[MARMAM] New paper: Moving towards dynamic ocean management: How well do modeled ocean products predict species distributions?

Elizabeth Becker ebecker77 at cox.net
Mon Feb 22 09:27:22 PST 2016


Dear MARMAM Colleagues,

 

We are pleased to announce the following open access publication in the
special issue of Remote Sensing of Biodiversity:

 

Becker, E.A., K.A. Forney, P.C. Fiedler, J. Barlow, S.J. Chivers, C.A.
Edwards, A.M. Moore, and J.V. Redfern. (2016) Moving towards dynamic ocean
management: How well do modeled ocean products predict species
distributions?  Remote Sensing 2016, 8, 149; doi:10.3390/rs8020149

Abstract:  Species distribution models are now widely used in conservation
and management to predict suitable habitat for protected marine species. The
primary sources of dynamic habitat data have been in situ and remotely
sensed oceanic variables (both are considered "measured data"), but now
ocean models can provide historical estimates and forecast predictions of
relevant habitat variables such as temperature, salinity, and mixed layer
depth. To assess the performance of modeled ocean data in species
distribution models, we present a case study for cetaceans that compares
models based on output from a data assimilative implementation of the
Regional Ocean Modeling System (ROMS) to those based on measured data.
Specifically, we used seven years of cetacean line-transect survey data
collected between 1991 and 2009 to develop predictive habitat-based models
of cetacean density for 11 species in the California Current Ecosystem. Two
different generalized additive models were compared: one built with a full
suite of ROMS output and another built with a full suite of measured data.
Model performance was assessed using the percentage of explained deviance,
root mean squared error (RMSE), observed to predicted density ratios, and
visual inspection of predicted and observed distributions. Predicted
distribution patterns were similar for models using ROMS output and measured
data, and showed good concordance between observed sightings and model
predictions. Quantitative measures of predictive ability were also similar
between model types, and RMSE values were almost identical. The overall
demonstrated success of the ROMS-based models opens new opportunities for
dynamic species management and biodiversity monitoring because ROMS output
is available in near real time and can be forecast.

 

The paper is freely available from Remote Sensing: 

 <http://www.mdpi.com/2072-4292/8/2/149>
http://www.mdpi.com/2072-4292/8/2/149

 

Best Regards,

 

Elizabeth A. Becker, Ph.D.
Contractor, Ocean Associates, Inc.
Marine Mammal & Turtle Division
Southwest Fisheries Science Center
National Marine Fisheries Service, NOAA
110 Shaffer Road
Santa Cruz, CA 95060
e-mail: ebecker77 at cox.net <mailto:ebecker77 at cox.net> 

 

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