Avian Community Response To A Recent Mountain Pine Beetle Epidemic

Recent epidemics of mountain pine beetles (Dendroctonus ponderosae) will fundamentally alter forests of the Intermountain West, impacting management decisions related to fire, logging, and wildlife habitat. We evaluated effects of a recent mountain pine beetle epidemic on site occupancy dynamics of > 60 avian species in four study units dominated by ponderosa pine (Pinus ponderosa) in the Helena National Forest. Point count data were collected during the avian breeding seasons (May-Jul) of 2003-2006 (pre-epidemic) and again during 2009-2010 (post-epidemic). We used a Bayesian hierarchical model that accounts for detection probability to obtain occupancy estimates for rare and elusive species as well as common ones. We estimated occupancy and detection for all species with respect to the occurrence of the beetle outbreak, live tree density at fine scale (1 ha), and live tree density at coarse (landscape) scale (100 ha). Preliminary analyses focus on trends in occupancy for species of interest, such as the American Three-toed Woodpecker (Picoides tridactylus), as well as patterns of occupancy for nesting and foraging guilds. Results indicated diverse responses among species, with occupancy rates increasing for some and declining for others

Occupancy Dynamics of Avian Species in Relation to A Mountain Pine Beetle Epidemic

Recent epidemics of mountain pine beetles (Dendroctonus ponderosae) will fundamentally alter Rocky Mountain forests, impacting management decisions related to fire, logging, and wildlife habitat. We evaluated effects of a recent mountain pine beetle epidemic on occupancy dynamics of 46 avian species. Seventy-six point count stations were randomly located in four, 250 ha study units within pine (Pinus spp.) forests in the Elkhorn Mountains, Montana. Each point was visited 3 times during the breeding seasons (May-Jul) 2003-2006 (pre-outbreak) and 2009-2011 (post-outbreak). We used a Bayesian hierarchical model of multi-species occupancy that accounts for imperfect detection and allows for estimates of rare, as well as common species. Occupancy was modeled for all species with respect to preoutbreak years, year since the outbreak, and proportion of ponderosa pine. Results supported our prediction that occupancy rates would increase after the outbreak for bark-drilling woodpeckers (Picoides spp.). Occupancy rates of foliage-gleaning chickadees (Poecile spp.) and bark-gleaning nuthatches (Sitta spp.) declined soon after the peak in beetle-induced tree mortality (2008); however, their rates began to rise within 3 years. Bark-gleaning species’ occupancy relationships with ponderosa pine changed after the outbreak. Our results will help inform forest management activities for the persistence of species that evolved with largescale disturbances

2018 Scholars at Work Conference Program

Program for the 2018 Scholars at Work Conference at Minnesota State University, Mankato on March 30, 2018

Priority research needs to inform amphibian conservation in the Anthropocene

The problem of global amphibian declines has prompted extensive research over the last three decades. Initially, the focus was on identifying and characterizing the extent of the problem, but more recently efforts have shifted to evidence‐based research designed to identify best solutions and to improve conservation outcomes. Despite extensive accumulation of knowledge on amphibian declines, there remain knowledge gaps and disconnects between science and action that hamper our ability to advance conservation efforts. Using input from participants at the ninth World Congress of Herpetology, a U.S. Geological Survey Powell Center symposium, amphibian on‐line forums for discussion, the International Union for Conservation of Nature Assisted Reproductive Technologies and Gamete Biobanking group, and respondents to a survey, we developed a list of 25 priority research questions for amphibian conservation at this stage of the Anthropocene. We identified amphibian conservation research priorities while accounting for expected tradeoffs in geographic scope, costs, and the taxonomic breadth of research needs. We aimed to solicit views from individuals rather than organizations while acknowledging inequities in participation. Emerging research priorities (i.e., those under‐represented in recently published amphibian conservation literature) were identified, and included the effects of climate change, community‐level (rather than single species‐level) drivers of declines, methodological improvements for research and monitoring, genomics, and effects of land‐use change. Improved inclusion of under‐represented members of the amphibian conservation community was also identified as a priority. These research needs represent critical knowledge gaps for amphibian conservation although filling these gaps may not be necessary for many conservation actions

Data from: Design- and model-based strategies for detecting and quantifying an amphibian pathogen in environmental samples

