Abstract
Mule deer (Odocoileus hemionus) are declining in abundance across their broad distribution in western North America. Identifying drivers of mule deer demography could inform habitat restoration. However, linking habitat quality to vital rates is challenging and often done indirectly using proxy metrics. We combine habitat selection with climate-related effects to identify synergistic influences affecting mule deer age ratios (fawn:doe). We used location data from 1473 female deer over 22 years in Wyoming to fit seasonal resource selection models, predict habitat suitability, and model age ratios as a function of drought conditions, winter severity, and seasonal habitat. Here we show temperature had the largest effect on mule deer recruitment with age ratios declining following hotter summers and colder winters. Age ratios increased with higher proportions of habitat with high-quality summer habitat of particular importance. Given the likely increases in summer temperatures and extreme winter weather events, populations may struggle to increase recruitment over the next half-century. Targeted management supporting forage quantity and quality, especially on summer range, could buffer the effects of decades-long drought conditions. Our findings also indicate mule deer avoid areas with high densities of oil and gas development. By delineating important mule deer habitat, we offer spatial tools for development siting and mitigation in Wyoming and a framework for broader application across the western United States.
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Data availability
Source datasets for resource selection and age ratio models and seasonal habitat suitability raster data are available35. Raw mule deer GPS location data used in this study is part of a broad collaboration with many partners and interest in access to this information should be relayed to the corresponding author.
Code availability
A description of the resource selection model formulated for use in NIMBLE is available in the Supplementary Information. No additional novel code was developed for these analyses; all software packages, versions, and programs used are documented within.
References
Cook, J. G. et al. Effects of Summer-Autumn Nutrition and Parturition Date on Reproduction and Survival of Elk. Wildl. Monogr. 155, 1–61 (2004).
Stephenson, T. R. et al. Linking population performance to nutritional condition in an alpine ungulate. J. Mammal. 101, 1244–1256 (2020).
Gaillard, J.-M., Festa-Bianchet, M., Delorme, D. & Jorgenson, J. Body Mass and Individual Fitness in Female Ungulates: Bigger Is Not Always Better. Proc. Biol. Sci. 267, 471–477 (2000).
Côté, S. D. & Festa-Bianchet, M. Birthdate, Mass and Survival in Mountain Goat Kids: Effects of Maternal Characteristics and Forage Quality. Oecologia 127, 230–238 (2001).
Bolger, D. T., Newmark, W. D., Morrison, T. A. & Doak, D. F. The need for integrative approaches to understand and conserve migratory ungulates. Ecol. Lett. 11, 63–77 (2008).
Bacon, M. M. & Boyce, M. S. Landscape of Fear for Naïve Prey: Ungulates Flee Protected Area to Avoid a Re-established Predator. Can. Wildl. Biol. Manag. 5, 1–9 (2016).
Post, E. & Forchhammer, M. C. Climate change reduces reproductive success of an Arctic herbivore through trophic mismatch. Philos. Trans. R. Soc. B Biol. Sci. 363, 2367–2373 (2008).
Ogutu, J. O. & Owen-Smith, N. ENSO, rainfall and temperature influences on extreme population declines among African savanna ungulates. Ecol. Lett. 6, 412–419 (2003).
Ito, T. Y. et al. Fragmentation of the Habitat of Wild Ungulates by Anthropogenic Barriers in Mongolia. PLOS ONE 8, e56995 (2013).
Mule Deer Working Group Technical Committee. Range-Wide Status of Black-Tailed and Mule Deer. 1–46 https://wafwa.org/wpdm-package/2025-rangewide-status-of-black-tailed-and-mule-deer/ (2025).
Singer, F. J. & Renkin, R. A. Effects of browsing by native ungulates on the shrubs in big sagebrush communities in Yellowstone National Park. Gt. Basin Nat. 55, 201–212 (1995).
Bergman, E. J., Watkins, B. E., Bishop, C. J., Lukacs, P. M. & Lloyd, M. Biological and socio-economic effects of statewide limitation of deer licenses in Colorado. J. Wildl. Manag. 75, 1443–1452 (2011).
