Biology subjects

Conn, P. B.

Publications and source records attributed to Conn, P. B..

5 recordsLinked to original sources

bioRxiv2024

Simulation-based inference for close-kin mark-recapture: implications for small populations and nonrandom mating

Close-kin mark-recapture (CKMR) uses data on the frequency of kin pair relationships (e.g. parent-offspring, half siblings) in genetic samples from animal populations to estimate parameters such as abundance and adult survival probability. To date, most applications of CKMR have relied on a pseudo-likelihood approximation where pairwise comparisons of relatedness are assumed to be independent. This approximation works well when abundance is high and the sampled fraction of the population is low (as with many marine fisheries), but resultant estimators have inflated nominal confidence interval coverage when applied to small populations. Small populations and nonrandom mating structures also lead to problems with using second-order kin for estimation because one cannot typically differentiate half-siblings from other kin pair types like aunt-niece. In this paper, I assess the utility of approximate Bayesian computation (ABC) as a way of relaxing the pseudo-likelihood independence assumption. Under this approach, one only needs the ability to simulate population and sampling dynamics and to summarize resulting statistics in an informative way (e.g., number of kin pairs of different types). After exploring bias and interval coverage in several simulation studies, I illustrate these procedures on CKMR data from a Canadian caribou population. I show that ABC substantially improves interval coverage, and allows inference for difficult biologies where it would be difficult to calculate the analytical probabilities necessary for a binomial pseudo-likelihood. Simulation-based approaches to inference have great potential for expanding the application of CKMR to small populations and for breeding dynamics that are difficult to model.

ecology↗

bioRxiv2024

Abundance and distribution of ringed and bearded seals in the Chukchi Sea: a reference for future trends

Ringed (Pusa hispida) and bearded (Erignathus barbatus) seals are vulnerable to decreasing sea ice habitat in the rapidly warming Arctic. In April and May of 2016, we conducted an aerial survey over the ice-covered areas of the Chukchi Sea using thermal and color cameras to detect and count these seals on sea ice. We related the seal counts to environmental variables, and used the relationships to estimate the species distributions and abundance throughout the Chukchi Sea. We accounted for incomplete detection due to seals missed by sensors or image processing errors, behavioral responses to aircraft, or incomplete availability (i.e. seals that are in water or in snow dens on the ice, called lairs). For the latter, we used wet/dry records from satellite-linked bio-loggers, and remotely sensed snow melt dates to estimate the proportion of ringed seal individuals that were visible on ice for each day and location surveyed. To our knowledge, this is the first study where use of lairs by ringed seals has been formally addressed while estimating abundance from aerial surveys. Ringed seal abundance was estimated as [Formula] = 592,577 (95% CI: 478,448-733,929), with highest densities near Kotzebue Sound, Alaska, USA. Bearded seal abundance was estimated as [Formula] = 147,421 (95% CI: 114,155-190,380), with highest densities in broken pack ice near Bering Strait. The influence of environmental variables, such as snow depth and ice type, was consistent with prior studies of the species natural history, particularly ringed seals preference for snow of adequate depth for lairs. Our study provides the first comprehensive abundance estimates for ringed and bearded seals in the Chukchi Sea and establishes a reference for monitoring how their populations respond to Arctic warming.

ecology↗

bioRxiv2022

Modeling vital rates and age-sex structure of Pacific arctic phocids: influence on aerial survey correction factors

To estimate abundance, surveys of marine mammals often rely on samples of satellite-tagged individuals to correct counts for the proportion of animals that are unavailable to be detected. However, naive application of this correction relies on the key assumption that availability of the tagged sample resembles that of the population. Here, we show how matrix population models can be used to estimate the stable stage structure of bearded seals (Erignathus barbatus), ribbon seals (Histriophoca fasciata), ringed seals (Pusa hispida), and spotted seals (Phoca largha) in the Bering and Chukchi Seas, and how these proportions can be used to adjust aerial survey correction factors so that they represent population-level availability. We find that correction factors ignoring age-sex composition can positively bias spotted seal abundance by an average of 13% and negatively bias ribbon seal abundance by an average of 5%. Note that we did not examine potential bias for bearded or ringed seals due to low sample sizes; as such, we urge caution in interpretation of abundance estimates for these species.

