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

Abstract

Capture–recapture techniques provide valuable information, but are often more cost‐prohibitive at large spatial and temporal scales than less‐intensive sampling techniques. Model development combining multiple data sources to leverage data source strengths and for improved parameter precision has increased, but with limited discussion on precision gain versus effort. We present a general framework for evaluating trade‐offs between precision gained and costs associated with acquiring multiple data sources, useful for designing future or new phases of current studies.We illustrated how Bayesian hierarchical joint models using detection/non‐detection and banding data can improve abundance, survival, and recruitment inference, and quantified data source costs in a northern Arizona, USA, western bluebird (Sialia mexicana) population. We used an 8‐year detection/non‐detection (distributed across the landscape) and banding (subset of locations within landscape) data set to estimate parameters. We constructed separate models using detection/non‐detection and banding data, and a joint model using both data types to evaluate parameter precision gain relative to effort.Joint model parameter estimates were more precise than single data model estimates, but parameter precision varied (apparent survival > abundance > recruitment). Banding provided greater apparent survival precision than detection/non‐detection data. Therefore, little precision was gained when detection/non‐detection data were added to banding data. Additional costs were minimal; however, additional spatial coverage and ability to estimate abundance and recruitment improved inference. Conversely, more precision was gained when adding banding to detection/non‐detection data at higher cost. Spatial coverage was identical, yet survival and abundance estimates were more precise. Justification of increased costs associated with additional data types depends on project objectives.We illustrate a general framework for evaluating precision gain relative to effort, applicable to joint data models with any data type combination. This framework evaluates costs and benefits from and effort levels between multiple data types, thus improving population monitoring designs.

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

“…While we often see improved precision with joint models (Besbeas et al, 2002;Schaub & Abadi, 2010) these will come with some cost associated with collecting additional data. Collecting some additional data sources may be more efficient (improved precision per unit effort) than others (Clement, 2016;Sanderlin, Block, Strohmeyer, Saab, & Ganey, 2018). These considerations are important during initial study design or when there is an opportunity to collect additional data.…”

A guide to state–space modeling of ecological time series

“…The importance of multiple covariates in a model (e.g., the effect of temperature and precipitation on bird survival) can be assessed by multiplying coefficients in a model by indicator variables which when equal to one include the covariate and when equal to zero exclude the covariate (O'Hara et al, 2009). Such techniques have been used to compare ecological SSMs (Sanderlin et al, 2019), but such an approach is designed for nested models only. Posterior predictive loss approaches appear to be suitable for time-series data (Hooten and Hobbs, 2015) and have been used to compare ecological SSMs (Mills Flemming et al, 2010).…”

Advances in modelling demographic processes: The Euring 2017 Analytical Meeting

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“…always result in substantial gains in inference; Sanderlin et al (2019) Throughout, the importance of developing software that ecologists can use to implement the methods has been paramount, most notable are the programs MARK (White & Burnham, 1999) and, in its various incarnations, SURGE (Choquet, Rouan, & Pradel, 2009…”