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USGS2019

The area under the precision‐recall curve as a performance metric for rare binary events

Species distribution models are used to study biogeographic patterns and guide decision‐making. The variable quality of these models makes it critical to assess whether a model's outputs are suitable for the intended use, but commonly used evaluation approaches are inappropriate for many ecological contexts. In particular, unrealistically high performance assessments have been associated with models for rare species and predictions over large geographic extents. We evaluated the area under the precision‐recall curve (AUC‐PR) as a performance metric for rare binary events, focusing on the assessment of species distribution models. Precision is the probability that a species is present given a predicted presence, while recall (more commonly called sensitivity) is the probability the model predicts presence in locations where the species has been observed. We simulated species at three levels of prevalence, compared AUC‐PR and the area under the receiver operating characteristic curve (AUC‐ROC) when the geographic extent of predictions was increased and assessed how well each metric reflected a model's utility to guide surveys for new populations. AUC‐PR was robust to species rarity and, unlike AUC‐ROC, not affected by an increasing geographic extent. The major advantages of AUC‐PR arise because it does not incorporate correctly predicted absences and is therefore less prone to exaggerate model performance for unbalanced datasets. AUC‐PR and precision were useful indicators of a model's utility for guiding surveys. We show that AUC‐PR has important advantages for evaluating models of rare species, and its benefits in the context of unbalanced binary responses will make it applicable for other ecological studies. By not considering the true negative quadrant of the confusion matrix, AUC‐PR ameliorates issues that arise when the geographic extent is increased beyond the species’ range or when a large number of background points are used when absence information is unavailable. However, no single metric captures all aspects of performance nor provides an absolute index that can be compared across datasets. Our results indicate AUC‐PR and precision can provide useful and intuitive metrics for evaluating a model's utility for guiding sampling, and can complement other metrics to help delineate a model's appropriate use.

Methods in Ecology and Evolution↗
USGS2019

A comparative analysis of common methods to identify waterbird hotspots

1. Hotspot analysis is a commonly used method in ecology and conservation to identify areas of high biodiversity or conservation concern. However, delineating and mapping hotspots is subjective and various approaches can lead to different conclusions with regard to the classification of particular areas as hotspots, complicating long-term conservation planning and implementation efforts. 2. We present a comparative analysis of recent approaches for identifying waterbird hotspots, with the goal of developing insights about the appropriate use of these methods. We selected four commonly used measures to identify persistent areas of high use: kernel density estimation, Getis-Ord Gi*, hotspot persistence, and hotspots conditional on presence, which represent the range of quantitative hotspot estimation approaches used in waterbird analyses. We applied each of the methods to aerial survey waterbird count data collected in the Great Lakes from 2012-2014 using a 5 km2 grid. For each approach, we identified areas of high use for seven species/species groups and then compared the results across all methods. 3. Our results indicate that formal hotspot analysis frameworks do not always lead to the same conclusions. The kernel density and Getis-Ord Gi* methods yielded the most similar results across all species analyzed. We found that these two models can differ substantially from the hotspot persistence and hotspots conditional on presence estimation approaches, which were not consistently similar to one another. The hotspot persistence approach differed most significantly from the other methods but is the only method to explicitly account for temporal variation. 4. We recommend considering the ecological question and scale of any conservation or management activities prior to designing survey methodologies. Deciding the appropriate definition and scale for analysis is critical for interpretation of hotspot analysis results. Combining methods using an integrative approach, either within a single analysis or post-hoc, could lead to greater consistency in the identification of waterbird hotspots.

