[MARMAM] New Paper: Predicting cetacean distributions in data-poor marine ecosystems
Jessica Redfern - NOAA Federal
jessica.redfern at noaa.gov
Tue Mar 7 16:42:45 PST 2017
We are pleased to announce publication of the following paper:
Redfern, J.V., Moore, T.J., Fiedler, P.C., de Vos, A., Brownell, R.L.,
Forney, K.A., Becker, E.A. & Ballance, L.T. (2017) Predicting cetacean
distributions in data-poor marine ecosystems. Diversity and Distributions.
The abstract appears below and the full article can be accessed here:
Please contact me (Jessica.Redfern at noaa.gov), if you would like a copy of
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.
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