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.

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.

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.

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.

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.

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).

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.