Lake Erie, Lake Huron, Lake Michigan↗
USGS2019

Cohesive framework for modeling plant cover class data

The study of plant distribution and abundance is a fundamental pursuit in ecology and conservation biology. Measuring plant abundance by visually assessing percent cover and recording a cover class is a common field method that yields ordinal data. Statistical models for ordinal data exist but entail cumbersome interpretations and sometimes restrictive assumptions. We propose a Bayesian hierarchical framework for analysing cover class data that allows for linking ordinal observations to a latent beta distribution and accounts for zero inflation. Harnessing a latent beta distribution supports interpreting changes in abundance in terms of mean percent cover rather than odds ratios of cumulative cover classes as for cumulative link models. The zero augmentation allows for simultaneous inferences on both occurrence (distribution) and abundance. We show how our model can account for true and false zeros, misclassification of cover classes, multiple species and hierarchical sampling designs, using empirical examples and simulations. Simulated observation errors, when ignored, led to models overestimating abundance and underestimating occurrence. Based on simulations, we found no substantial difference between mean percent cover estimates when analyzing ordinal cover classes versus continuous percent cover as the response. Our empirical datasets displayed high probability of detection (>0.85 on average for all species), likely due to the sampling design used and training of observers. Probability of occurrence was slightly underestimated for bare ground, Artemisia tridentata , Elycap medusae , and Poa secunda using a model that ignored imperfect detection. Estimated mean percent cover was not substantially impacted by ignoring measurement error for five plant species and bare ground. Our modelling framework for cover class data allows for an explicit separation of distribution from abundance and, importantly, allows for interpreting species–environment relationships in terms of variation in mean percent cover as compared to cumulative odds ratios. The beta distribution inherently accommodates heteroscedasticity and skewness, statistical properties that are a consequence of spatially aggregated patterns common to plant survey data. Recording cover classes provides a reliable, efficient way to measure plants and our simulations suggest little loss of information compared to assuming continuous percent cover. We provide JAGS and Stan model code for implementation.

Methods in Ecology and Evolution↗
USGS2019

Sampling and analysis frameworks for inference in ecology

1. Reliable statistical inference is central to ecological research, much of which seeks to estimate population attributes and their interactions. The issue of sampling design and its relationship to inference has become increasingly important due to rapid proliferation of modeling methodology (line transect modeling, capture-recapture, estimation of occurrence, model selection procedures, hierarchical modeling) and new sampling approaches (adaptive sampling, other specialized designs). It is important for ecologists using these advanced methods to be aware of how the linkages between sample selection and data analysis can potentially affect inference. 2. We examine design-based and model-based inference frameworks for ecological data collected randomly, purposively, or opportunistically. We elucidate differences in the probability structures for data arising from these frameworks, clarify the assumptions that underlie them, and demonstrate their differences. 3. Design-based inference builds on a probability structure inherited from randomized data collection, whereas model-based inference relies on an assumed stochastic model of the data. By itself, a design-based approach is of limited value for inferences about causal hypotheses. In contrast, model-based inference is dependent on a conditionality principle that can seldom be shown to be met for an ecological system. We describe the conditions under which one can safely ignore sampling design in model-based analysis, along with inferential implications if these conditions are not met. The special case of opportunistic sampling is discussed. 4. We present a combined framework that takes advantage of both approaches to inference, and provides a robust methodology that can deal with the modeling of sampling problems such as nondetection and misclassification, as well as the exploration of causal hypotheses. The combined framework can be useful for identifying optimal sampling strategies. 5. Each approach to inference has its strengths and weaknesses, and practitioners should be aware of these in order to tailor designs and analyses to specific questions. We use the approaches and their underlying rationales to provide guidelines for choosing designs and estimators for reliable inference.

Methods in Ecology and Evolution↗
USGS2020

Dermal denticle assemblages in coral reef sediments correlate with conventional shark surveys

1. It is challenging to assess long-term trends in mobile, long-lived, and relatively rare species such as sharks. Despite ongoing declines in many coastal shark populations, conventional surveys might be too fleeting and too recent to describe population trends over decades to millennia. Placing recent shark declines into historical context should improve management efforts as well as our understanding of past ecosystem dynamics. 2. A new paleoecological approach for surveying shark abundance on coral reefs is to quantify dermal denticle assemblages preserved in sediments. This approach assumes that denticle accumulation rates correlate with shark abundances. Here, we test this assumption by comparing the denticle record in surface sediments to three conventional shark survey methods at Palmyra Atoll, Line Islands, central Pacific Ocean, where shark density is high and spatially heterogeneous. 3. We generally found a significant positive correlation between denticle accumulation rates and shark abundances derived from underwater visual census, baited remote underwater video, and hook and line surveys. 4. Denticle accumulation rates reflected shark abundances, suggesting that denticle assemblages can preserve a signal of time-averaged shark abundance in low-energy coral reef environments. We offer suggestions for applying this tool to measure shark abundance over long timescales in other contexts.

