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Abiotic Factors Modify Ponderosa Pine Regeneration Outcomes After High-Severity Fire

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Abstract

Large high-severity burn patches are increasingly common in southwestern US dry conifer forests. Seed-obligate conifers often fail to quickly regenerate large patches because their seeds rarely travel the distances required to reach core patch area. Abiotic factors may further alter the distance seeds can travel to regenerate a patch, which would change expected post-fire regeneration patterns. We used the presence and density of ponderosa pine regeneration as a proxy for seed dispersal to quantify the effect of abiotic factors on seed dispersal into high-severity patches. We established 45 transects in burn patches across the Gila National Forest, NM, USA, to measure regeneration density in areas that varied by aspect, slope, and prevailing wind direction relative to intact forest. We modeled the effect of abiotic factors on regeneration presence and density, comparing density estimates against a distance-only model to assess differences in model performance and expected regeneration density. We found the highest regeneration densities on north-facing aspects that were near, downwind, and downslope of intact forest, which decreased in density and likelihood as conditions for seed dispersal became less favorable. Accounting for abiotic factors improved model performance and increased regeneration density estimates compared to the distance-only model. Our findings indicate that regeneration presence and density vary as a function of the interaction between abiotic factors and distance to the primary seed source, which is determined by patch characteristics. Therefore, abiotic factors will have a smaller effect on regeneration outcomes in large, simple patches, which have more area further from the patch edge.

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Data Availability

Data and code used in this research are available at: https://doi.org/https://doi.org/10.5061/dryad.2547d7wxh.

References

  • Abatzoglou JT, Williams AP. 2016. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci. 113:11770–11775.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Aicher RJ, Larios L, Suding KN. 2011. Seed supply, recruitment, and assembly: quantifying relative seed and establishment limitation in a plant community context. Am. Natl. 178:464–477.

    Article  Google Scholar 

  • Barton K, Barton MK. 2015. Package ‘mumin.’ Version 1:439.

    Google Scholar 

  • Bivand R, Keitt T, Rowlingson B. 2021. rgdal: bindings for the 'geospatial' data abstraction library. R package version 1.5–25. https://CRAN.R-project.org/package=rgdal.

  • Bivand R, Rundel C. 2020. rgeos: interface to geometry engine—open source ('GEOS'). R package version 0.5–5. https://CRAN.R-project.org/package=rgeos.

  • Bonnet VH, Schoettle AW, Shepperd WD. 2005. Postfire environmental conditions influence the spatial pattern of regeneration for Pinus ponderosa. Can. J. For. Res. 35:37–47.

    Article  Google Scholar 

  • Boucher PF, Moody RD. 1998. The historical role of fire and ecosystem management of fires: Gila national forest, New Mexico. In: Pruden T, Brennan L, Eds. Fire in ecosystem management: shifting the paradigm from suppression to prescription. Vol. 20. Tallahassee, FL: U.S. Department of Agriculture, Forest Service, Region 3. pp 74–9.

  • Burnham KP, Anderson DR. 2002. Model selection and multimodel inference: a practical information-theoretic approach. New York: Springer-Verlag.

    Google Scholar 

  • Burns RM, Honkala BH. 1990. Silvics of North America. Volume 1. Conifers. Agriculture Handbook (Washington). p 654.

  • Chambers ME, Fornwalt PJ, Malone SL, Battaglia MA. 2016. Patterns of conifer regeneration following high severity wildfire in ponderosa pine—dominated forests of the Colorado Front Range. For. Ecol. Manag. 378:57–67.

    Article  Google Scholar 

  • Clark JS, Silman M, Kern R, Macklin E, HilleRisLambers J. 1999. Seed dispersal near and far: patterns across temperate and tropical forests. Ecology 80:1475–1494.

    Article  Google Scholar 

  • Collins BM, Stevens JT, Miller JD, Stephens SL, Brown PM, North MP. 2017. Alternative characterization of forest fire regimes: incorporating spatial patterns. Landsc. Ecol. 32:1543–1552.

    Article  Google Scholar 

  • Compton LA. 2004. Ponderosa pine dispersal and recruitment: the role of seed-caching rodents. Northern Arizona University.

  • Coop JD, Parks SA, Stevens-Rumann CS, Crausbay SD, Higuera PE, Hurteau MD, Tepley A, Whitman E, Assal T, Collins BM, Davis KT, Dobrowski S, Falk DA, Fornwalt PJ, Fulé PZ, Harvey BJ, Kane VR, Littlefield CE, Margolis EQ, North M, Parisien M-A, Prichard S, Rodman KC. 2020. Wildfire-driven forest conversion in Western North American landscapes. BioScience 70:659.

