Geology topics

Ben Augustine

Publications and source records attributed to Ben Augustine.

8 recordsLinked to original sources

USGS2025

Spatially explicit capture-mark-recapture to evaluate demographic status of the Louisiana black bear

Louisiana black bears ( Ursus americanus luteolus ) occur in semi-isolated fragments of bottomland hardwood forest in the lower Mississippi Alluvial Valley and were listed as threatened under the United States Endangered Species Act in 1992. A population viability analysis based on radio-telemetry and capture-mark-recapture (CMR) data collected from 2002 to 2012 revealed that the probability of bears persisting in at least 1 subpopulation was >0.999, which prompted the United States Fish and Wildlife Service to remove the Louisiana black bear from the threatened species list in 2016. A post-delisting monitoring plan was developed, which included continued noninvasive CMR surveys to monitor subpopulation trends. We used genetic CMR data based on non-invasively collected hair samples for a post-delisting assessment of the demographic status of the black bear subpopulations in Louisiana, USA. The assessment included primary range in the Tensas River Basin (TRB; 2006–2020), the Upper Atchafalaya River Basin (UARB; 2007–2020), and the Three Rivers Complex (TRC; 2014 and 2021), where bears had been reintroduced beginning in 2001, and adjacent areas of possible range expansion (i.e., secondary range). We used spatially explicit closed-population capture-recapture models to estimate abundance ( N ), density ( D ), and the realized population growth rate ( λ ) for female bears at TRB, UARB, and TRC. Model-averaged estimates of N for primary range at TRB ranged from 135 (95% CI = 121–149) female bears in 2006 to 140 (95% CI = 123–157) in 2020 with mean λ of 1.003 (95% CI = 0.991–1.015), which suggested a stable primary population. An estimated 56 (95% CI = 19–92) additional female bears occurred in secondary range at TRB in 2020. Model-averaged estimates of N for primary range at UARB ranged from 35 (95% CI = 28–43) females in 2007 to 33 (95% CI = 28–43) in 2020, with mean λ = 0.996 (95% CI = 0.972–1.020), again suggesting a stable population. We estimated 3 (95% CI = 0–6) additional female bears occurred in secondary range at UARB during 2020. The estimate of N at TRC was 28 (95% CI = 18–44) females in 2014 and 42 (95% CI = 19–94) in 2021. The number of bears in the secondary ranges suggests some bears may have emigrated from the primary areas and colonized secondary areas, likely facilitated by increases in bottomland hardwood forests adjacent to the core populations. The stable primary populations, the reintroduced population at TRC, and the number of bears in secondary range adjacent to TRB indicate the overall number of Louisiana black bears has increased since monitoring began.

Louisiana↗

USGS2024

Abundance-mediated species interactions

Species interactions shape biodiversity patterns, community assemblage, and the dynamics of wildlife populations. Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, prevalent approaches for modeling species interactions predominantly center around occupancy states. Here, we use simulations to illuminate the inadequacies of modeling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to error in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an abundance-mediated interaction framework designed explicitly for modeling species interactions involving two or more species from detection/non-detection data. We present Markov chain Monte Carlo (MCMC) samplers tailored for diverse ecological scenarios, including intraguild predation, disease- or predator-mediated competition, and trophic cascades. Illustrating the practical implications of our approach, we compare inference from modeling the interactions in a three-species network involving coyotes ( Canis latrans ), fishers ( Pekania pennanti ), and American marten ( Martes americana ) in North America as a function of occupancy states and as a function of abundance. When modeling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Our study emphasizes that accounting for abundance-mediated interactions rather than simple co-occurrence patterns can fundamentally alter our comprehension of system dynamics. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modeling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.

Ecology↗

USGS2024

A hierarchical model for eDNA fate and transport dynamics accommodating low concentration samples

Environmental DNA (eDNA) sampling is an increasingly important tool for answering ecological questions and informing aquatic species management; however, several factors currently limit the reliability of ecological inference from eDNA sampling. Two particular challenges are (1) determining species source location(s) and (2) accurately and precisely measuring low concentration eDNA samples in the presence of multiple sources of ecological and measurement variability. The recently introduced eDNA Integrating Transport and Hydrology (eDITH) model provides a framework for relating eDNA measurements to source locations in riverine networks, but little empirical work has been done to test and refine model assumptions or accommodate low concentration samples, that can be systematically undermeasured. To better understand eDNA fate and transport dynamics and our ability to reliably quantify low concentration samples, we developed a hierarchical model and used it to evaluate a fate and transport experiment. Our model addresses several low concentration challenges by modeling the number of copies in each PCR replicate as a latent variable with a count distribution and conditioning detection and quantification on replicate copy number. We provide evidence that the eDNA removal rate declined through time, estimating that over 80% of eDNA was removed over the first 10 m, traversed in 41 s. After this initial period of rapid decay, eDNA decayed slowly with consistent detection through our farthest site 1 km from the release location, traversed in 67.8 min. Our model further allowed us to detect extra-Poisson variation in the allocation of copies to replicates. We extended our hierarchical model to accommodate a continuous effect of inhibitors and used our model to provide evidence for the inhibitor hypothesis and explore the potential implications. While our model is not a panacea for all challenges faced when quantifying low-concentration eDNA samples, it provides a framework for a more complete accounting of uncertainty.