Methods in Ecology and Evolution↗
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↗
USGS2020

msocc: Fit and analyse computationally efficient multi‐scale occupancy models in R

Environmental DNA (eDNA) sampling is a promising tool for the detection of rare and cryptic taxa, such as aquatic pathogens, parasites and invasive species. Environmental DNA sampling workflows commonly rely on multi‐stage hierarchical sampling designs that induce complicated dependencies within the data. This complex dependence structure can be intuitively modelled with Bayesian multi‐scale occupancy models. However, current software for such models are computationally demanding, impeding their use. We present an r package, msocc , that implements a data augmentation strategy to fit fully Bayesian, computationally efficient multi‐scale occupancy models. The msocc package allows users to fit multi‐scale occupancy models, to estimate and visualize posterior summaries of site, sample and replicate‐level occupancy, and to compare different models using Bayesian information criterion. Additionally, we provide a supplemental web application that allows users to investigate study design for multi‐scale occupancy models and acts as a graphical user interface to the msocc package. The utility of the msocc package is illustrated on a published dataset and the functions in msocc are compared to the primary Bayesian toolkit for multi‐scale occupancy modelling, eDNAoccupancy , using various computational benchmarks. These benchmarks indicate that msocc is capable of fitting models 50 times faster than eDNAoccupancy . We hope that access to software that efficiently fits, analyses and conducts study design investigations for multi‐scale occupancy models facilitates their implementation by the research and wildlife management communities.

Methods in Ecology and Evolution↗
USGS2020

Integrating airborne remote sensing and field campaigns for ecology and Earth system science

In recent years, the availability of airborne imaging spectroscopy (hyperspectral) data has expanded dramatically. The high spatial and spectral resolution of these data uniquely enable spatially explicit ecological studies including species mapping, assessment of drought mortality and foliar trait distributions. However, we have barely begun to unlock the potential of these data to use direct mapping of vegetation characteristics to infer subsurface properties of the critical zone. To assess their utility for Earth systems research, imaging spectroscopy data acquisitions require integration with large, coincident ground‐based datasets collected by experts in ecology and environmental and Earth science. Without coordinated, well‐planned field campaigns, potential knowledge leveraged from advanced airborne data collections could be lost. Despite the growing importance of this field, documented methods to couple such a wide variety of disciplines remain sparse. We coordinated the first National Ecological Observatory Network Airborne Observation Platform (AOP) survey performed outside of their core sites, which took place in the Upper East River watershed, Colorado. Extensive planning for sample tracking and organization allowed field and flight teams to update the ground‐based sampling strategy daily. This enabled collection of an extensive set of physical samples to support a wide range of ecological, microbiological, biogeochemical and hydrological studies. We present a framework for integrating airborne and field campaigns to obtain high‐quality data for foliar trait prediction and document an archive of coincident physical samples collected to support a systems approach to ecological research in the critical zone. This detailed methodological account provides an example of how a multi‐disciplinary and multi‐institutional team can coordinate to maximize knowledge gained from an airborne survey, an approach that could be extended to other studies. The coordination of imaging spectroscopy surveys with appropriately timed and extensive field surveys, along with high‐quality processing of these data, presents a unique opportunity to reveal new insights into the structure and dynamics of the critical zone. To our knowledge, this level of co‐aligned sampling has never been undertaken in tandem with AOP surveys and subsequent studies utilizing this archive will shed considerable light on the breadth of applications for which imaging spectroscopy data can be leveraged.

Colorado↗
USGS2020

Scenarios for valuing sample information in natural resources

Uncertainty is ubiquitous in natural resource systems, science and management. Sample data are obtained in order to reduce uncertainty, thereby increasing knowledge and improving resource management, but sampling always comes at a cost of some sort. Is that cost worthwhile? Analysis of the value of sample information ( VSI ) addresses this question. In this paper we develop the valuation of sample information in terms of five elements: (a) a system whose attributes are the focus of analysis; (b) a range of management actions that affect the system's status; (c) uncertainty about system status or structure, as characterized by initial (prior) probabilities of possible system states or structures; (d) an experiment or other information source that produces new data points and updated (posterior) probabilities; and (e) a value measure that is a function of the management action taken, conditional on either the system state or structure. We describe five scenarios for analysing the VSI under uncertainty about system structure and state. Scenarios 1–3 comprise analyses of conditional, expected and optimal expected values of sample information. They focus primarily on choice of management adaptations with new information. Scenarios 4 and 5 involve pre-selected management actions, and are useful for comparing designs of data collection rather than for choosing a management action. These last scenarios expand the framework for VSI to include actions that have been selected independently of the updating of uncertainty. We discuss other extensions of VSI analysis, which include spatial applications, hybrid scenarios, applications involving dynamic systems, and a focus on costs rather than net benefits. Value of sample information analysis holds promise in emerging areas of ecology such as ecological forecasting and the use of remote sensing in conservation, where potential new data from models and satellites can be evaluated in advance, thereby allowing more efficient prioritization of scientific efforts. More generally, VSI can contribute to better ecological understanding and more effective management in a wide range of ecological situations.

