The distribution of plant consumption traits across habitat types and the patterns of fruit availability suggest a mechanism of coexistence of two sympatric frugivorous mammals

Understanding the mechanisms governing the coexistence of organisms is an important question in ecology, and providing potential solutions contributes to conservation science. In this study, we evaluated the contribution of several mechanisms to the coexistence of two sympatric frugivores, using western lowland gorillas (Gorilla gorilla gorilla) and central chimpanzees (Pan troglodytes troglodytes) in a tropical rainforest of southeast Cameroon as a model system. We collected great ape fecal samples to determine and classify fruit species consumed; we conducted great ape nest surveys to evaluate seasonal patterns of habitat use; and we collected botanical data to investigate the distribution of plant species across habitat types in relation to their “consumption traits” (which indicate whether plants are preferred or fallback for either gorilla, chimpanzee, or both). We found that patterns of habitat use varied seasonally for both gorillas and chimpanzees and that gorilla and chimpanzee preferred and fallback fruits differed. Also, the distribution of plant consumption traits was influenced by habitat type and matched accordingly with the patterns of habitat use by gorillas and chimpanzees. We show that neither habitat selection nor fruit preference alone can explain the coexistence of gorillas and chimpanzees, but that considering together the distribution of plant consumption traits of fruiting woody plants across habitats as well as the pattern of fruit availability may contribute to explaining coexistence. This supports the assumptions of niche theory with dominant and subordinate species in heterogeneous landscapes, whereby a species may prefer nesting in habitats where it is less subject to competitive exclusion and where food availability is higher. To our knowledge, our study is the first to investigate the contribution of plant consumption traits, seasonality, and habitat heterogeneity to enabling the coexistence of two sympatric frugivores

Maximum entropy modeling of giant pangolin Smutsia gigantea (Illiger, 1815) habitat suitability in a protected forest-savannah transition area of central Cameroon

Across the planet, biodiversity is facing ever-growing threats including habitat loss, climate change, overexploitation, and pollution. Pangolins of the order Pholidota are the only scaly mammal species worldwide and are considered the most trafficked wild mammals in the world, being widely exploited for their meat and scales. The giant pangolin (Smutsia gigantea, GP) is one of the least studied species of this order, with little being known about their response to environmental and anthropogenic variables, as well as their distribution patterns in forest-savannah transition areas. Our study aimed to increase ecological knowledge about GP by investigating the environmental factors associated with the distribution of suitable habitat for GP within a protected forest/savannah transition area of Cameroon. Using data on the locations of GP resting burrows collected using line transects and employing a maximum entropy (MaxEnt) modelling approach, we explored GP habitat suitability within a forest-savannah transition area of Cameroon. Our model revealed a good level of accuracy based on the average test area under the Receiver Operator Curve metric. The jackknife test found that Euclidian distance to the national park’s boundaries, normalized difference vegetation index, elevation, and distance to river were the most important predictors determining the distribution of GP burrows. Areas predicted to be suitable for GP burrows were patchily distributed within dense forests, ecotone and savannah, with 19.24% of the study area being suitable and 1% very suitable. Overall, our study shows the possible importance of habitat suitability modeling for understanding GP distribution, as well as planning and prioritising their conservation actions

Population dynamics and genetic connectivity in recent chimpanzee history

The European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 864203) (to T.M.-B.). BFU2017-86471-P (MINECO/FEDER, UE) (to T.M.-B.). “Unidad de Excelencia María de Maeztu”, funded by the AEI (CEX2018-000792-M) (to T.M.-B.). Howard Hughes International Early Career (to T.M.-B.). NIH 1R01HG010898-01A1 (to T.M.-B.). Secretaria d’Universitats i Recerca and CERCA Program del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 880) (to T.M.-B.). UCL’s Wellcome Trust ISSF3 award 204841/Z/16/Z (to A.M.A. and J.M.S.). Generalitat de Catalunya (2017 SGR-1040) (to M. Llorente). Wellcome Trust Investigator Award 202802/Z/16/Z (to D.A.H.). The Pan African Program: The Cultured Chimpanzee (PanAf) is generously funded by the Max Planck Society, the Max Planck Society Innovation Fund, and the Heinz L. Krekeler Foundation.Knowledge on the population history of endangered species is critical for conservation, but whole-genome data on chimpanzees (Pan troglodytes) is geographically sparse. Here, we produced the first non-invasive geolocalized catalog of genomic diversity by capturing chromosome 21 from 828 non-invasive samples collected at 48 sampling sites across Africa. The four recognized subspecies show clear genetic differentiation correlating with known barriers, while previously undescribed genetic exchange suggests that these have been permeable on a local scale. We obtained a detailed reconstruction of population stratification and fine-scale patterns of isolation, migration, and connectivity, including a comprehensive picture of admixture with bonobos (Pan paniscus). Unlike humans, chimpanzees did not experience extended episodes of long-distance migrations, which might have limited cultural transmission. Finally, based on local rare variation, we implement a fine-grained geolocalization approach demonstrating improved precision in determining the origin of confiscated chimpanzees.Publisher PDFPeer reviewe

