DOI: 10.5061/dryad.30hd0
title: Data from: Contrasting effects of spatial heterogeneity and environmental stochasticity on population dynamics of a perennial wildflower

description: Understanding how variation in growth, survival and reproduction affect
 population dynamics is a fundamental question in ecology. Although the
 effects of among-year variation (environmental stochasticity) are well
 understood, the effects of among-site variation (spatial heterogeneity)
 are less clearly defined. I evaluated the effects of spatial and temporal
 variation on the population dynamics of Pulsatilla patens, pasqueflower, a
 perennial prairie forb. I conducted a 10-year demographic monitoring
 study, and quantified vital rate variation among sites and years using
 generalized linear models. I incorporated vital rate functions using this
 variation into integral projection models for stochastic and spatially
 heterogeneous environments. I also explored the effects of temporal and
 spatial autocorrelation, by exploring model predictions over the range of
 possible values for temporal autocorrelation and local seed dispersal.
 Vital rates varied more among years than among sites. However,
 environmental stochasticity and spatial heterogeneity had similar
 magnitude effects on population dynamics. These effects were also
 qualitatively different: environmental stochasticity reduced population
 growth rates relative to the average, whereas spatial heterogeneity
 increased population growth rates. Spatial autocorrelation and negative
 temporal autocorrelation led to higher population growth rates, although
 environmental stochasticity still reduced growth rates for all
 autocorrelation values, and spatial heterogeneity increased growth rates
 for all autocorrelation values. Some form of autocorrelation would be
 necessary for model projections to match observed population trends.
 Synthesis. Spatial heterogeneity is as important as environmental
 stochasticity for population dynamics, but it is much less often
 incorporated into population projection models. This study points to a
 number of interesting avenues for future research into the roles of
 spatial heterogeneity and spatiotemporal variation for long-term
 population dynamics.
description: GrowthSurvivalFloweringPrimary data file for estimating growth, survival, and flowering. "Cell" refers to blocks at the monitoring site, and spatial heterogeneity was estimated from the random-effects variation among blocks. "toX" refers to the probability that a plant died in the following year, i.e. 1 = death, and 0 = survival. Growth was estimated as the relationship between "Leaves" and "LvsNxt." Flowering was estimated as the relationship between Flowers and Leaves in a particular year. "toF" refers to whether a plant flowered in the next year. I explored separate models for the probability of future flowering and # flowers as a function of size, conditioned on flowering, but did not include this in the final models.recruit sizeSize of new recruits, estimated from the number of leaves per plant. "Include" refers to whether plants were included in the analysis in the Journal of Ecology 2015 paper. A few plants were excluded because I was less confident that they were really new recruits; one was very large and the others were at locations that had had flowers in the past, even though there had not been any recent leaves. It is also likely that some of the new recruits in the 2nd year of the study in high-numbered cells were ones that we had missed in the first year of monitoring.Recruits.per.plotTotal number of new recruits and total number of flowers per plot. Used to estimate the relationship between flower and seedling production. In high-numbered plots, some of the new recruits in the 2nd year were probably plants that we did not notice in the first year. (Monitoring in the first year took longer than expected, and I think some plants had senesced by the time we reached these high-numbered plots.)