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Kathryn M. Irvine

Publications and source records attributed to Kathryn M. Irvine.

At least 19 recordsLinked to original sources
USGS2025

Predicting bat roosts in bridges using Bayesian Additive Regression Trees

Human-built structures can provide important habitat for wildlife, but predicting which structures are most likely to be used remains challenging. To evaluate the predictive capabilities of data-driven ensemble modeling approaches, we conducted surveys for bats and signs of bat use, such as urine and guano staining, at bridges across the southwestern United States. We developed a bat roost discovery tool using Bayesian Additive Regression Trees (BART) and evaluated the predictive ability of this model against other commonly used approaches. We found that the lack of nearby water resources was associated with a lower predicted probability of bat presence or signs of bat use at bridges. While the presence of nearby water resources was associated with higher average predicted probability of bat presence or signs of bat use, high uncertainty surrounding these estimates indicates that other factors also play a role in determining which bridge roosts bats are more likely to use. As such, our model could be particularly useful for predicting which bridges can be excluded from survey efforts due to low probability of bat presence or signs of bat use. We extrapolated our model to unsurveyed bridges across the study region and provide an interactive dashboard application interface for the exploration of these results. Overall, this study demonstrates the application of BART as a predictive tool for prioritizing future bridge surveys for bats roosting in transportation structures.

Arizona, California, Nevada, New Mexico, Texas↗
USGS2022

Statistical assessment on determining local presence of rare bat species

Surveying cryptic, sparsely distributed taxa using autonomous recording units, although cost-effective, provides imperfect knowledge about species presence. Summertime bat acoustic surveys in North America exemplify the challenges with characterizing sources of uncertainty: observation error, inability to census populations, and natural stochastic variation. Statistical uncertainty, if not considered thoroughly, hampers determining rare species presence accurately and/or estimating rangewide status and trends with suitable precision. Bat acoustic data are processed using an automated workflow in which proprietary or open-source algorithms assign a species label to each recorded high-frequency echolocation sequence. A false-negative occurs, if a species is actually present but not recorded and/or all recordings from the species are of such poor quality that a correct species identity cannot be assigned to any observation. False positives for a focal species are a direct result of the presence and incorrect identification of a recording from another species. We compare four analytical approaches in terms of parameter estimation and their resulting (in)correct decisions regarding species presence or absence using realistic data-generating scenarios for bat acoustic data within a simulation study. The current standard for deciding species presence or absence uses a multinomial likelihood-ratio test p value (maximum likelihood estimate [MLE]-metric) that accounts for known species misidentifications, but not imperfect detection and only returns a binary outcome (evidence of presence or not). We found that the MLE-metric had estimated median correct decisions less than 60% for presence and greater than 85% for absence. Alternatively, a multispecies count detection model was equivalent to or better than the MLE-metric for correct claims of rare species presence or absence using the posterior probability a species was present at a site and, importantly, provided unbiased estimates of relative activity and probability of occurrence, creating opportunities for reducing posterior uncertainty through the inclusion of meaningful covariates. Single-species occupancy models with and without false-positive detections removed were insufficient for determining local presence because of substantially biased occurrence and detection probabilities. We propose solutions to potential barriers for integrating local, short-term and rangewide, long-term acoustic surveys within a cohesive statistical framework that facilitates determining local species presence with uncertainty concurrent with estimating species–environment relationships.