Using abundance and habitat variables to identify high conservation value areas for threatened mammals

The present study used abundance and habitat variables to design High Conservation Value Forests for wildlife protection. We considered great apes (Gorilla gorilla gorilla and Pan troglodytes troglodytes) as model species, and we used nest surveys, dietary analysis and botanical inventories to evaluate whether the traditional methods that use abundance data alone were consistent with the survival of the species. We assumed that setting a local priority area for animal conservation can be made possible if at least one variable (abundance or habitat variables) is spatially clustered and that the final decision for a species may depend on the pattern of spatial association between abundance, nesting habitat and feeding habitat. We used Kernel Density Estimation to evaluate the spatial pattern of each biological variable. The results indicate that all three variables were spatially clustered for both gorillas and chimpanzees. The abundance variables of both animal species were spatially correlated to their preferred nesting habitat variables. But while the chimpanzee feeding habitat variable was spatially correlated to the abundance and nesting habitat variables, the same pattern was not observed for gorillas. We then proposed different methods to be considered to design local priority areas for the conservation of each great ape species. Alone, the abundance variable does not successfully represent the spatial distribution of major biological requirements for the survival of wildlife species; we, therefore, recommend the integration of the spatial distribution of their food resources to overcome the mismatch caused by the existence of a biological interaction between congeneric species

2) Mean weight of fruits and mean number of seeds for each species

2-a) Species: species names to the genus level, for genus with multiple species consumed by great apes 2-b) Family: the family name of the species 2-c) Mean weight per fruit for the species 2-d) Mean number of seeds per fruit for the species. NA: the seeds were uncountabl

5) Data from faecal analysis

5-a) Animal species (gorilla, chimpanzee) 5-b) Date of sample collection 5-c) Month of collection 5-d) Year of collection 5-e) Sample ID 5-f) Species name: The plant species name observed in the faecal sample 5-g) Family: The family name of the plant species found in the sample 5-h) Number of seeds in the faecal sampl

1) Botanical data

Data collected on woody plant species found in faecal samples of gorillas and chimpanzees in 184 plots of 25 x 25 m in 2014. Data description 1-a) Plot ID: The unique code for each plot 1-b) Longitude: the longitude component of the geographical coordinates of the plots, in UTM 33 N 1-c) Latitude: the latitude component of the geographical coordinates of the plots, in UTM 33 N 1-d) Habitat type: vegetation characteristics of each plot. Values: Old secondary forest, Near primary forest, Young secondary forest, Swamp, Riparian forest 1-e) Plant ID: the unique identification of each individual plant 1-f) Plant form: Tree, liana, strangler (Ficus spp) 1-g) Species traits: the plant consumption traits indicating whether the species is preferred or fallback (see the main article, Table 2). Values: Preferred gorilla (preferred by gorillas only), Preferred chimpanzee (preferred by chimpanzee only), Preferred apes (preferred by both gorillas and chimpanzees), Fallback gorilla (fallback for gorillas only), Fallback apes (fallback for both gorillas and chimpanzees), UC (unclassified), NA (no species was found in the plot) 1-h) Scientific names genus level: the scientific names of the species, determined at the genus level in faecal samples for genus with several species consumed by great apes 1-i) Scientific names species level: the scientific names of species, determined at the species level, excepted for unidentified species 1-j) Family: the family names of species 1-k) DBH: the diameter at breast height of the individual plant (in centimetre

3) Phenological data

3-a) Date: the date of data collection 3-b) Month: the month in number 3-c) Season: Long dry season, long rainy season, short dry season, short rainy season 3-e) Plant ID: the unique identifier of each individual plant 3-f) Species: the scientific name of the species 3-g) Family: Family names 3-h) DBH: the diameter at breast height of the individual plant (in centimetre) 3-i) Basal area (in centimetre square) 3-j) Fruit score: 0 (none), 1 (few), 2 (many