Biology subjects

Linden, D. W.

Publications and source records attributed to Linden, D. W..

6 recordsLinked to original sources

bioRxiv2024

Using known births to account for delayed marking in population estimation of North Atlantic right whales

Population estimation using capture-recapture modeling typically requires that individuals are identifiable by unique marks. North Atlantic right whales (Eubalaena glacialis) can be identified by natural callosity patterns on their heads that are established nearly a year after birth, which has facilitated population monitoring using extensive aerial surveys. A well-maintained catalog of individual sightings has been used to annually estimate population size with a Jolly-Seber (J-S) model using a Bayesian state-space framework. Given that young animals cannot enter the catalog before an established callosity pattern, the terminal year population estimate never includes new calves despite breeding area surveys that provide a nearly complete census of births. Here, I illustrate a simple modification to the J-S likelihood whereby the number of expected entrants is a function of known births and a parameter representing initial offspring mortality. A simulation study was used as a proof of concept and indicated increased accuracy and precision of population estimates. The birth-integrated J-S model had more accurate terminal-year estimates of right whale population size that remained consistent during subsequent model fitting to additional years of sightings data. While the bias corrections were fairly small (5%) given low per capita calving rates, the demonstrated improvement in accuracy will be helpful to the conservation and management processes for this endangered species. Integrated modeling approaches make better use of available data and can improve inferences on population dynamics.

ecology↗

bioRxiv2024

A multistate capture-recapture model to estimate reproduction of North Atlantic right whales

The recent steep decline of critically endangered North Atlantic right whales (Eubalaena glacialis) can be attributed to high mortality combined with low reproduction. While the former is a clear result of anthro-pogenic activity, the latter involves more complexity. Evidence suggests that both short-term fluctuations in prey availability and long-term decline in health are responsible for depressed right whale calving rates. To facilitate an assessment of extinction risk, we developed a multistate capture-recapture model that estimated the probability of calving using extensive sightings data from 1990-2019. The model estimated sub-lethal effects of severe injury on calving probability, and modeled temporal variability in calving as related to indices of prey availability (Calanus spp. biomass) and an apparent regime shift. The average annual probability of calving for known-breeding females, given average prey conditions, decreased from 0.217 [0.162, 0.281] to 0.142 [0.067, 0.252] after the 2010 regime shift. The model indicated strong evidence of a relationship between calving probability and the prey index from the eastern Gulf of Maine, though this relationship effectively disappeared after 2010; moderate evidence for a relationship with prey from the southwest Gulf of St. Lawrence remained. Weak evidence of reduced calving probability due to severe injury resulted from low sample sizes, given increased mortality for individuals observed with severe injuries. The regime effect is hypothesized to be capturing a long-term decline in health due to a combination of decreasing habitat quality resulting from climate change and potentially chronic sublethal injuries (e.g., entanglements). Our reproduction model provides demographic parameter estimates that can be used in population projections for North Atlantic right whales, though uncertainty remains in the mechanisms responsible for recent declines in calving.

ecology↗

bioRxiv2023

A multistate capture-recapture model to estimate cause-specific injury and mortality of North Atlantic right whales

Understanding the causes of mortality for a declining species is essential for developing effective conservation and management strategies, particularly when anthropogenic activities are the primary threat. Using a competing hazards framework allows for robust estimation of the cause-specific variation that may exist across multiple dimensions, such as time and individual. Here, we estimated cause-specific rates of severe injury and mortality for North Atlantic right whales (Eubalaena glacialis), a critically endangered species that is currently in peril due to human-caused interactions. We developed a multistate capture-recapture model that leveraged 30 years of intensive survey effort yielding sightings of individuals with injury assessments and necropsies of carcass recoveries. We examined variation in the hazard rates of severe injury and mortality due to entanglements in fishing gear and vessel strikes as explained by year and the age and reproductive status of the individual. We found strong evidence for increased rates of severe entanglement injuries after 2013 and for females with calves, with consequently higher marginal mortality. The model results also suggested that despite vessel strikes causing a lower average rate of severe injuries, the higher mortality rate conditional on injury results in significant total mortality risk, particularly for females resting from a recent calving event. Large uncertainty in the estimation of carcass recovery rate for vessel strike deaths permeated into the apportionment of mortality causes. The increased rates of North Atlantic right whale mortality in the last decade, particularly for reproducing females, puts the species at risk of severe decline. By apportioning the human-caused threats using a quantitative approach with estimation of relevant uncertainty, this work can guide development of conservation and management strategies to facilitate species recovery.

