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Do tradeoffs govern plant species responses to different global change treatments?


Langley, J. Adam et al. (2021), Do tradeoffs govern plant species responses to different global change treatments?, Dryad, Dataset,


Plants are subject to tradeoffs among growth strategies such that adaptations for optimal growth in one condition can preclude optimal growth in another. Thus, we hypothesized that the response of plant species abundance to one global change treatment would relate inversely to the response to a second treatment, particularly for treatment combinations that accentuate distinct traits. To address this hypothesis, we examined plant species abundances in 39 global change experiments manipulating CO2, nitrogen, phosphorus, water, temperature, or disturbance. Overall, the directional response of a species to one treatment was 13% more likely than expected to oppose its response to a second. This tendency was detectable across the global dataset but held little predictive power for individual treatment combinations or within individual experiments. While tradeoffs in the ability to respond to different global change drivers exert detectable effects globally, other forces may obscure their influence in local communities.


We analyzed the CoRRE database (Komatsu et al. 2019;, which compiles species abundance information from herbaceous plant communities subjected to experimental manipulations mimicking global change drivers. We included studies from the database that manipulated at least two global change drivers (elevated CO2, nitrogen addition, phosphorus addition, multiple nutrient addition, water addition, droought, warming, and disturbance, where disturbance included burning, mowing, or clipping) for at least three years. For each of these experiments, we estimated each species' mean abundance across all years in the control plots and in each relevant treatment plot (single factor manipulations only). We calculated three different effect size metrics using species mean abundance in treatment plots (t) and control plots (c): 1) E=(t-c)/(t+c), 2) LRR=ln(t/c), 3) PS=(t-c)/c. We also include data on species mean abundance in the control plots. We coded data to indicate whether species were present in at least one treatment plot and one control plot (present), or if they were absent from either all treatment or all control plots.

We also include in this dataset the results of 999 runs of a null model where we randomly reshuffled the treatment assignments among all control and all treatment plots within each experiment. We then calculated species mean abundances and effect sizes as above for the observed data.

Usage Notes

"Permutation" column indicates a number (1-999) if the community data were simulated or "real" if the data were observed in the global change experiments in the field. The "site_project_comm" column gives an identifier for the experiment; see the manuscript supplement for more details on the sites and experiments. The "species" column gives the species name or code provided by the data collectors. The "global.change.treatment" column indicates which of the following types of manipulation was imposed upon the treatment plots: elevated CO2 ("CO2"), nitrogen addition ("N"), phosphorus addition ("P"), multiple nutrient addition ("mult_nutrient"), water addition ("irr"), drought ("drought"), warming ("temp"), and disturbance ("BMCT"). Species that were absent in either all treatment plots (coded "absent treatment" in the "code" column) or all control plots ("absent control") have NA for log response ratio (column "LRR") because this value is incalculable in either situation. Species absent in all control plots have NA for percent stimulation (column "PS") because this value is incalculable in that situaiton. All species have a value between -1 and 1 in the "E" column. Species mean abundance in control plots ("relabund.control" column) is given only for the "real" data and is NA whenever permutation is a number 1-999 or when a species was absent from all replicate control plots (code is "absent control").


National Science Foundation, Award: EF-0553768

National Science Foundation, Award: DEB-1545288

National Science Foundation, Award: DEB-0950080

National Science Foundation, Award: DEB-1457100

National Science Foundation, Award: DEB-1557009

U.S. Department of Energy, Award: 08-SC-NICCR-1073

Russian Science Foundation, Award: 19-14-00038

UK Natural Environment Research Council

Academy of Finland, Award: 297191

Academy of Finland, Award: 253385