Ecosphere↗
USGS2022

Coupling validation effort with in situ bioacoustic data improves estimating relative activity and occupancy for multiple species with cross-species misclassifications

The increasing complexity and pace of ecological change requires natural resource managers to consider entire species assemblages. Acoustic recording units (ARUs) require minimal cost and effort to deploy and inform relative activity, or encounter rates, for multiple species simultaneously. ARU-based surveys require post-processing of the recordings via software algorithms that assign a species label to each recording. The automated classification process can result in cross-species misidentifications that should be accounted for when employing statistical modelling for conservation decision-making. Using simulation and ARU-based detection counts from 17 bat species in British Columbia, Canada, we investigate three strategies for adjusting statistical inference for species misclassification: (a) ‘coupling’ ambiguous and unambiguous detections by validating a subset of survey events post-hoc, (b) using a calibration dataset on the software algorithm's (in)accuracy for species identification or (c) specifying informative Bayesian priors on classification probabilities. We explore the impact of different Bayesian prior specifications for the classification probabilities on posterior estimation. We then consider how the quantity of data validated post-hoc impacts model convergence and resulting inferences for bat species relative activity as related to nightly conditions and yearly site occupancy after accounting for site-level environmental variables. Coupled methods resulted in less bias and uncertainty when estimating relative activity and species classification probabilities relative to calibration approaches. We found that species that were difficult-to-detect and those that were often inaccurately identified by the software required more validation effort than more easily detected and/or identified species. Our results suggest that, when possible, acoustic surveys should rely on coupled validated detection information to account for false-positive detections, rather than uncoupled calibration datasets. However, if the assemblage of interest contains a large number of rarely detected or less prevalent species, an intractable amount of effort may be required, suggesting there are benefits to curating a calibration dataset that is representative of the observation process. Our findings provide insights into the practical challenges associated with statistical analyses of ARU data and possible analytical solutions to support reliable and cost-effective decision-making for wildlife conservation/management in the face of known sources of observation errors.

British Columbia↗
USGS2021

Spatial Gaussian processes improve multi-species occupancy models when range boundaries are uncertain and nonoverlapping

Species distribution models enable practitioners to analyze large datasets of encounter records and make predictions about species occurrence at unsurveyed locations. In omnibus surveys that record data on multiple species simultaneously, species ranges are often nonoverlapping and misaligned with the administrative unit defining the spatial domain of interest (e.g., a state or province). Consequently, some species display differentially restricted extents within a study area. Assuming hard boundaries based on expert opinion or published range maps to restrict species occurrence predictions implies a false sense of certainty in model-based inferences. We propose a multi-species occupancy model with a spatial Gaussian process on site-specific effects for each species as a model-based solution. Specifying informative Bayesian hyperpriors on the spatial hyperparameters encapsulates broad-scale correlation among site occupancy probabilities for each species. We fit this model to acoustic detection/nondetection data collected with autonomous recording units during summer of 2016–2019 throughout Oregon and Washington, USA, on 15 bat species. We found vast improvements in spatial predictions of spotted bat ( Euderma maculatum ), canyon bat ( Parastrellus hesperus ), and Brazilian free-tailed bat ( Tadarida brasiliensis ) when the available environmental predictors were insufficient for characterizing their restricted ranges within the region. In contrast, widespread species ( Lasionycteris noctivagans , Myotis californicus , Myotis evotis , Myotis volans ) were appropriately modeled using only environmental predictors, such as percentage forest cover and cliff and canyon cover. Utilizing spatial Gaussian processes within a community or multi-species model incorporates uncertainty in range boundaries and allows for simultaneous predictions for the entire faunal assemblage even if species have nonoverlapping or restricted ranges within a spatial domain of interest. Such modeling improvements are essential if species distribution models are to accurately inform monitoring, species recovery plans, and other conservation efforts.

