Data from: Quantifying the importance of geographic replication and representativeness when estimating demographic rates, using a coastal species as a case study
Field, Christopher R. et al. (2017), Data from: Quantifying the importance of geographic replication and representativeness when estimating demographic rates, using a coastal species as a case study, Dryad, Dataset, https://doi.org/10.5061/dryad.f5c50
Demographic rates are rarely estimated over an entire species range, limiting empirical tests of ecological patterns and theories, and raising questions about the representativeness of studies that use data from a small part of a range. The uncertainty that results from using demographic rates from just a few sites is especially pervasive in population projections, which are critical for a wide range of questions in ecology and conservation. We developed a simple simulation to quantify how this lack of geographic representativeness can affect inferences about the global mean and variance of growth rates, which has implications for the robust design of a wide range of population studies. Using a coastal songbird, saltmarsh sparrow (Ammodramus caudacutus), as a case study, we first estimated survival, fecundity, and population growth rates at 21 sites distributed across much of their breeding range. We then subsampled this large, representative dataset according to five sampling scenarios in order to simulate a variety of geographic biases in study design. We found spatial variation in demographic rates, but no large systematic patterns. Estimating the global mean and variance of growth rates using subsets of the data suggested that at least 10-15 sites were required for reasonably unbiased estimates, highlighting how relying on demographic data from just a few sites can lead to biased results when extrapolating across a species range. Sampling at the full 21 sites, however, offered diminishing returns, raising the possibility that for some species accepting some geographical bias in sampling can still allow for robust range-wide inferences. The sub-sampling approach presented here, while conceptually simple, could be used with both new and existing data to encourage efficiency in the design of long-term or large-scale ecological studies.