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Ecologists can learn a lot about species by studying the precise locations in which they do (and do not) occur, but the location information associated with many species records is imprecise. A prominent example of this is the North American Breeding Bird Survey (BBS), in which volunteer observers have surveyed birds at points along consistent routes across the United States for over fifty-five years. As the BBS was designed for large-scale analyses, detailed location information for each bird count is not recorded. We estimate location uncertainty, and the resulting uncertainty in land cover covariates, for the BBS data and present a modeling method that accounts for this uncertainty in a way that opens new possibilities for fine-scale uses of this extensive dataset, unlocking its potential to advance the study of the relationships between birds and their immediate habitat. More broadly, our methods and modeling framework could be used in a variety of situations in which covariate or location uncertainty is a challenge.

Risk analysis protocols for prioritizing the management of non-native species are numerous, yet few incorporate risk and management in the same analysis or accommodate a broad diversity of taxa outside of a specific geographic area. We adapted a protocol that accounts for these factors to address non-native animal species in the Southern California/Northern Baja California Coast Ecoregion near the international border in San Diego County, an area with high indigenous biodiversity and high numbers of species of conservation concern. This stepwise, semi-quantitative protocol is applicable to any animal group in any predefined geographic area, relies on consensus-building among taxonomic experts, and has been vetted through previous use and in peer-reviewed literature. Our results show that the final prioritization was driven mainly by management feasibility, with top-ranked species having multitrophic effects that favor other non-native invaders over native residents. Conditions within the assessment area required some modification to the protocol as it was originally designed, namely a shift in emphasis from eradication to control, given that eradication is implausible for most non-native species in the assessment area. We call attention to taxon-specific issues that surfaced during the analysis, identify areas for improvement in this first-ever risk assessment for invasive animal species in the Natural Communities Conservation Plan/Habitat Conservation Plan (NCCP/HCP) reserve system of San Diego County, and provide suggestions for further refinement of the protocol. This study builds on the effort to standardize risk analysis for invasive species globally, given that many of the same invaders present threats to indigenous biodiversity worldwide.

1. Surveillance programs are essential for detecting emerging pathogens and often rely on molecular methods to make inference about the presence of a target disease agent. However, molecular methods rarely detect target DNA perfectly. For example, molecular pathogen detection methods can result in misclassification (i.e., false positives and false negatives) or partial detection errors (i.e., detections with ‘ambiguous’, ‘uncertain’, or ‘equivocal’ results). Then, when data are to be analyzed, these?partial observations?are?either?discarded?or censored;?this, however, disregards information that could be used to make inference about the true state of the system. There is a critical need for more direction and guidance related to how many samples is enough to declare a unit of interest ‘pathogen-free’.
2. Here, we develop a Bayesian hierarchal framework that accommodates false negative, false positive, and uncertain detections to improve inference related to the occupancy of a pathogen. We apply our modeling framework to a case study of the fungal pathogen Pseudogymnoascus destructans (Pd) identified in Texas bats at the invasion front of white-nose syndrome. To improve future surveillance programs, we provide guidance on sample sizes required to be 95% certain a target organism is absent from a site.
3. We found that the presence of uncertain detections increased the variability of resulting posterior probability distributions of pathogen occurrence, and that our estimates of required sample size were very sensitive to prior information about pathogen occupancy, pathogen prevalence, and diagnostic test specificity. In the Pd case study, we found that the posterior probability of occupancy was very low in 2018, but occupancy probability approached 1 in 2020, reflecting increasing prior probabilities of occupancy and prevalence elicited from the site manager.
4. Our modeling framework provides the user a posterior probability distribution of pathogen occurrence, which allows for subjective interpretation by the decision-maker. To help readers apply and use the methods we developed, we provide an interactive?RShiny?app?that generates target species?occupancy estimation and sample size estimates to make these methods more accessible?to the scientific community (https://rmummah.shinyapps.io/ambigDetect_sampleSize).?This modeling framework and sample size guide may be useful for improving inferences from molecular surveillance data about emerging pathogens, non-native invasive species, and endangered species where misclassifications and ambiguous detections occur.

