Assessment of Chronic Wasting Disease Prion Shedding in Deer Saliva with Occupancy Modeling.

The detection of prions is difficult due to the peculiarity of the pathogen, which is a misfolded form of a normal protein. The specificity and sensitivity of detection methods are imperfect in complex samples, including in excreta. Here, we combined optimized prion amplification procedures with a statistical method that accounts for false-positive and false-negative errors to test deer saliva for chronic wasting disease (CWD) prions. This approach enabled us to discriminate the shedding of prions in saliva and the detection of prions in saliva-a distinction crucial to understanding the role of prion shedding in disease transmission and for diagnosis. We found that assay sensitivity and specificity were indeed imperfect, and we were able to draw several conclusions pertinent to CWD biology from our analyses: (i) the shedding of prions in saliva increases with time postinoculation, but is common throughout the preclinical phase of disease; (ii) the shedding propensity is influenced neither by sex nor by prion protein genotype at codon 96; and (iii) the source of prion-containing inoculum used to infect deer affects the likelihood of prion shedding in saliva; oral inoculation of deer with CWD-positive saliva resulted in 2.77 times the likelihood of prion shedding in saliva compared to that from inoculation with CWD-positive brain. These results are pertinent to horizontal CWD transmission in wild cervids. Moreover, the approach described is applicable to other diagnostic assays with imperfect detection.

Leveraging constraints and biotelemetry data to pinpoint repetitively used spatial features.

Satellite telemetry devices collect valuable information concerning the sites visited by animals, including the location of central places like dens, nests, rookeries, or haul-outs. Existing methods for estimating the location of central places from telemetry data require user-specified thresholds and ignore common nuances like measurement error. We present a fully model-based approach for locating central places from telemetry data that accounts for multiple sources of uncertainty and uses all of the available locational data. Our general framework consists of an observation model to account for large telemetry measurement error and animal movement, and a highly flexible mixture model specified using a Dirichlet process to identify the location of central places. We also quantify temporal patterns in central place use by incorporating ancillary behavioral data into the model; however, our framework is also suitable when no such behavioral data exist. We apply the model to a simulated data set as proof of concept. We then illustrate our framework by analyzing an Argos satellite telemetry data set on harbor seals (Phoca vitulina) in the Gulf of Alaska, a species that exhibits fidelity to terrestrial haul-out sites.

The basis function approach for modeling autocorrelation in ecological data.

Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many seemingly disparate statistical methods used to account for autocorrelation can be expressed as regression models that include basis functions. Basis functions also enable ecologists to modify a wide range of existing ecological models in order to account for autocorrelation, which can improve inference and predictive accuracy. Furthermore, understanding the properties of basis functions is essential for evaluating the fit of spatial or time-series models, detecting a hidden form of collinearity, and analyzing large data sets. We present important concepts and properties related to basis functions and illustrate several tools and techniques ecologists can use when modeling autocorrelation in ecological data.

Animal movement constraints improve resource selection inference in the presence of telemetry error.

Multiple factors complicate the analysis of animal telemetry location data. Recent advancements address issues such as temporal autocorrelation and telemetry measurement error, but additional challenges remain. Difficulties introduced by complicated error structures or barriers to animal movement can weaken inference. We propose an approach for obtaining resource selection inference from animal location data that accounts for complicated error structures, movement constraints, and temporally autocorrelated observations. We specify a model for telemetry data observed with error conditional on unobserved true locations that reflects prior knowledge about constraints in the animal movement process. The observed telemetry data are modeled using a flexible distribution that accommodates extreme errors and complicated error structures. Although constraints to movement are often viewed as a nuisance, we use constraints to simultaneously estimate and account for telemetry error. We apply the model to simulated data, showing that it outperforms common ad hoc approaches used when confronted with measurement error and movement constraints. We then apply our framework to an Argos satellite telemetry data set on harbor seals (Phoca vitulina) in the Gulf of Alaska, a species that is constrained to move within the marine environment and adjacent coastlines.

Use of land facets to design linkages for climate change.