White, K. S. et al. Mountain sentinels in a changing world: Review and conservation implications of weather and climate effects on mountain goats (Oreamnos americanus). Glob. Ecol. Conserv. 57, e03364 (2025).
Long, R. A., Kie, J. G., Bowyer, R. T. & Hurley, M. A. Resource Selection and Movements by Female Mule Deer Odocoileus hemionus: Effects of Reproductive Stage. Wildl. Biol. 15, 288–298 (2009).
Peterson, C. J., DeCesare, N. J., Hayes, T. A., Bishop, C. J. & Mitchell, M. S. Consequences of migratory strategy on habitat selection by mule deer. J. Wildl. Manag. 86, e22135 (2022).
Coe, P. K. et al. Multiscale models of habitat use by mule deer in winter. J. Wildl. Manag. 82, 1285–1299 (2018).
Monteith, K. L., Hayes, M. M., Kauffman, M. J., Copeland, H. E. & Sawyer, H. Functional attributes of ungulate migration: landscape features facilitate movement and access to forage. Ecol. Appl. 28, 2153–2164 (2018).
Sawyer, H., Nielson, R. M., Lindzey, F. & McDONALD, L. L. Winter Habitat Selection of Mule Deer Before and During Development of a Natural Gas Field. J. Wildl. Manag. 70, 396–403 (2006).
Sawyer, H., Kauffman, M. J. & Nielson, R. M. Influence of Well Pad Activity on Winter Habitat Selection Patterns of Mule Deer. J. Wildl. Manag. 73, 1052–1061 (2009).
Anton, C. B., DeCesare, N. J., Peterson, C., Hayes, T. A. & Bishop, C. J. Climate, habitat interactions, and mule deer resource selection on winter landscapes. J. Wildl. Manag. 86, e22299 (2022).
Heffelfinger, L. J., Stewart, K. M., Shoemaker, K. T., Darby, N. W. & Bleich, V. C. Balancing Current and Future Reproductive Investment: Variation in Resource Selection During Stages of Reproduction in a Long-Lived Herbivore. Front. Ecol. Evol. 8, 163 (2020).
Shoemaker, K. T. et al. A machine-learning approach for extending classical wildlife resource selection analyses. Ecol. Evol. 8, 3556–3569 (2018).
Marshal, J. P., Bleich, V. C., Krausman, P. R., Reed, M. L. & Andrew, N. G. Factors Affecting Habitat Use and Distribution of Desert Mule Deer in an Arid Environment. Wildl. Soc. Bull. 34, 609–619 (2006).
Lomas, L. A. & Bender, L. C. Survival and Cause-Specific Mortality of Neonatal Mule Deer Fawns, North-Central New Mexico. J. Wildl. Manag. 71, 884–894 (2007).
Atwood, T. C., Gese, E. M. & Kunkel, K. E. Spatial Partitioning of Predation Risk in a Multiple Predator-Multiple Prey System. J. Wildl. Manag. 73, 876–884 (2009).
Tollefson, T. N., Shipley, L. A., Myers, W. L., Keisler, D. H. & Dasgupta, N. Influence of Summer and Autumn Nutrition on Body Condition and Reproduction in Lactating Mule Deer. J. Wildl. Manag. 74, 974–986 (2010).
Hurley, M. A. et al. Demographic response of mule deer to experimental reduction of coyotes and mountain lions in southeastern Idaho. Wildl. Monogr. 178, 1–33 (2011).
Monteith, K. L. et al. Life-history characteristics of mule deer: Effects of nutrition in a variable environment. Wildl. Monogr. 186, 1–62 (2014).
Lamb, S. et al. From conception to recruitment: Nutritional condition of the dam dictates the likelihood of success in a temperate ungulate. Front. Ecol. Evol. 11, 1090116 (2023).
Tull, J. C., Krausman, P. R. & Steidl, R. J. Bed-Site Selection by Desert Mule Deer in Southern Arizona. Southwest. Nat. 46, 354 (2001).
