LANDFIRE Related Articles

Picotte, J.J., Peterson, B., Meier, G., and Howard, S.M., 2016, 1984–2010 trends in fire burn severity and area for the conterminous US: International Journal of Wildland Fire, v. 25, no. 4, p. 413–420, at https://doi.org/10.1071/wf15039.

Padilla, C.J., Martin, J.T., Cain, J.W., III, and Gompper, M.E., 2024, Abiotic and demographic drivers of flea parasitism on deer mice in a recovering mixed-conifer forest a decade postfire: The Journal of Parasitology, v. 110, no. 4, p. 375–385, at https://doi.org/10.1645/23-45.

Lewis, W.B., Chandler, R.B., Delancey, C.D., Rushton, E., Wann, G.T., McConnell, M.D., and Martin, J.A., 2022, Abundance and distribution of ruffed grouse Bonasa umbellus at the southern periphery of the range: Wildlife Biology, v. 2022, no. 5, article e01017, at https://doi.org/10.1002/wlb3.01017.

Lombardo, S., Kinney, J., Blake, D., Chase, S., Stovall, A., Siddiqi, A., Arquilla, K., Israel, S., Wood, D., et al., 2023, Accessible satellite data decision support systems for Yurok Tribe forest management: Acta Astronautica, v. 213, p. 777–791, at https://doi.org/10.1016/j.actaastro.2023.09.040.

Fusco, E.J., Rau, B.M., Falkowski, M., Filippelli, S., and Bradley, B.A., 2019, Accounting for aboveground carbon storage in shrubland and woodland ecosystems in the Great Basin: Ecosphere, v. 10, no. 8, article e02821, at https://doi.org/10.1002/ecs2.2821.

Chandler, H.C., Jenkins, C.L., and Bauder, J.M., 2021, Accounting for geographic variation in species-habitat associations during habitat suitability modeling: Ecological Applications, v. 32, no. 2, article e2504, at https://doi.org/10.1002/eap.2504.

Hurteau, M.D., Hungate, B.A., and Koch, G.W., 2009, Accounting for risk in valuing forest carbon offsets: Carbon Balance and Management, v. 4, article 1, at https://doi.org/10.1186/1750-0680-4-1.

White, M.A., Shaw, J.D., and Ramsey, R.D., 2005, Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south-western USA: International Journal of Remote Sensing, v. 26, no. 12, p. 2699–2704, at https://doi.org/10.1080/01431160500080626.

D’Elia, J., Haig, S.M., Johnson, M., Marcot, B.G., and Young, R., 2015, Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus): Biological Conservation, v. 184, p. 90–99, at https://doi.org/10.1016/j.biocon.2015.01.002.

Moody, M.J., Stoll, R., and Bailey, B.N., 2023, Adaptation of QES-Fire, a dynamically coupled fast response wildfire model for heterogeneous environments: International Journal of Wildland Fire, v. 32, no. 5, p. 749–766, at https://doi.org/10.1071/wf22190.

Calzado-Martinez, C., Brunson, M.W., Koutzoukis, S., Baggio, J., and Veblen, K.E., 2023, Addressing barriers to proactive restoration of at-risk sagebrush communities—A causal layered analysis: Restoration Ecology, v. 31, no. 7, article e13897, at https://doi.org/10.1111/rec.13897.

Liu, H., Shu, L., Liu, X., Cheng, P., Wang, M., and Huang, Y., 2025, Advancements in artificial intelligence applications for forest fire prediction: Forests, v. 16, no. 4, article 704, at https://doi.org/10.3390/f16040704.

Thompson, M.P., Calkin, D.E., Gilbertson-Day, J.W., and Ager, A.A., 2011, Advancing effects analysis for integrated, large-scale wildfire risk assessment: Environmental Monitoring and Assessment, v. 179, no. 4, p. 217–39, at https://doi.org/10.1007/s10661-010-1731-x.

