Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions

Among specific applications of species distribution models (SDMs), the use of SDMs probabilistic maps for guiding field surveys is increasingly applied. This approach is particularly used for poorly known and/or cryptic species in order to better assess their distribution. One of the most interesting aspects of these applications is that predictions could be clearly validated by real data, subsequently obtained in the field. Despite this important difference from other applications, to our knowledge, the efficiency of different algorithms, metrics for model evaluation and algorithm-specific settings have not yet been sufficiently investigated. This research performs a literature survey to investigate which species, study area characteristics, variables, techniques and settings were used or suggested by previous authors. We then applied the most common approaches to guide field surveys for a set of 70 vascular plants in an endemic-rich area of Sardinia (Italy) of approx. 9000 ha, the flora of which was deeply investigated during the last two years. Our main aims were: (1) to use pre-model records for predicting the potential occurrence of plant species with different sample size, detectability and habitat preference, (2) to apply results for guiding searches for new populations of poorly known species, (3) to calculate the model performance according to independent real presence/absence data (testAUC) and (4) to compare different modelling data input and settings on the testAUC basis. By emphasizing the importance of field verification, both the review and the worked example supported the reliability of SDMs for a wide range of species to understand where a species could potentially be present and therefore to optimise resources for the search of new species localities. This study may help understand and summarise the most applied methodological approaches and to highlight future directions for this practical application. Without underrating the importance of most common recommendations, practitioners are encouraged to test the ability of this SDMs’ application with their own data. Indeed, large gaps in biological groups (e.g. insects) and in regions covered by these kind of studies (e.g. many African and Asian territories) were found. Furthermore, eventual biases due to lack of data, experience or staff, have in this experimental case less irreparable consequences than others, such as conservation assessments based on future projections, which cannot be otherwise adjusted by explicit data from ground validation.