Ecology and Evolution↗
USGS2021

Resilience to fire and resistance to annual grass invasion in sagebrush ecosystems of US National Parks

Western North American sagebrush shrublands and steppe face accelerating risks from fire-driven feedback loops that transition these ecosystems into self-reinforcing states dominated by invasive annual grasses. In response, sagebrush conservation decision-making is increasingly done through the lens of resilience to fire and annual grass invasion resistance. Operationalizing resilience and resistance concepts requires place-based understanding of resilience and resistance variation among landscapes over time. Place-based insights allow for landscape prioritization in targeted areas of significance such as protected-area sagebrush ecosystems that exhibit inherently low resilience and are therefore at high risk of loss. We used a multi-scale approach to evaluate sagebrush resiliency and strategic planning across 1) the US National Park system, 2) a regional suite of five parks, and 3) for two specific park case studies. First, we summarized broad patterns of relative resilience to fire and resistance to annual grass invasion across all parks with sagebrush ecosystems. We found that national parks represented ~11% of US protected-area sagebrush ecosystems and reflected a similar low-resilience bias that occurs across the biome, broadly. Climate change is likely to shift both low- and high-resilience park sagebrush ecosystems towards moderate resiliency, creating new opportunities and constraints for park conservation. Approximately seventy park units include at least some sagebrush shrublands or steppe, but we identified 40 parks with substantial amounts (>20% of park area) that can be included in an agency-wide conservation strategy. Second, we examined detailed patterns of resilience and resistance, fire history and fire risk, cheatgrass ( Bromus tectorum ) invasion, and sagebrush shrub ( Artemisia spp.) persistence in five national park units in Columbia Basin and Snake River Plain sagebrush steppe, contextualized by the broader summary. In these five parks, fire frequency and size increased in recent decades. Cheatgrass invasion and sagebrush persistence correlated strongly with resilience, burn frequency (0–3 fires since ~1940), and burn probability, but with important variation, in part mediated by local-scale topography. Third, we used these insights to assemble strategic sagebrush ecosystem fire protection mapping scenarios in two additional parks – Lava Beds National Monument and Great Basin National Park. Readily available and periodically updated geospatial data including soil surveys, fire histories, vegetation inventories, and long-term monitoring support resiliency-based adaptive management through tactical planning of pre-fire protection, post-fire restoration, and triage. Our assessment establishes the precarious importance of the US national park system to sagebrush ecosystem conservation and an operational strategy for place-based and science-supported conservation.

Arizona, California, Colorado, Idaho, Montana, New↗
USGS2021

Advancements in analytical approaches improve whitebark pine monitoring results

Long-term monitoring programs track the status and trends of species in increasingly vulnerable environments. These monitoring results provide critical information for evaluating, understanding, and managing natural resources. To accurately interpret if and how conditions may be changing for select ecological indicators, it is essential that monitoring programs adopt methods to ensure exceptional data quality. To do this and remain relevant, practitioners need to be flexible and willing to embrace a degree of adaptivity in their protocols. They must periodically re-evaluate their statistical methods and field data collection techniques to provide contemporary, significant, and applicable inferences.

Idaho, Montana, Wyoming↗
USGS2020

Post-fire vegetation response in a repeatedly burned low-elevation sagebrush steppe protected area provides insights about resilience and invasion resistance

Sagebrush steppe ecosystems are threatened by human land-use legacies, biological invasions, and altered fire and climate dynamics. Steppe protected areas are therefore of heightened conservation importance but are few and vulnerable to the same impacts broadly affecting sagebrush steppe. To address this problem, sagebrush steppe conservation science is increasingly emphasizing a focus on resilience to fire and resistance to non-native annual grass invasion as a decision framework. It is well-established that the positive feedback loop between fire and annual grass invasion is the driving process of most contemporary steppe degradation. We use a newly developed ordinal zero-augmented beta regression model fit to large-sample vegetation monitoring data from John Day Fossil Beds National Monument, USA, spanning 7 years to evaluate fire responses of two native perennial foundation bunchgrasses and two non-native invasive annual grasses in a repeatedly burned, historically grazed, and inherently low-resilient protected area. We structured our model hierarchically to support inferences about variation among ecological site types and over time after also accounting for growing-season water deficit, fine-scale topographic variation, and burn severity. We use a state-and-transition conceptual diagram and abundances of plants listed in ecological site reference conditions to formalize our hypothesis of fire-accelerated transition to ecologically novel annual grassland. Notably, big sagebrush ( Artemisia tridentata ) and other woody species were entirely removed by fire. The two perennial grasses, bluebunch wheatgrass ( Pseudoroegneria spicata ) and Thurber's needlegrass ( Achnatherum thurberianum ) exhibited fire resiliency, with no apparent trend after fire. The two annual grasses, cheatgrass ( Bromus tectorum ) and medusahead ( Taeniatherum caput-medusae ), increased in response to burn severity, most notably medusahead. Surprisingly, we found no variation in grass cover among ecological sites, suggesting fire-driven homogenization as shrubs were removed and annual grasses became dominant. We found contrasting responses among all four grass species along gradients of topography and water deficit, informative to protected-area conservation strategies. The fine-grained influence of topography was particularly important to variation in cover among species and provides a foothold for conservation in low-resilient, aridic steppe. Broadly, our study demonstrates how to operationalize resilience and resistance concepts for protected areas by integrating empirical data with conceptual and statistical models.