Environmental and Ecological Statistics↗

USGS2024

Field trials of an autonomous eDNA sampler in lotic waters

Environmental DNA (eDNA) analysis has become a transformative technology, but sample collection methods lack standardization and sampling at effective frequencies requires considerable field effort. Autonomous eDNA samplers that can sample water at high frequencies offer potential solutions to these problems. We present results from four case studies using a prototype autonomous eDNA sampler as part of the U.S. Geological Survey’s Rapid Environmental eDNA Assessment and Deployment Initiative & Network (READI-Net) project. These case studies involved short-term deployments of an eDNA autosampler (Smith-Root) across a range of riverine habitats with the objectives of (a) identifying what insights could be gained from high-frequency autosampling and (b) benchmarking these autosamples against manually collected samples. The high frequency autosampling revealed high temporal variability of eDNA concentrations and provided valuable insights about eDNA associations with environmental covariates, such as discharge and turbidity. Benchmarking assessments indicated autosamples had similar detection rates to manual samples and obtained similar or greater eDNA quantities. We did find minimal carryover contamination in autosampler field controls. We conclude that eDNA autosamplers have potential to improve freshwater biosurveillance by reducing logistical sampling barriers, standardizing collection methods, and clarifying the influence of environmental covariates on eDNA results.

Idaho, Missouri, Montana, New York↗

USGS2024

Autonomous samplers and environmental DNA metabarcoding: Sampling day and primer choice have greatest impact on fish detection probabilities

Unprecedented rates of biodiversity loss and ecosystem function necessitate the use of rapid, efficacious, and cost-effective biomonitoring tools. The combination of autonomous samplers and high throughput sequencing (i.e., “metabarcoding”) of environmental DNA ( eDNA ) samples enables characterization of entire communities at high frequency and can be an important tool for conservation and management, allowing researchers to track fluctuations in biodiversity. We deployed two autonomous samplers at two U.S. Geological Survey streamgage sites in the upper Snake River (Wyoming and Idaho, USA) to collect eDNA samples from July-September 2021 and 2022 to characterize fish diversity. We used a probabilistic approach to evaluate the effects of water temperature, water discharge, filter pore size, water volume filtered, number of samples collected, timing, and primers on the probability of detecting eDNA from fish species known to be present. We detected eDNA from 13/15 species present in these areas of the Snake River. Overall, we did not find evidence that filter pore size, water volume filtered, water discharge, and water temperature affected the probability of detecting fish species’ eDNA . By contrast, primers and sampling day affected fish detection probabilities, indicating that primer choice and sampling day can either over- or under- estimate species diversity. These results indicate that users would ideally consider sampling on non-consecutive days and which primer set will maximize species detections.

Metabarcoding & Metagenomics↗

USGS2024

A hierarchical model for eDNA fate and transport dynamics accommodating low concentration samples

Environmental DNA (eDNA) sampling is an increasingly important tool for answering ecological questions and informing aquatic species management; however, several factors currently limit the reliability of ecological inference from eDNA sampling. Two particular challenges are 1) determining species source location(s) and 2) accurately and precisely measuring low concentration eDNA samples in the presence of multiple sources of ecological and measurement variability. The recently introduced eDNA Integrating Transport and Hydrology (eDITH) model provides a framework for relating eDNA measurements to source locations in riverine networks, but little empirical work has been done to test and refine model assumptions or accommodate low concentration samples, that can be systematically undermeasured. To better understand eDNA fate and transport dynamics and our ability to reliably quantify low concentration samples, we developed a hierarchical model and used it to evaluate a fate and transport experiment. Our model addresses several low concentration challenges by modeling the number of copies in each PCR replicate as a latent variable with a count distribution and conditioning detection and quantification on replicate copy number. We provide evidence that the eDNA removal rate declined through time, estimating that over 80% of eDNA was removed over the first 10 meters, traversed in 41 seconds. After this initial period of rapid decay, eDNA decayed slowly with consistent detection through our farthest site 1km from the release location, traversed in 250 seconds. Our model further allowed us to detect extra-Poisson variation in the allocation of copies to replicates. We extended our hierarchical model to accommodate a continuous effect of inhibitors and used our model to provide evidence for the inhibitor hypothesis and explore the potential implications. While our model is not a panacea for all challenges faced when quantifying low-concentration eDNA samples, it provides a framework for a more complete accounting of uncertainty.

BioRxiv↗

USGS2024

Estimating species-specific U.S. waterfowl harvest

The U.S. Fish and Wildlife Service monitors species-specific waterfowl (ducks, seaducks, geese, and brant) harvest through two hunter surveys, one that estimates the total harvest for each waterfowl group, and a second that estimates the species composition of each waterfowl group. Point estimates for species-specific harvest can be computed by multiplying the estimated total harvest by the estimated proportion of the total harvest of each species. However, to date, no uncertainty estimates have been available. Here, we combine these two data sources to provide species-specific harvest estimates at the state and flyway level while characterizing the uncertainty via Bayesian estimation. We take a similar approach to Smith et al . (2022) , providing both estimates that treat yearly data as independent and estimates that share information across years via a random walk process. We then discuss the advantages and disadvantages of each approach.

BioRxiv↗

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↗