Estimating the footprint of pollution on coral reefs with models of species turnover
Brown, Christopher J., and Hamilton, Richard J. (2018) Estimating the footprint of pollution on coral reefs with models of species turnover. Conservation Biology, 32 (4). pp. 949-958.
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Abstract
Ecological communities typically change along gradients of human impact, although it is difficult to estimate the footprint of impacts for diffuse threats such as pollution. We developed a joint model (i.e., one that includes multiple species and their interactions with each other and environmental covariates) of benthic habitats on lagoonal coral reefs and used it to infer change in benthic composition along a gradient of distance from logging operations. The model estimated both changes in abundances of benthic groups and their compositional turnover, a type of beta diversity. We used the model to predict the footprint of turbidity impacts from past and recent logging. Benthic communities far from logging were dominated by branching corals, whereas communities close to logging had higher cover of dead coral, massive corals, and soft sediment. Recent impacts were predicted to be small relative to the extensive impacts of past logging because recent logging has occurred far from lagoonal reefs. Our model can be used more generally to estimate the footprint of human impacts on ecosystems and evaluate the benefits of conservation actions for ecosystems.
Item ID: | 54875 |
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Item Type: | Article (Research - C1) |
ISSN: | 1523-1739 |
Keywords: | Bayesian modeling, beta diversity, ecological indicator, latent variable, logging, multispecies distribution modeling, water quality |
Copyright Information: | © 2018 Society for Conservation Biology. |
Funders: | Australian Research Council (ARC), Science for Nature and People Partnership (SNAPP) |
Projects and Grants: | ARC Discovery Early Career Researcher Award DE160101207 |
Date Deposited: | 01 Aug 2018 07:42 |
FoR Codes: | 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring @ 100% |
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