Using Spatial Datasets for Conservation Planning Under Climate Change in the Pacific Northwest
This guidebook provides user-friendly overviews of a variety of spatial datasets relevant to conservation and management of natural resources in the face of climate change in the Pacific Northwest, United States. Each guidebook chapter was created using a standardized template to summarize a spatial dataset or a group of closely related datasets. Datasets were selected according to standardized criteria based on input through a collaborative process involving researchers and natural-resource managers throughout the Pacific Northwest region.
In each chapter, basic spatial and temporal information is provided for the dataset, along with a conceptual overview, glossary of key terms, links to download data and supporting documentation, a brief methods summary describing how the dataset was created, guidelines for dataset interpretation, assessment of uncertainties along with evaluation of caveats and simplifying assumptions, and information about potential and actual conservation applications of the dataset.
Collectively, this information provides natural-resource managers with “snapshots” of a variety of datasets representing diverse processes and conditions, including climate projections, changes in hydrologic conditions, vegetation and fire-regime shifts, animal habitat changes, species movements, and topographic and soil conditions relevant to climate change. Along with other types of data and site-specific information, the datasets described in this guidebook have the potential to inform management of valued natural resources throughout the Pacific Northwest region in the context of adaptation to changing climate conditions.
Cartwright, J.M., ed. 2020. A guidebook to spatial datasets for conservation planning under climate change in the Pacific Northwest. U.S. Geological Survey. https://doi.org/10.5066/P92L1H7O
Jennifer Cartwright (U.S. Geological Survey)
Guidebook chapter contributing authors:
R. Travis Belote (The Wilderness Society)
Karen Bennett (USDA Forest Service)
Kyle Blasch (U.S. Geological Survey)
Steve Campbell (USDA Natural Resources Conservation Service)
Jennifer Cartwright (U.S. Geological Survey)
Jeanne Chambers (USDA Forest Service)
Raymond Davis (USDA Forest Service)
Solomon Dobrowski (University of Montana)
Jason Dunham (U.S. Geological Survey)
Diana Gergel (University of Washington)
Daniel Isaak (USDA Forest Service)
Kris Jaeger (U.S. Geological Survey)
Meade Krosby (University of Washington)
Jesse Langdon (Weyerhaeuser)
Joshua Lawler (University of Washington)
Caitlin Littlefield (University of Montana)
Charles Luce (USDA Forest Service)
Jeremy Maestas (USDA Natural Resources Conservation Service)
Anthony Martinez (USDA Forest Service)
Arjan Meddens (Washington State University)
Julia Michalak (University of Washington)
Sean Parks (USDA Forest Service)
Wendy Peterman (USDA Forest Service)
Ken Popper (The Nature Conservancy)†
Chris Ringo (Oregon State University)
Roy Sando (U.S. Geological Survey)
Michael Schindel (The Nature Conservancy)
Diana Stralberg (University of Alberta)
David Theobald (Conservation Science Partners)
Nathan Walker (USDA Forest Service)
Chad Wilsey (National Audubon Society)
Zhiqiang Yang (USDA Forest Service)
Andrew Yost (Oregon Department of Forestry)
Clockwise from upper left: (a) 2016 Pioneer Fire in Boise National Forest, Idaho, by Kari Greer, USDA Forest Service; (b) “A mountain lake way up high,” Cascade Mountains, by Glenna Barlow, CC BY 2.0; (c) “Primordial,” stream at Deception Falls, Washington, by John Westrock, CC BY 2.0; (d) Sagebrush steppe in Seedskadee National Wildlife Refuge, southwest Wyoming, by Tom Koerner, U.S. Fish and Wildlife Service
This guidebook was created using input from natural-resource managers and scientists throughout the Pacific Northwest region. In particular, the process of selecting datasets and creating a standardized template for guidebook chapters was guided by feedback from Charlie Schrader (USDA Forest Service), Laura Gephart (Columbia River Inter-Tribal Fish Commission), Leona Svancara (Idaho Department of Fish and Game), Louisa Evers (Bureau of Land Management), Tom Miewald (North Pacific Landscape Conservation Cooperative), Eric Jensen (Great Basin Landscape Conservation Cooperative), Jessica Halofsky (University of Washington), Ken Popper (The Nature Conservancy), Roy Sando (U.S. Geological Survey), Dave Theobald (Conservation Science Partners), Christian Torgerson (U.S. Geological Survey), Aaron Ramirez (Reed College), Gustavo Bisbal (Department of the Interior Northwest Climate Adaptation Science Center), and members of the Refugia Research Coalition, a project of the Department of the Interior Northwest Climate Adaptation Science Center.
