title: Are we predicting the actual or apparent distribution of temperate marine fishes?
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 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 km(2) 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.
authors.affiliation: School of Life and Environmental Sciences, Faculty of Science and Technology, Deakin University, Warrnambool, Victoria, Australia. jacquomo.monk@deakin.edu.au
authors.forename: Jacquomo
authors.initials: J
authors.lastname: Monk
authors.forename: Daniel
authors.initials: D
authors.lastname: Ierodiaconou
authors.forename: Euan
authors.initials: E
authors.lastname: Harvey
authors.forename: Alex
authors.initials: A
authors.lastname: Rattray
authors.forename: Vincent L
authors.initials: VL
authors.lastname: Versace
mesh_terms: D000818:Animals; D019540:Area Under Curve; D003198:Computer Simulation; D003247:Conservation of Natural Resources; D003625:Data Collection / Q000379:methods; D017753:Ecosystem; D005399:Fishes; D008954:Models, Biological; D009792:Oceans and Seas; D012372:ROC Curve; D014741:Video Recording