Ecology

Loose coupling and human intervention promote collective foraging successWe first determined group search performance by assessing the average search time, consumption time, and total targets found in each movement condition with and without intervention.Results showed that search performance as measured by mean trial time was better with loose coupling and human intervention, as seen in the lowest average trial times in Fig. 3. Movement type had a reliable effect on performance without human intervention, F(1,59) = 27.65, p  More

Study areaKogi East located in Kogi State, North Central Nigeria. It is a geographical region comprising of nine (9) Local Government Areas (LGAs); Ankpa, Bassa, Dekina, Ibaji, Idah, Igalamela/Odolu, Ofu, Olamaboro and Omala. The region is located between latitude 6º32′33.8′′N to 8º02′44.8′′N and longitude 6º42′08.5′′E to 7º51′50.3′′E. It occupies an area of 26,197 square kilometres sharing boundaries with six (6) states of Nigeria28. The population of the region at 2006 is 1,479,144 with a projected population of 1,996,700 at 201629.Ethical approval and informed consentEthical clearance was obtained from Research Ethics Committee, Kogi State Ministry of Health (KSMoH), Lokoja with reference number MOH/KGS/1376/1/82 and permission was obtained from the State Universal Basic Education Board (SUBEB), Lokoja with reference number KG/SUBEB/GEN/04/’T’ which was conveyed to the Education Secretaries of the 9 LGAs and the Headmasters (mistress) of the schools.This study follows guidelines for the care and use of human samples established by the Human Care and Use Committee of the Ahmadu Bello University, Zaria, Kaduna State, Nigeria and the Research Ethics Committee, Kogi State Ministry of Health (KSMoH), Lokoja.Statement of consent from participantsWritten consents were obtained from the guardians/parents of study participants, informing them of their rights and granting permission for their children to participate in the study.Source of epidemiological dataThe epidemiological data used for this study were obtained from an earlier district-wide survey carried out in 2018 (Table 1)25 in rural communities of Kogi East, Kogi State, Nigeria. The study obtained samples from school-children of age 5 to 14 years. Samples collected were examined using formal ether sedimentation technique. The study was carried out in schools that did not receive anthelminthic drugs during the yearly periodic deworming exercise carried out by the State Ministry of Health. During the survey, the geographical coordinates of each school and community were captured within the school premises using a handheld Global Positioning system (GPS) device, Garmin 12XL (Garmin Corp, USA).Table 1 Epidemiological Data from District Wide Survey Conducted in 2018 by Yaro et al. (2020) in Kogi East, North Central Nigeria.Full size tableSpatial analysis of STHsCo-ordinate of schools sampled and the mean prevalence of each parasites from the baseline study for A. lumbricoides, Hookworms and S. stercoralis were computed in Microsoft Excel version 2013 and converted to comma delimited file (.csv). These files were further converted from text files to shapefiles using DIVA-GIS version 7.5.0 and were geo-referenced on the map of Kogi East, Nigeria. The prevalence of these parasites were categorized; 0.0–1.0,  > 1.0–5.0,  > 5.0–10.0,  > 10.0–20.0,  > 20.0–50.0 and  > 50.0 on the map (Figs. 1 and 2).Figure 1Source of Satellite Imagery: Image Google Earth: Landsat/Copernicus (Data SIO, NOAA, U.S. Navy, NGA, GEBCO. Maps were visualized on ArcMap 10.1. https://www.google.com/maps/place/Kogi/@7.3195959,7.2632804,189324m/data=!3m1!1e3!4m5!3m4!1s0x104f41e9d61f12dd:0xbdc9f94f2d58aafd!8m2!3d7.7337325!4d6.6905836.Spatial Distribution of STHs in Communities of Kogi East, North Central Nigeria.Full size imageFigure 2Source of Satellite Imagery: Image Google Earth: Landsat/Copernicus (Data SIO, NOAA, U.S. Navy, NGA, GEBCO. Maps were visualized on ArcMap 10.1. https://www.google.com/maps/place/Kogi/@7.3195959,7.2632804,189324m/data=!3m1!1e3!4m5!3m4!1s0x104f41e9d61f12dd:0xbdc9f94f2d58aafd!8m2!3d7.7337325!4d6.6905836.Spatial Distribution of STHs in Local Government Areas of Kogi East, North Central Nigeria.Full size imageEnvironmental data collectionClimatic and elevation variablesRemotely sensed environmental data for altitude, temperature and precipitation were obtained from Worldclim database30. The climatic variables such as temperature and precipitation are at global and meso scales and topographic variables such as elevation and aspect likely affect species distributions at meso and topo-scales31. Hence, the use of the climatic and topographic variables in the prediction of distributions of soil transmitted helminths in Kogi East, Nigeria. Also, temperature was considered in the analysis because A. lumbricoides, hookworms and S. stercoralis have thermal thresholds of 38 °C, 40 °C and 40 °C respectively outside of which the survival of the infective stages in the soil decline32,33.In this study, a total of 19 bioclimatic factors of present climate for Nigeria were downloaded at 1 km spatial resolution (Table 2) from Worldclim