Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions
Abstract Species distribution modelling studies the relationship between species occurrence records and their environmental setting, providing a valuable approach to predicting species distribution in the Southern Ocean (SO), a challenging region to investigate due to its remoteness and extreme weat...
| Main Authors: | , , |
|---|---|
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/325775 https://dipot.ulb.ac.be/dspace/bitstream/2013/325775/3/Guillaumotetal2021PolBiol.pdf |
| Summary: | Abstract Species distribution modelling studies the relationship between species occurrence records and their environmental setting, providing a valuable approach to predicting species distribution in the Southern Ocean (SO), a challenging region to investigate due to its remoteness and extreme weather and sea conditions. The specificity of SO studies, including restricted field access and sampling, the paucity of observations and difficulties in conducting biological experiments, limit the performance of species distribution models. In this review, we discuss some issues that may influence model performance when preparing datasets and calibrating models, namely the selection and quality of environmental descriptors, the spatial and temporal biases that may affect the quality of occurrence data, the choice of modelling algorithms and the spatial scale and limits of the projection area. We stress the importance of evaluating and communicating model uncertainties, and the most common evaluation metrics are reviewed and discussed accordingly. Based on a selection of case studies on SO benthic invertebrates, we highlight important cautions to take and pitfalls to avoid when modelling the distribution of SO species, and we provide some guidelines along with potential methods and original solutions that can be used for improving model performance. SCOPUS: ar.j info:eu-repo/semantics/published |
|---|