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A New Species and New Records of Longhorned Beetles (Coleoptera: Cerambycidae) from Panama and Colombia

Sat, 20 Jun 2026 16:00:00 GMT

Abstract

A new species of Sternacutus Gilmour, 1961 (Lamiinae: Acanthocinini), Sternacutus crucolon Lanuza-Garay, Taboada-Verona, and Rivera, new species, is described from Panama. Five new country records are provided for various Cerambycidae species: Eburodacrys megaspilota White, 1853, Atrypanius irrorellus Bates, 1885, and Isthmiade perpulchra Linsley, 1961 from Colombia; Psyrassa maesi Audureau, 2010 and Sphaerion exutum (Newman, 1841) from Panama. Seven new departmental or provincial records from Panama and Colombia are provided: Ambonus electus (Gahan, 1904) (Colombia); Alastos batesi (Pascoe, 1888) (Colombia); Hippopsis septemlineata Breuning, 1940 (Colombia); Caciomorpha palliata (White, 1855) (Colombia); Mimasyngenes icuapara Galileo and Martins, 1996 (Colombia); and Ozodes xanthophasma Bates, 1872 (Panama). Brief comments about the distributional occurrences of Ambonus electus, Atrypanius irrorellus, Ozodes xanthophasma, Caciomorpha palliata, and Holonotus nigroaeneus Bates, 1869 are provided.

Introduction

Despite extensive study by many authors, especially in recent years, the Cerambycidae fauna of Central America and South America continues to reveal unknown species (LeTirant and Santos-Silva 2023). The latest count of species in the genus Sternacutus Gilmour, 1961 (Lamiinae: Acanthocinini) includes 38 species distributed from Mexico to southern South America (Roguet 2025). This genus includes species that differ in body shape, prothoracic shape, shape of the lateral tubercles of the prothorax, form of the elytral apices, and presence or absence of a humeral carina. The shape of the centrobasal crest of the elytra is one of the most notable diagnostic features (Santos-Silva et al. 2025). These pronounced differences suggest that the included species belong to more than one genus (Heffern et al. 2024).

In 2025, during an inspection of the Cerambycidae collections in Panama (MIUP), we found a specimen of Sternacutus that differed markedly from all previously known species of the genus. The specimen was collected in a light trap on Barro Colorado Island, Panama. Herein, we describe this as a new species. Additionally, we provide some new records of Cerambycidae species for the countries of Panama and Colombia and new records for their departments and provinces. Brief comments about the distributional occurrence of Ambonus electus (Gahan, 1904), Atrypanius irrorellus Bates, 1885, Ozodes xanthophasma Bates, 1872, Caciomorpha palliata (White, 1855), and Holonotus nigroaeneus Bates, 1869 are provided.

Materials and Methods

Photographs of the new species of Sternacutus, Psyrassa maesi Audureau, 2010, Sphaerion exutum (Newman, 1841), and Ozodes xanthophasma were taken in the Laboratories of Centro Regional Universitario de Colón, Universidad de Panamá, with a Canon Rebel T8i camera equipped with an AmScope 4× Plan close-up lens and a Nikkor Z MC 105-mm f/2.8 VR S aperture for angular frames, and with an EOS Rebel T100 camera, EF-S-18-55 III kit, then edited using the “tracking” technique in Affinity Photo, Adobe Photoshop, and Snapseed software.

Photographs of each of the new records treated herein for Colombia were taken in the Laboratorio de Entomología of Universidad del Tolima, with a Nikon D5600 camera equipped with a Laowa 100 mm f/2.8 2× aperture macro lens for angular frames; these were edited using Helicon Focus stacking software. Measurements were taken in millimeters using a Leica M165 C stereomicroscope with measuring ocular, also used in the study of the specimens. Identifications of specimens were made using original descriptions and available identification keys (Audureau 2010; Heffern et al. 2024; Martins 2005; Monné et al. 2020; Santos-Silva et al. 2024), as well as specimen photographs (Bezark 2025).

The taxonomic data and distributional records are based on Martínez (2000), Monné (2024a, b, c), Lanuza-Garay and Monné (2025), and Tavakilian and Chevillotte (2025).

The collection abbreviations used in the text are asfollows:CFPL,ColecciónFamiliaPardo-Locarno, Palmira, Valle del Cauca, Colombia; LABSUN, Laboratorio de Biología de Suelos, Universidad Nacional de Colombia, Bogotá, Colombia; MACO, Marianne Akers private collection, Colón, Panama; MIUP, Museo de Invertebrados G. B. Fairchild, Universidad de Panamá, Panama; STRI, Smithsonian Tropical Research Institute Insect Collection, Panamá, Panama.

Results

**Lamiinae Acanthocinini Sternacutus Gilmour, 1961**
Sternacutus crucolon Lanuza-Garay, Taboada-Verona, and Rivera, new species
zoobank.org/urn:lsid:zoobank.org:act: 3D15E34B-5F32-4ED0-BBA1-87FDD9CF756A
(Figs. 1–4)

Description. Female holotype. Integument mostly brownish; head orangish brown; base of antennal tubercle dark brown, scape brownish dorsally, orangish brown ventrally, basal two-thirds of antennomeres III–X orangish basally and apical third dark brown, light area gradually darker from VII to X, antennomere XI dark brown; anteclypeus and labrum yellowish; mandibles orangish brown basally, dark brown apically. Pronotum with dark-brown reniform macula on each side from about basal sulcus to middle, anteriorly with a small, dark-brown tubercle inside each macula, and dark-brown macula at base of lateral tubercles of prothorax. Elytron with 4 dark-brown maculae, one surrounding centrobasal crest, one oval-shaped located close to suture before middle of elytron, one laterally at middle of elytron, and a longitudinal macula located on center of dorsal surface posterior to middle of elytron; posterior two-thirds with sparse, dark-brown, rounded spots; epipleural base with a dark-brown macula. Coxae yellowish; pro- and mesofemora yellowish brown, lateral surfaces and most of ventral surface of femoral club dark brown, metafemur yellowish, dark brown toward apex; tibiae with basal half yellowish, apical half dark brown. Protarsomeres I–IV yellowish; protarsomere V yellowish brown; meso- and metatarsomeres I pale yellow on basal three-fourths, brown on apical fourth; meso- and metatarsomeres II–IV yellowish brown; meso- and metatarsomeres V yellowish basally, gradually becoming brown toward apex on remaining surface. Thoracic and abdominal ventrites yellowish brown. Head. Frons abundantly, minutely punctate; with a longitudinal, yellowish, pubescent band close to ventral eye lobes partially obscuring integument; centrally with slightly sparser, yellowish pubescence not obscuring integument. Area between antennal tubercles with dense, yellowish pubescence not obscuring integument; area between antennal tubercles and dorsal eye lobes with dense, yellowish pubescence obscuring integument, except glabrous median groove. Area behind dorsal eye lobes with dense, yellow pubescence close to eye, glabrous close to prothorax. Distance between dorsal eye lobes 0.26 times distance between outer margins of eyes; frontally, distance between ventral eye lobes 0.58 times distance between outer margins of eyes. Gena 0.42 times shorter than ventral eye lobe, with sparse, yellowish pubescence not obscuring integument. Antennal tubercle with abundant, pale-yellow pubescence frontally, sparse, yellowish-brown pubescence dorsally, except apex with dense, yellow pubescence, and abundant, yellow pubescence posteriorly. Wide central area of postclypeus with dense, yellowish pubescence not obscuring integument; sides of postclypeus glabrous; postclypeus with 2 long, erect, yellowish setae near central area. Labrum with sparse, yellowish pubescence posteriorly, glabrous on remaining surface, except fringe of yellowish-brown setae on anterior margin; posterior third with long, erect, brownish setae. Gulamentum smooth, glabrous. Antenna slender, reaching elytral apex at base of antennomere VII. Scape slightly sinuate ventrally; with a few long, erect, dark-brown setae near apex of ventral surface. Pedicel with moderately abundant, yellowish pubescence not obscuring integument on light integumental area, and moderately abundant, brown pubescence not obscuring integument on dark integumental area; with a few long, erect, thick, dark setae ventrally. Antennomeres III–IX with abundant, whitish pubescence not obscuring integument on light integumental area, and abundant, mostly dark pubescence not obscuring integument on dark integumental area; with short, erect, whitish setae interspersed throughout; ventral surface of III–V with a few short, thick, erect, dark setae; dorsal and lateral apex of III–IX with a few short, thick, dark setae. Antennomeres X–XI mostly with dark pubescence not obscuring integument and short, erect, whitish setae interspersed throughout. Antennal formula based on length of antennomere III: scape = 0.85; pedicel = 0.07; IV = 0.81; V = 0.81; VI = 1.00; VII = 1.09; VIII = 1.63; IX = 1.72; X = 1.38; XI = 0.78. Thorax. Prothorax 0.66 times wider than long; armed with acute lateral tubercles on posterior third, apex directed posteriorly, with a few long, erect, dark setae behind tubercle. Pronotum with yellowish pubescence not obscuring integument; finely punctate, punctures abundant centrally and posteriorly; with an arched row of coarse, deep punctures near anterior and posterior margins; central dark maculae covered with sparse, yellowish pubescence not obscuring integument. Sides of prothorax with abundant, pale-yellow pubescence. Prosternum with pale-yellow pubescence, distinctly sparser on wide central region. Prosternal process narrow, not laminiform, expanded apically, narrowest area 0.23 times procoxal width. Margins of mesanepisternum and mesepimeron with dense, pale-yellow pubescence, remaining surface with sparse, pale-yellow pubescence not obscuring integument; mesoventral process not laminiform, narrowest area 0.37 times mesocoxal width; metanepisternum and metaventrite with dense, yellow pubescence, pubescence paler and distinctly sparser on wide central region of metaventrite. Scutellar shield with abundant, yellowish-brown pubescence not obscuring integument. Elytra: Surface abundantly, coarsely punctate on anterior half, punctures gradually finer, sparser toward apex on posterior half; centrobasal crest slightly elevated, with a tuft of erect, black setae dorsally, slightly inclined posteriorly; apex emarginate with outer angle triangularly acuminate; with abundant, yellowish pubescence partially obscuring integument, except areas with dark-brown pubescence not obscuring integument. Legs: Femora with abundant, yellowish pubescence not obscuring integument, pubescence slightly denser and paler on some areas of femoral club, especially on meso- and metafemora, dense and longer on apex. Tibiae with abundant, yellowish pubescence not obscuring integument, except sparser pubescence on dorsal and lateral surfaces of posterior third, and dense, bristly, yellowish-brown pubescence on posterior third of ventral surface; dorsal surface of posterior third of mesotibia with abundant, short, thick, erect, blackish setae; dorsal surface of posterior third of metatibia with a few short, thick, erect, dark-brown setae. Tarsomeres with abundant, yellowish pubescence dorsally not obscuring integument; metatarsomere I distinctly longer than II–III together. Abdomen: Ventrites with abundant, pale-yellowish pubescence not obscuring integument. Ventrite 5 with sparse, long, thick, erect, blackish setae interspersed, especially on apical third; apex emarginate.

Measurements (mm). Total length, 5.50; prothoracic length, 1.00; prothoracic anterior width, 1.15; prothoracic posterior width, 1.60; elytral length, 4.10; humeral width, 1.90.

Etymology. We are pleased to name this species in honor of the Centro Regional Universitario de Colón (CRUC), an academic extension of the Universidad de Panamá in the province of Colón, founded in 1981. The name is treated as a noun in apposition.

Type Specimen. Female holotype, labeled “Isla Barro Colorado, Rep. Panamá, 19-25 oct. 1984, H. Wolda leg. (T. luz)” (MIUP).

Remarks. Sternacutus crucolon is similar to Sternacutus ursulae (Lanuza-Garay and Rivera, 2023) [see photograph of the holotype male and paratype female in Lanuza-Garay and Rivera (2023) and Bezark (2025)] by the prothoracic lateral tubercles slightly longer (some shorter in S. ursulae); pronotum with two reniform maculae along the central disc (deer-track shaped in S. ursulae), anterior third without maculae on each side (with one small, inverted, triangular-shaped macula in S. ursulae), and with a dark-brown macula on the base of the lateral tubercle of the prothorax (some small in S. ursulae) (Figs. 5–6); and by the centrobasal crest of the elytra not elevated (centrobasal crest in both sexes of S. ursulae slightly elevated, subconi-cal) (Figs. 11–12). Furthermore, the distribution of integumental pubescence and elytral maculae are different, mainly by the presence of an oval-shaped macula located close to the suture before the middle of the elytra in S. crucolon (absent in S. ursulae) (Figs. 8–9). It is similar to Sternacutus akersae (Lanuza-Garay and Rivera, 2024) [see photograph of the holotype female in Lanuza-Garay and Rivera (2024) and Bezark (2025)] but differs notably by the body color pattern: S. crucolon has a predominantly brownish coloration, except for the orangish-brown head and scape, while the epipleuron, coxae, and legs are yellowish brown; in S. akersae the coloration is uniformly brownish throughout the body. Moreover, the pattern of pronotal and elytral maculae and pronotal punctation between S. crucolon and S. akersae are different: in S. crucolon, the pronotum has an elongate, reniform macula on each side of the central disc, with a dark-brown tubercle inside the macula anteriorly, and the pronotal surface is finely punctate on the central disc; in S. akersae, the pronotum has a pair of rounded maculae centrally, the pronotal surface is coarsely punctate, and punctures are abundant on the central disc (Fig. 7). Patterns of elytral maculae are different in each species (Figs. 8, 10); in S. crucolon, the centrobasal crest of the elytron is not elevated and has no arched setae, while the centrobasal crest of the elytron in S. akersae is elevated and has arched setae (Fig. 13).

Figs. 1–4.

Sternacutus crucolon, new species, holotype female. 1) Habitus, dorsal view; 2) Habitus, ventral view; 3) Habitus, lateral view; 4) Head, frontal view.

New Geographical Records
Cerambycinae Eburiini Eburodacrys White, 1853
Eburodacrys megaspilota White, 1853
(Fig. 14)

Known Distribution. Ecuador and Brazil (Amazonas) (Monné 2024a).

Specimen Examined. 1 specimen, COLOMBIA (new country record), Meta department, Villavicencio, light trap, 23.XI.1994, various collectors (CFPL).

Figs. 5–13.

Characters of Sternacutus species. Pronotum, dorsal view: 5) S. crucolon, new species; 6) S. ursulae; 7) S. akersae. Right elytron, dorsal view: 8) S. crucolon, new species; 9) S. ursulae; 10) S. akersae. Elytral centrobasal crest, lateral view: 11) S. crucolon, new species; 12) S. ursulae; 13) S. akersae.

Elaphidiini Ambonus Gistel, 1848
Ambonus electus (Gahan, 1904)
(Fig. 15)

Known Distribution. Honduras (?), Nicaragua, Panama (Coclé), Colombia, Suriname, Venezuela, Brazil (Rondônia, Mato Grosso, Goiás, Mato Grosso do Sul, Rio Grande do Norte, Paraíba, Alagoas, Pernambuco, Bahia, Minas Gerais, Espírito Santo, Rio de Janeiro, São Paulo, Paraná, and Santa Catarina), Bolivia (Santa Cruz and Tarija), Paraguay, and Argentina (Salta, Jujuy, Santiago del Estero, and Córdoba) (Lanuza-Garay and Monné 2025; Monné 2024a).

Specimen Examined. 1♂, COLOMBIA, Vichada department (new departmental record), Nueva Antioquia, Hacienda San José, 5°55′42.31″N 69°38′0.00″W,25.IV.2025,C.Taboadaleg.(LABSUN).

Comments. This species was recorded from Colombia by Martins and Galileo (2003) based on a female specimen. However, the information on the label is ambiguous, as it mentions two departments, “Cundinamarca or Boyacá”, without clearly specifying the exact locality where the specimen was collected.

Psyrassa Pascoe, 1866
Psyrassa maesi Audureau, 2010
(Fig. 16)

Known Distribution. Nicaragua (Audureau 2010; Tavakilian and Chevillote 2025).

Specimens Examined. 1♀, PANAMA (new country record), Colón Province, Fort Espinar, 9.V.1991, M. Akers leg.; 1♂, same data except 6.V.1992; 1♂, same data except 5.V.1993; 1♀, same data except 7.V.1993; 1♀, same data except 17.V.1993; 1♀, same data except 4.V.1994 (MACO).

Sphaerion Audinet-Serville, 1834
Sphaerion exutum (Newman, 1841)
(Fig. 17)

Known Distribution. United States of America (Texas: Lower Rio Grande Valley), Mexico (Chiapas and Yucatán), Nicaragua, Brazil (Pará, Piauí, Bahia, Minas Gerais, Espírito Santo, Rio de Janeiro, São Paulo, and Santa Catarina), Argentina, and Uruguay (Tavakilian and Chevillotte 2025).

Specimens Examined. 1♂, PANAMA (new country record), Colón Province, Fort Espinar, 16.V.1992, M. Akers leg. (MACO); 1 specimen, PANAMA, Coclé, Interamerican Hwy at Rio Grande, 05.V.1993, H. Stockwell leg., det. E. Giesbert, STRI_ENT_0060541 (STRI).

Figs. 14–25.

Dorsal habitus of Cerambycidae. 14) Eburodacrys megaspilota; 15) Ambonus electus; 16) Psyrassa maesi; 17) Sphaerion exutum; 18) Alastos batesi; 19) Ozodes xanthophasma; 20) Isthmiade perpulchra; 21) Atrypanius irrorellus; 22) Hippopsis septemlineata; 23) Caciomorpha palliata; 24) Mimasyngenes icuapara; 25) Holonotus nigroaeneus.

Hesperophanini Alastos Napp and Martins, 1982
Alastos batesi (Pascoe, 1888)
(Fig. 18)

Known Distribution. Costa Rica (Cartago and Puntarenas), Colombia (Magdalena, Cundinamarca, Caquetá, Risaralda, and Córdoba), Venezuela, Ecuador, and Bolivia (Santa Cruz) (Monné 2024a).

Specimen Examined. 1 specimen, COLOMBIA, Valle del Cauca department (new departmental record), Calima, La Palmera, Río Bravo, light trap, 03.VI.1991, A. Papamija leg. (CFPL).

Necydalopsini Ozodes Audinet-Serville, 1834
Ozodes xanthophasma Bates, 1872 (Fig. 19)

Known Distribution. Mexico (Veracruz), Honduras, Nicaragua (Chontales), Costa Rica (Alajuela, Guanacaste, Heredia, Limón, and San José), Panama (Chiriquí), and Brazil (Goiás) (Lanuza-Garay and Monné 2025; Monné 2024a)

Specimen Examined. 1♀, PANAMA, Panama province (new provincial record), Los Pinos, Fortaleza, altos de Cerro Azul, elev. 801 m, 2.XII.2016, D. G. Zechiel leg. (MIUP).

Comments. Bates (1885) indicated this species' distribution as Tolé, Bugaba, and Volcán (Chiriquí province). Lanuza-Garay and Monné (2025) did not record any specimens of this species for other locations in Panama in their checklist.

Rhinotragini Isthmiade Thomson, 1864
Isthmiade perpulchra Linsley, 1961
(Fig. 20)

Known Distribution. Costa Rica and Panama (Panamá and Canal Zone) (Lanuza-Garay and Monné 2025; Monné 2024a).

Specimens Examined. 1 specimen, COLOMBIA (new country record), Valle del Cauca department, Palmira, VI.1982, A. Reina leg. (CFPL); 1 specimen, Valle del Cauca, Río Bravo Calima, II.1980, Diego Torres leg; 1 specimen, Valle del Cauca, Sabaletas, Río Anchicayá, Buenaventura, XII.1996, L. C. Pardo-Locarno leg. (CFPL).

Lamiinae Acanthocinini Atrypanius Bates, 1864
Atrypanius irrorellus Bates, 1885
(Fig. 21)

Known Distribution. Mexico (Chiapas and San Luis Potosí), Guatemala, El Salvador (San Salvador), Honduras, Nicaragua, Costa Rica (Puntarenas, Guanacaste, Limón, and San José), Panama (Panama and Chiriquí), Trinidad and Tobago, Colombia, Venezuela, Ecuador, and Bolivia (Cochabamba and Santa Cruz) (Monné 2024b).

Specimen Examined. 1 specimen, COLOMBIA (new country record), Sucre department, Los Palmitos, Finca el Socorro, 9°24′50.89″N, 75°16′22. 6″W, 192 m elevation, light trap, 06.VII.2016, P. Álvarez leg. (LABSUN).

Comments. This species had previously been erroneously cited for Colombia. Although Martínez (2000) included it in her study on Colombian Cerambycidae, she clarified that its presence in the country was only considered probable and remained unconfirmed.

Agapanthiini Hippopsis Lepeletier and Audinet-Serville, 1825
Hippopsis septemlineata Breuning, 1940
(Fig. 22)

Known Distribution. Panama (Canal Zone, Colón, Panamá, and Panamá Oeste), Colombia (Bolívar and Atlántico), and Ecuador (Lanuza-Garay and Monné 2025; Monné 2024b).

Specimen Examined. 1 specimen, COLOMBIA, Sucre department (new departmental record), Sincelejo, Campus Unisucre sede Puerta Roja, 9°19′3.87″N, 75°23′11.50″W, elevation 181 m, 24.V.2017, A. Olivero leg. (LABSUN).

Anisocerini Caciomorpha Thomson, 1864
Caciomorpha palliata (White, 1855)
(Fig. 23)

Known Distribution. Guatemala, Honduras, Nicaragua (Chontales and Matagalpa), Costa Rica (Alajuela, Limón, Guanacaste, and Puntarenas), Panama (Bocas del Toro, Canal Zone, Colón, and Panamá), and Colombia (Lanuza-Garay and Monné 2025; Monné 2024b).

Specimen Examined. 1 specimen, COLOMBIA, Valle del Cauca department (new departmental record), El Dovio, Río Dovio, 14–16.V.1996, L. Pardo-Locarno leg. (CFPL).

Comments. Apparently, this species was erroneously cited for Colombia in the catalogs of Monné, including the most recent one (Monné 2024b). It is very likely that the inclusion of Colombia resulted from the work of Galileo and Martins (1998), in which the country appears in the taxonomic key proposed for Caciomorpha (Guatemala to Colombia); however, no examined material from Colombia was reported in that study.

Desmiphorini Mimasyngenes Breuning, 1950
Mimasyngenes icuapara Galileo and Martins, 1996
(Fig. 24)

Known Distribution. Costa Rica (Guanacaste), Venezuela, Colombia (Atlántico), Brazil (São Paulo), and Argentina (Misiones) (Monné 2024b).

Specimen Examined. 1 specimen, COLOMBIA, Sucre department (new departmental record), Los Palmitos, Finca El Socorro, 9°24′50.89″N, 75°16′22.6″W, 192 m elevation, 04.VI.2016, C. Taboada leg. (LABSUN).

Prioninae Solenopterini Holonotus Thomson, 1861
Holonotus nigroaeneus Bates, 1869
(Fig. 25)

Known Distribution. Nicaragua (Chontales), Costa Rica (Guanacaste, Heredia, and Alajuela), and Colombia (Valle de Cauca) (Martínez 2000; Monné 2024c).

Specimen Examined. 1 specimen, COLOMBIA, Valle del Cauca department, Buenaventura, Río Calima, light trap, XI.2011, P. Álvarez leg. (CFPL).

Comments. This species was listed from Colombia by Martínez (2000), who reported its occurrence in the department of Valle del Cauca based on specimens stored in the Museo Francisco Luis Gallego, Universidad Nacional de Colombia (UNCM); nevertheless, no specific locality was provided in that publication.

Acknowledgments

We thank Celso Hernandez for providing photographs of Sternacutus crucolon, to Nemesio Melo (Asociación Biológica de Panamá, ABIOPA) and Omar Sánchez (Agrupación de Investigación Entomológica de Panamá, ADIEPA) for providing photographs of Psyrassa maesi, Sphaerion exutum, and Ozodes xanthophasma, and for the photographic editing of the beetles treated in this manuscript in collaboration with Celso Hernández. Additionally, we would like to thank Roberto Cambra (MIUP) for approving access to the specimen used to describe this new species; to Steve Lingafelter (USDA) and Alain Audureau for confirming the records of S. exutum and P. maesi, respectively, and Daniel Heffern for his useful comments and review of English language. The fourth author thanks Julián Salazar, Luis Miguel Constantino, and Carlos López Vahamonde for their support during fieldwork. He also expresses his gratitude to María Patricia Franco (RIP) for her valuable assistance in laboratory work, specimen mounting, and labeling.

