干旱区生态环境知识资源中心(Arid): Machine learning prediction of mortality in the common desert shrub Encelia farinosa

Arid

DOI	10.1016/j.ecoinf.2021.101376
	Machine learning prediction of mortality in the common desert shrub Encelia farinosa
	Bitter, Nicholas Q.; Ehleringer, James R.
通讯作者	Bitter, NQ (corresponding author), Univ Utah, Sch Biol Sci, 257 South 1400 East, Salt Lake City, UT 84112 USA.
来源期刊	ECOLOGICAL INFORMATICS
ISSN	1574-9541
EISSN	1878-0512
出版年	2021
卷号	64
英文摘要	Two populations of the common shrub Encelia farinosa in the northern and southern portions of the Mojave Desert have been surveyed each spring for nearly 40 years, providing an opportunity to assess highly variable shrub mortality in an arid ecosystem. Most of the newly established shrubs experienced mortality during the juvenile stage, with median survival time of about three years in both populations yet, a small number of shrubs lived for at least a dozen years or even decades. Applying machine learning techniques, we predicted shrub mortality at different life-history stages using random forest and logistic regression. First, we examined seedling survival to become yearlings (one-year old plants), finding that less than 3% of seedlings in both populations survived to become established yearling shrubs. Second, we predicted whether or not yearlings would die prior to reaching the mature adult stage (four years old). The models achieved an Area Under the Receiver Operating Characteristic (AUC) in the 0.80 range for the Oatman population (southern Mojave Desert) and 0.90 range for the Death Valley population (northern Mojave Desert). We found yearling characteristics of smaller shrub size, low leaf coverage, and location in specific microsites associated with experiencing mortality before reaching the mature stage. Third, using only the average juvenile plant characteristics over the first four years of life, we predicted whether or not new adult shrubs were likely to experience mortality within the next eight years. The performance in this application achieved AUC in the 0.72 range for both populations. We found adult Encelia farinosa shrubs that had juvenile characteristics of smaller size, flowered less frequently, and had smaller interplant distances for the Oatman population were associated with increased mortality within the next eight years. Overall, the size of the shrub was the most important feature for the mortality modeling applications. No significant difference in AUC was found for random forest and logistic regression.
英文关键词	Random Forest Logistic regression Plant mortality Plant survival Arid ecosystems
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000691773500003
WOS关键词	INDIVIDUAL TREE MORTALITY ; SEEDLING MORTALITY ; PLANT MORTALITY ; SURVIVAL ; MODELS ; GROWTH ; REPRODUCTION ; DROUGHT ; SPRUCE
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/2XILL650/363013
作者单位	[Bitter, Nicholas Q.; Ehleringer, James R.] Univ Utah, Sch Biol Sci, 257 South 1400 East, Salt Lake City, UT 84112 USA
推荐引用方式 GB/T 7714	Bitter, Nicholas Q.,Ehleringer, James R.. Machine learning prediction of mortality in the common desert shrub Encelia farinosa[J],2021,64.
APA	Bitter, Nicholas Q.,&Ehleringer, James R..(2021).Machine learning prediction of mortality in the common desert shrub Encelia farinosa.ECOLOGICAL INFORMATICS,64.
MLA	Bitter, Nicholas Q.,et al."Machine learning prediction of mortality in the common desert shrub Encelia farinosa".ECOLOGICAL INFORMATICS 64(2021).