Aune, K.T., Zaitchik, B.F., Curriero, F.C., Davis, M.F., and Smith, G.S., 2023, Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability: Frontiers in Epidemiology, v. 3, article 1128501, at https://doi.org/10.3389/fepid.2023.1128501.

Dorshow, W.B., and Wills, W.H., 2022, Agricultural suitability of the Chaco Canyon Region, New Mexico—Implications for settlement patterning during the great house period (ca. AD 850 to 1200): Journal of Archaeological Science - Reports, v. 46, article 103650, at https://doi.org/10.1016/j.jasrep.2022.103650.

Yu, H., Guenther, A., Gu, D., Warneke, C., Geron, C., Goldstein, A., Graus, M., Karl, T., Kaser, L., et al., 2017, Airborne measurements of isoprene and monoterpene emissions from southeastern U.S. forests: Science of the Total Environment, v. 595, p. 149–158, at https://doi.org/10.1016/j.scitotenv.2017.03.262.

Houtman, R.M., Montgomery, C.A., Gagnon, A.R., Calkin, D.E., Dietterich, T.G., McGregor, S., and Crowley, M., 2013, Allowing a wildfire to burn—Estimating the effect on future fire suppression costs: International Journal of Wildland Fire, v. 22, no. 7, p. 871–882, at https://doi.org/10.1071/WF12157.

Schafer, T.L.J., Breck, S.W., Baruch-Mordo, S., Lewis, D.L., Wilson, K.R., Mao, J.S., and Day, T.L., 2018, American black bear den-site selection and characteristics in an urban environment: Ursus, v. 29, no. 1, p. 25–31, at https://doi.org/10.2192/URSUS-D-17-00004.2.

Kochanski, A.K., Clough, K., Farguell, A., Mallia, D.V., Mandel, J., and Hilburn, K., 2023, Analysis of methods for assimilating fire perimeters into a coupled fire-atmosphere model: Frontiers in Forests and Global Change, v. 6, article 1203578, at https://doi.org/10.3389/ffgc.2023.1203578.

Page, W.G., Wagenbrenner, N.S., Butler, B.W., and Blunck, D.L., 2019, An analysis of spotting distances during the 2017 fire season in the Northern Rockies, USA: Canadian Journal of Forest Research, v. 49, no. 3, p. 317–325, at https://doi.org/10.1139/cjfr-2018-0094.

Ager, A.A., Barros, A.M.G., Day, M.A., Preisler, H.K., Spies, T.A., and Bolte, J., 2018, Analyzing fine-scale spatiotemporal drivers of wildfire in a forest landscape model: Ecological Modelling, v. 384, p. 87–102, at https://doi.org/10.1016/j.ecolmodel.2018.06.018.

Ager, A.A., Vaillant, N.M., Finney, M.A., and Preisler, H.K., 2012, Analyzing wildfire exposure and source-sink relationships on a fire prone forest landscape: Forest Ecology and Management, v. 267, p. 271–283, at https://doi.org/10.1016/j.foreco.2011.11.021.

Turco, M., Abatzoglou, J.T., Herrera, S., Zhuang, Y., Jerez, S., Lucas, D.D., AghaKouchak, A., and Cvijanovic, I., 2023, Anthropogenic climate change impacts exacerbate summer forest fires in California: Proceedings of the National Academy of Sciences of the United States of America, v. 120, no. 25, article e2213815120, at https://doi.org/10.1073/pnas.2213815120.

Squires, J.R., Olson, L.E., Ivan, J.S., McDonald, P.M., and Holbrook, J.D., 2025, Anthropogenically protected but naturally disturbed—A specialist carnivore at its southern range periphery: Biodiversity and Conservation, v. 34, p. 401–427, at https://doi.org/10.1007/s10531-024-02978-8.