The endangered mountain yellow-legged frog (Rana muscosa) has been reduced to <10 isolated populations in the wild. Due to frequent catastrophic events (floods, droughts, wildfires), the recent dynamics of these populations have been erratic, making the future of the species highly uncertain. In 2018, a recovery plan was developed to improve the species status by reducing the impacts of various threats (predation, disease, habitat destruction), as well as reinforcing wild populations through the reintroduction of captive-bred frogs. The short-term goal stated in this plan was to reach a minimum of 20 populations of 50 adults each (hereafter, the 20/50 target), before the species can be considered for downlisting from the U.S. Endangered Species Act. However, there is no guarantee that this 20/50 target will be sufficient to ensure the species persistence in the long run. Using 19 years of mark-recapture data, we estimated populations' demographic trends and assessed the viability of R. muscosa from a starting state of 20 populations of 50 adults each (i.e., the downlisting criteria). Our results reveal that, from this 20/50 state, the species has high chances of persistence only at a short time horizon (50 years). Moreover, >80% of populations would be extinct 50 years later. Therefore, the species will not be able to persist without implementation of the reintroduction program. We found that it is more important to increase the number of suitable sites occupied by R. muscosa than to simply reinforce or augment existing populations. Expanding the current distribution by establishing new populations at suitable sites, even after the “20 populations” mark has been reached, would increase the likelihood of the species' persistence in the longer term.

1. Most applications of single-level occupancy models do not differentiate between availability and detectability, even though species availability is rarely equal to one. The availability process includes elements of species movement, behavior, and phenology, and 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-level occupancy models are when availability is less than one.

2. 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-level 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?.

3. 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 (< https://0.40 on the probability scale), then single-level occupancy models severely underestimate occupancy by as much as https://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-level 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-level occupancy models performed similarly to the multi-scale occupancy model.

4. As a companion, we provide an RShiny app that allows users to further explore our results and determine optimal designs across different sampling scenarios https://gdirenzo.shinyapps.io/multi-scale-occ/. Our results suggest that unaccounted for availability can lead to underestimating species distributions using single-level occupancy models, which can have large implications on ecological inference and predictions for practitioners, such as those working at the front lines of invasion ecology, disease emergence, and species conservation.

Extensive urbanization in coastal southern California has reduced natural habitat in this biodiversity hotspot. To better conserve ecological communities, state and federal agencies, along with local jurisdictions and private stakeholders, developed regional conservation plans for southern California. Although many protected areas exist within this region, the patchwork nature of these protected areas might not provide good coverage for species that require multiple habitat components, such as amphibians with complex life histories. Because of declines in the past century, the status of the western spadefoot (Spea hammondii) in southern California is of concern to state and federal wildlife agencies. Species distribution models (SDMs) can aid in determining
the conservation status of imperiled species by projecting where suitable habitat remains and how much is protected from further development. We built SDMs that integrated site occupancy data from systematic pitfall trapping surveys and presence-only data from biodiversity databases and citizen science platforms to project the current distribution of western spadefoots in southern California. Western spadefoot occurrence was positively related to the cover of grassland or shrub/scrub and the % sand in the soil within a 1000 m buffer, and was negatively related to slope, elevation, and distance to ephemeral streams or vernal pools. Most of the remaining unprotected habitat for western spadefoots is in the southern half of its historical range in western San Diego and Riverside counties. A few large tracts of spadefoot habitat exist on U.S. Department of Defense lands and smaller tracts remain on ecological reserves owned by state and local government agencies. Only small patches of habitat remain in the northern half of this clade’s historical range in Ventura, Orange, Los Angeles, and San Bernardino counties. Existing regional conservation plans provide ostensible spatial coverage of the majority of extant habitat for western spadefoots in southern California, but most of the habitat within the jurisdiction of these plans lacks formal protection, exposing this species to further declines as urbanization continues in the 21st century.

