Testing expert groups for a habitat suitability model for the lynx Lynx lynx in the Swiss Alps

Modelling species distribution is an important aspect of conservation ecology. Empirical models are most commonly used. However, collecting data for these models is time-consuming and expensive. Expert models may be a good alternative method, though previous studies have found mixed results. The purpose of our study was first to create an expert model and evaluate it with independent lynx data, and second to use two discrete types of experts to control for prior radio-tracking experience. Two habitat suitability expert models (scientific and local experts) were constructed in a Geographical Information System using the Analytical Hierarchy Process and Compromise Programming. The models were evaluated with lynx data, taken from the study area in the northwestern Swiss Alps, using Resource Selection Index and Spearman correlation. The correlations showed that both models fitted the data well. However, the local expert model was better (rs == 0.964, P < 0.001) than the scientific expert model (rs == 0.833, P < 0.001). The models were also evaluated in the Jura Mountains to test the local nature of the models. It was found that the local expert model performed less well (rs == 0.939, P < 0.001) than the scientific expert model (rs == 0.967, P < 0.001) as expected. Comparison between weights for each expert group revealed some interesting differences, which showed the local nature of answers and how personal experience and theoretical knowledge can lead to different answers. Our study shows that expert knowledge, and especially local knowledge, can be employed to create a good habitat suitability model. This has important implications for conservation and science because it shows not only that expert knowledge may be used when no other data exist, but also that local ‘‘ground workers’’ should be employed more often in the development of habitat suitability models or conservation plans. However, there are limitations to the models and, as expert models are relatively new in ecology, more research is needed. Nevertheless, in a climate where there is pressure to keep up with human exploitation of natural resources and to adopt a more strategic approach to conservation, the findings of our study are encouraging.