Moll, R.J., Jackson, P.J., Wakeling, B.F., Lackey, C.W., Beckmann, J.P., Millspaugh, J.J., and Montgomery, R.A., 2021, An apex carnivore’s life history mediates a predator cascade: Oecologia, v. 196, no. 1, p. 223–234, at https://doi.org/10.1007/s00442-021-04927-6.

Rwanga, S.S., and Ndambuki, J.M., 2014, Application of geographical information systems and FARSITE in fire spread modelling: International Journal of Environment and Sustainable Development, v. 13, no. 2, p. 185–203, at https://doi.org/10.1504/IJESD.2014.060201.

Smith, J.E., Billmire, M., French, N.H.F., and Domke, G.M., 2024, Application of the wildland fire emissions inventory system to estimate fire emissions on forest lands of the United States: Carbon Balance and Management, v. 19, no. 1, article 26, at https://doi.org/10.1186/s13021-024-00274-0.

Williamson, C.R., Campa, H., III, Locher, A.B., Winterstein, S.R., and Beyer, D.E.J., 2021, Applications of integrating wildlife habitat suitability and habitat potential models: Wildlife Society Bulletin, v. 45, no. 1, p. 70–84, at https://doi.org/10.1002/wsb.1152.

Meunier, J., and Shea, M.E., 2020, Applying the usual rules to an unusual ecological situation—Fire rotation in Great Lakes Pine Forests: Forest Ecology and Management, v. 472, article 118246, at https://doi.org/10.1016/j.foreco.2020.118246.

Cameron, D.R., Cohen, B.S., and Morrison, S.A., 2012, An approach to enhance the conservation-compatibility of solar energy development: PLoS ONE, v. 7, no. 6, article e38437, at https://doi.org/10.1371/journal.pone.0038437.

Vaillant, N.M., and Ager, A.A., 2014, ArcFuels—An ArcMap toolbar for fuel treatment planning and wildfire risk assessment: Fire Management Today, v. 74, no. 2, p. 21–23, at https://www.frames.gov/documents/usfs/fmt/fmt_74-1.pdf.

Hu, F.S., Higuera, P.E., Duffy, P., Chipman, M.L., Rocha, A.V., Young, A.M., Kelly, R., and Dietze, M.C., 2015, Arctic tundra fires—Natural variability and responses to climate change: Frontiers in Ecology and the Environment, v. 13, no. 7, p. 369–377, at https://doi.org/10.1890/150063.

Polasky, S., Johnson, K., Keeler, B., Kovacs, K., Nelson, E., Pennington, D., Plantinga, A.J., and Withey, J., 2012, Are investments to promote biodiversity conservation and ecosystem services aligned?: Oxford Review of Economic Policy, v. 28, no. 1, p. 139–163, at https://doi.org/10.1093/oxrep/grs011.

Wion, A.P., Weisberg, P.J., Pearse, I.S., and Redmond, M.D., 2019, Aridity drives spatiotemporal patterns of masting across the latitudinal range of a dryland conifer: Ecography, v. 43, no. 4, p. 569–580, at https://doi.org/10.1111/ecog.04856.

Saeedimoghaddam, M., Nearing, G., Goodrich, D.C., Hernandez, M., Guertin, D.P., Metz, L.J., Wei, H., Ponce-Campos, G., Burns, S., et al., 2024, An artificial neural network to estimate the foliar and ground cover input variables of the Rangeland Hydrology and Erosion Model: Journal of Hydrology, v. 631, article 130835, at https://doi.org/10.1016/j.jhydrol.2024.130835.

Fremgen-Tarantino, M.R., Olsoy, P.J., Frye, G.G., Connelly, J.W., Krakauer, A.H., Patricelli, G.L., and Forbey, J.S., 2021, Assessing accuracy of GAP and LANDFIRE land cover datasets in winter habitats used by greater sage-grouse in Idaho and Wyoming, USA: Journal of Environmental Management, v. 280, article 111720, at https://doi.org/10.1016/j.jenvman.2020.111720.