For species with variable phenology, it is often challenging to produce reliable estimates of population dynamics or changes in occupancy. The Arizona Toad (Anaxyrus microscaphus) is a southwestern USA endemic that has been petitioned for legal protection, but status assessments are limited by a lack of information on population trends. Also, timing and consistency of Arizona Toad breeding varies greatly, making it difficult to predict optimal survey times or effort required for detection. To help fill these information gaps, we conducted breeding season call surveys during 2013–2016 and 2019 at 86 historically occupied sites and 59 control sites across the species’ range in New Mexico. We estimated variation in mean dates of arrival and departure from breeding sites, changes in occupancy, and site-level extinction since 1959 with recently developed multi-season staggered-entry models, which relax the within-season closure assumption common to most occupancy models. Optimal timing of surveys in our study areas was approximately March 5 - March 30. Averaged across years, estimated probability of occupancy was https://0.58 (SE = 0.09) for historical sites and https://0.19 (SE = 0.08) for control sites. Occupancy increased from 2013 through 2019. Notably, even though observer error was trivial, annual detection probabilities varied from https://0.23 to https://0.75 and declined during the study; this means naïve occupancy values would have been misleading, indicating apparent declines in toad occupancy. Occupancy was lowest during the first year of the study, possibly due to changes in stream flows and conditions in many waterbodies following extended drought and recent wildfires. Although within-season closure was violated by variable calling phenology, simple multi-season models provided nearly identical estimates as staggered-entry models. Surprisingly, extinction probability was unrelated to the number of years since the first or last record at historically occupied sites. Collectively, our results suggest a lack of large, recent declines in occupancy by Arizona Toads in New Mexico, but we still lack population information from most of the species’ range.

First-order dynamic occupancy models (FODOMs) are a class of state-space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to measure the covariates that generate ecological data, which are often spatio-temporally correlated. Consequently, the non-independent error structure of correlated data causes underestimation of parameter uncertainty and poor ecological inference. Here, we extend the FODOM framework with a second-order Markov process to accommodate site memory when covariates are not available. Our modeling framework can be used to make reliable inference about site occupancy, colonization, extinction, turnover, and detection probabilities. We present a series of simulations to illustrate the data requirements and model performance. We then applied our modeling framework to 13 years of data from an amphibian community in southern Arizona, USA and find that site memory helps describe dynamic processes for most species. Our approach represents a valuable advance in obtaining inference on population dynamics, especially as they relate to metapopulations.

1. Fragmentation within urbanized environments often leads to a loss of native species diversity; however, variation exists in responses among-species and among-populations within species.
2. We aimed to identify patterns in species biogeography in an urbanized landscape to understand anthropogenic effects on vertebrate communities and identify species that are more sensitive or resilient to landscape change.
3. We investigated patterns in species richness and species responses to fragmentation in southern Californian small vertebrate communities using multispecies occupancy models and determined factors associated with overall commonness and sensitivity to patch size for 45 small vertebrate species both among and within remaining non-developed patches.
4. In general, smaller patches had fewer species, with amphibian species richness being particularly sensitive to patch size effects. Mammals were generally more common, occurring both in a greater proportion of patches and a higher proportion of the sites within occupied patches. Alternatively, amphibians were generally restricted to larger patches but were more ubiquitous within smaller patches when occupied. Species range size was positively correlated with how common a species was across and within patches, even when controlling for only patches that fell within a species’ range. We found sensitivity to patch size was greater for more fecund species and depended on where the patch occurred within a species’ range. While all taxa were more likely to occur in patches in the warmer portions of their ranges, amphibians and mammals were more sensitive to fragmentation in these warmer areas as compared to the rest of their ranges. Similarly, amphibians occurred at a smaller proportion of sites within patches in drier portions of their ranges. Mammals occurred at a higher proportion of sites that were also in drier portions of their range while reptiles did not differ in their sensitivity to patch size by range position.
5. We demonstrate that taxonomy, life history, range size, and range position can predict commonness and sensitivity of species across this highly fragmented yet biodiverse landscape. The impacts of fragmentation on species communities within an urban landscape depend on scale, with differences emerging among and within species and populations.

