Geology topics

Nolan A. Helmstetter

Publications and source records attributed to Nolan A. Helmstetter.

4 recordsLinked to original sources

USGS2024

Predator-specific mortality of sage-grouse nests based on predator DNA on eggshells

Greater sage-grouse (hereafter sage-grouse; Centrocercus urophasianus ) populations have declined across their range. Increased nest predation as a result of anthropogenic land use is one mechanism proposed to explain these declines. However, sage-grouse contend with a diverse suite of nest predators that vary in functional traits (e.g., search tactics or hunting mode) and abundance. Consequently, generalizing about factors influencing nest fate is challenging. Identifying the explicit predator species responsible for nest predation events is, therefore, critical to understanding causal mechanisms linking land use to patterns of sage-grouse nest success. Cattle grazing is often assumed to adversely affect sage-grouse recruitment by reducing grass height (and hence cover), thereby facilitating nest detection by predators. However, recent evidence found little support for the hypothesized effect of grazing on nest fate at the pasture scale. Rather, nest success appears to be similar on pastures grazed at varying intensities. One possible explanation for the lack of observed effect involves a localized response by one or more nest predators. The presence of cattle may cause a temporary reduction in predator density and/or use within a pasture (the cattle avoidance hypothesis). The cattle avoidance hypothesis predicts a decreased probability of at least one sage-grouse nest predator predating sage-grouse nests in pastures with livestock relative to pastures without livestock present during the nesting season. To test the cattle avoidance hypothesis, we collected predator DNA from eggshells from predated nests and used genetic methods to identify the sage-grouse nest predator(s) responsible for the predation event. We evaluated the influence of habitat and grazing on predator-specific nest predation. We evaluated the efficacy of our genetic method by deploying artificial nests with trail cameras and compared the results of our genetic method to the species captured via trail camera. Our molecular methods identified at least one nest predator captured predating artificial nests via trail camera for 33 of 35 (94%) artificial nests. We detected nest predators via our molecular analysis at 76 of 114 (67%) predated sage-grouse nests. The primary predators detected at sage-grouse nests were coyotes ( Canis latrans ) and corvids ( Corvidea ). Grazing did not influence the probability of nest predation by either coyotes or corvids. Sagebrush canopy cover was negatively associated with the probability a coyote predated a nest, distance to water was positively associated with the probability a corvid predated a nest, and average minimum temperature was negatively associated with the probability that either a coyote or a corvid predated a nest. Our study provides a framework for implementing an effective, non-invasive method for identifying sage-grouse nest predators that can be used to better understand how management actions at local and regional scales may impact an important component of sage-grouse recruitment.

Idaho↗

USGS2024

The influence of grazing on the spatiotemporal activity patterns of a primary sage-grouse nest predator

Perturbations in ecological processes can occur when wildlife alter their spatiotemporal activity patterns to avoid human activities that they perceive as a risk. Such perturbations can have cascading effects throughout wildlife communities. For greater sage-grouse ( Centrocercus urophasianus ; hereafter sage-grouse), nest predation plays an important role in population dynamics. Domestic cattle ( Bos taurus ) grazing has been hypothesized to increase nest predation by reducing grass height, and therefore reducing nest concealment, which may facilitate nest detection by predators. Grass height is lower on grazed pastures, but sage-grouse nest success appears similar on pastures grazed at varying intensities in several recent studies. Any reductions in nest concealment caused by grazing could potentially be offset by a localized response of one or more nest predators to the presence of cattle (i.e., the cattle avoidance hypothesis). A reduction in nest predator density or relative use within pastures could explain similar patterns of nest success on pastures grazed at varying intensities. Also, wildlife can potentially partition themselves temporally to avoid risks associated with human activities. For example, a shift in diel activity patterns by nest predators in response to cattle could result in predators being active during portions of the day when they are less efficient at locating sage-grouse nests. Thus, the effects of grazing could be offset by a temporal avoidance of cattle by predators. We deployed motion sensor cameras across six pastures to evaluate whether coyotes ( Canis latrans ; a primary sage-grouse nest predator) altered spatiotemporal activity patterns in response to cattle. We found that the probability of detecting coyotes had a positive relationship with cattle detections at camera sites (β = 0.22; 95% CI = 0.14,0.30). We also found that coyotes did not shift their diel activity patterns in response to cattle being in the pastures. Thus, in our system, similar sage-grouse nest success among pastures with different grazing intensities cannot be explained by the cattle avoidance hypothesis, at least for coyotes.

