Predicting cetacean abundance and distribution in a changing climate

Abstract

Aim Changes in abundance and shifts in distribution as a result of a warming climate have been documented for many marine species, but opportunities to test our ability to forecast such changes have been limited. This study evaluates the ability of habitat‐based density models to accurately forecast cetacean abundance and distribution during a novel year with unprecedented warm ocean temperatures caused by a sustained marine heatwave. Location California Current Ecosystem, USA. Methods We constructed generalized additive models based on cetacean sighting and environmental data from 1991 to 2009 for eight species with a diverse range of habitat associations. Models were built with three different sets of predictor variables to compare performance. Models were then used to forecast species abundance and distribution patterns during 2014, a year with anomalously warm ocean temperatures. Cetacean sighting data collected during 2014 were used to assess model forecasts. Results Ratios of model‐predicted abundance to observed abundance were close to 1:1 for all but one species and accurately captured changes in the number of animals in the study area during the anomalous year. Predicted distribution patterns also showed good concordance with the 2014 survey observations. Our results indicate that habitat relationships were captured sufficiently to predict both changes in abundance and shifts in distribution when conditions warmed, for both cool‐ and warm‐temperate species. Main conclusions Models built with multidecadal datasets were able to forecast abundance and distribution in a novel warm year for a diverse set of cetacean species. Models with the best explanatory power did not necessarily have the best predictive power. Also, they revealed species‐specific responses to warming ocean waters. Results have implications for modelling effects of climate change on cetaceans and other marine predators.

Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species

“…Presence and pseudo‐absence data were matched to dynamic surface and subsurface environmental variables as well as to static seafloor relief variables. Variables were examined based on hypothesized drivers of habitat and those shown to be significantly associated with blue whale space use (Becker et al, , ; Hazen et al, ). Daily environmental data at 0.1° resolution were sourced from historical and near‐real‐time data‐assimilative versions of the Regional Ocean Modelling System (ROMS) configured for the CCE (Moore et al, ; Neveu et al, ) (obtained from oceanmodeling.ucsc.edu).…”

“…GAMMs were fitted using the binomial family and a logit link function, with individuals nested as a random effect. A tensor product smooth between latitude and longitude was explored as predictors in GAMMs to account for spatial autocorrelation (Becker et al, ). A latitudinal interaction term with SST was also considered in the GAMMs to account for the latitudinal temperature gradient over the study area (Becker et al, ).…”

“…A tensor product smooth between latitude and longitude was explored as predictors in GAMMs to account for spatial autocorrelation (Becker et al, ). A latitudinal interaction term with SST was also considered in the GAMMs to account for the latitudinal temperature gradient over the study area (Becker et al, ). Multiple candidate GAMM models were generated based on published and hypothesized predictor variable combinations (Becker et al, ; Hazen et al, ) and were initially evaluated using the Area Under the receiver operating Curve (AUC; Table ) before selecting the top three GAMMs for further model evaluation.…”

“…Currently, voluntary seasonal slowdowns are the only tool used to manage ship strike risk in this region (Rockwood et al, ). The existence of multiple shipping lanes for marine traffic offers potential opportunity for redirecting vessels based on strike risk (Redfern et al, ), but existing blue whale models for the region are either limited by partial temporal coverage (Becker et al, ) or by coarse resolution (Hazen et al, ). In addition to better matching the timescales of species occurrences, finer scale distribution estimates may be better able to inform dynamic management decisions in the SCB given the migratory behaviour of blue whales (Bailey et al, ) and the variable biophysical conditions in the CCE (Checkley & Barth, ).…”

esdm: A tool for creating and exploring ensembles of predictions from species distribution and abundance models

“…Researchers are frequently updating and improving SDMs (e.g. new blue whale models have been published by Becker et al, , Abrahms et al, , and Palacios et al, since we undertook our example analysis). Consequently, we do not intend our results to be considered the current best set of predictions for blue whales in the CCE.…”