Merems, J. L. et al. Nutritional-Landscape Models Link Habitat Use to Condition of Mule Deer (Odocoileus hemionus). Front. Ecol. Evol. 8, 98 (2020).
Johnson, H. E. et al. Increases in residential and energy development are associated with reductions in recruitment for a large ungulate. Glob. Change Biol. 23, 578–591 (2017).
Cook, R. C. et al. Regional and seasonal patterns of nutritional condition and reproduction in elk. Wildl. Monogr. 184, 1–45 (2013).
Proffitt, K. M., Hebblewhite, M., Peters, W., Hupp, N. & Shamhart, J. Linking landscape-scale differences in forage to ungulate nutritional ecology. Ecol. Appl. 26, 2156–2174 (2016).
Janousek, W. M. et al. Predicted habitat suitability and associated covariate data used to explain changes in mule deer age ratios from 2001 to 2023, Wyoming, USA. U. S. Geological Survey https://doi.org/10.5066/P1SJAMX7 (2025).
Hobbs, N. T., Baker, D. L. & Gill, R. B. Comparative Nutritional Ecology of Montane Ungulates during Winter. J. Wildl. Manag. 47, 1–16 (1983).
Torstenson, W. L. F., Mosley, J. C., Brewer, T. K., Tess, M. W. & Knight, J. E. Elk, Mule Deer, and Cattle Foraging Relationships on Foothill and Mountain Rangeland. Rangel. Ecol. Manag. 59, 80–87 (2006).
Merkle, J. A. et al. Large herbivores surf waves of green-up during spring. Proc. R. Soc. B Biol. Sci. 283, 20160456 (2016).
Aikens, E. O. et al. The greenscape shapes surfing of resource waves in a large migratory herbivore. Ecol. Lett. 20, 741–750 (2017).
Sawyer, H., Merkle, J. A., Middleton, A. D., Dwinnell, S. P. H. & Monteith, K. L. Migratory plasticity is not ubiquitous among large herbivores. J. Anim. Ecol. 88, 450–460 (2019).
Morrison, T. A. et al. Drivers of site fidelity in ungulates. J. Anim. Ecol. 90, 955–966 (2021).
Hegewisch, K. C. & Abatzoglou, J. T. ‘Future Boxplots’ web tool. https://climatetoolbox.org/ (2025).
Wood, D. J. A., Powell, S., Stoy, P. C., Thurman, L. L. & Beever, E. A. Is the grass always greener? Land surface phenology reveals differences in peak and season-long vegetation productivity responses to climate and management. Ecol. Evol. 11, 11168–11199 (2021).
Balting, D. F., AghaKouchak, A., Lohmann, G. & Ionita, M. Northern Hemisphere drought risk in a warming climate. npj Clim. Atmos. Sci. 4, 61 (2021).
Aikens, E. O. et al. Drought reshuffles plant phenology and reduces the foraging benefit of green-wave surfing for a migratory ungulate. Glob. Change Biol. 26, 4215–4225 (2020).
LaSharr, T. N. et al. Behavior, nutrition, and environment drive survival of a large herbivore in the face of extreme winter conditions. Ecosphere 14, e4601 (2023).
Cohen, J., Agel, L., Barlow, M., Garfinkel, C. I. & White, I. Linking Arctic variability and change with extreme winter weather in the United States. Science 373, 1116–1121 (2021).
Cohen, J., Francis, J. A. & Pfeiffer, K. Anomalous Arctic warming linked with severe winter weather in Northern Hemisphere continents. Commun. Earth Environ. 5, 557 (2024).
Wyoming Game and Fish Department. Wyoming Range mule deer population declines after severe winter of 2022-23. (2024).
Wyoming Game and Fish Department. Game and Fish conducts annual mule deer surveys. (2025).
Wyoming Game and Fish Department. Mule Deer in Wyoming – 2025 Snapshot. (2025).
Department of Interior. Improving Habitat Quality in Western Big Game Winter Range and Migration Corridors. Secretarial Order vol. 3362 (2018).
