Comparing California Current cetacean–habitat models developed using in situ and remotely sensed sea surface temperature data

Abstract

Generalized linear and generalized additive habitat models were used to predict cetacean densities for 10 species in an 818 000 km 2 area off California. The performance of models built with remotely sensed oceanic data was compared to that of models built with in situ measurements. Cetacean sighting data were collected by the Southwest Fisheries Science Center on 4 systematic line-transect surveys during the summer and fall of 1991, 1993, 1996, and 2001. Predictor variables included temporally dynamic, remotely sensed environmental variables (sea surface temperature and measures of its variance) and more static geographical variables (water depth, bathymetric slope, and a categorical variable representing oceanic zone). The explanatory and predictive power of different spatial and temporal resolutions of satellite data were examined and included in the models for each of the 10 species. Alternative models were built using in situ analogs for sea surface temperature and its variance. The remotely sensed and in situ models with the highest predictive ability were selected based on a pseudo-jackknife cross validation procedure. Environmental predictors included in the final models varied by species, but, for each species, overall explanatory power was similar between the remotely sensed and in situ models. Cetacean-habitat models developed using satellite data at 8 d temporal resolution and from 5 to 35 km spatial resolution were shown to have predictive ability that generally met or exceeded models developed with analogous in situ data. This suggests that the former could be an effective tool for resource managers to develop near real-time predictions of cetacean density.KEY WORDS: Cetacean density · Habitat modeling · GAM · GLM · California Current · Remote sensing · Whale · Dolphin · Porpoise Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 413: 2010 Ferguson et al. 2006). Although some relied on satellite data to investigate cetacean-habitat associations (e.g. Waring et al. 1993, Jaquet & Whitehead 1996, Moore et al. 2002, satellite data typically were used to augment in situ data or when equipment failure precluded the collection of along-track data (Davis et al. 1998, Baumgartner et al. 2001, Davis et al. 2002, Hamazaki 2002. However, satellite data provide synoptic spatial coverage in near real-time, and this can be an important advantage if remotely sensed data are as effective at capturing species-environment relationships as in situ data. To date, there have been no direct comparisons of cetacean habitat models based solely on in situ and solely on remotely sensed oceanic variables.Generalized linear models (GLMs) and generalized additive models (GAMs) have been used effectively to model cetacean sighting rates (Hedley et al. 1999, Forney 2000 and cetacean density (Ferguson et al. 2006) as a function of environmental variables. Cetacean densities are typically estimated by line-transect surveys and generally result in estimates for large geogr...

Habitat-Based Density Models for Three Cetacean Species off Southern California Illustrate Pronounced Seasonal Differences

“…The ratios of observed to predicted density for all species summarized over all years for the entire study area were close to unity for short-beaked common dolphin and humpback whale, and within 8% of unity for Dall's porpoise ( Table 2). Similar to previous analyses of summer/fall data, the individual yearly ratios were highly variable, reflecting the reduced predictive ability for any specific year, due in large part to the smaller sample sizes resulting from data stratification (Barlow et al, 2009;Becker et al, 2010;Forney et al, 2012).…”

“…Rather, this band of predicted low density is likely a result of prevailing ocean conditions during winter and spring, when cold water extends from Point Conception southeastward, serving to bifurcate the study area (Figure 7). Short-beaked common dolphins are a warm temperate to tropical species, and based on the models developed here as well as in previous studies (Becker et al, 2010(Becker et al, , 2014Forney et al, 2012), densities are greatest when waters are warmest, so there appears to be some avoidance of these areas with cool, upwelled water. Interestingly, genetic differences between short-beaked populations have been identified, with an apparent separate stock occurring in the Southern California Bight (Chivers et al, 2003), corresponding roughly to the nearshore areas of higher density identified by the habitat model ( Figure 4A).…”

“…These habitat models and the underlying methodology have been extensively validated using cross validation (Forney, 2000;Forney et al, 2012;Barlow et al, 2009;Becker et al, 2010Becker et al, , 2016, predictions on novel data sets (Barlow et al, 2009;Becker et al, 2012Becker et al, , 2014Forney et al, 2012;Calambokidis et al, 2015), and expert opinion (Barlow et al, 2009;Forney et al, 2012). Although the study area for the Becker et al (2016) summer/fall models was larger than our CalCOFI study area, the spatial and temporal resolution of the developed models was identical, and similar habitat predictors were used.…”

“…Samples for modeling were created by dividing transects into approximate 5 km segments of continuous survey effort as described by Becker et al (2010). To maximize samples sizes for estimation of the detection function, all data collected in Beaufort sea states 0-5 while observers were systematically searching for marine mammals ("on-effort") were used.…”

“…As part of marine mammal and ecosystem assessments, systematic surveys have been conducted in the California Current since 1991, and these data have been used to develop SDMs for many of the cetacean species known to occur in this region (Forney, 2000;Barlow et al, 2009;Becker et al, 2010Becker et al, , 2012Becker et al, , 2014Becker et al, , 2016Forney et al, 2012;Redfern et al, 2013). Density predictions from these models have been used by the U.S. Navy to assist in the evaluation of potential impacts on cetaceans (U.S. Department of the Navy, 2015), to assess overlap between fisheries and cetaceans (Saez et al, 2013;Feist et al, 2015), and to evaluate potential ship-strike risk to large whales in waters off Southern California (Redfern et al, 2013).…”