Complex multivariate model predictions for coral diversity with climatic change
Abstract Models of the future of coral reefs are potentially sensitive to theoretical assumptions, variable selectivity, interactions, and scales. A number of these aspects were evaluated using boosted regression tree models of numbers of coral taxa trained on ~1000 field surveys and 35 spatially co...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Ecosphere |
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| Online Access: | https://doi.org/10.1002/ecs2.70057 |
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| author | Tim R. McClanahan Maxwell K. Azali Nyawira A. Muthiga Sean N. Porter Michael H. Schleyer Mireille M. M. Guillaume |
| author_facet | Tim R. McClanahan Maxwell K. Azali Nyawira A. Muthiga Sean N. Porter Michael H. Schleyer Mireille M. M. Guillaume |
| author_sort | Tim R. McClanahan |
| collection | DOAJ |
| description | Abstract Models of the future of coral reefs are potentially sensitive to theoretical assumptions, variable selectivity, interactions, and scales. A number of these aspects were evaluated using boosted regression tree models of numbers of coral taxa trained on ~1000 field surveys and 35 spatially complete influential environmental proxies at moderate scales (~6.25 km2). Models explored influences of climate change, water quality, direct human‐resource extraction, and variable selection processes. We examined the predictions for numbers of coral taxa using all variables and compared them to models based on variables commonly used to predict climate change and human influences (eight and nine variables). Results indicated individual temperature variables alone had lower predictive ability (R2 < 2%–7%) compared to human influence variables (6%–18%) but overall climate had a higher training–testing fit (70%) than the human influence (63%) model. The full variable model had the highest fit to the full data (27 variables; R2 = 85%) and indicated the strongly interactive and complex role of environmental and human influence variables when making moderate‐scale biodiversity predictions. Projecting changes using Coupled Model Intercomparison Project (CMIP) 2050 Representative Concentration Pathways (RCP2.6 and 8.5) water temperature predictions indicated high local variability and fewer negative effects than predictions made by coarse scale threshold and niche models. The persistence of coral reefs over periods of rapid climate change is likely to be caused by smaller scale variability that is poorly simulated with coarse scale modeled predictions. |
| format | Article |
| id | doaj-art-ace0ce8899fb46fc91208d976914c2b8 |
| institution | Kabale University |
| issn | 2150-8925 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecosphere |
| spelling | doaj-art-ace0ce8899fb46fc91208d976914c2b82025-01-27T14:51:33ZengWileyEcosphere2150-89252024-12-011512n/an/a10.1002/ecs2.70057Complex multivariate model predictions for coral diversity with climatic changeTim R. McClanahan0Maxwell K. Azali1Nyawira A. Muthiga2Sean N. Porter3Michael H. Schleyer4Mireille M. M. Guillaume5Wildlife Conservation Society, Global Marine Programs Bronx New York USAWildlife Conservation Society, Kenya Marine Program Mombasa KenyaWildlife Conservation Society, Kenya Marine Program Mombasa KenyaOceanographic Research Institute South African Association for Marine Biological Research Durban South AfricaOceanographic Research Institute South African Association for Marine Biological Research Durban South AfricaMuséum national d'Histoire naturelle (MNHN), UMR BOrEA (MNHN, CNRS 2030, Sorbonne Université, IRD 207, Uni Caen‐Normandie, Université des Antilles) Paris FranceAbstract Models of the future of coral reefs are potentially sensitive to theoretical assumptions, variable selectivity, interactions, and scales. A number of these aspects were evaluated using boosted regression tree models of numbers of coral taxa trained on ~1000 field surveys and 35 spatially complete influential environmental proxies at moderate scales (~6.25 km2). Models explored influences of climate change, water quality, direct human‐resource extraction, and variable selection processes. We examined the predictions for numbers of coral taxa using all variables and compared them to models based on variables commonly used to predict climate change and human influences (eight and nine variables). Results indicated individual temperature variables alone had lower predictive ability (R2 < 2%–7%) compared to human influence variables (6%–18%) but overall climate had a higher training–testing fit (70%) than the human influence (63%) model. The full variable model had the highest fit to the full data (27 variables; R2 = 85%) and indicated the strongly interactive and complex role of environmental and human influence variables when making moderate‐scale biodiversity predictions. Projecting changes using Coupled Model Intercomparison Project (CMIP) 2050 Representative Concentration Pathways (RCP2.6 and 8.5) water temperature predictions indicated high local variability and fewer negative effects than predictions made by coarse scale threshold and niche models. The persistence of coral reefs over periods of rapid climate change is likely to be caused by smaller scale variability that is poorly simulated with coarse scale modeled predictions.https://doi.org/10.1002/ecs2.70057AfricabiodiversityCoupled Model Intercomparison Project (CMIP)environmental driversIntergovernmental Panel on Climate Change (IPCC)machine learning |
| spellingShingle | Tim R. McClanahan Maxwell K. Azali Nyawira A. Muthiga Sean N. Porter Michael H. Schleyer Mireille M. M. Guillaume Complex multivariate model predictions for coral diversity with climatic change Ecosphere Africa biodiversity Coupled Model Intercomparison Project (CMIP) environmental drivers Intergovernmental Panel on Climate Change (IPCC) machine learning |
| title | Complex multivariate model predictions for coral diversity with climatic change |
| title_full | Complex multivariate model predictions for coral diversity with climatic change |
| title_fullStr | Complex multivariate model predictions for coral diversity with climatic change |
| title_full_unstemmed | Complex multivariate model predictions for coral diversity with climatic change |
| title_short | Complex multivariate model predictions for coral diversity with climatic change |
| title_sort | complex multivariate model predictions for coral diversity with climatic change |
| topic | Africa biodiversity Coupled Model Intercomparison Project (CMIP) environmental drivers Intergovernmental Panel on Climate Change (IPCC) machine learning |
| url | https://doi.org/10.1002/ecs2.70057 |
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