Assistant Professor Rice University Houston, Texas, United States
Background/Question/Methods:
One of the fundamental goals of ecology is to identify the factors governing biodiversity. While there are many well-supported hypotheses that address global variation in biodiversity patterns, variation at the regional scale is not as well studied. Here we use a general linear model to quantify the relative impacts of spatial and environmental variables on species richness within primate communities in the Afrotropical, Indomalayan, Malagasy, and Neotropical regions. Each of the spatial and environmental variables used in this assessment (i.e. latitude, longitude, elevation, and precipitation) were selected to test a priori hypotheses, such as the mid-domain effect, and known patterns of biodiversity, such as the latitudinal diversity gradient. Longitude and precipitation were used to assess hypotheses suggesting that species richness is higher with increased habitat productivity and heterogeneity.
Results/Conclusions:
We found regional differences in the predictors governing primate species richness. For example, the latitudinal diversity gradient was supported in the Afrotropical (p < 0.001; ꞵ = -0.03; 95% CI = -0.04, -0.02) and Neotropical (p < 0.001; ꞵ =-0.04; 95% CI = -0.06, -0.02) regions, but not in the Indomalayan or Malagasy regions. In addition, we identified a longitudinal gradient of species richness within the Indomalayan (p < 0.001; ꞵ = 0.02; 95% CI = 9.59e-03, 0.03) and Neotropical (p < 0.001; ꞵ= -0.01; 95% CI = -0.02, -6.14e-03) regions. The mid-domain hypothesis was not supported for any region, whereas precipitation positively and significantly influenced species richness in all regions (Afrotropical p< 0.001, ꞵ = 1.505e-03, 95% CI = 1.10e-03, 1.92e-03; Indomalayan p = 0.02, ꞵ = 5.84e-04, 95% CI = 8.89e-05, 1.10e-03; Malagasy p = 0.03, ꞵ = 9.13e-04, 95% CI = 1.12e-04, 1.72e-03; Neotropical p < 0.001, ꞵ = 1.03e-03, 95% CI = 3.43e-04, 1.76e-03). As extinction rates increasingly accelerate, it is more important than ever to understand these and other mechanisms underlying patterns of biodiversity so that we can predict community responses to change and respond proactively.
