Purpose & Motivation

Recent advancements in climate modeling, remote sensing, and ecological science have produced a variety of digital geospatial datasets representing many aspects of climate-change ecology. New datasets are published regularly illustrating spatial patterns in climate vulnerability for terrestrial and aquatic ecosystems. Collectively, these datasets represent a wealth of valuable information that can be applied to conservation and natural-resource management in the face of climate change. The ever-increasing body of climate-ecology spatial datasets provides opportunities for natural-resource managers to anticipate climate-driven changes to ecosystems, habitats, and the larger landscape. Consideration of these projected changes may support the long-term effectiveness of management for protected areas, working lands, and other areas managed for biodiversity, recreation, or natural resources.

However, natural-resource managers face many challenges when trying to incorporate these diverse sources of information into on-the-ground decision-making. Managers must commonly juggle multiple priorities with limited resources. Urgent and time-sensitive management needs may leave limited time available to read, digest, and critically assess the technical publications (e.g. journal articles) in which newly published spatial datasets are typically presented. The computer modeling processes and methods that produce spatial datasets are often complex and may require consultation of multiple publications or technical experts to fully evaluate.

Furthermore, different datasets that appear to represent similar landscape processes or characteristics may differ in the landscape patterns they depict. Such differences arise in part because different datasets are produced by different research teams with different modeling approaches, input datasets, conceptual definitions, starting assumptions, and model parameters. Furthermore, important information needed for accurate dataset interpretation may require searching through multiple sections of publications (e.g., methods and discussion sections, supplementary materials) and sometimes across multiple publications—some of which may not be readily accessible to natural-resource managers. Such critically important information can include model validation and accuracy statistics, simplifying assumptions, caveats on interpretation, and components of landscape processes that are not fully represented by the dataset. All these considerations can result in both uncertainty and information overload, creating barriers for managers seeking to apply climate-ecology spatial datasets to their decision-making.

The purpose of this guidebook is to present user-friendly overviews for a variety of published spatial datasets relevant to conservation and natural-resource management in the face of climate change for the Pacific Northwest region of the United States. Datasets are summarized and key features are described briefly, allowing readers to select datasets of interest for further investigation and potential application to their work. Ultimately, the goals of this guidebook are to:

1. Support natural-resource managers to discover and examine spatial datasets that might be relevant to their ongoing efforts
2. Increase the usefulness and usability of climate-ecology spatial datasets for real-world conservation decision-making
3. Help bridge the gap between scientific publications and conservation practice

Guidebook Organization

This guidebook is divided into chapters, with each chapter summarizing a spatial dataset or a group of closely related datasets produced by the same research team. Each guidebook chapter is subdivided into the following sections:

1. Dataset overview, including a map of the dataset and glossary of key terms
2. Data access information including hyperlinks to download datasets and accompanying documentation
3. Basic conceptual information about what the dataset represents
4. Spatial information
5. Temporal information
6. Methods summary describing how the dataset was produced along with information on input datasets and models used
7. Guidelines for dataset interpretation
8. Evaluation of uncertainties including dataset assumptions, simplifications, and caveats
9. Peer-review information
10. Information on potential or actual conservation applications.

Because the same terms (e.g., “refugia”) were sometimes defined slightly differently by different research teams in the production of datasets, a universal glossary of terms for this guidebook was not possible; instead key terms are defined in a dedicated glossary for each chapter using the definitions most appropriate for the dataset in question.

The appendices provide additional information: Appendix 1 provides detailed information on climate models used by datasets in the guidebook, appendix 2 explains greenhouse-gas scenarios used in climate-change projections, and appendix 3 discusses spatial scale considerations for applying spatial datasets to management applications.

Datasets described in the chapters of this guidebook address a wide array of landscape processes relevant to management and conservation. Datasets generally belong to one or more of the following broad categories. Search for datasets based on these categories on the guidebook homepage.

• Animal habitat: datasets that provide information relevant to management of a particular taxonomic group (such as birds) or type of habitat (such as stream-dwelling animals)
• Climate: datasets that rely primarily on climate projections and/or those that represent changing climate conditions such as temperature or precipitation
• Fire: datasets that characterize or predict factors that influence wildfires
• Hydrology: datasets that represent water-cycle processes and conditions in watersheds (such as snowpack and soil moisture) or in streams
• Landscape connectivity: datasets that represent how plants or animals might move across landscapes in response to climate change, including human-made barriers to movement
• Stream and riparian: datasets that represent information only for streams or riparian areas
• Topoedaphic: datasets that represent topographic and soil conditions that may be relevant to changing climate conditions, for example soil drought vulnerability or the diversity of topographic settings (and hence the diversity of microclimates) across a landscape
• Vegetation: datasets that provide information about changing plant communities.