    Article  PubMed  PubMed Central  Google Scholar 

  • Covington WW, Moore MM. 1994. Post settlement changes in natural fire regimes and forest structure: ecological restoration of old-growth ponderosa pine forests. J. Sustain. For. 2:153–181.

    Article  Google Scholar 

  • Crockett JL, Hurteau MD. 2022. Post-fire early successional vegetation buffers surface microclimate and increases survival of planted conifer seedlings in the southwestern United States. Can. J. For. Res. 52:416–425.

    Article  CAS  Google Scholar 

  • Crockett JL, Hurteau MD. 2023. Ability of seedlings to survive heat and drought portends future demographic challenges for five southwestern US conifers. Tree Physiol. https://doi.org/10.1093/treephys/tpad136.

    Article  Google Scholar 

  • Damschen EI, Baker DV, Bohrer G, Nathan R, Orrock JL, Turner JR, Brudvig LA, Haddad NM, Levey DJ, Tewksbury JJ. 2014. How fragmentation and corridors affect wind dynamics and seed dispersal in open habitats. Proc. Natl. Acad. Sci. 111:3484–3489.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • DeWald LE, Mahalovich MF. 2008. Historical and contemporary lessons from ponderosa pine genetic studies at the fort valley experimental forest, Arizona. In: Olberding SD, Moore MM Eds., Fort valley experimental forest—a century of research 1908–2008, Proceedings RMRS-P-55, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO.

  • Dovčak M, Frelich LE, Reich PB. 2005. Pathways in old-field succession to white pine: seed rain, shade, and climate effects. Ecol. Monogr. 75:363–378.

    Article  Google Scholar 

  • EDAC. 2021. Earth data analysis center. Available from https://edac.unm.edu/ [accessed 31 March 2021].

  • Eidenshink J, Schwind B, Brewer K, Zhu Z-L, Quayle B, Howard S. 2007. A project for monitoring trends in burn severity. Fire Ecol. 3:3–21.

    Article  Google Scholar 

  • ESRI. 2020. ArcGIS desktop: release 10.8. Redlands, CA: Environmental systems research institute.

  • Fox J, Weisberg S. 2019. An R Companion to Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.

  • Fulé PZ, Covington WW, Moore MM. 1997. Determining reference conditions for ecosystem management of southwestern ponderosa pine forests. Ecol. Appl. 7:895–908.

    Article  Google Scholar 

  • Haffey C, Sisk TD, Allen CD, Thode AE, Margolis EQ. 2018. Limits to ponderosa pine regeneration following large high-severity forest fires in the United States Southwest. Fire Ecol. 14:21.

    Article  Google Scholar 

  • Haire SL, McGarigal K. 2010. Effects of landscape patterns of fire severity on regenerating ponderosa pine forests (Pinus ponderosa) in New Mexico and Arizona, USA. Landsc. Ecol. 25:1055–1069.

    Article  Google Scholar 

  • Hanbury-Brown AR, Ward RE, Kueppers LM. 2022. Forest regeneration within earth system models: current process representations and ways forward. New Phytol. 235:20–40.

    Article  PubMed  Google Scholar 

  • Hankin LE, Higuera PE, Davis KT, Dobrowski SZ. 2019. Impacts of growing-season climate on tree growth and post-fire regeneration in ponderosa pine and douglas-fir forests. Ecosphere 10:e02679.

    Article  Google Scholar 

  • Hartig F. 2021. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.4.1. https://CRAN.R-project.org/package=DHARMa.

  • Hastie T, Tibshirani R, Friedman JH, Friedman JH. 2009. The elements of statistical learning: data mining, inference, and prediction. New York: springer. 2: 1–758.

  • Hijmans RJ. 2021. raster: geographic data analysis and modeling. R package version 3.4–13. https://CRAN.R-project.org/package=raster.

  • Jung CG, Keyser AR, Remy CC, Krofcheck D, Allen CD, Hurteau MD. 2023. Topographic information improves simulated patterns of post-fire conifer regeneration in the southwest United States. Glob. Change Biol. 29:4342–4353. https://doi.org/10.1111/gcb.16764.

    Article  CAS  Google Scholar 

  • Keane RE, Mincemoyer SA, Schmidt KM, Long DG, Garner JL. 2000. Mapping vegetation and fuels for fire management on the Gila national forest complex, New Mexico. Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station https://www.fs.usda.gov/treesearch/pubs/4555. Last accessed 05/06/2018.

  • Keeley JE. 2009. Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int. J. Wildland Fire 18:116. https://doi.org/10.1071/WF07049.

