Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models

Sampling bias is a common phenomenon in records of plant and animal distribution. Yet, models based on such records usually ignore the potential implications of bias in data collection on the accuracy of model predictions. This study was designed to investigate the effect of roadside bias, one of the most common sources of bias in biodiversity databases, on the accuracy of predictive maps produced by bioclimatic models. Using data on the distribution of 129 species of woody plants in Israel, we tested the following hypotheses: (1) that data collected on woody plant distribution in Israel suffer from roadside bias, (2) that such bias affects the accuracy of model predictions, (3) that the road network of Israel is biased with respect to climatic conditions, and (4) that the impact of roadside bias on model predictions depends on the magnitude of climatic bias in the geographic distribution of the road network. As expected, the frequency of plant observations near roads was consistently greater than that expected from a spatially random distribution. This bias was most pronounced at distances of 500-2000 m from roads, but it was statistically significant also at larger scales. Predictive maps based on near-road observations were less accurate than those based on off-road or "rectified" observations (observations corrected for roadside bias). However, the magnitude of these differences was extremely low, indicating that even a strong bias in the distribution of species observations does not necessarily deteriorate the accuracy of predictive maps generated by bioclimatic models. Further analysis of the data indicated that the road network of Israel is relatively unbiased in terms of temperature, and only weakly biased in terms of rainfall conditions. The overall results are consistent with the hypothesis that the impact of roadside bias on model predictions depends on the magnitude of climatic bias in the geographic distribution of the road network. We discuss some theoretical and practical considerations of bias correction in biodiversity databases.