Methods in Ecology and Evolution↗
USGS2021

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Methods in Ecology and Evolution↗
USGS2021

A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems

Accurately quantifying rates and patterns of biological nitrogen fixation (BNF) in terrestrial ecosystems is essential to characterize ecological and biogeochemical interactions, identify mechanistic controls, improve BNF representation in conceptual and numerical modelling, and forecast nitrogen limitation constraints on future carbon (C) cycling. While many resources address the technical advantages and limitations of different methods for measuring BNF, less systematic consideration has been given to the broader decisions involved in planning studies, interpreting data, and extrapolating results. Here, we present a conceptual and practical road map to study design, study execution, data analysis and scaling, outlining key considerations at each step. We address issues including defining N‐fixing niches of interest, identifying important sources of temporal and spatial heterogeneity, designing a sampling scheme (including method selection, measurement conditions, replication, and consideration of hotspots and hot moments), and approaches to analysing, scaling and reporting BNF. We also review the comparability of estimates derived using different approaches in the literature, and provide sample R code for simulating symbiotic BNF data frames and upscaling. Improving and standardizing study design at each of these stages will improve the accuracy and interpretability of data, define limits of extrapolation, and facilitate broader use of BNF data for downstream applications. We highlight aspects—such as quantifying scales of heterogeneity, statistical approaches for dealing with non‐normality, and consideration of rates versus ecological significance—that are ripe for further development.

Methods in Ecology and Evolution↗
USGS2021

Instrumental variable methods in structural equation models

Instrumental variable regression (RegIV) provides a means for detecting and correcting parameter bias in causal models. Widely used in economics, recently several papers have highlighted its potential utility for ecological applications. Little attention has thus far been paid to the fact that IV methods can also be implemented within structural equation models (SEMIV). In this paper I present the motivations, requirements and basic procedures for using SEMIV. I first consider causal inference and IVs from the perspective of a randomized experiment with partial control of the cause of interest. I consider common sources of bias, the role of randomization and limits to its capacity to exclude bias. Sources of bias include omitted confounders, reciprocal causation, reverse causation and measurement error, all of which can all be seen as a single problem—endogeneity. The approach to estimating IV models most commonly used in econometric practice, two-stage least squares regression (2SLS), is explained, followed by a brief exposition of the covariance modelling approach used in SEM. Using data from an ecological field experiment, I illustrate the use of the treatment variable as an IV and then illustrate procedures for evaluating candidate variables that might serve as additional IVs. IV methods are shown to be useful for both detecting endogeneity and removing its influences. I illustrate some of the ways that bias can be generated, as well as diagnostic capabilities and means for remedy embedded within SEM. Procedures for screening and evaluating additional IVs reveal valuable lessons regarding the theoretical requirements and empirical standards for IVs. SEMIV provides a useful way to detect and control for bias. I suggest that the use of IVs within the SEM framework can support the simultaneous pursuit of causal inference and explanatory modelling, a common pair of aspirations for ecologists. Moving forward, there is a need for a better understanding of the capabilities of SEMIV and requirements for successful application.

Methods in Ecology and Evolution↗
USGS2021

Pedigree accumulation analysis: Combining methods from community ecology and population genetics for breeding adult estimation