This guidebook was improved based on reviews by Tatiana Eaves (Johns Hopkins University, Krieger School of Arts and Sciences) and by Crystal Raymond and Heidi Roop (University of Washington, Climate Impacts Group. Development of this guidebook was supported by the Department of the Interior Northwest Climate Adaptation Science Center. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Datasets that provide information relevant to management of a particular taxonomic group (such as birds) or type of habitat (such as stream-dwelling animals):
Datasets that rely primarily on climate projections and/or those that represent changing climate conditions such as temperature or precipitation:
- Chapter 1: Climate dissimilarity for North America
- Chapter 2: Multivariate climate velocity
- Chapter 3: North American climate refugia
- Chapter 4: Changes in snowpack and snow residence time
- Chapter 5: Changes in snowpack, soil moisture, and fuel moisture
- Chapter 6: Forest suitability for large wildfires
- Chapter 9: Stream temperature and climate velocity predictions in the PNW
- Chapter 14: Species movement to analog climates
- Chapter 15: Minimum cumulative exposure and minimum exposure distance
- Chapter 16: Animal species turnover
- Chapter 17: Spatial priorities for conserving birds of the PNW
Datasets that characterize or predict factors that influence wildfires:
Datasets that represent water-cycle processes and conditions in watersheds (such as snowpack and soil moisture) or in streams:
- Chapter 4: Changes in snowpack and snow residence time
- Chapter 5: Changes in snowpack, soil moisture, and fuel moisture
- Chapter 9: Stream temperature and climate velocity predictions in the PNW
- Chapter 10: Streamflow metric projections
- Chapter 11: Probability of Streamflow Permanence (PROSPER) Model
Datasets that represent how plants or animals might move across landscapes in response to climate change, including human-made barriers to movement:
Datasets that represent information only for streams or riparian areas:
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:
- Chapter 19: Environmental diversity datasets for North America
- Chapter 20: Landforms and physiographic diversity of the United States
- Chapter 21: Topoclimate diversity of the Pacific Northwest
- Chapter 22: Soil sensitivity index for western Washington, Oregon, and California
- Chapter 23: Soil drought probability for Pacific Northwest forests
- Chapter 24: Sagebrush ecosystem resilience and resistance
Datasets that represent future projections under climate-change scenarios are typically created using general circulation models (GCMs) as inputs. A GCM is a type of mathematical climate model that simulates the circulation of oceans and the Earth’s atmosphere (Randall et al., 2007; Rupp et al. 2013). Some climate models, known as Earth System Models (ESMs), also include carbon-cycle and ocean biogeochemical effects. Using future projections for greenhouse-gas emissions and sequestration (see appendix 2), GCMs can be used to generate future projections for climate variables such as temperature and precipitation.
GCMs differ in the ways in which they simulate ocean and atmospheric processes, including the equations and parameters they use and the horizontal and vertical spatial resolutions at which they operate. As a result, GCMs may differ in their sensitivities to climate forcing, such as how increased greenhouse gas concentrations influence global temperatures. Because the outputs of GCMs are often coarse in spatial resolution, downscaling methods are commonly employed to generate finer-resolution datasets. Downscaling methods are described in section 6 of each guidebook chapter for datasets that employed downscaled climate projections.
Table A1 presents a selection of GCMs that were used in the creation of datasets described in this guidebook. As GCMs are improved and updated, newer versions of GCMs are regularly published. Additional information on GCMs may be obtained by consulting the dataset citations in each chapter, and references therein. Detailed information about GCMs, including model evaluation and comparison among models, is available from Randall et al. (2007) and Rupp et al. (2013).
Bentsen, M., I. Bethke, J. Debernard, and T. Iversen. 2013. The Norwegian Earth system model, NorESM1-M - Part 1 : description and basic evaluation of the physical climate. Geoscientific Model Development 6:687–720.
Chylek, P., J. Li, M. K. Dubey, M. Wang, and G. Lesins. 2011. Observed and model simulated 20th century Arctic temperature variability: Canadian Earth System Model CanESM2. Atmospheric Chemistry and Physics Discussions 11:22893–22907.
