Geology topics

Peter L. Boveng

Publications and source records attributed to Peter L. Boveng.

4 recordsLinked to original sources

USGS2022

Marine mammal hotspots across the circumpolar Arctic

Aim Identify hotspots and areas of high species richness for Arctic marine mammals. Location Circumpolar Arctic. Methods A total of 2115 biologging devices were deployed on marine mammals from 13 species in the Arctic from 2005 to 2019. Getis-Ord G i * hotspots were calculated based on the number of individuals in grid cells for each species and for phylogenetic groups (nine pinnipeds, three cetaceans, all species) and areas with high species richness were identified for summer (Jun-Nov), winter (Dec-May) and the entire year. Seasonal habitat differences among species’ hotspots were investigated using Principal Component Analysis. Results Hotspots and areas with high species richness occurred within the Arctic continental-shelf seas and within the marginal ice zone, particularly in the “Arctic gateways” of the north Atlantic and Pacific oceans. Summer hotspots were generally found further north than winter hotspots, but there were exceptions to this pattern, including bowhead whales in the Greenland-Barents Seas and species with coastal distributions in Svalbard, Norway and East Greenland. Areas with high species richness generally overlapped high-density hotspots. Large regional and seasonal differences in habitat features of hotspots were found among species but also within species from different regions. Gap analysis (discrepancy between hotspots and IUCN ranges) identified species and regions where more research is required. Main conclusions This study identified important areas (and habitat types) for Arctic marine mammals using available biotelemetry data. The results herein serve as a benchmark to measure future distributional shifts. Expanded monitoring and telemetry studies are needed on Arctic species to understand the impacts of climate change and concomitant ecosystem changes (synergistic effects of multiple stressors). While efforts should be made to fill knowledge gaps, including regional gaps and more complete sex and age coverage, hotspots identified herein can inform management efforts to mitigate the impacts of human activities and ecological changes, including creation of protected areas.

Diversity and Distributions↗

USGS2021

Re-examination of population structure in Arctic ringed seals using DArTseq genotyping

Although Arctic ringed seals Phoca hispida hispida are currently abundant and broadly distributed, their numbers are projected to decline substantially by the year 2100 due to climate warming. While understanding population structure could provide insight into the impact of environmental changes on this subspecies, detecting demographically important levels of exchange can be difficult in taxa with high abundance. We used a next-generation sequencing approach (DArTseq) to genotype ~5700 single nucleotide polymorphisms in 79 seals from 4 Pacific Arctic regions. Comparison of the 2 most geographically separated strata (eastern Bering vs. northeastern Chukchi-Beaufort Seas) revealed a statistically significant level of genetic differentiation ( F ST = 0.001, p = 0.005) that, while small, was 1 to 2 orders of magnitude greater than expected based on divergence estimated for similarly sized populations connected by low (1% yr -1 ) dispersal. A relatively high proportion (72 to 88%) of individuals within these strata could be genetically assigned to their stratum of origin. These results indicate that demographically important structure may be present among Arctic ringed seals breeding in different areas, increasing the risk that declines in the number of seals breeding in areas most negatively affected by environmental warming could occur.

Alaska↗

USGS2018

A multi-species synthesis of satellite telemetry data in the Pacific Arctic (1987–2015): Overlap of marine mammal distributions and core use areas

We collated available satellite telemetry data for six species of ice-associated marine mammals in the Pacific Arctic: ringed seals ( Pusa hispida ; n = 118), bearded seals( Erignathus barbatus, n = 51), spotted seals ( Phoca largha , n = 72), Pacific walruses ( Odobenus rosmarus divergens, n = 389); bowhead whales ( Balaena mysticetus , n = 46), and five Arctic and sub-arctic stocks of beluga whales ( Delphinapterus leucas , n = 103). We also included one seasonal resident, eastern North Pacific gray whales ( Eschrichtius robustus , n = 12). This review summarized the distribution of daily locations from satellite-linked transmitters during two analysis periods, summer (May–November) and winter (December–April), and then examined the overlap among species. Six multi-species core use areas were identified during the summer period: 1) Chukotka/Bering Strait; 2) Norton Sound; 3) Kotzebue Sound; 4) the northeastern Chukchi Sea; 5) Mackenzie River Delta/Amundsen Gulf; and 6) Viscount Melville Sound. During the winter period, we identified four multi-species core use areas: 1) Anadyr Gulf/Strait; 2) central Bering Sea; 3) Nunivak Island; and 4) Bristol Bay. During the summer period, four of the six areas were centered on the greater Bering Strait region and the northwestern coast of Alaska and included most of the species we examined. The two remaining summer areas were in the western Canadian Arctic and were largely defined by the seasonal presence of Bering-Chukchi-Beaufort stock bowhead whales and Eastern Beaufort Sea stock beluga whales, whose distribution overlapped during both summer and winter periods. During the winter period, the main multi-species core use area was located near the Gulf of Anadyr and extended northwards through Anadyr and Bering Straits. This area is contained within the Bering Sea “green belt”, an area of enhanced primary and secondary productivity in the Bering Sea. We also described available telemetry data and where they can be found as of 2017. These data are important for understanding ice-associated marine mammal movements and habitat use in the Pacific Arctic and should be archived, with appropriate metadata, to ensure they are available for future retrospective analyses.

Deep Sea Research Part II: Topical Studies in Ocea↗

USGS2015

Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts

Ecologists often fit models to survey data to estimate and explain variation in animal abundance. Such models typically require that animal density remains constant across the landscape where sampling is being conducted, a potentially problematic assumption for animals inhabiting dynamic landscapes or otherwise exhibiting considerable spatiotemporal variation in density. We review several concepts from the burgeoning literature on spatiotemporal statistical models, including the nature of the temporal structure (i.e., descriptive or dynamical) and strategies for dimension reduction to promote computational tractability. We also review several features as they specifically relate to abundance estimation, including boundary conditions, population closure, choice of link function, and extrapolation of predicted relationships to unsampled areas. We then compare a suite of novel and existing spatiotemporal hierarchical models for animal count data that permit animal density to vary over space and time, including formulations motivated by resource selection and allowing for closed populations. We gauge the relative performance (bias, precision, computational demands) of alternative spatiotemporal models when confronted with simulated and real data sets from dynamic animal populations. For the latter, we analyze spotted seal ( Phoca largha ) counts from an aerial survey of the Bering Sea where the quantity and quality of suitable habitat (sea ice) changed dramatically while surveys were being conducted. Simulation analyses suggested that multiple types of spatiotemporal models provide reasonable inference (low positive bias, high precision) about animal abundance, but have potential for overestimating precision. Analysis of spotted seal data indicated that several model formulations, including those based on a log-Gaussian Cox process, had a tendency to overestimate abundance. By contrast, a model that included a population closure assumption and a scale prior on total abundance produced estimates that largely conformed to our a priori expectation. Although care must be taken to tailor models to match the study population and survey data available, we argue that hierarchical spatiotemporal statistical models represent a powerful way forward for estimating abundance and explaining variation in the distribution of dynamical populations.

Ecological Monographs↗