Oregon↗
USGS2020

The use of Bayesian priors in Ecology: The good, the bad and the not great

Bayesian data analysis (BDA) is a powerful tool for making inference from ecological data, but its full potential has yet to be realized. Despite a generally positive trajectory in research surrounding model development and assessment, far too little attention has been given to prior specification. Default priors, a sub‐class of non‐informative prior distributions that are often chosen without critical thought or evaluation, are commonly used in practice. We believe the fear of being too ‘subjective’ has prevented many researchers from using any prior information in their analyses despite the fact that defending prior choice (informative or not) promotes good statistical practice. In this commentary, we provide an overview of how BDA is currently being used in a random sample of articles, discuss implications for inference if current bad practices continue, and highlight sub‐fields where knowledge about the system has improved inference and promoted good statistical practices through the careful and justified use of informative priors. We hope to inspire a renewed discussion about the use of Bayesian priors in Ecology with particular attention paid to specification and justification. We also emphasize that all priors are the result of a subjective choice, and should be discussed in that way.

Methods in Ecology and Evolution↗
USGS2019

Using the beta distribution to analyze plant cover data

Most plant species are spatially aggregated. Local demographic and ecological processes (e.g. vegetative growth and limited seed dispersal) result in a clustered spatial pattern within an environmentally homogenous area. Spatial aggregation should be considered when modelling plant abundance data. Commonly, plant abundance is quantified by measuring cover within multiple areal plots or along multiple lines randomly placed within a study area. A common practice for analyzing plant cover is to use statistical methods that rely on the normal distribution for quantifying uncertainty. This is problematic because plant cover data tend to be left‐skewed (J‐shaped), right skewed (L‐shaped) or U‐shaped and, therefore, commonly violate classic statistical assumptions, such as normality. We outline statistical analyses that explicitly account for spatial aggregation by assuming that plant cover is beta‐distributed. The beta distribution is a flexible choice because within the open unit interval it can take on a wide range of shapes (L, J, U, or a bell‐shaped). We discuss and introduce extensions to the beta distribution that address common analysis issues encountered in plant cover datasets, such as i) the treatment of zero and one cover values, ii) hierarchical data structures, and iii) observations errors. For heuristic purposes, we focus on single species analyses, but we demonstrate how the outlined methods can be generalized to more species. The assumption that plant cover is beta‐distributed allows us to estimate the degree of spatial aggregation, and the ecological significance of this new knowledge is discussed. We provide a summary of available software for analyses (emphasizing standard R packages) and include worked examples and a simulation study comparing analysis options as supplemental information. Synthesis . Previously, the state of the statistical software made it practically difficult for empirical plant ecologists to analyze their cover data correctly, but new theory and R‐packages have been developed, and this difficulty no longer exists. We recommend that empirical plant ecologists embrace the new statistical possibilities for exploring the exciting ecological features in spatial variation of plant cover.

