Challenges of transferring models of fish abundance between coral reefs

Sequeira, Ana M.M., Mellin, Camille, Lozano-Montes, Hector M., Meeuwig, Jessica J., Vanderklift, Mathew A., Haywood, Michael D.E., Babcock, Russell C., & (2018) Challenges of transferring models of fish abundance between coral reefs. PeerJ, 6, Article number: e4566.

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<p>Reliable abundance estimates for species are fundamental in ecology, fisheries, and conservation. Consequently, predictive models able to provide reliable estimates for un- or poorly-surveyed locations would prove a valuable tool for management. Based on commonly used environmental and physical predictors, we developed predictive models of total fish abundance and of abundance by fish family for ten representative taxonomic families for the Great Barrier Reef (GBR) using multiple temporal scenarios. We then tested if models developed for the GBR (reference system) could predict fish abundances at Ningaloo Reef (NR; target system), i.e., if these GBR models could be successfully transferred to NR. Models of abundance by fish family resulted in improved performance (e.g., 44.1% < R<sup>2</sup> < 50:6% for Acanthuridae) compared to total fish abundance (9% < R<sup>2</sup> < 18:6%). However, in contrast with previous transferability obtained for similar models for fish species richness from the GBR to NR, transferability for these fish abundance models was poor. When compared with observations of fish abundance collected in NR, our transferability results had low validation scores (R<sup>2</sup> < 6%, p > 0:05). High spatio-temporal variability of patterns in fish abundance at the family and population levels in both reef systems likely affected the transferability of these models. Inclusion of additional predictors with potential direct effects on abundance, such as local fishing effort or topographic complexity, may improve transferability of fish abundance models. However, observations of these local-scale predictors are often not available, and might thereby hinder studies on model transferability and its usefulness for conservation planning and management.</p>

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ID Code: 229627
Item Type: Contribution to Journal (Journal Article)
Refereed: Yes
Additional Information: Funding Information: Ana MM Sequeira was funded by a collaborative Post-doctoral Fellowship from UWA, AIMS and CSIRO, through the Indian Ocean Marine Research Centre (IOMRC) and by an ARC Grant (DE170100841). Camille Mellin was funded by an ARC Grant (DE140100701). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Measurements or Duration: 23 pages
Keywords: Generalized linear mixed-effects modelling, Great Barrier Reef, Ningaloo Reef, Species distribution models, Underwater visual counts
DOI: 10.7717/peerj.4566
ISSN: 2167-8359
Pure ID: 108280925
Divisions: Past > QUT Faculties & Divisions > Science & Engineering Faculty
Current > Schools > School of Mathematical Sciences
Funding Information: We are grateful to R Pitcher andMCase for providing access to the environmental variables used as predictors in this work, and to all participants in the fish data collection both at NR and the GBR. CSIRO Ningaloo work in 2013 was funded by the Australian Government's Caring For Our Country Program. Ana MM Sequeira was funded by a collaborative Post-doctoral Fellowship from UWA, AIMS and CSIRO, through the Indian Ocean Marine Research Centre (IOMRC) and by an ARC Grant (DE170100841). Camille Mellin was funded by an ARC Grant (DE140100701). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The following grant information was disclosed by the authors: UWA. AIMS. CSIRO. Indian Ocean Marine Research Centre (IOMRC). ARC Grant: DE170100841, DE140100701.
Copyright Owner: 2018 The Author(s)
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Deposited On: 12 Apr 2022 05:49
Last Modified: 03 Mar 2024 06:38