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Remote Sens. 2016, 8(2), 149; doi:10.3390/rs8020149

Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?

1
NOAA, Southwest Fisheries Science Center, 110 Shaffer Rd, Santa Cruz, CA 95060, USA
2
ManTech International Corporation, 420 Stevens Ave, Solana Beach, CA 92075, USA
3
NOAA, Southwest Fisheries Science Center, 8901 La Jolla Shores Dr., La Jolla, CA 92037, USA
4
Ocean Sciences Department, University of California, Santa Cruz, CA 95064, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Susan L. Ustin, Xiaofeng Li and Prasad S. Thenkabail
Received: 12 November 2015 / Revised: 16 January 2016 / Accepted: 1 February 2016 / Published: 16 February 2016
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
View Full-Text   |   Download PDF [4160 KB, uploaded 16 February 2016]   |  

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. View Full-Text
Keywords: abundance; California Current Ecosystem; cetaceans; dynamic ocean management; generalized additive model; habitat-based density model; remote sensing; ROMS; species distribution model abundance; California Current Ecosystem; cetaceans; dynamic ocean management; generalized additive model; habitat-based density model; remote sensing; ROMS; species distribution model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Becker, E.A.; Forney, K.A.; Fiedler, P.C.; Barlow, J.; Chivers, S.J.; Edwards, C.A.; Moore, A.M.; Redfern, J.V. Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions? Remote Sens. 2016, 8, 149.

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