Data from: Joint spatial modeling of cluster size and density for a heavily hunted primate persisting in a heterogeneous landscape

Shared landscapes in which humans and wildlife coexist, are increasingly
recognized as integral to conservation. Fine-scale data on the
distribution and density of threatened wildlife are therefore critical to
promote long-term coexistence. Yet, the spatial complexity of habitat,
anthropic threats and animal behaviour in shared landscapes challenges
conventional survey techniques. For social wildlife in particular, the
size of sub-groups or clusters is likely to both vary in space and
influence detectability, biasing density estimation and spatial
prediction. Using the R package inlabru, we develop a full-likelihood
joint log-Gaussian Cox process to simultaneously perform spatial distance
sampling and model a spatially varying cluster size distribution, which we
condition upon detection probability to mitigate cluster-size detection
bias. We accommodate spatial dependencies by incorporating a
non-stationary Gaussian Markov random field, enabling the explicit
inclusion of geographical barriers to wildlife dispersal. We demonstrate
this model using 136 georeferenced detections of Campbell’s monkey
(Cercopithecus campbelli) clusters, collected with 398.56-km of line
transects across a shared agroforest landscape mosaic (1067-km2) in
Guinea-Bissau. We assess a suite of anthropogenic and environmental
spatial covariates, finding that normalized difference vegetation index
(NDVI) and proximity to mangroves are both powerful spatial predictors of
density. We captured strong spatial variation in cluster size, likely
driven by fission-fusion in response to the complex distribution of
resources and risk in the landscape. If left unaccounted for under
existing approaches, such variation may bias density surface estimation.
We estimate a population of 10,301 (95% CI [7606-14,104]) individuals and
produce a fine-scale predictive density map, revealing the importance of
mangrove-habitat interfaces for the conservation of this heavily hunted
primate. This work demonstrates a powerful, widely applicable approach for
monitoring socially flexible wildlife and informing evidence-based
conservation in complex, heterogeneous landscapes moving forward.