Coe, P. K. et al. Identifying migration corridors of mule deer threatened by highway development. Wildl. Soc. Bull. 39, 256–267 (2015).
Doherty, K. E. et al. State of the Sagebrush: Implementing the Sagebrush Conservation Design to Save a Biome. Rangel. Ecol. Manag. 97, 1–11 (2024).
Northrup, J. M., Anderson, C. R. & Wittemyer, G. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Glob. Change Biol. 21, 3961–3970 (2015).
Northrup, J. M., Anderson, C. R. Jr., Gerber, B. D. & Wittemyer, G. Behavioral and Demographic Responses of Mule Deer to Energy Development on Winter Range. Wildl. Monogr. 208, 1–37 (2021).
U.S. Environmental Protection Agency. Level III ecoregions of the continental United States: Corvallis, Oregon, U.S. EPA – National Health and Environmental Effects Research Laboratory, map scale 1:500,000. (2023).
Manly, B. F. J., MacDonald, L. L., Thomas, D. L., McDonald, T. L. & Erickson, W. P. Resource Selection by Animals: Statistical Design and Analysis for Field Studies. (Kluwer Academic Publishers, Dordrecht, 2004). https://doi.org/10.1007/0-306-48151-0.
Bunnefeld, N. et al. A model-driven approach to quantify migration patterns: individual, regional and yearly differences. J. Anim. Ecol. 80, 466–476 (2011).
Merkle, J. A., Gage, J., Sawyer, H., Lowrey, B. & Kauffman, M. J. Migration Mapper: Identifying movement corridors and seasonal ranges for large mammal conservation. Methods Ecol. Evol. 13, 2397–2403 (2022).
Kie, J. G. et al. The home-range concept: are traditional estimators still relevant with modern telemetry technology?. Philos. Trans. R. Soc. B Biol. Sci. 365, 2221–2231 (2010).
Northrup, J. M., Hooten, M. B., Anderson, C. R. & Wittemyer, G. Practical guidance on characterizing availability in resource selection functions under a use—availability design. Ecology 94, 1456–1463 (2013).
Allred, B. W. et al. Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty. Methods Ecol. Evol. 12, 841–849 (2021).
Pettorelli, N. et al. The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology. Clim. Res. 46, 15–27 (2011).
Theobald, D. M., Harrison-Atlas, D., Monahan, W. B. & Albano, C. M. Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLOS ONE 10, e0143619 (2015).
Wu, Q. & Brown, A. whitebox: ‘WhiteboxTools’ R Frontend. 2.4.3 https://doi.org/10.32614/CRAN.package.whitebox (2022).
Jarvis, A., Guevara, E., Reuter, H. I. & Nelson, A. D. Hole-filled SRTM for the globe: version 4: data grid. https://research.utwente.nl/en/publications/hole-filled-srtm-for-the-globe-version-4-data-grid/ (2008).
Ironside, K. E. et al. Geomorphometry in Landscape Ecology: Issues of Scale, Physiography, and Application. Environ. Ecol. Res. 6, 397–412 (2018).
Thornton, M. M., Shrestha, R., Wei, Y., Thornton, P. E. & Kao, S.-C. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1 (Version 4.1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2129 (2022).
Hayes, T. A. & Graves, T. A. Wyoming statewide time-stamped oil and gas activity, 1900-2020. U.S. Geological Survey https://doi.org/10.5066/P92CPSVR (2023).
U. S. Census Bureau. TIGER/Line Shapefile, Wyoming, Primary and Secondary Roads State-based Shapefile. U.S. Department of Commerce (2023).
U.S. Geological Survey. National Hydrography Dataset (NHD) – USGS National Map Downloadable Data Collection. USGS – National Geospatial Technical Operations Center (NGTOC) (2023).
Hijmans, R. J. terra: Spatial Data Analysis. 1.8-86 https://doi.org/10.32614/CRAN.package.terra (2024).
Core, R. Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2024).