Cardil, A., Monedero, S., Ramírez, J., and Silva, C.A., 2019, Assessing and reinitializing wildland fire simulations through satellite active fire data: Journal of Environmental Management, v. 231, p. 996–1003, at https://doi.org/10.1016/j.jenvman.2018.10.115.

Theobald, D.M., Crooks, K.R., and Norman, J.B., 2011, Assessing effects of land use on landscape connectivity—Loss and fragmentation of western U.S. forests: Ecological Applications, v. 21, no. 7, p. 2445–2458, at https://doi.org/10.1890/10-1701.1.

Wimberly, M.C., Cochrane, M.A., Baer, A.D., and Kari, P., 2009, Assessing fuel treatment effectiveness using satellite imagery and spatial statistics: Ecological Applications, v. 19, no. 6, p. 1377–1384, at https://doi.org/10.1890/08-1685.1.

Woodie, M.B., Tomcho, A., Barnhill, L.M., and McComb, B.C., 2023, Assessing golden-winged warbler dispersal habitat in a highly parcelized landscape: Wildlife Society Bulletin, v. 47, no. 3, article e1473, at https://doi.org/10.1002/wsb.1473.

Paveglio, T.B., Edgeley, C.M., and Stasiewicz, A.M., 2018, Assessing influences on social vulnerability to wildfire using surveys, spatial data and wildfire simulations: Journal of Environmental Management, v. 213, p. 425–439, at https://doi.org/10.1016/j.jenvman.2018.02.068.

Buchholtz, E.K., Kreitler, J., Shinneman, D.J., Crist, M., and Heinrichs, J., 2023, Assessing large landscape patterns of potential fire connectivity using circuit methods: Landscape Ecology, v. 38, p. 1663–1676, at https://doi.org/10.1007/s10980-022-01581-y.

Halofsky, J.E., Hemstrom, M.A., Conklin, D.R., Halofsky, J.S., Kerns, B.K., and Bachelet, D., 2013, Assessing potential climate change effects on vegetation using a linked model approach: Ecological Modelling, v. 266, p. 131–143, at https://doi.org/10.1016/j.ecolmodel.2013.07.003.

Li, M., Huang, C., Zhu, Z., Shi, H., Lu, H., and Peng, S., 2009, Assessing rates of forest change and fragmentation in Alabama, USA, using the vegetation change tracker model: Forest Ecology and Management, v. 257, no. 6, p. 1480–1488, at https://doi.org/10.1016/j.foreco.2008.12.023.

Ghimire, S.R., Schumacher, B., Swanson, S., Hall, R., Hall, E.S., Zambrana, J., and Johnston, J.M., 2025, Assessing riparian functioning condition for improved ecosystem services—A case study of the Back Creek watershed (Virginia, USA): Journal of Environmental Management, v. 375, article 124154, at https://doi.org/10.1016/j.jenvman.2025.124154.

Scott, V., Juran, L., Ling, E.J., Benham, B., and Spiller, A., 2020, Assessing strontium and vulnerability to strontium in private drinking water systems in Virginia: Water, v. 12, no. 4, article 1053, at https://doi.org/10.3390/w12041053.

Shams, S.B., Boehnert, J., and Wilhelmi, O., 2025, Assessing the impact of conservation practices on post-wildfire recovery of evergreen and conifer forests using remote sensing data: Fire, v. 8, no. 3, article 92, at https://doi.org/10.3390/fire8030092.

Viteri, M.C., Stegner, M.A., and Hadly, E.A., 2021, Assessing the reliability of raptor pellets in recording local small mammal diversity: Quaternary Research, v. 106, p. 1–10, at https://doi.org/10.1017/qua.2021.59.

Wilson, A.C., Nolin, A.W., and Bladon, K.D., 2021, Assessing the role of snow cover for post-wildfire revegetation across the Pacific Northwest: Journal of Geophysical Research—Biogeosciences, v. 126, no. 11, article e2021JG006465, at https://doi.org/10.1029/2021jg006465.

