|Document Type:||Journal Article|
|Title:||Integrating count and detectionnondetection data to model population dynamics|
|Author:||Elise Zipkin, Sam Rossman, Charles B. Yackulic, J. David Wiens, James T. Thorson, Raymond J. Davis, Evan Campbell Grant|
|Keywords:||Detection probability, Integrated population model, N-mixture model,|
There is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. Current work on this topic primarily focuses on combining capture–recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection–nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection–nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection–nondetection data (1995–2014) with newly collected count data (2015–2016) from a growing population of Barred Owl (Strix varia) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.
|Full Text URL:||http://onlinelibrary.wiley.com/doi/10.1002/ecy.1831/full|
|Theme:||Recovery and rebuilding of marine and coastal species|
Characterize the population biology of species, and develop and improve methods for predicting the status of populations.
Develop methods to use physiological, biological and behavioral information to predict population-level processes.