Statistical integration of tracking and vessel survey data to incorporate life history differences in habitat models

Yamamoto, Takashi; Watanuki, Yutaka; Hazen, Elliott L.; Nishizawa, Bungo; Sasaki, Hidetada; Takahashi, Akinori

doi:10.1890/15-0142.1

Cited by 14 publications

(15 citation statements)

References 84 publications

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“…; Yamamoto et al . ). Running SDMs in a predictive mode allows for habitat estimates at finer temporal scales (days to months) limited by environmental data availability.…”

Section: Discussionmentioning

confidence: 97%

“…; Yamamoto et al . ). These two approaches provide complementary data; however, telemetry data are generally underutilized in management because the data are presence‐only, violate statistical assumptions including independence of observations (Maunder et al .…”

Section: Introductionmentioning

confidence: 97%

“…However, in marine systems, these approaches are often spatially and temporally constrained to a single season and navigable waters within a country's exclusive economic zone. In contrast, telemetry data provide a Lagrangian view of individuals moving through the environment and allow additional inference on their behaviour (Hazen et al 2012;Yamamoto et al 2015). These two approaches provide complementary data; however, telemetry data are generally underutilized in management because the data are presence-only, violate statistical assumptions including independence of observations (Maunder et al 2006), require a large number of tags to adequately represent population patterns (Block et al 2011;Wakefield et al 2013) and in a worst case, can modify behaviour or fitness (Fossette et al 2008).…”

Section: Introductionmentioning

confidence: 99%

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WhaleWatch: a dynamic management tool for predicting blue whale density in the California Current

Hazen

Palacios

Forney

et al. 2016

Journal of Applied Ecology

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155

196

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Summary1. Management of highly migratory species is reliant on spatially and temporally explicit information on their distribution and abundance. Satellite telemetry provides time-series data on individual movements. However, these data are underutilized in management applications in part because they provide presence-only information rather than abundance information such as density. 2. Eastern North Pacific blue whales are listed as threatened, and ship strikes have been suggested as a key factor limiting their recovery. Here, we developed a satellite-telemetry-based habitat model in a case-control design for Eastern North Pacific blue whales Balaenoptera musculus that was combined with previously published abundance estimates to predict habitat preference and densities. Further, we operationalize an automated, near-real-time whale density prediction tool based on up-to-date environmental data for use by managers and other stakeholders. 3. A switching state-space movement model was applied to 104 blue whale satellite tracks from 1994 to 2008 to account for errors in the location estimates and provide daily positions (case points). We simulated positions using a correlated random walk model (control points) and sampled the environment at each case and control point. Generalized additive mixed models and boosted regression trees were applied to determine the probability of occurrence based on environmental covariates. Models were used to predict 8-day and monthly resolution, year-round density estimates scaled by population abundance estimates that provide a critical tool for understanding seasonal and interannual changes in habitat use. 4. The telemetry-based habitat model predicted known blue whale hot spots and had seasonal agreement with sightings data, highlighting the skill of the model for predicting blue whale habitat preference and density. We identified high interannual variability in occurrence emphasizing the benefit of dynamic models compared to multiyear averages. 5. Synthesis and applications. This near-real-time tool allows a more accurate examination of the year-round spatio-temporal overlap of blue whales with potentially harmful human activities, such as shipping. This approach should also be applicable to other species for which sufficient telemetry data are available. The dynamic predictive product developed here is an important tool that allows managers to consider finer-scale management areas that are more economically feasible and socially acceptable.

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“…; Yamamoto et al . ). Running SDMs in a predictive mode allows for habitat estimates at finer temporal scales (days to months) limited by environmental data availability.…”

Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

WhaleWatch: a dynamic management tool for predicting blue whale density in the California Current

Hazen

Palacios

Forney

et al. 2016

Journal of Applied Ecology

Self Cite

155

196

View full text Add to dashboard Cite

show abstract

“…Telemetry‐based models may therefore underestimate offshore habitat suitability, though model performance remained high when compared to sightings data that were more broadly distributed offshore (Figure ). Testing the performance of models developed using a combination of data types such as telemetry, transect survey and acoustic monitoring data would therefore be a valuable exercise for exploring biases based on data types used (Fithian, Elith, Hastie, & Keith, ; Yamamoto et al, ).…”

