Chimpanzee non-avoidance of hyper-proximity to humans

Abstract

A species, especially when it is endangered and surrounded by anthropogenic elements, can be threatened by habitat fragmentation. Food resource availability in the species’ usual or surrogate habitats may reinforce or decrease its use of certain areas. Our objective was to to determine the influence of natural and anthropogenic variables on spatial distribution of eastern chimpanzees (Pan troglodytes schweinfurthii). We first determined the home range of a wild chimpanzee community (hereafter Sebitoli Chimpanzee Community [SCC]) based on global positioning system (GPS) point locations (n = 2,586 direct observations and feces locations) collected between 2009 and 2013 in Sebitoli, Kibale National Park, Uganda. We described SCC home range using grid cells (2,959 cells of 100 m × 100 m) referencing environmental, spatial, and topographical variables (n = 15). We then determined diet and food species abundance within their territory (n = 63 vegetation plots and 18 months phenological survey) and predicted distribution of the 10 most foraged fruit species within SCC home range, using environmental, spatial, and topographical variables (n = 10) by applying a maximum entropy model (maxent). We then predicted chimpanzee presence as a function of environmental, spatial, and topographical variables (n = 15) using the maxent model and assessed its truthfulness with the kernel model, based only on GPS point locations. Chimpanzees in Sebitoli were mostly observed in a core area of 5.42 km2 within the 25‐km2 home range. They did not avoid forest edges in contact with human populations, especially males who used larger core areas than females. Factors with the greatest positive impact on the chimpanzee distribution model (maxent area under the curve [AUC] = 0.907) were related to 3 different food resources: 1) proximity to forest edges considered as attractive because of presence of crops cultivated by local farmers; 2) proximity to a tarmac road that crosses the SCC home range with shoulders covered by attractive terrestrial herbaceous vegetation; and 3) presence of wild forest fruiting resources. The results obtained with the maxent predictive model, applied on a fine scale, were consistent with the kernel model, based on real observations. Therefore, such an approach may be recommended for surveys or action plans interested in sustainable management of wildlife in an anthropogenic environment and may be a useful tool to better understand parameters of a prefered zone for an endangered species. © 2016 The Wildlife Society.

Status of Road Ecology Research in Africa: Do We Understand the Impacts of Roads, and How to Successfully Mitigate Them?

“…We used broad search terms in our review, and thus discovered many more publications than Bennett (2017). We also found that many road ecology studies were hidden or buried in studies that were ostensibly on other topics, for example, where road density was an explanatory factor to help understand the distribution or abundance of a certain species, and not the primary focus (e.g., Blom et al, 2005;Aubréville, 2015;Bortolamiol et al, 2016).…”

“…For example, far more papers alluded to wildlife with few considering areas such as pollution (Inchaouh et al, 2017;Megido et al, 2017), biological invasions or off-roading (see Figures 6, 7). Furthermore, whilst some of the papers described animal behaviour around roads (e.g., road avoidance, foraging near roads; Bortolamiol et al, 2016), a broader search that includes additional terms, specifically "behaviour" will likely source further road ecology-related studies (for example, Shepard et al, 2008;Neumann et al, 2012). Our study has highlighted the need for the accelerated study of the impacts of roads and appropriate mitigation measures, and adoption of recommendations made in the African road ecology publications, so that mitigation be applied and rigorously assessed.…”

Recent vegetation phenology variability and wild reindeer migration in Hardangervidda plateau (Norway)

“…Mounting GRiD toolbox has been incrementally encouraged by the needs of automation processing for the survey of wild reindeer migration and densities in Hardangervidda Plateau (Norway). The wide range spatial behavior of reindeer is an interesting case study to apply this method, more adapted to large territories than simple grid multi-data analysis previously realized by other authors in smaller areas (Jolivet 2014, Bortolamiol et al, 2016. Indeed, by choosing the core point of each grid cell as the common reference to cross all the datasets, the process is realized within a reasonable computation time while avoiding important bias according to the preliminary test realized in our study.…”

Variation in behavioral traits of two frugivorous mammals may lead to differential responses to human disturbance

“…This divergence in animal behavior is often called “disturbance” when it is caused by humans (Frid & Dill, 2002). Humans are now either permanently present (Bortolamiol et al, 2016; Leblond et al, 2011; Scholte & Iyah, 2016) or temporarily present (Gehr et al, 2017; Paton, Ciuti, Quinn, & Boyce, 2017) in almost all ecosystems, and human settlements are often found close to wildlife populations. Hunting‐related disturbance, but also activities such as gathering, and logging, or even the presence of villages and roads, is perceived as a threat by wildlife species, inducing some changes in their spatial distribution (Frid & Dill, 2002; Koerner, Poulsen, Blanchard, Okouyi, & Clark, 2017; Lindshield, Danielson, Rothman, & Pruetz, 2017; Paton et al, 2017; Tagg et al, 2018; Tucker et al, 2018; Vanthomme, Kolowski, Korte, & Alonso, 2013).…”