Muff, S., Signer, J. & Fieberg, J. Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation. J. Anim. Ecol. 89, 80–92 (2020).
de Valpine, P. et al. Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE. J. Comput. Graph. Stat. 26, 403–413 (2017).
NIMBLE Development Team NIMBLE: MCMC, Particle Filtering, and Programmable Hierarchical Modeling. Zenodo https://doi.org/10.5281/ZENODO.1211190 (2024).
Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).
Coates, P. S. et al. Spatially Explicit Modeling of Annual and Seasonal Habitat for Greater Sage-Grouse (Centrocercus Urophasianus) in Nevada and Northeastern California—An Updated Decision-Support Tool for Management. Open-File Rep. https://doi.org/10.3133/ofr20161080 (2016).
Wyoming Game and Fish Department. Wildlife Observation System – concatenated, 1985 to 2020. (2021).
Hayes, T. A. et al. Age ratios and landscape change covariates for mule deer (Odocoileus hemionus) herd units in Wyoming, USA, 1985-2019. U. S. Geol. Surv. https://doi.org/10.5066/P9T0K8BI (2023).
Hayes, T. A. et al. Integrating climate and anthropogenic dynamics can inform multifaceted management for declining mule deer populations. Ecol. Appl. 36, e70107 (2026).
Johnson, B. K., Coe, P. K. & Green, R. L. Abiotic, bottom-up, and top-down influences on recruitment of Rocky Mountain elk in Oregon: A retrospective analysis. J. Wildl. Manag. 77, 102–116 (2013).
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Wells, N., Goddard, S. & Hayes, M. J. A Self-Calibrating Palmer Drought Severity Index. J. Clim. 17, 2335–2351 (2004).
Daly, C., Smith, J. I. & Olson, K. V. Mapping Atmospheric Moisture Climatologies across the Conterminous United States. PLOS ONE 10, e0141140 (2015).
Bürkner, P.-C. brms: An R Package for Bayesian Multilevel Models Using Stan. J. Stat. Softw. 80, 1–28 (2017).
Lowrey, B. et al. Niche similarities among introduced and native mountain ungulates. Ecol. Appl. 28, 1131–1142 (2018).
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).
Acknowledgements
Funding for this study was provided by the U.S. Geological Survey Wyoming Landscape Conservation Initiative, Species Management and Biothreats programs. We thank the Wyoming Game and Fish Department for in-kind support along with many others who supported this project through mule deer data acquisition and contributions during the planning stages (Justin Binfet, Todd Cornish, Teal Cufaude, Melia Devivo, Sam Dwinnell, Gary Fraylick, Pat Hnilicka, Rusty Kaiser, Lee Knox, J. Terril Patterson, Erika Peckham, Jill Randall, Hall Sawyer, Jeff Short, Cheyenne Stewart, Tim Thomas, Mark Thonhoff, Brandon Werner). We also thank the Wind River Inter-Tribal Council for sharing their mule deer data. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. The findings and conclusions in this article are those of the author(s) and do not necessarily represent the views of the Bureau of Land Management.
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W.M.J., A.N.J., S.L.B., S.R.D., K.S.H., T.N.L., B.L., R.P.J., T.A.G., E.H., T.A.H., M.J.K., and K.M. contributed to the conceptual development of the study, devised analytical approaches, and supported data acquisition. W.M.J. and T.A.G. analyzed the data and wrote the first draft. W.M.J., A.N.J., S.L.B., S.R.D., K.S.H., T.N.L., B.L., R.P.J., T.A.G., E.H., T.A.H., M.J.K., and K.M. were involved in interpretation of results, provided input on figures, revised multiple manuscript drafts, and approved the final submitted version.
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Janousek, W.M., Johnston, A.N., Bullock, S.L. et al. The interplay of habitat quality and temperature shape demographic patterns of mule deer (Odocoileus hemionus) in North America.
Commun Biol (2026). https://doi.org/10.1038/s42003-026-09687-8
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DOI: https://doi.org/10.1038/s42003-026-09687-8
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