    Article  Google Scholar 

  • Keyes CR, Maguire DA. 2007. Seed rain of ponderosa pine beneath partial overstories. New For. 34:107–114. https://doi.org/10.1007/s11056-007-9040-0.

    Article  Google Scholar 

  • Keyser AR, Krofcheck DJ, Remy CC, Allen CD, Hurteau MD. 2020. Simulated increases in fire activity reinforce shrub conversion in a Southwestern US Forest. Ecosystems 23:1702–1713.

    Article  Google Scholar 

  • Kim M, Lee S, Lee S, Yi K, Kim H-S, Chung S, Chung J, Kim HS, Yoon TK. 2022. Seed dispersal models for natural regeneration: a review and prospects. Forests 13:659.

    Article  Google Scholar 

  • Kuhn M. 2008. Building predictive models in R using the caret package. J Stat Soft. http://www.jstatsoft.org/v28/i05/. Last accessed 14/06/2022.

  • Law BE, Sun OJ, Campbell J, Van Tuyl S, Thornton PE. 2003. Changes in carbon storage and fluxes in a chronosequence of ponderosa pine. Glob. Change Biol. 9:510–524. https://doi.org/10.1046/j.1365-2486.2003.00624.x.

    Article  Google Scholar 

  • LePage PT, Canham CD, Coates KD, Bartemucci P. 2000. Seed abundance versus substrate limitation of seedling recruitment in northern temperate forests of British Columbia. Can. J. For. Res. 30:415.

    Article  Google Scholar 

  • Marsh C, Krofcheck D, Hurteau MD. 2022. Identifying microclimate tree seedling refugia in post-wildfire landscapes. Agric. For. Meteorol. 313:108741.

    Article  Google Scholar 

  • McDonald PM. 1980. Seed dissemination in small clearcuttings in north-central California. Berkeley, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station https://www.fs.usda.gov/treesearch/pubs/28821. Last accessed 22/10/2018.

  • Moles AT, Westoby M. 2006. Seed size and plant strategy across the whole life cycle. Oikos 113:91–105.

    Article  Google Scholar 

  • Nathan R, Muller-Landau HC. 2000. Spatial patterns of seed dispersal, their determinants and consequences for recruitment. Trends Ecol. Evol. 15:278–285.

    Article  CAS  PubMed  Google Scholar 

  • NRCS. 2022. Distribution maps of dominant soil orders. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/class/maps/?cid=nrcs142p2_053589. Accessed 14 June 2022.

  • Ouzts J, Kolb T, Huffman D, Sánchez Meador A. 2015. Post-fire ponderosa pine regeneration with and without planting in Arizona and New Mexico. Forest Ecol. Manag. 354:281–290.

    Article  Google Scholar 

  • Puhlick JJ, Laughlin DC, Moore MM. 2012. Factors influencing ponderosa pine regeneration in the southwestern USA. Forest Ecol. Manag. 264:10–19.

    Article  Google Scholar 

  • R Core Team. 2020. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from https://www.R-project.org/.

  • Reid KA, Day NJ, Alfaro-Sánchez R, Johnstone JF, Cumming SG, Mack MC, Turetsky MR, Walker XJ, Baltzer JL. 2023. Black spruce (Picea mariana) seed availability and viability in boreal forests after large wildfires. Ann. For. Sci. 80:4. https://doi.org/10.1186/s13595-022-01166-4.

    Article  Google Scholar 

  • Reilly MJ, Dunn CJ, Meigs GW, Spies TA, Kennedy RE, Bailey JD, Briggs K. 2017. Contemporary patterns of fire extent and severity in forests of the Pacific Northwest, USA (1985–2010). Ecosphere 8:e01695. https://doi.org/10.1002/ecs2.1695.

    Article  Google Scholar 

  • Rixon TF. 1905. Forest conditions in the Gila River forest reserve, New Mexico. Vol. 39. US Government Printing Office.

  • Rother MT, Veblen TT. 2016. Limited conifer regeneration following wildfires in dry ponderosa pine forests of the Colorado front range. Ecosphere 7:e01594.

    Article  Google Scholar 

  • Ruel J-C, Pin D, Cooper K. 1998. Effect of topography on wind behaviour in a complex terrain. Forestry 71:261–265.

    Article  Google Scholar 

  • Sheppard P, Comrie A, Packin G, Angersbach K, Hughes M. 2002. The climate of the US Southwest. Clim. Res. 21:219–238.

    Article  Google Scholar 

  • Shive KL, Preisler HK, Welch KR, Safford HD, Butz RJ, O’Hara KL, Stephens SL. 2018. From the stand scale to the landscape scale: predicting the spatial patterns of forest regeneration after disturbance. Ecol Appl. https://doi.org/10.1002/eap.1756.