zoology↗

bioRxiv2022

Haul-out behavior and aerial survey detectability of seals in the Bering and Chukchi seas

Ice-associated seals rely on sea ice for a variety of activities, including pupping, breeding, molting, and resting. In the Arctic, many of these activities occur in spring (April - June) as sea ice begins to melt and retreat northward. Rapid acceleration of climate change in Arctic ecosystems is therefore of concern as the quantity and quality of suitable habitat is forecast to decrease. Improved estimates of seal population abundance are needed to properly monitor the impacts of these changes over time. In this paper, we use hourly percent-dry data from satellite-linked bio-loggers deployed between 2005 and 2021 to quantify the proportion of seals hauled out on ice. This information is needed to accurately estimate abundance from aerial survey counts of ice-associated seals (i.e., to correct for the proportion of animals that are in the water while surveys are conducted). In addition to providing essential data for survey availability calculations, our analysis also provides insights into the seasonal timing and environmental factors affecting haul-out behavior by ice-associated seals. We specifically focused on bearded (Erignathus barbatus), ribbon (Histriophoca fasciata), and spotted seals (Phoca largha) in the Bering and Chukchi seas. Because ringed seals (Phoca (pusa) hispida) can be out of the water but hidden from view in snow lairs analysis of their availability to surveys requires special consideration; therefore,they were not included in this analysis. Using generalized linear mixed pseudo-models to properly account for temporal autocorrelation, we fit models with covariates of interest (e.g., day-of-year, solar hour, age-sex class, wind speed, barometric pressure, temperature, precipitation) to examine their ability to explain variation in haul-out probability. We found evidence for strong diel and within-season patterns in haul-out behavior, as well as strong weather effects (particularly wind and temperature). In general, seals were more likely to haul out on ice in the middle of the day and when wind speed was low and temperatures were higher. Haul-out probability increased through March and April, peaking in May and early June before declining again. The timing and frequency of haul-out events also varied based on species and age-sex class. For ribbon and spotted seals, models with year effects were highly supported, indicating that the timing and magnitude of haul-out behavior varied among years. However, we did not find broad evidence that haul-out timing was linked to annual sea-ice extent. Our analysis emphasizes the importance of accounting for seasonal and temporal variation in haul-out behavior, as well as associated environmental covariates, when interpreting the number of seals counted in aerial surveys.

ecology↗

bioRxiv2016

Confronting preferential sampling in wildlife surveys: diagnosis and model-based triage

SummaryO_LIWildlife surveys are often used to estimate the density, abundance, or distribution of animal populations. Recently, model-based approaches to analyzing survey data have become popular because one can more readily accommodate departures from pre-planned survey routes and construct more detailed maps than one can with design-based procedures.\nC_LIO_LISpecies distribution models fitted to wildlife survey data often make the implicit assumption that locations chosen for sampling and animal abundance at those locations are conditionally independent given modeled covariates. However, this assumption is likely violated in many cases when survey effort is non-randomized, leading to preferential sampling.\nC_LIO_LIWe develop a hierarchical statistical modeling framework for detecting and alleviating the biasing effects of preferential sampling in species distribution models fitted to count data. The approach works by jointly modeling wildlife state variables and the locations selected for sampling, and specifying a dependent correlation structure between the two models.\nC_LIO_LIUsing simulation, we show that moderate levels of preferential sampling can lead to large (e.g. 40%) bias in estimates of animal density, and that our modeling approach can considerably reduce this bias.\nC_LIO_LIWe apply our approach to aerial survey counts of bearded seals (Erignathus barbatus) in the eastern Bering Sea. Models that included a preferential sampling effect led to lower estimates of abundance than models without, but the effect size of the preferential sampling parameter decreased in models that included explanatory environmental covariates.\nC_LIO_LIWhen wildlife surveys are conducted without a well-defined sampling frame, ecologists should recognize the potentially biasing effects of preferential sampling. Joint models, such as those described in this paper, can be used to test and correct for such biases. Predictive covariates are also useful for bias reduction, but ultimately the best way to avoid preferential sampling bias is to incorporate design-based principles such as randomization and/or systematic sampling into survey design.\nC_LI

ecology↗