Resilience of the endangered sperm whale Physeter macrocephalus to foraging disturbance in the Gulf of Mexico, USA: a bioenergetic approach

Farmer, Nicholas A.; Noren, Dawn P.; Fougeres, Erin; Machernis, Abigail F.; Baker, Kyle

doi:10.3354/meps12457

Cited by 18 publications

(33 citation statements)

References 96 publications

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“…In this study, the integration of multiple data sources allowed translating observed behavioral changes into a potential loss of foraging time and associated energy, which will provide a common metric to evaluate the implications of these short-term responses for individual fitness using models for the population consequences of disturbance. This reinforces the results of previous work on other marine mammal ( Noren et al, 2016 ; Farmer et al, 2018 ; Guilpin et al, 2020 ), mammal ( Bradshaw et al, 1998 ; Houston et al, 2012 ) and bird species ( West et al, 2002 ; Masden et al, 2010 ). Importantly, we were able to partially reconcile the mismatch between the scale of data collection (detailed individual movement and diving behavior, collected in specific locations and times, at extremely high resolution) and the scale of a corresponding model ( Pirotta et al, 2019 ) for population-level effects (dealing with the population, modelled across the entire year and range, at daily temporal resolution and in large spatial units), thereby condensing the data into a compatible input for the model.…”

Section: Discussionsupporting

confidence: 90%

Context-dependent variability in the predicted daily energetic costs of disturbance for blue whales

Pirotta

Booth

Cade

et al. 2021

Conservation Physiology

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Assessing the long-term consequences of sub-lethal anthropogenic disturbance on wildlife populations requires integrating data on fine-scale individual behavior and physiology into spatially and temporally broader, population-level inference. A typical behavioral response to disturbance is the cessation of foraging, which can be translated into a common metric of energetic cost. However, this necessitates detailed empirical information on baseline movements, activity budgets, feeding rates and energy intake, as well as the probability of an individual responding to the disturbance-inducing stressor within different exposure contexts. Here, we integrated data from blue whales (Balaenoptera musculus) experimentally exposed to military active sonar signals with fine-scale measurements of baseline behavior over multiple days or weeks obtained from accelerometry loggers, telemetry tracking and prey sampling. Specifically, we developed daily simulations of movement, feeding behavior and exposure to localized sonar events of increasing duration and intensity and predicted the effects of this disturbance source on the daily energy intake of an individual. Activity budgets and movements were highly variable in space and time and among individuals, resulting in large variability in predicted energetic intake and costs. In half of our simulations, an individual’s energy intake was unaffected by the simulated source. However, some individuals lost their entire daily energy intake under brief or weak exposure scenarios. Given this large variation, population-level models will have to assess the consequences of the entire distribution of energetic costs, rather than only consider single summary statistics. The shape of the exposure-response functions also strongly influenced predictions, reinforcing the need for contextually explicit experiments and improved mechanistic understanding of the processes driving behavioral and physiological responses to disturbance. This study presents a robust approach for integrating different types of empirical information to assess the effects of disturbance at spatio-temporal and ecological scales that are relevant to management and conservation.

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Section: Discussionsupporting

confidence: 90%

Context-dependent variability in the predicted daily energetic costs of disturbance for blue whales

Pirotta

Booth

Cade

et al. 2021

Conservation Physiology

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“…() examined the relation between foraging activity and energy stores (estimated from changes in buoyancy) of female southern elephant seals ( Mirounga leonina ) over the course of a foraging trip. Other applications have inferred changes in energy stores from models of foraging activity that either treat energy explicitly using a bioenergetic approach (Beltran, Testa, & Burns, ; Christiansen & Lusseau, ; Farmer, Noren, Fougères, Machernis, & Baker, ; McHuron, Costa, Schwarz, & Mangel, ; McHuron, Mangel, Schwarz, & Costa, ; Noren, ; Pirotta, Mangel, et al., ; Villegas‐Amtmann et al., , ) or use an arbitrarily scaled energy metric that represents an underlying motivational state (Nabe‐Nielsen et al., , ; New, Harwood, et al., ; Pirotta, Harwood, et al., ; Pirotta, New, Harwood, & Lusseau, ). Although technologies that can measure the morphometrics of individuals remotely may make it easier to estimate changes in body condition directly (e.g., Christiansen, Dujon, Sprogis, Arnould, & Bejder, ; Miller, Best, Perryman, Baumgartner, & Moore, ), extensive health assessment in cetaceans will probably remain limited to a few closely monitored coastal populations, due to logistical constraints (Wells et al., ).…”

