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At least 253 records · Page 14Linked to original sources
USGS2014

Using auxiliary information to improve wildlife disease surveillance when infected animals are not detected: A Bayesian approach

There are numerous situations in which it is important to determine whether a particular disease of interest is present in a free-ranging wildlife population. However adequate disease surveillance can be labor-intensive and expensive and thus there is substantial motivation to conduct it as efficiently as possible. Surveillance is often based on the assumption of a simple random sample, but this can almost always be improved upon if there is auxiliary information available about disease risk factors. We present a Bayesian approach to disease surveillance when auxiliary risk information is available which will usually allow for substantial improvements over simple random sampling. Others have employed risk weights in surveillance, but this can result in overly optimistic statements regarding freedom from disease due to not accounting for the uncertainty in the auxiliary information; our approach remedies this. We compare our Bayesian approach to a published example of risk weights applied to chronic wasting disease in deer in Colorado, and we also present calculations to examine when uncertainty in the auxiliary information has a serious impact on the risk weights approach. Our approach allows “apples-to-apples” comparisons of surveillance efficiencies between units where heterogeneous samples were collected

PLoS ONE↗
USGS2018

Time series sightability modeling of animal populations

Logistic regression models—or “sightability models”—fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose ( Alces alces ) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.

PLoS ONE↗
USGS2021

Statistical methods used in research concerning endangered and threatened animal species of Puerto Rico: A meta-study

A concern about statistics in wildlife studies, particularly of endangered and threatened species, is whether the data collected meet the assumptions necessary for the use of parametric statistics. This study identified published papers on the nine endangered and six threatened species found only on Puerto Rico using five different databases. The results from the Zoological Record database identified the most articles, including all identified by the other databases. Of the 222 identified articles, 108 included some form of statistics, 26 used only descriptive statistics, 34 included only parametric statistics, 26 used only nonparametric statistics, and 22 reported both parametric and nonparametric statistical analyses. This meta-study showed that the percentage of articles with no statistical treatment decreased in the most recent 20 years, and that although parametric statistics continue to be the most commonly used in published wildlife studies of Puerto Rican wildlife, there has been a distinct increase in the use of nonparametric statistics over time.

Caribbean Journal of Science↗
USGS2010

Quantifying landscape ruggedness for animal habitat analysis: A case study using bighorn sheep in the Mojave Desert

Terrain ruggedness is often an important variable in wildlife habitat models. Most methods used to quantify ruggedness are indices derived from measures of slope and, as a result, are strongly correlated with slope. Using a Geographic Information System, we developed a vector ruggedness measure (VRM) of terrain based on a geomorphological method for measuring vector dispersion that is less correlated with slope. We examined the relationship of VRM to slope and to 2 commonly used indices of ruggedness in 3 physiographically different mountain ranges within the Mojave Desert of the southwestern United States. We used VRM, slope, distance to water, and springtime bighorn sheep (Ovis canadensis nelsoni) adult female locations to model sheep habitat in the 3 ranges. Using logistic regression, we determined that the importance of ruggedness in habitat selection remained consistent across mountain ranges, whereas the relative importance of slope varied according to the characteristic physiography of each range. Our results indicate that the VRM quantifies local variation in terrain more independently of slope than other methods tested, and that VRM and slope distinguish 2 different components of bighorn sheep habitat.

Journal of Wildlife Management↗
USGS1979

Robust estimation of population size when capture probabilities vary among animals

A model is given for multiple recapture studies on closed populations which allows capture probabilities to vary among individuals. The capture probability of each individual is assumed to be constant over time. Based on this model we give a nonparametric estimation procedure for population size. The estimator involves selecting one of a sequence of estimator which are each linear combinations of the capture frequencies. The individual estimators are derived from the generalized jackknife method. We also give a goodness of fit test for the model's assumption that individual capture probabilities do not change during the study. The robustness of the estimation procedure is investigated with a simulation study. By virtue of this study, and the theoretical nature of the estimator, it is judged to be robust to moderate variations in individual capture probabilities which may occur in commonly used short-term livetrapping studies.

Ecology↗
USGS1994

On the importance of sampling variance to investigations of temporal variation in animal population size

Our purpose here is to emphasize the need to properly deal with sampling variance when studying population variability and to present a means of doing so. We present an estimator for temporal variance of population size for the general case in which there are both sampling variances and covariances associated with estimates of population size. We illustrate the estimation approach with a series of population size estimates for black-capped chickadees (Parus atricapillus) wintering in a Connecticut study area and with a series of population size estimates for breeding populations of ducks in southwestern Manitoba.

Oikos↗