    Article  PubMed  Google Scholar 

  • Singleton MP, Thode AE, Sánchez Meador AJ, Iniguez JM. 2019. Increasing trends in high-severity fire in the southwestern USA from 1984 to 2015. For. Ecol. Manag. 433:709–719.

    Article  Google Scholar 

  • Singleton MP, Thode AE, Sánchez Meador AJ, Iniguez JM. 2021a. Moisture and vegetation cover limit ponderosa pine regeneration in high-severity burn patches in the southwestern US. Fire Ecol. 17:14.

    Article  Google Scholar 

  • Singleton MP, Thode AE, Sánchez Meador AJ, Iniguez JM, Stevens JT. 2021b. Management strategy influences landscape patterns of high-severity burn patches in the southwestern United States. Landsc. Ecol. 36:3429–3449.

    Article  Google Scholar 

  • Stevens JT, Haffey CM, Coop JD, Fornwalt PJ, Yocom L, Allen CD, Bradley A, Burney OT, Carril D, Chambers ME, Chapman TB, Haire SL, Hurteau MD, Iniguez JM, Margolis EQ, Marks C, Marshall LAE, Rodman KC, Stevens-Rumann CS, Thode AE, Walker JJ. 2021. Tamm Review: Postfire landscape management in frequent-fire conifer forests of the southwestern United States. For. Ecol. Manag. 502:119678.

    Article  Google Scholar 

  • Stevens-Rumann CS, Morgan P. 2019. Tree regeneration following wildfires in the western US: a review. Fire Ecol. 15:15.

    Article  Google Scholar 

  • Stewart JAE, Mantgem PJ, Young DJN, Shive KL, Preisler HK, Das AJ, Stephenson NL, Keeley JE, Safford HD, Wright MC, Welch KR, Thorne JH. 2021. Effects of postfire climate and seed availability on postfire conifer regeneration. Ecol Appl. https://doi.org/10.1002/eap.2280.

    Article  PubMed  Google Scholar 

  • Swetnam TW, Dieterich JH. 1985. Fire history of ponderosa pine forests in the Gila Wilderness, New Mexico. In: Missoula MT: US department of agriculture, forest service, intermountain forest and range experiment station. pp 390–7.

  • Trakhtenbrot A, Katul GG, Nathan R. 2014. Mechanistic modeling of seed dispersal by wind over hilly terrain. Ecol. Modell. 274:29–40.

    Article  Google Scholar 

  • Vander Wall SB. 1994. Removal of wind-dispersed pine seeds by ground-foraging vertebrates. Oikos 69:125.

    Article  Google Scholar 

  • Vander Wall SB. 2023. Seed dispersal in pines (pinus). Bot. Rev. 89:275–307. https://doi.org/10.1007/s12229-023-09288-8.

    Article  Google Scholar 

  • Yarranton GA, Morrison RG. 1974. Spatial dynamics of a primary succession: nucleation. J. Ecol. 62:417–428.

    Article  Google Scholar 

  • Zachariassen J, Zeller KF, Nikolov N, McClelland T. 2003. A review of the Forest Service remote automated weather station (RAWS) network. USDA forest service, rocky mountain research station. General technical report RMRS-GTR-119.

  • Zeng H, Peltola H, Talkkari A, Venäläinen A, Strandman H, Kellomäki S, Wang K. 2004. Influence of clear-cutting on the risk of wind damage at forest edges. For. Ecol. Manag. 203:77–88.

    Article  Google Scholar 

  • Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM. 2009. Mixed effects models and extensions in ecology with R. Vol. 574. New York: springer.

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Acknowledgements

We acknowledge and appreciate the assistance of Joseph Crockett, Marissa Goodwin, and Carolina May for their help with data collection.

Funding

This work was supported by the Interagency Carbon Cycle Science program grant no. 2017-67004-26486/project accession no. 1012226 from the USDA National Institute of Food and Agriculture and the Joint Fire Science Program under Project JFSP 16-1-05-8. This work was also supported by the Agriculture and Food Research Initiative—Education and Workforce Development program grant no. 2022-67011-36462/project accession no. 1028071 from the USDA National Institute of Food and Agriculture.

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Correspondence to Kevin G. Willson.

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KW and MH designed the study, performed research, analyzed data, and wrote the paper.

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Willson, K.G., Hurteau, M.D. Abiotic Factors Modify Ponderosa Pine Regeneration Outcomes After High-Severity Fire. Ecosystems (2024). https://doi.org/10.1007/s10021-024-00911-2

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