A generalized framework for the detection of range shifts from “resurvey” studies

Morgan W. Tingley 1
1 Department of Ecology and Evolutionary Biology, University of Connecticut, 75 N. Eagleville Rd Unit 3043, Storrs CT 06103 USA,

morgan.tingley@uconn.edu, @mwtingley

Long-­‐term, large-­‐scale environmental change necessitates empirical studies that span time frames from decades to centuries. As this length of time often precludes planned experiments, an increasingly popular option in ecology is the “resurvey” study, or the revisitation of past research sites. An important emerging issue is how researchers can use past survey data to make analytically robust comparisons. Previous work has largely acknowledged this problem while employing a diverse set of strategies, statistical or otherwise, to account for bias. While no single analytical framework will satisfy the needs of all researchers, the increasing popularity of resurvey studies demands a generalized accounting of inferential problems as well as common strategies for overcoming them. This presentation outlines how statistical methods can be used to account for bias derived from issues such as imperfect detection, shifting taxonomies, unknown or changing spatial sampling, differing survey methodologies, and varying levels of survey effort. Among various methodological options, emphasis will be placed on the flexible nature of state-­‐space models to allow the inclusion, characterization, and estimation of multiple sources of uncertainty deriving from both state and observation processes. While such analytical tools show great potential in resurvey studies, their prospective success relies on the standards by which both past data and new data are collected. Thus, whether we are resurveying the past or setting baselines for the future, changing how we survey – in addition to changing how we analyze data – will play a key role in the future measurement and documentation of species on the move.

Twitter: @mwtingley

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