This open-access article is distributed under the CC BY-NC-ND 4.0 license

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Seven Guideposts of Agricultural Education: Linking Agronomy With Biology Teaching & Learning

Mon, 25 May 2026 16:00:00 GMT

Abstract

This article introduces a useful framework of seven guideposts for integrating agricultural concepts into biology and environmental science education. Despite agricultural literacy being essential for understanding contemporary environmental challenges, research shows many students lack foundational knowledge about food production systems. We propose agronomy—the science of crop production—as “applied biology” that provides a unique context for teaching biological concepts through tangible experiences that directly connect to curricular standards. Our framework systematically integrates plant agriculture through seven strategic guideposts: agriculture's evolution, current agricultural systems, seeds, soil health, food access, ecological connections, and technological solutions. Each guidepost maps directly to biology standards while cultivating agricultural literacy crucial for addressing sustainability challenges. Plant agriculture is emphasized due to four practical advantages: classroom feasibility requiring minimal resources, accessibility for direct observation over reasonable timeframes, natural alignment with existing curriculum standards, and relevance to global food security issues. This framework, supported by evidence from garden-based learning research, enables biology teachers to integrate agricultural literacy without additional courses or curricular overhaul. By contextualizing abstract biological concepts through concrete agricultural examples, educators can simultaneously deepen students' biological understanding and prepare them to engage with pressing food system challenges that will shape their futures.

Restoring Freshwater Mussels to the Clinch and Powell Rivers: Monitoring and Evaluation of the Certus Inc. and Lone Mountain Processing Inc. Natural R...

Sun, 03 May 2026 16:00:00 GMT

Abstract

Freshwater mussels are particularly susceptible to injury from releases of hazardous substances. Natural recolonization of injured mussel populations can take decades because of their complex life history. Hence, hatchery propagation and stocking of mussels is commonly used for recovering injured populations. In recent decades, several Natural Resource Damage Assessment and Restoration (NRDAR) cases have involved freshwater mussels, but none have analyzed whether restoration was successful. Our study represents the first evaluation of restoration success of freshwater mussels in an NRDAR context. Its purpose was to determine whether mussel restoration was successful for two large-scale, multiyear (>10 years) NRDAR cases in the Clinch and Powell rivers of Virginia and Tennessee. We used mussel release data compiled from 2004–2017 and a Leslie matrix model to estimate the expected abundance of mussels at nine restoration and monitoring sites. We then compared expected abundances to abundance values estimated from quadrat surveys conducted from 2015–2021 at these same nine sites. Estimated abundances were 57–85% lower than expected. We conducted mark-recapture surveys at two sites and the data from this independent method supported our quadrat survey results; i.e., abundance estimates were much lower than the expected abundance values. However, we observed evidence of successful restoration, such as released mussels reaching breeding ages and presence of mussels at low- to medium densities (0.02–0.48 m^–2) at restoration sites, and we confirmed limited recruitment of two species. Nonetheless, lower-than-expected abundance suggests that either mussels are settling or recruiting outside of restoration sites and/or that survival and recruitment of released mussels are lower than expected. Further study is needed to determine to what extent each of these factors explain lower-than-expected abundance to better estimate the scope of restoration required in future NRDAR cases. Finally, we developed a set of metrics to determine whether restoration was successful in this study and for application to future cases involving freshwater mussels.

INTRODUCTION

Freshwater mussels provide many ecosystem services, including regulating services (biofiltration of water), supporting services (nutrient cycling and storage, habitat modification, and environmental monitoring), provisioning services (food and products made from shell), and cultural services (providing cultural and existence values) (Vaughn 2018). Unfortunately, freshwater mussels (Unionida) are among the most imperiled groups of freshwater organisms in North America (Vaughn and Taylor 1999; Lopes-Lima et al. 2018). Of the approximately 300 recognized species, 88 are listed as federally endangered and 15 are listed as federally threatened under the Endangered Species Act (U.S. Fish and Wildlife Service 2018). Water pollution and water quality degradation are among the most frequently cited causes of mussel decline (Downing et al. 2010). Due to their sessile nature, mussels are highly susceptible to injury from releases of hazardous substances into aquatic ecosystems. Releases of contaminants into rivers can drastically reduce the diversity and abundance of local mussel populations (Sheehan et al. 1989). Further, the limited dispersal capabilities of mussels and their complex life history (which involves fish hosts for dispersal) make natural recolonization difficult and unlikely in the short term (∼10–20 years) (Patterson et al. 2018; Irmscher and Vaughn 2018).

Natural Resource Damage Assessment and Restoration (NRDAR) regulations allow the federal government to assess injury to natural resources resulting from the release of a hazardous substance and to recover damages from responsible parties (43 CFR § 11). There have been numerous NRDAR cases involving injury to freshwater mussels in recent decades. Two are particularly relevant to our study. In 1996, a release of coal slurry from the Lone Mountain Processing Inc. (LMPI) facility near St. Charles, Virginia, impacted a large section of the Powell River, affecting 15 species of federally listed endangered mussels present in the river (U.S. Fish and Wildlife Service 2003). Two years later, a tanker truck operated by Certus Inc. overturned in Cedar Bluff, Virginia (U.S. Fish and Wildlife Service 2004); the resulting spill of a hazardous chemical killed an estimated 18,621 mussels of 14 species, three of which were listed as federally endangered at the time and one other that was listed as endangered after the spill (Hyde and Jones 2021). Additional examples include a decades-long mercury release from the DuPont-Waynesboro facility that affected mussel populations in the South River from 1929 to 1950, resulting in a $4 million settlement for mussel restoration in the South River and South Fork Shenandoah River (U.S. Fish and Wildlife Service 2017); the 1999 release of hazardous substances from a ferro-alloy manufacturing facility that killed over 990,000 mussels in the Ohio River, including individuals of two federally listed endangered species (U.S. Fish and Wildlife Service 2007); and a 2014 coal ash spill in the Dan River (Dan River Natural Resource Trustee Council 2020) that adversely affected the federally endangered James Spinymussel (Parvaspina collina) and other mussel species. Together, these cases show that injury to freshwater mussel populations is an ongoing concern.

In these cases, injury assessment and restoration goals varied considerably. For the DuPont-Waynesboro case, an injury of 650,000 mussels was estimated by using reference sites, historical species composition, and impacted habitat to determine expected density and applying the estimated density to the area injured (U.S. Fish and Wildlife Service 2017). For the Ohio River NRDAR case (U.S. Fish and Wildlife 2007), injury was established by documenting the presence of dead mussels immediately downstream from the discharge area of the facility. Surveys at one downstream site found mussel mortality to be 100 percent, and additional surveys estimated 990,000 mussels killed. The selected restoration alternative included translocation of adult mussels, release of infested host fishes, and propagation and release of juvenile mussels. The goal was to restore density in the affected areas to one mussel per square meter, requiring long-term survival of 195,000 individuals to age five. In the Dan River NRDAR case, a Habitat Equivalency Analysis (HEA) was used to determine the level of restoration needed for gains in services to equal the loss of services from the injury. In this case, mussel propagation was not among the selected restoration activities, although three species (Yellow Lampmussel (Lampsilis cariosa), Green Floater (Lasmigona subviridis), and Notched Rainbow (Venustaconcha constricta)) were successfully propagated by the Virginia Fisheries and Aquatic Wildlife Center as part of the pre-NRDAR restoration process (Brian Watson, Virginia Department of Wildlife Resources, personal communication). Rather, several habitat restoration and conservation alternatives were used, including the transfer of 618 acres of land to North Carolina and Virginia state parks and the removal of the Pigg River Power Dam (Dan River Natural Resource Trustee Council 2020). Injury to mussel populations for the Certus Inc. NRDAR case was quantified by counting the number of fresh-dead mussels in the affected area and multiplying by three to account for mussels buried in the substrate (U.S. Fish and Wildlife Service 2001). The primary restoration goal for the Certus Inc. case was to restore the mussel assemblage to approximate baseline conditions, i.e., “the condition of the natural resources and services that would have existed had the incident not occurred,” which was accomplished by propagating and releasing most species injured in the spill (U.S. Fish and Wildlife Service 2004). Injury for the LMPI NRDAR case was largely sublethal and defined, but not quantified, as acute (time of spill) and chronic (resuspension over time) toxicity from exposure to hazardous substances, indirect mortality of glochidia due to loss of host fishes, and indirect losses due to habitat degradation from silt and sedimentation (U.S. Fish and Wildlife Service 2003). The primary goal was to restore mussel assemblages to approximate baseline conditions, which was accomplished by propagating and releasing mussels from a targeted suite of injured species in the mussel assemblage. Compared to more recent cases, baseline conditions for the Certus Inc. and LMPI NRDAR cases were less explicitly defined, and it was difficult to measure success of restoration based on the resulting restoration goals to restore to pre-injury baseline. These examples clearly demonstrate that the methods for determining the extent of injury and subsequent required restoration vary widely from case to case.

Natural recolonization of injured mussel assemblages may take many years, during which time the services they provide would be lost. Given this lag time, release of propagated mussels to restoration sites is a common action in NRDAR cases. Stocking mussels satisfies the “restoration or rehabilitation of injured natural resources to a condition where they can provide the level of services available at baseline” criteria of NRDAR regulations (43 CFR § 11). Further, NRDAR regulations also allow for “the replacement and/or acquisition of equivalent natural resources capable of providing such services. . .” in lieu of, or in addition to, restoration/rehabilitation (43 CFR § 11). In cases involving injury to freshwater mussels, replacement of equivalent services equates to releasing mussels outside of the impacted area or the use of species capable of providing services similar to those afforded by the injured species (replacement/acquisition). It also may be necessary to correct for differences in services provided by juvenile vs. adult mussels because most propagation involves releases of mussels <5 years old, which might not provide the full suite of services afforded by older individuals (Patterson et al. 2018). The extent to which mussels successfully establish at a site poses implications for the number of mussels that need to be released for successful restoration. A lower rate of establishment and survival at a site would require releasing a higher number of mussels over a longer period compared to a site with higher rates of mussel establishment and survival. Further, if mussels are experiencing higher mortality after release than what they would experience naturally, there would be a corresponding decrease in the expected services provided over their lifetime.

Few published studies have analyzed the success of mussel restoration, particularly in a NRDAR context. Although long-term monitoring is conducted in some cases (e.g., restored populations of P. collina have been monitored for over 10 years), results of such monitoring are seldom published in the primary literature (Lavictoire and West 2024). Thus, the purpose of our study was to monitor restored mussel populations in the Clinch and Powell rivers for the Certus Inc. and LMPI NRDAR cases to determine whether, and to what extent, restoration efforts for these cases were successful. The successful restoration of injured resources assumes that released mussels are establishing and reproducing at restoration sites. We developed the following metrics for assessing successful mussel restoration: (1) settling into suitable habitat and surviving after release, (2) surviving at rates high enough to reach breeding age, (3) being fertilized, resulting in gravid females, (4) producing recruits that successfully establish, and finally (5) that released mussels and their recruits continue successfully breeding to the point that the mussel assemblage is self-sustaining and stable in the long term (Table 1). These criteria are predicated on the successful establishment of released mussels in sufficient numbers. We focused only on abundance, density, and growth for the Certus Inc. and LMPI cases, because these metrics were developed after the design and implementation of the monitoring program for each case. Future monitoring designs should attempt to measure as many of these metrics as feasible. The objectives of this study were to (1) estimate the expected number of mussels surviving at restoration sites, (2) estimate abundance and density of mussel species at restoration sites, (3) determine whether estimated abundance and density differed from expected abundance and density at restoration sites, (4) determine the shell length growth rate of released mussels, and (5) determine whether, and to what extent, restoration goals were achieved for the Certus Inc. and LMPI NRDAR cases.

Detailed Case Background

The Certus Inc. and LMPI Natural Resource Damage Assessment and Restoration (NRDAR) cases in the upper Tennessee River basin of Virginia are among the first and largest cases in the United States involving injury to freshwater mussels due to release of hazardous substances (Hyde and Jones 2021). The Certus Inc. chemical spill released 5,110 liters of Octocure-554 revised, a rubber accelerant, into a tributary of the Clinch River when a tanker truck overturned in Tazewell County, Virginia, on August 27, 1998. An estimated 18,621 mussels, including 750 individuals of three endangered species (Golden Riffleshell (Epioblasma aureola), Purple Bean (Venustaconcha trabalis), and Rough Rabbitsfoot (Theliderma strigillata)), were killed along an 11-kilometer section of stream (U.S. Fish and Wildlife Service 2004). Further, since the spill, both Fluted Kidneyshell (Ptychobranchus subtentus) and Slabside Pearlymussel (Pleuronaia dolabelloides) have been listed as endangered. The loss of their local Clinch River populations likely contributed to the listing of these species.

The LMPI coal slurry spill occurred when a holding pond failed at a processing plant in Lee County, Virginia, on October 24, 1996. The spill released 22.7 million liters of coal slurry into a series of tributaries of the Powell River. The resulting “blackwater” impacted a large section of the Powell River, and coal fines and sediment ultimately were deposited in Norris Reservoir, Tennessee, 105 kilometers downstream from the release. Although no dead mussels were found, coal fines later were detected in mussel gut tissues (U.S. Fish and Wildlife Service 2003). Additionally, at least 11,240 fish of various species were killed, some of which are host fishes for the 15 federally endangered mussel species found in the impacted river reach (U.S. Fish and Wildlife Service 2003). Coal fines and sediment also were deposited in the substrate throughout the affected length of the Powell River and likely continued to impose chronic, sub-lethal impacts due to resuspension during high flow events in 1996 and 1997 (U.S. Fish and Wildlife Service 2003). In contrast to the acute, lethal effects of the Certus Inc. spill, the LMPI spill represented a chronic, sub-lethal effect on the mussel fauna in the impacted river reach (Michalak et al. 2017).

Table 1.

Monitoring criteria and evidence required for documenting successful establishment and reproductive success of restoring freshwater mussel populations.

The principal goal for each case was “to restore the mussel assemblage and its supporting habitats to approximate baseline conditions” (U.S. Fish and Wildlife Service 2003; U.S. Fish and Wildlife Service 2004). Baseline condition for the Certus Inc. NRDAR case was the estimated number of mussels (18,621) and respective species composition present in the impact zone before the spill (Hyde and Jones 2021). Consequently, many mussels released as part of restoration were at sites in the immediate impact zone of the Clinch River between Cedar Bluff, Virginia (River Mile (RM) 324) and Richlands, Virginia (RM 318). However, mussels also were released downstream at other restoration sites in the Clinch River in Russell County, Virginia (RM 270–277.5), to reduce the risk that released mussels would all be impacted by another single, catastrophic event or degradation of habitat in the areas of Cedar Bluff and Richlands. Further, the ability to propagate some affected species was limited. Notably, Oyster Mussel (Epioblasma capsaeformis) and Cumberlandian Comb-shell (E. brevidens) were used as surrogates for the critically endangered E. aureola due to the greater availability of brood-stock and ease of propagating these two species at mussel hatcheries. Specifically, these two Epioblasma species were used as surrogates to develop propagation, culture, and monitoring techniques for E. aureola. Baseline condition of the mussel assemblage was not quantified for the LMPI NRDAR case; however, the goal was to propagate a selected suite of the federally listed and non-listed mussel species affected by the spill in the Powell River. Not all federally listed mussel species impacted by the spill could be propagated and restored due to technological limitations (e.g., undeveloped propagation techniques including unknown host fishes); thus, restoration efforts in this case mainly focused on releasing E. capsaeformis and E. brevidens, as well as numerous non-endangered species, at sites in the Powell River to establish robust populations of these species and to restore their local populations and respective ecosystem services.

METHODS

Study Area

Mussels were sampled at six release and monitoring sites in the Clinch River (Fig. 1) and three of six release and monitoring sites in the Powell River (Fig. 1). These nine sites were sampled from 2015 to 2017 and again in either 2020 or 2021 (Table 2). This period was chosen because, by 2015, most of the restoration had been conducted for the Certus and LMPI NRDAR cases. However, monitoring at the Oakley Property in the Powell River, Tennessee, only began in 2016 and therefore it was not sampled in 2015.

Local mussel populations at two of the sites in the Clinch River, the Sycamore Lane site near Richlands, Virginia (RM 320), and the Payne Property site near Cedar Bluff, Virginia (RM 322.1), in Tazewell County, Virginia, were impacted directly in 1998 by the Certus Inc. chemical spill. These sites were selected for restoration because they represented some of the best and largest available mussel habitat patches in the impact zone. Four additional sites located further downstream in Russell County—Bennett Property (RM 277.5), Artrip (RM 274.5), Whited Property (RM 272.7), and Cleveland Island (RM 270)—were not directly impacted by the spill. However, they were chosen as additional restoration sites for the Certus Inc. NRDAR project to reduce risk for potential future impacts to the two sites located in the impact zone between Cedar Bluff and Richlands, Virginia, as well as to replace/acquire natural resources and services equivalent to those lost during the spill. Additionally, these four sites had suitable habitat for mussels, a viable mussel assemblage, and suitable host fishes. All three Powell River monitoring sites were within the area affected by the LMPI coal slurry spill and are in Claiborne County, Tennessee—upper Brooks Bridge (RM 95.3), lower Brooks Bridge (RM 94.7), and Oakley Property (RM 89.7).

In the Clinch River, the Bennett Property had the highest number of released mussels >6 months old (28,538), most of which were E. capsaeformis and E. brevidens (see Table 1.18 in Hyde and Jones 2021). The Sycamore Lane (21,417) and Payne Property (15,314) sites in Tazewell County had the next highest numbers of released mussels, followed by Artrip (11,066), Cleveland Islands (7,344), and Whited (1,297).

In the Powell River, most of the mussels released were at the Lower (4,583) and Upper Brooks Bridge (4,211) sites, although an additional 1,301 mussels were released at the Oakley Property. Many of the released mussels at the Powell River sites were either E. capsaeformis or E. brevidens. The number and timing of mussel releases varied widely based upon the species released at restoration sites, how difficult those species were to propagate (e.g., how available gravid females were from year to year), and year-to-year differences in culture success.

Quadrat Sampling

We used a systematic quadrat sampling design at all nine restoration sites (Strayer and Smith 2003). We used multiple random starts for the location of the first quadrat of each systematic sample and all subsequent. We used a quadrat size of 0.25 m² because it is generally more accurate and precise than 1.0 m² quadrats when used to estimate abundance (Pooler and Smith 2005). We used three to four random starts at each site. The number of quadrats sampled at each site in 2015 depended on the expected density of mussel species and the desired level of precision. We determined expected densities using 2004–2014 mussel release data from the Freshwater Mollusk Conservation Center (FMCC) and the Aquatic Wildlife Conservation Center (AWCC). A 95% annual survival rate was applied to each cohort to estimate the population density of each species released at each site (Jones et al. 2012). Survival was assumed to be high because mussels are generally long-lived species (Hart et al. 2001; Villella et al. 2004; Hua 2015). Recruitment from released mussels in the wild was assumed to be zero because released mussels were sub-adults. Assuming no recruitment and high survival also ensured that sufficient quadrats were sampled the first year because the density estimate was lower than if we had assumed recruitment, i.e., lower densities require more quadrats. We used a power analysis (Strayer et al. 1997) to determine the number of quadrats needed to achieve a given level of precision:

where n is the number of quadrats, m is the mean number of mussels expected per quadrat, and CV is the desired coefficient of variation (standard error/mean), i.e., a 15% target level of precision was used in this study. We calculated n starting with the most common species at each site and added fewer common species until the number of quadrats became too high (e.g., >400 per site) to reasonably sample. These data were used to determine the target number of quadrats at each site in 2015. For 2016 and 2017, we used actual density estimates from the 2015 quadrat sampling, rather than estimates based on past releases, to determine the target number of quadrats.

Figure 1.

Locations of mussel population restoration and monitoring sites (red dots) in the upper Clinch River, Russell and Tazewell counties, Virginia, for the Certus Inc. NRDAR case and the Powell River, Claiborne County, Tennessee, and Lee County, Virginia, for the Lone Mountain Processing Inc. NRDAR case. Only monitoring data from lower three sites in the Powell were assessed and included in his study. Blue area in inset is the Clinch (a) and Powell (b) river watersheds. Mussel releases occurred from 2003–2019.

Table 2.

Nine sites quantitatively sampled for the Certus Inc. and Lone Mountain Processing Inc. NRDAR mussel-restoration cases in the Clinch and Powell rivers, Tennessee and Virginia. Site length, mean width, and area are all rounded to nearest whole number (area is calculated using unrounded length and width). Sample sizes for quadrat surveys are number of quadrats sampled and sample sizes for mark-recapture surveys are number of individuals sampled. Dash (-) indicates the site was not sampled that year. We used river mile because it corresponds to the unit used in USGS topographic maps.

The distance between quadrats varied among sites and was determined using the formula:

where L is the total length of a site, W is the mean width of a site, n is the target number of quadrats to be sampled, and k is the number of random starts (Strayer and Smith 2003). The distance between quadrats determined the size of the start area where the first quadrat for each systematic sample was placed. For example, a distance of 8 m resulted in an 8 × 8 m start area, and each random start was randomly placed in this box. Random starts at each site were determined using the RAND() function in Microsoft Excel 2015.

The upper and lower boundaries of each site were determined based on the location of past mussel releases and location of suitable habitat. River width was measured at 10-m intervals along the length of each site using a laser rangefinder with 0.5-m precision. Area in each segment was calculated and used to convert population size estimates to densities per m² (see Hyde and Jones 2021, Appendix C for Google Earth images of sites). We also used these measurements to calculate distance between quadrats using the above formula.

The initial quadrat for each random start was placed, and then all subsequent quadrats were spaced at even intervals along a transect perpendicular to stream flow. The distance between each transect along the stream was the same as the interval between quadrats. Any distance between the last quadrat on a transect and the stream bank was subtracted from the distance between the bank and the first quadrat on the next transect. For example, an interval of 8 m would result in a distance of 8 m between each quadrat within a transect and a distance of 8 m between each transect. If there were 5 m between the last quadrat of one transect and the stream bank, the first quadrat on the next transect would be 3 m from the bank.

Quadrats were excavated to an approximate depth of 20 cm or until bedrock or hardpan was reached. For mussels found in each quadrat, we identified them to species, sexed them as male/female (for dimorphic species), and measured them to the nearest tenth millimeter using dial calipers (length only). We recorded any mussels visible on the surface as “surface,” whereas those not visible were recorded as “subsurface.” For mussels previously tagged at AWCC and FMCC prior to release, we also recorded the tag color and number.

We used the data from the quadrat surveys to estimate abundance of each species by multiplying the mean number of individuals found in a systematic sample by the total number of possible systematic samples in the area surveyed. Density was determined by dividing abundance by the area of the site sampled. We calculated 95% confidence intervals for abundance using the formula

where is the estimate of abundance and var() is the estimate of the variance of the abundance estimate (Smith et al. 2001). The variance of the abundance estimate was calculated using the formula

where M is the number of possible systematic samples, m is the number of random starts, x̄ is the mean number of mussels per systematic sample, and x_i is the number of mussels in random start (Smith et al. 2001). Variance for density can be calculated by dividing var() by the squared area. We performed the same calculations on the subset of mussels found on the surface of the substrate for comparison to mark-recapture estimates.

Mark-Recapture Sampling

Because Sycamore Lane and Payne Property were in the impact zone of the Certus Inc. chemical spill, we decided to use mark-recapture sampling to independently estimate abundance and density at those two sites. We used a robust design, mark-recapture framework (Pollock 1982) to sample these Clinch River sites during the late summer/early fall from 2015 to 2017. Each year's sampling represented a single primary period under the robust design framework. The population is assumed to be open to changes due to births, deaths, immigration, or emigration between primary periods, i.e., years. Each primary period consisted of two secondary sampling days as close to each other as possible, usually consecutive, when the population is assumed to be closed to changes due to births, deaths, immigration, or emigration. Each site was divided into 20-m wide transects oriented perpendicular to stream flow. Transects were divided into 1-m wide lanes oriented parallel to flow to ensure full spatial coverage of the site. Each lane was sampled visually by snorkeling from the downstream to upstream end. In areas too shallow to snorkel, we used view-scopes or slowly walked through transect areas and visually inspected for mussels. Substrate was not excavated during sampling. Each individual mussel was identified to species, sexed for dimorphic species, and measured for length to the nearest tenth millimeter using dial calipers. We also noted the collector of each mussel. Mussels already tagged had their tag number and tag color recorded. Any untagged mussels were tagged using Hallprint^® glue-on shellfish tags and cyanoacrylate glue. After processing, mussels were returned to the location from which they were sampled.

