Predicting the odds of chronic wasting disease with Habitat Risk software.

Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in captive cervids in Colorado, United States (US) in 1967, but has since spread into free-ranging white-tailed deer (Odocoileus virginianus) across the US and Canada as well as to Scandinavia and South Korea. In some areas, the disease is considered endemic in wild deer populations, and governmental wildlife agencies have employed epidemiological models to understand long-term environmental risk. However, continued rapid spread of CWD into new regions of the continent has underscored the need for extension of these models into broader tools applicable for wide use by wildlife agencies. Additionally, efforts to semi-automate models will facilitate access of technical scientific methods to broader users. We introduce software (Habitat Risk) designed to link a previously published epidemiological model with spatially referenced environmental and disease testing data to enable agency personnel to make up-to-date, localized, data-driven predictions regarding the odds of CWD detection in surrounding areas after an outbreak is discovered. Habitat Risk requires pre-processing publicly available environmental datasets and standardization of disease testing (surveillance) data, after which an autonomous computational workflow terminates in a user interface that displays an interactive map of disease risk. We demonstrated the use of the Habitat Risk software with surveillance data of white-tailed deer from Tennessee, USA.

Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning.

Continued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigated at state scales, but a regional model to predict locations of new infections can guide increasingly efficient surveillance efforts. We predicted CWD incidence by county using CWD surveillance data depicting white-tailed deer (Odocoileus virginianus) in 16 eastern and midwestern US states. We predicted the binary outcome of CWD-status using four machine learning models, utilized five-fold cross-validation and grid search to pinpoint the best model, then compared model predictions against the subsequent year of surveillance data. Cross validation revealed that the Light Boosting Gradient model was the most reliable predictor given the regional data. The predictive model could be helpful for surveillance planning. Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD. However, disagreements in positives and negatives between the CWD Prediction Web App predictions and the on-the-ground surveillance data one year later underscore the need for state wildlife agency professionals to use a layered modeling approach to ensure robust surveillance planning. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/ .

OCCURRENCE OF MANGE IN AMERICAN BLACK BEARS (URSUS AMERICANUS) IN NEW YORK STATE, USA.

Mange, a parasitic skin disease caused by various species of mites, is found in free-ranging wildlife populations and has been increasingly reported in American black bears (Ursus americanus) over the last decade in New York State (NYS), USA. Our goal was to describe the geographic, seasonal, and demographic factors associated with mange in this species in NYS. Our retrospective study used historic, opportunistic data from diagnostic necropsy records and visual sighting reports collected by the NYS Wildlife Health Program from 2009 to 2018. We used chi-square tests for independence and odds ratios to examine whether geographic location, year, season, sex, age, and reason for laboratory submission were associated with mange in bears. We used maps and seasonal analysis to investigate emerging patterns. We confirmed increased black bear mange reports in recent years. Necropsy data revealed more bears submitted to the laboratory because of mange, mainly caused by Sarcoptes scabiei; females were more likely than males to present with sarcoptic mange. We found that cases of mange in the Northern Zone were widely disseminated throughout the region, whereas cases in the Southern Zone were concentrated in two areas along the Pennsylvania border. Seasonally, mange cases showed peaks occurring in late spring to early summer and in fall. Our results were on the basis of available data; a comprehensive statewide surveillance program would be useful to better understand the apparent increase in mange and its potential impact on both the welfare of individual animals and the population of black bears in NYS. Additional research on the timing of transmission dynamics associated with females in winter dens may be helpful to wildlife managers to identify strategies to mitigate deleterious spread of the disease in black bears.

Another look at the eigenvalues of a population matrix model.

Population matrix models are important tools in resource management, in part because they are used to calculate the finite rate of growth ("dominant eigenvalue"). But understanding how a population matrix model converts life history traits into the finite rate of growth can be tricky. We introduce interactive software ("IsoPOPd") that uses the characteristic equation to display how vital rates (survival and fertility) contribute to the finite rate of growth. Higher-order interactions among vital rates complicate the linkage between a management intervention and a population's growth rate. We illustrate the use of the software for investigating the consequences of three management interventions in a 3-stage model of white-tailed deer (Odocoileus virginianus). The software is applicable to any species with 2- or 3-stages, but the mathematical concepts underlying the software are applicable to a population matrix model of any size. The IsoPOPd software is available at: https://cwhl.vet.cornell.edu/tools/isopopd.

How can we augment the few that remain? Using stable population dynamics to aid reintroduction planning of an iteroparous species.

Restoration of depleted populations is an important method in biological conservation. Reintroduction strategies frequently aim to restore stable, increasing, self-sustaining populations. Knowledge of asymptotic system dynamics may provide advantage in selecting reintroduction strategies. We introduce interactive software that is designed to identify strategies for release of females that are immediately aligned with stable population dynamics from species represented by 2-, 3-, 4-, and 5-stage life history strategies. The software allows managers to input a matrix of interest, the desired number of breeding females, and the desired management timeline, and calls upon stable population theory to give release strategies that are in concert with both stable population status and the management goals. We demonstrate how the software can aid in assessing various strategies ahead of a hypothetical restoration. For the purpose of demonstration of the tool only, we use published vital rates of an ungulate species, but remark that the selection of species for demonstration is not central to the use of this tool. Adaption of this tool to real-life restorations of any 2-, 3-, 4-, or 5-stage iteroparous species may aid in understanding how to minimize undesirable recovery complications that may naturally arise from transient population dynamics. The software is freely available at: https//cwhl.vet.cornell.edu/tools/stapopd.