Collier, M. A., S. J. Jeffrey, L. D. Rotstayn, S. M. Dravitzki, C. Moeseneder, C. Hamalainen, J. I. Syktus, R. Suppiah, J. Antony, A. El Zein, and M. Atif. 2011. The CSIRO-Mk3.6.0 atmosphere-ocean GCM: participation in CMIP5 and data publication. 19th International Congress on Modelling and Simulation, December 12-16 2011. Perth, Australia.
Collins, W. J., N. Bellouin, N. Gedney, P. Halloran, T. Hinton, J. Hughes, and C. D. Jones. 2011. Development and evaluation of an Earth-system model – HadGEM2. Geoscientific Model Development 4:1051–1075.
Diansky, N., and E. Volodin. 2002. Simulation of the present-day climate with a coupled atmosphere-ocean general circulation model. Izvestia, Atmospheric and Oceanic Physics 38:732–747.
Dufresne, J., M. Foujols, S. Denvil, A. Caubel, O. Marti, O. Aumont, Y. Balkanski, S. Bekki, H. Bellenger, R. Benshila, S. Bony, L. Bopp, P. Braconnot, P. Brockmann, P. Cadule, F. Cheruy, L. Fairhead, T. Fichefet, F. Codron, A. Cozic, D. Cugnet, N. De Noblet, C. Ethe, S. Flavoni, P. Friedlingstein, L. Guez, E. Guilyardi, D. Hauglustaine, F. Hourdin, A. Idelkadi, J. Ghattas, S. Joussaume, M. Kageyama, G. Krinner, S. Labetoulle, A. Lahellec, F. Lefevre, C. Levy, Z. X. Li, J. Lloyd, F. Lott, G. Madec, M. Mancip, M. Marchand, S. Masson, Y. Meurdesoif, J. Mignot, I. Musat, S. Parouty, J. Polcher, C. Rio, M. Schulz, D. Swingedouw, S. Szopa, C. Talandier, P. Terray, N. Viovy, and N. Vuichard. 2013. Climate change projections using the IPSL-CM5 Earth system model: from CMIP3 to CMIP5. Climate Dynamics 40:2123–2165.
Griffies, S., M. Winton, L. Donner, L. Horowitz, S. Downes, R. Farneti, A. Gnanadesikan, W. Hurlin, H.C. Lee, Z. Liang, J. B. Palter, B. L. Samuels, A.T. Wittenberg, B. L. Wyman, J. Yin, and N. Zadeh. 2011. The GFDL CM3 coupled climate model: characteristics of the ocean and sea ice simulations. Journal of Climate 24:3520–3544.
Ji, D., L. Wang, J. Feng, Q. Wu, H. Cheng, Q. Zhang, J. Yang, W. Dong, Y. Dai, D. Gong, and R. Zhang. 2014. Description and basic evaluation of Beijing Normal University Earth System Model (BNU-ESM) version 1. Geoscientific Model Development 7:2039–2064.
Jones, C. D., J. K. Hughes, N. Bellouin, S. C. Hardiman, G. S. Jones, J. Knight, S. Liddicoat, F. M. O. Connor, and I. P. Laplace. 2011. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geoscientific Model Development 4:543–570.
Lewis, S., and D. Karoly. 2014. Assessment of forced responses of the Australian Community Climate and Earth System Simulator (ACCESS) 1.3 in CMIP5 historical detection and attribution experiments. Australian Meteorological and Oceanographic Journal 64:87–101.
Martin, G., N. Bellouin, W. J. Collins, I. D. Culverwell, and P. R. Halloran. 2011. The HadGEM2 family of Met Office Unified Model climate configurations. Geoscientific Model Development 4:723–757.
Meehl, G., W. Washington, J. Arblaster, A. Hu, H. Teng, J. Kay, A. Gettelman, D. Lawrence, B. Sanderson, and W. Strand. 2013. Climate change projections in CESM1 (CAM5) compared to CCSM4. Journal of Climate 26:6287–6308.
Meehl, G., W. Washington, J. Arblaster, A. Hu, H. Teng, and C. Tebaldi. 2012. Climate system response to external forcings and climate change projections in CCSM4. Journal of Climate 25:3661–3683.
Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi, and K.E. Taylor. 2007. Climate models and their evaluation. in S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. Tignor, and H. Miller, editors. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York, NY.