A set of eight candidate models was developed for estimating abundance. These models contained the following parameters:

S_i = Apparent survival during primary period i
γ' = probability of not being available for capture during primary period i, given that an individual was not available for capture during primary period
i – 1 (i.e., the probability of not immigrating back into study area)
γ ” = probability of not being available for capture during primary period i, given that an individual was available for capture during period i – 1 (i.e., the probability of temporarily emigrating)
p_ij = probability of being captured during secondary sampling occasion j of primary period i
c_ij = probability of being recaptured during secondary sampling occasion j of primary period i

All models assumed that capture probability was constant within a primary period (i.e., across the two secondary surveys) but could vary from one primary period to another {i.e., (p₁₁ = p₁₂) 6 = (p₂₁ = p₂₂)}. Temporary emigration was assumed to be constant and random {i.e., γ'(.) = γ”(.)}.

We created various a priori models as follows: Model 1 was the most general model, allowing both initial capture (p) and recapture (c) probabilities to vary with time between primary periods (interval between primary sampling period) and not equal each other between secondary sampling occasions within each primary period i.e., a behavior response to being captured initially. Model 2 still allowed capture and recapture probabilities to vary with time (interval between primary sampling periods) but they were equal for secondary sampling occasions within each primary period (i.e., no behavior response). Capture and recapture were constant between primary sampling periods in models 3 and 4, but model 3 had no behavior response, whereas model 4 had a behavior response. Survival varied with time between primary periods for all four models. Models 5–8 were equivalent to models 1–4 except that survival was constant.

We analyzed our candidate model set using Program MARK (White and Burnham 1999) to determine the model with the highest likelihood (Villella et al. 2004; Meador et al. 2011). Likelihood estimates were based on Akaike's Information Criterion (AIC) (Akaike 1973) modified for small sample sizes (AICc) (Sugiura 1978):

where L() is the likelihood of the parameter estimates, given the data, K is the number of parameters, and n is the sample size. We considered the best model as the one with the lowest AIC score and models were considered competing if ΔAIC <2.0. To estimate the abundance of both the total mussel assemblage and the population of Rainbow Mussel (Cambarunio iris) at the Payne Property, we used the top model in each case.

Table 3.

Mussel species that were assessed for expected abundance and density from 2015–2017 and again in either 2020 or 2021 at sites outside of the impact zone of the Certus Inc. chemical spill in the Clinch River, Virginia, and Powell River, Tennessee. These six species did not occur at restoration and monitoring sites before being released or occurred at very low densities. X indicates species was released at and assessed for expected abundance at that site.

Due to low recapture rates, we could not use the robust design model to estimate abundance at Sycamore Lane, although it was used to estimate abundance for both C. iris and the total mussel assemblage at the Payne Property. Therefore, at the Sycamore Lane site, we used the modified Lincoln-Petersen estimator (also known as the Chapman Estimator) to estimate mussel abundance. The formula used was

where is the estimated abundance, n₁ is the number of individuals caught on the first occasion, n₂ is the number caught on the second, and m₂ is the number of marked individuals caught on the second occasion (Chapman 1951). Standard error was calculated using the formula

from Pollock et al. (1990). Abundance estimates from the mark-recapture estimators (Lincoln-Petersen and the robust design model) were compared to estimates of mussels from quadrats, i.e., the combined surface and subsurface mussels and the surface-only mussels.

Expected vs. Estimated Mussel Abundance

We used a Leslie matrix model developed in collaboration with U.S. Department of the Interior economist Kristin Skrabis to estimate the expected number of total mussels at all nine restoration and monitoring sites in 2017, and in either 2020 or 2021. For the model, we assumed all mussels released at these sites could achieve a maximum age of 40 years, began breeding at 5 years old, and had an annual recruitment rate of 7.6% per year. Annual survival was set as 95% until age class 30, when survival began to decrease annually to a survival rate of 60% to the final age class. Maximum age and breeding age were chosen to represent a typical mussel species. The recruitment rate was set so that the population growth rate would be stable over the long-term. Survival rates were based on Jones et al. (2012), who based their survival rates on an empirical study of dead shells and a catch-curve analysis of shell-length at age and unpublished survival rates from field studies by the Virginia Department of Wildlife Resources, and survival rates reported for other long-lived mussel species (Musick 1999; Akçakaya et al. 2004). We assumed all mussels died after reaching 40 years of age.

We used the mussel release data compiled in Hyde and Jones (2021) as input for the model. Only mussels >6 months old at time of release were included in the analysis. We set mussels at 1 year-old at time of release (i.e., in the 1- and 2-year age-class). We included all mussel species released at the Payne Property and Sycamore Lane sites in the model. At the remaining monitoring sites, we included only those species released at the site that did not occur at those sites prior to restoration (Table 3.). Hence, the natural mussel assemblage at sites in the Clinch River in Russell County, Virginia, and in the Powell River, Tennessee, was not included in our analysis of expected versus estimated mussel abundance. We compared the expected number of mussels at all sites in 2015, 2016, 2017, and either 2020 or 2021 with actual abundance estimates based on quadrat and mark-recapture estimates and calculated the percentage of expected mussels not found during monitoring.

Mussel Length and Growth Rates

The shell growth rate of each tagged mussel sampled more than once was calculated using the following formula:

where M_f is the final measurement and M_i is the initial measurement. When an individual was sampled more than twice, G was calculated for each interval. In cases where the later measurement was less than the first measurement, we set the growth rate to zero rather than negative, as this was likely due to measurement error (since shell length, unlike mass, typically cannot decrease), and included the zero in the calculation of the mean and standard deviation.

Figure 2.

Estimated abundances (a) and densities (b) of freshwater mussels at population restoration and monitoring sites in the Clinch and Powell rivers, Virginia and Tennessee, based on quadrat sampling conducted from 2015–2017, and again in 2020 and 2021. Sites are ordered from upstream to downstream within each river, and error bars represent 95% confidence intervals.

We calculated mean lengths of tagged mussels released in 2013 at the Payne and Sycamore Lane sites for Wavyrayed lampmussel (Lampsilis fasciola), Kidneyshell (Ptychobranchus fasciolaris), and Mountain Creekshell (Leaunio vanuxemensis). Individuals from the 2013 cohort that were sampled from 2015–2017 during our quadrat and mark-recapture sampling were measured and mean lengths calculated for each year. We also calculated mean lengths of C. iris initially tagged during 2015 mark-recapture sampling and tracked the mean lengths of this cohort in 2016 and 2017. Finally, we compared mean lengths of P. fasciolaris among all sites using 95% confidence intervals.

RESULTS

Quadrat Monitoring Data

Across all nine monitoring sites, mussel abundances and densities were generally higher in 2017 compared to 2016, but lower than the first year of monitoring in 2015 (Fig. 2). Sampling in 2020 and 2021 found similar estimates as prior years. In the Clinch River, the Bennett Property had the highest abundances and densities of all sites in all four monitoring years. In the Powell River, Lower Brooks Bridge generally had the highest abundances across all sites and years and the highest densities in 2015 (2.03 m²) and 2016 (1.25/m²), but the highest density observed during the study was at the Oakley Property in 2021 (2.10/m²).

Clinch River.—Total mussel assemblage abundance and density at the Payne Property ranged from 1,257 individuals (0.36/m²) in 2016 to 2,537 individuals (0.72/m²) in 2015 (Tables 4 and 5; Fig. 2). Cambarunio iris was the most abundant species, followed by Lampsilis fasciola and Ptychobranchus fasciolaris. All three of these species were released at the site in relatively high numbers for restoration. Pocketbook (Lampsilis ovata), which also was released in high numbers at the site, was not found during any of the four monitoring years. However, this species has been found at the Payne Property more recently (Tim Lane, Virginia Department of Wildlife Resources, personal observation).

Table 4.

Estimated abundances of freshwater mussels at population restoration and monitoring sites based on quadrat sampling in the Clinch River, Virginia from 2015–2017, and again in either 2020 or 2021. Site at Cleveland Islands was located in the lower-half of the right-descending channel.

Table 5.

Estimated densities of freshwater mussels at population restoration and monitoring sites based on quadrat sampling in the Clinch River, Virginia from 2015 to 2017. Density is reported as individuals per m². Site at Cleveland Islands was located in the lower-half of the right-descending channel.

Total mussel assemblage abundance and density at Sycamore Lane ranged from 1,590 individuals (0.32/m²) in 2016 to 2,836 individuals (0.67/m²) in 2017 (Tables 4 and 5; Fig. 2). Cambarunio iris was the most common species, followed by P. fasciolaris and Ptychobranchus subtentus. Cambarunio iris was released in the highest numbers during restoration at this site, followed by L. fasciola and Cumberland Moccasinshell (Medionidus conradicus).

Total mussel assemblage abundance and density at the Bennett Property ranged from 22,920 individuals (2.73/m²) in 2016 to 42,360 individuals (5.07/m²) in 2020 (Tables 4 and 5; Fig. 2). The most common species was Pheasantshell (Actinonaias pectorosa), which was not released during restoration and was already present at the site. Epioblasma capsaeformis and Epioblasma brevidens, which were not present at the site before being released there, were the third and fourth most abundant species, respectively. In addition, 1,547 individuals of the federally endangered Snuffbox (Epioblasma triquetra) were released from 2017–2019 (most in 2018). This species was found during monitoring in 2020 with an estimated abundance of 652 individuals (0.8/m²).

Total mussel assemblage abundance and density at Artrip ranged from 4,423 individuals (1.02/m²) in 2016 to 11,359 individuals (2.58/m²) in 2015 (Tables 4 and 5; Fig. 2). The most common species in all years at the site was A. pectorosa. Epioblasma brevidens had the highest number of individuals released at the site, followed by releases of E. capsaeformis, which was detected during all monitoring years.

Total mussel assemblage abundance and density at the Whited Property ranged from 5,789 individuals (1.04/m²) in 2016 to 14,454 individuals (2.59/m²) in 2015 (Tables 4 and 5; Fig. 2). A. pectorosa was the most common species at the site in all years. However, mussel releases at the Whited Property were relatively low, and all occurred before 2013. Epioblasma capsaeformis was the species with the most individuals released but was only detected during the first monitoring year in 2015.

Total mussel assemblage abundance and density at Cleveland Islands in the right descending channel ranged from 2,423 individuals (0.58/m²) in 2016 to 8,529 individuals (1.93/m²) in 2015 (Tables 4 and 5; Fig. 2). Actinonaias pectorosa was the most common species, followed by Eurynia dilatata and Pleuronaia spp. Epioblasma capsaeformis had the highest number of released individuals at the site and was found in all monitoring years. Epioblasma brevidens was released in 2013 (N = 789) but was not detected during 2015 and 2016 monitoring. Epioblasma brevidens was released again in both 2017 and 2018 (>1,000 in both years) and was found in both 2017 and 2020.

Powell River.—Total mussel assemblage abundance and density at Upper Brooks Bridge ranged from 3,232 individuals (0.63/m²) in 2016 to 8,392 individuals (1.67/m²) in 2015 (Tables 6 and 7; Fig. 2). The most common species were A. pectorosa, followed by Mucket (Ortmanniana ligamentina), and M. conradicus. E. capsaeformis and E. brevidens had the most released individuals at the site and were last released in 2013, but only E. capsaeformis was found in 2021.

Lower Brooks Bridge had the highest abundance and density of the Powell River sites across all years. Total mussel assemblage abundance and density ranged from 8,860 individuals (1.24/m²) in 2016 to 14,367 individuals (2.04/m²) in 2015 (Tables 6 and 7; Fig. 2). Actinonaias pectorosa and O. ligamentina were the most dominant species. Epioblasma capsaeformis and E. brevidens were released as late as 2017. Epioblasma capsaeformis was not found in 2021, but E. brevidens still occurred at a density of 0.11 mussels/m².

Because of its small size, the Oakley site had the lowest abundance of the Powell River sites across all years but had the highest density in 2017 and 2021 and the second-highest density in 2016. Total mussel assemblage abundance and density ranged from 906 individuals (0.80/m²) in 2016 to 2,426 individuals (2.10/m²) in 2021 (Tables 6 and 7; Fig. 2). Epioblasma capsaeformis had the most released individuals at the site, almost all of which occurred in 2012. In 2016, abundances ranged from 32 individuals of Purple Wartyback (Cyclonaias tuberculata), L. fasciola, L. ovata, and P. fasciolaris to 259 individuals of E. capsaeformis, with densities ranging from 0.03–0.23/m² for these species, respectively. In 2017, abundances ranged from 21 individuals of E. brevidens to 418 individuals of E. capsaeformis, with densities ranging from 0.02 to 0.36/m², respectively. Overall, E. capsaeformis was the most common species at this site.

Mark-Recapture Monitoring Data

During mark-recapture sampling in 2015, we collected and tagged 105 untagged mussels in the Clinch River at the Payne Property. We also collected 26 mussels that were tagged from previous releases. The total number of observations (including mussels collected on both sampling days) was 137 at the Payne Property (Table 8). In 2016, we collected and tagged 92 untagged mussels at the Payne Property. Including mussels that were already tagged (42) and mussels observed on both days, we had a total of 147 observations. Of these observations, only 11 were recaptures from 2015. In 2017, we collected and tagged 99 untagged mussels at the Payne Property. We also sampled 31 previously tagged mussels and had a total of 141 observations, 18 of which were recaptures from 2015 and 2016. An individual E. capsaeformis collected at the Payne Property in 2015 was likely an inadvertent release from a past study or from hatchery-produced sources and the individual was removed from the site.

During mark-recapture sampling in 2015, we sampled and tagged 101 untagged mussels in the Clinch River at the Sycamore Lane site. We also collected 84 mussels that were already tagged from previous releases/studies for a total of 194 observations at Sycamore Lane (Table 8). During mark-recapture sampling in 2016, we sampled and tagged 184 untagged mussels at Sycamore Lane. Including sampled mussels that were already tagged (213), we had a total of 418 observations at Sycamore Lane. Of these mussels, only 13 were recaptures from 2015. During mark-recapture sampling in 2017, we sampled and tagged 253 untagged mussels at the Sycamore Lane site. Including sampled mussels that were already tagged (331), we had a total of 644 observations at Sycamore Lane, 49 of which were recaptures in 2015 and 2016. An individual E. brevidens collected at the Sycamore Lane site in 2016 was likely an inadvertent release from a past study or from hatchery-produced sources and was removed from the site.

Table 6.

Estimated abundances of freshwater mussels at population restoration and monitoring sites based on quadrat sampling in the Powell River, Tennessee from 2015–2017, and again in 2021.

We could not estimate abundance at Sycamore Lane using the robust design model (Pollock 1982), possibly due to lower recapture rates compared to the Payne Property, especially in 2016 when only 3% of observations were of previously observed mussels. Estimates using the Lincoln-Petersen estimator ranged from 976–1,872 individuals comprising the total mussel assemblage at this site. These estimates were generally higher than the quadrat abundance estimates calculated using only mussels found at the substrate surface during quadrat sampling, but not higher than quadrat estimates using combined surface and subsurface mussels (Fig. 3). For C. iris at Sycamore Lane, Lincoln-Petersen estimates ranged from 357–914 and were generally higher than surface quadrat estimates but lower than combined quadrat estimates (Fig. 3). We were unable to estimate apparent survival at Sycamore Lane for either the total assemblage or C. iris, despite having three years of data.

The top model for the total mussel assemblage at the Payne Property was Model 5, suggesting that detectability varied among years, and recapture rates of individuals marked on the first sampling day of each year were lower the next day (behavior response – likely due to captured mussels burrowing into the substrate after being returned to the stream). Abundance estimates for the total assemblage at the Payne Property ranged from 155–186 individuals and the estimate for apparent survival was 86% (95% CI [5%, 99%]). For C. iris at the Payne property, the top model was Model 8, suggesting that detectability was similar among years and recapture rates were lower on the second day of sampling. Abundance estimates for C. iris at the Payne Property ranged from 113–135 individuals and the estimate for apparent survival was 96% (95% CI [0%, 100%]). The lowest estimates of abundance for C. iris and the total assemblage at the Payne Property were calculated using the robust design model (Fig. 3). For the total assemblage, estimates of abundance based on surface quadrat data and the Lincoln-Petersen estimator of our mark-recapture data were similar, whereas the Lincoln-Petersen estimate of abundance was slightly higher for C. iris. At the Payne Property, estimates of abundance from combined quadrat data (surface and subsurface mussels) were comparatively higher for both the total assemblage and C. iris.

Table 7.

Estimated densities of freshwater mussels at population restoration and monitoring sites based on quadrat sampling in the Powell River, Tennessee from 2015–2017, and again in 2021. Density is reported as individuals per m².

Expected vs. Estimated Mussel Abundance

Estimated abundance was lower than expected abundance across all years at all sites, although this effect was especially pronounced at sites in the impact zone of the Clinch River (Payne Property and Sycamore Lane) (Fig. 4). Overall, the percentage of expected mussels not found during quadrat monitoring across sites and years ranged from 42.6%–97.6%, with a mean of 75.4% (Table 9). Mean discrepancies were similar for the Clinch and Powell River sites (75.7% and 74.8%, respectively). The Payne Property had the highest mean discrepancy across all years (85.3%), followed by Cleveland Islands RDC (83.8%) and Sycamore Lane (81.8%). The Bennett Property had the lowest discrepancy at 57%.

Mussel Length and Growth Rates

Shell growth rates of mussels sampled during mark-recapture surveys were calculated only for C. iris at the Payne Property and Sycamore Lane sites due to low recapture rates of other species. The mean length of C. iris increased by 1.34 mm, or 3.7%, from 2015–2017, with a mean growth of 0.67 mm (1.85%) per year. No tagged mussels from 2015 were recaptured in 2020.

The mean lengths of L. fasciola, P. fasciolaris, and L. vanuxemensis from the 2013 release cohort all increased substantially from 2013–2015, with a much slower increase from 2015–2017 (Fig. 5). Only two individuals of P. fasciolaris and one individual of L. vanuxemensis from the 2013 cohort were observed in 2020. This 2013 cohort tracks mussels released in 2013 and later sampled during mark-recapture surveys from 2015–2017 (Fig. 5). Growth rates of C. iris from the 2015 mark-recapture cohort were similar to the other three species from 2015–2017 (3.8 mm) (Fig. 5). This cohort represents untagged mussels that were first sampled during mark-recapture surveys in 2015 and later sampled in 2016 and 2017. Based on confidence intervals, mean lengths of P. fasciolaris were significantly lower at the Payne Property and Sycamore Lane sites compared to most other monitored sites (Fig. 6).

Table 8.

Numbers of mussels sampled at two population restoration and monitoring sites in the impact zone for the Certus Inc. NRDAR case in the Clinch River, Tazewell County, Virginia, using transect guided mark-recapture sampling from 2015–2017. An asterisk (*) indicates inadvertent release and individual was removed from site. These values indicate observations during each pass, including observations of the same mussel during both passes.

DISCUSSION

There are several potential causes of lower estimated abundance relative to expected abundance. Estimates of mussel abundance from quadrat surveys were 57% to 85% lower than the expected number of mussels (based on past releases and expected survival and recruitment rates) at all restoration and monitoring sites for both the Certus and LMPI NRDAR cases (Table 9; Fig. 4). First, survival might be lower than we are currently assuming in the Leslie matrix model (e.g., 95% per year). Possibly, the release of propagated individuals into the wild might result in a higher-than-expected mortality. High initial mortality after releases for reintroduction are common for many taxa (Sarrazin and Legendre 2000). At the Payne Property, there were anecdotal reports of Canadian geese possibly feeding on mussels for several days after the mussels were released (Tim Lane, Virginia Department of Wildlife Resources, personal communication). However, estimates of apparent survival from our mark-recapture survey suggest survival is relatively high at the Payne Property (86%–96%), although these data do not cover the timeframe of initial release. Further, freshwater mussels typically have high annual survival rates. A study of naturally occurring Threeridge (Amblema plicata) in the Mississippi and Otter Tail rivers, Minnesota, found that annual survival was greater than 97% in natural habitats (Hart et al. 2001). Meador et al. (2011) found high annual survival of naturally occurring mussels in slack-water and pool habitats (>90%) in the Altamaha River, Georgia, in 2006 and 2007, although mussels in swift-water habitats had somewhat lower survival (75%). Villella et al. (2004) found annual survival was >90% for three species of naturally occurring adult mussels of Eastern Elliptio (Elliptio complanata), Northern Lance (E. fisheriana), and Yellow Lampmussel (Lampsilis cariosa) in the Cacapon River, West Virginia. Carey et al. (2015) found that 65–70% of laboratory-propagated E. capsaeformis released in 2010 and 2011 in the Clinch River at Cleveland Islands survived when the population was sampled in 2011 and 2012. A recovery survival rate of 82% also was observed a year after the release of laboratory-propagated E. brevidens into cages in the Powell River, Tennessee (Hua et al. 2011), although the cages may have contributed to high survival. However, recovery of PIT tagged E. brevidens also found high month-to-month survival (0.98) at this same site over a 2-year period (Hua 2015). Thus, available data suggest lower-than-expected annual survival is not the major contributor to the lower-than-expected abundance found at our sites. Regardless, given the potential for high initial mortality, it would be prudent to bury mussels when released, rather than spreading them on top of the substrate, especially if the presence of predators has been observed.

Figure 3.

A comparison of abundance estimates for the total mussel assemblage and Cambarunio iris at the Sycamore Lane and Payne Property sites, Clinch River, Tazewell County, Virginia, from 2015–2017 based on quadrat and mark-recapture surveys. Surface quadrat abundance was calculated using only mussels found on the surface of the substrate during quadrat surveys. All quadrat abundance includes surface and subsurface mussels. Both the modified Lincoln Petersen estimator and robust design model were used to estimate abundance from data collected from mark-recapture surveys. Error bars represent standard error.

Another possibility is that mussels released at restoration sites are dispersing downstream from the immediate release and monitoring areas. For example, out of 100 mussels relocated in the Kishwaukee River, Illinois, 20 were detected outside of the relocation area over the course of three years, one of which moved approximately 50 m downstream over two months (Tiemann et al. 2016). However, this study only included a buffer zone of 75 m downstream of their immediate sampling area. Other studies have found limited downstream movement. Balfour and Smock (1995) found that the mean net movement downstream of 84 naturally occurring E. complanata (out of 160 initially tagged) in a first-order stream in Virginia over the course of a year was 27 cm, although three mussels (i.e., outliers) moved much further than 27 cm (12.5 m upstream, 25.5 m upstream, and 46.2 m downstream). Another study found the probability of moving downstream among twelve 20-m sections of stream was less than 1% over a period of four years with most movement within 40 m (Villella et al. 2004). Increasing the recapture area compared to the initial sampling area can detect greater movement of mobile organisms such as fish (Albanese et al. 2003). This might also apply to mussels, although the effect would likely be less pronounced. None of the above studies were explicitly examining downstream dispersal. Further, both Balfour and Smock (1995) and Villella et al. (2004) were examining natural populations of mussels. Propagated mussels released into the wild or translocated mussels released at a different site might have higher downstream dispersal than natural populations, possibly due to a failure to burrow sufficiently and thus being more susceptible to high-flow events (Stodola et al. 2017). We also found some evidence of downstream dispersal in our study when qualitatively sampling (visual/snorkel) other potential mussel habitat in the impact zone of the Certus Inc. spill. Two tagged mussels were found at least a kilometer downstream from where they were released at the Sycamore Lane site (one C. iris and one A. pectorosa), and we observed a dead, tagged L. fasciola ∼150 m downstream of its release location at the Payne Property. In 2015, we observed a tagged Flutedshell (Lasmigona costata) in the downstream section of Sycamore Lane, which was released at the Payne Property in 2009, approximately 2.5 km upstream. Several E. capsaeformis and E. brevidens released in the Powell River, Tennessee, in 2012 were observed alive 300 meters downstream of Upper Brooks Bridge in June 2022, and females were observed displaying their mantle lures (Tim Lane, Virginia Department of Wildlife Resources, personal observation). Finally, P. collina have been observed about 2 km downstream from release sites in Rock Island Creek (Brian Watson, Virginia Department of Wildlife Resources, personal communication). Future monitoring should include some form of sampling farther downstream of the immediate monitoring area to account for dispersal.

Figure 4.

Comparison of expected versus estimated abundance of released mussels that did not previously occur at nine monitoring sites in the Clinch and Powell rivers, Virginia and Tennessee (see Table 3 for a list of mussels included). Expected abundance was determined using release data inputted to a Leslie matrix model assuming 95% survival, and estimated abundance was determined from quadrat sampling data. Bars represent discrepancy (in percentage) from expected abundance. Abundance was not estimated for the Whited Property in 2016 and 2017 because the species released at the sites were not detected in those years.