database30 and were used in the prediction of soil transmitted helminths distribution in Kogi East. Elevation data derived from the Shuttle Radar Topography Mission (SRTM) (aggregated to 30 arc-seconds, “1 km”) were also downloaded from WorldClim database30.Table 2 Characteristics of Environmental Variables Used in Predicting the Distribution of STHs in Nigeria.Full size tableEdaphic variableThe influence of edaphic factors on the distribution of STHs have been reported by several researchers globally34,35,36 as important factors in the biology of STH parasites. In view of this, data for soil pH, soil moisture content, soil organic carbon and soil clay content for Africa continent were downloaded from International Soil Reference Centre (ISRIC) soil database as spatial layers (Table 2)37.File conversions and resamplingThe 19 bioclimatic factors downloaded from WorldClim data are in geographic coordinates of latitudes and longitudes which comes as .bil files were extracted into a folder. These data were transformed into predefined geographic coordinate system (GCS_WGS_1984), this projection was done on ArcMap 10.1 and were converted to asci files on DIVA-GIS 7.5. These files were transferred back to ArcMap and assigned a projected coordinate system of Universal Transverse Mercator (UTM) Zone 32 N (Nigeria is located on UTM Zone 31, 32 and 33). Also, the edaphic factors obtained were also assigned a projected coordinate system. The projected raster files (i.e. climatic, elevation and edaphic) were all clipped into a layer using the administrative boundary map of the study area, this was downloaded on DIVA-GIS database38.Prior to modelling, all variables were resampled from their native resolution to a common resolution of 1 km spatial resolution using the nearest neighbour technique on ArcMap 10.1 to enable overlaying of variables. The resampled raster files were converted to float files on ArcMap 10.1 and transferred to DIVA-GIS 7.5. Float files were converted to grid files and then to asci files on DIVA-GIS 7.5 and were used on MaxEnt tool for modelling the distribution of STHs in Kogi East.Ecological niche modellingThe potential distribution of STHs were modelled using maximum entropy (MaxEnt) software version 3.3.3k39. MaxEnt uses environmental data at occurrence and background locations to predict the distribution of a species across a landscape31,40. This modelling tool was selected based on the reasons of Sarma et al.41, they stated that this tool allows the use of presence only datasets and model robustness is hardly influenced by small sample sizes. It has been shown to be one of the top performing modelling tools42.Probability of presence of each of the STH was estimated by MaxEnt using the prevalence of each of the STH parasites obtained for 45 sampled communities in the 9 LGAs of Kogi East during the district-wide survey carried out in 201825 served as the presence records to generate background points were used41. Regularization of the prevalence was performed to control over-fitting. This modelling tool uses five different features to perform its statistics; linear, quadratic, product, threshold and hinge features to produce a geographical distribution of species within a define area. The MaxEnt produces a logistic output format used in the production of a continuous map that provides a visualization with an estimated probability of species between 0 and 1. This map distinguish areas of high and low risk for STH infections41.The 19 bioclimatic factors, elevation data and the edaphic factors obtained were used for the ecological niche modelling. The level of significance of contribution of the altitude and 19 bioclimatic factors was used to calculate the area under the receiver operating characteristics curve (AUC) was used to evaluate the model performance. The AUC values varies from 0.5 to 1.0; an AUC value of 0.5 indicates that model predictions are not better than random, values  0.9 indicates high model performance43.Model validation was performed as follows41, using the ‘sub-sampling’ procedure in MaxEnt. 75% of the parasites prevalence data were used for model calibration and the remaining 25% for model validation. Ten replicates were run and average AUC values for training and test datasets were calculated. Maximum iterations were set at 5000. Sensitivity, which is also named the true positive rate, can measure the ability to correctly identify areas infected. Its value equals the rate of true positive and the sum value of true positive and false negative. Specificity, which is also named the true negative rate, can measure the ability to correctly identify areas uninfected. Its value equals the rate of true negative and the sum value of false positive and true negative.Ethics approvalThis study follows guidelines for the care and use of experimental animals established by the Animal Care and Use Committee of the Ahmadu Bello University, Zaria for the purpose of control and supervision of experiments on animals and ethical permission for the study was obtained from the ethical Board of Kogi State Ministry of Health, Lokoja with reference number: MOH/KGS/1376/1/82. More