Section: Discussionmentioning

confidence: 99%

Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species

Abrahms

Welch

Brodie

et al. 2019

Diversity and Distributions

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113

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Aim Advances in ecological and environmental modelling offer new opportunities for estimating dynamic habitat suitability for highly mobile species and supporting management strategies at relevant spatiotemporal scales. We used an ensemble modelling approach to predict daily, year‐round habitat suitability for a migratory species, the blue whale (Balaenoptera musculus), and demonstrate an application for evaluating the spatiotemporal dynamics of their exposure to ship strike risk. Location The California Current Ecosystem (CCE) and the Southern California Bight (SCB), USA. Methods We integrated a long‐term (1994–2008) satellite tracking dataset on 104 blue whales with data‐assimilative ocean model output to assess year‐round habitat suitability. We evaluated the relative utility of ensembling multiple model types compared to using single models, and selected and validated candidate models using multiple cross‐validation metrics and independent observer data. We quantified the spatial and temporal distribution of exposure to ship strike risk within shipping lanes in the SCB. Results Multi‐model ensembles outperformed single‐model approaches. The final ensemble model had high predictive skill (AUC = 0.95), resulting in daily, year‐round predictions of blue whale habitat suitability in the CCE that accurately captured migratory behaviour. Risk exposure in shipping lanes was highly variable within and among years as a function of environmental conditions (e.g., marine heatwave). Main conclusions Daily information on three‐dimensional oceanic habitats was used to model the daily distribution of a highly migratory species with high predictive power and indicated that management strategies could benefit by incorporating dynamic environmental information. This approach is readily transferable to other species. Dynamic, high‐resolution species distribution models are valuable tools for assessing risk exposure and targeting management needs.

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“…Efforts to combine Eulerian and Lagrangian perspectives using seabird counts within quantitative models have been conducted (Hyrenbach, Keiper, Allen, Ainley, & Anderson, 2006;Louzao et al, 2009;Yamamoto et al, 2015), and methods continue to be refined (Watanuki et al, 2016). Development of separate habitat models using data from each sampling perspective, and then comparing and integrating results across models, presents a powerful tool to quantify factors influencing marine mammal and seabird distributions and habitat use (Watanuki et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Does perspective matter? A case study comparing Eulerian and Lagrangian estimates of common murre (Uria aalge) distributions

Phillips

Horne

Zamon

et al. 2019

Ecology and Evolution

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Studies estimating species' distributions require information about animal locations in space and time. Location data can be collected using surveys within a predetermined frame of reference (i.e., Eulerian sampling) or from animal‐borne tracking devices (i.e., Lagrangian sampling). Integration of observations obtained from Eulerian and Lagrangian perspectives can provide insights into animal movement and habitat use. However, contemporaneous data from both perspectives are rarely available, making examination of biases associated with each sampling approach difficult. We compared distributions of a mobile seabird observed concurrently from ship, aerial, and satellite tag surveys during May, June, and July 2012 in the northern California Current. We calculated utilization distributions to quantify and compare variability in common murre ( Uria aalge ) space use and examine how sampling perspective and platform influence observed patterns. Spatial distributions of murres were similar in May, regardless of sampling perspective. Greatest densities occurred in coastal waters off southern Washington and northern Oregon, near large murre colonies and the mouth of the Columbia River. Density distributions of murres estimated from ship and aerial surveys in June and July were similar to those observed in May, whereas distributions of satellite‐tagged murres in June and July indicated northward movement into British Columbia, Canada, resulting in different patterns observed from Eulerian and Lagrangian perspectives. These results suggest that the population of murres observed in the northern California Current during spring and summer includes relatively stationary individuals attending breeding colonies and nonstationary, vagile adults and subadults. Given the expected growth of telemetry studies and advances in survey technology (e.g., unmanned aerial systems), these results highlight the importance of considering methodological approaches, spatial extent, and synopticity of distribution data sets prior to integrating data from different sampling perspectives.

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Statistical integration of tracking and vessel survey data to incorporate life history differences in habitat models

Cited by 14 publications

References 84 publications

WhaleWatch: a dynamic management tool for predicting blue whale density in the California Current

WhaleWatch: a dynamic management tool for predicting blue whale density in the California Current

Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species

Does perspective matter? A case study comparing Eulerian and Lagrangian estimates of common murre (Uria aalge) distributions

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