Accurate pathogen detection is essential for developing management strategies to address emerging infectious diseases, an increasingly prominent threat to wildlife. Sampling for free-living pathogens outside of their hosts has benefits for inference and study efficiency, but is still uncommon. We used a laboratory experiment to evaluate the influences of pathogen concentration, water type, and qPCR inhibitors on the detection and quantification of Batrachochytrium dendrobatidis (Bd) using water filtration. We compared results pre- and post-inhibitor removal, and assessed inferential differences when single versus multiple samples were collected across space or time. We found that qPCR inhibition influenced both Bd detection and quantification in natural water samples, resulting in biased inferences about Bd occurrence and abundance. Biases in occurrence could be mitigated by collecting multiple samples in space or time, but biases in Bd quantification were persistent. Differences in Bd concentration resulted in variation in detection probability, indicating that occupancy modeling could be used to explore factors influencing heterogeneity in Bd abundance among samples, sites, or over time. Our work will influence the design of studies involving amphibian disease dynamics and studies utilizing environmental DNA (eDNA) to understand species distributions

scen2_toad_2N

Generated data for the SRM boreal toad scenario with 160 sites to be used with the dynamic 2-species model

Data from: Host-pathogen metapopulation dynamics suggest high elevation refugia for boreal toads

Emerging infectious diseases are an increasingly common threat to wildlife. Chytridiomycosis, caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd), is an emerging infectious disease that has been linked to amphibian declines around the world. Few studies exist that explore amphibian-Bd dynamics at the landscape scale, limiting our ability to identify which factors are associated with variation in population susceptibility and to develop effective in situ disease management. Declines of boreal toads (Anaxyrus boreas boreas) in the Southern Rocky Mountains are largely attributed to chytridiomycosis but variation exists in local extinction of boreal toads across this metapopulation. Using a large-scale historic dataset, we explored several potential factors influencing disease dynamics in the boreal toad-Bd system: geographic isolation of populations, amphibian community richness, elevational differences, and habitat permanence. We found evidence that boreal toad extinction risk was lowest at high elevations where temperatures may be sub-optimal for Bd growth and where small boreal toad populations may be below the threshold needed for efficient pathogen transmission. In addition, boreal toads were more likely to recolonize high elevation sites after local extinction, again suggesting that high elevations may provide refuge from disease for boreal toads. We illustrate a modeling framework that will be useful to natural resource managers striving to make decisions in amphibian-Bd systems. Our data suggest that in the southern Rocky Mountains high elevation sites should be prioritized for conservation initiatives like reintroductions

Data from: Inferential biases linked to unobservable states in complex occupancy models

Modeling of species distributions has undergone a shift from relying on equilibrium assumptions to recognizing transient system dynamics explicitly. This shift has necessitated more complex modeling techniques, but the performance of these dynamic models has not yet been assessed for systems where unobservable states exist. Our work is motivated by the impacts of the emerging infectious disease chytridiomycosis, a disease of amphibians that associated with declines of many species worldwide. Using this host-pathogen system as a general example, we first illustrate how misleading inferences can result from failing to incorporate pathogen dynamics into the modeling process, especially when the pathogen is difficult or impossible to survey in the absence of a host species. We found that traditional modeling techniques can underestimate the effect of a pathogen on host species occurrence and dynamics when the pathogen can only be detected in the host, and pathogen information is treated as a covariate. We propose a dynamic multistate modeling approach that is flexible enough to account for the detection structures that may be present in complex multistate systems, especially when the sampling design is limited by a species’ natural history or sampling technology. When multistate occupancy models are used and an unobservable state is present, parameter estimation can be influenced by model complexity, data sparseness, and the underlying dynamics of the system. We show that, even with large sample sizes, many models incorporating seasonal variation in vital rates may not generate reasonable estimates, indicating parameter redundancy. We found that certain types of missing data can greatly hinder inference, and we make study design recommendations to avoid these issues. Additionally, we advocate the use of time-varying covariates to explain temporal trends in the data, and the development of sampling techniques that match the biology of the system to eliminate unobservable states when possible

Inferring pathogen presence when sample misclassification and partial observation occur

Abstract Surveillance programmes 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 analysed, 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 are enough to declare a unit of interest ‘pathogen free’. 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 modelling 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 programmes, we provide guidance on sample sizes required to be 95% certain a target organism is absent from a site. 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. Our modelling 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 modelling 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

Successful molecular detection studies require clear communication among diverse research partners

Molecular detection techniques are powerful tools used in ecological applications ranging from diet analyses to pathogen surveillance. Research partnerships that use these tools often involve collaboration among professionals with expertise in field biology, laboratory techniques, quantitative modeling, wildlife disease, and natural resource management. However, in many cases, each of these collaborators lacks specific knowledge about the approaches, decisions, methods, and terminology used by their research partners, which can impede effective communication and act as a barrier to the efficient use of molecular data for ecological inferences and subsequent conservation decision making. We outline a collaborative framework to assist colleagues with diverse types of expertise to effectively translate their scientific and management needs to research partners from other specialties. The molecular techniques used to detect organisms will continue to advance both in sophistication and in the breadth of ecological applications. Our objective is to enable ecologists to harness the full utility of these methods by developing effective collaborative partnerships