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In this study, we confirmed that P. longissimus is nonbioluminescent, despite its close relationship to the luminous species P. litoralis (Supplementary Fig. S2)8. The presence of both luminous and nonluminous species in a single genus is likely widespread, but only a few examples have been confirmed; for example, the genera Vibrio and Photobacterium (marine bacteria)9, Epigonus (deep-sea fishes)10, Mycena (bonnet mushrooms)11 and Eisenia (terrestrial earthworms)12 have been reported to contain both luminous and nonluminous species. P. litoralis and P. longissimus can easily be collected at the same beach8 and reared in a laboratory; thus, they are suitable for studying the ecology and evolution of bioluminescence.In vitro luciferin-luciferase cross-reaction tests of P. longissimus and P. litoralis confirmed that the luminescence ability of P. litoralis is due to the presence of multiple bioluminescent components in coelomic fluid, i.e., luciferin, luciferase and the light emitter. Cross-reaction tests have previously indicated that luminous earthworms in the genera Pontodrilus (Megascolecidae), Microscolex and Diplocardia (Acanthodrilidae) share the same basic bioluminescence mechanisms5,7,13,14, despite their distant relationships to each other15,16. It is expected that the ancestral state of Pontodrilus is nonbioluminescent because the nearest extant relatives of Pontodrilus belong to the genus Plutellus Perrier, 1873, and all members of this group are nonbioluminescent6,17. These findings suggested that P. litoralis secondarily acquired bioluminescent properties through parallel evolution, similar to the case of bioluminescence in lampyrid and elaterid beetles18. We detected a clear difference in the protein composition of the secreted fluid between P. litoralis and P. longissimus (Supplementary Fig. S1). Luciferase and other bioluminescent components of luminous earthworms were not identified, and further comparative analyses of the protein bands, which appear only in the secreted fluid of luminous species, will be useful to understand the mechanisms of bioluminescence and its parallel evolution.In Thailand, P. longissimus was found sympatrically with P. litoralis at the beaches along the coast, but the microhabitats of the two congeners are different; P. litoralis was collected on the beach surface (under trash or leaf litter on sandy beaches), whereas P. longissimus was found at a greater depth than P. litoralis, i.e., a depth of more than 10 cm, where trash and leaves are scarce8 (Fig. 4A–D). It has been hypothesized that the biological function of bioluminescence in Annelida, including P. litoralis, is to stun or divert attention as an anti-predator defense19,20,21,22,23,24,25, but experiments and observations of the prey are limited. Sivinski & Forrest25 reported that the luminescence of Microscolex phosphoreus deterred predation by the mole cricket Scapteriscus acletus under laboratory conditions, although the specimen was ultimately consumed. A British television program26 presented by David Attenborough showed that the French luminous earthworm Avelona ligra glowed when attacked by the carabid beetle, but the beetle consumed the luminescent worm without any hesitation. We suggest that the absence of bioluminescence in P. longissimus may be associated with its presence in habitats with low predation pressure, whereas P. litoralis acquired a bioluminescent property during evolution that enabled it live on the surface of the beach, which is rich in nutrition and food sources3,27 as well as potential predators.Figure 4(A) The microhabitat of Pontodrilus litoralis from Aichi Prefecture, Japan. (B) The microhabitat of P. longissimus in Ranong, Thailand; sympatric Pontodrilus specimens were collected from this location8. (C) P. longissimus was found at a depth of 10–30 cm in muddy sand; the earthworm is shown by an arrow. (D) Bright field image of the Pontodrilus species included in this study. (E) An earwig (Anisolabis maritima) (a potential Pontodrilus predator) grooming its forelegs after attacking P. litoralis. (F) A. maritima (arrowhead) was found in the same microhabitat as P. litoralis in Aichi Prefecture, Japan.Full size imageIndeed, while we observed some burrowing bivalves, no potential predators were observed in the deep sand inhabited by P. longissimus. In contrast, various carnivorous invertebrates, such as earwigs, rove beetles and carabid beetles, were observed on the surface of beaches in Thailand and Japan, where P. litoralis live (Seesamut pers. obs.). We therefore performed a feeding experiment using maritime earwigs sympatrically distributed in a P. litoralis habitat. The maritime earwig Anisolabis maritima (Dermaptera, Anisolabididae) is a cosmopolitan species that can be found in Japan. It has well-developed compound eyes (Fig. 4E) and is considered a carnivorous animal that forages for prey at night28, 29. A. maritima (body length ≤ 30 mm) was the predominant predator at the beach where P. litoralis was collected (Fig. 4F). Some rove beetles (Coleoptera, Staphylinidae) were found in the same habitat, but they seemed to be too small ( More

