Geology topics

Kathryn Irvine

Publications and source records attributed to Kathryn Irvine.

13 recordsLinked to original sources

USGS2025

Estimating disease prevalence from preferentially sampled, pooled data

After the onset of the COVID-19 pandemic, scientific interest in coronaviruses endemic in animal populations has increased dramatically. However, investigating the prevalence of disease in animal populations across the landscape, which requires finding and capturing animals can be difficult. Spatial random sampling over a grid could be extremely inefficient because animals can be hard to locate, and the total number of samples may be small. Alternatively, preferential sampling, using existing knowledge to inform sample location, can guarantee larger numbers of samples, but estimates derived from this sampling scheme may exhibit bias if there is a relationship between higher probability sampling locations and the disease prevalence. Sample specimens are commonly grouped and tested in pools which can also be an added challenge when combined with preferential sampling. Here we present a Bayesian method for estimating disease prevalence with preferential sampling in pooled presence-absence data motivated by estimating factors related to coronavirus infection among Mexican free-tailed bats ( Tadarida brasiliensis ) in California. We demonstrate the efficacy of our approach in a simulation study, where a naive model, not accounting for preferential sampling, returns biased estimates of parameter values; however, our model returns unbiased results regardless of the degree of preferential sampling. Our model framework is then applied to data from California to estimate factors related to coronavirus prevalence. After accounting for preferential sampling impacts, our model suggests small prevalence differences between male and female bats.

California↗

USGS2025

Integrated distribution modeling resolves asynchrony between bat population impacts and occupancy trends through latent abundance

Monitoring populations is challenging for cryptic species with seasonal life cycles, where data from multiple field techniques are commonly collected and analyzed as multiple lines of evidence. Data integration can provide comprehensive inferences while improving accuracy, precision, and scope but faces challenges in modeling misaligned resolutions and observational uncertainties. We developed a multi-scale, integrated species distribution model (MS-iSDM) for North American bats to combine data across monitoring types and seasons using joint likelihood methods, observational models with false-negatives and false-positives, and seasonal migratory connectivity. We applied this model to 11 years of data for an imperiled bat species (tricolored bat, Perimyotis subflavus ). Relative abundance and occupancy were linked with multi-scale predictors, revealing clear patterns of population declines, but with important differences in spatial trends (abundance: corresponded with white-nose syndrome impacts, occupancy: at the range periphery) and overall severity (abundance: -74.8%, 95% CRI: -79.7% to -69.3%; occupancy: -35.5%, 95% CRI: -41.1% to -30.2%). The asynchrony between occupancy trends and population impacts was explained as an emergent pattern of spatiotemporal variation in abundance in the integrated distribution model. Compared to multiple lines of evidence, the integrated model provided consensus-estimates, increased precision and spatiotemporal scope, and strengthened evidence of population declines.

Communications Biology↗

USGS2025

Bayesian model selection to investigate meaningful spatial scales

Ecologists and other statistical practitioners with access to high-resolution spatial data lack guidance on best approaches for discerning meaningful spatial scales for environmental covariates which is necessary when spatial factors influence environmental processes. Recently developed methods have attempted to automate investigating spatial scales for covariates by evaluating models for which potential explanatory variables are derived from concentric circles of increasing size centered at survey locations. However, these methods make a strong assumption on the inclusion of the covariate and do not help discern whether a covariate should be included in the model. We present an approach that utilizes researcher guidance to create informative priors on the model space that, along with parallelizable Reversible Jump MCMC techniques, enables efficient estimation of posterior model probabilities to assist with the choice of meaningful spatial scales for environmental covariates.

Authorea↗

USGS2025

Statistical review of systematic reconnaissance flight (SRF) surveys (2008-2023) for monitoring American alligator (Alligator mississippiensis) nests in Everglades National Park

We reviewed current (2008-2023) protocols for monitoring American alligator nests in Everglades National Park (ENP) using a qualitative statistical review framework that emphasizes connecting measurable objectives with evaluation of survey design elements. Our review outlined the statistical assumptions that, if severely violated, would prevent defensible inferences from being drawn regarding the three alligator nesting metrics monitored in ENP’s systematic reconnaissance flight (SRF) surveys: nest effort (number of nests), nest distribution, and nest fate. After evaluating the current survey and inference methods, we offer potential adjustments or augmentations to the design and models currently used to better account for potential error sources and align the stated objectives with statistical inferences.