Least-cost modeling for focal species is the most widely used method for designing conservation corridors and linkages. However, these linkages have been based on current species' distributions and land cover, both of which will change with large-scale climate change. One method to develop corridors that facilitate species' shifting distributions is to incorporate climate models into their design. But this approach is enormously complex and prone to error propagation. It also produces outputs at a grain size (km2) coarser than the grain at which conservation decisions are made. One way to avoid these problems is to design linkages for the continuity and interspersion of land facets, or recurring landscape units of relatively uniform topography and soils. This coarse-filter approach aims to conserve the arenas of biological activity rather than the temporary occupants of those arenas. In this paper, we demonstrate how land facets can be defined in a rule-based and adaptable way, and how they can be used for linkage design in the face of climate change. We used fuzzy c-means cluster analysis to define land facets with respect to four topographic variables (elevation, slope angle, solar insolation, and topographic position), and least-cost analysis to design linkages that include one corridor per land facet. To demonstrate the flexibility of our procedures, we designed linkages using land facets in three topographically diverse landscapes in Arizona, USA. Our procedures can use other variables, including soil variables, to define land facets. We advocate using land facets to complement, rather than replace, existing focal species approaches to linkage design. This approach can be used even in regions lacking land cover maps and is not affected by the bias and patchiness common in species occurrence data.

A model-based solution for observational errors in laboratory studies.

Molecular techniques for detecting microorganisms, macroorganisms and infectious agents are susceptible to false-negative and false-positive errors. If left unaddressed, these observational errors may yield misleading inference concerning occurrence, prevalence, sensitivity, specificity and covariate relationships. Occupancy models are widely used to account for false-negative errors and more recently have even been used to address false-positive errors, too. Current modelling options assume false-positive errors only occur in truly negative samples, an assumption that yields biased inference concerning detection because a positive sample could be classified as such not because the target agent was successfully detected, but rather due to a false-positive test result. We present an extension to the occupancy modelling framework that allows false-positive errors in both negative and positive samples, thereby providing unbiased inference concerning occurrence and detection, as well as reliable conclusions about the efficacy of sampling designs, handling protocols and diagnostic tests. We apply the model to simulated data, showing that it recovers known parameters and outperforms other approaches that are commonly used when confronted with observation errors. We then apply the model to an experimental data set on Batrachochytrium dendrobatidis, a pathogenic fungus that is implicated in the global decline or extinction of hundreds of amphibian species. The model-based approach we present is not only useful for obtaining reliable inference when data are contaminated with observational errors, but also eliminates the need for establishing arbitrary thresholds or decision rules that have hidden and unintended consequences.

Comparing linkage designs based on land facets to linkage designs based on focal species.

Least-cost modeling for focal species is the most widely used method for designing conservation corridors and linkages. However, these designs depend on today's land covers, which will be altered by climate change. We recently proposed an alternative approach based on land facets (recurring landscape units of relatively uniform topography and soils). The rationale is that corridors with high continuity of individual land facets will facilitate movement of species associated with each facet today and in the future. Conservation practitioners might like to know whether a linkage design based on land facets is likely to provide continuity of modeled breeding habitat for species needing connectivity today, and whether a linkage for focal species provides continuity and interspersion of land facets. To address these questions, we compared linkages designed for focal species and land facets in three landscapes in Arizona, USA. We used two variables to measure linkage utility, namely distances between patches of modeled breeding habitat for 5-16 focal species in each linkage, and resistance profiles for focal species and land facets between patches connected by the linkage. Compared to focal species designs, linkage designs based on land facets provided as much or more modeled habitat connectivity for 25 of 28 species-landscape combinations, failing only for the three species with the most narrowly distributed habitat. Compared to land facets designs, focal species linkages provided lower connectivity for about half the land facets in two landscapes. In areas where a focal species approach to linkage design is not possible, our results suggest that conservation practitioners may be able to implement a land facets approach with some confidence that the linkage design would serve most potential focal species. In areas where focal species designs are possible, we recommend using the land facet approach to complement, rather than replace, focal species approaches.