气候变化领域集成服务门户(CCPortal): Estimating multivariate ecological variables at high spatial resolution using a cost-effective matching algorithm

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DOI	10.1002/ecs2.4811
	Estimating multivariate ecological variables at high spatial resolution using a cost-effective matching algorithm
	Renne, Rachel R.; Schlaepfer, Daniel R.; Palmquist, Kyle A.; Lauenroth, William K.; Bradford, John B.
发表日期	2024
ISSN	2150-8925
起始页码	15
结束页码	3
卷号	15 期号:3
英文摘要	Simulation models are valuable tools for estimating ecosystem response to environmental conditions and are particularly relevant for investigating climate change impacts. However, because of high computational requirements, models are often applied over a coarse grid of points or for representative locations. Spatial interpolation of model output can be necessary to guide decision-making, yet interpolation is not straightforward because the interpolated values must maintain the covariance structure among variables. We present methods for two key steps for utilizing limited simulations to generate detailed maps of multivariate and time series output. First, we present a method to select an optimal set of simulation sites that maximize the area represented for a given number of sites. Then, we introduce a multivariate matching approach to interpolate simulation results to detailed maps for the represented area. This approach links simulation output to environmentally analogous matched sites according to user-defined criteria. We demonstrate the methods with case studies using output from (1) an individual-based plant simulation model to illustrate site selection, and (2) an ecosystem water balance simulation model to illustrate interpolation. For the site selection case study, we identified 200 simulation sites that represented 96% of a large study area (1.12 x 10(6) km(2)) at a similar to 1-km resolution. For the interpolation case study, we generated similar to 1-km resolution maps across 4.38 x 10(6) km(2) of drylands in North America from a 10 x 10 km grid of simulated sites. Estimates of interpolation errors using cross validation were low (<10% of the range of each variable). Our point selection and interpolation methods, which are available as an easy-to-use R package, provide a means of cost-effectively generating detailed maps of expensive, complex simulation output (e.g., multivariate and time series) at scales relevant for local conservation planning. Our methods are flexible and allow the user to identify relevant matching criteria to balance interpolation uncertainty with areal coverage to enhance inference and decision-making at management-relevant scales across large areas.
英文关键词	ecohydrology; ecological modeling; multivariate interpolation; multivariate matching; sagebrush; sampling design; time series interpolation
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Ecology
WOS记录号	WOS:001184294400001
来源期刊	ECOSPHERE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305135
作者单位	Yale University; United States Department of the Interior; United States Geological Survey; Northern Arizona University; Marshall University
推荐引用方式 GB/T 7714	Renne, Rachel R.,Schlaepfer, Daniel R.,Palmquist, Kyle A.,et al. Estimating multivariate ecological variables at high spatial resolution using a cost-effective matching algorithm[J],2024,15(3).
APA	Renne, Rachel R.,Schlaepfer, Daniel R.,Palmquist, Kyle A.,Lauenroth, William K.,&Bradford, John B..(2024).Estimating multivariate ecological variables at high spatial resolution using a cost-effective matching algorithm.ECOSPHERE,15(3).
MLA	Renne, Rachel R.,et al."Estimating multivariate ecological variables at high spatial resolution using a cost-effective matching algorithm".ECOSPHERE 15.3(2024).