Census data aggregation decisions can affect population‐level inference in heterogeneous populations. Issue 14 (25th June 2020)

• Record Type: Journal Article
• Authors: Engbo, Søs; Bull, James C.; Börger, Luca; Stringell, Thomas B.; Lock, Kate; Morgan, Lisa; Jones, Owen R.
• Is Part Of: Ecology and evolution. Volume 10:Issue 14(2020)
• Page Start: 7487
• Page End: 7496
• Publication Date: 2020-06-25
• Subjects: conservation -- grey seal -- matrix population modeling -- population dynamics -- population management -- survey methods; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.6475
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: Conservation and population management decisions often rely on population models parameterized using census data. However, the sampling regime, precision, sample size, and methods used to collect census data are usually heterogeneous in time and space. Decisions about how to derive population‐wide estimates from this patchwork of data are complicated and may bias estimated population dynamics, with important implications for subsequent management decisions. Here, we explore the impact of site selection and data aggregation decisions on pup survival estimates, and downstream estimates derived from parameterized matrix population models (MPMs), using a long‐term dataset on grey seal ( Halichoerus grypus ) pup survival from southwestern Wales. The spatiotemporal and methodological heterogeneity of the data are fairly typical for ecological census data and it is, therefore, a good model to address this topic. Data were collected from 46 sampling locations (sites) over 25 years, and we explore the impact of data handling decisions by varying how years and sampling locations are combined to parameterize pup survival in population‐level MPMs. We focus on pup survival because abundant high‐quality data are available on this developmental stage. We found that survival probability was highly variable with most variation being at the site level, and poorly correlated among sampling sites. This variation could generate marked differences in predicted population dynamics depending on sampling strategy. The sample size required for a confident survival estimate also varied markedly geographically. We conclude that for populations with highly variable vital rates among sub‐populations, site selection and data aggregation methods are important. In particular, including peripheral or less frequently used areas can introduce substantial variation into population estimates. This is likely to be context‐dependent, but these choices, including the use of appropriate weights when summarizing across sampling areas, should be explored to ensure that management actions are successful. Abstract : Population management planning often uses models parameterised from survey data. These data are often aggregated across time and space. We illustrate how data aggregation decisions can influence inferences and highlight that these choices could have important consequences for future management.