The incompleteness of information on biodiversity distribution is a major issue for ecology and conservation. Researchers have made many attempts to quantify the amount of biodiversity that still remains unknown. We evaluated whether models that integrate ecogeographical variables with measures of the effectiveness of sampling can be used to estimate biodiversity patterns (species richness) of reptiles in remote areas that have received limited surveys.
The Western Palaearctic (Europe, Northern Africa, the Middle East and Central Asia).
We gathered data on the distribution of turtles, amphisbaenians and lizards. We used regression models integrating spatial autocorrelation (spatial eigenvector mapping and Bayesian autoregressive models) to analyse species richness, and identified relationships between species richness, ecogeographical features and large-scale measures of accessibility.
The two regression techniques were in agreement. Known species richness was dependent on ecogeographical factors, peaking in areas with high temperature and annual actual evapotraspiration, and intermediate cover of natural vegetation. However, richness declined sharply in the least accessible areas. Our models revealed regions where reptile richness is likely to be higher than currently known, particularly in the biodiversity hotspots in the south of the Arabian Peninsula, the Irano-Anatolian region, and the Central Asian mountains. An independent validation data set, with distribution data collected recently throughout the study region, confirmed that combining accessibility measures with ecogeographical variables allows a good estimate of reptile richness, even in remote areas that have received limited monitoring so far. Some remote regions that support very rich communities are covered very little by protected areas.
Integrating accessibility measures into species distribution models allows biologists to identify areas where current knowledge underestimates the actual richness of reptiles. Our study identifies regions requiring future biodiversity research, proposes a novel approach to biodiversity prediction in poorly studied areas, and identifies potential regions for conservation.