干旱区生态环境知识资源中心(Arid): High-integrity human intervention in ecosystems: Tracking self-organization modes

Arid

DOI	10.1371/journal.pcbi.1009427
	High-integrity human intervention in ecosystems: Tracking self-organization modes
	Zelnik, Yuval R.; Mau, Yair; Shachak, Moshe; Meron, Ehud
通讯作者	Zelnik, YR (corresponding author), CNRS, Ctr Biodivers Theory & Modelling, Theoret & Expt Ecol Stn, Moulis, France. ; Zelnik, YR (corresponding author), Swedish Univ Agr Sci, Dept Ecol, Uppsala, Sweden.
来源期刊	PLOS COMPUTATIONAL BIOLOGY
ISSN	1553-734X
EISSN	1553-7358
出版年	2021
卷号	17 期号:9
英文摘要	Author summary Human intervention in ecosystems is motivated by various functional needs, such as provisioning ecosystem services, but often has unexpected detrimental outcomes. A major question in ecology is how to manage human intervention so as to achieve its goal without impairing ecosystem function. The main idea pursued here is the need to identify the inherent response ways of ecosystems to disturbances, and use them as road maps for conducting interventions. This approach is demonstrated mathematically using two contexts, grazing management and vegetation restoration, and compared to remote sensing data for the latter. Among the surprising insights obtained is the beneficial effect of grazing, in terms of resilience to droughts, that can be achieved by managing it non-uniformly in space. Humans play major roles in shaping and transforming the ecology of Earth. Unlike natural drivers of ecosystem change, which are erratic and unpredictable, human intervention in ecosystems generally involves planning and management, but often results in detrimental outcomes. Using model studies and aerial-image analysis, we argue that the design of a successful human intervention form calls for the identification of the self-organization modes that drive ecosystem change, and for studying their dynamics. We demonstrate this approach with two examples: grazing management in drought-prone ecosystems, and rehabilitation of degraded vegetation by water harvesting. We show that grazing can increase the resilience to droughts, rather than imposing an additional stress, if managed in a spatially non-uniform manner, and that fragmental restoration along contour bunds is more resilient than the common practice of continuous restoration in vegetation stripes. We conclude by discussing the need for additional studies of self-organization modes and their dynamics.
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000701270000001
WOS关键词	PATTERN-FORMATION ; ECOLOGICAL THRESHOLDS ; TREE MORTALITY ; REGIME SHIFTS ; LAND-USE ; VEGETATION ; SYSTEMS ; DESERTIFICATION ; MANAGEMENT ; REDUCTION
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
来源机构	Ben-Gurion University of the Negev ; Hebrew University of Jerusalem
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364369
作者单位	[Zelnik, Yuval R.] CNRS, Ctr Biodivers Theory & Modelling, Theoret & Expt Ecol Stn, Moulis, France; [Zelnik, Yuval R.] Swedish Univ Agr Sci, Dept Ecol, Uppsala, Sweden; [Mau, Yair] Hebrew Univ Jerusalem, Robert H Smith Fac Agr Food & Environm, Inst Environm Sci, Rehovot, Israel; [Shachak, Moshe] Ben Gurion Univ Negev, Blaustein Inst Desert Res, Mitrani Dept Desert Ecol, Sede Boqer Campus, Beer Sheva, Israel; [Meron, Ehud] Ben Gurion Univ Negev, Blaustein Inst Desert Res, Dept Solar Energy & Environm Phys, Sede Boqer Campus, Beer Sheva, Israel; [Meron, Ehud] Ben Gurion Univ Negev, Dept Phys, Beer Sheva, Israel
推荐引用方式 GB/T 7714	Zelnik, Yuval R.,Mau, Yair,Shachak, Moshe,et al. High-integrity human intervention in ecosystems: Tracking self-organization modes[J]. Ben-Gurion University of the Negev, Hebrew University of Jerusalem,2021,17(9).
APA	Zelnik, Yuval R.,Mau, Yair,Shachak, Moshe,&Meron, Ehud.(2021).High-integrity human intervention in ecosystems: Tracking self-organization modes.PLOS COMPUTATIONAL BIOLOGY,17(9).
MLA	Zelnik, Yuval R.,et al."High-integrity human intervention in ecosystems: Tracking self-organization modes".PLOS COMPUTATIONAL BIOLOGY 17.9(2021).