A comparison of approaches for modelling the occurrence of marine animals

Abstract

Approaches for modelling the distribution of animals in relation to their environment can be divided into two basic types, those which use records of absence as well as records of presence and those which use only presence records. For terrestrial species, presence-absence approaches have been found to produce models with greater predictive ability than presence-only approaches. This study compared the predictive ability of both approaches for a marine animal, the harbour porpoise (Phoceoena phocoena). Using data on the occurrence of harbour porpoises in the Sea of Hebrides, Scotland, the predictive abilities of one presence-absence approach (generalised linear modelling-GLM) and three presence-only approaches (Principal component analysis-PCA, ecological niche factor analysis-ENFA and genetic algorithm for rule-set prediction-GARP) were compared. When the predictive ability of the models was assessed using receiver operating characteristic (ROC) plots, the presence-absence approach (GLM) was found to have the greatest predictive ability. However, all approaches were found to produce models that predicted occurrence significantly better than a random model and the GLM model did not perform significantly better than ENFA and GARP. The PCA had a significantly lower predictive ability than GLM but not the other approaches. In addition, all models predicted a similar spatial distribution. Therefore, while models constructed using presence-absence approaches are likely to provide the best understanding of species distribution within a surveyed area, presence-only models can perform almost as well. However, careful consideration of the potential limitations and biases in the data, especially with regards to representativeness, is needed if the results of presence-only models are to be used for conservation and/or management purposes.

Comparing species distribution models: a case study of four deep sea urchin species

“…However, these differences were not significant for all the analyzed species and the differences varied between MAXENT and ENFA. In general, these results are consistent with other studies that compared both approaches (Hirzel et al, 2001;Brotons et al, 2004;Bedia et al, 2011), but not with all of them (MacLeod et al, 2008). Macleod et al (2008) used AUC values to compare three different models (ENFA, PCA and GARP) using presence-only data with one presence-absence model (GLM).…”

“…They found significant differences between PCA and GLM, but not between ENFA and GLM. However, their results were obtained for only one species, the harbor porpoise Phocoena phocoena, which is hard to detect and hence there is a higher risk of false absences (Macleod et al, 2008). When a higher number of species was included, reducing the risk associated with the inclusion of cryptic species, the presence-absence approach performed better than the presence-only approach (Brotons et al, 2004;Bedia et al, 2011).…”

“…Four distinct modeling approaches were used, two presence-only models (MAXENT and ENFA) and two presenceabsence models (GAM and Random Forest). These techniques were selected as they are some of the most widely used model species distribution in marine ecosystems (MacLeod et al, 2008;Monk et al, 2010;Reiss et al, 2011;Darr et al, 2014). The presenceonly data models use different mathematical algorithms to calculate the ecological niche of the target species based on the environmental variable values at the presence point (Monk et al, 2010).…”

“…Recent works (MacLeod et al, 2008;Monk et al, 2010) focusing on the marine environment suggested differences between the presence-only models and presence/absence models could be less important. MacLeod et al (2008) suggested that the presenceonly models can perform almost as well as the presence-absence models and Monk et al (2010) encouraged the ongoing use of presence-only approaches.…”

“…MacLeod et al (2008) suggested that the presenceonly models can perform almost as well as the presence-absence models and Monk et al (2010) encouraged the ongoing use of presence-only approaches. On the other hand, Zaniewski et al (2002) showed that the presence-only models inevitably tend to over-predict habitat suitability and Brotons et al (2004) suggested that if absence data are available, methods using this information should be preferred in most situations.…”