Dear MARMAM community,
We are pleased to announce the publication of the following article in
Diversity and Distributions:
*Derville S, Torres LG, Iovan C, Garrigue C. Finding the right fit:
Comparative cetacean distribution models using multiple data sources and
statistical approaches. Divers Distrib. 2018;00:1–17.
https://doi.org/10.1111/ddi.12782*
***Abstract***
*Aim. *Accurate predictions of cetacean distributions are essential to
their conservation but are limited by statistical challenges and a
paucity of data. This study aimed at comparing the capacity of various
statistical algorithms to deal with biases commonly found in
nonsystematic cetacean surveys and to evaluate the potential for citizen
science data to improve habitat modelling and predictions. An endangered
population of humpback whales (Megaptera novaeangliae) in their breeding
ground was used as a case study.
*Location.* New Caledonia, Oceania.
*Methods. *Five statistical algorithms were used to model the habitat
preferences of humpback whales from 1,360 sightings collected over 14
years of nonsystematic research surveys. Three different background
sampling approaches were tested when developing models from 625
crowdsourced sightings to assess methods accounting for citizen science
spatial sampling bias. Model evaluation was conducted through
cross‐validation and prediction to an independent satellite tracking
dataset.
*Results.* Algorithms differed in complexity of the environmental
relationships modelled, ecological interpretability and transferability.
While parameter tuning had a great effect on model performances, GLMs
generally had low predictive performance, SVMs were particularly hard to
interpret, and BRTs had high descriptive power but showed signs of
overfitting. MAXENT and especially GAMs provided a valuable complexity
trade‐off, accurate predictions and were ecologically intelligible.
Models showed that humpback whales favoured cool (22–23°C) and shallow
waters (0–100 m deep) in coastal as well as offshore areas. Citizen
science models converged with research survey models, specifically when
accounting for spatial sampling bias.
*Main conclusions.* Marine megafauna distribution models present
specific challenges that may be addressed through integrative
evaluation, independent testing and appropriately tuned statistical
algorithms. Specifically, controlling overfitting is a priority when
predicting cetacean distributions for large‐scale conservation
perspectives. Citizen science data appear to be a powerful tool to
describe cetacean habitat.
The paper may be downloaded on
https://onlinelibrary.wiley.com/doi/abs/10.1111/ddi.12782
Feel free to contact me directly for a PDF copy: [email protected]
<mailto:[email protected]>
Best,
--
Solène Derville
PhD student - Spatial Ecology
UMR Entropie - Institut de Recherche pour le Développement
Université Pierre et Marie Curie
Association Opération Cétacés
----------
101 Promenade Roger Laroque, BPA5
98848 Noumea cedex, New Caledonia
Phone: +687 912299
https://www.researchgate.net/profile/Solene_Derville
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