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Geographic range determinants of two commercially important marine molluscs
Mellin, C.; Russell, B.D.; Connell, S.D.; Brook, B.W.; Fordham, D.A. (2012). Geographic range determinants of two commercially important marine molluscs. Diversity Distrib. 18(2): 133-146. https://dx.doi.org/10.1111/j.1472-4642.2011.00822.x
In: Diversity and Distributions. Blackwell: Oxford. ISSN 1366-9516; e-ISSN 1472-4642, more
Peer reviewed article  

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Keyword
    Marine/Coastal

Authors  Top 
  • Mellin, C.
  • Russell, B.D.
  • Connell, S.D.
  • Brook, B.W.
  • Fordham, D.A.

Abstract
    Aim We modelled the spatial abundance patterns of two abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814) inhabiting inshore rocky reefs to better understand the importance of current sea surface temperature (SST) (among other predictors) and, ultimately, the effect of future climate change, on marine molluscs.

    Location Southern Australia.

    Methods We used an ensemble species distribution modelling approach that combined likelihood-based generalized linear models and boosted regression trees. For each modelling technique, a two-step procedure was used to predict: (1) the current probability of presence, followed by (2) current abundance conditional on presence. The resulting models were validated using an independent, spatially explicit dataset of abalone abundance patterns in Victoria.

    Results For both species, the presence of reef was the main driver of abalone occurrence, while SST was the main driver of spatial abundance patterns. Predictive maps at c. 1-km resolution showed maximal abundance on shallow coastal reefs characterized by mild winter SSTs for both species.

    Main conclusions Sea surface temperature was a major driver of abundance patterns for both abalone species, and the resulting ensemble models were used to build fine-resolution predictive range maps (c. 1 km) that incorporate measures of habitat suitability and quality in support of resource management. By integrating this output with structured spatial population models, a more robust understanding of the potential impacts of threatening human processes such as climate change can be established.


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