Roeckner, E., G. Bäuml, L. Bonaventura, R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, I. Kirchner, L. Kornblueh, E. Manzini, A. Rhodin, U. Schlese, U. Schulzweida, and A. Tompkins. 2003. The atmospheric general circulation model ECHAM5. Part I: model description. Report No. 349. Max Planck Institute for Meteorology, Hamburg, Germany.
Rupp, D. E., J. T. Abatzoglou, K. C. Hegewisch, and P. W. Mote. 2013. Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. Journal of Geophysical Research 118: 884–907.
Schmidt, G. A., M. Kelley, L. Nazarenko, R. Ruedy, G. L. Russell, I. Aleinov, M. Bauer, S. E. Bauer, M. K. Bhat, R. Bleck, V. Canuto, Y. Chen, Y. Cheng, T. L. Clune, A. Del Genio, R. De Fainchtein, G. Faluvegi, J. E. Hansen, R. J. Healy, N. Y. Kiang, D. Koch, A. A. Lacis, A. N. Legrande, J. Lerner, K. K. Lo, E. E. Matthews, S. Menon, R. L. Miller, V. Oinas, and A. O. Oloso. 2014. Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. Journal of Advances in Modeling Earth Systems 6:141–184.
Voldoire, A., E. Sanchez-Gomez, D. Salas y Melia, B. Decharme, C. Cassou, S. Senesi, S. Valcke, I. Beau, A. Alias, M. Chevallier, M. Déqué, J. Deshayes, H. Douville, E. Fernandez, G. Madec, E. Maisonnave, M.-P. Moine, S. Planton, D. Saint-Martin, S. Szopa, S. Tyteca, R. Alkama, S. Belamari, A. Braun, L. Coquart, and F. Chauvin. 2013. The CNRM-CM5.1 global climate model: description and basic evaluation. Climate Dynamics 40:2091–2121.
Watanabe, M., T. Suzuki, R. Oishi, Y. Komuro, S. Watanabe, S. Emori, T. Takemura, M. Chikira, T. Ogura, M. Sekiguchi, K. Takata, D. Yamazaki, T. Yokohata, T. Nozawa, H. Hasumi, H. Tatebe, and M. Kimoto. 2010. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. Journal of Climate 23:6312–6335.
Watanabe, S., T. Hajima, K. Sudo, T. Nagashima, T. Takemura, H. Okajima, T. Nozawa, and H. Kawase. 2011. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development 4:845–872.
Wu, T. 2012. A mass-flux cumulus parameterization scheme for large-scale models: description and test with observations. Climate Dynamics 38:725–744.
Yukimoto, S., Y. Adachi, M. Hosaka, and T. Sakami. 2012. A new global climate model of the meteorological research institute: MRI-CGCM3 — model description and basic performance. Journal of the Meteorological Society of Japan 90:23–64.
Climate models, including general circulation models (GCMs), are produced by research institutions around the world (see appendix 1). The Coupled Model Intercomparison Project (CMIP) is a standardized framework for GCMs, which allows GCMs to be compared and their uncertainties to be studied (Knutti and Sedláček, 2013). CMIP Phase 5 (CMIP5) is a more recent effort that builds upon CMIP Phase 3 (CMIP3) and uses a different set of scenarios to represent future changes, such as greenhouse-gas emissions and sequestration, that may influence Earth’s climate. Details and updates on the most recent CMIP Phase 6 (CMIP6) are available from the World Climate Research Programme (2019).
CMIP3 used a family of greenhouse-gas scenarios known as Special Report on Emissions Scenarios (SRES) scenarios A1, A2, B1, and B2, which were subdivided into groups of sub-scenarios (Intergovernmental Panel on Climate Change, 2000). Although greenhouse-gas scenarios are commonly referred to as “emissions scenarios”, some scenarios also incorporate the effects of carbon sequestration in addition to emissions projections. Scenarios differ in how they represent future global conditions related to a range of factors that are expected to control greenhouse-gas emission and sequestration rates, including human population growth, geographic patterns of economic development, technological change, and patterns of natural-resource use (Intergovernmental Panel on Climate Change, 2000). Commonly used SRES scenarios include B1, A1B, and A2, listed in order of increasing predicted global surface warming by the year 2100 (Knutti and Sedláček, 2013). The B1, A1B, and A2 scenarios predict approximately 1.5°C, 2.5°C, and 3.5°C increases in global surface temperatures, respectively, between the mid-2000s and the year 2100 (see figure 1 in Knutti and Sedláček, 2013).
