Decreased snowpack and warmer temperatures reduce the negative effects of interspecific competitors on regenerating conifers
The persistence and distribution of species under changing climates can be affected by both direct effects of the environment and indirect effects via biotic interactions. However, the relative importance of direct and indirect climate effects on recruitment stages is poorly understood. We conducted a manipulative experiment to test the multiway interaction of direct and competition-mediated effects of climate change on vegetation dynamics. Following stand-replacing fire in California mixed-conifer forest, we seeded two conifer species, Pinus ponderosa and Abies concolor, in two consecutive years, one relatively normal and the other with an unusually wet and snowy winter followed by a hot summer. We additionally manipulated snow amount and competitive environment for both years. We found the effects of the snowpack treatment were contingent upon other abiotic factors (year of seeding) and biotic factors (shrub competition). Under ambient snowpack, shrubs reduced recruitment of P. ponderosa seedlings, but this negative effect disappeared with reduced snowpack. Additionally, the effects of shrubs on seedlings differed between cohorts and by life stage. In a warmer future, decreased snowpack may increase seedling emergence, but hotter and drier summers will decrease seedling survival; the effects of shrubs on conifers may become less negative as temperatures increase.
KeywordsClimate change Fire Interannual variation Pinus ponderosa Year effects
This work was funded by Henry A. Jastro Graduate Research Awards, a University of California Davis Department of Plant Sciences MacDonald Fellowship, and a National Science Foundation Graduate Research Fellowship (to CW). Site location and research permissions were obtained with the help of Dana Walsh of the US Forest Service, and herbicide application was conducted under the supervision of Kurt Vaughn. We are grateful to University of California Davis students who assisted with fieldwork. Finally, we thank two anonymous reviewers and editor Kendi Davies for their detailed feedback on this manuscript.
Author contribution statement
CW, HS, and TY conceived the ideas and designed methodology; CW collected the data; CW and DY analyzed the data; CW led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
This work was funded by Henry A. Jastro Graduate Research Awards, a University of California Davis Department of Plant Sciences MacDonald Fellowship, and a National Science Foundation Graduate Research Fellowship (to CW).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Data and code
Data and R code are available in a figshare repository, https://doi.org/10.6084/m9.figshare.3172468.
- Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EHT, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim JH, Allard G, Running SW, Semerci A, Cobb N (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 259:660–684. https://doi.org/10.1002/2014GL062308 CrossRefGoogle Scholar
- Barton K (2017) MuMIn: multi-model inference. R package version 1.40.0. https://CRAN.R-project.org/package=MuMIn. Accessed 27 Apr 2019
- Buck JM, Adams RS, Cone J, Conkle MT, Libby WJ, Eden CJ, Knight MJ (1970) California tree seed zones. U.S. Forest Service, San FranciscoGoogle Scholar
- Gómez-Aparicio L, Gómez JM, Zamora R, Boettinger JL (2005) Canopy vs. soil effects of shrubs facilitating tree seedlings in Mediterranean montane ecosystems. J Veg Sci 16:191–198. https://doi.org/10.1111/j.1654-1103.2005.tb02355.x CrossRefGoogle Scholar
- Gray AN, Zald HSJ, Kern RA, North M (2005) Stand conditions associated with tree regeneration in Sierran mixed-conifer forests. For Sci 51:198–210Google Scholar
- Grolemund G, Wickham H (2011) Dates and times made easy with lubridate. J Stat Softw 40:1–25Google Scholar
- Kassambara A, Kosinski M (2018) survminer: drawing survival curves using ‘ggplot2’. R package version 0.4.3. https://CRAN.R-project.org/package=survminer
- Kitzberger T, Steinaker DF, Veblen TT (2000) Effects of climatic variability on facilitation of tree establishment in northern Patagonia. Ecology 81:1914–1924. https://doi.org/10.1890/0012-9658(2000)081%5b1914:eocvof%5d2.0.co;2 CrossRefGoogle Scholar
- McDonald PM, Fiddler GO (2010) Twenty-five years of managing vegetation in conifer plantations in northern and central California: results, application, principles, and challenges. Department of Agriculture, Forest Service, Pacific Southwest Research Station, Albany. https://doi.org/10.2737/PSW-GTR-231 CrossRefGoogle Scholar
- North MP, Collins BM, Safford HD, Stephenson NL (2016) Montane forests. In: Mooney HA, Zavaleta E (eds) Ecosystems of California. University of California Press, Berekely, pp 553–577Google Scholar
- Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2017) nlme: linear and nonlinear mixed effects models. R package version 3.1-131. https://CRAN.R-project.org/package=nlme. Accessed 27 Apr 2019
- PRISM Climate Group (2016) August. http://prism.oregonstate.edu. Accessed 8 Feb 2018
- R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed 27 Apr 2019
- Safford HD, Stevens JT (2017) Natural range of variation for yellow pine and mixed-conifer forests in the Sierra Nevada, southern Cascades, and Modoc and Inyo National Forests, California, USA. http://fs.usda.govGoogle Scholar
- Serra-Diaz JM, Franklin J, Sweet LC, McCullough IM, Syphard AD, Regan HM, Flint LE, Flint AL, Dingman JR, Moritz MA, KmRedmond L Hannah, Davis FW (2015) Averaged 30 year climate change projections mask opportunities for species establishment. Ecography 39:844–845. https://doi.org/10.1111/ecog.02074 CrossRefGoogle Scholar
- Therneau T (2015) A package for survival analysis in S. version 2.38. https://CRAN.R-project.org/package=survival. Accessed 27 Apr 2019
- Wickham H (2011) The split-apply-combine strategy for data analysis. J Stat Softw 40:1–29Google Scholar
- Wickham H (2017) tidyverse: easily install and load the ‘tidyverse’. R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse. Accessed 27 Apr 2019
- Wilke CO (2019) cowplot: streamlined plot theme and plot annotations for ‘ggplot2’. R package version 0.9.4. https://CRAN.R-project.org/package=cowplot. Accessed 27 Apr 2019