Animals that display plasticity in behavioral, ecological, and morphological traits are better poised to cope with environmental disturbances. Here, we examined individual plasticity and intraspecific variation in the morphometrics, movement patterns, and dive behavior of an enigmatic apex predator, the leopard seal ( Hydrurga leptonyx ). Satellite/GPS tags and time-depth recorders were deployed on 22 leopard seals off the Western Antarctic Peninsula. Adult female leopard seals were significantly larger (454±59 kg) and longer (302±11 cm) than adult males (302±22 kg, 276±11 cm). As females were 50% larger than their male counterparts, leopard seals are therefore one of the most extreme examples of female-biased sexual size dimorphism in marine mammals. Female leopard seals also spent more time hauled-out on land and ice than males. In the austral spring/summer, three adult female leopard seals hauled-out on ice for 10+ days, which likely represent the first satellite tracks of parturition and lactation for the species. While we found sex-based differences in morphometrics and haul-out durations, other variables, including maximum distance traveled and dive parameters, did not vary by sex. Regardless of sex, some leopard seals remained in near-shore habitats, traveling less than 50 kilometers, while other leopard seals traveled up to 1,700 kilometers away from the tagging location. Overall, leopard seals were short (3.0±0.7 min) and shallow (29±8 m) divers. However, within this general pattern, some individual leopard seals primarily used short, shallow dives, while others switched between short, shallow dives and long, deep dives. We also recorded the single deepest and longest dive made by any leopard seal—1, 256 meters for 25 minutes. Together, our results showcased high plasticity among leopard seals tagged in a single location. These flexible behaviors and traits may offer leopard seals, an ice-associated apex predator, resilience to the rapidly changing Southern Ocean.
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Foraging dive frequency predicts body mass gain in the Adélie penguin
Abstract Quantifying food intake in wild animals is crucial to many ecological and evolutionary questions, yet it can be very challenging, especially in the marine environment. Because foraging behavior can be inferred from dive recordings in many marine creatures, we hypothesized that specific behavioral dive variables can indicate food intake. To test this hypothesis, we attached time-depth recorders to breeding Adélie penguins also implanted with RFID tags that crossed a weighbridge as they traveled to and from the ocean to feed their chicks. The weighbridge reported how much mass the penguin had gained during a foraging trip. The variables that explained a significant amount of the change in body mass while at sea were the number of foraging dives per hour (46%) and the number of undulations per hour (12%). Most importantly, every increment of 1 in the rate of foraging dives per hour equated to a penguin gaining an average 170 g of mass, over the course of a 6–60 h foraging trip. These results add to a growing understanding that different metrics of foraging success are likely appropriate for different species, and that assessing the types and frequencies of dives using time-depth recorders can yield valuable insights.
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- Award ID(s):
- 1935870
- NSF-PAR ID:
- 10357862
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Vertical movements can expose individuals to rapid changes in physical and trophic environments—for aquatic fauna, dive profiles from biotelemetry data can be used to quantify and categorize vertical movements. Inferences on classes of vertical movement profiles typically rely on subjective summaries of parameters or statistical clustering techniques that utilize Euclidean matching of vertical movement profiles with vertical observation points. These approaches are prone to subjectivity, error, and bias. We used machine learning approaches on a large dataset of vertical time series (
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-021-02451-4
