Challenges of transferring models of fish abundance between coral reefs.
| Autor: | Sequeira AMM; IOMRC and The UWA Oceans Institute, The University of Western Australia, Crawley, Western Australia, Australia., Mellin C; Australian Institute of Marine Science, Townsville, Queensland, Australia.; The Environment Institute and School of Biological Sciences, The University of Adelaide, Adelaide, South Australia, Australia., Lozano-Montes HM; Indian Ocean Marine Research Centre, CSIRO Oceans and Atmosphere, Crawley, Western Australia, Australia., Meeuwig JJ; Centre for Marine Futures and School of Biological Sciences, The University of Western Australia, Crawley, Western Australia, Australia., Vanderklift MA; Indian Ocean Marine Research Centre, CSIRO Oceans and Atmosphere, Crawley, Western Australia, Australia., Haywood MDE; Dutton Park, CSIRO Oceans and Atmosphere, Brisbane, Queensland, Australia., Babcock RC; Dutton Park, CSIRO Oceans and Atmosphere, Brisbane, Queensland, Australia., Caley MJ; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Queensland, Australia. |
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| Jazyk: | angličtina |
| Zdroj: | PeerJ [PeerJ] 2018 Apr 17; Vol. 6, pp. e4566. Date of Electronic Publication: 2018 Apr 17 (Print Publication: 2018). |
| DOI: | 10.7717/peerj.4566 |
| Abstrakt: | 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 2 < 50.6% for Acanthuridae) compared to total fish abundance (9% < R 2 < 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 2 < 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. Competing Interests: AIMS and CSIRO supported the field data collection and sampling design in the GBR and NR, respectively. The authors declare that there no additional competing interests. |
| Databáze: | MEDLINE |
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