@article{kendall_conn_hines_2006, title={Combining multistate capture-recapture data with tag recoveries to estimate demographic parameters}, volume={87}, number={1}, journal={Ecology (Brooklyn, New York, N.Y.)}, author={Kendall, W. L. and Conn, P. B. and Hines, J. E.}, year={2006}, pages={169–177} }
 @article{conn_arthur_bailey_singleton_2006, title={Estimating the abundance of mouse populations of known size: Promises and pitfalls of new methods}, volume={16}, ISSN={["1939-5582"]}, DOI={10.1890/1051-0761(2006)016[0829:ETAOMP]2.0.CO;2}, abstractNote={Knowledge of animal abundance is fundamental to many ecological studies. Frequently, researchers cannot determine true abundance, and so must estimate it using a method such as mark-recapture or distance sampling. Recent advances in abundance estimation allow one to model heterogeneity with individual covariates or mixture distributions and to derive multimodel abundance estimators that explicitly address uncertainty about which model parameterization best represents truth. Further, it is possible to borrow information on detection probability across several populations when data are sparse. While promising, these methods have not been evaluated using mark-recapture data from populations of known abundance, and thus far have largely been overlooked by ecologists. In this paper, we explored the utility of newly developed mark-recapture methods for estimating the abundance of 12 captive populations of wild house mice (Mus musculus). We found that mark-recapture methods employing individual covariates yielded satisfactory abundance estimates for most populations. In contrast, model sets with heterogeneity formulations consisting solely of mixture distributions did not perform well for several of the populations. We show through simulation that a higher number of trapping occasions would have been necessary to achieve good estimator performance in this case. Finally, we show that simultaneous analysis of data from low abundance populations can yield viable abundance estimates.}, number={2}, journal={ECOLOGICAL APPLICATIONS}, author={Conn, PB and Arthur, AD and Bailey, LL and Singleton, GR}, year={2006}, month={Apr}, pages={829–837} }
 @article{conn_kendall_samuel_2004, title={A  general model for the analysis of mark-resight, mark-recapture, and band-recovery data under tag loss}, volume={60}, number={4}, journal={Biometrics}, author={Conn, P. B. and Kendall, W. L. and Samuel, M. D.}, year={2004}, pages={900–909} }
 @article{conn_kendall_2004, title={Evaluating mallard adaptive management models with time series}, volume={68}, ISSN={["1937-2817"]}, DOI={10.2193/0022-541X(2004)068[1065:EMAMMW]2.0.CO;2}, abstractNote={Abstract Wildlife practitioners concerned with midcontinent mallard (Anas platyrhynchos) management in the United States have instituted a system of adaptive harvest management (AHM) as an objective format for setting harvest regulations. Under the AHM paradigm, predictions from a set of models that reflect key uncertainties about processes underlying population dynamics are used in coordination with optimization software to determine an optimal set of harvest decisions. Managers use comparisons of the predictive abilities of these models to gauge the relative truth of different hypotheses about density-dependent recruitment and survival, with better-predicting models giving more weight to the determination of harvest regulations. We tested the effectiveness of this strategy by examining convergence rates of “predictor” models when the true model for population dynamics was known a priori. We generated time series for cases when the a priori model was 1 of the predictor models as well as for several cases when the a priori model was not in the model set. We further examined the addition of different levels of uncertainty into the variance structure of predictor models, reflecting different levels of confidence about estimated parameters. We showed that in certain situations, the model-selection process favors a predictor model that incorporates the hypotheses of additive harvest mortality and weakly density-dependent recruitment, even when the model is not used to generate data. Higher levels of predictor model variance led to decreased rates of convergence to the model that generated the data, but model weight trajectories were in general more stable. We suggest that predictive models should incorporate all sources of uncertainty about estimated parameters, that the variance structure should be similar for all predictor models, and that models with different functional forms for population dynamics should be considered for inclusion in predictor model sets. All of these suggestions should help lower the probability of erroneous learning in mallard AHM and adaptive management in general.}, number={4}, journal={JOURNAL OF WILDLIFE MANAGEMENT}, author={Conn, PB and Kendall, WL}, year={2004}, month={Oct}, pages={1065–1081} }