The newer CMIP5 uses representative concentration pathways (RCPs) as greenhouse-gas scenarios, including RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 (Meinshausen et al., 2011), again listed in order of increasing predicted global surface warming (Knutti and Sedláček, 2013). RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 predict approximately 1°C, 1.75°C, 2.5°C, and 4°C increases in global surface temperatures, respectively, between the mid-2000s and the year 2100 (see figure 1 in Knutti and Sedláček, 2013). While the CMIP3 and CMIP5 scenarios are not directly comparable because they include different assumptions related to environmental and socioeconomic forces that influence greenhouse-gas emissions and sequestration, Knutti and Sedláček (2013) provide a useful basis of comparison of the temperature and precipitation projections derived from the different sets of scenarios.
Some datasets described in this guidebook include future projections based on more than one scenario (e.g., A1B and A2, or RCP 4.5 and RCP 8.5). Natural-resource managers may wish to compare these projections to help address uncertainty about the magnitude and rate of future climate change. For example, future projections under the RCP 8.5 scenario represent conditions under more severe global climate change than projections under RCP 4.5. Additionally, managers may wish to compare future projections within a given greenhouse-gas scenario across future time periods, e.g., mid-21st-century compared to the end of the 21st century.
Intergovernmental Panel on Climate Change. 2000. Special Report on Emissions Scenarios. N. Nakićenović and R. Swart, Eds. Cambridge University Press, Cambridge, UK.
Knutti, R., and J. Sedláček. 2013. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change 3:369–373.
Meinshausen, M., S. J. Smith, K. Calvin, J. S. Daniel, and M. L. T. Kainuma. 2011. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climate Change 109:213–241.
World Climate Research Programme. 2019. CMIP Phase 6 (CMIP6). https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6.
Spatial resolution is a key consideration in the selection of geospatial datasets for conservation and management purposes. Requirements for levels of spatial detail to inform decision-making may vary depending on management applications and conservation goals. Scale mismatches can occur when datasets are too coarse in resolution relative to the spatial scale and conservation purpose for which they are applied (Guerrero et al., 2013). For example, at a regional scale, a 1-km resolution dataset (meaning a gridded raster dataset with each square pixel having sides 1-km long) might be appropriate for a variety of conservation applications requiring detailed assessment of spatial variability. However, the same dataset might be too coarse for those applications at a local scale, such as a 5-km2 protected area. In this example, the protected area would be covered by only five pixels of the dataset. At this scale, the dataset might help inform conservation in other ways that do not require detailed evaluation of spatial variability, for example to anticipate general changes over time for the entire protected area or to compare average dataset values for the projected area to average values for other locations in the region.
To help inform selection of datasets for appropriate conservation and management applications at a variety of spatial scales, tables A2 and A3 provide basic quantitative information on the dataset spatial resolutions needed to achieve specified numbers of pixels (100 and 1,000, respectively) within land-management units of varying sizes. Rows in tables A2 and A3 are in approximately increasing order by size. For each type of management unit, the tables list the median and 10th percentile land areas based on distributions of management units (e.g., protected areas, watersheds, and counties) across the Pacific Northwest, along with approximations of the coarsest resolution of spatial data needed to ensure 100 and 1,000 pixels (for tables A2 and A3, respectively) within the median and 10th percentile sizes of those management units. The choice of 100 and 1,000 pixels for calculations in these tables is arbitrary; however, the two tables can be compared to gain a sense of how spatial resolution needs change based on criteria for how many pixels are required within a given management unit. The median and 10th percentile land areas in tables A2 and A3 could be used to perform similar calculations at other thresholds of number of pixels desired within management units.
2. Methods for assessing spatial resolution at different management scales
To create tables A2 and A3, polygons representing various management unit types were compiled from several sources. Polygons representing protected areas within Washington, Oregon, and Idaho were obtained from the Gap Analysis Program (GAP) Protected Areas Database (U.S. Geological Survey, 2016) and were subdivided by landowner and land manager using the “owner type” and “manager type” fields. Polygons representing state-owned lands were additionally screened to include only polygons with “manager name” as “State Dept Natural Resources,” “State Fish and Wildlife,” or “State Parks and Recreation” and having a “unit name” that included the search terms “natural”, “conservation”, “wildlife”, or “park.” This additional filtering was required to screen out very small localized areas such as gravesites, local greenways, and river access sites. Watershed polygons representing 8-digit and 12-digit hydrologic unit codes (HUC-8s and HUC-12s, respectively) were obtained from the National Hydrography Dataset (U.S. Geological Survey, 2013). Polygons representing U.S. Environmental Protection Agency Level 3 ecoregions were obtained from (Omernik and Griffith, 2014).
