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Data and code for: Functional traits mediate individualistic species-environment distributions at broad spatial scales while fine-scale species’ associations remain unpredictable

Citation

Beck, Jared et al. (2022), Data and code for: Functional traits mediate individualistic species-environment distributions at broad spatial scales while fine-scale species’ associations remain unpredictable, Dryad, Dataset, https://doi.org/10.5061/dryad.98sf7m0n3

Abstract

Ecological communities are structured by a diverse set of processes acting at different spatial scales. In plant communities, assembly processes like ecological sorting, limiting similarity, and stochastic events are all expected to influence plant distributions and co-occurrence patterns. We assembled a data set describing the distribution of 139 herbaceous plant species within and among 257 forest stands in Wisconsin (USA) to elucidate the spatial scales at which these assembly processes operate. Analyses of these data in conjunction with detailed information about environmental conditions, plant functional traits, and phylogenetic relationships provided new insights into the scale-dependent drivers of plant community assembly in temperate forest understories. Traits like leaf height, specific leaf area, and seed mass all influenced individualistic plant distributions along landscape-scale gradients in soil texture, soil fertility, light availability, and climate while phylogenetic relationships did not predict species-environment relationships. These findings point to the importance of trait-mediated ecological sorting in shaping individualistic plant distributions at broad spatial scales. Contrary to our expectations about the importance of limiting similarity at local scales, neither functionally similar nor phylogenetically related herbs segregated among microsites within forest stands. We hypothesize strong ecological sorting among forest stands coupled with stochastic fine-scale interactions among species appear deterministic, niche-based assembly processes at local scales.

Methods

Vegetation surveys and measures of soil fertility, soil texture, and tree basal area were collected following the protocols outlined in Waller et al. (2012). Climate data were obtained following the protocols outlined in Beck et al. (2022). Species functional trait means were obtained from the database and protocols described in Waller et al. (2022). The phylogeny used in the analysis was obtained following the procedures described in Spalink et al. (2018). All data were proofed and verified for accuracy.

References:

Beck, J. J., D. Li, S. E. Johnson, D. A. Rogers, K. M. Cameron, K. J. Sytsma, T. J. Givnish, and D. M. Waller. In press. Functional traits mediate individualistic species-environment distributions at broad spatial scales while fine-scale species’ associations remain unpredictable. American Journal of Botany.

Spalink, D., R. Kriebel, P. Li, M. C. Pace, B. T. Drew, J. G. Zaborsky, J. Rose, C. P. Drummond, M. A. Feist, W. S. Alverson, D. M. Waller, K. M. Cameron, T. J. Givnish, and K. J. Sytsma. 2018. Spatial phylogenetics reveals evolutionary constraints on the assembly of a large regional flora. American Journal of Botany 105:1938–1950.

Waller, D. M., K. Amatangelo, S. Johnson, and D. Rogers. 2012. Wisconsin Vegetation Database – plant community survey and resurvey data from the Wisconsin Plant Ecology Laboratory. Biodiversity & Ecology 4:255–264.

Waller, D. M., A. K. Paulson, J. H. Richards, W. S. Alverson, K. L. Amatangelo, C. Bai, S. E. Johnson, D. Li, G. Sonnier, and R. H. Toczydlowski. 2022. Functional trait data for vascular plant species from northeastern North America. Ecology 103:e03527.

Usage Notes

Species distribution data as well as environmental and trait data are saved in .csv files. The phylogeny is saved in a .tre file. Code provided in R script (.R) file. The script uses functions from a handful of open-source packages readily available through CRAN. Analyses require the 'phyr' package (version 1.0.3): https://github.com/daijiang/phyr

Funding

National Science Foundation