Ager, A.A., Palaiologou, P., Evers, C.R., Day, M.A., and Barros, A.M.G., 2018, Assessing transboundary wildfire exposure in the southwestern United States: Risk Analysis, v. 38, no. 10, p. 2105–2127, at https://doi.org/10.1111/risa.12999.

Monroe, A.P., Nauman, T.W., Aldridge, C.L., O’Donnell, M.S., Duniway, M.C., Cade, B.S., Manier, D.J., and Anderson, P.J., 2022, Assessing vegetation recovery from energy development using a dynamic reference approach: Ecology and Evolution, v. 12, no. 2, article e8508, at https://doi.org/10.1002/ece3.8508.

Malashin, I.P., Masich, I., Nelyub, V., Borodulin, A., Gantimurov, A., and Tynchenko, V., 2025, Assessing wildfire extents in Siberian forests using machine learning: Scientific Reports, v. 15, no. 1, article 32834, at https://doi.org/10.1038/s41598-025-17465-5.

Sparklin, B.D., Doherty, K.E., Rodhouse, T.J., Lonneker, J.J., Spaak, J., Cross, T.B., and Warren, J.M., 2024, An assessment of conservation opportunities within sagebrush ecosystems of US national parks and wildlife refuges: Rangeland Ecology & Management, v. 97, p. 94–106, at https://doi.org/10.1016/j.rama.2024.09.005.

Palomaki, R.T., Rittger, K., Lenard, S.J.P., Bair, E., Dozier, J., Skiles, S.M., and Painter, T.H., 2025, Assessment of methods for mapping snow albedo from MODIS: Remote Sensing of Environment, v. 326, article 114742, at https://doi.org/10.1016/j.rse.2025.114742.

Reeves, M.C., Hanberry, B.B., Wilmer, H., Kaplan, N.E., and Lauenroth, W.K., 2020, An assessment of production trends on the Great Plains from 1984 to 2017: Rangeland Ecology & Management, v. 78, p. 165–179, at https://doi.org/10.1016/j.rama.2020.01.011.

Rashidi, E., Medal, H., and Hoskins, A., 2018, An attacker-defender model for analyzing the vulnerability of initial attack in wildfire suppression: Naval Research Logistics, v. 65, no. 2, p. 120–134, at https://doi.org/10.1002/nav.21792.

Henderson, C.R., Mitchell, M.S., Myers, W.L., Lukacs, P.M., and Nelson, G.P., 2018, Attributes of seasonal home range influence choice of migratory strategy in white-tailed deer: Journal of Mammalogy, v. 99, no. 1, p. 89–96, at https://doi.org/10.1093/jmammal/gyx148.

Azim, M.R., Keskin, M., Do, N., and Gul, M., 2022, Automated classification of fuel types using roadside images via deep learning: International Journal of Wildland Fire, v. 31, no. 10, p. 982–987, at https://doi.org/10.1071/WF21136.

Julianus, E., Hollingsworth, T.N., McGuire, A.D., and Kielland, K., 2019, Availability and use of moose browse in response to post-fire succession on Kanuti National Wildlife Refuge, Alaska: Alces, v. 55, p. 67–89, at https://www.alcesjournal.org/index.php/alces/article/view/231.

Jorge, M.H., Conner, L.M., Garrison, E.P., and Cherry, M.J., 2022, Avian species richness in a frequently burned ecosystem—A link between pyrodiversity and biodiversity: Landscape Ecology, v. 37, no. 4, p. 983–996, at https://doi.org/10.1007/s10980-022-01399-8.

Hysen, L., Nayeri, D., Cushman, S., and Wan, H.Y., 2022, Background sampling for multi-scale ensemble habitat selection modeling—Does the number of points matter?: Ecological Informatics, v. 72, article 101914, at https://doi.org/10.1016/j.ecoinf.2022.101914.

