Quantitative Developments

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.