Environmental drivers of humpback whale foraging behavior in the remote Southern Ocean

Riekkola, Leena; Andrews‐Goff, Virginia; Friedlaender, Ari S.; Constantine, Rochelle; Zerbini, Alexandre N.

doi:10.1016/j.jembe.2019.05.008

Cited by 34 publications

(57 citation statements)

References 109 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, using design-based estimators, such the DSM technique applied here invaluable. Satellite tracking of humpback whales in the West Antarctic Peninsula feeding grounds has revealed that they travel large distances of 17-75 km/day (Dalla Rosa, Secchi, Maia, Zerbini, & Heide-Jørgensen, 2008) and recent studies from animals tagged north of the Balleny's at the Kermadec Islands show similar results (Riekkola et al, 2019). Given that the Balleny Islands survey area is only approximately 150 km in both This article is protected by copyright.…”

Section: Limitations and Future Directionsmentioning

confidence: 84%

“…Due to the remote location of the Balleny Islands it is financially and logistically difficult to conduct long term vessel surveys to assess the role that this feeding ground plays in E1 humpback whale feeding strategies. However, with recent technical advancements it becomes possible to count whales in geographically isolated locations using unmanned aerial surveys (Linchant, Lisein, Semeki, Lejeune, & Vermeulen, 2015), gliders (Baumgartner et al, 2013), fixed passive acoustic sensors (Marques, Thomas, Ward, DiMarzio, & Tyack, 2009), high resolution satellite imagery (Fretwell, Staniland, & Forcada, 2014) and of course satellite tagging (Andrews-Goff et al, 2018;Riekkola, Andrews-Goff, Friedlaender, Constantine, & Zerbini, 2019).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

A Southern Ocean archipelago enhances feeding opportunities for a krill predator

Harrison

Goetz

Cox

et al. 2019

Marine Mammal Science

View full text Add to dashboard Cite

show abstract

Section: Limitations and Future Directionsmentioning

confidence: 84%

Section: Limitations and Future Directionsmentioning

confidence: 99%

A Southern Ocean archipelago enhances feeding opportunities for a krill predator

Harrison

Goetz

Cox

et al. 2019

Marine Mammal Science

View full text Add to dashboard Cite

show abstract

“…We compiled published and unpublished satellite tracking data from 378 individual humpback whales, totaling 291,628 location records. Argos satellite-linked telemetry tags were deployed on humpback whales in their breeding areas and Antarctic foraging areas from 2002-2018 [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] (Supplementary Table S1). Adult individuals were selected for tagging in these study areas indiscriminately; specific individuals were not targeted.…”

Section: Whale Tracking Datamentioning

confidence: 99%

“…In the Southern Hemisphere, seven breeding populations ("breeding stocks" A-G) [30] from discrete low-latitude winter breeding areas in the Atlantic, Indian, and Pacific Oceans migrate to summer foraging areas in the Southern Ocean around Antarctica. Published humpback whale tracking data for the Southern Ocean [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] (suggest that there is region-specific habitat selection in Southern Ocean humpback whales. This case thus presents a good scenario for testing how region-specific habitat selection patterns can be included in large-scale predictions.…”

Section: Introductionmentioning

confidence: 99%

Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales

et al. 2021

Self Cite

View full text Add to dashboard Cite

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection.

show abstract

“…The likelihood of area-restricted search behaviour in summer and winter covaried with ice concentration variability (Figures 5, 6), which serves as a proxy for sea-ice edge zones and accessibility of ice-covered areas (Wege et al, 2020). The sea-ice edge zones are productive areas due to the input of nutrients in the water column as the sea ice melts, which promote phytoplankton blooms which in turn trigger grazers and other mid-level and higher-order predators to aggregate to forage (Nicol, 2006;Arrigo et al, 2008;Riekkola et al, 2019). The thick multi-year pack-ice present in the Weddell Sea and an increased swimming distance to the open ocean likely makes the southern Weddell Sea an unsuitable habitat for Ross seals, which would explain the low habitat suitability within the Weddell Sea embayment year-round (Figure 3).…”

Section: Distribution and Dietmentioning

confidence: 99%

Distribution and Habitat Suitability of Ross Seals in a Warming Ocean

et al. 2021

View full text Add to dashboard Cite

Understanding the determinants of poorly studied species’ spatial ecology is fundamental to understanding climate change impacts on those species and how to effectively prioritise their conservation. Ross seals (Ommatophoca rossii) are the least studied of the Antarctic pinnipeds with a limited knowledge of their spatial ecology. We present the largest tracking study for this species to date, create the first habitat models, and discuss the potential impacts of climate change on their preferred habitat and the implications for conservation. We combined newly collected satellite tracking data (2016–2019: n = 11) with previously published data (2001: n = 8) from the Weddell, King Haakon VII and Lazarev seas, Antarctica, and used 16 remotely sensed environmental variables to model Ross seal habitat suitability by means of boosted regression trees for summer and winter, respectively. Five of the top environmental predictors were relevant in both summer and winter (sea-surface temperature, distance to the ice edge, ice concentration standard deviation, mixed-layer depth, and sea-surface height anomalies). Ross seals preferred to forage in waters ranging between −1 and 2°C, where the mixed-layer depth was shallower in summer and deeper in winter, where current speeds were slower, and away from the ice edge in the open ocean. Receding ice edge and shoaling of the mixed layer induced by climate change may reduce swimming distances and diving depths, thereby reducing foraging costs. However, predicted increased current speeds and sea-surface temperatures may reduce habitat suitability in these regions. We suggest that the response of Ross seals to climate change will be regionally specific, their future success will ultimately depend on how their prey responds to regional climate effects and their own behavioural plasticity.

show abstract

Environmental drivers of humpback whale foraging behavior in the remote Southern Ocean

Cited by 34 publications

References 109 publications

A Southern Ocean archipelago enhances feeding opportunities for a krill predator

A Southern Ocean archipelago enhances feeding opportunities for a krill predator

Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales

Distribution and Habitat Suitability of Ross Seals in a Warming Ocean

Contact Info

Product

Resources

About