Dataset Selection & Guidebook Design Process

The following criteria were used to guide dataset selection for this guidebook:

1. Datasets in this guidebook are spatially explicit, i.e. digital maps. This means that datasets can be plotted in geographic space using geospatial information systems (GIS) software.
2. Because this guidebook focuses on the Pacific Northwest, datasets cover all or most of the states of Oregon, Washington, and Idaho, or they cover a major ecosystem type within that geographic area (e.g., forests or streams).
3. The landscape processes or characteristics represented by the datasets directly relate to a feature or component of potential climate-change vulnerability for species, ecosystems, or other natural resources. These components include climate-change exposure (the magnitude and rate of climate change), sensitivity (the degree to which the fitness or health of a species, ecosystem, or other resource depends on the prevailing climate), and adaptive capacity (the ability to cope with climate change by persisting in place, shifting to other local habitats, or migrating to regions of more suitable climate). Datasets that do not explicitly address components of climate vulnerability—and thus not included in this guidebook—may still provide important information for management and conservation purposes and can be consulted in conjunction with datasets in this guidebook. Examples include land cover maps and maps of human alteration of the landscape.
4. The spatial resolution of datasets in this guidebook is sufficiently fine to enable conservation applications at the regional scale or finer for the Pacific Northwest (Oregon, Washington, and Idaho); see appendix 3 for more information on dataset spatial resolution and conservation applications. Most datasets described in this guidebook are in a gridded (raster) format, such that dataset resolution refers to the size of an individual pixel (i.e., grid cell).
5. All datasets are free of charge to access and use. Most datasets in this guidebook are publicly available for download; the remaining datasets are freely available by contacting the corresponding author listed in the respective chapter.
6. In planning this guidebook, we considered including datasets that were already published or on-track to be published by April 2019. We considered datasets for inclusion that were published in 2010 or later, with an emphasis on new datasets. The majority of chapters in this guidebook (15 out of 24) represent datasets published in 2017 and 2018, with 22 out of 24 chapters representing datasets published between 2015 and 2018.
7. To be included in the guidebook, dataset primary authors needed to be willing to assist with writing, editing, and revising their respective chapters.

Although this guidebook includes information about a diverse array of spatial datasets relevant to conservation and climate change in the Pacific Northwest, it is not exhaustive. In many cases natural-resource managers will need to consult other datasets beyond those presented here to best inform conservation planning and decision-making. Examples of datasets not included in this guidebook but of potential conservation importance include:

• Coarse-resolution datasets intended for use at continental-to-global scales
• Site-scale datasets representing specific protected areas (e.g., national parks)
• Datasets representing land cover, vegetation, fire events, hydrology, human impacts, or other landscape features that do not explicitly represent potential climate-change effects
• Point locations of features of interest, e.g., monitoring sites or wetland locations
• Raw remote-sensing datasets
• Datasets that are not spatially explicit, e.g., data about particular species of conservation concern
• Unpublished datasets created by natural-resource managers for internal use

Selection of datasets for inclusion in this guidebook benefited from guidance and recommendations from natural-resource managers and researchers from State and Federal agencies, Tribal organizations, non-profit organizations, landscape conservation cooperatives, and colleges and universities throughout the Pacific Northwest (see Acknowledgements section). Recommendations for potential datasets to include came from targeted outreach (e.g., to the Stakeholder Advisory Committee of the Northwest Climate Adaptation Science Center), workshops convened by the Refugia Research Coalition, and ongoing communication with researchers and managers throughout the Pacific Northwest region. This process was participatory and focused on co-production of a useful product through collaboration between researchers and managers. However, the process was not comprehensive or systematic such that this guidebook does not represent a systematic review or a formal meta-analysis.

Future Considerations

Because of the rapid rate at which new climate-ecology spatial datasets are published, this guidebook may have a useful lifespan of 3 to 5 years from the date of publication, after which it would benefit from updates to reflect recently published datasets. Additionally, the research processes that guide models and dataset development are continually being refined. In some cases, these research processes may be improved based on input and feedback from natural-resource managers. The contact information presented in each chapter of the guidebook for dataset corresponding authors may be useful for natural-resource managers who wish to provide such feedback. Questions that managers may wish to consider in providing feedback to dataset authors include:

1. In what ways is the dataset in question most relevant to your work? What management questions does it help answer?
2. Are there ways the dataset could be refined that would make it more useful or usable for on-the-ground decision-making (e.g., changes in spatial resolution, species or ecosystems represented, time periods for future projections, etc.)?
3. Are there related or similar characteristics or processes that could be represented in future refinements of the dataset?