Where the whales are: using habitat modeling to support changes in shipping regulations within National Marine Sanctuaries in Central California

Dransfield, Andrea; Hines, Ellen; McGowan, Jennifer; Holzman, Barbara A.; Nur, Nadav; Elliott, Meredith L.; Jahncke, Jaime

doi:10.3354/esr00627

Cited by 46 publications

(60 citation statements)

References 77 publications

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“…Haida Gwaii), and thus maximum humpback densities in our study area can reasonably be expected to occur in somewhat deeper water. In California, Dransfield et al (2014) similarly predicted that hump backs had an affinity for the shelf break and occurred at greatest frequencies near the 200 m isobath. As with our model, Dalla Rosa et al (2012) determined that latitude was a significant predictor of humpback densities, and suggested this might occur because the extent of preferred, on-shelf habitat varies greatly by latitude in BC.…”

Section: Discussionmentioning

confidence: 91%

Risk of lethal vessel strikes to humpback and fin whales off the west coast of Vancouver Island, Canada

Nichol¹,

Wright²,

O’Hara³

et al. 2017

Endang. Species. Res.

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Vessel strikes are a source of mortality and injury for baleen whales, which can have population-level impacts. Spatial analysis of whale and marine traffic distributions provides a valuable approach for identifying zones with high collision risk. We conducted 34 systematic aerial surveys to estimate humpback Megaptera novaeangliae and fin whale Balaenoptera physalus densities off the west coast of Vancouver Island, Canada, including approaches to major shipping lanes in Juan de Fuca Strait, a gateway to the ports of southern British Columbia and Washington State. To predict whale densities, we fit negative binomial generalized additive models (GAMs) to sightings data, incorporating survey effort as an offset, and depth, slope, and latitude as environmental covariates. Humpbacks were primarily observed on the continental shelf, with highest predicted densities along the shelf edge (~200 m isobath), whereas fin whales were primarily distributed west of the shelf break (> 450 m depth). We combined GAM-predicted whale densities with vessel traffic data to estimate the relative risk of ship strikes. Since vessel speed is an important determinant of lethality, we also calculated the relative risk of lethal injuries, given the probability that a collision occurs. Humpbacks were most likely to be struck along the shelf edge, the inshore approaches to Juan de Fuca Strait, and within the strait itself. Fin whales were most likely to be struck in the offshore approaches to Juan de Fuca and inside the western portion of the strait. Our study is the first to assess ship strike risk in this region of high whale density and marine traffic use.

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Section: Discussionmentioning

confidence: 91%

Risk of lethal vessel strikes to humpback and fin whales off the west coast of Vancouver Island, Canada

Nichol¹,

Wright²,

O’Hara³

et al. 2017

Endang. Species. Res.

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“…Availability of higher resolution remotely sensed data and ocean model output could improve models for all species and ultimately increase our understanding of fine-scale distribution patterns. This is especially important for species with coastal habitats given their generally higher vulnerability to anthropogenic impacts (e.g., ship strikes [10,25]). …”

Section: Discussionmentioning

confidence: 99%

“…In a recent feasibility study, Becker et al [13] demonstrated the nowcast and forecast capabilities of cetacean habitat models based on remotely sensed and ocean modeled SST data. The results provide a foundation for building more complex cetacean habitat models using ocean modeled predictor variables such as sea surface salinity, mixed layer depth [MLD], and SSH, all shown to be important habitat predictors for cetaceans [1,11,25,[34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…Through data assimilation, controls of ocean model output, such as model initial conditions and/or forcing fields, are adjusted so that model-data misfit is reduced relative to an unconstrained model [20]. Ocean circulation models have the potential to enhance marine species distribution forecasting because they provide detailed physical characteristics describing features known to be important to marine predators [1,4,10,12,[21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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“…Given the level of effort that has been devoted to characterizing their marine mammal populations and physical and biological components, the California Current and Bering Sea ecosystems are particularly important areas for such modeling efforts (Moore, 2008;Friday et al, 2013;Redfern et al, 2013;Dransfield et al, 2014;Becker et al, 2016). Continuation of these studies should be encouraged because ample environmental and species distribution data already exist, linkages between species occurrence and their environmental drivers have been explored, and these systems have been extensively modeled.…”

Section: Priority Actions and Exemplar Speciesmentioning

confidence: 99%

Projecting Marine Mammal Distribution in a Changing Climate

et al. 2017

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Climate-related shifts in marine mammal range and distribution have been observed in some populations; however, the nature and magnitude of future responses are uncertain in novel environments projected under climate change. This poses a challenge for agencies charged with management and conservation of these species. Specialized diets, restricted ranges, or reliance on specific substrates or sites (e.g., for pupping) make many marine mammal populations particularly vulnerable to climate change. High-latitude, predominantly ice-obligate, species have experienced some of the largest changes in habitat and distribution and these are expected to continue. Efforts to predict and project marine mammal distributions to date have emphasized data-driven statistical habitat models. These have proven successful for short time-scale (e.g., seasonal) management activities, but confidence that such relationships will hold for multi-decade projections and novel environments is limited. Recent advances in mechanistic modeling of marine mammals (i.e., models that rely on robust physiological and ecological principles expected to hold under climate change) may address this limitation. The success of such approaches rests on continued advances in marine mammal ecology, behavior, and physiology together with improved regional climate projections. The broad scope of this challenge suggests initial priorities be placed on vulnerable species or populations (those already experiencing declines or projected to undergo ecological shifts resulting from climate Silber et al.Projecting Marine Mammal Distributions changes that are consistent across climate projections) and species or populations for which ample data already exist (with the hope that these may inform climate change sensitivities in less well observed species or populations elsewhere). The sustained monitoring networks, novel observations, and modeling advances required to more confidently project marine mammal distributions in a changing climate will ultimately benefit management decisions across time-scales, further promoting the resilience of marine mammal populations.

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Where the whales are: using habitat modeling to support changes in shipping regulations within National Marine Sanctuaries in Central California

Cited by 46 publications

References 77 publications

Risk of lethal vessel strikes to humpback and fin whales off the west coast of Vancouver Island, Canada

Risk of lethal vessel strikes to humpback and fin whales off the west coast of Vancouver Island, Canada

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

Projecting Marine Mammal Distribution in a Changing Climate

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