Integrating count and detection–nondetection data to model population dynamics

Abstract

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.

Integrating auxiliary data in optimal spatial design for species distribution modelling

“…logistic, probit or more recently Warton and Shepherd () and Zipkin et al. () use the cloglog link). Spatial dependence in the random effects is captured by modelling the random effects θ = (θ 1 , …, θ N ) T aswhere X is a known N × p covariate matrix, β is a vector (of length p ) of unknown regression coefficients, and Σ is an N × N spatial covariance matrix.…”

The recent past and promising future for data integration methods to estimate species’ distributions

“…Other recent examples exploit the relationship between individual abundance and probability of detecting a species that occurs at a site (i.e. Zipkin et al., ). This relationship has previously been recognized in the occupancy literature (Royle & Nichols, ).…”

“…Opportunities for data integration are not limited to traditional static species distribution models, but also could be utilized when distribution dynamics are of interest (Zipkin et al., ). Dynamic models range from simple abundance models that estimate local changes in occurrence or abundance across space and time (Amburgey et al., ; Miller et al, ) to more complex models that incorporate demographic parameters and life‐stage‐specific abundances (Davis, Hooten, Phillips, & Doherty, ; Zipkin et al., ).…”

Precision gain versus effort with joint models using detection/non‐detection and banding data

“…For example, capture–recapture techniques provide valuable information, but are often cost‐prohibitive to use at large spatial and temporal scales (landscape or regional, >5 years) (but see the Monitoring Avian Productivity and Survivorship program [MAPS]; Saracco, Royle, DeSante, & Gardner, ; Saracco, Royle, DeSante, & Gardner, ). Consequently, projects frequently employ these techniques at smaller scales to reduce effort (time and money), potentially limiting the statistical inference spatial scale (Zipkin et al, ; Zipkin & Saunders, ). Alternatively, presence–absence data for use in occupancy models (MacKenzie et al, ) and count data are considerably less expensive and thus can be collected at larger scales for similar cost.…”

“…Alternatively, presence–absence data for use in occupancy models (MacKenzie et al, ) and count data are considerably less expensive and thus can be collected at larger scales for similar cost. These data types historically provided less information than more intensive sampling approaches, but new analytical approaches now allow estimation of survival, population gains from local recruitment and immigration, and abundance using these data types with dynamic N ‐occupancy models (Rossman et al, ; Zipkin et al, ).…”