It is also possible that a high proportion of newly transformed juvenile mussels are excysting from host fish outside of the monitoring areas, i.e., fish that were infected with glochidia from mussels released at these restoration sites. For example, C. iris, L. ovata, and L. fasciola use mobile Centrarchids such as Rockbass (Ambloplites rupestris), Large-mouth Bass (Micropterus salmoides), and Smallmouth Bass (Micropterus dolomieu) as hosts and their transformed glochidia wouldn't be expected to settle in the immediate areas where fish hosts were initially infected. Hence, setting recruitment to zero in our Leslie matrix model decreases the expected number of mussels in 2017 at the Payne Property to 10,800 individuals and at Sycamore Lane to 10,996 individuals. However, zero recruitment alone cannot account for the large discrepancies between our expected densities and estimated densities from quadrat samples, especially at these two sites.

Table 9.

Percentage of expected mussels not accounted for in quadrat estimates at each restoration and monitoring site in the Clinch and Powell rivers in Tennessee and Virginia. Percentage unaccounted mussels was likely a function of both emigration and additional mortality. Cleveland Islands was the lower-half of the right-descending channel.

Figure 5.

Mean lengths of selected mussel species at the Payne Property and Sycamore Lane sites in the Clinch River, Virginia, from 2013–2020. Subfigures a–c track cohorts of mussels released in 2013 and their mean lengths when recaptured during monitoring. Subfigure d tracks mussels tagged in 2015 during mark-recapture surveys and their mean lengths when captured during subsequent mark-recapture surveys. Numbers above means represent sample size and error bars show standard deviation. Standard deviation was not calculated for Ptychobranchus fasciolaris in 2020 because both mussels were the same length.

Another possible explanation is that we failed to collect 100% of the individuals present in our sampling units (e.g., sampling lanes or quadrats) at the surveyed sites. For example, Balfour and Smock (1995) found that most mussels <3 years old remained buried in the sediment year-round. Amyot and Downing (1991) found that mussels that were buried in mid-summer tended to be smaller and were likely juveniles. The expected age distribution of mussels at the Payne Property and Sycamore Lane sites in 2017 suggests that 35% of the mussels might be <5 years old. This observation might have caused a negative bias in our mark-recapture estimates, given that we were searching only on the surface. However, if detectability was near 100% in the quadrat survey, buried juveniles should have been detected (and were in our study) and thus would not have affected our abundance. Collector experience can also affect detectability (Wisniewski et al. 2014), suggesting that differences in the experience of collectors may have influenced the survey results of both methods. For example, during quadrat surveys there was a consistent decline in estimated abundance across all sites in 2016 compared to 2015 (Fig. 2). While this decrease may be partly due to a real decrease in abundance, it seems unlikely that such a consistent decrease in estimated abundance would be entirely a result of an actual decrease in abundance, given that our sites were in two different watersheds.

Figure 6.

Mean lengths of Ptychobranchus fasciolaris at restoration and monitoring sites in the Clinch and Powell rivers, Virginia and Tennessee, from 2015–2017 and in 2020 and 2021. Error bars represent 95% confidence intervals. The error bar for Oakley property in 2016 was not calculated because only one mussel was sampled.

Taken together, the reasons for the discrepancy between expected and estimated abundance have substantial implications for future planning of mussel restoration via propagation. Based on Leslie matrix analysis and monitoring, up to 85% of the expected number of mussels (based on number released and expected survival) were unaccounted for. Mussels that emigrate downstream from a release site and are alive should still be credited toward restoration, even if they are no longer at the immediate restoration site, because they satisfy NRDAR's criterion of replacement and/or acquisition of equivalent natural resources. However, mussels that have died because of higher-than-expected mortality should not be credited. Knowing what proportion of this discrepancy is due to higher-than-expected mortality rather than emigration is important for planning, as it would allow for a more realistic estimate of the necessary yearly production to result in the targeted abundance. Future studies should examine dispersal rates of live mussels downstream. Survival also could be studied more thoroughly using PIT tags or estimates from the recovery of dead shells, especially in the period immediately following releases. Thus, future sampling designs should include assessment of areas downstream of the immediate release area to determine site-specific emigration and survival rates.

The large mussel assemblage present in the Clinch River at the Bennett Property is mostly due to one species (A. pectorosa) that was already naturally present at the site and was not released there as part of ongoing propagation efforts. However, E. capsaeformis, E. brevidens, E. triquetra, and several other mussel species listed as endangered were not present at the Bennett Property prior to propagation efforts and they are now among the most common species at this site. The only species released in the Clinch River at the Whited Property was E. capsaeformis, which was not detected in quadrat samples in 2016 or 2017, although a few individuals were collected there in 2015. Similarly, E. brevidens was detected only in 2017 in the right descending channel of Cleveland Islands in the Clinch River, although E. capsaeformis was collected in this channel at a higher density and abundance in 2015 compared to 2016 and 2017. In the Powell River, at the Upper and Lower Brooks Bridge sites, as well as at the Oakley Property, a high proportion of E. capsaeformis and E. brevidens were collected relative to the number of mussels released, suggesting survival was higher than expected at these sites.

Compared to quadrat sampling and the Lincoln-Petersen estimator, a robust design model tended to underestimate abundance. This outcome is likely due to the very low recapture rate, both within and among primary periods, making modeling difficult. For example, of the 39 C. iris sampled in 2015 on the first sampling day at Sycamore Lane, only six were recaptured the next day. Of the 88 C. iris that were sampled on both days in 2015, only 8 were sampled again in 2016, whereas 183 were sampled for the first time that year. Thus, one should expect actual abundance to be much higher than the number sampled and likely higher than the estimates from the robust design model. It is also possible that smaller individuals were buried in the sediment and unavailable for capture during our mark-recapture survey, which also would underestimate abundance.

The Lincoln-Petersen estimator provided a better, lower-bound estimate of abundance compared to the robust design model. It generally yielded higher estimates of abundance relative to abundance estimates made using the surface quadrat data because it accounts for <100% detectability. Both estimates accounted only for mussels found on the substrate surface. However, the mark-recapture surveys were typically conducted during the early fall when detectability at the substrate surface was expected to be higher, whereas the quadrat surveys were conducted in mid-to-late summer, when detectability at the substrate surface was likely lower (Carey et al. 2015).

The mean length of P. fasciolaris was significantly lower at the Payne Property and Sycamore Lane sites than at the other sites. Physicochemical factors, such as habitat, temperature and degree of eutrophication, can affect the growth rates and sizes of freshwater mussels via effects on productivity of the habitat and metabolism of mussels (Bauer 1992). However, the majority of P. fasciolaris released before monitoring in 2015 and 2016 was at the Payne Property and Sycamore Lane sites (741 and 608, respectively). Only 196 mussels were released at the Bennett Property, and none were released at the other 6 restoration and monitoring sites. Many of the mussels released at the Payne Property and Sycamore Lane sites also were released before 2013, and because these sites were in the impact zone of the Certus Inc. chemical spill, there was no population of these species present before releases. The smaller size of the P. fasciolaris populations at these sites is likely because the populations there are much younger than populations at other restoration sites. Further, the mean length of P. fasciolaris, L. fasciola, and L. vanuxemensis in the impact zone sites increased 20–28 mm from 2013–2017. Growth of C. iris from 2015–2017 was only 3.7 mm, but this was not much lower than the 4.8–7.4 mm that the other three species grew during the same period. Since three of the four species were released before 2013, it is likely that the much lower growth from 2015–2017 was a result of mussels reaching an age where overall growth rate begins to slow down.

Growth rates of C. iris at the Payne Property and Sycamore Lane sites were similar to comparably sized C. iris sampled at three sites in the Clinch River from 1988–1993 (Scott 1994). The same study found that growth rates of L. fasciola also were similar to comparably sized L. fasciola at four sites in the Clinch River. Hence, our results suggest that growth has not been negatively affected at the Payne Property and Sycamore Lane sites in the impact zone of the Certus Inc. chemical spill.

Overall, there is evidence of successful restoration for the Certus NRDAR case. Although current abundances in the impact zone have not reached baseline conditions (i.e., 18,621 mussels), we observed released mussels that had grown to breeding ages, displaying females, and recruitment of one species (C. iris) in all three monitoring years (length <20 mm). Because the local mussel assemblage at these sites was completely extirpated, released mussels are clearly breeding successfully. Collectively, our observations indicate moderate restoration success based on the criteria presented in Table 1. Abundance at the two monitoring sites in the immediate impact zone is lower than expected based on assumed survival, but there are populations of numerous species that have low-to-medium densities. Moreover, 37,101 mussels >6 months old, representing 14 species, were released in the impact zone, and another 60,486 mussels representing 20 species have been released at restoration and monitoring sites downstream in the Clinch River in Russell County (see Table 1.18 in Hyde and Jones 2021). Together, this total is far greater than the estimated 18,621 mussels killed during the spill, and therefore restoration at these sites satisfies the NRDAR criteria of recovering or acquiring equivalent natural resources as those injured. Further, the estimated kill was calculated by multiplying dead mussels by three to account for mussels buried in the substrate (U.S. Fish and Wildlife Service 2001). If the spill caused a significant number (e.g., 80%) of mussels to migrate to the surface before dying, then that 3x multiplier overestimated the injury. However, no quantitative sampling was conducted after the spill to validate the use of the 3x multiplier. Future spill assessment studies should include some quantitative sampling, such as excavation of quadrats, to more accurately determine the best multiplier for estimating injury. Although it is unknown why estimated abundance is lower than expected in the impact zone, if released mussels are migrating downstream, then they should still be counted toward restoration for the Certus Inc. NRDAR case. In 2016 and 2017, 731 E. aureola were reintroduced in the Clinch River, 300 of which were released in the impact zone at Sycamore Lane. However, no individuals from this release have been observed alive since 2019 (Sarah Colletti, Virginia Department of Wildlife Resources, personal communication). Further, E. aureola surrogates–E. capsaeformis and E. brevidens–have been well established at other augmentation sites in the Clinch River, Virginia, and they are now the second and third most common species at the Bennett Property, despite not occurring there before restoration.

Restoration success for the LMPI NRDAR case was harder to measure as the impacts to mussels were potentially chronic and sub-lethal. Nevertheless, E. capsaeformis, one of the primary species released in the Powell River, Tennessee, and one which did not occur at restoration sites prior to release, is currently found at low-to-moderate densities at all three sites in the Powell River. These mussels include breeding-age individuals, and both gravidity and evidence of recruitment have been confirmed. Quantification of success for LMPI could have been improved by setting clear, explicit goals to define success.

To document full success, i.e., long-term presence of a stable population, in these and future NRDAR cases involving freshwater mussels, requires long-term monitoring well past the point of final restoration activities, i.e., >20+ years. This recommendation is due to many mussel species having periods of low recruitment punctuated by years with exceptionally high recruitment (Jones et al. 2012). More moderate strength of evidence could be obtained over the medium-term by documenting an increase in the number of recruits in the years (5-10) immediately following restoration activity. Ideally, this increase should be documented long enough after restoration activities that the increase could be attributed to successful breeding of 1^st-generation recruits of released mussels, i.e., recruitment is not solely due to released mussels. Regardless, determination of restoration success in future NRDAR cases require both clear, concrete metrics for what constitutes baseline conditions, as well as medium- to long-term monitoring of restored populations.

It may not always be feasible to release enough mussels to reach baseline conditions or to recover the value of their lost ecosystem services. A case such as Certus may result in hundreds of thousands of lost mussel-years and associated services because of the services that would have been provided over the lifetime of the injured mussels and their offspring (Jones et al. 2012). Nonetheless, recruitment from released mussels must be sufficient to eventually reach restoration goals. To determine the appropriate amount of restoration, assumptions about survival and recruitment rates must be made. This study used a Leslie matrix with reasonable age-specific survival rates for long-lived species and sufficient recruitment to maintain a stable population to estimate the expected number of mussels at restoration and monitoring sites for two NRDAR cases. Monitoring of these sites suggested that either survival and/or recruitment of released mussels were lower than expected or that mussels are settling and/or recruiting downstream of the monitoring area. Further study is needed to determine the reasons for this discrepancy and to inform the amount of restoration needed for future NRDAR cases involving freshwater mussels.

Finally, we have developed a set of metrics that can be used to assess whether mussel restoration was successful (Table 1). These metrics range from easy, such as survival of mussels to breeding age, to difficult, such as determining the establishment of a self-sustaining population. They will be useful for designing monitoring programs for future restoration mussel-restoration activities, including determining what metrics are feasible given case-specific time and monetary restraints. Although the Certus Inc. and LMPI NRDAR cases did not assess all of these monitoring metrics, our study represents the largest evaluation to date of restoration of freshwater mussels in a NRDAR context, and we urge future monitoring programs to assess as many of these metrics as reasonable.

ACKNOWLEDGMENTS

Financial support for this project was received from the U.S. Department of the Interior's Office of Restoration and Damage Assessment, Washington, D.C., the U.S. Fish and Wildlife Service and the Virginia Department of Wildlife Resources, with whom we have collaborated extensively on this project. We thank economist Dr. Kristin Skrabis from the U.S. Department of the Interior for her invaluable help with developing the Leslie Matrix used for analysis. We also thank students and technicians at the FMCC, Virginia Tech, who helped with the field and laboratory work for the project, including Aaron Adkins, Anna Dellapenta, John Moore and Andrew Phipps, staff from Virginia Department of Wildlife Resources including Sarah Colletti and Tiffany Leach, and Dr. Catherine Gatenby, U.S. Fish and Wildlife Service. We also thank Dr. Paul Angermeier, U.S. Geological Survey, Blacksburg, Virginia, and several anonymous journal referees, all of whom reviewed and helped improve the quality of the manuscript.

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Water Pollution Assessment and Community Perception of Major Rivers in Ghana

Tue, 14 Apr 2026 16:00:00 GMT

Abstract

Water pollution poses risks to health and livelihoods of communities that depend on river systems. The major rivers in Ghana have experienced significant pollution, but limited studies have examined water quality conditions and community perceptions of pollution sources. This study assessed water quality and community perceptions of major rivers in Ghana. Water samples from 6 major rivers were analysed for physical, chemical, and biological parameters based on APHA Standard Methods for Water Examination (22nd Edition). A survey was conducted on 400 residents using a stratified random sampling technique. ANOVA, Pearson correlation, PCA, and regression were employed to process the data. Analysis revealed arsenic (0.58-0.67 mg/L), lead (0.66-0.95 mg/L), mercury (0.10-0.14 mg/L), and E. coli (up to 1349.75 MPN/100 mL) concentrations. Nitrate increased from 15.12 mg/L upstream to 19.06 mg/L downstream, while E. coli was high midstream (1349.75 MPN/100 mL). Principal Component Analysis (PCA) identified arsenic, lead and mercury as major contributors to overall water pollution. Age, Education level, and gender influence pollution perceptions (R² = .654-.69). Mining activities (59%) dominated water pollution compared to industrial discharges, agricultural runoff, and domestic sewage. Effective strategies identified included stricter regulations (97.14%), sustainable farming (97.14%), and community campaigns (95.71%). Findings revealed that river pollution contributes significantly to economic, health, and psychological issues. Public awareness and demographic characteristics shape pollution perception, behaviour and policy responses. The Environmental Protection Agency (EPA), the Ministry of Sanitation and Water Resources, and the Water Resources Commission (WRC) should prioritise stricter regulations, sustainable practices, and community engagement to protect rivers in Ghana.

Background to the Study

River resources have become crucial in ensuring safe drinking water, good sanitation and hygiene, and promoting food production, economic growth and development. However, river pollution has become a major global, national and local issue due to industrialisation, human population growth, its root cause, and associated health risks. River pollution contributes significantly to about 80% of global waterborne diseases, including cholera, cryptosporidiosis, diarrhoea, and typhoid.^1-3 According to the World Health Organisation,³ it contributes to approximately 1.2 million deaths annually, specifically among children under 5 years old. This threatens the achievement of the United Nations Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 14 (Life Below Water), and SDG 15 (Life on Land).^4-7 Globally, laws and wastewater treatment plants have been designed to control the discharge of pollutants.^8-10 Similarly, community-led programmes, including river clean-up campaigns, have been promoted to ensure environmental awareness.^11,12 However, weak enforcement of regulations, lack of adequate and efficient wastewater treatment plants, and economic reasons, as well as sporadic and very short-lived efforts without adequate sustained support, have impeded the success of some of these efforts.^13-15

Custodio et al¹⁶ and Ouma et al¹⁷ revealed that unregulated mining activities in regions such as South America discharge hazardous substances, heavy metals (mercury and arsenic), and sediments in nearby rivers. The Yangtze River in China and the Amazon Basin in South America, among the largest rivers in the world, have experienced a significant decline in water quality as a result of elevated human and industrial activities.^18,19 The level of pollution in the Surabaya River, a major river of Surabaya in Indonesia, has increased greately beyond human consumption.^20,21 This has significantly affected freshwater ecosystems, posing risks to biodiversity, human health, agriculture, and economic stability, particularly in regions with limited technological capacity. The World Health Organisation (WHO),³ Inyinbor Adejumoke et al,²² Ogidi and Akpan,²³ and Kay et al²⁴ claimed that the pollution of rivers affects agricultural productivity by reducing crop yield, as a result of toxic substances absorbed through contaminated rain irrigation, leading to food poisoning and economic losses among growers of the produce.²⁵ Moreover, the industrial sectors, especially those that rely on the use of freshwater for manufacturing and processing, face increased production costs due to water purification requirements.²⁵ The total global economic impact of health problems due to contaminated water exceeds $260 billion/year, particularly in developing nations.³

Water pollution in Ghana has become an alarmingly serious issue with the presence of heavy metals, plastics and sediment entering the major rivers.²⁶ About 60% of rural Ghana rely on untreated surface water sources containing emerging pollutants.^27,28 Ghana Water Company and the Ghana Health Service indicated that the remaining 40% of the rural water sources are unsafe for drinking purposes due to the presence of a considerable amount of microbial contaminants.²⁹ Industrialisation, unauthorised mining activities and farmland runoff also affect water quality of Ankobra, Pra, and Volta Rivers.^30-32 Shockingly, studies have revealed high concentrations of mercury in both the Pra and Ankobra Rivers, exceeding WHO recommendations for safe drinking.^33-36 According to the findings of these studies, this high concentration results in serious health risks, including mercury poisoning, for communities that depend on them for drinking and fishing.

Karikari et al³⁷ proved that river pollution in Ghana has contributed to the occurrence of poor bone and child formation, absence of body parts at birth, and respiratory, kidney, and cardiovascular diseases. Agricultural practices also exacerbate the problem of water pollution by releasing harmful Escherichia coli (E. coli), nitrates, and phosphates into water sources. This increases the likelihood of cholera, dysentery, and gastroenteritis outbreaks.³⁸ These health risks necessitate urgent interventions, as projections suggest that Ghana may be forced to import potable water by 2030 if pollution trends persist.³⁹ Farmers who use polluted river water for irrigation experience reduced soil fertility and lower crop yields due to contamination from nitrates, phosphates, turbidity, and industrial chemicals. Additionally, the elevated levels of turbidity caused by sedimentation and pollution in the waters not only affect the aquatic life but also lead to a decline in fish population, which plays a crucial role in the diet and economy of many Ghanaian communities.

The Ghanaian government introduced various initiatives to protect the river bodies. These include a media campaign launched in 2017 called “Stop Galamsey,” aimed at raising the level of awareness among the populace about the destruction caused by environmental degradation emanating from illegal mining activities.^40-42 Nevertheless, such efforts criminalise and stigmatise artisanal small-scale miners rather than providing a solution to the water pollution created by such activities. Additionally, the government established the Minerals and Mining Act of 2006 and the Inter-Ministerial Committee on Illegal Mining to control small-scale miners and ensure that the country’s environment is properly governed.⁴³ Additionally, it has established the Community Mining Scheme and the National Alternative Employment and Livelihood Programme. These not only form the solution to providing minimal miners with the chance to mine legally, but it has further established socio-economic interventions such as the Youth in Agriculture Programme and Alternative Livelihood Projects.^44,45 These projects train the involved miners to shift their roles to legitimate job opportunities. Nevertheless, the above-mentioned efforts have been hampered by inadequate law enforcement and corruption within the involved agencies.

Land and water resource restoration initiatives, such as the Ghana Landscape Restoration and Small-Scale Mining Project (GLRSSMP), focus on the restoration and rehabilitation of the affected land and water bodies but are also hampered by the sustainability of the project.^46,47 Projections indicate that Ghana could face severe water scarcity by 2030 if current pollution trends persist.⁴⁸ However, the most important question at this juncture is how Ghana can sure that there is no water crisis in the future despite the water pollution and degradation of the environment at the current moment. Darko et al⁴⁹ examined water quality issues in urban rivers in Kumasi and found significant contamination by heavy metals such as arsenic (As), cadmium (Cd), and lead (Pb). Alhassan et al⁵⁰ and Yirenkyi-Fianko and Ottou⁵¹ studied water pollution in Birim River in the Eastern part of the country, detecting a high concentration of arsenic, mercury, and lead elements in water sources around the mining communities. Egbi et al⁵² assessed water quality in the Volta River and reported significant increase in mercury concentrations in water bodies near artisanal gold mining areas. Craswell⁵³ assessed water pollution level in major water bodies in the Western Region, measuring the contribution of agricultural runoffs to nitrate and phosphate concentrations. The findings indicated that water bodies in close proximity to farming communities exhibited significantly higher concentrations of these nutrients. However, these studies and others are usually centred on specific regions and rivers.

According to Amponsah et al.⁵⁴ Lima et al.⁵⁵ and Vasistha and Ganguly,⁵⁶ the lack of complete assessment of the physical, chemical and biological water pollution indicators across many rivers over extended periods makes it difficult to evaluate the full extent of water pollution and its impact on aquatic ecosystems. Additionally, Olisah et al⁵⁷ and Xu et al^58,59 asserted that variability in approaches and the failure to consider seasonal variations significantly impact the concentration of pollutants. This could hinder policymakers and organisations from providing evidence-based policies to control and mitigate water pollution. There is a need to track changes in pollution levels, quantify the exact sources of pollution and their spatial distribution across major river basins (Ankobra, Bia, Densu, Pra, Tano, and Volta) in Ghana. Nonetheless, Anthonj et al,⁶⁰ Benameur et al,⁶¹ and Mustafa et al⁶² found that understanding public awareness and behavioural responses to water contamination is key to informing policy interventions and improving public health. Despite growing awareness of water pollution, traditional ecological knowledge and local perceptions about water resource management among Ghanaian communities are often given less attention in modern environmental management approaches.⁶³

Very few studies, according to the review, have investigated the local perception of water pollution as well as its impact on the health of local communities, as noted by Abraham et al,⁴⁶ Baffoe et al,⁴⁸ and Abanyie et al.⁶⁴ This implies that local perceptions of the major rivers in Ghana is very low. Therefore, studies are needed to integrate local knowledge into modern water management strategies and track shifts in public attitudes and behaviours regarding water quality, pollution prevention and conservation efforts. The purpose of the study is to examine the current state of water quality and identify pollution sources and perceptions of major rivers in Ghana. The study seeks to (1) assess the physical, chemical, and biological quality of water in the 6 major rivers, and (2) determine the primary sources of pollution affecting the rivers.

Furthermore, the study aims to (3) examine the views of local communities on water pollution and its effects. In addition, the study aims to explore the practical approaches towards improved water management and pollution control, based on local and international best practices for sustainable development. The findings of this study provide scientific information on health hazards associated with water pollution, hence providing a scientific basis for efforts aimed at improving water management, pollution control, and health education of the public. This study also contributes to the achievement of Ghana’s national development priorities and the Sustainable Development Goals (SDGs) and provides necessary data for policymakers to develop effective strategies for improving water quality and public health.

Materials and Methods

Research Design

A quantitative cross-sectional design was employed to assess the levels of pollution and perceptions influencing the pollutants in major rivers in Ghana. This design allows the collection of data at 1 point in time only. It measured pollutant concentrations, water quality indicators, and obtained data on community perceptions via structured surveys and the sampling of water (see Figure 1). Water samples were collected from 6 major rivers in strategically three (3) chosen sampling points: upstream, midstream, and downstream, to capture variations in pollution levels. Laboratory analysis measured pollutant concentrations, water quality indicators, presence of heavy metals and microbial contaminants. This provides an efficient impact on the assessment of pollution sources affecting the river ecosystems.