ecology↗

bioRxiv2020

Spatial Proximity Moderates Genotype Uncertainty in Genetic Tagging Studies

Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower quality samples cannot be used, and they do not allow for errors in individual identification. We introduce the Genotype Spatial Partial Identity Model (SPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers (Pekania pennanti) in which 37% of samples were originally discarded because of uncertainty in individual identity. The Genotype SPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected 3 errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63% and 42%, respectively, using 3 PCRs per genetic sample. Further, the simulations showed that the Genotype SPIM model parameters are identifiable with only one PCR per sample, and that accuracy and precision are relatively insensitive to the number of PCRs for high quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high quality samples, but when using the Genotype SPIM, genotyping protocols could be more efficient by devoting more resources to low quality samples. SignificanceWe present a new statistical framework for the estimation of animal demographic parameters, such as abundance, density, and growth rate, from noninvasive genetic samples (e.g., hair, scat). By integrating a genetic classification model with a spatial population model, we show that accounting for spatial proximity of samples reduces genotype uncertainty and improves parameter estimation. Our method produces a fundamentally different approach to genetic capture-recapture by sharing information between the normally disjunct steps of assigning individual identities to genetic samples and modeling population processes. Further, it leads to more efficient protocols for processing genetic samples, which can lower project costs and expand opportunities for applying noninvasive genetics to conservation and management problems.

ecology↗

bioRxiv2019

Challenges and opportunities in population monitoring of cheetahs

Population monitoring is key to wildlife conservation and management but is challenging at the spatial and temporal extents necessary for understanding changes. Non-invasive survey methods and spatial capture-recapture (SCR) models have revolutionized wildlife monitoring by providing the means to more easily acquire data at large scales and the framework to generate spatially-explicit predictions, respectively. Despite the opportunities for improved monitoring, challenges can remain in the study design and model fitting phases of an SCR approach. Here, we used a search-encounter design with multi-session SCR models to collect spatially-indexed photographs and estimate the changes in density of cheetahs between 2005 and 2013-2016 in the Masai Mara National Reserve (MMNR) in southwestern Kenya. Our SCR models of cheetah encounters suggested little change in cheetah density from 2005 to 2013-2016, though there was some evidence that density fluctuated annually in the MMNR. The sampling period length (5 vs. 10 months) and timing (early, late, full year) over which spatial encounters were included in the modeling did not substantially alter inferences about density when sample sizes were adequate (>20 spatially distinct encounters). We estimated an average cheetah density of ~1.2 cheetahs/100 km2, consistent with the impression that the MMNR provides important habitat for cheetahs in Africa. During most years and seasonal periods, the spatial distribution of vegetation greenness (a proxy for ungulate habitat quality) accounted for important variation in encounter rates. The search-encounter design used here could be applied to other regions for the purposes of cheetah monitoring. While snap-shot estimates of population size across time are useful for wildlife monitoring, open population models could identify the mechanisms behind changes and further facilitate better conservation and management decision making.

ecology↗

bioRxiv2017

Integrating Telemetry Data Into Spatial Capture-Recapture Modifies Inferences On Multi-Scale Resource Selection

Estimating population size and resource selection functions (RSFs) are common approaches in applied ecology for addressing wildlife conservation and management objectives. Traditionally such approaches have been undertaken separately with different sources of data. Spatial capture-recapture (SCR) provides a framework for jointly estimating density and multi-scale resource selection, and data integration techniques provide opportunities for improving inferences from SCR models. Here we illustrate an application of integrated SCR-RSF modeling to a population of American marten (Martes americana) in alpine forests of northern New England. Spatial encounter data from camera traps were combined with telemetry locations from radio-collared individuals to examine how density and space use varied with spatial environmental features. We compared multi-model inferences between the integrated SCR-RSF model with telemetry and a standard SCR model with no telemetry. The integrated SCR-RSF model supported more complex relationships with spatial variation in third-order resource selection (i.e., individual space use), including selection for areas with shorter distances to mixed coniferous forest and rugged terrain. Both models indicated increased second-order selection (i.e., density) for areas close to mixed coniferous forest, while the integrated SCR-RSF model had a lower effect size due to modulation from spatial variability in space use. Our application of the integrated SCR-RSF model illustrates the improved inferences from spatial encounter data that can be achieved from integrating auxiliary telemetry data. Integrated modeling allows ecologists to join empirical data to ecological theory using a robust quantitative framework to better address conservation and management objectives.

ecology↗