Abstract: Spatially explicit information on the species composition and structure of forest vegetation is needed at broad spatial scales for natural resource policy analysis and ecological research. We present a method for predictive vegetation mapping that applies direct gradient analysis and nearest-neighbor imputation to ascribe detailed ground attributes of vegetation to each pixel in a digital landscape map. The gradient nearest neighbor method integrates vegetation measurements from regional grids of field plots, mapped environmental data, and Landsat Thematic Mapper (TM) imagery. In the Oregon coastal province, species gradients were most strongly associated with regional climate and geographic location, whereas variation in forest structure was best explained by Landsat TM variables. At the regional level, mapped predictions represented the range of variability in the sample data, and predicted area by vegetation type closely matched sample-based estimates. At the site level, mapped predictions maintained the covariance structure among multiple response variables. Prediction accuracy for tree species occurrence and several measures of vegetation structure and composition was good to moderate. Vegetation maps produced with the gradient nearest neighbor method are appropriately used for regional-level planning, policy analysis, and research, not to guide local management decisions. Résumé: Afin d’effectuer l’analyse des politiques touchant les ressources naturelles et appuyer la recherche écologique, il est nécessaire d’obtenir une information spatiale précise sur la structure de la végétation forestière et sur la composition des espèces et ce, à une vaste échelle spatiale. Nous présentons une méthode de cartographie prévisionnelle

1 Canonical community ordination comprises a collection of methods that relate species assemblages to their environment, in both observational studies and designed experiments. Canonical ordination differs from ordination sensu stricto in that species and environment data are analyzed simultaneously. Part I reviews the theory in a non-mathematical way with emphasis on new insights for the interpretation of ordination diagrams. The interpretation depends on the ordination method used to create the diagram. After the basic theory, Part I is focused on the ordination diagrams in linear methods of canonical community ordination, in particular principal components analysis, redundancy analysis and canonical correlation analysis. Special attention is devoted to the display of qualitative environmental variables. Key words: principal components analysis, redundancy analysis, canonical correlation analysis, biplot, ordination diagram, species-environment relations. 2

Authors Reliable predictions of how changing climate and disturbance regimes will affect forest ecosystems are crucial for effective forest management. Current fire and climate research in forest ecosystem and community ecology offers data and methods that can inform such predictions. However, research in these fields occurs at different scales, with disparate goals, methods, and context. Often results are not readily comparable among studies and defy integration. We discuss the strengths and weaknesses of three modeling paradigms: empirical gradient models, mechanistic ecosystem models, and stochastic landscape disturbance models. We then propose a synthetic approach to multi-scale analysis of the effects of climatic change and disturbance on forest ecosystems. Empirical gradient models provide an anchor and spatial template for stand-level forest ecosystem models by quantifying key parameters for individual species and accounting for broad-scale geographic variation among them. Gradient imputation transfers predictions of fine-scale forest composition and structure across geographic space. Mechanistic ecosystem dynamic models predict the responses of biological variables to specific environmental drivers and facilitate understanding of temporal dynamics and disequilibrium. Stochastic landscape dynamics models predict frequency, extent, and severity of broad-scale disturbance. A robust linkage of these three modeling paradigms will facilitate prediction of the effects of altered fire and other disturbance regimes on forest ecosystems at multiple scales and in the context of climatic variability and change.

OF THE DISSERTATION A Markov Chain Monte Carlo Method for Approximating 2-Way Contingency Tables with Applications in the Stability Analysis of Ecological Ordination by Stanley S. Bentow Doctor of Philosophy in Statistics University of California, Los Angeles, 1999 Professor N. Donald Ylvisaker, Chair This dissertation develops a Markov Chain Monte Carlo method for approximating 2-way contingency tables with an eye toward assessing the stability of ecological ordination. Ecology is a part of biology that deals with the interrelationships between populations, communities and ecosystems and their environment. It draws on knowledge from many other disciplines such as climatology, physical geography, agronomy, and pedology [52]. Odum [75] prefers the de nition \ Ecology is the study of structure and function of nature," and stresses the role of ecosystem research in relation to the use of nature by man. Krebs [58] prefers to think of Ecology as the scienti c study of the interactions t...

this paper -- the statistical complication is that classical methods used to quantify environment-abundance associations assume independence of observations. However, the distribution or abundance of a species is typically spatially autocorrelated due to locomotory constraints (e.g., Orians and Pearson 1979, Abrahams Accepted 23 January 2002 Copyright ECOGRAPHY 2002 ISSN 0906-7590 1986), social organization (e.g., Stamps 1988, Morris et al. 1992), or aggregative responses to cues from conspecifics (Turchin and Kareiva 1989, Turchin and Thoeny 1993). At the same time the environment is usually also spatially autocorrelated (e.g., Manly 1986, Leduc et al. 1992, Legendre 1993). Careless regression (or correlation) of autocorrelated variables across an explicit or implicit spatial surface may, as a consequence, highlight spurious associations (Lennon 2000). Thus, models of abundance-environment relationships that ignore autocorrelated spatial pattern may place undue emphasis on environmental factors that in truth have little or no bearing on a species' distribution and abundance. Perhaps more importantly, models that ignore spatial autocorrelation may fail to place sufficient emphasis on true abundance-environment relationships, and thus lead to omission of important variables during model selection

The present study evaluates the relative importance of environmental factors in affecting the species composition and abundance of the plant communities on ultramafic soils in Tuscany, Italy. We used rigorous sampling techniques to test hypotheses generated from exploratory studies performed previously. Vegetation–environmental relationships were analyzed using 50 plots, each 1 m 2, randomly located throughout a 22-ha area in the Upper Tiber Valley. We confirm that the exchangeable fraction of nickel in the soil is almost never high enough to affect the vegetation. However, physical factors (e.g. substrate setting and elevation) are important in controlling the distribution of plant species. Tree cover (almost exclusively due to the introduced plantation pines) also had a significant affect on the vegetation composition and on soil features such as the C/N ratio. Other important factors significantly related to the gradients in vegetation composition (e.g. rockiness and total soil nitrogen) are interpreted as factors related to the vegetation composition through a positive feedback mechanism. Key words: endemic plants; monitoring; positive feedback; restoration; ultramafic soils.

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Despite being conspicuous and influential features of the biosphere, urban ecosystems have been neglected in ecological research. Arthropods are abundant in urban settings, but little is known about how these animals respond to urbanization. We systematically monitored the structure of ground arthropod communities for 12 months at 16 sites representing the four most abundant forms of urban land use (residential, industrial, agricultural, and desert remnant) in a rapidly growing metropolitan area (Phoenix, AZ). Although taxonomic richness was comparable among land-use types, community composition differed, with certain taxa being uniquely associated with each form of land use. Three taxa (springtails, ants, and mites) were extremely widespread and abundant, accounting for over 92 % of captures; when these three taxa were excluded from analysis, however, differences were revealed in arthropod community composition with urban land use. Trophic dynamics also varied with land use: predators, herbivores, and detritivores were most abundant in agricultural sites, while omnivores were equally abundant in all forms of land use. These community-level differences resulted from taxon-specific responses to habitat structure, which varied with land use. Because arthropod community structure is affected by habitat structure and land use, and because arthropods play key roles in nutrient cycling, organic matter decomposition, pollination, and soil aeration, the spatial heterogeneity of urban ecosystems therefore may affect ecosystem functioning. (Q) 2001 Elsevier Science B. V. All rights reserved.