ALBERT

Description: Understanding the dynamics of cetacean distribution in ecologically vulnerable regions is essential to interpret the impact of environmental changes on species ecology and ecosystem functioning. Species distribution models (SDMs) are helpful tools that link species occurrences to environmental variables in order to predict a species’ potential distribution. Studies on baleen whale distribution in polar regions are comparably rare, mainly due to financial and logistic constraints. Here we use SDMs to predict habitat suitability for fin whales (Balaenoptera physalus) in Arctic waters. A combination of opportunistic and systematically collected visual observations from 2007 to 2018 was used. Opportunistic data were collected during ten RV Polarstern cruises in the Arctic Ocean (including the Barents-, Norwegian and Greenland Sea). Complementary visual data were obtained from open source databases. Environmental variables were chosen based on ecological relevance to the species, comprising both static and dynamic variables. We used MaxEnt software to model the distribution of fin whales, using presence-only data as a function of carefully chosen environmental covariates. MaxEnt’s predictive performance has been shown to be consistently competitive with the highest performing methods. We were able to reveal important factors affecting the distribution of fin whales in the Arctic Ocean and how they respond to them. Results demonstrate the effective use of SDMs to predict species distributions in highly remote areas, constituting a cost-effective method for targeting future surveys and prioritizing the limited conservation resources. Results can be applied in a variety of purposes, such as designing marine protected areas and support the further use of opportunistic data to understand the ecological drivers of species distribution.

Description: Detailed information on cetacean distribution is crucial to identify large-scale conservation actions and management decisions. Understanding the ecological drivers behind their spatial patterns in the Southern Ocean is complicated by whales’ mobility and the logistic restrictions in collecting data in polar environments. Species distribution models have become essential tools in ecology and conservation. They relate information on species occurrence with environmental predictors thought to influence its habitat use, to predict its potential distribution and explain environmental drivers of the observed patterns. In this study, we compiled opportunistic presenceonly data for seven whale species in the Southern Ocean from multiple sources. A quality-controlled data set was then used to model species distributions using Maxent software (under the point process modelling framework). Environmental predictors were prepared from multiple in-situ and remotely-sensed sources, based on our experience of the study area and species ecology. We estimated the best combinations of Maxent’s parameters & evaluated model performance on a species-specific spatial block cross-validation to maintain spatial independence between training and testing data. For each species, block size and their spatial allocation into crossvalidation folds was objectively determined according to how much spatial-autocorrelation exists at occurrences. For each of species, we 1) predicted circumpolar potential distribution, 2) determined the most important variables, and 3) showed the relationship between habitat suitability and environmental variables. We believe that our results would be of great importance to explain the habitat preference of species in the Southern Ocean, for the first time for the majority of studied species. However, we argue that these models can only represent a hypothetical, mean state (which actually never becomes manifest) of the potential distribution of the species in space, and hence another set of dynamic models are required to consider the high dynamic environment in the Southern Ocean and the migratory nature of whales.