Abstract
AbstractPlant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely sensed, species‐specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28‐year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long‐term predictions are rooted in small‐scale and short‐term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.
Highlights
Forecasting the impacts of climate change on plant populations and communities is a central challenge for ecology (Clark et al 2001, Petchey et al 2015)
The lagPpt climate covariate can be considered important based on a 90% credible interval, and it had a positive effect on sagebrush percent cover change (Fig. 3)
We introduced a new approach to fitting and simulating population models at large spatial extents with plant population data derived from state-of-the-art remote sensing
Summary
Forecasting the impacts of climate change on plant populations and communities is a central challenge for ecology (Clark et al 2001, Petchey et al 2015). Local-s cale demographic data make building population projection models an easy task (Ellner and Rees 2006, Rees and Ellner 2009, Adler et al 2012), but it is very difficult to extrapolate small-s cale studies to large spatial extents with any certainty because the data likely only represent a small subset of parameter space and environmental conditions (Freckleton et al 2011, Queenborough et al 2011). The real challenge is not to make population forecasts, but to do so at spatial scales relevant to policy and management decisions (Queenborough et al 2011)
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