Habitat predictive modelling of demersal fish species in the Azores

Dissertação de Mestrado, Estudos Integrados dos Oceanos, 25 de Março de 2013, Universidade dos Açores.

Species distribution modelling of the marine environment has been extensively used to assess species–environment relationships to predict fish spatial distributions accurately. In this study we explored the application of two distinct modelling techniques, maximum entropy model (MaxEnt) and generalized linear models (GLMs) for predicting the potential distribution in the Azores economic exclusive zone (EEZ) of four economically important demersal fish species: blackbelly rosefish, Helicolenus dactylopterus dactylopterus, forkbeard, Phycis phycis, wreckfish, Polyprion americanus and offshore rockfish, Pontinus kuhlii. Models were constructed based on 13 years of fish presence/absence data derived from bottom longline surveys performed in the study area combined with high resolution (300 m) topographic and biogeochemical habitat seafloor variables. The most important predictors were depth and slope followed by sediment type, oxygen saturation and salinity, with relative contributions being similar among species. GLMs provided ‘outstanding’ model predictions (AUC>0.9) for two of the four fish species while MaxEnt provided ‘excellent’ model predictions (AUC=0.8–0.9) for three of four species. The level of agreement between observed and predicted presence/absence sites for both modelling techniques was ‘moderate’ (K=0.4–0.6) for three of the four species with P. americanus models presenting the lowest level of agreement (K<0.1). For the scope of this study, both modelling approaches presented here were determined to produce viable presence/absence maps which represent a snap–shot of the potential distributions of the investigated species. This information provides a better description of demersal fish spatial ecology and can be of a great deal of interest for future fisheries management and conservation planning.