Florida↗

USGS2024

Clustering and unconstrained ordination with Dirichlet process mixture models

Assessment of similarity in species composition or abundance across sampled locations is a common goal in multi-species monitoring programs. Existing ordination techniques provide a framework for clustering sample locations based on species composition by projecting high-dimensional community data into a low-dimensional, latent ecological gradient representing species composition. However, these techniques require specification of the number of distinct ecological communities present in the latent space, which can be difficult to determine in advance. We develop an ordination model capable of simultaneous clustering and ordination that allows for estimation of the number of clusters present in the latent ecological gradient. This model draws latent coordinates for each sample location from a Dirichlet process mixture model, affording researchers with probabilistic statements about the number of clusters present in the latent ecological gradient. The model is compared to existing methods for simultaneous clustering and ordination via simulation and applied to two empirical datasets; JAGS code to fit the proposed model is provided in an appendix. The first dataset concerns presence-absence records of fish in the Doubs river in eastern France and the second dataset describes presence-absence records of plant species in Craters of the Moon National Monument and Preserve (CRMO) in Idaho, USA. Results from both analyses align with existing ecological gradients at each location. Development of the Dirichlet process ordination model provides wildlife managers with data-driven inferences about the number of distinct communities present across monitored locations, allowing for more cost-effective monitoring and reliable decision-making for conservation management.

Methods in Ecology and Evolution↗

USGS2024

Using mobile acoustic monitoring and false-positive N-mixture models to estimate bat abundance and population trends

Estimating the abundance of unmarked animal populations from acoustic data is challenging due to the inability to identify individuals and the need to adjust for observation biases including detectability (false negatives), species misclassification (false positives), and sampling exposure. Acoustic surveys conducted along mobile transects were designed to avoid counting individuals more than once, where raw counts are commonly treated as an index of abundance. More recently, false-positive abundance models have been developed to estimate abundance while accounting for imperfect detection and misclassification. We adapted these methods to model summertime abundance and trends of three species of bats at multiple spatial scales using acoustic recordings collected along mobile transects by partners of the North American Bat Monitoring Program (NABat) from 2012 to 2020. This multiscale modeling spanned individual transect routes, larger NABat grid cells (10 km × 10 km), and across the entire extent of modeled species ranges. We estimated relationships between species abundances and a suite of abiotic and biotic predictors (landcover types, climatological variables, physiographic diversity, building density, and the impacts of white-nose syndrome [WNS]) and found varying levels of support between species. We present clear evidence of substantial declines in populations of tricolored bats ( Perimyotis subflavus ) and little brown bats ( Myotis lucifugus ), declines that corresponded in space and time with the progression of WNS, a devastating disease of hibernating bats. In contrast, our analysis revealed that similar population-wide declines probably have not occurred in big brown bats ( Eptesicus fuscus ), a species known to be less affected by WNS. This study provides the first abundance-based species distribution predictions and population trends for bats in their summer ranges in North America. These models will probably be applicable to assessing wildlife populations in other monitoring programs where acoustic data are used or where false-negative and false-positive detections are present. Finally, our abundance framework (as a spatial point pattern process) can serve as a foundation from which more sophisticated integrated species distribution models that incorporate additional streams of monitoring data (e.g., stationary acoustics, captures) can be developed for North American bats.

Ecological Monographs↗

USGS2024

Joint spatial modeling bridges the gap between disparate disease surveillance and population monitoring efforts informing conservation of at-risk bat species

White-Nose Syndrome (WNS) is a wildlife disease that has decimated hibernating bats since its introduction in North America in 2006. As the disease spreads westward, assessing the potentially differential impact of the disease on western bat species is an urgent conservation need. The statistical challenge is that the disease surveillance and species response monitoring data are not co-located, available at different spatial resolutions, non-Gaussian, and subject to observation error requiring a novel extension to spatially misaligned regression models for analysis. Previous work motivated by epidemiology applications has proposed two-step approaches that overcome the spatial misalignment while intentionally preventing the human health outcome from informing estimation of exposure. In our application, the impacted animals contribute to spreading the fungus that causes WNS, motivating development of a joint framework that exploits the known biological relationship. We introduce a Bayesian, joint spatial modeling framework that provides inferences about the impact of WNS on measures of relative bat activity and accounts for the uncertainty in estimation of WNS presence at non-surveyed locations. Our simulations demonstrate that the joint model produced more precise estimates of disease occurrence and unbiased estimates of the association between disease presence and the count response relative to competing two-step approaches. Our statistical framework provides a solution that leverages disparate monitoring activities and informs species conservation across large landscapes. Stan code and documentation are provided to facilitate access and adaptation for other wildlife disease applications.

