Spatio‐temporal changes in chimpanzee density and abundance in the Greater Mahale Ecosystem, Tanzania

• Published in: Ecological applications Vol. 32; no. 8; pp. e2715 - n/a
• Main Authors: Carvalho, Joana S., Stewart, Fiona A., Marques, Tiago A., Bonnin, Noemie, Pintea, Lilian, Chitayat, Adrienne, Ingram, Rebecca, Moore, Richard J., Piel, Alex K.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01-12-2022; Ecological Society of America
• Subjects: Adaptive management; Animals; Chimpanzees; Confidence intervals; Conservation; Conservation of Natural Resources; Deforestation; density surface modeling; detection function estimation; eastern chimpanzee; Ecosystem; Ecosystems; Endangered species; Forest protection; Forests; generalized additive models; great apes; Human settlements; Humans; Landscape; line‐transect distance sampling; Logging; Monitoring; Monkeys & apes; National parks; Pan troglodytes; Pan troglodytes schweinfurthii; Population changes; Population decline; Population density; Population number; Riparian forests; spatially explicit models; Tanzania; Vegetation; Wildlife; Wildlife conservation; Wildlife habitats; Wildlife management; Woodlands; Tanzania; eastern chimpanzee; conservation; density surface modeling; line-transect distance sampling; generalized additive models; spatially explicit models; detection function estimation; great apes
• ISSN: 1051-0761; 1939-5582
• DOI: 10.1002/eap.2715

Species conservation and management require reliable information about animal distribution and population size. Better management actions within a species' range can be achieved by identifying the location and timing of population changes. In the Greater Mahale Ecosystem (GME), western Tanzania, deforestation due to the expansion of human settlements and agriculture, annual burning, and logging are known threats to wildlife. For one of the most charismatic species, the endangered eastern chimpanzee (Pan troglodytes schweinfurthii), approximately 75% of the individuals are distributed outside national park boundaries, requiring monitoring and protection efforts over a vast landscape of various protection statuses. These efforts are especially challenging when we lack data on trends in density and population size. To predict spatio‐temporal chimpanzee density and abundance across the GME, we used density surface modeling, fitting a generalized additive model to a 10‐year time‐series data set of nest counts based on line‐transect surveys. The chimpanzee population declined at an annual rate of 2.41%, including declines of 1.72% in riparian forests (from this point forward, forests), 2.05% in miombo woodlands (from this point forward, woodlands) and 3.45% in nonforests. These population declines were accompanied by ecosystem‐wide declines in vegetation types of 1.36% and 0.32% per year for forests and woodlands, respectively; we estimated an annual increase of 1.35% for nonforests. Our model predicted the highest chimpanzee density in forests (0.86 chimpanzees/km2, 95% confidence intervals (CIs) 0.60–1.23; as of 2020), followed by woodlands (0.19, 95% CI 0.12–0.30) and nonforests (0.18, 95% CI 0.10–1.33). Although forests represent only 6% of the landscape, they support nearly one‐quarter of the chimpanzee population (769 chimpanzees, 95% CI 536–1103). Woodlands dominate the landscape (71%) and therefore support more than a half of the chimpanzee population (2294; 95% CI 1420–3707). The remaining quarter of the landscape is represented by nonforests and supports another quarter of the chimpanzee population (750; 95% CI 408–1381). Given the pressures on the remaining suitable habitat in Tanzania, and the need of chimpanzees to access both forest and woodland vegetation to survive, we urge future management actions to increase resources and expand the efforts to protect critical forest and woodland habitat and promote strategies and policies that more effectively prevent irreversible losses. We suggest that regular monitoring programs implement a systematic random design to effectively inform and allocate conservation actions and facilitate interannual comparisons for trend monitoring, measuring conservation success, and guiding adaptive management.