Arid
DOI10.1111/2041-210X.12686
Do we need demographic data to forecast plant population dynamics?
Tredennick, Andrew T.1,2; Hooten, Mevin B.3,4,5; Adler, Peter B.1,2
通讯作者Tredennick, Andrew T.
来源期刊METHODS IN ECOLOGY AND EVOLUTION
ISSN2041-210X
EISSN2041-2096
出版年2017
卷号8期号:5页码:541-551
英文摘要

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. Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction. In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types. In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist for many plant species. Modelers should exploit these data to predict the impacts of environmental change.


英文关键词climate change forecasting grassland integral projection model population model ridge regression statistical regularization
类型Article
语种英语
国家USA
收录类别SCI-E
WOS记录号WOS:000400823400002
WOS关键词INTEGRAL PROJECTION MODELS ; CLIMATE ; COEXISTENCE ; INFERENCE ; VARIABILITY ; PREDICTION
WOS类目Ecology
WOS研究方向Environmental Sciences & Ecology
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/201086
作者单位1.Utah State Univ, Dept Wildland Resources, 5230 Old Main Hill, Logan, UT 84322 USA;
2.Utah State Univ, Ctr Ecol, 5230 Old Main Hill, Logan, UT 84322 USA;
3.US Geol Survey, Colorado Cooperat Fish & Wildlife Res Unit, Ft Collins, CO 80523 USA;
4.Colorado State Univ, Dept Fish Wildlife & Conservat Biol, Ft Collins, CO 80523 USA;
5.Colorado State Univ, Dept Stat, Ft Collins, CO 80523 USA
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Tredennick, Andrew T.,Hooten, Mevin B.,Adler, Peter B.. Do we need demographic data to forecast plant population dynamics?[J],2017,8(5):541-551.
APA Tredennick, Andrew T.,Hooten, Mevin B.,&Adler, Peter B..(2017).Do we need demographic data to forecast plant population dynamics?.METHODS IN ECOLOGY AND EVOLUTION,8(5),541-551.
MLA Tredennick, Andrew T.,et al."Do we need demographic data to forecast plant population dynamics?".METHODS IN ECOLOGY AND EVOLUTION 8.5(2017):541-551.
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