In Europe, bumblebees pollinate the flowers called foxgloves, but foxgloves that spread to the Americas are also pollinated by hummingbirds and have evolved as a result. Credit: Getty

Ecology
16 April 2021
Flowers adapt to welcome the birds — but not the bees

Once in the Americas, foxgloves swiftly evolved under pressure by pollinating hummingbirds.

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Evolution can forge new relationships between plants and pollinators in fewer than 85 generations.The showy purple flowers called common foxgloves (Digitalis purpurea) are native to Europe, where they are pollinated by bumblebees. When admiring humans took the foxglove to the Americas, it was enthusiastically embraced by a new guild of nectar-drinkers — the hummingbirds.Maria Clara Castellanos at the University of Sussex in Brighton, UK, and her colleagues tallied visitors to foxgloves in the United Kingdom, Colombia and Costa Rica during more than 2,000 3-minute study periods. They found that hummingbirds pollinate up to 27% of foxgloves in Colombia and Costa Rica, where the flowers’ corollas — the long purple tubes that gardeners love so much — are 13% and 26% longer, respectively, than those of UK foxgloves.So why would foxgloves with longer corollas do better? Plants with corollas too long for bumblebees to reach their nectar are guaranteed to be pollinated by hummingbirds, which are more effective than bees at depositing pollen on the next flower. The longer corolla also creates a more comfortable fit for a hovering hummingbird, perhaps improving pollination rates.Hummingbirds can travel further between flowers than can bees, which might reduce plant inbreeding.

J. Ecol. (2021)

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