Helmstetter, N.A., Conway, C.J., Stevens, B.S., and Goldberg, A.R., 2020, Balancing transferability and complexity of species distribution models for rare species conservation: Diversity and Distributions, v. 27, no. 1, p. 95–108, at https://doi.org/10.1111/ddi.13174.

Ren, J., Hanan, E.J., Hicke, J.A., Kolden, C.A., Abatzoglou, J.T., Tague, C.N.L., Bart, R.R., Kennedy, M.C., Liu, M., et al., 2023, Bark beetle effects on fire regimes depend on underlying fuel modifications in semiarid systems: Journal of Advances in Modeling Earth Systems, v. 15, no. 1, article e2022MS003073, at https://doi.org/10.1029/2022MS003073.

Carroll, R.W.H., Manning, A.H., Niswonger, R., Marchetti, D., and Williams, K.H., 2020, Baseflow age distributions and depth of active groundwater flow in a snow-dominated mountain headwater basin: Water Resources Research, v. 56, no. 12, article e2020WR028161, at https://doi.org/10.1029/2020WR028161.

Yoo, M., and Wikle, C.K., 2024, A Bayesian spatio-temporal level set dynamic model and application to fire front propagation: Annals of Applied Statistics, v. 18, no. 1, p. 404–423, at https://doi.org/10.1214/23-AOAS1794.

Wells, A.G., Yackulic, C.B., Kostelnik, J., Bock, A., Zuellig, R.E., Carlisle, D.M., Roberts, J.J., Rogers, K.B., and Munson, S.M., 2024, Before the fire—Predicting burn severity and potential post-fire debris-flow hazards to conservation populations of the Colorado River cutthroat trout (Oncorhynchus clarkii pleuriticus): International Journal of Wildland Fire, v. 33, no. 11, article WF23199, at https://doi.org/10.1071/Wf23199.

Gray, S.M., Humphreys, J.M., Montgomery, R.A., Etter, D.R., VerCauteren, K.C., Kramer, D.B., and Roloff, G.J., 2022, Behavioral states in space and time—Understanding landscape use by an invasive mammal: The Journal of Wildlife Management, v. 86, no. 4, article e22211, at https://doi.org/10.1002/jwmg.22211.

Picardi, S., Coates, P., Kolar, J., O'Neil, S., Mathews, S., and Dahlgren, D., 2021, Behavioural state-dependent habitat selection and implications for animal translocations: Journal of Applied Ecology, v. 59, no. 2, p. 624–635, at https://doi.org/10.1111/1365-2664.14080.

Donnelly, J.P., Jensco, K., Kimball, J.S., Moore, J.N., Ketchum, D., Collins, D.P., and Naugle, D.E., 2024, Beneficial ‘inefficiencies’ of western ranching—Flood-irrigated hay production sustains wetland systems by mimicking historic hydrologic processes: Agriculture, Ecosystems and Environment, v. 370, article 109051, at https://doi.org/10.1016/j.agee.2024.109051.

Swaty, R., 2016, The best way to spend an hour—Reviewing LANDFIRE ecosystem descriptions and models: The Bulletin of the Ecological Society of America, v. 97, no. 2, p. 180–182, at https://doi.org/10.1002/bes2.1234.

Barnett, K., Parks, S.A., Miller, C., and Naughton, H.T., 2016, Beyond fuel treatment effectiveness—Characterizing interactions between fire and treatments in the US: Forests, v. 7, no. 10, article 237, at https://doi.org/10.3390/f7100237.

Hudson, T.D., Reeves, M.C., Hall, S.A., Yorgey, G.G., and Neibergs, J.S., 2020, Big landscapes meet big data—Informing grazing management in a variable and changing world: Rangelands, v. 43, no. 1, p. 17–28, at https://doi.org/10.1016/j.rala.2020.10.006.

