Ecology

Preserving the biodiversity of rainforests, and limiting the effects of climate change on them, are global challenges that are recognized in international policy agreements and commitments1. The Central African rainforests are the second largest area of continuous rainforest in the world, after the Amazon rainforest. They store more carbon per hectare than does the Amazon and, on average, have a higher density of large trees2 than does any other continent — a feature attributed to the effects of big herbivores, particularly elephants, on the competition between trees for light, water and space3. Human activities, notably logging and over-hunting, facilitated by an expanding road network4, pose a serious threat to Central African rainforests and their value for society5.
Read the paper: Unveiling African rainforest composition and vulnerability to global change
How important is climate change, when acting on top of these existing human-generated pressures, for the future of these rainforests? Writing in Nature, Réjou-Méchain et al.6 provide an answer, and show that expected changes in climate in the region pose serious risks to the rainforests. Some forests in locations that have so far been relatively undisturbed by humans are more vulnerable to climate change than are those in areas already affected. For those areas already affected, the lower tree diversity as a consequence of human intervention reduces the capacity of forests to respond to climate change.The authors had access to an impressive commercial forest-inventory data set from 105 logging concessions (designated areas in which commercial operators are allowed to harvest timber), across five Central African countries. Analysing the abundance distribution of 6.1 million trees across 185,665 plots, the authors generate maps of floristically unique forest types — forests characterized by distinct sets of tree species. The spatial extent of these forest types is predominantly shaped by climate gradients, with further effects arising from human-induced pressures and variation in soil type.Previous research into links between species distribution and environmental variation used approaches such as ecological niche models, which are mechanistic or correlative models that relate field observations of species with environmental variables to predict habitat suitability. But the resulting predictions of how various species will be affected by climate change have been highly uncertain. This is mainly because of sampling bias, challenges such as spatial autocorrelation (locations closer together in space tend to be more similar to each other than do locations farther apart)7, and high variation in the responses of individual species to environmental drivers of distribution, including human-induced factors.
Satellites could soon map every tree on Earth
Réjou-Méchain et al. instead applied a modelling approach called supervised component generalized linear regression, which can identify the main predictive factors from an array of possibilities. This enabled them to detect distribution patterns at the scale of species assemblages (the set of species in a community), rather than focusing on individual species, and to model species and assemblage distribution in response to predictive variables, such as those of climate and human pressures, that potentially show linear dependencies on each other (collinearity). Collinearity is a challenge in niche models, and commonly occurs between climate variables, producing results that are unreliable and difficult to interpret.By combining their approach with a method called cluster analysis, Réjou-Méchain and colleagues show that the Central African rainforests are not a single bloc of forests, but instead encompass at least ten distinct forest types. This includes climate-driven types of forest such as the Atlantic coastal evergreen forest in Gabon, which harbours tree species that prefer cool, dark areas for the dry season. Another grouping, semi-deciduous forest, is found along the northern margin of the Central African region studied, and is characterized by species that can tolerate higher rates of water loss to the atmosphere (evapotranspiration).Such spatial variability in the species composition of Central African rainforests has many implications. For example, it will affect forest vulnerability to climate change, how warming might interact with human pressures to change biodiversity, and how it might affect the potential of these forests to mitigate the rise in atmospheric carbon. Global warming is projected to result in a drier, hotter environment in Central Africa, and previous research has suggested potentially dangerous implications for the fate of the rainforests there8. They might respond to limited water availability by opening canopies and becoming more prone to fires and less carbon dense. Using climate-model projections for the year 2085, Réjou-Méchain and colleagues conclude that the current climate niches associated with the ten forest types they have identified might disappear, or move to locations that would be difficult for the forests to reach through dispersal of tree seeds (by means such as wind and animals), and would hence become inaccessible.
Prioritizing where to restore Earth’s ecosystems
What do these findings mean for the future, and how can we manage the forests to minimize the threat from climate change? To provide an answer, Réjou-Méchain et al. looked at three components that characterize the vulnerability of forest communities to warming: their sensitivity, exposure and adaptive capacity. The authors conclude that some areas are more sensitive than others, which means that the dominant tree species in some forest types will be less able to tolerate environmental change than will those in other areas — for example, species in the northern and southwestern edge of the rainforest. Some areas, particularly those in the east, are expected to be more exposed to climate change than others. And some, especially areas under pressure from human activities, have lower local biodiversity, and might thus have less capacity to adapt compared with areas of greater biodiversity.Réjou-Méchain et al. report that the areas most vulnerable to climate change and predicted to be highly vulnerable to future human-induced pressures include forests in coastal Gabon, the Democratic Republic of the Congo (Fig. 1) and the northern margin of the domain studied. This finding suggests priority regions for targeted actions to protect forests from environmental changes. One such region under human pressure is in Cameroon and contains a forest group called degraded semi-deciduous forest. Protecting this type of forest offers a fast way of generating a carbon sink that will operate over a long time frame9. This is because it features long-lived ‘pioneer’ taxa, which colonize areas after a disturbance — whether natural or human induced. Such species frequently have a high requirement for light, and in this region have the potential to reach great heights in the absence of further disturbance.

Figure 1 | Kahuzi-Biéga National Park, Democratic Republic of the Congo. The road marks the boundary of this forest, which is one of the few remaining forest habitats for the eastern lowland gorilla (Gorilla beringei graueri). Rainforests are under threat from human-induced pressures, such as the deforestation visible outside this park. Réjou-Méchain et al.6 present maps of Central African rainforests that could aid conservation work.Credit: Adam Amir

As for elsewhere in sub-Saharan Africa, climate-change predictions for 2085 are uncertain for Central Africa. Réjou-Méchain and colleagues’ projections for the effects of human pressures for that year are probably underestimates, especially considering that road expansions are likely to continue to push the frontier of wilderness deeper into remote forest areas. Nevertheless, the research offers convincing evidence enabling land users and managers to take decisive actions. This could include efforts to protect the areas most vulnerable to climate change from human pressures, for example by setting up protection schemes, and actions that could include boosting forest connectivity in areas that have already experienced high levels of human pressure. To ensure the effectiveness of any interventions, it will be imperative to engage with local people in developing management solutions. Conservation and the sustainable management of rainforest carbon stocks have key roles in the reduction of carbon emissions.Perhaps most crucially, rainforests in Central Africa and the ecosystem services they provide are intertwined with people’s livelihoods and food security. Developing sustainable management plans that recognize the diversity of the ways in which people interact with and depend on these forests will be a huge challenge. It will require concerted cross-disciplinary and cross-sectoral efforts that move beyond national boundaries. More

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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