Journal of Ecology↗
USGS2019

Identifying occupancy model inadequacies: Can residuals separately assess detection and presence?

Occupancy models are widely applied to estimate species distributions, but few methods exist for model checking. Thorough model assessments can uncover inadequacies and allow for deeper ecological insight by exploring structure in the observed data not accounted for by a model. We introduce occupancy model residual definitions that utilize the posterior distribution of the partially latent occupancy states. Residual‐based assessments are valuable because they can target specific assumptions and identify ways to improve a model, such as adding spatial correlation or meaningful covariates. Our approach defines separate residuals for occupancy and detection, and we use simulation to examine whether missing structure for modeling detection probabilities can be distinguished from that for occupancy probabilities. In many scenarios, our residual diagnostics were able to successfully separate inadequacies at the different model levels, but we describe other situations when this may not be the case. Applying Moran's I residual diagnostics to assess models for silver‐haired (Lasionycteris noctivagans) and little brown (Myotis lucifugus) bats only provided evidence of residual spatial correlation among detections. Targeting specific model assumptions using carefully chosen residual diagnostics is valuable for any analysis, and we remove previous barriers for occupancy analyses — lack of examples and practical advice.

Ecology↗
USGS2019

Statistical power of dynamic occupancy models to identify temporal change: Informing the North American Bat Monitoring Program

Dynamic occupancy models provide a flexible framework for estimating and mapping species occupancy patterns over space and time for large-scale monitoring programs (e.g., the North American Bat Monitoring Program (NABat), the Amphibian Research and Monitoring Initiative). Challenges for designing surveys using the dynamic occupancy modeling framework include defining appropriate derived trend parameters, and providing usable tools for researchers to conduct project-specific sample size investigations. We present a simulation-based power analysis framework for dynamic occupancy models that allows for the incorporation of the underlying environmental space (i.e., as covariates) within a specific study region to inform sample size estimation. We investigate two definitions of temporal trend: (1) a gradual, sustained (linear or nonlinear) change over a period of many years, and (2) an abrupt increase or decrease between two time periods. We draw upon pilot data collected following NABat protocols to inform assumed data generating values in a demonstration of our approach. Due to the complicated parameter structure of dynamic occupancy models, we emphasize the importance of visualizing simulated changes over time based on different parameter settings prior to conducting a power analysis. Our simulations revealed that the linearity of short-term trends (five years in our investigation) conferred higher power with lower sample size than longer trends where occupancy probabilities approached zero (ten years in our investigation). We provide an example of how to use our tools to conduct customized investigations using questions posed by NABat, and in doing so, we shed light on general guidelines that can be applied to programs monitoring species occupancy for other taxa. Importantly, we created an R package to execute our approach for informing program-, species-, and study-specific investigations aimed at identifying changes in species occupancy.

Ecological Indicators↗
USGS2019

Wetland drying linked to variations in snowmelt runoff across Grand Teton and Yellowstone national parks

In Grand Teton and Yellowstone national parks wetlands offer critical habitat and play a key role in supporting biological diversity. The shallow depths and small size of many wetlands make them vulnerable to changes in climate compared with larger and deeper aquatic habitats. Here, we use a simple water balance model to generate estimates of biophysical drivers of wetland change. We then examine the relationship between wetland inundation status and four principal drivers (i.e., temperature, precipitation, evapotranspiration, and runoff) spanning varying meteorological conditions over an 8-year time series from Grand Teton and Yellowstone national parks. We found that a higher percentage of surveyed wetlands were dry in years characterized by lower snowmelt runoff. While runoff-based models were most supported, wetland drying was also related to variations in April to June precipitation and temperatures. Our work shows that wetland drying was widespread across both parks, but sub-regional variations were best described at the hydrologic subbasin-level. Documenting the varying responses of wetlands to meteorological drivers is a necessary first step to identifying which subbasins are most sensitive to recent change and contemplating how future change may alter the distribution of wetlands and their dependent taxa.

