Using density surface models to assess the ecological effectiveness of a protected area network in Tanzania

Abstract

Abstract Given recent global endeavors to increase protected area coverage, it is crucial to comprehensively evaluate the efficacy of various area‐based conservation strategies in effectively reducing biodiversity loss. Here, we investigated the responses of wildlife populations to different protection levels and environmental variables at the landscape scale in the Katavi–Rukwa Ecosystem, western Tanzania. To this end, we conducted line distance sampling surveys and counted the dung of six target mammal species (elephant, giraffe, buffalo, zebra, topi, and hartebeest) along foot transects within areas differing in protection levels (from strict to less‐strictly protected: national park, game reserve, forest reserve, game‐controlled area, and unprotected areas). Based on these dung counts, we modeled the spatial distribution of these six mammal species using a species‐specific density surface modeling framework. We found consistent effects of protection level and land use variables on the spatial distribution of the target mammal species: dung densities were highest in the national park and game reserves, intermediate in less‐strictly protected areas, and lowest in unprotected areas. Beyond species‐specific environmental predictors for dung densities, our results highlight consistent negative associations between dung densities of the target species and distance to cropland and avoidance of areas in proximity to houses. Our findings underpin differences in ecological effectiveness of protected areas within one ecosystem. Protection level and land use play crucial roles in moderating the spatial distribution of all considered mammal species. Our findings suggest that a landscape approach needs to guide effective conservation across the entire protection gradient of the Katavi–Rukwa Ecosystem.

Keywords

• conservation effectiveness
• conservation evidence
• distance sampling
• Miombo
• protected and conserved areas
• spatial distribution models