Geology topics

Daniel W. Linden

Publications and source records attributed to Daniel W. Linden.

8 recordsLinked to original sources

USGS2025

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

The recent steep decline of the endangered North Atlantic right whale Eubalaena glacialis can be attributed to high mortality combined with low reproduction. While the former is a clear result of anthropogenic 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 [95% CI: 0.162, 0.281] to 0.142 [95% CI: 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, although 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, although uncertainty remains in the mechanisms responsible for recent declines in calving.

Endangered Species Research↗

USGS2024

Quantifying uncertainty in anthropogenic causes of injury and mortality for an endangered baleen whale

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 in risk 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 temporal patterns 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, has been responsible for the severe decline in the species. 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. Our approach is relevant to other monitored populations where cause-specific injuries from multiple threats can be observed in live and dead individuals.

Ecosphere↗

USGS2024

Multistage time-to-event models improve survival inference by partitioning mortality processes of tracked organisms

Advances in tagging technologies are expanding opportunities to estimate survival of fish and wildlife populations. Yet, capture and handling effects could impact survival outcomes and bias inference about natural mortality processes. We developed a multistage time-to-event model that can partition the survival process into sequential phases that reflect the tagged animal experience, including handling and release mortality, post-release recovery mortality, and subsequently, natural mortality. We demonstrate performance of multistage survival models through simulation testing and through fish and bird telemetry case studies. Models are implemented in a Bayesian framework and can accommodate left, right, and interval censorship events. Our results indicate that accurate survival estimates can be achieved with reasonable sample sizes ( n ≈ 100 + ) "> 𝑛≈100+) and that multimodel inference can inform hypotheses about the configuration and length of survival stages needed to adequately describe mortality processes for tracked specimens. While we focus on survival estimation for tagged fish and wildlife populations, multistage time-to-event models could be used to understand other phenomena of interest such as migration, reproduction, or disease events across a range of taxa including plants and insects.

Scientific Reports↗

USGS2023

A management-focused population viability analysis for North Atlantic right whales

The North Atlantic right whale ( Eubalaena glacialis ) is among the most endangered whale species in the world and has been in decline since 2010. Considerable effort is directed toward its recovery by striving to remove threats. In this report, we describe the development of a population viability analysis for right whales that is designed to assess the current status, evaluate the contributions of various threats, and explore the management interventions needed to achieve recovery. The individual-based model that underlies this analysis accounts for age- and stagespecific survival and reproductive rates, the effects of severe injury from entanglement or vessel strike, and future changes in prey availability and accessibility. Several new or updated empirical analyses supplied parameter estimates, and parametric uncertainty was carefully incorporated into the model results. We find that under the status quo conditions of 2019, prior to the enactment of new regulations by the U.S. and Canada after 2020, the North Atlantic right whale population would be expected to continue to fall, with a median decline of 75% in 100 years (95% projection interval, –98% to +9% change) and a probability of falling below 50 proven females of 0.934 in 100 years. If the recently enacted regulations reduce entanglement risk by 25%, however, the population would be expected to decrease by 42% over 100 years (95% projection interval –92% to +154% change), with a risk of falling below 50 proven females in 100 years of 0.705. If, instead, the recently enacted regulations reduce entanglement risk by 50%, the population would be expected to increase by 52% in 100 years (95% projection interval –83% to +497% change), with a probability of falling below 50 proven females of 0.349. Of the 3 primary threats explored in this analysis, the risk of entanglement contributes the most to the long-term risk of quasi-extinction, followed closely by the risk of vessel strike, and much more distantly by a decrease in prey availability. In hypothetical scenarios that fully remove one threat at a time, removal of the entanglement threat alone reduces the probability of falling below 50 proven females in 100 years from 0.934 to 0.053; removal of the vessel strike threat alone reduces it to 0.343; and a return to higher prey conditions, but with both human-related threats still in place, reduces it to 0.875. We explored a wide range of management intervention scenarios that changed the rate of entanglement risk (e.g., endline reductions, closures, implementation of ropeless/on-demand gear); the effect of entanglement (through use of weak rope technology); the rate of vessel traffic increase over time; and the severity of vessel strike risk through speed restrictions. We found, for example, that reducing entanglement risk alone by 25% reduces the risk of quasi-extinction from 0.934 to 0.705; reducing vessel strike risk alone by 25% reduces the risk of quasi-extinction from 0.934 to 0.846; but the combination of reducing both entanglement risk and vessel strike risk by 25% reduces the risk of quasi-extinction to 0.528. This model and the results it produced are meant to represent an assessment of the current status of North Atlantic right whales using the best available scientific and commercial data and state-of-the-art analytical tools. Our knowledge of the future of the right whale population, however, has limitations. We have endeavored to fully incorporate uncertainty into this model, but there are many areas for continued improvement. We view this model as a living tool that can be improved, adapted, and extended as new data, new methods, and new questions arise.