Keane, R.E., Holsinger, L.M., and Loehman, R., 2020, Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates: Forest Ecology and Management, v. 477, article 118498, at https://doi.org/10.1016/j.foreco.2020.118498.

Costanza, J.K., Abt, R.C., McKerrow, A.J., and Collazo, J.A., 2017, Bioenergy production and forest landscape change in the southeastern United States: GCB Bioenergy, v. 9, no. 5, p. 924–939, at https://doi.org/10.1111/gcbb.12386.

Stevens, J.T., Kling, M.M., Schwilk, D.W., Varner, J.M., and Kane, J.M., 2020, Biogeography of fire regimes in western U.S. conifer forests—A trait-based approach: Global Ecology and Biogeography, v. 29, no. 5, p. 944–955, at https://doi.org/10.1111/geb.13079.

Riggs, R.A., Keane, R.E., Cimon, N., Cook, R., Holsinger, L., Cook, J., DelCurto, T., Baggett, L., Justice, D., et al., 2015, Biomass and fire dynamics in a temperate forest-grassland mosaic—Integrating multi-species herbivory, climate, and fire with the FireBGCv2/GrazeBGC system: Ecological Modelling, v. 296, p. 57–78, at https://doi.org/10.1016/j.ecolmodel.2014.10.013.

Smith, T.C., Bishop, T.B.B., Duniway, M.C., Villarreal, M.L., Knight, A.C., Munson, S.M., Waller, E.K., Jensen, R., and Gill, R.A., 2023, Biophysical factors control invasive annual grass hot spots in the Mojave Desert: Biological Invasions, v. 25, p. 3839–3858, at https://doi.org/10.1007/s10530-023-03142-z.

Stephens, J.L., Dinger, E.C., Alexander, J.D., Mohren, S.R., Ralph, C.J., and Sarr, D.A., 2016, Bird communities and environmental correlates in southern Oregon and northern California, USA: PLoS ONE, v. 11, no. 10, article e0163906, at https://doi.org/10.1371/journal.pone.0163906.

Greenler, S.M., Lake, F.K., Tripp, W., McCovey, K., Tripp, A., Hillman, L.G., Dunn, C.J., Prichard, S.J., Hessburg, P.F., et al., 2024, Blending indigenous and western science—Quantifying cultural burning impacts in Karuk Aboriginal Territory: Ecological Applications, v. 34, no. 4, article e2973, at https://doi.org/10.1002/eap.2973.

Kasprak, A., Hough-Snee, N., Beechie, T., Bouwes, N., Brierley, G., Camp, R., Fryirs, K., Imaki, H., Jensen, M., et al., 2016, The blurred line between form and process—A comparison of stream channel classification frameworks: PLoS ONE, v. 11, no. 3, p. 1–31, at https://doi.org/10.1371/journal.pone.0150293.

Sauder, J.D., and Rachlow, J.L., 2014, Both forest composition and configuration influence landscape-scale habitat selection by fishers (Pekania pennanti) in mixed coniferous forests of the Northern Rocky Mountains: Forest Ecology and Management, v. 314, p. 75–84, at https://doi.org/10.1016/j.foreco.2013.11.029.

Beauregard, N.D., Theimer, T.C., Drost, C.A., and Sferra, S.J., 2024, Breeding by western Yellow-billed Cuckoos in xeroriparian habitat in southeastern Arizona: Journal of Field Ornithology, v. 95, no. 4, article 1, at https://doi.org/10.5751/JFO-00539-950401.

Davison, J.E., Coe, S., Finch, D., Rowland, E., Friggens, M., and Graumlich, L.J., 2012, Bringing indices of species vulnerability to climate change into geographic space—An assessment across the Coronado National Forest: Biodiversity and Conservation, v. 21, no. 1, p. 189–204, at https://doi.org/10.1007/s10531-011-0175-0.

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