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.

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."

This book offers the first guide to landscape ecologists on the art and science of doing experiments, both observational and manipulative. How do you conduct an experiment when your study subject is as big as a landscape? Issues of scale, spatial heterogeneity and limitations on replication may challenge scientists seeking to carry out robust experiments in landscape ecology. Beginning with an overview of the history and philosophy of the scientific method, and tracing the development of experimental approaches in ecology broadly, the first half of the book discusses the broader issues of what makes a good experiment. Individual chapters describe unique aspects of landscape ecology that present challenges to experimentation, with suggestions for solutions on issues of scale, and how to apply controls, randomization and adequate replication in a landscape setting. The second half of the book describes different kinds of landscape ecology experimental approaches including: large-scale manipulations experimental model landscapes mesocosms and microcosms in silico experiments novel landscapes Each chapter describes the advantages and disadvantages of each approach, and identifies the types of landscape ecology concepts and questions that a research can address. Examples from around the world, in a myriad of different environments, help to illustrate the ideas in each chapter. Together with an annotated resources section, this book aims to stimulate ideas and inspire creativity for graduate students and early career researchers who want to conduct better experiments in landscape ecology.