Figure 1.

Method flowchart.

Community perceptions (households, fishermen, and farmers) regarding the water quality of rivers (for drinking water, fishing, agriculture and health risks) were measured using a structured questionnaire. The questionnaire consisted of closed-ended questions, including Likert-scale items assessing knowledge of sources of pollution, attitude towards water conservation, and awareness of health risks associated with the rivers. This method ensured that responses could be quantified and statistically analysed to determine how awareness and behaviours correlate with pollution levels in affected communities. However, the cross-sectional design allowed for the comparative assessment between pollution levels and public perceptions. This study design ensured that environmental and social data were simaltaneously collected, allowing statistical analysis of the relationships between pollution levels and community awareness and behavioural patterns. Additionally, the design was cost-effective and efficient in gathering data quick, which is particularly beneficial in rural areas where extended data collection could pose logistical challenges.

Population and Study Area

The study population included major rivers and communities (households, fishermen, and farmers) along the rivers. The rivers consisted of River Pra, Densu, Tano, Ankobra, Bia, and Volta. Pra River flows through Dunkwa on Offin, Twifo Praso, and Beposo, where mining activities have significantly affected water quality. Densu River sustains communities such as Nsawam, Amasaman, and Weija, which are endangered by urbanisation and mismanaged domestic waste. Tano River sustains communities including Ntotroso, Techiman, and Elubo, which have impaired quality of water due to agricultural runoff. Ankobra River supports Prestea, Ankwaaso, and Dominase communities that have been threatened by the mining activities. Ankobra River flows through Prestea, Ankwaaso, and Dominase, and is impacted by mining operations. Ankobra River is one of the sources of water for Prestea, Ankwaaso, and Dominase communities and its quality has been influenced by mining effluent. Bia River is close to Dadieso, Kwadwo Addaikrom, and Pomakrom and is threatened by pollutants from agricultural sources such as deforestation and pesticides. Volta River is a source of drinking water for Akosombo, Sogakope, and Kete Krachi and is exposed to pollutants from industries, agricultural runoff, and domestic sewage. These rivers are assessed to better understand the impact of human activity on their quality, as their deterioration poses risks to biodiversity, public health, agriculture, and economic development in Ghana (Figure 2).

Figure 2.

Study area.

This study covered eighteen communties (Dunkwa on Offin, Twifo Praso, Beposo, Nsawam, Amasaman, Weija, Ntotroso, Techiman, Elubo, Prestea, Ankwaaso, Dominase, Dadieso, Kwadwo Addaikrom, Pomakrom, Akosombo, Sogakope and Kete Krachi). Dunkwa on Offin, Twifo Praso, and Beposo are positioned along the Pra River. Nsawam, Amasaman and Weija are located along the Densu River. Ntotroso, Techiman, and Elubo are situated along the Tano River, while Prestea, Ankwaaso, and Dominase are found along the Ankobra River. The Bia River communities include Dadieso, Kwadwo Addaikrom, and Pomakrom. Relevant towns along the Volta River include Akosombo, Sogakope, and Kete Krachi. Dunkwa on Offin has a population of approximately 33 379, while Twifo Praso is home to about 23 000 residents. Beposo has an estimated population of 5000, and Nsawam has about 93 799 inhabitants. Amasaman has approximately 30 000 residents, Weija has around 85 000 and Dadieso has a population of 12 000. Kwadwo Addaikrom has about 8000 residents, Pomakrom has around 6000, and Akosombo has a population of 10 000. Sogakope is home to roughly 5000 people, Kete Krachi has about 20 000, and Ntotroso’s population is estimated at 26 909. Techiman has the largest population among the selected areas, with approximately 243 335 residents. Elubo has about 23 952, Prestea has around 35 760, Ankwaaso has approximately 5000, and Dominase is estimated to have 5000 residents. These communities depend significantly on rivers for drinking water, fishing, and irrigation, yet they frequently face contamination risks from agricultural runoff, inadequate waste management, and industrial activities.

Pra, Ankobra, and Tano rivers serve as a source of drinking water but are highly contaminated with mining activities. Densu and Volta rivers are sources of livelihood for fishermen, while Bia River aids in irrigation of plantain and cocoa plantations. However, water pollution in these communities has led to severe health concerns. The Ghana Health Service reported that outbreaks of cholera, dysentery, and typhoid are common due to microbial pollution. Cases of mercury poisoning from artisanal mining have led to kidney, skin, gastrointestinal and neurological ailments in children and pregnant women. High nitrate content from fertilisers has led to Blue Baby Syndrome in infants.^65,66 Consequently, this study evaluated the magnitude of this health concern and its relation to pollution and water use.^67-69 Neverthless, a total population of 671 134 from rural communities along these major rivers was used for the study. This population was based on Ghana Statistical Service records and local administrative data. To assess how community perception influences pollution of the major rivers in Ghana, the study targeted individuals aged 18 years and above. This composed of farmers (40%), businessmen (25%), fisherfolk (15%), miners (10%), and civil servants (10%).

Sample Size

The study estimated sample size comprised respondents from Dunkwa on Offin, Twifo Praso, Beposo, Nsawam, Amasaman, Weija, Ntotroso, Techiman, Elubo, Prestea, Ankwaaso, Dominase, Dadieso, Kwadwo Addaikrom, Pomakrom, Akosombo, Sogakope, and Kete Krachi. The communities were chosen due to their reliance on the river resources and the exposure they have had to the river contaminants from industrial and agricultural operations. Furthermore, the sample size was calculated using Cochran’s formula, and this took into account the confidence level and error margin and proportion estimation influenced by river pollution.⁷⁰ With a 95% confidence interval, a 5% error margin, and 50% assumed prevalence rate, a total of 384 participants were identified for this study. Taking into consideration non-respondents and incomplete data, 400 respondents was used for this study. This was essential for dealing with low literacy rates and restricted mobility, along with a lack of willingness to take part. It enabled a higher level of study validity and reliability, as well as a higher level of subgroup interpretation and overall generalisation to a similar setting in rural Ghana.⁷¹

Sampling Procedure

Purposive sampling method was employed first to identify the 6 major rivers (major basins, pollution hotspots, and hydrological importance) in Ghana affected by serious levels of pollution. This technqiue ensured that the research findings and results are relevant and applicable to environmental and health policies. Multi-stage sampling methods were employed for obtaining a representative and diverse sample from the desired population. A total of three (3) communities were selected along each of the 6 major rivers in the study. These include Dunkwa-On-Offin, Twifo Praso, and Beposo (Pra River), Nsawam, Amasaman, and Weija (Densu River), Techiman, Elubo, and Ntotroso (Tano River), Prestea, Ankwaaso, and Dominase (Ankobra River), Dadieso, Kwadwo Addaikrom (Bia River), and Akosombo, Sogakope, and Kete Krachi (Volta River). These communities were selected based on their proximity to the rivers and their dependence on river resources for drinking water, fishing and irrigation.

To additionally limit the possibility for selection bias and increase the level of representativeness in the surveys, individual respondents in each community were selected using simple random sampling technique. In this approach, each individual had an equal chance of being selected, thus preventing any particular group from being overly sampled. In addition to these technqiues applied to limit the possibility for any biases to occur in the survey, other factors specifically aimed at dealing with biases include oversampling for groups that are considered to be underrepresented and following up for better response rates. The combination of the approach for selecting respondents using purposive sampling and multi-stage sampling helped the study tap into the required environmental information and represented the characteristics of the major rivers and the community in Ghana affected by the phenomenon.

Research Instruments

The instruments used were Hach HQ2200 Portable pH/EC/TDS/DO Metre, Hanna Instruments HI-93102 Metre, Nephelometric Turbidity Metre (ISO 7027), Atomic Absorption Spectroscopy (AAS) with hydride generation (APHA 3114B) and Graphite Furnace AAS (APHA 3113B). Additional instruments comprised Cold Vapour AAS (APHA 3112B), UV Spectrophotometry (APHA 4500-NO3-), a calibrated electrode-based pH metre (APHA 4500-H+), and the Membrane Filtration Technique (ISO 9308-1:2014). These instruments were used to obtain quantitative measurements of physical, chemical, and biological parameters for analysing river pollution levels. Instrument calibration and standardisation, which is part of quality control in laboratories, ensured accuracy and consistency in the results.

Likewsie, structured questionnaire was designed and implemented for 18 communities. It consisted of 5 sections, including demographic information of the respondents, vulnerability to river pollution, socio-economic parameters, water quality perception, and health concerns. The determination of demographic information gathered were age, gender, education, and occupation. The health concerns captured skin conditions, respiratory infections, and waterborne diseases. Additionally, water quality perception and vulnerability to river water pollution were determined using a Likert scale. The scale identified river water pollution sources such as agricultural runoff, industrial effluent discharge, and domestic sewage.

Instrument Validity and Verification

Instruments were calibrated to ensure accuracy of readings. Specificlly, Hach HQ2200 pH/EC/TDS/DO Metre, pH metre, Hanna Instruments HI-93102, and Nephelometric Turbidity Metre were calibrated using certified buffer solutions of pH 4.0, 7.0, and 10.0. The calibration of the Hanna Instruments HI-93102 and the Nephelometric Turbidity Metre was done using Formazin turbidity standards. The Atomic Absorption Spectroscopy calibration was carried out using multi-point calibration techniques that used certified materials for metals such as arsenic, lead, and mercury. The calibration of the UV Spectrophotometry was carried out using prepared nitrate standards, while the Membrane Filtration Technique was calibrated using positive and negative controls, consisting of E. coli strain and sterile distilled water blank. This process ensured methodological accuracy and absence of contamination.

To ensure content validity, a panel of experts in environmental science and public health assessed the relevance, accuracy and comprehensiveness of the questionnaire items on river pollution and its effects on health. A pilot study was conducted with 30 respondents from Brewaniase (Volta Region), Jukwa (Central Region), and Beposo (Western Region), who share similar characteristics with the final test regions. The test helped verify the clarity and relevance of the items on the questionnaire, the validity of the methods of data collection and the feasibility of extracting the water samples. Minor revision were made to the wording of questionnaire items. The Cronbach Alpha coefficient (α = 0.72) indicated a strong internal consistency among questionnaire items, consistent with recommendation by Taber.⁷² To increase the accuracy of the results, water quality data were validated using multiply analytical procedures the results for the quality of the water were checked on the basis of a set of analyses. This thereby made the questionnaires free from personal biases.

Data Collection Procedure

The collection of water samples was conducted from May 2024 to August 2024, representing the rainy and dry periods. This helped observe the maximum levels of pollutants as well as the probable dangers associated with the pollutants for aquatic life and human life. The collection of samples was conducted in accordance with the American Public Health Association (APHA) Standard Methods for Water and Wastewater Examination (22nd Edition), as recommended in Yasin et al,⁷³ Lukubye and Andama,⁷⁴ and Shigut et al.⁷⁵ The samples were collected from 6 rivers (Ankobra, Densu, Bia, Pra, Tano, and Volta Rivers). The 6 rivers in turn were systematically sampled at 3 points (upstream, mid-point, and downstream).^76,77 This was based on the hydrological flow of the water bodies, as well as the land use and their closeness to the sources of pollution, helping account for the differences in the quality of the water at various points of study.^78,79

A total of 162 water samples were collected from 6 rivers to ensure spatial representativeness. The water samples were collected from 3 communities in each of the 6 rivers. In each of the 18 communities, 3 sampling points were selected (upstream, midstream, and downstream), with 3 replications in each sampling point, totalling 9 water samples in each river. This aided in representing spatially representative variations in water qualities, as well as ensuring that water qualities in different spatial areas of the 6 rivers are dependable. The water samples were collected 0.5 m below the surface and about 1 metre away from the shoreline to avoid external interference in the water.^2,80 The Grab Sampling Protocol (APHA 1060B) was followed in water sample collection, ensuring that parameters were measured instantly and preserved accordingly for further analysis in laboratories.^81,82 The water samples used in determining physicochemical properties were collected in clean HDPE bottles, while glass bottles were used to store water samples used to determine microbiological properties. The process of preservation included acidification (for heavy metal ions), refrigeration at 4°C + 2°C, and ice storage. The water samples were handled to prevent compositional changes within 6 hours.

Analysis was conducted at both the Ghana Water Research Institute (WRI) and the Council for Scientific and Industrial Research (CSIR) Environmental Quality Laboratory. Calibration standards were used to establish the precision levels in each piece of equipment. pH, electrical conductivity, TDS, and DO were determined using the Hach HQ2200 Portable pH/EC/TDS/DO Metre, with an accuracy level of ±0.01 pH. Turbidity standards were measured using both Nephelometric Turbidity Metre (in accordance with ISO 7027) and Hanna Turbidity Instrument HI-93102, with an accuracy level of ±0.02. Arsenic, lead, and mercury levels were analysed using Atomic Absorption Spectroscopy (AAS), with hydride generation (APHA 3114B), graphite furnace (APHA 3113B), and Cold Vapour techniques (APHA 3112B), respectively. Concentrations of nitrate were evaluated using UV Spectrophotometry (APHA 4500-NO₃⁻). Representations of Escherichia coli contamination were analysed using the Membrane Filtration Technique (ISO 9308-1:2014), with.

QGIS 3.34 was used to extract geographic coordinates of sampling locations and compute Euclidean distances between river sampling points and adjacent communities. This captured pollutant dispersion along upstream, midstream and downstream sections of the rivers. Assumptions were tested to ensure the validity of statistical tests include normality (Shapiro-Wilk Test), homoscedasticity (Levene’s Test), and independence (Durbin-Watson Test) to ensure the validity of the linear and correlation tests performed. Error sources, including drift, cross-contamination and sampling inconsistency, were controlled by triplicate sampling, recalibration, competence measurement, and data verification audit processes.⁸³ A structured questionnaire was formalised over a period of 6 weeks; validated through expert review; and pilot-tested in a rural setting. The questionnaire, which required 15 to 20 minutes for completion, was self-administered. Research assistants were employed to help respondents overcome any literacy limitations by providing translation in local dialects. The process of data collection added strength to the study as it helped in examining the link between water pollution of the river and health in the rural setting. Data collection was done over a period of 3 months.

Data Analysis Procedure

Data analysis was conducted using Python statistical environment, ensuring efficient data processing and statistical computations. The collected data from water quality analysis and field surveys were coded, cleaned, and transformed before analysis. Data cleaning procedures included the removal of duplicate entries, standardisation of units for water quality parameters, and handling of missing values through mean imputation for continuous variables and mode imputation for categorical variables. Outliers were identified and assessed using boxplots and z-scores, ensuring data integrity. Descriptive statistics, including frequencies, percentages, means, and standard deviations, summarised the water quality parameters and demographic characteristics of respondents. The water quality data were evaluated against the Ghana Standard (2021) and the WHO (2017) drinking water guidelines, assessing the suitability of each river for consumption, as shown in Table 1.

Table 1.

Acceptable Water Standards for Safe Drinking Water.

Distance analysis examined the impact of proximity to pollution sources on health outcomes. Communities’ geographic locations were used to estimate spatial proximity between communities and river sampling points. Euclidean distances to the nearest river sampling sites were calculated from community centroid coordinates. A distance analysis graph visualised these spatial patterns, showing the gradient of contamination levels across sampled areas.

The assumptions preceding statistical tests were checked before performing inferential statistical analysis. The Shapiro-Wilk tests were used to check for normality, Levene’s tests for homoscedasticity, and the Durbin-Watson test for independence among residuals for regression models. Pearson’s correlation analysis measured the strength and direction of relationships between water quality parameters. Multiple regression analysis was performed using a mutiple linear regression model, with pollution perception as the dependent variable and socio-economic factors, distance to pollution sources, and reported health issues as predictor variables. The model’s goodness-of-fit was assessed using R-squared and adjusted R-squared values, ensuring the robustness of findings. Analysis of variance (ANOVA) identified significant mean differences in contamination levels across sampling points. Significance levels for all tests were set at .05. Results were carefully presented using tables, figures, and geographical resource maps, each equipped with descriptions that linked test outcomes to existing scholarly work on environmental and health concerns. These methods helped to build a comprehensive understanding of river pollution behaviour, associated societal perceptions, and health risks associated with such phenomena in Ghana.

Results

This section presents results and discussions on the influence of community perception on pollution of the major rivers in Ghana. Table 2 and Figure 3 display the variation and distribution of the water quality parameters of Ankobra, Bia, Densu, Pra, Tano, and Volta Rivers. The 6 major rivers recorded mean Arsenic of 0.61 mg/L (SD = 0.05), lead of 0.83 mg/L (SD = 0.03), and mercury of 0.11 mg/L (SD = 0.02). Notwithstanding, the high levels of arsenic, lead, and mercury might be due to inputs from mining activities, industrial effluents and improper disposal of electronic waste in the surrounding communities.^86,87 The small difference within the quartiles for lead and mercury illustrates that pollutants are prevalent within the 6 rivers. According to Tchounwou et al.⁸⁸ this is a serious issue, for chronic exposure to arsenic and lead contributes to neurological and developmental disorders, cancer and kidney damage.

Table 2.

Descriptive Statistics of Chemical Contaminants and Bacterial Levels of Rivers.

Figure 3.

Distribution of water quality indicators across rivers.

Studies carried out by Al-Sulaiti et al⁸⁹ and Liu et al⁹⁰ proved that the bioaccumulation of mercury affects aquatic life and food chains. These authors added that mercury builds up in the aquatic environment cause food poisoning from plants to the smallest prey and the top predator, and consequently human beings. Among humans, this results in diseases such as the Minamata disease, including tremors, seizures, and memory loss. The maximum mercury concentration (0.21 mg/L) implies localised contamination, possibly due to mining activities.⁹¹ Anang and Lawson⁹¹ documented mercury and lead contamination in Aboabo and Wiwi Rivers,³⁴ in groundwater for domestic and agricultural purposes, arsenic in Bonsa River and Gbogbo et al⁹² in the shells of 7 species of fish. This demonstrates widespread arsenic, lead and mercury pollution in Ghana. There is the need to monitor and effectively manage these pollutants in the water bodies.

Similarly, the mean nitrate concentration (17.3 mg/L, SD = 1.9) found in Table 2 and Figure 3, which is lower than the Ghana Standard Authority⁸⁵ standard and the WHO² limit (50 mg/L) might result from the use of inorganic fertiliser along the rivers. Mishra,⁹³ who cautioned against a minimal increase in nitration, asserted that nitrate pollution influences eutrophication, as well as algal blooms that deplete oxygen and adversely affect aquatic life. pH level ranged from slightly acidic to neutral (SD = 0.25 and range = 5.45-7.65), with a mean value of 6.09 (SD = 0.25) and spanned from 5.45 to 7.65. These values, though, are within the Ghana Standard Authority⁸⁵ standard and the WHO² acceptable limits (from 6.5 to 8.5), they might influence the solubility and mobility of metals, such as mercury, in the rivers.^94,95 Furthermore, mean E. coli (1243.75 MPN/100 mL, SD = 145.3), ranging from 1020 to 1420 MPN/100 mL, exceeded the Ghana Standard Authority⁸⁵ standard and the WHO² safe limits (0 MPN/100 mL) for potable water. This finding demonstrates that the water sources are being affected by untreated human or animal waste, likely due to open defecation, direct sewage discharges, and poor sanitation infrastructure.

However, the outliers of E. coli approaching 1420 MPN/100 mL show persistent microbial contamination, raising significant public health concerns.⁹⁶ High E. coli counts correlate with inadequate sanitation facilities and sewage discharge, a pattern found by Usang et al⁹⁷ and Dagher et al.⁹⁸ Furthermore, the high electrical conductivity (mean = 801.25 µS/cm, SD = 99.8, and range = 675-910 µS/cm) and total dissolved solids (mean = 1505.45 mg/L, SD = 95.34 and range = 1300 to 1760 NTU) reflect a high concentration of dissolved ions, likely from geogenic sources or anthropogenic pollution. The high turbidity concentration (mean = 1525.75 NTU, SD = 197.55) further demonstrates the presence of suspended particles, possibly organic pollutants and sediments, compromising water clarity and quality.

Table 3 and Figure 4 show the relationships among water quality parameters. The correlation coefficients show complex interactions among water quality parameters. According to Cohen’s (1988) guidelines, correlations can be interpreted as weak (r = .10-.29), moderate (r = .30-.49) and strong (r ⩾ .50). As presented in Table 3, there was a strong positive correlation between Arsenic (As) and nitrate (NO₃^-; r = .98), and this suggests that these pollutants have common sources such as agricultural runoff and mining waste. A strong correlation also occurred between arsenic and E. coli (r = .83). Solgi et al.⁹⁹ linked arsenic contamination to agricultural activity and nitrate accumulation. Arsenic also strongly correlated with turbidity (r = .74), indicating the influence of suspended particles in transporting heavy metals in water. Ofori et al³⁵ and Daud et al³⁶ reported that when heavy metals like Arsenic adsorb onto sediments and microorganisms, they can then move with the water flow, spreading contamination among aquatic life upon uptake. A strong association was also observed between arsenic (As) and turbidity (r = .74), indicating that suspended particles play a key role in the adsorption and mobility of heavy metals in aquatic systems. Similar observations have been reported by Ofori et al³⁵ and Daud et al,³⁶ who noted that heavy metals can attach to sediments and organic particles, facilitating their dispersion in water bodies.

Table 3.

Pearson Moment Correlation Analysis Showing Association Between Water Quality Parameters.

Figure 4.

Pearson correlation coefficient matrix.

Nevertheless, EC correlated with TDS (r = .88) and pH (r = .97), and this indicates the presence of dissolved ions (Ca²⁺, Na+, Mg²⁺, Cl^-, and HCO₃^-) in the rivers. Gqomfa et al¹⁰⁰ explained that dissolved minerals increase ion concentration, including TDS and EC. The very strong relationship between EC and pH (r = .97) further indicates that ion-rich waters are regulated by acid–base chemistry, where the presence of hydrogen, carbonate, and bicarbonate ions significantly alters conductivity. Such conditions are frequently encountered in areas affected by mining, improper waste disposal, or intensive farming, where chemical inputs disrupt natural buffering systems. Additionally, the moderate negative correlations between pH and mercury (r = −.82) and nitrates (r = −.69) affirm that acidic conditions enhance the solubility and mobility of these contaminants, increasing the likelihood of their spread in aquatic environments. Acidic waters can release mercury from sediments into the water column, facilitating its transformation into more toxic forms such as methylmercury, while also promoting nitrate persistence, which contributes to eutrophication and associated fish kills. The negative relationship between E. coli and pH (r = −.82) indicates that bacterial survival declines under neutral to alkaline conditions, implying that acidic, polluted waters may favour microbial persistence. In contrast, the weak correlation between lead (Pb) and E. coli (r = .11) confirms that these pollutants are from different sources and have transport pathways. Lead contamination is particularly associated with geogenic inputs, ageing plumbing systems, or industrial discharges rather than faecal contamination.¹⁰¹

Table 4 and Figure 5 present water quality parameters that affect major rivers in Ghana. From PCA, the first 3 components (PC1, PC2, and PC3) explain 92.86% of the total variation in the pollution of the major rivers. PC1, consisting of arsenic (As) and Lead (Pb), explained 50% of this variation. This connotes that river pollution in Ghana is as results of mining and industry discharges. TDS and nitrates (NO₃⁻) were identified for PC2, and they explain 25.71% of the variation. This level of variation might result from agricultural runoffs as well as domestic sewage or water waste with high levels of dissolved substances.¹⁰² PC3 captured mercury (Hg) and explained 17.14%. This identified agricultural runoffs from small-scale gold mining activities as a water pollution factor for these water bodies. The Kaiser-Meyer-Olkin measure of 0.63 confirms data adequacy for further factor analysis. The presence of large eigenvalues shows that pollution of major rivers in Ghana is influenced by diverse sources.^97,103

Table 4.

Principal Components Analysis (PCA) Showing Pollutant Loads in Major Rivers in Ghana.

Figure 5.

Scree plot of eigenvalues and variance explained by principal components.

Figure 6 displays the distance and pollutant concentration analysis of the 6 major rivers in Ghana. The figure shows that pollutant concentration inversely relates to pollutant distance downstream. The maximum value of arsenic (1.25 mg/L), turbidity (2499.9 NTU), E. coli (1780.5 MPN/100 mL), and total dissolved solids (2432 mg/L) was obtained within the 0.5 to 1 km range from the source and decreased gradually towards 2 km. This, therefore, indicates that pollutant sources are upstream and midstream of the rivers, most likely around villages, mining areas, and farm fields where wastes and untreated effluents directly enter the rivers. These findings are supported by Gwira et al¹⁰⁴ and Kusimi and Kusimi¹⁰⁵ who reported high arsenic and lead levels in rivers within mining and industrial areas, such as Tarkwa. Thus, the very high turbidity (2499.9 NTU) and arsenic levels of 1.25 mg/L at 0.5 km could arise due to small-scale mining activities or agricultural runoff. Downstream, however, natural processes such as sedimentation, dilution, and microbial breakdown may be involved in lowering the levels of contaminants.^87,105

Figure 6.