A principal challenge impeding strong inference in analyses of wild populations is the lack of robust and long-term data sets. Recent advancements in analytical tools used in wildlife science may increase our ability to integrate smaller data sets and enhance the statistical power of population estimates. One such advancement, the development of spatial capture-recapture (SCR) methods, explicitly accounts for differences in spatial study designs, making it possible to equate multiple study designs in one analysis. SCR has been shown to be robust to variation in design as long as minimal sampling guidance is adhered to. However, these expectations are based on simulation and have yet to be evaluated in wild populations. Here we conduct a rigorously designed field experiment by manipulating the arrangement of artificial cover objects (ACOs) used to collect data on red-backed salamanders (Plethodon cinereus) to empirically evaluate the effects of design configuration on inference made using SCR. Our results suggest that, using SCR, estimates of space use and detectability are sensitive to study design configuration, namely the spacing and extent of the array, and that caution is warranted when assigning biological interpretation to these parameters. However, estimates of population density remain robust to design except when the configuration of detectors grossly violates existing recommendations.

Occupancy methods propelled the quantitative study of species distributions forward by separating the observation process, or the imperfect detectability of species, from the ecological processes of interest governing species distributions. Occupancy studies come at a cost, however: the collection of additional data to account for nondetections at sites where the species is present. The most common occupancy designs (repeated measures designs) require repeat visits to sites or the use of multiple observers or detection methods. Time-to-detection methods have been identified as a potentially efficient alternative, requiring only one visit to each site by a single observer. A comparison of time-to-detection methods to repeated measures designs for visual encounter surveys would allow researchers to evaluate whether time-to-detection methods might be appropriate for their study system and can inform optimal survey design. We collected time-to-detection data during two different repeated measures design occupancy surveys for four amphibians and compared the performance of time-to-detection methods to the other designs using the location (potential bias) and precision of posterior distributions for occurrence parameters. We further used results of time-to-detection surveys to optimize survey design. Time-to-detection methods performed best for species that are widespread and have high detection probabilities and rates, but performed less well for cryptic species with lower probability of occurrence or whose detection was strongly affected by survey conditions. In all cases single surveys were most efficient in terms of person-hours expended, but under some conditions the survey duration required to achieve high detection probabilities would be prohibitively long for a single survey. Regardless of occupancy survey design, time-to-detection methods provide important information that can be used to optimize surveys, allowing researchers and resource managers to efficiently achieve monitoring and conservation goals. Collecting time-to-detection data while conducting repeated measures occupancy surveys requires only small modifications to field methods but could have large benefits in terms of time spent surveying in the long-term.

The conservation of biological resources relies on the successful management of ecological and physiological research data. The Western Ecological Research Center of the U.S. Geological Survey is working with researchers, land managers and decision makers from non-government organizations, and city, county, state and federal resource agencies to develop data management methods. Access to the most current and applicable research data available in making sound decisions to conserve species diversity is foundational. We sought to accomplish several goals in developing the data management strategy used in the Multi-Taxa database (MTX). Data persistence and availability are primary goals of well-developed databases. By documenting and sharing the structure and definitions of MTX, we hope to further the successful management of these crucial data.

In metapopulations, dispersal and population growth rates are influenced by patch quality, spatial structure, and local population density. Recently developed spatial metapopulation models allow inferences about distance effects on dispersal, but these models typically focus on patch occupancy rather than abundance of animals. Spatial occupancy models are useful for studying colonization-extinction dynamics, but richer insights can be gained from estimating abundance and density-dependent demographic rates. We used presence-absence and count data from an 11-year study of a reintroduced metapopulation of federally-threatened Chiricahua leopard frogs (Lithobates chiricahuensis) to develop an integrated abundance-based metapopulation model to draw inferences about the processes contributing to spatiotemporal variation in density. Pond-specific population growth was influenced by pond hydroperiod and frog density, such that permanent and semi-permanent ponds with low densities of adult frogs experienced the highest annual population growth. Immigration rate declined as the distance among ponds increased. Metapopulation-level abundance increased from 2004 until 2015, when it stabilized around 1323 adult frogs (95% CI, 1166–1539). Further, changes in metapopulation abundance were driven mostly by changes in abundance at a few ponds. These high-density populations, which would not have been identifiable with traditional metapopulation models, are likely especially important for species recovery in the area. Our study extends existing statistical models of metapopulation dynamics by focusing on abundance and making it possible to test hypotheses regarding the influence of pond quality and density on local dynamics and colonization.

