Estimating species richness and modelling habitat preferences of tropical forest mammals from camera trap data

Abstract

Medium-to-large mammals within tropical forests represent a rich and functionally diversified component of this biome; however, they continue to be threatened by hunting and habitat loss. Assessing these communities implies studying species' richness and composition, and determining a state variable of species abundance in order to infer changes in species distribution and habitat associations. The Tropical Ecology, Assessment and Monitoring (TEAM) network fills a chronic gap in standardized data collection by implementing a systematic monitoring framework of biodiversity, including mammal communities, across several sites. In this study, we used TEAM camera trap data collected in the Udzungwa Mountains of Tanzania, an area of exceptional importance for mammal diversity, to propose an example of a baseline assessment of species' occupancy. We used 60 camera trap locations and cumulated 1,818 camera days in 2009. Sampling yielded 10,647 images of 26 species of mammals. We estimated that a minimum of 32 species are in fact present, matching available knowledge from other sources. Estimated species richness at camera sites did not vary with a suite of habitat covariates derived from remote sensing, however the detection probability varied with functional guilds, with herbivores being more detectable than other guilds. Species-specific occupancy modelling revealed novel ecological knowledge for the 11 most detected species, highlighting patterns such as 'montane forest dwellers', e.g. the endemic Sanje mangabey (Cercocebus sanjei), and 'lowland forest dwellers', e.g. suni antelope (Neotragus moschatus). Our results show that the analysis of camera trap data with account for imperfect detection can provide a solid ecological assessment of mammal communities that can be systematically replicated across sites.

Figure 1. Map of the study area, the Udzungwa Mountains of south-central Tanzania.

The map shows the main habitat types and blocks with closed-canopy forest (adapted from [53]). The study forest was Mwanihana in the northeastern portion of the range, which is zoomed in inset (A) where the 60 camera trap sites are shown as white dots and the background is a Digital Elevation Model (dark is lower elevation); (B) shows the position of Udzungwa in Tanzania.

Figure 2. Species accumulation curve for the community of medium-to-large mammals detected by camera trapping.

Detection of species is randomized 1000 times and results used to derive the 95% confidence intervals of the mean.

Figure 3. Posterior distribution of species richness.

The analysis follows Dorazio et al. (2006). The posterior probability that the community comprises only 26 species (vertical line is the observed species richness) is essentially zero, and the estimated median and mean values of species richness are 32.0 (±7.04 SD) and 34.3, respectively (26–54 CRI 95%; CRI = credible intervals).

Figure 4. Detection probability by functional guild.

Values are from the model averaging of relative species richness of the mammal community in the Udzungwa Mountains of Tanzania. Bars are 95% confidence intervals.

Figure 5. Spatially-explicit occupancy models.

Maps of predicted occupancy (left) and functional relationship between the most relevant covariate and ψ (right, with confidence intervals indicated by dashed lines) for four mammals in the Udzungwa Mountains of Tanzania, representing limiting cases in occupancy pattern: (A) Sanje mangabey, a montane evergreen forest species; (B) suni, a lowland deciduous forest species; (C) Harvey’s duiker, an edge-lover and disturbance-tolerant species; (D) grey-faced sengi, an edge-avoider and disturbance-sensitive species.

Figure 6. Graphs of predicted detection probability.

Values are modelled with distance from the park border for (A) the bushy-tailed mongoose, and (B) the Abbott’s duiker, in the Udzungwa Mountains of Tanzania. Confidence intervals are indicated by dashed lines.