Cost concentration analysis of river quality parameters.

Conversely, pH levels showed slight variation across distances (ranging from 5.45 to 7.65), with lower pH values closer to 0.5 km, indicating mildly acidic conditions in upstream locations. Nitrate levels declined from 24.87 mg/L at 0.5 km to 10.43 mg/L at 2 km, demonstrating nutrient loading from agriculturally influenced watersheds. Nitrate concentrations peaked at 0.065 mg/L·km, consistent with Coka,¹⁰⁶ who linked nitrate presence to agricultural runoff in farming zones. In the current analysis, E. coli levels decreased from 1780.5 MPN/100 mL at 0.5 km to 850.25 MPN/100 mL at 2 km, reflecting reduced faecal contamination downstream. This is potentially due to natural die-off and dilution processes. Nevertheless, these findings imply that communities situated near upstream locations may face greater exposure to carcinogenic and pathogenic pollutants, resulting in waterborne diseases and chronic health conditions. This highlights the need for focussed monitoring and mitigation strategies at specific points along river systems.

Table 5 presents the mean differences in water quality pollution across major rivers in Ghana. The table captures the sum of squares, degrees of freedom, mean square, F-values, and P-values for each parameter. Significantly, arsenic (P = .049), lead (P = .039), nitrates (P = .001), turbidity (P = .001), conductivity (P = .001), total dissolved solids (TDS, P = .001), and E. coli (P = .001) are less than .05. This indicates that the rivers have different levels of contamination and pollution sources. Conversely, mercury and pH did not show significant differences among the rivers. This presages that these pollutants have similar impacts on the pollution of the rivers understudy.

Table 5.

Mean Differences Between and Within Water Quality Parameters Across Rivers.

Figures 7 and 8 compare pollutant concentrations at downstream, midstream and upstream. From the figure, arsenic increased from a concentration of 0.43 mg/L upstream to 0.81 mg/L downstream, lead from 0.66 to 0.95 mg/L, mercury from 0.10 to 0.14 mg/L, and nitrate from 15.12 to 19.06 mg/L. Although arsenic, lead, and mercury showed moderate increases and remained within acceptable limits, gradual accumulation downstream suggests a long-term risk of heavy metal buildup. The increasingly heavy metal burden downstream could be linked to intensified illegal mining and effluent discharge from settlements along the riverbanks.¹⁰⁰ The electrical conductivity increased from 734.4 µS/cm upstream to 876.14 µS/cm downstream, total dissolved solids from 1413.18 to 1621.5 mg/L, and turbidity levels increased from 1203 NTU upstream to 1737.5 NTU downstream. The pH levels decreased from 7.03 upstream to 5.2 downstream. E. coli. was highest midstream at 1349.75 MPN/100 mL but decreased to 1174.50 MPN/100 mL downstream. Rapid population growth and poor sanitation practices in communities, including human or animal waste discharge, might have contributed to the increased E. coli levels. Usang et al⁹⁷ and Djagba et al¹⁰⁷ reported elevated nitrate levels downstream in the Niger River due to agricultural intensification and poor waste management. Likewise, turbidity doubled from 30 NTU to 60 NTU, potentially resulting from soil erosion, mining activities, and urban runoff and construction activities. This finding indicates water quality of river resources decreases from upstream to downstream. Therefore, the communities and authorities should reduce pollution by preventing direct dumping of waste and limiting agricultural runoff into the river to protect downstream water quality.

Figure 7.

Comparison of pollutant concentrations across downstream, midstream, and upstream.

Figure 8.

Pollutant concentration distributions across downstream, midstream, and upstream.

Table 6 presents gender, age group, marriage status, educational attainment, employment status, and duration in the community of 400 respondents. Most of the respondents are aged between 18 and 29 years old (33.89%), followed by the age bracket between 30 and 39 (24.44%). About 40% are married, and 35% are single, while 11.67% are divorced, with 8.06% widowed. This indicates diverse household dynamics that could influence water consumption practices as well as environmental health concern arrangements. Educational status of respondents indicates that 40.28% have attained tertiary education, while 28.61% secondary education. This educational level implies that major of the population is aware of the health risks associated with water pollution. Conversely, 17.50% of respondents have no formal education, and this connotes a lack of awareness concerning the risks associated with river pollution. Employment status and the tenure of residence revealed that 51.94% are unemployed, and the 34.44% are employed. A notwithstanding, 34.44% of the respondents have resided for over 10 years, indicating that they have an understanding of the changes in water quality over time and the effectiveness of past and present pollution mitigation strategies.

Table 6.

Demographic Information of Respondents (N = 400).

Table 7 presents the perception communities along Pra, Densu, Tano, Ankobra, Bia, and Volta have about river pollution. The Likert scaling method used a 5-choice system that included Strongly Agree (1 point), Agree (2 points), Neutral (3 points), Disagree (4 points), and Strongly Disagree (5 points). The average score was calculated for each statement, and the total points from all responses were summed and divided by the number of respondents as follows: [(1 + 2 + 3 + 4 + 5)/5] = 3 points. This gives a possible score range between 3 and 5, and any score above 3 indicates that it is mostly true or preferred and below 3 indicates something negative. From Table 7, the average scores for the overall water quality of all the rivers are 3.12, suggesting respondents generally accept that the water quality is poor. However, the standard deviation for this value is 0.87, and it indicates that some of the respondents are more strongly positive, while others are not. This perception reflects what Groh et al¹⁰⁸ found about rural communities, that they often undervalue pollution severity due to a lack of information and awareness about the actual water conditions. That is, in regions where information is scarce, residents rely on personal experiences or anecdotal evidence, leading to a skewed perception of river health. Regarding pollution levels, the responses indicated that pollution severity is lowly recognised. The majority of the residents recognise pollution existence and issues, while others downplay its seriousness. Perception of aquatic life biodiversity of the rivers appeared relatively optimistic, featuring a mean of 3.31. This is irrespective of the industrial discharge and agricultural runoff that have been observed to cause significant biodiversity decline.¹⁰⁹ Perceptions of a decline in the condition of the rivers appeared to be relatively lower, registering a mean of 2.85. This significant decline in the condition of the rivers is due to inadequate management of waste associated with rapid urbanisation.¹¹⁰ Confidence level in the local administration was approximately 3.01, suggesting some level of trust in the administration. Jackson et al.¹¹¹ found that limited funding, lack of infrastructure, and regulatory oversight hinder effective water resource management in local communities.

Table 7.

Current State of Rivers in Ghana (N = 400).

Table 8 presents the influence of age, education, gender, marital status, occupation, and duration of residence on perceptions about the current state of rivers. The model explains 65.4% of the variation in perceptions about the current state of rivers. Gender, particularly men, influences perception about the current state of rivers. This shows that male respondents perceive the current state of rivers more negatively than their female counterparts. This aligns with Vicente-Molina et al,¹¹² who showed that men tend to have greater concerns about environmental pollutants. Education (tertiary) significantly predicts perception about the current state of rivers. This suggests that awareness and concern about river conditions increase with enhanced levels of education. Debrah et al¹¹³ support this finding and stated that individuals acquire greater knowledge and understanding of river pollution through education.

Table 8.

Multiple Linear Regression Analysis Showing the Influence of Demographic Information of Respondents on the Current State of Rivers (N = 400).

Furthermore, the age group between 30 and 39, and married persons, with coefficients 0.180 and 0.210, respectively, influence perception about the current state of rivers. This implies that certain ages and marital statuses are more aware of the impact of river pollution in their communities. This awareness, perhaps, might be due to past personal experiences and community responsibilities related to family health. Employment (0.290) also influences perception about the current state of rivers. Afsar and Umrani¹¹⁴ and Liobikienė and Poškus¹¹⁵ found that employment increases environmental awareness. Those who actively involve themselves in their jobs appear to notice and seek more information on environmental matters. This implies that increasing employment opportunities in Ghana would help improve environmental awareness and enforce pollution control policies and activities, helping reduce river pollution in Ghana.

Table 9 and Figure 9 present the key pollutants affecting the rivers of Ghana. As shown in the figure, 59% of the respondents chose mining as the main source of river pollution in Ghana. This percentage indicates the high environmental impacts associated with mining activities, especially where gold and other minerals are being mined. This is because research indicates that high levels of metals and other hazardous wastes associated with toxicity found in mining activities get concentrated in the ecosystems of the rivers. According to Adu,¹¹⁶ the absence of control in mining activities results in the high diffusion of mercury and cyanide into the environment.

Table 9.

Pollution Sources in Rivers in Ghana (N = 400).

Figure 9.

Major pollutants in Ghanaian Rivers.

Moreover, industrial discharges (8%) and agricultural runoff (7%) are the next perceived pollution sources. According to Lisetskii and Buryak¹¹⁷ and Weldeslassie et al,¹¹⁸ industrial discharges introduce toxic chemicals and heavy metals into water systems, while agricultural runoff often carries fertilisers and pesticides that contribute to nutrient pollution, leading to eutrophication and degradation of aquatic ecosystems. Domestic sewage and waste disposal were perceived by 5.5% and 4.5% of respondents, respectively, indicating waste management practices among communities along the rivers understudy. Tariq and Mushtaq¹¹⁹ and Yohannes and Elias¹²⁰ asserted that in communities where there are the lack of efficient sewage and waste management systems, untreated waste is discharged directly into rivers. The relatively lower rates of oil spills (4%), urban runoff (3%), overfishing (2%), and erosion and sedimentation (1%) suggest that these pollution sources significantly degrade water quality and aquatic life. Therefore, the study recommends policy interventions that consider the multiple facets of pollution affecting Ghanaian rivers.

Table 10 presents perceptions of health, economic and psychological effects of river pollution associated with major rivers in Ghana. A high level of concern over waterborne diseases, especially cholera and typhoid (mean = 3.45). Adelodun et al¹²¹ reported that polluted rivers serve as major sources of infectious diseases in low-income communities. A study conducted by Adelodun et al¹²¹ revealed that polluted rivers with heavy flow of untreated sewage, solid waste, and industrial effluents become breeding places for disease-carrying pathogens. These pollutants reduce accessibility to clean drinking water, thereby compromising personal hygiene and increasing infection rates. In addition, respiratory infections (mean = 3.35) and skin conditions (mean = 3.15) also ranked high. Long-term health concerns, such as kidney and liver damage (mean = 3.2) and maternal complications (mean = 2.85) received lower scores. These are usually perceived as less acute because they take longer to manifest through regular exposure to lead, mercury, and arsenic, among other toxins. Bedu-Addo et al³³ noted that chronic contact with such pollutants can result in severe organ damage and developmental problems, especially in children. Relatively lower means indicate that the public may not be fully aware of such health consequences. Many individuals fail to connect these conditions to river pollution because the symptoms are slow to appear and less visible than acute infections. Lack of awareness may delay diagnosis and intervention, allowing problems to worsen silently.

Table 10.

Issues Associated with Pollution of Major Rivers in Ghana (n = 400).

Respondents expressed strong concern about the economic effects of river pollution. The decline in fish populations and reduced income for fishermen ranked highest (mean = 3.4). This reflects that water pollution has an immediate impact on people’s economic activities, most particularly those living near the rivers. Prip¹²² establishes that water pollution reduces biological diversity as well as fish production, hence undermining the economic activity of fishing households. Industrial effluvia as well as plastic waste reduce river water levels of oxygen and destroy fish environments, giving rise to poor fish harvests. This impacts rivers directly and makes people pool money for water treatment (mean = 3.12). Dirty rivers mean that people invest more in cleaner water for drinking, cooking, or bathing. Water pollution also negatively impacts agricultural production. Farmers using polluted river water for irrigation often obtain low yields and produce crops that may be contaminated and unsafe for consumptioners can neither harvest much nor ensure that the food is clean from polluted water (mean = 2.95). Chemicals in polluted water degrade soil quality and transfer toxins into crops, harming both local consumption and market sales.

Additionally, tourism has declined (mean = 3.18). Visitors avoid polluted rivers due to bad odours, unattractive scenery, and health risks. This decline reduces income from eco-tourism, recreation, and hospitality. Sompolska-Rzechuła et al¹²³ emphasised that environmental degradation severely limits Ghana’s tourism potential. River pollution not only affects subsistence but also hampers broader economic development in affected regions. Besides, results further show that river pollution has also created widespread psychological stress. Sadness ranked as one of the most intense emotional response (mean = 3.45). People feel defeated by watching their living rivers become polluted and lifeless. Rivers not only have economic uses but also cultural and spiritual significance to people’s lives. Adding to this community stress are people observing children suffering from polluted water (mean = 3.3). People feel hopeless because there are no other safe employment alternatives and are frightened by the absence of solutions to their economic hardship. Emotional disturbance (mean = 3.25) and general worry (mean = 3.08) highlight the mental toll of constant exposure to environmental degradation. These emotional responses stem not only from health concerns but also from uncertainty about the future. Yirenkyi-Fianko and Ottou⁵¹ found that environmental degradation increases depression and anxiety, particularly when individuals feel incapable of addressing the problem. In polluted communities, residents witness the slow destruction of natural resources and live with the daily stress of illness, financial instability, and ecological loss. Images of dirty water, dead fish, and struggling children serve as constant reminders. This psychological distress calls for urgent mental health support, alongside environmental remediation and public education campaigns.

Table 11 presents possible strategies for river protection in Ghana. The majority (97.14%) in Table 9 strongly agree that establishing stricter regulations reduces the impacts of industrial discharges and safeguards the waterways of the rivers. Stricter regulations force industries to adopt and implement measures that mandate waste treatment before discharge. As reported by Bataineh et al¹²⁴ and Lah and Kotnik¹²⁵ stricter regulations make companies operate within environmental laws, ensuring that businesses compete on innovation and efficiency rather than externalising environmental costs. However, 95.71% believed community awareness campaigns can raise public consciousness about river pollution. Commodore et al¹²⁶ found that increasing awareness among communities helps to better understand the consequences of pollution, encouraging individuals and groups to participate in environmental protection initiatives actively. Respondents (94.29%) believe that regular monitoring and assessment of river water quality can reduce the concentration of pollutants in the rivers. This suggests that regular monitoring and assessing the quality of water bodies ensure that sources of pollution are identified in time, leading to the development of targeted interventions aimed at ensuring effective management of water quality.

Table 11.

Strategies for Effective Rivers Protection (N = 400).

Furthermore, community engagement in river clean-up initiatives (95.71%) indicates that local communities cannot be excluded from protecting the rivers understudy. This finding implies that communities should exhibit responsible behaviours such as proper waste disposal, improved sanitation and reduced pollution of water sources. Lema¹²⁷ reported that local stewardship and shared responsibility help to sustain the protection measures of water and enhance long-term water resource management. This study also found that promoting sustainable agricultural practices (97.14%) and investing in wastewater treatment facilities (97.14%) protect rivers in Ghana. Xia et al¹²⁸ and Zhu et al¹²⁹ indicate that sustainable agricultural methods, including conservation of trees around water bodies, significantly reduce runoff pollution into water bodies. Another aspect observed from the study was the role of governance and accountability in managing rivers (92.86%). This emphasises the significance of a transparent and participatory process in decision-making if rivers are to be preserved. Accountability and governance help communities have trust and confidence in the effectiveness of river conservation initiatives by the government.¹³⁰

Conclusion and Recommendation

This study examined water quality of 6 major rivers in Ghana and how socio-economic, health, psychological, and environmental impact water pollution in rural communities besieging the rivers. Arsenic, lead and mercury levels of the major rivers in Ghana have increased. Notwithstanding, arsenic, lead, mercury, total dissolved solids, and nitrates primarily influence the water quality of the rivers. Moreover, mining activities contribute more to the pollution of the 6 major rivers in Ghana than industrial discharges, agricultural runoff and domestic sewage. There are significant concentration differences among water quality parameters such as nitrates, turbidity, electrical conductivity, total dissolved solids, and E. coli. Community perceptions influence the pollution and impacts of major rivers. Multiple regression analysis demonstrates that education, gender and age significantly influence community perceptions of river pollution. Pollution of major rivers in Ghana significantly contributes to emotional distress, financial hardship, and waterborne diseases among affected communities. Therefore, campaign efforts that strengthen regulations, promote sustainable farming practices and support community-based initiatives are necessary to maintain the health of the rivers, and educate the community about the dangers of river pollution and the significance of sustainable water resource management. Consequently, this would contribute to the achievement of the Sustainable Development Goals (SDGs) 6 (Clean Water and Sanitation) and 3 (Good Health and Well-being).

Acknowledgements

The authors acknowledge every respondent who participated in this study.

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

Consent to Participate

The authors, with written informed consent prior to their involvement, understand the study’s aims and procedures, and the right to withdraw at any time without facing any costs.

Author Contributions

Michael Aboah: Conceptualization, Methodology, Data Collection, Funding, Rriting - Review, Editing, Formal analysis and Writing - Original Draft. Emmanuel Agbo Tei: Funding, Data Collection, Data Analysis, Writing – Review, Editing and Formal Analysis. Michael Miyittah: Funding, Data Anakysis, Writing - Review & Editing. Writing - Review and Editing. Christian Julien Isac Gnimadi: Data Collection and Data Analysis.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Access to the data is available upon request.

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Invasive and Taxonomically Messy – The First Record of Olepa schleini (Lepidoptera: Noctuoidea: Erebidae: Arctiinae) in the Afrotropics

Thu, 09 Apr 2026 16:00:00 GMT

Abstract

Transoceanic introductions of alien species are especially dangerous in the case of pests. This study reports the unexpected but well-documented establishment of a population of Olepa schleini Witt et al., 2005 in the Afrotropics (Comoros). This is the first population record of this potentially severe pest of castor (Ricinus communis L.) outside the Asian continent. We discuss the taxonomic identity of the recorded species, based on morphological and genetic data, comparing it especially to the Israeli population of Olepa Watson, 1890. Our analyses reveal significant inaccuracies in the molecular data publicly available for the genus, and the urgent need for systematic revision of Olepa based on extensive material, particularly from India. We highlight that the relationships between the two most commonly reported taxa, O. ricini and O. schleini, are unclear and require critical re-examination. We also discuss the possible source of the Comoros population, the routes of transportation of the founding individuals, and the threats to the castor market in continental Africa. It is very likely that O. schleini will soon colonise this region and may become the major pest of castor in East African countries and Madagascar.

INTRODUCTION

Olepa Watson, 1980 is a genus of tiger moths (Lepidoptera: Noctuoidea: Erebidae: Arctiinae) belonging to the tribe Arctiini. Disregarding controversies regarding the taxonomic identity of some of the described forms, the most recent review lists 14 valid species and 2 subspecies within the genus (Kalawate et al. 2024). Most of the Olepa species are distributed in South-East Asia, and the majority of them are known only from India. Surprisingly, a single species, O. schleini Witt et al., 2005, was described relatively recently from Israel. Its peculiar distribution prompted discussion on the biogeographic relationships of the new taxon. In the original description, it was referred to as “an old relic species” associated with “the Mediterranean coastal marshes”. However, after more detailed study of the host plants, Rittner et al. (2012) suggested that O. schleini might not be a native species, but rather a new incomer from another region. They came to such a conclusion based on the larvae not being recorded from any indigenous plant, but observed only on six non-native host plants, with Ricinus communis L. (Euphorbiaceae) being the major one. Finally, they suggested that the species originated from the Indian subcontinent, although it had not been known there at the time. This hypothesis found its positive verification when Kalawate et al. (2020a) published results of their genetic barcoding studies, confirming the presence of O. schleini populations in western India (Maharashtra). Following this discovery, Kalawate and Dinesh (2021) published a thorough study aiming to clarify and confirm that the species is indeed an indigenous Indian taxon artificially introduced to Israel, where it became an invasive pest of castor. However, not only do the biogeographic relationships of O. schleini remain obscured. The same refers to its taxonomic status, which was questioned by Zhang et al. (2022), who synonymised O. schleini with O. ricini (Fabricius, 1775) based on their morphogenetic similarity. Finally, Kalawate et al. (2024) reanalysed all available genetic sequences (new and previously published) and, using additional comparative morphological data, argued that O. ricini and O. schleini are well-supported, separate taxa and formally resurrected the latter from synonymy. As for now, O. schleini is regarded as a morphologically variable species distributed in India, China and Israel.

The Union of the Comoros is situated in the Mozambican Channel in the Indian Ocean, between the African continent and Madagascar (11°23′-13°00′S 43°13′-45°18′E). It is an archipelagic country occupying three (Grande-Comore, Anjouan, Mohéli) of the four large islands of the Comoro Islands archipelago. The fourth, Mayotte, is an overseas department of France. These tropical islands, thanks to their volcanic origin, topography and the heterogeneity of ecological conditions, harbour a unique biotic diversity of global importance, composed of both marine and terrestrial fauna and flora (Louette et al. 2004). They are characterised not only by exceptional biodiversity but also by a high number of endemic species (Caldecott 1996), combined with rapidly growing pressures on the local environment (Maéva 2015).

Due to the rapid development of agriculture and the growing market of food products, invasions of alien phytophagous insects in the Comoros are increasing (Guillemaud et al. 2011). Unintentionally introduced pest insects cause growing problems of alimentary security for this developing country, where economic means and human resources are limited. In 2000, the Comoros experienced an invasion of coconut whiteflies, Aleurotrachelus atratus Hempel, 1922 and Paraleyrodes bondari Peracchi, 1971 (Streito et al. 2004). The oriental fruit fly, Bactrocera dorsalis (Hendel, 1912), was first reported in the country, specifically in Grande Comore, in 2005 (DeMeyer et al. 2012). In 2018, the Comoros recorded an invasion of Drosophila suzukii (Matsumura, 1931) (Hassani et al. 2020). The recent Lepidoptera invasions are those of fall armyworm Spodoptera frugiperda (Smith, 1797) and African armyworm Spodoptera exempta (Walker, 1857), well-known pests belonging to the family Noctuidae. The first one was reported in the Comoros in 2018 and the second in 2012 on Mayotte (Germain et al. 2014, Nagoshi et al. 2022).

This study aims to report the first case of introduction of Olepa schleini to the Comoros, and the entire Afrotropical region. Based on morphological and genetic data, we discuss the taxonomic position of the specimens from the Comoros, critically analysing all sequences of Olepa available in the public databases. We describe in detail the damages caused by the caterpillars and discuss the putative ways of introduction of the species to the Comoros. Finally, we discuss the potential risks of establishing the new population for the castor crops in continental Africa.

MATERIAL AND METHODS

Studied material. Live larvae of the last instar have been collected by HM in Moroni (Ngazidja Island) on 25 November 2023, and reared in laboratory conditions until pupation. The details of the collecting localities are provided in Results and on the map (Fig. 1). After hatching, the fresh specimens were killed in the freezer. Eight dead specimens were transferred to glassine envelopes and sent to Poland (ISEA PAS, Kraków) for further examination. Morphologicalstudies. Upon delivery in Poland, but prior to remoistening, a single foreleg of each individual was sampled for molecular study. All specimens were subsequently prepared, dried and labelled following the standard methods used in Lepidoptera studies (Schauff 1986), and photographed using a Canon 70D digital camera with a macro lens EF 50 mm. All eight voucher specimens are incorporated into the scientific collection of the ISEA PAS. To examine the morphology of internal reproductive organs, two males and two females were dissected. For photography, the preparations were mounted in glycerine on microscope slides. Finally, they were transferred into Euparal and mounted under cover slips as permanent slides. Pictures were taken using a stereoscope microscope (Leica S9i system), and edited with the Adobe Photoshop CC program. The morphological terminology of genitalia follows Volynkin (2024).

Molecular studies. For the molecular identification of the specimens, the barcode region of the cytochrome c oxidase subunit I gene (COI) was selected. Genomic DNA extraction from legs was conducted with NucleoSpin Tissue kit (Machery-Nagel, Germany), following the manufacturer's protocol. The first part of the COI gene was amplified with HCO/LCO primers (Wahlberg and Wheat 2008), according to the PCR protocol described by Zenker et al. (2017). Obtained products were sequenced with BrilliantDye Terminator v.3.1 kit (NimagGen, the Netherlands) and read with a ABI Prism 3130xl sequencing machine at ISEA PAS, Kraków. The sequences and the respective chromatograms were checked manually in BioEdit (Hall 1999), and ambiguous sites were encoded according to IUPAC nucleotide codes. Final sequences were uploaded to the VoSeq database (Peña and Malm 2012) and subsequently deposited in GenBank as accession numbers PV872405-PV872411.

