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Are we predicting the actual or apparent distribution of temperate marine fishes?
Citation
Monk, J and Ierodiaconou, D and Harvey, E and Rattray, A and Versace, VL, Are we predicting the actual or apparent distribution of temperate marine fishes?, PLoS ONE, 7, (4) Article e34558. ISSN 1932-6203 (2012) [Refereed Article]
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Copyright Statement
Licensed under Creative Commons Attribution 3.0 Unported (CC BY 3.0) http://creativecommons.org/licenses/by/3.0/
DOI: doi:10.1371/journal.pone.0034558
Abstract
Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform
conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological
function in the face of global environmental change – particularly changes in climate and resource exploitation. In the
absence of direct biological and ecological information for many marine species, scientists are increasingly using spatiallyexplicit,
predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video
technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously
not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution
depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine
temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized
linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on colocated
occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale
multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not
considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity
between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared
(relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to
which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species
apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also
encourage researchers and marine managers to carefully interpret model predictions.
Item Details
Item Type: | Refereed Article |
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Keywords: | demersal fishes, species distribution model |
Research Division: | Agricultural, Veterinary and Food Sciences |
Research Group: | Fisheries sciences |
Research Field: | Aquaculture and fisheries stock assessment |
Objective Division: | Environmental Management |
Objective Group: | Marine systems and management |
Objective Field: | Marine biodiversity |
UTAS Author: | Monk, J (Dr Jacquomo Monk) |
ID Code: | 99546 |
Year Published: | 2012 |
Web of Science® Times Cited: | 34 |
Deposited By: | IMAS Research and Education Centre |
Deposited On: | 2015-03-27 |
Last Modified: | 2017-11-01 |
Downloads: | 257 View Download Statistics |
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