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High-integrity human intervention in ecosystems: Tracking self-organization modes

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.
Conservation of Natural Resources, QH301-705.5, Climate Change, 710, Animals, Humans, Computer Simulation, Biomass, Herbivory, Biology (General), Animal Husbandry, Ecosystem, Stochastic Processes, Conservation of Water Resources, Ecology, Computational Biology, Grassland, Droughts, Bioinformatics and Systems Biology (methods development to be 10203), Remote Sensing Technology, Research Article
Conservation of Natural Resources, QH301-705.5, Climate Change, 710, Animals, Humans, Computer Simulation, Biomass, Herbivory, Biology (General), Animal Husbandry, Ecosystem, Stochastic Processes, Conservation of Water Resources, Ecology, Computational Biology, Grassland, Droughts, Bioinformatics and Systems Biology (methods development to be 10203), Remote Sensing Technology, Research Article
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).4 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
