Maps Reveal Where Poachers and Chimpanzees Cross Paths on Mount Nimba

Deep in the rain-forested slopes of the Nimba Mountains, where Guinea meets Côte d’Ivoire, one of Africa’s most endangered great apes is quietly sharing its refuge with an unwelcome guest. The western chimpanzee, the only chimpanzee subspecies classified as Critically Endangered by the IUCN, has lost roughly 80 percent of its population since 1990, leaving an estimated 52,800 individuals scattered across a shrinking range. Now, a year-long field study in the Seringbara region of the Mount Nimba Strict Nature Reserve has combined GPS tracking, machine learning and multi-scale ecological modelling to map, with unprecedented fine detail, exactly where illegal hunters and chimpanzees are most likely to collide — and the results carry a surprising twist about the protective power of scientific research camps.

The study, published in the journal Ecology and Evolution, focused on a roughly 30 square kilometre area on the western flank of the Nimba range, home to two chimpanzee communities totalling around 89 individuals. Although local customs and national law strictly prohibit chimpanzee hunting — a taboo documented in the region since the 1970s — hunters from outside the area do not necessarily share those prohibitions. Even when chimpanzees are not the target, poaching for smaller mammals generates by-catch, injuries and pervasive human disturbance that can reshape how these intelligent apes use their forest home. Previous research has shown that where hunting occurs, chimpanzees tend to spatially avoid human presence, making fine-scale disturbance a genuine conservation concern even without direct killing.

Between August 2022 and August 2023, teams of Guinean researchers combed the mountainous terrain twenty days per month, logging 901 GPS occurrence points: 633 records of chimpanzee feeding and travelling, 185 nesting records, and 83 points of hunting evidence ranging from empty bullet casings and snare traps to hunting camps and campfires. The researchers deliberately separated nesting from feeding and travelling, reasoning that the two activities — one vulnerable and stationary, the other mobile — might respond differently to human pressure. After thinning the data to one record per 30-metre pixel, they generated pseudo-absence points only along surveyed tracks, a careful design choice that prevented the models from mistaking unsurveyed areas for genuinely unused habitat.

The methodological heart of the study lies in its multi-scale approach. Recognising that species respond to their environment at different spatial grains, the team transformed sixteen environmental variables — including elevation, slope, topographic position, vegetation indices, wetness and distances to villages, roads, rivers and research camps — into seven spatial scales ranging from 30 metres to 2 kilometres using Gaussian kernel smoothing in Google Earth Engine. For each activity type, univariate models identified the optimal scale for every predictor, a step that fewer than 5 percent of habitat modelling studies typically perform. The team then compared two complementary modelling frameworks: generalised linear models, prized for their statistical interpretability, and boosted regression trees, a machine learning method capable of capturing non-linear relationships and threshold effects that simpler models miss.

The results paint a nuanced picture of two species — human and chimpanzee — navigating the same landscape with partly overlapping, partly divergent logic. For hunters, distance to research camps emerged as the single most consistent predictor, with hunting probability rising sharply away from the camps. In the boosted regression tree models, tree cover was the most influential variable at the optimised 500-metre scale, with hunting activity dropping markedly where canopy cover exceeded 90 percent — dense forest apparently presenting obstacles and limited visibility that deter hunters pursuing smaller prey. Wetness at 1,500 metres and proximity to rivers also shaped hunting patterns, suggesting that difficult, waterlogged terrain offers chimpanzees a measure of natural protection.

Chimpanzees, for their part, revealed their own sophisticated habitat calculus. Feeding and travelling activity peaked above 750 metres in elevation, with the animals favouring steeper slopes, warmer south-facing aspects and areas with high values of the Enhanced Vegetation Index — a satellite-derived signal of dense, healthy primary forest. Intriguingly, the multi-scale models showed that chimpanzees responded to warm slopes at a broad 2-kilometre scale while selecting steeper terrain at a medium 250-metre scale, evidence that these apes integrate landscape features across nested spatial hierarchies. Nesting behaviour followed a subtly different recipe: slope was key, with a pronounced threshold at 15 degrees above which nesting suitability climbed, and landscape-scale topographic position and wetness at 2,000 metres featured prominently, indicating that elevated ground relative to surroundings provides the security these apes seek during their most vulnerable resting hours.