Journal of Agricultural, Biological and Environmen↗

USGS2022

NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions. Here, we present NABat ML , an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and trained it on >600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future ‘unseen’ data. We evaluated model performance using a comprehensive, independent, holdout dataset. NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted-average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud-environment computing instance, the entire model training process took <16 h. Synthesis and applications . Our convolutional neural network (CNN)-based model, NABat ML , classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species-level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open-source and reproducible, enabling future implementations as software on end-user devices and cloud-based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big-data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad-scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species.

Journal of Applied Ecology↗

USGS2021

NABat: A top-down, bottom-up solution to collaborative continental-scale monitoring

Collaborative monitoring over broad scales and levels of ecological organization can inform conservation efforts necessary to address the contemporary biodiversity crisis. An important challenge to collaborative monitoring is motivating local engagement with enough buy-in from stakeholders while providing adequate top-down direction for scientific rigor, quality control, and coordination. Collaborative monitoring must reconcile this inherent tension between top-down control and bottom-up engagement. Highly mobile and cryptic taxa, such as bats, present a particularly acute challenge. Given their scale of movement, complex life histories, and rapidly expanding threats, understanding population trends of bats requires coordinated broad-scale collaborative monitoring. The North American Bat Monitoring Program (NABat) reconciles top-down, bottom-up tension with a hierarchical master sample survey design, integrated data analysis, dynamic data curation, regional monitoring hubs, and knowledge delivery through web-based infrastructure. NABat supports collaborative monitoring across spatial and organizational scales and the full annual lifecycle of bats.

Ambio↗

USGS2020

Scientist’s guide to developing explanatory statistical models using causal analysis principles

Recent discussions of model selection and multimodel inference highlight a general challenge for researchers, which is how to clearly convey the explanatory content of a hypothesized model or set of competing models. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis, now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create “well‐formed hypotheses”, as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well‐thought‐out coherent models, and scientists, who would like some practical advice for exactly how to do that.

Ecology↗

USGS2019

Climatic correlates of white pine blister rust infection in whitebark pine in the Greater Yellowstone Ecosystem

Whitebark pine, a foundation species at tree line in the Western U.S. and Canada, has declined due to native mountain pine beetle epidemics, wildfire, and white pine blister rust. These declines are concerning for the multitude of ecosystem and human benefits provided by this species. Understanding climatic correlates associated with spread is needed to successfully manage impacts from forest pathogens. In the Greater Yellowstone Ecosystem since 2000 mountain pine beetles have killed 75 percent of the mature cone-bearing trees, and 40.9 percent of monitored trees have been infected with white pine blister rust. We identified models of white pine blister rust infection that indicate an August and September interaction between relative humidity and temperature were better predictors of white pine blister rust infection in whitebark pine than location and site characteristics in the Greater Yellowstone Ecosystem. The climate conditions conducive to white pine blister rust occur throughout the ecosystem, but larger trees in relatively warm and humid conditions were more likely to be infected between 2000 and 2018. We mapped the infection probability over the past two decades to identify coarse-scale patterns of climate conditions conducive to white pine blister rust infection in whitebark pine.

Wyoming↗

USGS2019

Monitoring five-needle pine on Bureau of Land Management lands in Wyoming summary report for 2013, 2014, 2016, 2017