Idaho, Montana, Wyoming↗
USGS2019

Assessing spatial and temporal patterns in sagebrush steppe vegetation communities 2012-2018: Grand Teton National Park

Visual cover class data were collected on over 80 species across 30 permanent sampling frames in sagebrush steppe vegetation communities in Grand Teton National Park from 2012 to 2018. In this report, temporal and spatial patterns in species composition were assessed and used to inform potential sampling strategies for future monitoring. Specifically, the viability of a reduction in sampling effort was evaluated based on the similarity in species composition within each frame over time and among frames within each year. Using distance-based ordination techniques, we found little to no evidence of differences in species composition within each frame over time. Furthermore, there was little evidence of heterogeneity in species composition among frames within each year, though there was some evidence of differences in composition between the two principle sagebrush community types (sagebrush dry shrubland and sagebrush-bitterbrush) aggregated across frames. Based on these results, we propose that a reduction in sampling effort is viable and suggest a new monitoring schedule.

Wyoming↗
USGS2018

Occupancy modeling species–environment relationships with non‐ignorable survey designs

Statistical models supporting inferences about species occurrence patterns in relation to environmental gradients are fundamental to ecology and conservation biology. A common implicit assumption is that the sampling design is ignorable and does not need to be formally accounted for in analyses. The analyst assumes data are representative of the desired population and statistical modeling proceeds. However, if data sets from probability and non‐probability surveys are combined or unequal selection probabilities are used, the design may be non‐ignorable. We outline the use of pseudo‐maximum likelihood estimation for site‐occupancy models to account for such non‐ignorable survey designs. This estimation method accounts for the survey design by properly weighting the pseudo‐likelihood equation. In our empirical example, legacy and newer randomly selected locations were surveyed for bats to bridge a historic statewide effort with an ongoing nationwide program. We provide a worked example using bat acoustic detection/non‐detection data and show how analysts can diagnose whether their design is ignorable. Using simulations we assessed whether our approach is viable for modeling data sets composed of sites contributed outside of a probability design. Pseudo‐maximum likelihood estimates differed from the usual maximum likelihood occupancy estimates for some bat species. Using simulations we show the maximum likelihood estimator of species–environment relationships with non‐ignorable sampling designs was biased, whereas the pseudo‐likelihood estimator was design unbiased. However, in our simulation study the designs composed of a large proportion of legacy or non‐probability sites resulted in estimation issues for standard errors. These issues were likely a result of highly variable weights confounded by small sample sizes (5% or 10% sampling intensity and four revisits). Aggregating data sets from multiple sources logically supports larger sample sizes and potentially increases spatial extents for statistical inferences. Our results suggest that ignoring the mechanism for how locations were selected for data collection (e.g., the sampling design) could result in erroneous model‐based conclusions. Therefore, in order to ensure robust and defensible recommendations for evidence‐based conservation decision‐making, the survey design information in addition to the data themselves must be available for analysts. Details for constructing the weights used in estimation and code for implementation are provided.

Ecological Applications↗
USGS2018

Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification

Acoustic recording units (ARUs) enable geographically extensive surveys of sensitive and elusive species. However, a hidden cost of using ARU data for modeling species occupancy is that prohibitive amounts of human verification may be required to correct species identifications made from automated software. Bat acoustic studies exemplify this challenge because large volumes of echolocation calls could be recorded and automatically classified to species. The standard occupancy model requires aggregating verified recordings to construct confirmed detection/non‐detection datasets. The multistep data processing workflow is not necessarily transparent nor consistent among studies. We share a workflow diagramming strategy that could provide coherency among practitioners. A false‐positive occupancy model is explored that accounts for misclassification errors and enables potential reduction in the number of confirmed detections. Simulations informed by real data were used to evaluate how much confirmation effort could be reduced without sacrificing site occupancy and detection error estimator bias and precision. We found even under a 50% reduction in total confirmation effort, estimator properties were reasonable for our assumed survey design, species‐specific parameter values, and desired precision. For transferability, a fully documented r package, OCacoustic, for implementing a false‐positive occupancy model is provided. Practitioners can apply OCacoustic to optimize their own study design (required sample sizes, number of visits, and confirmation scenarios) for properly implementing a false‐positive occupancy model with bat or other wildlife acoustic data. Additionally, our work highlights the importance of clearly defining research objectives and data processing strategies at the outset to align the study design with desired statistical inferences.