For each type of management unit in tables A2 and A3, the distribution of polygon areas was used to calculate the median area and 10th percentile area. Although median area may be useful to consider for regional-scale conservation planning, managers may also wish to ensure that a dataset is of appropriate resolution even for relatively small management units within each type (represented by the 10th percentile area, such that approximately 90% of polygons of a given management unit type are equal to or larger than the listed area).
To calculate the coarsest resolution needed to ensure a minimum of P pixels within the median (and 10th percentile) of areas:
where R is the coarsest resolution (linear distance along an edge of a square pixel) and A is the area in question (median or 10th percentile area). This equation can be used to modify the calculations in tables A2 and A3, by substituting other desired minimum number of pixels (P) instead of 100 and 1,000.
3. Scale considerations for applying datasets for management and conservation
Along with other considerations, tables A2 and A3 can be used to help guide selection of datasets for management and conservation applications at various spatial scales to avoid potential scale mismatches. Section 10 of each guidebook chapter (‘Conservation applications’) lists a series of geographic scales for which the dataset may be appropriate for conservation applications that require detailed assessment of spatial variation within a geographic boundary such as a protected area, informed by the analysis in table A3. This section also lists the finer geographic scales at which the dataset might be useful for other conservation purposes, such as to assess general patterns or for comparison to other locations.
It is important to note that these guidelines are based only on the resolution of datasets relative to the size distributions of management units and do not account for the local magnitude of variation for a given dataset at a given scale. Local variation in dataset values varies between datasets. For example, two datasets of the same resolution might differ in their usefulness for a given management purpose in a given protected area if one dataset shows a large amount of spatial variability within the protected area (allowing managers to discern which parts of the protected area have relatively high values and low values) and the other shows fairly homogenous values across the protected area. In addition to tables A2 and A3, managers may also wish to consider the nature of the information presented in each dataset because landscape characteristics and processes (e.g., climate variation, habitat availability, soil and geologic patterns, and species movements) operate across a broad range of scales (Carroll et al., 2017). In general, spatial resolution relative to management scale should be one of several considerations used in selecting datasets for conservation or management applications. Other important considerations are described in section 10 of each guidebook chapter.
Carroll, C., T. Wang, D. R. Roberts, J. L. Michalak, J. J. Lawler, S. E. Nielsen, D. Stralberg, A. Hamann, and B. H. McRae. 2017. Scale-dependent complementarity of climatic velocity and environmental diversity for identifying priority areas for conservation under climate change. Global Change Biology 23:4508–4520.
Guerrero, A. M., R. R. J. M. C. Allister, J. Corcoran, and K. A. Wilson. 2013. Scale mismatches , conservation planning , and the value of social-network analyses. Conservation Biology 27:35–44.
Omernik, J., and G. Griffith. 2014. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environmental Management 54:1249–1266.
U.S. Geological Survey. 2013. The National Hydrography Dataset (NHD). https://nhd.usgs.gov/.
U.S. Geological Survey. 2016. National Gap Analysis Program, Protected Areas of the U.S. (PAD-US) dataset, version 1.4. https://gapanalysis.usgs.gov/padus/data/.
BLM: Bureau of Land Management
CMIP: Climate Model Intercomparison Project
DEM: digital elevation model
GCM: general circulation model
GIS: geospatial information systems
HLI: heat-load index
HUC: hydrologic unit code
NAD: North American Datum
NHD: National Hydrography Dataset
PCA: principal components analysis
RCP: representative concentration pathway
SDM: species-distribution model
SNOTEL: snow telemetry
SRES: Special Report on Emissions Scenarios
SSURGO: Soil survey geographic data
STATSGO: State soils geographic data
SWE: snow-water equivalent
TPI: topographic position index
USDA: United States Department of Agriculture
VIC: variable infiltration capacity
WGS: World Geodetic System