When the researchers intersected the predicted surfaces, the maps exposed a striking geography of risk. The western lowlands of the study area showed high hunting probability but low chimpanzee suitability, while the highest-elevation zones — precisely the steep, remote areas chimpanzees favour for nesting — emerged as the zones of greatest potential overlap between apes and hunters. Correlation analyses confirmed strong spatial associations between the predictive surfaces, meaning chimpanzees and hunters largely select and avoid similar terrain. The authors suggest this overlap may indicate that chimpanzees either tolerate human presence or employ temporal displacement, avoiding areas only when hunting activity is underway. Over the long term, however, sustained human activity in these shared zones could gradually suppress habitat use and force chimpanzees into increasingly marginal environments.

The most unexpected finding concerns the research camps themselves. Both chimpanzees and hunters avoided areas near the camps, but the hunters’ avoidance was markedly stronger. Because the reduction in predicted hunting activity exceeded the reduction in chimpanzee suitability, the camps effectively cast a protective buffer across the surrounding forest — a case of conservation science inadvertently shielding the very animals it studies. The authors caution, however, that this benefit comes with a caveat: because chimpanzees generally avoid humans, siting new camps in the heart of prime chimpanzee habitat could paradoxically drive the apes away from resources they need, so strategic placement must balance deterrence of hunters against disturbance of the studied communities.

Methodologically, the study offers lessons that extend well beyond Nimba. Scale-optimised boosted regression tree models consistently outperformed their single-scale counterparts, and the multi-scale approach improved the hunting models across both frameworks, confirming that habitat features invisible to single-scale analyses can prove critical for conservation planning. The two modelling approaches also diverged instructively: the linear models delivered clearer inference through model-averaged coefficients, while the machine learning models captured thresholds and extrapolated more reliably beyond surveyed ground. The authors are candid about limitations — survey effort targeted chimpanzee-frequented areas, potentially biasing the pseudo-absence sample, and variables such as fruit availability could not be included — and they call for extended surveys, ground-truthing and additional data on disturbances like slash-and-burn agriculture.

For the Mount Nimba Strict Nature Reserve, a UNESCO World Heritage Site listed as In Danger, the practical implications are immediate. High-elevation zones should be prioritised for intensive monitoring and patrolling, dense forests deserve protection as natural refuges, and the strategic positioning of research and monitoring infrastructure could be harnessed deliberately as a conservation tool. More broadly, the study demonstrates how pairing fine-scale remote sensing with multi-scale ecological niche modelling can turn scattered field observations into actionable maps of human-wildlife conflict — a template that, if replicated across other montane refuges, could help secure a future for the western chimpanzee in the fragmented forests of West Africa.

Subject of Research: The impact of illegal hunting on the spatial distribution of western chimpanzees in the Mount Nimba Strict Nature Reserve, Guinea

Article Title: Illegal Hunting and Chimpanzee Distribution in the Seringbara Region of the Mount Nimba Strict Nature Reserve: A Multi‐Scale Modelling Approach

Article References: Degrève, L., Erazo, D., Fitzgerald, M., Camara, H. D., Mamy, G., Dellicour, S., Kaszta, Ż., & Koops, K. (2026). Illegal Hunting and Chimpanzee Distribution in the Seringbara Region of the Mount Nimba Strict Nature Reserve: A Multi‐Scale Modelling Approach. Ecology and Evolution, 16(10), Article e74384. https://doi.org/10.1002/ece3.74384

Image Credits: AI Generated

DOI: 10.1002/ece3.74384

Keywords: western chimpanzee, Mount Nimba, illegal hunting, poaching, ecological niche modelling, boosted regression trees, habitat suitability, conservation, Guinea, UNESCO World Heritage, spatial overlap, multi-scale modelling