One of the leading causes of global amphibian decline is emerging infectious disease. We summarize the disease ecology of four major emerging amphibian infectious agents: chytrids, ranaviruses, trematodes, and Perkinsea. We focus on recently developed quantitative advances that build on well-established ecological theories and aid in studying epizootic and enzootic disease dynamics. For example, we identify ecological and evolutionary selective forces that determine disease outcomes and transmission pathways by borrowing ideas from population and community ecology theory. We outline three topics of general interest in disease ecology: (i) the relationship between biodiversity and disease risk, (ii) individual, species, or environmental transmission heterogeneity, and (iii) pathogen coinfections. Finally, we identify specific knowledge gaps impeding the success of conservation-related decisions for disease mitigation and the future of amphibian conservation success.

Natural resource managers are increasingly faced with threats to managed ecosystems that are largely outside of their control. Examples include land development, climate change, invasive species, and emerging infectious diseases. All of these are characterized by large uncertainties in timing, magnitude, and effects on species. In many cases, the conservation of species will only be possible through concerted action on the limited elements of the system that managers can control. However, before an action is taken, a manager must decide how to act, which is ? if done well ? not easy. In addition to dealing with uncertainty, managers must balance multiple potentially competing objectives, often in cases when the management actions available to them are limited. Guidance in making these types of challenging decisions can be found in the practice known as decision analysis. We demonstrate how using a decision-analytic approach to frame decisions can help identify and address impediments to improved conservation decision making. We demonstrate the application of decision analysis to two high-elevation amphibian species. An inadequate focus on the decision-making process, and an assumption that scientific information is adequate to solve conservation problems, must be overcome to advance the conservation of amphibians and other highly threatened taxa.

Many natural wetlands around the world have disappeared or been replaced, resulting in the dependence of many wildlife species on small, artificial earthen stock ponds. These ponds provide critical wildlife habitat, such that the accurate detection of water and assessment of inundation extent is required. We applied a full (linear spectral mixture analysis; LSMA) and partial (matched filtering; MF) spectral unmixing algorithm to a 2007 Landsat 5 and a 2014 Landsat 8 satellite image to determine the ability of a time-intensive (i.e., more spectral input; LSMA) vs. a more efficient (less spectral input; MF) spectral unmixing approach to detect and estimate surface water area of stock ponds in southern Arizona, USA and northern Sonora, Mexico. Spearman rank correlations (rs) between modeled and actual inundation areas less than a single Landsat pixel (< 900 m2) were low for both techniques (rs range = https://0.22 to 0.62), but improved for inundation areas > 900 m2 (rs range = https://0.34 to 0.70). Our results demonstrate that the MF approach can model ranked inundation extent of known pond locations with results comparable to or better than LSMA, but further refinement is required for estimating absolute inundation areas and mapping wetlands < 1 Landsat pixel.

