ORCID Profile
0000-0002-7873-208X
Current Organisation
University of Tennessee College of Veterinary Medicine
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Publisher: Wiley
Date: 02-2023
DOI: 10.1002/ECE3.9790
Abstract: Information on resource use and trophic dynamics of marine predators is important for understanding their role in ecosystem functioning and predicting population‐level responses to environmental change. Where separate populations experience different local environmental conditions, geographic variability in their foraging ecology is often expected. Within populations, in iduals also vary in morphology, physiology, and experience, resulting in specialization in resource use. In this context, isotopic compositions of incrementally grown tissues such as keratinous hairs offer a valuable opportunity to study long‐term variation in resource and habitat use. We investigated the trophic ecology of female Cape fur seals ( Arctocephalus pusillus pusillus ) using carbon and nitrogen isotopic compositions of serially s led whiskers collected at four breeding sites along the coast of South Africa. Drawing on over 900 isotopic measurements, we assessed geographic variability in isotopic niche width between colonies and the degree of in idual specialization. We found slight, but clear geographic differences in isotopic ratios and isotopic niche widths, seemingly related to ecological setting, with niche widths being proportional to the area of available shelf and shelf‐slope habitat surrounding the colony. We further identified periodic oscillations in isotopic ratios, which likely reflect temporal patterns in foraging distribution and prey type, linked to shifts in the availability of prey resources and their interaction with constraints on in idual females throughout their breeding cycle. Finally, in idual specialization indices revealed that each of the study populations contain specialist in iduals that utilize only a small subset of the total population niche width. The degree of in idual specialization was, however, not consistent across colonies and may reflect an interactive influence between density‐dependent effects and habitat heterogeneity. Overall, this study provides important information on the trophic ecology of Cape fur seals breeding in South Africa and highlights the need to consider geographic and in idual variability when assessing the foraging ecology of marine predators.
Publisher: MDPI AG
Date: 25-05-2021
DOI: 10.3390/RS13112074
Abstract: 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.
Publisher: Wiley
Date: 24-08-2018
DOI: 10.1111/JFD.12701
Abstract: The aim of this study was to describe two epizootics of high mortalities from infection with Streptococcus agalactiae, occurring in captive rays held in a marine display aquarium in south-east Queensland, Australia, in 2009 and 2010. Five different species of rays were affected, including mangrove whiprays (Himantura granulata), estuary rays (Dasyatis fluviorum), eastern shovelnose rays (Aptychotrema rostrata), white-spotted eagle rays (Aetobatus narinari) and blue-spotted mask rays (Neotrygon kuhlii). This report describes the history of both epizootics including collection, quarantine and husbandry of rays, the disease epizootics, clinico-pathological features of the disease, antimicrobial therapy, autogenous vaccine production, and laboratory studies including clinical and histopathology, bacteriology, PCR, molecular serotyping and sequencing of the bacterium S. agalactiae.
Location: United States of America
No related grants have been discovered for Simon Mduduzi Seakamela.