To verify the taxonomic status of the Comoro specimens, we included all Olepa COI sequences available in GenBank as the primary resource, supplemented with BOLD records (Table 1). To visualise their position within Olepa, we conducted a phylogenetic analysis within the Maximum Likelihood framework, using IQ-TREE (Nguyen et al. 2015) on the web server (Trifinopoulos et al. 2016), with automatic substitution model selection. Ultrafast Bootstrap (Hoang et al. 2017) and SH-like approximate likelihood test (Guindon et al. 2010) were used to verify nodal support, both with 1000 replicates.

After verification of publicly available sequence codes, authors, specimen collecting dates, and localities, we found most of the BOLD records to be uploaded to GenBank, but in some cases not crossreferenced, and also some records to be duplicated in BOLD (Table 1).

In total, our initial dataset consisted of 57 unique Olepa barcode sequences, with an Arctia caja KF533444 sample used as an outgroup. In the tree inferred with this dataset, we found the O. ghatmatha sample MT318099 (Kalawate et al. 2020b) and five identical O. ricini samples AM050280-AM050284 (Witt et al. 2005) forming long branches (Fig. 2). In manual checking, the sequence MT318099 appeared relatively short (351 bp) compared to the other samples, with ‘messy’ beginning and ending, and differing in 15 amino acids when codons were translated (although without stop codons, not shown). Further, a BLAST query of the sequence AM050280 showed its closest similarity to a few different Geometridae species, suggesting that samples AM050280-AM050284 are mismatched or contaminated. Thus, we excluded the above-mentioned samples and repeated the analysis on a dataset of 51 sequences.

Abbreviations. INRAPE – Institut National de Recherche pour l'Agriculture, la Pêche et l'Environnement, Moroni, Union des Comores; ISEA PAS – Institute of Systematics and Evolution of Animals Polish Academy of Sciences, Kraków, Poland; ŁP – Łukasz Przybyłowicz, Kraków, Poland; BAK – Boinahadji Ahamada Karihila, Moroni, Union des Comores; HM – Hakimou Mahamoudou, Moroni, Union des Comores; MW – Marcin Wiorek, Kraków, Poland.

Figure 1.

Distribution of Olepa schleini in Comoros, Ngazidja Island.

RESULTS

Identity of the species

Olepa schleini Witt, Müller, Kravchenko, Miller, Hausmann & Speidel, 2005

Materialexaminedmorphologically and genetically (1♀was not sequenced): 5♀♀, 3♂♂ Comoros, Moroni, Graphica Imprimerie, 11°43′27.96″S 43°14′49.37″E, 25 XI 2023 (larvae ex situ), 22 I 2024 (imagines ex cult.).

Morphological determination. Due to the lack of a modern, extensive revision of the genus and an identification key to species, for the morphological determination of the Comoro specimens we followed the descriptions and illustrations of the morphological characters available in the published papers on Olepa. The examination of the variability of the forewing pattern and the hindwing background colouration (which are intensively pink-red in Comoros specimens, Fig. 3A–B) of all available illustrations of Olepa specimens did not provide a base for useful discrimination between taxa. These characters are rather uniform across the genus and express high intraspecific variability. The examination of the male genitalia turned out to be much more informative. Within Olepa, two main types of uncus and valva can be distinguished. The uncus can be either elongate and narrow or broad and triangular, whilst the valva can gradually narrow towards the tip or be equipped in the terminal portion with more or less extensive, blunt or more pointed protrusions.

The genitalia of the two examined male specimens are characterised by a clearly narrow, elongate uncus and valva directed inwards, narrowing and devoid of any marginal protrusions (Fig. 3C1–C2). This combination undoubtedly indicates them to represent the nominotypical subgenus (Singh and Singh 2013). Within this group of 10 species, such an arrangement is typical for only three taxa: O. ricini, O. schleini and O. neumuthi Orhant, 2012. Thus, further examination was narrowed down to the published data for these three species. The examination of all available illustrations of the male genitalia published in Orhant (1986, 2012), Witt et al. (2005), Singh and Singh (2013), Zhang et al. (2022), Kalawate et al. (2020a), and the summarising review of Kalawate et al. (2024) did not result in the convincing ascription of the Comoros specimens to any of the taxa. The valva, albeit more similar to the morphotype referred to as schleini, can also be well matched with some illustrations determined as ricini. It should be noted that the genitalia of O. neumuthi, known from a single male, taking into account the morphological similarity combined with intraspecific variation of ricini and schleini, cannot serve as a significant determinant of the taxonomic affiliation to this taxon. For the purpose of this study, which is not aimed at revision of the ricini group of species, the Comoros invasive population is tentatively determined by morphology as belonging to O. schleini. Additionally, the genitalia of a single dissected female are depicted (Fig. 3D) to illustrate the morphological details of the Comoros specimens.

Molecular determination. In our final phylogenetic tree (Fig. 4), the Olepa samples from the Comoros are placed in the large terminal clade, together with 13 samples identified as O. schleini in Kalawate et al. (2020a), Rönkä et al. (2016) and Witt et al. (2005), four samples of O. ricini from the paper by Zhang et al. (2020), the holotype of O. neumuthi (LEPGO009-14), and a few further, unpublished sequences assigned to O. ricini and O. schleini in GenBank. In the clade, there are seven Comoros samples, 17 O. schleini samples, seven O. ricini samples, and one O. neumuthi sample in total.

All samples assigned to O. schleini are grouped in the terminal clade, whilst six further samples of O. ricini are scattered in other places in the tree.

Considering the obtained topology, we interpret the samples from Comoros as belonging to O. schleini. Distribution in Comoros. In December 2023, an invasion of O. schleini caterpillars was observed in the south of Moroni (Graphica cartier). In February 2024, the same species invaded the neighbourhoods around Graphica and later in early March, the Zilmadjou district, a few kilometres from Graphica. In total, the species has been recorded in 10 localities within an area of a few square kilometres (Fig. 1).

Host plants in Comoros. Caterpillars of O. schleini in the Comoros were recorded predominantly on Castor Bean (R. communis), which is the species' main hostplant (Fig. 5A, E). The caterpillars were observed to be able to destroy castor leaves completely, which is congruent with the observations from Israel (Müller et al. 2005). In the Comoros, other plants growing in the vicinity of infested castor individuals were also observed to be attacked, including the economically significant Musa x paradisiaca L. (Fig. 5B), Artocarpus altilis (Parkinson) Fosberg, Persea americana Mill., Passiflora edulis Sims, and Carica papaya L. (Fig. 5C), and further Achyranthes aspera L., Tragia benthamii Baker, Paederia foetida L., Morinda citrifolia L., Calanchoe pinnata Pers., Plectranthus sp., Ficus sp., Dracaena sp., Xanthosoma sp., Acalypha wilkesiana Müll. Arg., Jatropha curcas L., Epipremnum pinnatum L. (Engl.), Ipomoea obscura L. (Ker) Gawl., Solanum torvum Sw. (Fig. 5D), Urena lobate L., Cordyline fruticose L. (A) Chev., and Achyranthes aspera L. The full list of observed host plants is provided in Table 2. It should be stressed that except for plants from the genera Ipomoea, Musa, Ricinus and Solanum, all remaining 23 recorded plants represent new host genera for Olepa. They belong to various orders of monocotyledon and eudicotyledon plants, highlighting the polyphagous nature of O. schleini.

Table 1.

Accession numbers of samples used in the final phylogenetic analysis (see text).

Figure 2.

Maximum Likelihood phylogenetic tree of Olepa based on the initial dataset of COI gene sequences (see text). Support values at nodes represent SH-like/Ultrafast Bootstrap, respectively.

Figure 3.

Olepa schleini from the Comoros. (A) female; (B) male; (C) male genitalia (Genital Slide No S555); (D) female genitalia (Genital Slide No S556).

Figure 4.

Maximum Likelihood phylogenetic tree of Olepa based on the final dataset of COI gene sequences (see text). Support values at nodes represent SH-like/Ultrafast Bootstrap, respectively.

DISCUSSION

Members of Arctiinae are rarely reported as alien invasive species, and Erebidae generally are not very successful invaders, compared to some other families of Lepidoptera (Mally et al. 2022). Furthermore, at least three out of seven Arctiinae invasive species listed in Mally et al. (2022) are known to have been intentionally released, and the origins of the others are uncertain, with Tyria jacobeae (Linnaeus, 1758) as a biological control of Ragwort being one of the most striking cases. One of the best examples of unintentional introductions within tiger moths is the North American Fall Webworm Hyphantria cunea (Drury, 1773). This was first reported in Europe in 1949, and subsequently it has successfully colonised the entire temperate area of Asia, reaching Japan. The species is regarded as one of the most polyphagous of all insects, with 636 species recorded as food plants (Warren and Tadic 1970).

Figure 5.

Selected plants attacked by Olepa schleini in Comoros. (A) Ricinus communis; (B) Musa x paradisiaca; (C) Carica papaya; (D) Solanum torvum; (E) many larvae on a Castor stem.

Table 2.

Details of the infestation of different plants in Comoros by larvae of O. schleini.

Table 2.

Details of the infestation of different plants in Comoros by larvae of O. schleini. Continued

Molecular data on the genus Olepa. Molecular data on the genus Olepa published so far are preliminary, consisting predominantly of the COI (DNA barcode) sequences published by Kalawate et al. (2020a, 2020b), Witt et al. (2005), and Zhang et al. (2022), with only six nuclear gene sequences available for O. schleini (Rönkä et al. 2016). The most comprehensive and up-to-date Olepa phylogeny was published by Kalawate et al. (2024), but unfortunately, based exclusively on the COI gene. Analysis of the trees obtained by Kalawate et al. (2024) and by us leads to the conclusion that the taxonomic status and relationship of O. ricini and O. schleini cannot be inferred with the available data, and much less the evolutionary history of the entire genus, or the genetic variability of the extant taxa and populations. However, the much-needed taxonomic revision of the genus Olepa is beyond the aim of this paper. Since the Comoro specimens unequivocally fall within the O. schleini clade, which is supported by the morphological analyses, we hypothesise that they belong to this taxon. The analyses conducted in our paper cannot answer the questions about the geographic origin of the newly detected Comoro population either. The samples from Israel, India, China and Thailand are mixed within the O. schleini clade, and none of them can be indicated with certainty as the most probable source of introduction.

Nevertheless, following our finding of erroneous sequences AM050280-AM050284 and the low quality of the MT318099 sequence, we can argue that the alleged polyphyletic character of Olepa, suggested in Kalawate et al. (2024), results solely from the data structure and is incorrect. This is also supported by the data available on BOLD, where the samples cluster together with some European geometrids (BIN, BOLD:AAE1311).

The samples identified as O. ricini are scattered in three different lineages across the tree, making the species appear paraphyletic. It raises substantial doubts about the accuracy of the specimens' determination and the taxonomic status of some other taxa included in the dataset. The molecular and morphological data obtained so far support the presence of diverse lineages within Olepa, some of them being recognised as species or subspecies (Kalawate et al. 2024). Currently (September 2025) on BOLD, the majority of Olepa individuals, identified as O. schleini, O. neumuthi, and O. ricini, represent the same DNA barcode cluster (BIN, BOLD:AAC3385), with up to 1% variability. By contrast, O. zedesi (BIN, BOLD: AAK3571) and O. suryamal (BIN, BOLD: ACH0336) are over 4% distinct. Nevertheless, the overall diversity of the genus appears tangled, with more complex research required to address these issues. Considering the unclear and recently changed status of some species (Kalawate et al. 2024), O. ricini could have been used as the ‘default’ identification of ambiguous individuals, which cannot be verified without access to these specimens and their genitalia slides. This leads to an even more important question: whether the actual O. ricini is present in our dataset at all, which could be fully answered only by sequencing the type specimens of this species that are deposited at the Zoological Museum, University of Copenhagen (see Orhant 1986).

The most unequivocal result concerning the remaining Olepa species is the question about the taxonomic distinctiveness of O. neumuthi Orhant, 2012, synonymised with O. ricini by Singh and Singh (2013), and subsequently resurrected as a valid species by Kalawate et al. (2024). According to our molecular results, the single specimen of O. neumuthi does not belong to a separate evolutionary lineage but is arranged together with the Comoro samples of O. schleini. As illustrated in the original description (Orhant 2012: figs 9–11), eggs and caterpillars of O. neumuthi were found in a Castor Bean plantation, suggesting it to be an introduced pest rather than a Ricinus herbivore native to Thailand. With the overall morphological similarity to the other members of the “ricini-schleini complex”, it is plausible that the specimen does not represent a separate, previously unknown taxon but rather an alien, likely invasive species of Olepa introduced to Thailand, similar to the case reported here from the Comoros.

Parasites or parasitoids. Müller et al. (2005) did not mention any parasitoids reared within four years of their study. Instead, they report numerous lethal infections caused by microsporidial fungi. There is no other published data on the natural enemies, especially parasitoids of Olepa species.

Olepa as a pest. Host plants of 10 out of 14 species of Olepa remain unknown (Kalawate and Dinesh 2021). Ricinus communis is utilised by O. koslandana Orhant, 1986, O. neumuthi, O. ricini and O. schleini. In the case of the first two species, it is the only known food plant (Orhant 2012, Farooqui et al. 2022), and in both cases, larvae were collected in a single locality. Thus, there is no information about the severity of infestation of the castor plants in the studied areas in India and Thailand.

Almost all information related to the importance of the Olepa species in agriculture is based on observations of O. ricini. The genus is frequently reported in India as the pest on aubergine, banana, castor, cotton, lablab, maize, sunflower and sesame. Depending on the plant species and source it is regarded as a minor or major pest (David and Ananthakrishnan 2004, Singh and Gandhi 2012). The caterpillars were also collected, probably incidentally, on leaves of vanilla (Vanitha et al. 2011).

The first reports of the potentially calamitous impact of O. schleini caterpillars on castor plantations were in Müller et al. (2005). These authors, who described the taxon some months earlier from Israel, noted that the larvae occurring in larger numbers were able to completely defoliate castor individuals. Additionally, instead of searching for new plants (by dispersing on the ground), they utilised all remaining edible parts of the infested individual, such as green twigs, green seeds and the bark of small branches. They reported a single case in which the small castor tree was “killed […] within a few days”.

Based on the study of Rittner et al. (2012), O. schleini seems to be a highly polyphagous species whose larvae were reported to utilise nine plant species from eight different families. He was also the first to suggest that this moth can be “a potential pest species in agriculture and horticulture” and considered Brassica oleracea L. as the vegetable potentially most susceptible to the loss.

The published data, as well as the field observations gathered in the course of the present study, indicate that regardless of the true taxonomic status of the O. ricini/schleini complex, this pest can constitute a significant threat to commercial castor plantations in Africa. In the Comoros, Olepa apparently has adapted very quickly to the local climatic and ecological conditions and has become a locally abundant taxon. Although the islands lie on the opposite side of the Equator from India, they both belong to the tropical climate zone. The distance from the Equator is similar in case of both Africa and the Comoros, which are about 1300 km distant, which is equal to the location of Bengaluru in the southern portion of the Indian Peninsula, from where O. cf. ricini was recorded (Kalawate et al. 2024). The concerns about a population establishment on the African continent seem to be justified, the more that the existence of the population of O. schleini in Israel proves that the species can easily adapt to relatively dry conditions.

The discovery of so numerous larvae of O. schleini defoliating R. communis in many localities across Moroni may herald a serious threat to the local population of this plant. It may potentially have a significant socioeconomic influence. Castor, which is thought to have an original origin in East Africa (Kenya and Ethiopia), was introduced to the Comoros and Madagascar by colonists more than a century ago as a source of income that could attract major investors (Anziz 2021, Xu et al. 2021). Soon after its introduction, castor oil was adopted in traditional Comorian medicine (Matthew et al. 2020). Although this plant, growing in many dry, tropical and subtropical regions as well as in temperate regions with hot summers, is currently not cultivated in the Comoros (Ghnimi 2015), it has become a widespread weed present in various habitats. Castor grain is used in the Comoros as the source of multi-purpose oil with many potential applications, thanks to the reactivity of its main fatty acid, ricinoleic acid (Mutlu and Meier 2010, Kunduru et al. 2015, Patel et al. 2016, Mubofu 2016). Thus, the plant is of increasing importance on the domestic market, with the emergence of new, small cosmetic companies, which produce and sell castor oil made in the Comoros, making it an increasingly important part of the local economy. Threats to castor oil production in Africa. According to official data published by TRIDGE ( https://www.tridge.com/intelligences/castor-beancastor-seed/production), the largest castor oil producer as of 2022 was India with 1.6 million tonnes, significantly exceeding all other producers from the top 10 list. Among them, there are two East African countries, Mozambique and Ethiopia, and the former is the second largest producer of castor oil in the world. However, virtually all East African countries are significant producers and exporters of castor oil ( https://www.helgilibrary.com/charts/which-countryproduces-the-most-castor-oil/). In this light, the discovery of a potential pest of R. communis close to the African mainland raises the question of the potential impact of O. schleini on Castor Bean plantations in those countries. If the moth was able to colonise – certainly in the case of human involvement – a land located about 5000 km from the known range of the species, the risk of its unintentional transportation to the African mainland, only 300–415 km away from the Comoros, is very high. The observation data retrieved from iNaturalist confirm the presence of wild Castor Bean plants in different locations along the eastern coast of Africa, with numerous coastal records from Kenya and Tanzania. The records also confirm the presence of the host plants in Mombasa and Dar es Salaam (iNaturalist), which are the largest seaports of these countries, respectively. Records of castor along the sea coast of Mozambique are also frequent.

Origins and means of transport of O. schleini to the Comoros. The first record of the originally Asian O. schleini in the Afrotropics raises questions about its possible origins and routes of its introduction to the Comoros. The species is present in both India and Israel, and could have been introduced to the Comoros from the former country, as many of the products used in the Comoros come from India. For example, the rice consumed in the Comoros comes from India and Thailand. According to the unpublished report of INRAPE from 2023, approximately 2,880 tons of luxury rice, distributed across 114 containers, were imported from India and Thailand to the Comoros. Of these 2,880 tons, 97.74% (2,815 tons) originated from India. Also, according to the unpublished rapport of INRAPE from 2024, the quantity decreased to 2,780 tons in 109 containers, with 2,715 tons (97.66%) coming from India.

Without prior studies, it is difficult to explain how exactly the species was transported to the Comoros, but one can consider two major routes: via air or sea. The latter one seems more likely, by transportation with goods such as containers of rice or other plant products, since the main cargo associated with unintentional alien insect species introductions is plant and wood products (Fenn-Moltu et al. 2022). This scenario is also supported by the localisation of transport infrastructure objects in Grand Comoro. The main airport on the island, Prince Said Ibrahim International Airport, is located 19 km in a straight line from the collecting localities. In contrast, the Moroni Sea Port borders the north margin of the area where the species has been recorded. Shipping cargo from the Comoros to India typically takes around 2 to 3 weeks, depending on the route and port of destination. Sea freight transit time from India to Mombasa is estimated at 3–5 weeks by the online calculators of the largest shipping companies, such as Maersk or Hapag-Lloyd ( https://www.maersk.com/schedules/pointToPoint, https://www.hapag-lloyd.com/solutions/schedule/#/). Such a period is short enough to transport any developmental stage of a moth, perhaps except for the imago, which needs regular feeding and is generally the shortest living stage. The transported individual(s) can potentially also continue to develop in any of the four stages (egg, larva, pupa, imago). It should be emphasised that not just an adult female is required to effectively establish a new population. It can be easily initiated by pupae, which, after disembarking of transported goods in the destination port, quickly turn into much more mobile imagines.

CONCLUSIONS

In conclusion, despite the still unclear taxonomic status of the population of discovered Olepa in the Comoros, this observation is very important from an economic perspective. It is the first record of this potentially severe pest of Castor in the Afrotropics and serves as a warning for the quarantine services in continental Africa. The study undertaken highlights the importance of proper curation and allocation of genetic data submitted to publicly available databases, as well as their critical evaluation before application in more general studies.

Finally, the morphological and genetic study conducted shows how superficial our knowledge of the biological variability of the described entities ascribed to the genus Olepa is. Clarification of this intriguing issue will benefit not only basic science but is vital for more applied aspects related to agriculture or pest control. We are convinced that regular monitoring of the Comorian population of O. schleini is needed. Taking into account the apparently aggressive nature of both Israeli and Comoros populations of Olepa, active eradication of the species from the Comoros territory should also be considered.

AUTHOR CONTRIBUTIONS

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by LP, HM and MW. The first draft of the manuscript was written by LP, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

STATEMENTS AND DECLARATIONS

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

COMPETING INTERESTS

The authors have no relevant financial or nonfinancial interests to disclose.

ACKNOWLEDGMENTS

We thank Patryk Skraba (ISEA PAS) for photographing the specimens and preparing the figures. Beata Babicz (ISEA PAS) is acknowledged for acquiring hard-to-reach literature. David Lees (NHMUK) kindly corrected the linguistic quality of the text. The National Research Institute for Agriculture, Fisheries and the Environment, Comoros, is acknowledged for facilitating field missions for sampling.

This open-access article is distributed under the CC BY-NC-ND 4.0 license

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The combined effect of proteinaceous feeding and seasonal changes on the activity and productivity of honey bee (Apis mellifera L.) colonies

Sun, 05 Apr 2026 16:00:00 GMT

Abstract

Nectar and pollen resources are not sustainable year-round in most parts of the world, and the lack of either results in reduced colony productivity. To address this challenge, we investigated the effects of a high-protein pollen substitute diet on honey bee (Apis mellifera L.) colony performance over a one-year period. Ten colonies were fed a diet (50 g/colony, twice weekly) comprising 15 g defatted soybean flour + 15 g brewer's yeast + 5 g sugar powder + 5 g skimmed milk + 10 g cotton honey. Another ten colonies served as a control group without protein supplementation. Colony performance metrics including foraging activity, stored pollen area, worker and drone brood production, and overall colony strength were monitored and compared. Additionally, monthly surveys identified the dominant nectar and pollen sources in the Kafrelsheikh district. Faba bean, Egyptian clover, sunflower, eucalyptus, and sesame were the dominant sources of nectar and pollen. Maize was the main pollen source, and cotton was a major nectar source in the Kafrelsheikh district. Compared with the unfed colonies, the fed colonies showed significant superiority in colony activity and productivity. The highest values of the number of forager bees, pollen forager, stored pollen area, worker sealed brood area, and colony population size were recorded during May, followed by August, then March, while the lowest values were recorded during December. The highest values of drone sealed brood area were recorded during March, followed by May, then August, while no drone broods were reared from October to January. Honey yield from fed colonies increased by 36.62% and 35.10% more than honey from unfed colonies for Egyptian clover and cotton honey, respectively. Supplementing honey bee colonies with a high-protein diet during periods of natural pollen scarcity significantly enhances colony performance, strength, and honey production. We recommend protein supplementation during scarcity periods and between major nectar flows to sustain colony health and maximize productivity.

Pollen and nectar are the major natural protein, carbohydrate, and lipid sources of honey bee colonies. They are essential for brood production, growth, and health of the honey bee colony (Taha et al., 2019a; Al-Kahtani et al., 2021). There is a positive relationship between the low seasons of nectar and pollen and all honey bee activities and their products under suitable environmental conditions, according to the rule that states 'no pollen, no bees' (Taha and Al-Kahtani, 2019; Shawer et al., 2021). Development, reproduction, colony population size, and food reserves are among the criteria used to determine whether honey bee colonies are successful in a region (Helal et al., 2003; Shawer et al., 2003; Elwakeil et al., 2025; Taha et al., 2025a).

Providing honey bee colonies with supplemental pollen feed is critical to their health and productivity. Honey bees need nectar and pollen to generate heat for thermo-regulation within their nests and to rear their brood (Degrandi-Hoffman et al., 2008). The production of a colony depends on the availability of high-quality nutritional sources. When natural pollen sources are not available, artificial pollen diets can be used to supplement honey bee colonies, which is essential for the development of young bees, reproduction, brood rearing, honey production, and colony maintenance (Taha, 2015b; Elwakeil et al., 2025). Bees in their developing stages and nurse bees require pollen supplements that are abundant and diverse in amino acids, fatty acids, and mineral elements, since certain pollens may lack essential amino acids they cannot synthesize for themselves (Al-Kahtani and Taha, 2020; Taha and Al-Kahtani, 2020b).

