New knowledge from wildlife-habitat relationship models is often difficult to implement in a management context. This can occur because researchers do not always consider whether managers have access to information about environmental covariates that permit the models to be applied. Moreover, ecosystem management requires knowledge about the condition of habitats over large geographic regions, whereas most research projects have limited spatial inference. For example, research has revealed much about the habitat of fishers (Martes pennanti) at various research sites in California, yet this work has not been translated into practical tools that managers can use to monitor fisher habitat regionally, or to evaluate and mitigate the effects of proposed forest management on fisher habitat. This led us to create new habitat models that are intimately linked to agency approaches to forest monitoring and software tools used by forest managers to plan timber harvests and vegetation management. We created habitat models that were integrated with these approaches and tools that forest managers use for two purposes: to inventory forest resources (i.e., Forest Inventory and Analysis [FIA] plots) and to simulate the response of stands to harvest, fire, insects, disease, and other disturbances (i.e., Forest Vegetation Simulator [FVS]). In this paper we provide an example of how to assess and monitor wildlife habitat using FIA vegetation monitoring protocols. We also provide an example of how to integrate an existing FIA-based model of fisher resting habitat into FVS, software that simulates the effect of alternative silvicultural treatments on vegetation data collected from field plots. Using these tools we produce quantitative predictions of the status of resting habitat quality for fishers, and describe how it can be monitored over time. We also provide an example of the effect of vegetation treatments on predicted fisher resting habitat, which illustrates a process that can be used to understand, reduce, or mitigate the effects of these activities on fisher habitat. This work on the fisher provides one example of how habitat assessments for wildlife could be advanced if they were developed with management applicability and implementation success as a goal.

New knowledge from wildlife-habitat relationship models is often difficult to implement in a management context. This can occur because researchers do not always consider whether managers have access to information about environmental covariates that permit the models to be applied. Moreover, ecosystem management requires knowledge about the condition of habits over large geographic regions, whereas most research projects have limited spatial inference. For example, research has revealed much about the habitat of fishers (Martes pennanti) at various research sites in California, yet this work has not been translated into practical tools that managers can use to monitor fisher habitat regionally, or to evaluate and mitigate the effects of proposed forest management on fisher habitat. This led us to create new habitat models that are intimately linked to agency approaches to forest monitoring and software tools used by forest managers to plan timber harvests and vegetation management. We created habitat models that were integrated with these approaches and tools that forest managers use for two purposes: to inventory forest resources (i.e. Forest Inventory and Analysis [FIA] plots) and to simulate the response to stands to harvest, fire, insects, disease, and other disturbances (i.e., Forest Vegetation Simulator [FVS]). In this paper we provide an example of how to assess and monitor wildlife habitat using FIA vegetation monitoring protocols. We also provide an example of how to integrate an existing FIA-based model of fisher resting habitat into FVS, software that simulates the effect of alternative silvicultural treatments on vegetation data collected from field plots. Using these tools we produce quantitative predictions of the status of resting habitat quality for fishers, and describe how it can be monitored over time. We also provide an example of the effect of vegetation treatments on predicted fisher resting habitat, which illustrates a process that can be used to understand, reduce, or mitigate the effects of these activities on fisher habitat. This work on the fisher provides one example of how habitat assessments for wildlife could be advanced if they were developed with management applicability and implementation success as a goal.

The study of animal movement has always been a key element in ecological science, because it is inherently linked to critical processes that scale from individuals to populations and communities to ecosystems. Rapid improvements in biotelemetry data collection and processing technology have given rise to a variety of statistical methods for characterizing animal movement. The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement. Animal Movement is an essential reference for wildlife biologists, quantitative ecologists, and statisticians who seek a deeper understanding of modern animal movement models. A wide variety of modeling approaches are reconciled in the book using a consistent notation. Models are organized into groups based on how they treat the underlying spatio-temporal process of movement. Connections among approaches are highlighted to allow the reader to form a broader view of animal movement analysis and its associations with traditional spatial and temporal statistical modeling. After an initial overview examining the role that animal movement plays in ecology, a primer on spatial and temporal statistics provides a solid foundation for the remainder of the book. Each subsequent chapter outlines a fundamental type of statistical model utilized in the contemporary analysis of telemetry data for animal movement inference. Descriptions begin with basic traditional forms and sequentially build up to general classes of models in each category. Important background and technical details for each class of model are provided, including spatial point process models, discrete-time dynamic models, and continuous-time stochastic process models. The book also covers the essential elements for how to accommodate multiple sources of uncertainty, such as location error and latent behavior states. In addition to thorough descriptions of animal movement models, differences and connections are also emphasized to provide a broader perspective of approaches.

In the face of so many unprecedented changes in our environment, the pressure is on scientists to lead the way toward a more sustainable future. Written by a team of ecologists, Monitoring Animal Populations and Their Habitats: A Practitioner’s Guide provides a framework that natural resource managers and researchers can use to design monitoring programs that will benefit future generations by distilling the information needed to make informed decisions. In addition, this text is valuable for undergraduate- and graduate-level courses that are focused on monitoring animal populations. With the aid of more than 90 illustrations and a four-page color insert, this book offers practical guidance for the entire monitoring process, from incorporating stakeholder input and data collection, to data management, analysis, and reporting. It establishes the basis for why, what, how, where, and when monitoring should be conducted; describes how to analyze and interpret the data; explains how to budget for monitoring efforts; and discusses how to assemble reports of use in decision-making. The book takes a multi-scaled and multi-taxa approach, focusing on monitoring vertebrate populations and upland habitats, but the recommendations and suggestions presented are applicable to a variety of monitoring programs. Lastly, the book explores the future of monitoring techniques, enabling researchers to better plan for the future of wildlife populations and their habitats. Monitoring Animal Populations and Their Habitats: A Practitioner’s Guide furthers the goal of achieving a world in which biodiversity is allowed to evolve and flourish in the face of such uncertainties as climate change, invasive species proliferation, land use expansion, and population growth.