NOAA Technical Memorandum↗

USGS2021

Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands

Forests are often affected by management that could influence demographics of breeding and post-breeding birds that reside within. Numerous studies have focused on immediate effects from management on wildlife soon after forestry treatment (e.g., 0–5 years), however, fewer studies have examined changes in focal species abundance over longer durations as a forest regenerates after disturbance. We examined how forest management influenced 18 conifer-associated birds during breeding and post-breeding over the forest regeneration period in a landscape dominated by forestry. To achieve this, we paired avian detection data from point count surveys in lowland conifer and mixed-wood forests with Bayesian distance-removal models and an information-theoretic framework. We estimated abundance and associations with seven common forestry treatment categories applied at the stand scale, years-since-harvest (YSH; 5–120+), and seven vegetation variables measured within stands. Forestry treatment categories and YSH were poor predictors of abundance, and none of the 14 species with good-fitting models had associations with these covariates. Twelve of 13 species with good-fitting models had important associations between abundance and vegetation variables. All vegetation variables were associated with abundance of some species, irrespective of the forestry treatment in which the site occurred, including spruce-fir tree composition (seven species), tree basal area (six species), midstory cover (five species), live crown ratio (three species), shrub cover (three species), tree diameter at breast height (two species), and shrub composition (one species). In a companion study, several species assemblages were associated with vegetation variables (i.e., spruce-fir tree composition, tree basal area, and tree diameter at breast height) that varied with YSH and forestry treatments, suggesting that some forestry treatments may indirectly influence avian abundance when certain vegetation outcomes are achieved. Our results suggest that managers should target species-specific vegetation outcomes rather than more broadly categorized forestry treatment types when managing for individual focal species because of large variations in vegetative outcomes across stands within a forest treatment category. Our study informs management and conservation of biodiversity in regions such as the Atlantic Northern Forest where commercial forestry is the dominant human land use.

Maine, New Hampshire, Vermont↗

USGS2020

Spatial capture–recapture with random thinning for unidentified encounters

Spatial capture–recapture (SCR) models have increasingly been used as a basis for combining capture–recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark–resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of “marked” and “unmarked” individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites. Here we describe a “random thinning” SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE. We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low‐density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear ( Ursus arctos ) density in Oriental Cantabrian Mountains (North Spain). Our model can improve density estimation for noninvasive sampling studies for low‐density populations with low rates of individual identification, by making use of available data that might otherwise be discarded.

Ecology & Evolution↗

USGS2020

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 assume individuals are identified without error. We introduce the genotype spatial partial identity model (gSPIM), 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 hair samples were originally discarded because of uncertainty in individual identity. The gSPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected three 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 three replicated assignments (e.g., PCRs for microsatellites) per genetic sample. Further, the simulations showed that the gSPIM model parameters are identifiable with only one replicated assignment per sample and that accuracy and precision are relatively insensitive to the number of replicated assignments for high-quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high-quality samples, but when using the gSPIM, genotyping protocols could be more efficient by devoting more resources to low-quality samples.

Proceedings of the National Academy of Sciences↗

USGS2016

Management decision making for fisher populations informed by occupancy modeling

Harvest data are often used by wildlife managers when setting harvest regulations for species because the data are regularly collected and do not require implementation of logistically and financially challenging studies to obtain the data. However, when harvest data are not available because an area had not previously supported a harvest season, alternative approaches are required to help inform management decision making. When distribution or density data are required across large areas, occupancy modeling is a useful approach, and under certain conditions, can be used as a surrogate for density. We collaborated with the New York State Department of Environmental Conservation (NYSDEC) to conduct a camera trapping study across a 70,096-km 2 region of southern New York in areas that were currently open to fisher ( Pekania [ Martes ] pennanti ) harvest and those that had been closed to harvest for approximately 65 years. We used detection–nondetection data at 826 sites to model occupancy as a function of site-level landscape characteristics while accounting for sampling variation. Fisher occupancy was influenced positively by the proportion of conifer and mixed-wood forest within a 15-km 2 grid cell and negatively associated with road density and the proportion of agriculture. Model-averaged predictions indicated high occupancy probabilities (>0.90) when road densities were low (<1 km/km 2 ) and coniferous and mixed forest proportions were high (>0.50). Predicted occupancy ranged 0.41–0.67 in wildlife management units (WMUs) currently open to trapping, which could be used to guide a minimum occupancy threshold for opening new areas to trapping seasons. There were 5 WMUs that had been closed to trapping but had an average predicted occupancy of 0.52 (0.07 SE), and above the threshold of 0.41. These areas are currently under consideration by NYSDEC for opening a conservative harvest season. We demonstrate the use of occupancy modeling as an aid to management decision making when harvest-related data are unavailable and when budgetary constraints do not allow for capture–recapture studies to directly estimate density.

Journal of Wildlife Management↗