Global patterns and predictors of tropical reef fish species richness
Abstract
In the marine realm, the tropics host an extraordinary diversity of taxa but the drivers underlying the global distribution of marine organisms are still under scrutiny and we still lack an accurate global predictive model. Using a spatial database for 6336 tropical reef fishes, we attempted to predict species richness according to geometric, biogeographical and environmental explanatory variables. In particular, we aimed to evaluate and disentangle the predictive performances of temperature, habitat area, connectivity, mid‐domain effect and biogeographical region on reef fish species richness. We used boosted regression trees, a flexible machine‐learning technique, to build our predictive model and structural equation modeling to test for potential ‘mediation effects’ among predictors. Our model proved to be accurate, explaining 80% of the total deviance in fish richness using a cross‐validated procedure. Coral reef area and biogeographical region were the primary predictors of reef fish species richness, followed by coast length, connectivity, mid‐domain effect and sea surface temperature, with interactions between the region and other predictors. Important indirect effects of water temperature on reef fish richness, mediated by coral reef area, were also identified. The relationship between environmental predictors and species richness varied markedly among biogeographical regions. Our analysis revealed that a few easily accessible variables can accurately predict reef fish species richness. They also highlight concerns regarding ongoing environmental declines, with region‐specific responses to variation in environmental conditions predicting a variable response to anthropogenic impacts.
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Non-parametric ML techniques like Random Forest have been successfully used to predict species richness and have shown significant error reduction with respect to traditional counterparts used in ecology, for example in fishes, spiders, and small mammals.
“Non-parametric ML techniques like Random Forest126 have been successfully used to predict species richness and have shown significant error reduction with respect to the traditional counterparts used in ecology, for example in the estimation of richness distributions of fishes127,128, spiders 129, and small mammals130.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [127]
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Perspectives in machine learning for wildlife conservation
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- Authors
- Parravicini, V., Kulbicki, M., Bellwood, D. R., Friedlander, A. M., Arias‐Gonzalez, J. E., Chabanet, P., Floeter, S. R., Myers, R., Vigliola, L., D’Agata, S., & Mouillot, D.
- Affiliations
- Institut de Recherche pour le Développement (CG)
- James Cook University (AU)
- University of Hawaiʻi at Mānoa (US)
- Institut de Recherche pour le Développement (BJ)
- Universidade Federal de Santa Catarina (BR)
- Ifremer (FR)
- ORCID
- Venue
- Ecography
- Publisher
- Wiley
- Published
- 2013-04-30
- Volume
- 36
- Issue
- 12
- Pages
- 1254-1262
- ISSN
- 0906-7590 (ISSN-L)
- 1600-0587
- OpenAlex
- W2095461656
- Cited by
- 186
- References
- 54
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 17 · 2023: 18 · 2022: 14 · 2021: 13 · 2020: 17 · 2019: 13 · 2018: 13 · 2017: 16 · 2016: 13 · 2015: 12 · 2014: 8 · 2013: 3
- FWCI
- 10.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ecography (publishedVersion)
- ResearchOnline - JCU (James Cook University) (submittedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Ecology › Coral and Marine Ecosystems Studies
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Marine Biology and Ecology Research
- Keywords
- tropical reef fish, species richness, biogeographic regions, sea surface temperature, habitat connectivity, mid-domain effect, environmental drivers of biodiversity, mediating effect, anthropogenic impacts, biodiversity prediction
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