Irina Trukhanova - Academia.edu (original) (raw)
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Papers by Irina Trukhanova
<p>Note that these data do not include seals harvested in the west coast of Kamchatka (unde... more <p>Note that these data do not include seals harvested in the west coast of Kamchatka (under jurisdiction of Kamchatrybvod). Abundance estimates are based on available population aerial survey results.</p
<p>Note that the Y-axis scales vary with species.</p
Marine Pollution Bulletin
<p>The boxes show the number and range of 50% of observations in each group; bold horizonta... more <p>The boxes show the number and range of 50% of observations in each group; bold horizontal lines in boxes indicate median number of seals caught, dots indicate outliers in the data. The left Y-axis is for the GAM smooths for each year and multi-year mean. Note that both Y-axis scales vary with species.</p
European Whales, Dolphins, and Porpoises, 2020
Seals, polar bears, and polar bear track counts were summarized from U.S. and Russian survey flig... more Seals, polar bears, and polar bear track counts were summarized from U.S. and Russian survey flights over the Chukchi Sea during aerial surveys in April and May of 2016. Seal detections were made using using infrared imagery, with corresponding digital photographs providing information on species identification, where possible. Polar bear detections were made using a variety of methods, including infrared (thermal) detections, in situ observations by human observers, and through post hoc searching of photographs. These observations are summarized within a discretized study area, where grid cells are approximately 625 km^2, with the intention of estimating the distribution and abundance of all three species (bearded seals, Erignathus barbatus; ringed seals, Pusa hispida; and polar bears, Ursus maritimus). Data are provided in 5 .csv (comma delimited text) files, summarizing (1) the location of grid cells, and some associated environmental and physiographic covariates (CHESS_grid_axio...
Knowledge of life‐history parameters is frequently lacking in many species and populations, often... more Knowledge of life‐history parameters is frequently lacking in many species and populations, often because they are cryptic or logistically challenging to study, but also because life‐history parameters can be difficult to estimate with adequate precision. We suggest using hierarchical Bayesian analysis (HBA) to analyze variation in life‐history parameters among related species, with prior variance components representing shared taxonomy, phenotypic plasticity, and observation error. We develop such a framework to analyze U‐shaped natural mortality patterns typical of mammalian life history from a variety of sparse datasets. Using 39 datasets from seals in the family Phocidae, we analyzed 16 models with different formulations for natural morality, specifically the amount of taxonomic and data‐level variance components (subfamily, species, study, and dataset levels) included in mortality hazard parameters. The highest‐ranked model according to DIC included subfamily‐, species‐, and dataset‐level parameter variance components and resulted in typical U‐shaped hazard functions for the 11 seal species in the study. Species with little data had survival schedules shrunken to the mean. We suggest that evolutionary and population ecologists consider employing HBA to quantify variation in life‐history parameters. This approach can be useful for increasing the precision of estimates resulting from a collection of (often sparse) datasets, and for producing prior distributions for populations missing life‐history data
PLOS ONE, 2021
Polar bears are of international conservation concern due to climate change but are difficult to ... more Polar bears are of international conservation concern due to climate change but are difficult to study because of low densities and an expansive, circumpolar distribution. In a collaborative U.S.-Russian effort in spring of 2016, we used aerial surveys to detect and estimate the abundance of polar bears on sea ice in the Chukchi Sea. Our surveys used a combination of thermal imagery, digital photography, and human observations. Using spatio-temporal statistical models that related bear and track densities to physiographic and biological covariates (e.g., sea ice extent, resource selection functions derived from satellite tags), we predicted abundance and spatial distribution throughout our study area. Estimates of 2016 abundance (N ^ *) ranged from 3,435 (95% CI: 2,300-5,131) to 5,444 (95% CI: 3,636-8,152) depending on the proportion of bears assumed to be missed on the transect line during Russian surveys (g(0)). Our point estimates are larger than, but of similar magnitude to, a r...
Conservation Biology, 2018
Izvestiya TINRO, 2019
An instrumental aerial survey was conducted in the Russian part of the Chukchi Sea and the easter... more An instrumental aerial survey was conducted in the Russian part of the Chukchi Sea and the eastern East-Siberian Sea in the spring of 2016 to investigate new technical capabilities for estimating abundance and distribution of ringed and bearded seals on the spring ice. Density of both species decreased with distance to the mainland; the largest concentrations of ringed seals were detected in coastal waters, including the Koluchinskaya and Chaunskaya Bays. Taking into account the portion of seals in the water (on average 32 %) and the portion of seals that were disturbed by the aircraft engine noise and dove (on average 30.2 % of ringed seals and 5.9 % of bearded seals), the number of ringed seals in the surveyed area was estimated as 50,839 (СI 95 %: 25,400–73,859; CV = 23.8 %), and the number of bearded seals as 14,590 (CI 95 %: 6,404–24,560; CV = 31.1 %). These estimates are considered to be biased low, primarily due to asynchronic collapse of the ringed seal snow lairs in differe...