Conservation of at-risk species is aided by reliable forecasts of the consequences of environmental change and management actions on population viability. Forecasts from conventional population viability analysis (PVA) are made using a two-step procedure in which parameters are estimated, or elicited from expert opinion, and then plugged into a stochastic population model without accounting for parameter uncertainty. Recently-developed statistical PVAs differ because forecasts are made conditional on models that are fitted to empirical data. The statistical forecasting approach allows for uncertainty about parameters, but it has rarely been applied in metapopulation contexts where spatially-explicit inference is needed about colonization and extinction dynamics and other forms of stochasticity that influence metapopulation viability. We conducted a statistical metapopulation viability analysis (MPVA) using 11 years of data on the federally-threatened Chiricahua leopard frog to forecast responses to landscape heterogeneity, drought, environmental stochasticity, and management. We evaluated several future environmental scenarios and pond restoration options designed to reduce extinction risk. Forecasts over a 50-yr time horizon indicated that metapopulation extinction risk was <8% for all scenarios, but uncertainty was high. Without pond restoration, extinction risk is forecasted to be 5.6% (95% CI: 0?60%) by year 2060. Restoring six ponds by increasing hydroperiod reduced extinction risk to 1.0% (0 ? 11%) in year 2060. We found little evidence that drought influences metapopulation viability when managers have the ability to maintain ponds that hold water throughout the year and are free of invasive species. Our study illustrates the utility of the spatially explicit statistical forecasting approach to MPVA in conservation planning efforts.

Molecular techniques are powerful conservation tools used in applications ranging from early detection of invasive species to understanding host-pathogen dynamics. However, communication barriers among resource managers, ecologists, and laboratories often preclude the efficient use of molecular data for ecological inference and conservation decision-making. The disconnect largely stems from a lack of specific knowledge about the approaches, decisions, methods, and terminology that each partner uses. As a result, data generated by molecular assays are sometimes of limited utility to managers. We outline a collaborative framework to assist partners with different areas of expertise to more effectively translate their scientific and management needs to other partners. The use of molecular methods in conservation science will continue to expand; therefore, the aim of our paper is to enable the conservation community to harness the full utility of these methods by developing effective collaborative partnerships among managers, ecologists, and laboratory scientists.

Life history information sets the foundation for our understanding of ecology and conservation requirements. For many species, this information is lacking even for basic demographic rates such as survival and movement. When survival and movement estimates are available, they are often derived from mixed demographic groups and do not consider differences among life stages or sexes, which is critical because life stages and sexes often contribute differentially to population dynamics. We used hierarchical models informed with spatial capture-mark-recapture data of Ascaphus montanus (Rocky Mountain tailed frog) in 5 streams and A. truei (coastal tailed frog) in 1 stream to estimate variation in survival and movement by sex and age, represented by size. By incorporating survival and movement into a single model, we were able to estimate both parameters with limited bias. Annual survival was similar between sexes of A. montanus (females = https://0.885 [95% CI: 0.614?1], males = https://0.901 [0.657?1]), but was slightly higher for female A. truei (https://0.836 [0.560?0.993]) than for males (https://0.664 [0.354?0.962]). Survival of A. montanus peaked at mid-age, suggesting that lower survival of young and actuarial senescence may influence population demographics. Our models suggest that younger A. montanus moved farther than older individuals, and that females moved farther than males in both species. Our results provide uncommon insight into age- and sex-specific rates of survival and movement that are crucial elements of life-history strategies and are important for modeling population growth and prescribing conservation actions.

Understanding the distribution of pathogens across landscapes and within host populations is a common aim of wildlife managers. Despite the need for unbiased inferences about these parameters to plan effective management interventions, many researchers fail to account for imperfect pathogen detection and instead report only raw data, which may lead to improper management. We demonstrate analyses of ranavirus detection data in the Patuxent Research Refuge using an occupancy modeling approach, which yields unbiased estimates of pathogen occurrence and prevalence. To improve inference and reporting of pathogen and disease parameters, we describe our approach in-text and provide a written tutorial on how to fit these models using the freely available software Program PRESENCE. In our case study, ranavirus prevalence was underestimated up to 30% if imperfect detection was ignored. After accounting for imperfect detection, estimates of ranavirus prevalence in larval wood frogs (Lithobates sylvaticus) were higher than in larval spotted salamanders (Ambystoma maculatum). In addition, we found that the odds of detecting ranavirus in tail samples were 6.7 times higher than detecting ranavirus in liver samples. We discuss how this information can be used to design ranavirus surveillance studies. Our tutorial provides a clear guide for practitioners and researchers wishing to make unbiased inference in a variety of host-pathogen systems.