Section: Effect Of Behavioral and Physiological Changes On Healthmentioning

confidence: 99%

“…For example, New et al (2014) and Schick, New, et al (2013) examined the relation between foraging activity and energy stores (estimated from changes in buoyancy) of female southern elephant seals (Mirounga leonina) over the course of a foraging trip. Other applications have inferred changes in energy stores from models of foraging activity that either treat energy explicitly using a bioenergetic approach (Beltran, Testa, & Burns, 2017;Farmer, Noren, Fougères, Machernis, & Baker, 2018;Noren, 2011;Pirotta, Mangel, et al, 2018;Villegas-Amtmann et al, 2015 or use an arbitrarily scaled energy metric that represents an underlying motivational state (Nabe-Nielsen et al, 2014New, Harwood, et al, 2013;Pirotta, Harwood, et al, 2015;Pirotta, New, Harwood, & Lusseau, 2014).…”

Section: Effec T Of B Ehavi or Al And Phys Iolog Ic Al Chang E S Onmentioning

confidence: 99%

Understanding the population consequences of disturbance

Pirotta

Booth²,

Costa

et al. 2018

Ecology and Evolution

194

217

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Managing the nonlethal effects of disturbance on wildlife populations has been a long‐term goal for decision makers, managers, and ecologists, and assessment of these effects is currently required by European Union and United States legislation. However, robust assessment of these effects is challenging. The management of human activities that have nonlethal effects on wildlife is a specific example of a fundamental ecological problem: how to understand the population‐level consequences of changes in the behavior or physiology of individual animals that are caused by external stressors. In this study, we review recent applications of a conceptual framework for assessing and predicting these consequences for marine mammal populations. We explore the range of models that can be used to formalize the approach and we identify critical research gaps. We also provide a decision tree that can be used to select the most appropriate model structure given the available data. Synthesis and applications: The implementation of this framework has moved the focus of discussion of the management of nonlethal disturbances on marine mammal populations away from a rhetorical debate about defining negligible impact and toward a quantitative understanding of long‐term population‐level effects. Here we demonstrate the framework's general applicability to other marine and terrestrial systems and show how it can support integrated modeling of the proximate and ultimate mechanisms that regulate trait‐mediated, indirect interactions in ecological communities, that is, the nonconsumptive effects of a predator or stressor on a species' behavior, physiology, or life history.

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“…where E B is the individual's mean energy store (MJ). A lipid energy density of 39.42 MJ/kg was used as per Blaxter (1989), which is consistent with other estimates used in marine mammal energetics models (Rechsteiner et al, 2013;Christiansen and Lusseau, 2015;Beltran et al, 2017;Farmer et al, 2018). Not all blubber is accessible as an energy store, so the total potential energy in an individual's lipids was halved to give the lipid stores, E S .…”

Section: Energy Storesmentioning

confidence: 86%

Extreme Effects of Extreme Disturbances: A Simulation Approach to Assess Population Specific Responses

et al. 2020

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In South Australia, discrete populations of bottlenose dolphins inhabit two large gulfs, where key threats and population estimates have been identified. Climate change, habitat disturbance (shipping and noise pollution), fishery interactions and epizootic events have been identified as the key threats facing these populations. The Population Consequences of Disturbance (PCoD) framework has been developed to understand how disturbances can influence population dynamics. We used population estimates combined with population specific bioenergetics models to undertake a partial PCoD assessment, comparing how the two populations respond to the identified regional threats. Populations were modeled over a 5 year period looking at the influence of each disturbance separately. As expected, the most extreme epizootic and climate change disturbance scenarios with high frequency and intensity had the biggest influence on population trends. However, the magnitude of the effect differed by population, with Spencer Gulf showing a 43% and Gulf St Vincent a 23% decline under high frequency and high impact epizootic scenarios. Epizootic events were seen to have the strongest influence on population trends and reproductive parameters for both populations, followed by climate change. PCoD modeling provides insights into how disturbances may affect different populations and informs management on how to mitigate potential effects while there is still time to act.

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Resilience of the endangered sperm whale Physeter macrocephalus to foraging disturbance in the Gulf of Mexico, USA: a bioenergetic approach

Cited by 18 publications

References 96 publications

Context-dependent variability in the predicted daily energetic costs of disturbance for blue whales

Context-dependent variability in the predicted daily energetic costs of disturbance for blue whales

Understanding the population consequences of disturbance

Extreme Effects of Extreme Disturbances: A Simulation Approach to Assess Population Specific Responses

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