Ecology and Evolution↗
USGS2018

Impacts of temporal revisit designs on the power to detect trend with a linear mixed model: An application to long-term monitoring of Sierra Nevada lakes

Long-term ecological monitoring programs often use linear mixed models to estimate trend in an ecological indicator sampled across large landscapes. A linear mixed model is versatile for estimating a linear trend in time as well as components of spatial and temporal variationin the case of unbalanced data structures , which are common in complex monitoring designs where limited sampling effort must be optimized over time and space. A power analysis was used to inform a lake chemistry monitoring design, including selecting the most appropriate temporal revisit design. Pilot data from surveys of lakes across large wilderness national parks (Sequoia, Kings Canyon, and Yosemite national parks) were used to obtain variance components for a Monte Carlo power simulation. Using a linear mixed model for a range of temporal revisit designs, sample sizes, and trend magnitudes, we evaluated the power to detect trend, the trend test size, and the relative bias of trend coefficient estimates for four continuous and normally distributed indicators. Contrary to prior research based on large-sample approximations that identified a single panel of sites visited annually as the revisit design generating the highest power, we found that the power to detect a 12-year trend based on the Wald t-test from a linear mixed model may be optimized by obtaining unbalanced data sets with limited to no annual replication. We emphasize the importance of examining variance composition, sample size, and the power and size of the trend test with Monte Carlo simulation when allocating sampling effort over time and space.

Ecological Indicators↗
USGS2017

Statistical design and analysis for plant cover studies with multiple sources of observation errors

Effective wildlife habitat management and conservation requires understanding the factors influencing distribution and abundance of plant species. Field studies, however, have documented observation errors in visually estimated plant cover including measurements which differ from the true value (measurement error) and not observing a species that is present within a plot (detection error). Unlike the rapid expansion of occupancy and N-mixture models for analysing wildlife surveys, development of statistical models accounting for observation error in plants has not progressed quickly. Our work informs development of a monitoring protocol for managed wetlands within the National Wildlife Refuge System. Zero-augmented beta (ZAB) regression is the most suitable method for analysing areal plant cover recorded as a continuous proportion but assumes no observation errors. We present a model extension that explicitly includes the observation process thereby accounting for both measurement and detection errors. Using simulations, we compare our approach to a ZAB regression that ignores observation errors (naïve model) and an “ad hoc” approach using a composite of multiple observations per plot within the naïve model. We explore how sample size and within-season revisit design affect the ability to detect a change in mean plant cover between 2 years using our model. Explicitly modelling the observation process within our framework produced unbiased estimates and nominal coverage of model parameters. The naïve and “ad hoc” approaches resulted in underestimation of occurrence and overestimation of mean cover. The degree of bias was primarily driven by imperfect detection and its relationship with cover within a plot. Conversely, measurement error had minimal impacts on inferences. We found >30 plots with at least three within-season revisits achieved reasonable posterior probabilities for assessing change in mean plant cover. For rapid adoption and application, code for Bayesian estimation of our single-species ZAB with errors model is included. Practitioners utilizing our R-based simulation code can explore trade-offs among different survey efforts and parameter values, as we did, but tuned to their own investigation. Less abundant plant species of high ecological interest may warrant the additional cost of gathering multiple independent observations in order to guard against erroneous conclusions.

Methods in Ecology and Evolution↗