A Systematic Review of Marine-Based Species Distribution Models (SDMs) with Recommendations for Best Practice - Robinson, Néstor M.; Nelson, Wendy A.; Costello, Mark J.; Sutherland, Judy E.; Lundquist, Carolyn J. (Frontiers in Marine Science, 2017)
In the marine environment Species Distribution Models (SDMs) have been used in hundreds of papers for predicting the present and future geographic range and environmental niche of species. We have analysed ways in which SDMs are being applied to marine species in order to recommend best practice in future studies. This systematic review was registered as a protocol on the Open Science Framework: https://osf.io/tngs6/. The literature reviewed (236 papers) was published between 1992 and July 2016. The number of papers significantly increased through time (R2=0.92, p<0.05). The studies were predominantly carried out in the Temperate Northern Atlantic (45%) followed by studies of global scale (11%) and studies in Temperate Australasia (10%). The majority of studies reviewed focused on theoretical ecology (37%) including investigations of biological invasions by non-native organisms, conservation planning (19%), and climate change predictions (17%). Most of the studies were published in ecological, multidisciplinary or biodiversity conservation journals. Most of the studies (94%) failed to report the amount of uncertainty derived from data deficiencies and model parameters. Best practice recommendations are proposed here to ensure that novice and advanced SDM users can (a) understand the main elements of SDMs, (b) reproduce standard methods and analysis, and (c) identify potential limitations with their data. We suggest that in the future, studies of marine SDMs should report on key features of the approaches employed, data deficiencies, the selection of the best explanatory model, and the approach taken to validate the SDM results. In addition, based on the literature reviewed, we suggest that future marine SDMs should account for uncertainty levels as part of the modelling process.