Data Analysis In Community And Landscape Ecology

Ecological data has several special properties: the presence or absence of species on a semi-quantitative abundance scale; non-linear relationships between species and environmental factors; and high inter-correlations among species and among environmental variables. The analysis of such data is important to the interpretation of relationships within plant and animal communities and with their environments. In this corrected version of Data Analysis in Community and Landscape Ecology, without using complex mathematics, the contributors demonstrate the methods that have proven most useful, with examples, exercises and case-studies. Chapters explain in an elementary way powerful data analysis techniques such as logic regression, canonical correspondence analysis, and kriging.

Vegetation Description and Data Analysis: A PracticalApproach, Second Edition is a fully revised and up-datededition of this key text. The book takes account of recent advancesin the field whilst retaining the original reader-friendly approachto the coverage of vegetation description and multivariate analysisin the context of vegetation data and plant ecology. Since the publication of the hugely popular first edition therehave been significant developments in computer hardware andsoftware, new key journals have been established in the field andscope and application of vegetation description and analysis hasbecome a truly global field. This new edition includes fullcoverage of new developments and technologies. This contemporary and comprehensive edition of this well-known andrespected textbook will prove invaluable to undergraduate andgraduate students in biological sciences, environmental science,geography, botany, agriculture, forestry and biologicalconservation. * Fully international approach * Includes illustrative case studies throughout * Now with new material on: the nature of plant communities;transitional areas between plant communities; induction anddeduction of plant ecology; diversity indices and dominancediversity curves; multivariate analysis in ecology. * Accessible, reader-friendly style * Now with new and improved illustrations

Interactions between species are of fundamental importance to all living systems and the framework we have for studying these interactions is community ecology. This is important to our understanding of the planets biological diversity and how species interactions relate to the functioning of ecosystems at all scales. Species do not live in isolation and the study of community ecology is of practical application in a wide range of conservation issues. The study of ecological community data involves many methods of analysis. In this book you will learn many of the mainstays of community analysis including: diversity, similarity and cluster analysis, ordination and multivariate analyses. This book is for undergraduate and postgraduate students and researchers seeking a step-by-step methodology for analysing plant and animal communities using R and Excel. Microsoft's Excel spreadsheet is virtually ubiquitous and familiar to most computer users. It is a robust program that makes an excellent storage and manipulation system for many kinds of data, including community data. The R program is a powerful and flexible analytical system able to conduct a huge variety of analytical methods, which means that the user only has to learn one program to address many research questions. Its other advantage is that it is open source and therefore completely free. Novel analytical methods are being added constantly to the already comprehensive suite of tools available in R. Mark Gardener is both an ecologist and an analyst. He has worked in a range of ecosystems around the world and has been involved in research across a spectrum of community types. His knowledge of R is largely self-taught and this gives him insight into the needs of students learning to use R for complicated analyses.

This book presents a new method for assessing spatial pattern in raster land cover maps based on satellite imagery in a way that incorporates multiple pixel resolutions. This is combined with more conventional single-resolution measurements of spatial pattern and simple non-spatial land cover proportions to assess predictability of both surface water quality and ecological integrity within watersheds of the state of Pennsylvania (USA).

The fusion of individualistic community ecology with the Hutchinsonian niche concept enabled a broad integration of ecological theory, spanning all the way from the niche characteristics of individual species, to the composition, structure, and dynamics of ecological communities. Landscape ecology has been variously described as the study of the structure, function, and management of large heterogeneous land areas. Any reading of the published landscape ecology literature shows near uniformity in the adoption of a categorical patch-mosaic paradigm. However, if biological communities are multivariate gradients of species composition, with each species responding individualistically to particular combinations of limiting factors, is a categorical patch-based representation appropriate? We evaluate the sufficiency of several patch-based representations of vegetation at the landscape level to explain the composition of the plant community. Classified vegetation maps all performed poorly in explaining the composition and structure of forest trees among plots. Different categorical vegetation maps provided largely independent explanations of species variability. Individual species models based on spectral, topographic, and climatic variables vastly out-performed those produced using the classified maps. By moving from a landscape ecological paradigm based on categorical patches to one based on quantitative species and environmental responses across continuous space, it will be possible to both produce much more effective predictions of species distributions and ecological processes and remove much of the disjunction between landscape ecology and mainstream community ecology theory.

An essential guide for graduates, researchers and professionals to spatial analysis and the fast-growing range of methods available.

Most projects in Landscape Ecology, at some point, define a species-habitat association. These models are inherently spatial, dealing with landscapes and their configurations. Whether coding behavioral rules for dispersal of simulated organisms through simulated landscapes, or designing the sampling extent of field surveys and experiments in real landscapes, landscape ecologists must make assumptions about how organisms experience and utilize the landscape. These convenient working postulates allow modelers to project the model in time and space, yet rarely are they explicitly considered. The early years of landscape ecology necessarily focused on the evolution of effective data sources, metrics, and statistical approaches that could truly capture the spatial and temporal patterns and processes of interest. Now that these tools are well established, we reflect on the ecological theories that underpin the assumptions commonly made during species distribution modeling and mapping. This is crucial for applying models to questions of global sustainability. Due to the inherent use of GIS for much of this kind of research, and as several authors’ research involves the production of multicolored map figures, there would be an 8-page color insert. Additional color figures could be made available through a digital archive, or by cost contributions of the chapter authors. Where applicable, would be relevant chapters’ GIS data and model code available through a digital archive. The practice of data and code sharing is becoming standard in GIS studies, is an inherent method of this book, and will serve to add additional research value to the book for both academic and practitioner audiences.