Colony performance is influenced by environmental factors, which in turn impact colony productivity. The beekeeper feeds supplementary pollen or pollen substitutes to colonies during times of pollen dearth to maintain colony strength (Morais et al., 2013; Taha, 2015b; Elwakeil et al., 2025). Bee diets often contain protein-rich substances such as soy, pea, yeast, casein, etc. (Ricigliano et al., 2022; Elwakeil et al., 2025). Pollen substitutes should meet the nutritional needs of honey bees throughout the year, be cost-effective, and provide comprehensive nutrition (Elwakeil et al., 2025). A pollen substitute diet should contain protein, lipids, and other nutritional sources (Brodschneider and Crailsheim, 2010; Taha et al., 2025a). A consistent supply of pollen or a protein-supplemented diet can encourage brood rearing and promote colony expansion (Morais et al., 2013; Elwakeil et al., 2025). However, adult workers who are nutritionally stressed, such as larvae, might experience weight loss, shorter survival, less foraging activity, and more aggressive dance behavior (Noordyke et al., 2021). Most beekeepers commonly use commercial or artificial diets to avoid apparent nutritional deficiencies (Taha, 2015b; Elwakeil et al., 2025).

Colony growth and maintenance are limited by the amount of protein available typically found in pollen. Protein is an essential nutrient for honey bee larvae, and a shortage will greatly affect brood production (Brodschneider and Crailshein, 2010; Taha et al., 2019a; Al-Kahtani et al., 2020). During the first 3 days of larval age, bee larvae feed on royal jelly produced by nurse bees. Nurse bees feed on pollen to develop their hypopharyngeal glands and mandibular glands, which release royal jelly. Therefore, colonies that have no access to protein during the active season have a reduced capacity to rear new bees to replace the old ones when they die, resulting in a quick decline in population and potential death (Shawer et al., 2021). The wintering ability, brood areas, colony population size, and adult survival rates are positively influenced by feeding (Elwakeil et al., 2025; Taha et al., 2025a). The evaluation of pollen substitutes should include their attractiveness to bee workers, and their effects on brood area, survival of bees, and colony development (Taha, 2015b; Lamontagne-Drolet et al., 2019; Elwakeil et al., 2025).

Colony strength before the nectar flow is a critical factor for honey production and yield, allowing effective use of early flow seasons. Pollen supplements or substitutes may accelerate colony growth and improve colony performance by stimulating brood rearing during scarcity periods (Mattila and Otis, 2006; Elwakeil et al., 2025). While adult bees can survive on carbohydrates alone, access to pollen significantly extends worker longevity (Manning et al., 2007); this can be seen in the colony population size and can have effects on colony performance and productivity.

In most regions, seasonal fluctuations in nectar and pollen availability lead to weakened colonies, reduced productivity, and potential colony decline. To address this issue, this study investigates the effects of a high-protein diet on honey bee colony performance, with particular focus on: foraging activity (monthly variations), stored pollen reserves, worker and drone brood production, colony population dynamics, and honey yield. By evaluating these key metrics, we assess how protein supplementation sustains colony strength and productivity during resource-limited periods.

MATERIALS AND METHODS

Apiary Location and Experimental Colonies

Experiments were conducted at the Faculty of Agriculture apiary (31° 5′ 54″ N, 30° 57′0″ E) at Kafrelsheikh University, Kafrelsheikh, Egypt, during the period extended from October 2022 to September 2023. The oxalic acid vaporization method was used to control Varroa mite (Varroa destractor) in experimental colonies in mid-September 2022. By the beginning of October 2022, 20 Carniolan hybrid honey bee (Apis mellifera carnica Pollmann × A. m. lamarkii Cockerell) colonies (14000 bees for each) headed by sister open-mated queens and having relatively similar brood area, and storage food were prepared for the experiments. The colonies were divided into 2 groups, each 10 colonies. The 1st group was fed a diet of Taha et al. (2025a). The diet (50.00 g/colony) comprises 15 g brewer's yeast (Saccharomyces cerevisiae) + 15 g defatted soybean (Glycine max) flour + 5 g skimmed milk + 10 g cotton (Gossypium barbadense) honey + 5 g sugar powder. The diet was introduced fresh to the colonies as a paste placed on waxed paper directly above the brood nest at a rate of 50 g/colony twice a week. The proximate analysis of the experimental diet has been determined (Taha et al., 2025a). The 2nd group was left without proteinaceous feeding (control). Sugar feed was provided equally to all colonies as needed during periods of scarcity.

Colony Performance

Colony performance was determined over one year. The number of incoming workers without and with pollen loads to a colony within one minute at the flight activity peak was counted monthly, once a week. The counts were taken at 09.00–10.00 hrs during June–September and at 12.00–13.00 hrs during October–May. A plastic sheet divided into square inches was utilized to measure the areas (square inches) of stored pollen and worker and drone sealed broods at 12-day intervals, and the monthly areas were counted. The number of combs covered with bees/hive was calculated monthly to determine the number of bees (colony population size), as the bees covering one comb equal 2000 bees (Taha, 2007). The Egyptian clover (Trifolium alexandrinum L.) honey yield was harvested by the beginning of June and cotton (Gossypium herbaceum L.) honey was harvested by the end of August. The honey yield of each colony was determined by calculating the difference between the weight of honeycombs before and after extraction. The monthly increments due to feeding in the previous aspects were calculated (Eq. 1)

Survey of Major Bee Plants

The most important nectar and/or pollen-producing plant species in the experimental area were recorded throughout the year. The scientific name, common name, and plant family were identified for each plant. The flowering date of each plant and its value to bees as a source of nectar and/or pollen were recorded.

Meteorological Factors

The mean values of maximum and minimum air temperature, rainfall, wind velocity, and relative humidity in Kafrelsheikh district, Egypt during experimental months (Table 1) were obtained from the meteorological station of Rice Research and Training Center, Sakha, Kafrelsheikh, Egypt.

Table 1.

The mean values of some meteorological factors in Kafrelsheikh district, Egypt during experimental months in 2022/2023.

Statistical Analysis

The two-way analysis of variance was used to test the differences between the fed and unfed colonies for foraging activity, stored pollen area, worker and drone sealed brood areas, colony population size, and honey yield via the PROC GLM function in SAS version 9.1 (SAS Institute, 2003). The treatment means were compared by using Tukey's HSD post-hoc test.

RESULTS

Data presented in Table 1 show that the maximum air temperatures were above 32 °C during June to September, above 25 °C during April, May, October, and November, and above 20 °C during March and December. The minimum air temperature declined under 10°C only in January and under 15 °C during December, February, and March. The relative humidity values were above 65% during October, November, and January, above 60% during February, July, and August, above 55% during March, June, and September, and above 50% during April and May. Wind velocity was above 4 km/hr from March to May.

Data listed in Table 2 show the most important nectar and/or pollen flora for honey bees in the Kafrelsheikh district during the experimental year. Faba bean (Vicia faba L.) was the predominant source of nectar and pollen from January to March. Egyptian clover (Trifolium alexandrinum L.) was the major source of nectar and pollen during May and June. Maize (Zea mays L.) was the main source of pollen for honey bees from June to November. Sunflower (Helianthus annuus L.), cotton (Gossypium herbaceum L.), and sesame (Sesamum indicum L.) were the dominant sources of nectar; sunflower and sesame were the major sources of pollen during July and August. Eucalyptus (Eucalyptus globulus Labill) was the predominant source of nectar and pollen from October to April.

Table 2.

Major nectar and pollen floral resources in the Kafrelsheikh district in 2022/2023.

Data presented in Figs (1-7) show the impact of proteinaceous feeding on the monthly changes in colony performance. The monthly variations in the number of forager bees, pollen foragers, stored pollen area, worker sealed brood area, and number of bees per colony (colony population size) showed the highest values during May, followed by August, then March, while the lowest values were recorded during December. The highest significant (P < 0.01) values of drone sealed brood area were recorded during March, followed by May, then August, while no drone brood was reared from October to January. Compared with the unfed colonies, the fed colonies showed significant (P < 0.01) superiority in all colony activities and honey yield. Honey yield in fed colonies was significantly larger than honey yield in unfed colonies (7.35 vs 5.38 kg/colony) in the Egyptian clover flowering season, and (5.35 vs 3.96 kg/colony) in the cotton flowering season.

Figure 1.

Monthly fluctuation of the number of forager bees/colony/min in fed and unfed Carniolan hybrid honey bee colonies in Kafrelsheikh district during 2022/2023.

Figure 2.

Monthly fluctuation of the number of pollen foragers/colony/min in fed and unfed Carniolan hybrid honey bee colonies in Kafrelsheikh district during 2022/2023.

Figure 3.

Monthly fluctuation of stored pollen in fed and unfed Carniolan hybrid honey bee colonies in Kafrelsheikh district during 2022/2023.

Figure 4.

Monthly fluctuation of worker sealed brood area in fed and unfed Carniolan hybrid honey bee colonies in Kafrelsheikh district during 2022/2023.

Figure 5.

Monthly fluctuation of drone sealed brood area in fed and unfed Carniolan hybrid honey bee colonies in Kafrelsheikh district during 2022/2023.

Figure 6.

Monthly fluctuation of population size in fed and unfed Carniolan hybrid honey bee colonies in Kafrelsheikh district during 2022/2023.

Figure 7.

Honey yield (kg)/colony in fed and unfed Carniolan hybrid honey bee colonies in the Kafrelsheikh district during 2022/2023.

DISCUSSION

Honey bees primarily get carbohydrates from nectar, while pollen provides them with protein, fats, minerals, vitamins, and other nutrients. For honey bees, Taha et al. (2019b) have recorded 15 major sources of pollen and/or nectar, in addition to 95 minor sources in Kafrelsheikh province. In the current study, 7 major bee plants were recorded in the study area. The most important pollen resources for honey bees were faba bean (January–March), Egyptian clover (May and June), maize (June–November), sunflower and sesame (July and August), and eucalyptus (October–April), and the most important nectar resources were sunflower and cotton (July and August), sesame, Egyptian clover, eucalyptus, and faba bean. Besides, there were a large number of secondary sources of pollen and/or nectar that contributed to maintaining colony growth throughout the year. From the 6 major nectar sources, only Egyptian clover honey and cotton honey were harvested. The previous plants have been recorded as important sources of nectar and/or pollen for honey bees in Kafrelsheikh province; similar results have been recorded by Taha et al. (2019b). The major and minor nectar and/or pollen sources play a vital role in honey bee colony growth and colony strength maintenance (Shawer et al., 2003; Taha and Al-Kahtani, 2013; Shawer et al., 2021).

Foraging for food was fluctuated throughout the months of the year and affected by the availability of nectar and/or pollen flora (Taha et al., 2006; Seitz et al., 2020; Sponsler et al., 2020; Verweij et al., 2025), weather and environmental factors (Taha and Al-Kahtani, 2019; Taha et al., 2025b), colony population size (Taha and Al-Kahtani, 2013), comb age (Taha and Al-Kahtani, 2020), and presence of bee enemies like birds of migratory bee-eater, Merops spp. (Ali and Taha, 2012). Three peaks of flight activity were noticed; the biggest was during May (the flow season of Egyptian clover), followed by August when cotton, sunflower, maize, and sesame bloomed, and then March the flowering period of faba bean and eucalyptus. These results confirmed the findings of Helal et al. (2003) and Shawer et al. (2021) who have recorded the highest numbers of incoming bees and incoming bees with pollen loads during May, followed by July and August the blooming season of cotton, maize, and sunflower. In the current study, a decline in the numbers of forager bees was observed during October to December, and after flow seasons. Flight activity decreased during October–December, April, and June due to a shortage of pollen and nectar flora. Relatively similar results have been reported by Taha (2005) and Shawer et al. (2021). However, a decrease in air temperature during January caused a decline in foraging activity. Compared with the unfed colonies, the number of incoming workers in fed colonies increased by 0.58, 17.17, 18.09, 27.41, 12.29, 20.53, 24.40, 23.57, .71, 26.04, 26.90, and 22.17% during the months from October 2022 to September 2023, respectively. A similar trend was observed with the number of incoming workers carrying pollen. The impact of feeding colonies with a rich-protein diet became very clear. The fed colonies outperformed the unfed colonies in the number of incoming workers carrying pollen by 8.14, 50.00, 51.79, 30.23, 22.89, 14.94, 36.00, 50.54, 54.55, 15.39, 21.36, and 32.50% during the months from October 2022 to September 2023, respectively. The positive effect of feeding colonies on foraging activity has been confirmed (Elwakeil et al., 2025; Taha et al., 2025a).

The stored pollen area reflects pollen-gathering activity in a colony, as mentioned in different studies by Pankiw and Page (2000); Döke et al. (2015); Taha and Al-Kahtani (2020a); Taha et al. (2021); Elwakeil et al. (2025). The stored pollen area reflects pollen-gathering activity in a colony (Taha and Al-Kahtani, 2020a; Taha et al., 2021; Elwakeil et al., 2025). The stored pollen area displayed three peaks; the biggest during May (the flow season of Egyptian clover), followed by March, the flowering period of faba bean, and then August when sunflower, maize, and sesame bloomed. Similar peaks in pollen collection have been observed in Kafrelsheikh province (Taha et al., 2019b; Taha et al., 2021), and in Saudi Arabia (Taha, 2015a; Taha and Al-Kahtani, 2019). There was a significant increase in the area of stored pollen due to feeding colonies a protein-rich diet. Compared with the unfed colonies, the stored pollen area in fed colonies increased by 2.95, 26.60, 48.98, 27.59, 24.65, 23.97, 56.68, 62.92, 76.29, 63.13, 55.50, and 71.51% during the months from October 2022 to September 2023, respectively. These results emphasize the findings of Taha (2015b), Amera et al. (2024), Elwakeil et al. (2025), and Taha et al. (2025a). The above ratios reveal that feeding colonies a high-protein diet was most effective during periods of scarcity.

Beekeepers give pollen or pollen substitutes to colonies to encourage them to build. In addition to the colony strength, bee race, and environmental factors, brood rearing and colony growth in a colony are affected by nutritional factors (Taha and Al-Kahtani, 2019; Kavitha et al., 2021; Elwakeil et al., 2025). At the beginning of the experiment, all colonies were headed by openly mated young sister queens, colony population, and brood areas were similar, so the effects should have been the same, except for nutritional factors. The fed colonies showed superiority in the worker sealed brood area by 2.15, 7.19, 22.19, 25.66, 30.41, 31.84, 34.80, 31.86, 35.71, 22.59, 25.58, and 37.03% more than the unfed colonies during the months from October 2022 to September 2023, respectively. These results are endorsed by the findings of Mattila and Otis (2006), Kumar et al. (2013), Topal et al. (2019), Elwakeil et al. (2025), García-Vicente et al. (2025), and Taha et al. (2025a). Three peaks of worker sealed brood area occurred; the biggest was during May, followed by August, and then March. These results are emphasized by the findings of Shawer et al. (2021) and Taha et al. (2021). The peaks of worker sealed brood area coincided with the peaks of foraging activity and stored pollen area. Significant positive correlations have been found between worker sealed brood area, stored pollen area, and the number of pollen foragers (Taha and Al-Kahtani, 2019; Elwakeil et al., 2025; Taha et al., 2025a). Three peaks of drone sealed brood area occurred; the biggest was during March, followed by May, and then August, Meanwhile, no drone broods were reared during the period from October to January. Similar results have been reported by Taha et al. (2021). However, providing colonies with pollen substitutes encouraged drone brood rearing since the fed colonies outperformed unfed colonies in drone brood production by 58.82%, 39.10%, 71.13%, 41.67%, 48.84%, 36.05%, 80.85%, and 62.07% during the months of February 2023 to September 2023, respectively.

The colony population size results from the difference between eggs produced by the queen that emerge as adult workers and the old workers that died. The fed colonies displayed significant superiority in population size during all months. Compared with the unfed colonies, the colony size in fed colonies increased by 2.11, 2.62, 9.17, 9.63, 13.38, 18.19, 16.60, 20.42, 17.49, 21.38, 20.35, and 20.97% during the months from October 2022 to September 2023, respectively. These results confirm those obtained by Topal et al. (2019), Amera et al. (2024), Elwakeil et al. (2025), García-Vicente et al. (2025), and Taha et al. (2025a). Three peaks of colony population size occurred; the biggest was during May, followed by August, and then March. These peaks coincided with the peaks of stored pollen and worker sealed brood areas. Taha and Al-Kahtani (2019), Elwakeil et al. (2025), Taha et al. (2025a) have found significant positive correlations between colony size, worker sealed brood area, stored pollen area, and the number of pollen foragers.

The production of honey yield is affected by several factors, including the honey bee race (Al-Ghamdi et al., 2017; Taha and Al-Kahtani, 2019), availability of nectar and/or pollen flora (Taha et al., 2006; Seitz et al., 2020; Sponsler et al., 2020; Verweij et al., 2025), colony population size (Taha and Al-Kahtani, 2013; Brar et al., 2018), feeding of colony (Amera et al., 2024; García-Vicente et al., 2025), comb age (Taha and El-Sanat, 2007; Taha and Al-Kahtani, 2020a; Taha et al., 2021), and queen status (Shawer et al., 2021). Here, the fed colonies outperformed the unfed colonies in honey production by 36.62% and 35.10% for Egyptian clover honey and cotton honey, respectively. The superiority of fed colonies in honey production was related to the superiority in foraging activity, brood rearing, and colony population size. Significant positive correlations have been found between honey yield, worker sealed brood area, stored pollen area, and the number of pollen foragers (Taha and Al-Kahtani, 2013; Elwakeil et al., 2025; Taha et al., 2025a).

CONCLUSION

The results demonstrate that a protein-rich diet significantly enhances colony growth performance and honey yield. To maintain optimal colony strength and maximize honey production, protein supplementation should be provided during periods of scarcity and between flow seasons.

FUNDING

This research was funded by Deanship of Scientific Research at King Faisal University, Project number (KFU 252554).

ACKNOWLEDGMENTS

The authors extend their appreciation to the Deanship of Scientific Research at King Faisal University for funding the Project number (KFU 252554), King Faisal University, Al-Ahsa, Saudi Arabia.

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Vampyriscus brocki (Chiroptera: Phyllostomidae)

Sun, 15 Mar 2026 16:00:00 GMT

Abstract

Vampyriscus brocki (Peterson, 1968), Brock's Yellow-eared Bat, is endemic to the South American rainforests east of the Andes and is distinguished from its congeners by its diploid number, smaller size, and other morphological characteristics. Vampyriscus brocki prefers primary and riverine forests and feeds on fruits. Despite its broad distribution, it is rarely encountered, contributing to limited ecological data. Although currently listed as “Least Concern” (LC) by the International Union for Conservation of Nature (IUCN), its dependence on mature forest habitats underscores the importance of habitat preservation.

Vampyriscus brocki (Peterson, 1968), el murciélago de orejas amarillas de Brock, es endémico de los bosques tropicales sudamericanos de los Andes orientales, y se distingue de sus congéneres por un conteo cromosómico único, su menor tamaño y otras características morfológicas. Vampyriscus brocki prefiere los bosques primarios y ribereños y se alimenta de frutas. A pesar de su amplia distribución, a la especie se la registra con dificultad, lo que contribuye a la escasez de datos ecológicos. Aunque actualmente listado como “Preocupación menor” por la Unión Internacional para la Conservación de la Naturaleza (IUCN), su dependencia de hábitats forestales maduros resalta la importancia de la preservación de su hábitat.

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Introduction

Materials and Methods

Results

Lamiinae Acanthocinini Sternacutus Gilmour, 1961 Sternacutus crucolon Lanuza-Garay, Taboada-Verona, and Rivera, new species zoobank.org/urn:lsid:zoobank.org:act: 3D15E34B-5F32-4ED0-BBA1-87FDD9CF756A (Figs. 1–4)

Figs. 1–4.

New Geographical Records Cerambycinae Eburiini Eburodacrys White, 1853 Eburodacrys megaspilota White, 1853 (Fig. 14)

Figs. 5–13.

Elaphidiini Ambonus Gistel, 1848 Ambonus electus (Gahan, 1904) (Fig. 15)

Psyrassa Pascoe, 1866 Psyrassa maesi Audureau, 2010 (Fig. 16)

Sphaerion Audinet-Serville, 1834 Sphaerion exutum (Newman, 1841) (Fig. 17)

Figs. 14–25.

Hesperophanini Alastos Napp and Martins, 1982 Alastos batesi (Pascoe, 1888) (Fig. 18)

Necydalopsini Ozodes Audinet-Serville, 1834 Ozodes xanthophasma Bates, 1872 (Fig. 19)

Rhinotragini Isthmiade Thomson, 1864 Isthmiade perpulchra Linsley, 1961 (Fig. 20)

Lamiinae Acanthocinini Atrypanius Bates, 1864 Atrypanius irrorellus Bates, 1885 (Fig. 21)

Agapanthiini Hippopsis Lepeletier and Audinet-Serville, 1825 Hippopsis septemlineata Breuning, 1940 (Fig. 22)

Anisocerini Caciomorpha Thomson, 1864 Caciomorpha palliata (White, 1855) (Fig. 23)

Desmiphorini Mimasyngenes Breuning, 1950 Mimasyngenes icuapara Galileo and Martins, 1996 (Fig. 24)

Prioninae Solenopterini Holonotus Thomson, 1861 Holonotus nigroaeneus Bates, 1869 (Fig. 25)

Acknowledgments

References Cited

Seven Guideposts of Agricultural Education: Linking Agronomy With Biology Teaching & Learning

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INTRODUCTION

Detailed Case Background

Table 1.

METHODS

Study Area

Quadrat Sampling

Figure 1.

Table 2.

Mark-Recapture Sampling

Table 3.

Expected vs. Estimated Mussel Abundance

Mussel Length and Growth Rates

Figure 2.

RESULTS

Quadrat Monitoring Data

Table 4.

Table 5.

Mark-Recapture Monitoring Data

Table 6.

Table 7.

Expected vs. Estimated Mussel Abundance

Mussel Length and Growth Rates

Table 8.

DISCUSSION

Figure 3.

Figure 4.

Table 9.

Figure 5.

Figure 6.

ACKNOWLEDGMENTS

LITERATURE CITED

Water Pollution Assessment and Community Perception of Major Rivers in Ghana

Background to the Study

Materials and Methods

Research Design

Figure 1.

Population and Study Area

Figure 2.

Sample Size

Sampling Procedure

Research Instruments

Instrument Validity and Verification

Data Collection Procedure

Data Analysis Procedure

Table 1.

Results

Table 2.

Figure 3.

Table 3.

Figure 4.

Table 4.

Figure 5.

**Lamiinae Acanthocinini Sternacutus Gilmour, 1961**
Sternacutus crucolon Lanuza-Garay, Taboada-Verona, and Rivera, new species
zoobank.org/urn:lsid:zoobank.org:act: 3D15E34B-5F32-4ED0-BBA1-87FDD9CF756A
(Figs. 1–4)

New Geographical Records
Cerambycinae Eburiini Eburodacrys White, 1853
Eburodacrys megaspilota White, 1853
(Fig. 14)

Elaphidiini Ambonus Gistel, 1848
Ambonus electus (Gahan, 1904)
(Fig. 15)

Psyrassa Pascoe, 1866
Psyrassa maesi Audureau, 2010
(Fig. 16)

Sphaerion Audinet-Serville, 1834
Sphaerion exutum (Newman, 1841)
(Fig. 17)

Hesperophanini Alastos Napp and Martins, 1982
Alastos batesi (Pascoe, 1888)
(Fig. 18)

Necydalopsini Ozodes Audinet-Serville, 1834
Ozodes xanthophasma Bates, 1872 (Fig. 19)

Rhinotragini Isthmiade Thomson, 1864
Isthmiade perpulchra Linsley, 1961
(Fig. 20)

Lamiinae Acanthocinini Atrypanius Bates, 1864
Atrypanius irrorellus Bates, 1885
(Fig. 21)

Agapanthiini Hippopsis Lepeletier and Audinet-Serville, 1825
Hippopsis septemlineata Breuning, 1940
(Fig. 22)

Anisocerini Caciomorpha Thomson, 1864
Caciomorpha palliata (White, 1855)
(Fig. 23)

Desmiphorini Mimasyngenes Breuning, 1950
Mimasyngenes icuapara Galileo and Martins, 1996
(Fig. 24)

Prioninae Solenopterini Holonotus Thomson, 1861
Holonotus nigroaeneus Bates, 1869
(Fig. 25)