Idaho↗

USGS2023

Geographic and taxonomic variation in adaptive capacity among mountain-dwelling small mammals: implications for conservation status and actions

Contemporary climate change is modifying the distribution, morphology, phenology, physiology, evolution, and interspecific interactions of species. Effects of climate change are mediated not only through the magnitude of change experienced (exposure) and an animal's sensitivity to such changes, but also through the ability of the population or species to adjust to climatic variability and change genetically, behaviorally, or spatially (via its distribution) (i.e., adaptive capacity; AC). Here, we used an attribute-based framework to systematically evaluate and compare the AC of American pikas ( Ochotona princeps ) against four other mountain-dwelling small mammals of North America to determine whether pikas are disproportionately vulnerable to climate change, as has been postulated. Unlike previous analyses, we also compared AC across O. princeps lineages and across three taxonomic (and thus, spatial) scales. Our results indicate that pikas have markedly lower adaptive capacity than all compared species except bushy-tailed woodrats ( Neotoma cinerea ), and that our assessments of species generally align with earlier characterizations of climate-change vulnerability based on life-history characteristics. Although AC did not differ dramatically among pika lineages, some attributes are likely constraining AC differently in various parts of the geographic range. Comparisons across taxonomic levels of pikas illustrated that, although AC levels were comparable in pika lineages versus range-wide, AC was assessed as lower in interior-Great-Basin pikas than across the entire O.p. schisticeps lineage. We conclude that the comparatively lower AC of pikas results in particularly high susceptibility to anthropogenic climate change, corroborating results from numerous other recent investigations of pikas' climate-responsiveness. Adaptive-capacity evaluations appear useful as a consistent way to identify sentinel species or populations and for conservation prioritization.

Biological Conservation↗

USGS2020

Balancing transferability and complexity of species distribution models for rare species conservation

Aim Species distribution models (SDMs) are valuable for rare species conservation and are commonly used to extrapolate predictions of habitat suitability geographically to regions where species occurrence is unknown (i.e., transferability). Spatially structured cross-validation can be used to infer transferability, yet, few studies have evaluated how delineation of cross-validation folds affects model complexity and predictions. We developed SDMs using multiple cross-validation approaches to understand the implications for predicting habitat suitability for northern Idaho ground squirrels, a rare, federally threatened species that has been extensively surveyed in regions where known populations occur, resulting in >8000 presence locations. Location Idaho, USA. Methods We delineated cross-validation folds by mimicking the manner in which predictions would be geographically extrapolated or by using existing dispersal barriers. We varied the distance between, number, and directionality of folds. We conducted a grid search on statistical regularization parameters to optimize model complexity, covering a range of values exceeding that typically implemented. For each cross-validation approach, we selected optimal regularization and model complexity based on out-of-sample predictive ability. Results Delineation of cross-validation folds substantially affected resulting model complexity and extrapolated predictions. All cross-validation approaches resulted in models with apparently high out-of-sample predictive ability, yet optimal model complexity varied substantially among the approaches. Regularization demonstrated a noisy relationship between model complexity and prediction, where local optima in predictive performance were common at small values. Main conclusion Subtle modelling decisions can have large consequences for predictions of habitat suitability and transferability of SDMs. When transferability is the goal, cross-validation approaches should be considered carefully and mimic the manner in which spatial extrapolation will occur, else overly complex models with inflated assessments of predictive accuracy may result. Further, spatially structured cross-validation may not guard against over-parameterization, and assessing a broader range of regularization parameters may be necessary to optimize model complexity for transferability.

Idaho↗