Biometrics and Population Modeling II

Contributed Paper
ROOM: Room 220 – Ruidoso

10:30AM Estimating Florida Manatee Abundance from Sparse Data Using Integrated and Retrospective Population Models
Jeffrey A. Hostetler; Julien Martin; Michael Kosempa; Michael C. Runge; Catherine A. Langtimm; Holly H. Edwards
Population projection models have been used extensively to project future wildlife abundance. A less common application of population models is to reconstruct abundances from the past. Integrated population models (IPM) can be used to improve estimates of past abundances, but generally use abundance or count data from each year. Here we use the example of models for Florida manatees (Trichechus manatus latirostris) to illustrate benefits and limitations of retrospective approaches using sparse abundance data. We developed two approaches that allowed us to estimate historical trends in manatee abundance and stage structure of the manatee population. The first approach uses survival and reproductive estimates from each year and an abundance estimate from a single year; the second (based on IPMs) also utilizes carcass recovery counts. We derive expected mortalities and recovery rates from each model. Our analyses also show that reasonably precise estimates of abundance could be derived from these models; from the first approach abundance CV increased from 0.15 in the abundance survey year to 0.17 10 years earlier, and from the second it was always under 0.13. This result has implication for frequency of costly surveys to estimate abundance. The estimated stage distribution seldom varied far from the stable stage structure, especially for the calf and subadult stages. Our study shows that retrospective analyses can be useful: (1) to infer historical trends in abundance, improving our understanding of population dynamics and therefore our ability to forecast; (2) to model the transient dynamics of stage distribution; and (3) as a communication tool and as a way to assess the conservation status of wild populations.
10:50AM Inferring Infection Hazard in Wildlife Populations by Linking Data Across Individual and Population scales
Kim M. Pepin; Shannon L. Kay; Ben D. Golas; Susan A. Shriner; Amy T. Gilbert; Ryan S. Miller; Andrea L. Graham; Steven Riley; Paul Cross; Michael D. Samuel; Mevin B. Hooten; Jennifer A. Hoeting; James O. Lloyd-Smith; Colleen T. Webb; Michael G. Buhnerkemp
Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations, case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes) and Bayesian inference, we show that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference of wildlife disease dynamics, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease. We finish by discussing other refinements that could be pursued for applying this framework more broadly for inference of wildlife disease dynamics. The work presented here was described in detail in Ecology Letters 20(3): 275-292.
11:10AM Time Series Sightability Modeling of Animal Populations
Althea ArchMiller; Robert M. Dorazio; Katherine St. Clair; John Fieberg
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 was developed for analyzing combined detection/non-detection and detection-only data. 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.
11:30AM Bayesian Model-Based Age Classification Using Morphometrics and Capture Dates
Nathanael Lichti; Kenneth F. Kellner; Timothy J. Smyser; Scott A. Johnson
Accurate age determination is a fundamental prerequisite for demographic studies as well as population monitoring efforts that provide information for management and conservation. Yet, common age determination methods suffer from low accuracy rates, impose additional handling and time costs on animals and biologists, or rely on invasive techniques such as tooth extraction. We introduce an alternative, mixture modeling approach for age determination that exploits mammalian growth patterns to classify newly encountered animals as juveniles or adults, and present an example analysis that classifies Allegheny woodrats (Neotoma magister) based solely on their capture dates and mass at capture, in combination with data from known adults. We also introduce and validate a simulation-based heuristic to evaluate potential classification accuracy when no known-age test cases are available. In the Allegheny woodrat example, the mixture model achieved a 90-92% accuracy rate (heuristic range: 89-94%), far better than the 36-43% achieved with a fixed-mass criterion, and comparable to accuracies reported in other species using more data-intensive, multivariate classification techniques. The model can be extended to classify multiple age groups, estimate chronological age, or further improve accuracy by including additional morphometric measures.
11:50AM Hunters, Harvest and Herd Health: Investigating The Sociological and Ecological Dynamics of CWD through Data Integration.
Daniel Walsh; Mat Alldredge
Game management agencies are charged with managing multiple aspects of wildlife populations including herd size and health. The former generally includes meticulous tracking of hunter license sales to individual hunters, and contacting these individuals post-hunting season to determine harvest success rates. The success of these harvest surveys rely on the response rates and the truthfully reporting of harvest by hunters. Additionally, hunters have the opportunity to submit their harvested animals to the agency for disease-testing, and this information provides the basis for tracking disease rates to assess herd health of game species. These activities create parallel data information streams that historically have been analyzed independently; however, the integration of these data can permit the examination of questions not previously possible and improve the precision of estimates of interest. Using the three largest datasets maintained by the state of Colorado, hunter license sales, harvest survey and CWD testing data collected on mule deer (Odocoileus hemionus) from 2002-2010, we demonstrate the benefits of data integration. First, we used a Bayesian, spatio-temporal model to estimate the CWD prevalence rates in the harvested population by estimating submission probabilities, alleviating the assumption that the submitted population was a random sample from the harvested population. Secondly, we were able to estimate the impact of sociological factors and other covariates influencing individual hunter harvest success probabilities, response probabilities to the survey, probability of responding truthfully to the harvest survey, and likelihood of submitting an individual for CWD-testing. Lastly, our integrated data model allowed us to estimate the sex-age structure of the harvest, which was previously not possible in a rigorous manner. The results of this modeling endeavor provide a framework of generating a wealth of information from data currently being collected that is critical to agencies managing wildlife populations.


Contributed Paper
Location: Albuquerque Convention Center Date: September 25, 2017 Time: 10:30 am - 12:10 pm