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Climate change

2016

Wildland Resources Faculty Publications

Articles 1 - 4 of 4

Full-Text Articles in Life Sciences

Do We Need Demographic Data To Forecast Plant Population Dynamics?, Andrew T. Tredennick Nov 2016

Do We Need Demographic Data To Forecast Plant Population Dynamics?, Andrew T. Tredennick

Wildland Resources Faculty Publications

  1. Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts.
  2. Here ...


Forecasting Climate Change Impacts On Plant Populations Over Large Spatial Extents, Andrew T. Tredennick, Mevi B. Hooten, Cameron L. Aldridge, Collin G. Homer, Andrew R. Kleinhesselink, Peter Adler Oct 2016

Forecasting Climate Change Impacts On Plant Populations Over Large Spatial Extents, Andrew T. Tredennick, Mevi B. Hooten, Cameron L. Aldridge, Collin G. Homer, Andrew R. Kleinhesselink, Peter Adler

Wildland Resources Faculty Publications

Plant 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 ...


Forecasting Climate Change Impacts On Plant Populations Over Large Spatial Extents, Andrew T. Tredennick, Mevin B. Hooten, Cameron L. Aldridge, Collin G. Homer, Andrew R. Kleinhesselink, Peter B. Adler Oct 2016

Forecasting Climate Change Impacts On Plant Populations Over Large Spatial Extents, Andrew T. Tredennick, Mevin B. Hooten, Cameron L. Aldridge, Collin G. Homer, Andrew R. Kleinhesselink, Peter B. Adler

Wildland Resources Faculty Publications

Plant 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 ...


Forecasting Climate Change Impacts On Plant Populations Over Large Spatial Extent, Andrew T. Tredennick, Mevin B. Hooten, Cameron L. Aldridge, Collin G. Homer, Andrew R. Kleinhesselink, Peter B. Adler Jul 2016

Forecasting Climate Change Impacts On Plant Populations Over Large Spatial Extent, Andrew T. Tredennick, Mevin B. Hooten, Cameron L. Aldridge, Collin G. Homer, Andrew R. Kleinhesselink, Peter B. Adler

Wildland Resources Faculty Publications

Plant 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 ...