Predicting cetacean distributions in data‐poor marine ecosystems

Abstract

Aim Human activities are creating conservation challenges for cetaceans. Spatially explicit risk assessments can be used to address these challenges, but require species distribution data, which are limited for many cetacean species. This study explores methods to overcome this limitation. Blue whales (Balaenoptera musculus) are used as a case study because they are an example of a species that have well-defined habitat and are subject to anthropogenic threats.Location Eastern Pacific Ocean, including the California Current (CC) and eastern tropical Pacific (ETP), and northern Indian Ocean (NIO).Methods We used 12 years of survey data (377 blue whale sightings and c. 225,400 km of effort) collected in the CC and ETP to assess the transferability of blue whale habitat models. We used the models built with CC and ETP data to create predictions of blue whale distributions in the data-poor NIO because key aspects of blue whale ecology are expected to be similar in these ecosystems. Results We found that the ecosystem-specific blue whale models performed well in their respective ecosystems, but were not transferable. For example, models built with CC data could accurately predict distributions in the CC, but could not accurately predict distributions in the ETP. However, the accuracy of models built with combined CC and ETP data was similar to the accuracy of the ecosystem-specific models in both ecosystems. Our predictions of blue whale habitat in the NIO from the models built with combined CC and ETP data compare favourably to hypotheses about NIO blue whale distributions, provide new insights into blue whale habitat, and can be used to prioritize research and monitoring efforts.Main conclusions Predicting cetacean distributions in data-poor ecosystems using habitat models built with data from multiple ecosystems is potentially a powerful marine conservation tool and should be examined for other species and regions.

Integrating multiple data sources for assessing blue whale abundance and distribution in Chilean Northern Patagonia

“…However, CNP is a transitional zone between areas of seasonal upwelling dominance and areas of permanent downwelling dominance (south of 45° S), thus presenting a large synoptic variability even during spring/summer “upwelling‐favourable season” (Strub, Mesias, Montecino, Rutllant, & Salinas, ). In this scenario, most covariates associated with the occurrence of upwelling waters are not expected to be directly transferable to CNP as proxies for krill availability (Redfern et al., ). Based in BW (and other large whales) ecology and CNP oceanographic characteristics, two main environmental factors could be inspected when assessing BW distribution and abundance.…”

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

“…Using the esdm GUI, we successfully created an ensemble of mean blue whale predictions from Becker et al (), Hazen et al (), and Redfern et al () despite their different spatial resolutions, data sources, and prediction value types. The best ensemble predictions identified known blue whale habitat in the CCE, while generally improving evaluation metrics and minimizing biases associated with any single SDM.…”

“…Redfern et al, ), which represent the largest sources of anthropogenic injury or mortality for blue whales in the CCE (Carretta et al, ). Becker et al (), Hazen et al (), and Redfern et al () developed models of blue whale distributions in this region (henceforth Model_B, Model_H, and Model_R, respectively) that can provide information for risk assessments. However, the predictions from these models differ in some areas (Figure ), making them challenging to use for management purposes.…”

“…Maps of the original predictions and associated uncertainty for Model_B (Becker et al, ), Model_H (Hazen et al, ) and Model_R (Redfern et al, ). In the top row, predictions are colour‐coded using the numerical prediction value for each SDM.…”

Temporal resolutions in species distribution models of highly mobile marine animals: Recommendations for ecologists and managers

“…They have been used to predict important species habitats with a high potential for delineation of marineprotected areas and implementation of mitigation measures (Mannocci, Roberts, Miller, & Halpin, 2017;Redfern et al, 2017;Torres et al, 2013). These SDMs are most appropriate in the context of area-based planning processes launched globally by intergovernmental processes such as the description of Ecologically or Biologically Significant Areas by the Convention on Biological Diversity (Bax et al, 2016).…”