Conservation Biology, 2018
Journal of Urban Ecology, 2018
Russian Journal of Theriology, 2013
Journal of Tropical Ecology, 2018
<p>Note that these data do not include seals harvested in the west coast of Kamchatka (unde... more <p>Note that these data do not include seals harvested in the west coast of Kamchatka (under jurisdiction of Kamchatrybvod). Abundance estimates are based on available population aerial survey results.</p
<p>Note that the Y-axis scales vary with species.</p
Marine Pollution Bulletin
<p>The boxes show the number and range of 50% of observations in each group; bold horizonta... more <p>The boxes show the number and range of 50% of observations in each group; bold horizontal lines in boxes indicate median number of seals caught, dots indicate outliers in the data. The left Y-axis is for the GAM smooths for each year and multi-year mean. Note that both Y-axis scales vary with species.</p
European Whales, Dolphins, and Porpoises, 2020
Seals, polar bears, and polar bear track counts were summarized from U.S. and Russian survey flig... more Seals, polar bears, and polar bear track counts were summarized from U.S. and Russian survey flights over the Chukchi Sea during aerial surveys in April and May of 2016. Seal detections were made using using infrared imagery, with corresponding digital photographs providing information on species identification, where possible. Polar bear detections were made using a variety of methods, including infrared (thermal) detections, in situ observations by human observers, and through post hoc searching of photographs. These observations are summarized within a discretized study area, where grid cells are approximately 625 km^2, with the intention of estimating the distribution and abundance of all three species (bearded seals, Erignathus barbatus; ringed seals, Pusa hispida; and polar bears, Ursus maritimus). Data are provided in 5 .csv (comma delimited text) files, summarizing (1) the location of grid cells, and some associated environmental and physiographic covariates (CHESS_grid_axio...
Knowledge of life‐history parameters is frequently lacking in many species and populations, often... more Knowledge of life‐history parameters is frequently lacking in many species and populations, often because they are cryptic or logistically challenging to study, but also because life‐history parameters can be difficult to estimate with adequate precision. We suggest using hierarchical Bayesian analysis (HBA) to analyze variation in life‐history parameters among related species, with prior variance components representing shared taxonomy, phenotypic plasticity, and observation error. We develop such a framework to analyze U‐shaped natural mortality patterns typical of mammalian life history from a variety of sparse datasets. Using 39 datasets from seals in the family Phocidae, we analyzed 16 models with different formulations for natural morality, specifically the amount of taxonomic and data‐level variance components (subfamily, species, study, and dataset levels) included in mortality hazard parameters. The highest‐ranked model according to DIC included subfamily‐, species‐, and dataset‐level parameter variance components and resulted in typical U‐shaped hazard functions for the 11 seal species in the study. Species with little data had survival schedules shrunken to the mean. We suggest that evolutionary and population ecologists consider employing HBA to quantify variation in life‐history parameters. This approach can be useful for increasing the precision of estimates resulting from a collection of (often sparse) datasets, and for producing prior distributions for populations missing life‐history data
PLOS ONE, 2021
Polar bears are of international conservation concern due to climate change but are difficult to ... more Polar bears are of international conservation concern due to climate change but are difficult to study because of low densities and an expansive, circumpolar distribution. In a collaborative U.S.-Russian effort in spring of 2016, we used aerial surveys to detect and estimate the abundance of polar bears on sea ice in the Chukchi Sea. Our surveys used a combination of thermal imagery, digital photography, and human observations. Using spatio-temporal statistical models that related bear and track densities to physiographic and biological covariates (e.g., sea ice extent, resource selection functions derived from satellite tags), we predicted abundance and spatial distribution throughout our study area. Estimates of 2016 abundance (N ^ *) ranged from 3,435 (95% CI: 2,300-5,131) to 5,444 (95% CI: 3,636-8,152) depending on the proportion of bears assumed to be missed on the transect line during Russian surveys (g(0)). Our point estimates are larger than, but of similar magnitude to, a r...
Conservation Biology, 2018
Izvestiya TINRO, 2019
An instrumental aerial survey was conducted in the Russian part of the Chukchi Sea and the easter... more An instrumental aerial survey was conducted in the Russian part of the Chukchi Sea and the eastern East-Siberian Sea in the spring of 2016 to investigate new technical capabilities for estimating abundance and distribution of ringed and bearded seals on the spring ice. Density of both species decreased with distance to the mainland; the largest concentrations of ringed seals were detected in coastal waters, including the Koluchinskaya and Chaunskaya Bays. Taking into account the portion of seals in the water (on average 32 %) and the portion of seals that were disturbed by the aircraft engine noise and dove (on average 30.2 % of ringed seals and 5.9 % of bearded seals), the number of ringed seals in the surveyed area was estimated as 50,839 (СI 95 %: 25,400–73,859; CV = 23.8 %), and the number of bearded seals as 14,590 (CI 95 %: 6,404–24,560; CV = 31.1 %). These estimates are considered to be biased low, primarily due to asynchronic collapse of the ringed seal snow lairs in differe...
Conservation Biology, 2018
Journal of Urban Ecology, 2018
Russian Journal of Theriology, 2013
Journal of Tropical Ecology, 2018