A matter of timing: how temporal scale selection influences cetacean ecological niche modelling

• Published in: Marine Ecology Progress Series
• Main Authors: Fernandez, M, Yesson, C, Gannier, A, Miller, PI, Azevedo, JMN
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2018
• Subjects: Conservation; Earth Observation - Remote Sensing; Marine Sciences; baleen whales
• Online Access: http://plymsea.ac.uk/id/eprint/7939/; http://plymsea.ac.uk/id/eprint/7939/1/Fernandez_et_al_MEPS_2018_cetacean_temporal_niche.pdf; https://www.int-res.com/abstracts/meps/v595/p217-231/; https://doi.org/10.3354/meps12551

Modelling in the marine environment faces unique challenges that place greater emphasis on model accuracy. The spatio-temporal variability of this environment presents challenges when trying to develop useful habitat models. We tested how different temporal scales influence model predictions for cetaceans with different ecological requirements. We used 7 years of (opportunistic) whale watching data (>16000 cetacean sightings) collected in the Azores archipelago under the MONICET platform. We modelled the distribution of 10 cetacean species with a sampling bias correction. Distribution modelling was performed at 2 spatial scales (2 and 4 km) and 2 temporal resolutions (8 d vs. monthly averages). We used a MAXENT analysis with 3 different validation procedures. Generally, the 8 d means produced better results. In some cases (e.g. baleen whales), predictions using monthly means were no better than null models. Finer temporal grains provided essential insights, especially for species influenced by dynamic variables (e.g. sea surface temperature). For species more influenced by static variables (e.g. bathymetry), differences between temporal scales were smaller. The selection of the right temporal scale can be essential when modelling the niches of cetaceans. Datasets with high temporal resolution (e.g. whale watching data) can provide an excellent basis for these analyses, allowing use of finer temporal grains. Our models showed good predictive performance; however, limitations related to the spatial coverage were found. Merging datasets with different temporal and spatial resolutions could help to improve niche estimates. Models with better predictive capacity and transferability are needed to implement more efficient protection and conservation measures.