Estimates of the number of successfully breeding adults ( N S ) in a population can predict levels of recruitment. However, assessments of N S are often difficult to obtain because encounters with adults are limited due to life-history characteristics, low abundance or other constraints associated with access to critical habitats. Alternatively, efforts to sample individuals at earlier ontogenetic stages can be more tractable, resulting in more comprehensive samples. To estimate N S , we describe, evaluate and apply two nonparametric species richness estimators to information associated with genetic pedigree reconstruction. Simulations compared bias and precision associated with Chao and Jackknife methods when estimating N S . We also evaluated N S estimation sensitivity to two sources of variation associated with species reproductive ecology (variance in reproductive success and sex ratio skew) and genetic pedigree assignment error. Finally, the application of our novel method was demonstrated in two different species and systems (Chinook Salmon in Oregon, USA and Lake Sturgeon in Michigan, USA). We found unbiased N S estimates were generated across a broad range of offspring sample sizes using the Chao method. Empirical results corroborated simulation-based expectations and highlighted applications where parents and offspring are sampled, and when only offspring are sampled. When offspring sample sizes are adequate and pedigree reconstruction errors are low, the combination of established methods from community ecology and genetic pedigree reconstruction provides an accurate alternative method to estimate N S that can facilitate population assessments.

Methods in Ecology and Evolution↗
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↗
USGS2022

Estimating species misclassification with occupancy dynamics and encounter rates: A semi-supervised, individual-level approach

1. Large-scale, long-term biodiversity monitoring is essential to conservation, land management, and identifying threats to biodiversity. However, multispecies surveys are prone to various types of observation error, including false positive/negative detection, and misclassification, where a species is thought to have been encountered but not correctly identified. Previous methods assume an imperfect classifier produces species-level classifications, but in practice, particularly with human observers, we may end up with extraspecific classifications including `unknown', morphospecies designations, and taxonomic identifications coarser than species. Disregarding these types of species misclassification in biodiversity monitoring datasets can bias estimates of ecologically important quantities such as demographic ratess, occurrence, and species richness. 2. Here we present a joint classification-occupancy model that accounts for species non-detection and misclassification. Our framework accommodates extinction and colonization dynamics, allows for additional uncertain `morphospecies' designations, and makes use of individual specimens with known species identities in a semi-supervised setting. We compare the performance of our model to a classification-only model that discards information about occupancy and encounter rate. We illustrate our model with an empirical case study of the carabid beetle (Carabidae) community at the National Ecological Observatory Network Niwot Ridge Mountain Research Station, near Boulder, CO, USA. We also use simulations to evaluate model performance through validation metrics where varying fractions of the data are confirmed. 3. The model supported imperfect classifier accuracy and favored certain true species classifications strongly for some morphospecies. The model outperformed (e.g., precision) the reduced model that discarded occupancy information, and these differences were most pronounced for abundant species. 4. Spatial and temporal dynamics from modeled occupancy and encounter rates may inform species misclassification probability, but this idea has not yet been tested. Our statistical framework explores this opportunity, and can be applied to datasets with imperfect species detection and classification, limited verification data, and non-species classifications.

Methods in Ecology and Evolution↗
USGS2022

Ignoring species availability biases occupancy estimates in single-scale occupancy models

Most applications of single-scale occupancy models do not differentiate between availability and detectability, even though species availability is rarely equal to one. Species availability can be estimated using multi-scale occupancy models; however, for the practical application of multi-scale occupancy models, it can be unclear what a robust sampling design looks like and what the statistical properties of the multi-scale and single-scale occupancy models are when availability is less than one. Using simulations, we explore the following common questions asked by ecologists during the design phase of a field study: (Q1) what is a robust sampling design for the multi-scale occupancy model when there are a priori expectations of parameter estimates? (Q2) what is a robust sampling design when we have no expectations of parameter estimates? and (Q3) can a single-scale occupancy model with a random effects term adequately absorb the extra heterogeneity produced when availability is less than one and provide reliable estimates of occupancy probability? Our results show that there is a tradeoff between the number of sites and surveys needed to achieve a specified level of acceptable error for occupancy estimates using the multi-scale occupancy model. We also document that when species availability is low (<0.40 on the probability scale), then single-scale occupancy models underestimate occupancy by as much as 0.40 on the probability scale, produce overly precise estimates, and provide poor parameter coverage. This pattern was observed when a random effects term was and was not included in the single-scale occupancy model, suggesting that adding a random-effects term does not adequately absorb the extra heterogeneity produced by the availability process. In contrast, when species availability was high (>0.60), single-scale occupancy models performed similarly to the multi-scale occupancy model. Users can further explore our results and sampling designs across a number of different scenarios using the RShiny app https://gdirenzo.shinyapps.io/multi-scale-occ/ . Our results suggest that unaccounted for availability can lead to underestimating species distributions when using single-scale occupancy models, which can have large implications on inference and prediction, especially for those working in the fields of invasion ecology, disease emergence, and species conservation.

