Estimating circumpolar distributions of lanternfish using 2D and 3D ecological niche models.

• Published in: Marine Ecology Progress Series
• Main Authors: Freer, Jennifer, Tarling, Geraint, Collins, Martin, Partridge, J.C., Genner, M.J.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Inter-Research 2020
• Subjects: Southern Ocean
• Online Access: http://nora.nerc.ac.uk/id/eprint/528494/; https://www.int-res.com/abstracts/meps/v647/p179-193/

Ecological niche models (ENMs) can be a practical approach for investigating distributions and habitat characteristics of pelagic species. In principle, to reflect the ecological niche of a species well, ENMs should incorporate environmental predictors that consider its full vertical habitat, yet examples of such models are rare. Here we present the first application of ‘3D’ ENMs to 10 Southern Ocean lanternfish species. This 3D approach incorporates depth-specific environmental predictor data to identify the distribution of suitable habitat across multiple depth levels. Results were compared to those from the more common ‘2D’ approach, which uses only environmental data from the sea surface. Measures of model discriminatory ability and overfitting indicated that 2D models often outperform 3D methods, even when accounting for reduced available sample size in the 3D models. Nevertheless, models for species with a known affinity for deeper habitat benefitted from the 3D approach, and our results suggest that species can track their ecological niche in latitude and depth leading to equatorward or poleward range extensions beyond that expected from incorporating only surface data. However, since 3D models require comprehensive depth-specific data, both data availability and the need for depth-specific model outputs must be considered when choosing the appropriate modelling approach. We advocate increased effort to include depth-resolved environmental parameters within marine ENMs. This will require collection of mesopelagic species occurrence data using appropriate temporal and depth-stratified methods, and inclusion of accurate depth information when occurrence records are submitted to global biodiversity databases.