Valuing Chaparral
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Valuing Chaparral
Chaparral shrubland ecosystems are an iconic feature of the California landscape, and a highly biodiverse yet highly flammable backdrop to some of the fastest growing urban areas in the United States. Chaparral-type ecosystems are a common element of all of the world’s Mediterranean-type climate regions – of which California is one – yet there is little public appreciation of the intrinsic value and the ecosystem services that these landscapes provide. Valuing Chaparral is a compendium of contributions from experts in chaparral ecology and management, with a focus on the human relationship with chaparral ecosystems. Chapters cover a wide variety of subjects, ranging from biodiversity to ecosystem services like water provision, erosion control, carbon sequestration and recreation; from the history of human interactions with chaparral to current education and conservation efforts; and from chaparral restoration and management to scenarios of the future under changing climate, land use, and human population. Valuing Chaparral will be of interest to resource managers, the research community, policy makers, and the public who live and work in the chaparral dominated landscapes of California and other Mediterranean-type climate regions.
Interpretation and Implications of Variability in Ecological Systems
Numerous hypotheses have been proposed to explain the dynamics in abundance of individual species, how species interact, how communities assemble, and how interactions between biotic and abiotic processes shape ecosystem stability. Many if not most of these hypotheses find some degree of support, but often only within relatively narrow spatial and temporal ranges. This is because conditions vary over time and from place to place, and so the strength and extent of processes that were the focus of a given a hypothesis become altered by other forces. Ecologists have confronted variability from two perspectives; conceptual and statistical. Conceptually, spatial and temporal variability are now recognized as being scale dependent and hierarchical. Statistically, there are many models that ecologists readily use that account for the hierarchical and scale-dependence of variability present in many datasets. But linking the two perspectives into a meaningful understanding of what variability means in real systems has been much less successful. For example, it is common to see studies where the fixed effects of a generalized linear mixed model are reported, but very often random effects are completely ignored or, at best, given scant attention. The likelihood of this being a significant problem increases greatly in what are rapidly becoming more common studies that utilize datasets spanning long temporal and/or large spatial scales, or when extreme and often unpredictable events (gray and black swans) occur.
Advances in Visual Computing
This two-volume set of LNCS 13598 and 13599 constitutes the refereed proceedings of the 17th International Symposium on Visual Computing, ISVC 2022, which was held in October 2022. The 61 papers presented in these volumes were carefully reviewed and selected from 110 submissions. They are organized in the following topical sections: Part I: deep learning I; visualization; object detection and recognition; deep learning II; video analysis and event recognition; computer graphics; ST: biomedical imaging techniques for cancer detection, diagnosis and management. Part II: ST: neuro-inspired artificia intelligence; applications; segmentation and tracking; virtual reality; poster.