Whitebark pine (Pinus albicaulis) grows at high elevations and in subalpine communities in the Pacific Northwest and Northern Rocky Mountains. Limber pine (Pinus flexilis) occurs in western North America across a broad elevational gradient from the Canadian Rocky Mountains into parts of New Mexico and Arizona and from southern California eastward to the few, isolated populations existing on the western boundary of the Dakotas and Nebraska (Steele 1990, Schoettle and Rochelle 2000). Both of these five-needle pine species play a variety of ecological roles and are considered key components in the their environments. Currently, whitebark pine and limber pine are being impacted by multiple ecological disturbances. White pine blister rust, caused by the introduced fungus Cronartium ribicola, mountain pine beetle (Dendroctonus ponderosae), dwarf mistletoe (Arceuthobium spp.), wildfires, and drought all pose significant threats to the persistence of healthy five-needle populations. An effort was initiated in 2013 by the National Park Service and the Wyoming Bureau of Land Management (WYBLM) to evaluate and monitor the long-term health trajectory of five-needle pines on WYBLM lands within the Greater Yellowstone Ecosystem (GYE). With guidance from the Interagency Whitebark Pine Monitoring Program protocol, and employing a rapid assessment survey technique specifically designed for this endeavor, we monitored whitebark pine trees in 2013, 2014, 2016, 2017. We estimated the proportion of live, five-needle pine trees (>1.4 m tall) infected with white pine blister rust, documented blister rust infection severity by the occurrence and location of persisting and new infections, determined mortality of five-needle pine trees and described potential factors contributing to the death of trees, and assessed the multiple components of recruitment of understory five-needle pine into the reproductive population. White pine blister rust was widespread throughout WYBLM lands within the GYE. Using a combined ratio estimator we found that the proportion of live, >1.4 m tall five-needle pine trees infected with white pine blister rust was 0.156 (±0.054 SE; this estimate combines all surveyed trees). Bole cankers were 25% more prevalent than branch cankers in all five-needle pines observed. Mortality of surveyed trees on WYBLM lands was predominantly attributed to mountain pine beetle. For seedlings and saplings, a total of 4003 live, ≤1.4 m tall five-needle pines were documented. Cones or cone scars were recorded on 745 of the live trees. Of these reproducing trees, 44 were recorded with white pine blister rust infection. Long-term monitoring on five-needle pines on WYBLM lands will continue into the future.

Wyoming↗

USGS2019

North American Bat Monitoring Program regional protocol for surveying with stationary deployments of echolocation recording devices: Narrative version 1.0, Pacific Northwestern US

The outbreak of white-nose syndrome (WNS) and the growing awareness of the risks to bats from wind power generating facilities have driven radical changes to North American bat conservation. Over the last decade, formerly common species such as the little brown myotis (Myotis lucifugus) and hoary bat (Lasiurus cinereus) have experienced unprecedented mortality rates and are now facing non-trivial extinction risk. In response to this change, federal land management agencies such as the US National Park Service, US Fish and Wildlife Service, US Forest Service, US Bureau of Land Management and state wildlife management agencies such as the Oregon Department of Fish and Wildlife and Idaho Fish and Game have invested in collaborative, interagency bat monitoring to close the gap in information about bat welfare and to inform bat conservation strategies. Bats are notoriously difficult to track and study and there remains a paucity of fundamental information about the seasonal patterns of bat activity and habitat use and population distributions and abundances. Moreover, because bats are so highly mobile and difficult to survey (e.g., nocturnal flight), this information needs to be contextualized at broad regional (e.g., 10,000 km2) and range-wide extents. Delimiting bat populations at local scales (e.g., 100 km2) is very difficult and it is not clear, for example, how a declining trend in local (e.g., a small park unit) patterns of bat activity or relative abundance should be interpreted without broader context. In recognition of these challenges, a plan for coordinated continental-scale monitoring of bats, the North American Bat Monitoring Program (NABat) was developed (Loeb et al. 2015). The centerpiece of the plan is the use of a spatially-balanced randomized master sample of grid-cell sample units from a grid-based sampling frame to provide the architecture for collaboration and the statistical foundation for making inferences about bat populations across broad regions and entire bat geographic ranges. The plan outlines general goals, survey design, and field methods for both summertime acoustic surveys of bats as well as winter and summer counts of bats in hibernacula and maternity colonies but it does not provide field-level protocol and standard operating procedures for consistent and efficient implementation. This regional protocol provides these details for one component of NABat, the deployment of stationary acoustic detectors to record bats during summer, as is called for by the NABat plan. This protocol was written specifically to provide guidance and consistency across the Pacific Northwestern US (N. California [California Department of Fish and Wildlife Northern Region], Idaho, Washington, and Oregon; US Fish and Wildlife Service Region 1 and portion of Region 8 [in Northern California and Klamath Basin]; US Forest Service Region 6 and portions of Regions 1 and 5 in Idaho; and the Upper Columbia Basin, North Coast Cascades, and Klamath Networks of the National Park Service). This region has internal cohesion, sharing a distinct bat faunal assemblage of 15 species (with several additional species occurring on the southern periphery of the region), and a long history of collaborative bat monitoring beginning with the interagency Bat Grid Program which operated from 2003-2010 across Oregon and Washington (US Forest Service Region 6). This protocol will be coordinated and implemented by the Northwestern Bat Hub, on behalf of the collective interagency partnership. The Northwestern Bat Hub is housed on the Oregon State University-Cascades campus and leverages pooled partner funds and resources to maintain a small staff that coordinates and conducts monitoring, provides training and oversight, ensures high-quality data quality and control, and analyzes data and reports on results.

California, Idaho, Oregon, Washington↗