Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes?
MetadataShow full item record
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 spatially-explicit, 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 co-located 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.
Showing items related by title, author, creator and subject.
Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationshipsMoore, Cordelia; Harvey, Euan; Van Niel, K. (2009)We used species distribution modelling to identify key environmental variables influencing the spatial distribution of demersal fish and to assess the potential of these species–environment relationships to predict fish ...
Improving essential fish habitat designation to support sustainable ecosystem-based fisheries managementMoore, Cordelia; Drazen, J.; Radford, Ben; Kelley, C.; Newman, Stephen (2016)A major limitation to fully integrated ecosystem based fishery management approaches is a lack of information on the spatial distribution of marine species and the environmental conditions shaping these distributions. ...
Environmental niche modelling fails to predict Last Glacial Maximum refugia: Niche shifts, microrefugia or incorrect palaeoclimate estimates?Worth, J.; Williamson, G.; Sakaguchi, S.; Nevill, Paul; Jordan, G. (2014)Aim: Many predictions of responses to future climate change utilize ecological niche models (ENMs). We assess the capacity of these models to predict species distributions under conditions that differ from the current ...