Using modelled prey to predict the distribution of a highly mobile marine mammal

Pendleton, Daniel E.; Holmes, Elizabeth E.; Redfern, Jessica V.; Zhang, Jinlun

doi:10.1111/ddi.13149

Cited by 20 publications

(18 citation statements)

References 65 publications

(103 reference statements)

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“…with low associated measurement error, unlike remote sensed or modelled variables which may help improving the explanatory power of the models. Pendleton et al [79] observed similar results for bowhead whales (Balaena mysticetus). They compared a model using only environmental and biological variables such as sea ice thickness, sea temperature, diatoms, flagellates, copepods and zooplankton from the Biology Ice Ocean Modeling and Assimilation System (BIO-MAS) to a model using these variables plus bathymetry and showed that the best model included bathymetry and BIOMAS variables.…”

Section: Ability Of Seapodym and Environmental Variables To Model Deep-diver Distributionsmentioning

confidence: 63%

Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?

et al. 2021

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In habitat modelling, environmental variables are assumed to be proxies of lower trophic levels distribution and by extension, of marine top predator distributions. More proximal variables, such as potential prey fields, could refine relationships between top predator distributions and their environment. In situ data on prey distributions are not available over large spatial scales but, a numerical model, the Spatial Ecosystem And POpulation DYnamics Model (SEAPODYM), provides simulations of the biomass and production of zooplankton and six functional groups of micronekton at the global scale. Here, we explored whether generalised additive models fitted to simulated prey distribution data better predicted deep-diver densities (here beaked whales Ziphiidae and sperm whales Physeter macrocephalus) than models fitted to environmental variables. We assessed whether the combination of environmental and prey distribution data would further improve model fit by comparing their explanatory power. For both taxa, results were suggestive of a preference for habitats associated with topographic features and thermal fronts but also for habitats with an extended euphotic zone and with large prey of the lower mesopelagic layer. For beaked whales, no SEAPODYM variable was selected in the best model that combined the two types of variables, possibly because SEAPODYM does not accurately simulate the organisms on which beaked whales feed on. For sperm whales, the increase model performance was only marginal. SEAPODYM outputs were at best weakly correlated with sightings of deep-diving cetaceans, suggesting SEAPODYM may not accurately predict the prey fields of these taxa. This study was a first investigation and mostly highlighted the importance of the physiographic variables to understand mechanisms that influence the distribution of deep-diving cetaceans. A more systematic use of SEAPODYM could allow to better define the limits of its use and a development of the model that would simulate larger prey beyond 1,000 m would probably better characterise the prey of deep-diving cetaceans.

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Section: Ability Of Seapodym and Environmental Variables To Model Deep-diver Distributionsmentioning

confidence: 63%

Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?

et al. 2021

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“…Such changes have clear bioenergetic implications for marine mammals and the ecosystems they inhabit ( Costa, 2008 ; Laidre et al, 2020 ; Gallagher et al, 2022 ). Since the prey landscape is a major driver of the spatiotemporal distribution of marine mammals ( Sveegaard et al, 2012 ; Zerbini et al, 2016 ; Sigler et al, 2017 ; Straley et al, 2018 ; Pendleton et al, 2020 ), knowledge of prey fields and how they may be changing provides insight into the potential impact of anthropogenic disturbances on energy budgets ( Keen et al, 2021 ). As such, prey fields are critical components of many PCoD models ( Nabe-Nielsen et al, 2018 ; Pirotta et al, 2019 ; McHuron et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Key questions in marine mammal bioenergetics

McHuron

Adamczak

Arnould

et al. 2022

Conservation Physiology

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Bioenergetic approaches are increasingly used to understand how marine mammal populations could be affected by a changing and disturbed aquatic environment. There remain considerable gaps in our knowledge of marine mammal bioenergetics, which hinder the application of bioenergetic studies to inform policy decisions. We conducted a priority-setting exercise to identify high-priority unanswered questions in marine mammal bioenergetics, with an emphasis on questions relevant to conservation and management. Electronic communication and a virtual workshop were used to solicit and collate potential research questions from the marine mammal bioenergetic community. From a final list of 39 questions, 11 were identified as ‘key’ questions because they received votes from at least 50% of survey participants. Key questions included those related to energy intake (prey landscapes, exposure to human activities) and expenditure (field metabolic rate, exposure to human activities, lactation, time-activity budgets), energy allocation priorities, metrics of body condition and relationships with survival and reproductive success and extrapolation of data from one species to another. Existing tools to address key questions include labelled water, animal-borne sensors, mark-resight data from long-term research programs, environmental DNA and unmanned vehicles. Further validation of existing approaches and development of new methodologies are needed to comprehensively address some key questions, particularly for cetaceans. The identification of these key questions can provide a guiding framework to set research priorities, which ultimately may yield more accurate information to inform policies and better conserve marine mammal populations.

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“…This methodology has been applied solely to Auk species using the Barents Sea as a closed domain; however, it could also be applied to other species and other regions, including vulnerable marine mammals such as Baleen Whales, Ross Seals ( Ommatophoca rossi ) and dolphins in the Southern Ocean: several previous studies have demonstrated the importance of ocean thermal conditions to these species when seeking prey at depth by using MaxENT to model their occupancy ranges (El‐Gabbas et al., 2021; Pendleton et al., 2020; Wege et al., 2021). These studies also benefit from the use of multiple environment variables which is not the case for this study (see Skov et al.…”

Section: Discussionmentioning

confidence: 99%

Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea

Hodges

Erikstad

Reiertsen

2022

Ecol Sol and Evidence

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The conservation of seabirds is increasingly important for their role as indicator species of ocean ecosystems, which are predicted to experience increasing levels of exploitation this century. Safeguarding these ecosystems will require predictive, spatial studies of seabird foraging hotspots. Current research on seabird foraging hotspots has established a significant relationship between probability of presence and several environmental variables, including Sea Surface Temperature (SST). However, inter‐annual, basin‐wide variation has the potential to invalidate these models, which depend on seasonal mesoscale variability. In this study, we present a novel solution to predict presence from spatially and temporally variable environmental predictors, while reducing the influence of large‐scale basin‐wide variation. We model the Maximum Entropy (MaxENT) Model‐derived relationship between Standardized Monthly SST (StdSST) and Habitat Suitability using Gaussian curve models, and then apply these models to independent StdSST data to produce heatmaps of predicted seabird presence. In this study, we demonstrate StdSST to be a functional environmental predictor of seabird presence, within a Gaussian curve model framework. We demonstrate accurate predictions of the model's training data and of independent seabird presence data to a high degree of accuracy (area under the receiver operator characteristic curve > 0.65) for four species of Auk: Common Guillemots (Uria aalge), Razorbills (Alca torda), Atlantic Puffins (Fratercula arctica) and Brunnich's Guillemots (Uria lomvia). We believe that the methodology we have developed and tested in this study can be used to guide ecosystem management practices by converting coupled‐climate model predictions into predictions of future presence based on Habitat Suitability for the species, allowing us to consider the possible effects of climate change and yearly variation of SST on foraging seabird hotspots in the Barents Sea.

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Using modelled prey to predict the distribution of a highly mobile marine mammal

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 20 publications

References 65 publications

Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?

Towards a better characterisation of deep-diving whales’ distributions by using prey distribution model outputs?

Key questions in marine mammal bioenergetics

Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea

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