Methods in Ecology and Evolution↗
USGS2022

Estimating occupancy from autonomous recording unit data in the presence of misclassifications and detection heterogeneity

1. Autonomous Recording Units (ARUs) are now widely used to survey communities of species. These surveys generate spatially and temporally replicated counts of unmarked animals, but such data typically include false negatives and misclassified detections, both of which may vary across sites in proportion to abundance. These data challenges can bias estimates of occupancy, and the typical approach of verifying individual detections is expensive. 2. We developed a Bayesian implementation of a two-species, false-positive N-mixture model for estimating occupancy from ARU data or other counts of unmarked animals that does not require manual verification. The model accounts for species misclassification and abundance-induced detection heterogeneity, as well as false negatives. To evaluate this model, we simulated 200 data sets for each of 29 scenarios, including scenarios in which misclassifications outnumbered correct classifications for rare species. We also applied the model to acoustic surveys of bats conducted on Fort Carson Army Post and Piñon Canyon Maneuver Site, Colorado, USA. 3. In the simulation study, bias, coverage, and root mean square error for occupancy estimates obtained from the two-species false-positive N-mixture model were superior to metrics obtained from two competing two-species false-positive occupancy models. Across 29 scenarios, absolute bias was consistently low (range: -0.03–0.07), while coverage averaged 93% (range: 74%–98%). For alternative occupancy models, absolute bias was often high (range: -0.36–0.39), and coverage averaged from 47%–65%. Although our model included an abundance parameter, abundance estimates were not reliable. For two species of Myotis bats, we estimated that 1%–5% of field-recorded detections were misclassified. Estimated occupancy (0.91 and 0.76) was lower than naïve estimates (1.00 and 0.94). Competing occupancy models implausibly estimated local occupancy of 0.00 at sites with numerous detections. 4. Our two-species, false-positive N-mixture model is significant because it accounts for detection heterogeneity and improves occupancy estimates without expensive manual verification of detections. Our field application indicated that misclassifications were not common, yet affected occupancy inferences. Given that ARUs are increasingly used to survey a broad range of taxa, such an occupancy model could be widely useful.

Methods in Ecology and Evolution↗
USGS2022

Spatially explicit management of genetic diversity using ancestry probability surfaces

1. Ecological restoration and conservation efforts are increasing worldwide and the management of intraspecific genetic variation in plants and animals, an important component of biodiversity, is increasingly valued. As a result, tailorable, spatially explicit approaches to map genetic variation are needed to support decision-making and management frameworks related to the recovery of threatened and endangered species and the maintenance of genetic resources in species utilized by humans, such as for restoration or agricultural purposes. 2. Here, we describe and demonstrate a workflow to spatially interpolate patterns of genetic differentiation using novel functions in the R package POPMAPS ( Pop ulation M anagement using A ncestry P robability S urfaces). Our approach uses empirical genetic data to estimate ancestry coefficients across a user-defined landscape correlated with patterns of differentiation in the focal species. The resulting surface, which we term the ancestry probability surface, includes two components: hard population boundaries and estimations of uncertainty that represent confidence in population assignments (i.e., ancestry probabilities). 3. An ancestry probability surface developed for Hilaria jamesii , an important graminoid utilized in restoration across the western United States, demonstrates the functionality of POPMAPS . Genetic distances among empirical sites correlated better with least-cost distances across suitable habitat than with geographic distances, informing the surface over which the interpolation was conducted (i.e., a model indicating habitat suitability). A jackknifing procedure identified parameter values resulting in robust population assignments across the species’ range, which were utilized in downstream analyses to estimate ancestry coefficients from empirical data. Ancestry coefficients were translated into ancestry probabilities, which tended to be low for cells that were intermediate in distance between empirical sampling locations representing different populations or when influenced by empirical sampling locations with mixed genetic ancestry. 4. POPMAPS allows users to tailor parameter values and analytical approaches and thereby incorporate species-specific biological characteristics and desired levels of uncertainty into maps illustrating patterns of genetic differentiation. Ancestry probability surfaces may be used to guide management or investigate further ecological or evolutionary hypotheses. We discuss how maps produced by POPMAPS can inform multiple management challenges including species recovery planning and the utilization of commonly used species in restoration.

Methods in Ecology and Evolution↗