Forecasting climate change impacts on plant populations over large spatial extents
Abstract
Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, 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. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data tomore »
- Authors:
-
[1];
- Utah State Univ., Logan, UT (United States). Dept. of Wildland Resources and the Ecology Center
- Colorado State Univ., Fort Collins, CO (United States). U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit; Colorado State Univ., Fort Collins, CO (United States). Dept. of Fish, Wildlife, and Conservation Biology; Colorado State Univ., Fort Collins, CO (United States). Dept. of Statistics
- Colorado State Univ., Fort Collins, CO (United States). Dept. of Ecosystem Science and Sustainability, Natural Resource Ecology Lab.; U.S. Geological Survey, Fort Collins, CO (United States). Fort Collins Science Center
- U.S. Geological Survey, Sioux Falls, SD (United States). Earth Resources Observation and Science (EROS) Center
- Publication Date:
- Research Org.:
- Utah State Univ., Logan, Utah (United States)
- Sponsoring Org.:
- National Science Foundation (NSF)
- OSTI Identifier:
- 1418516
- Grant/Contract Number:
- DEB-1054040; DBI-1400370
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Ecosphere
- Additional Journal Information:
- Journal Volume: 7; Journal Issue: 10; Journal ID: ISSN 2150-8925
- Publisher:
- Ecological Society of America
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Artemisia; climate change; dimension reduction; forecasting; population model; remote sensing; sagebrush; spatiotemporal model
Citation Formats
Tredennick, Andrew T., Hooten, Mevin B., Aldridge, Cameron L., Homer, Collin G., Kleinhesselink, Andrew R., and Adler, Peter B. Forecasting climate change impacts on plant populations over large spatial extents. United States: N. p., 2016.
Web. doi:10.1002/ecs2.1525.
Tredennick, Andrew T., Hooten, Mevin B., Aldridge, Cameron L., Homer, Collin G., Kleinhesselink, Andrew R., & Adler, Peter B. Forecasting climate change impacts on plant populations over large spatial extents. United States. https://doi.org/10.1002/ecs2.1525
Tredennick, Andrew T., Hooten, Mevin B., Aldridge, Cameron L., Homer, Collin G., Kleinhesselink, Andrew R., and Adler, Peter B. Mon .
"Forecasting climate change impacts on plant populations over large spatial extents". United States. https://doi.org/10.1002/ecs2.1525. https://www.osti.gov/servlets/purl/1418516.
@article{osti_1418516,
title = {Forecasting climate change impacts on plant populations over large spatial extents},
author = {Tredennick, Andrew T. and Hooten, Mevin B. and Aldridge, Cameron L. and Homer, Collin G. and Kleinhesselink, Andrew R. and Adler, Peter B.},
abstractNote = {Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, 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. Finally, 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.},
doi = {10.1002/ecs2.1525},
journal = {Ecosphere},
number = 10,
volume = 7,
place = {United States},
year = {Mon Oct 24 00:00:00 EDT 2016},
month = {Mon Oct 24 00:00:00 EDT 2016}
}
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