Changes in species composition in a complex environment - a challenge for the tools

One of the main aims in identifying conservation solutions is to ensure the preservation of high diversity areas while minimizing the cost to economic and social activities. Therefore, tools for determining priority areas in... [ view full abstract ]

One of the main aims in identifying conservation solutions is to ensure the preservation of high diversity areas while minimizing the cost to economic and social activities. Therefore, tools for determining priority areas in which the ratio between these goals is optimized are becoming increasingly popular among conservation scientists. While providing a good foundation for MPA identification, these tools are not without challenges, particularly for data-deficient areas such as Chilean Patagonia. Species distribution modelling (SDM) is a way of rapidly filling spatial knowledge gaps and thus reducing bias in the output of decision support tools. Apart from binary species distribution models, which show whether a species is present or not based on a variety of environmental variables, the response of a species to environmental change can be an effective method for predicting patterns of biodiversity. “gradientForest” is a R package that allows for the detection of biological composition patterns or ‘beta diversity’ along environmental gradients. It emphasizes where along a range of environmental gradients important changes of composition in species assemblages occur and the magnitude of the change along environmental gradients. By determining the relative importance of a predictor, it can help to optimize data assessment with limited time and resources and allows to make predictions on the impact that climate change related alterations of gradients may have on the biological patterns. This helps to plan effective MPA networks even under a changing environment.