Statistical Analysis
We used mixed-effect models with appropriate error structures (see below) to test the impact of drought on survival, growth and photosynthesis. Preliminary models for growth showed that trends did not vary between growth rate (Appendix Table A4) or relative growth rate (Appendix Table A7) and we used the latter for interpretation since relative growth rates were standardized by initial height.
For the first question concerning differences in species response to drought in relation to their affinity for less vs. more seasonal areas, we modeled species’ performance as a function of seasonality index, drought treatment, and their interaction. For question 2, we tested the role of each trait in governing drought response. We implemented separate models per trait, where species’ performance was modeled as a function of their trait values, drought treatment, and drought-trait interaction. In all models, we used species-level trait means. Species ID and block ID were included as random intercepts. Survival was modeled using a Bernoulli error structure and Gaussian errors were assumed for growth and photosynthesis. Because species differed in the extent of variation in their response, we added a weighting term to separately model the variance per species. Trait models for SRL were implemented with and without the species Actinodaphne malabarica , which had small seedlings with very light roots and high SRL values that were an outlier compared to other species.
Model structure in Wilkinson-Rogers notation:
Performance variable ~ trait + treatment + trait:treatment + (1|species) + (1|block)), weights=varIdent(form=~1|species)
Finally, to understand the impact of the overall trait-based phenotype on performance during drought (question 3), we used a two-step approach. First, we performed a pairwise correlation among traits (Appendix Figure A5) and a principal component analysis (PCA) on traits to obtain composite phenotypes defined by trait combinations. Then, we modeled individual survival, growth, and photosynthesis in control vs. drought plants using GLMM with species-level intercepts and slopes. From these, we extracted each species’ baseline performance (survival, growth, photosynthesis) in well-watered conditions and the change in performance with drought and conducted a PCA on these six variables to get species’ composite response to drought in relation to their baseline differences in growth, survival and photosynthesis. For both PCAs, we performed varimax rotation to simplify loadings for each factor. In the second step, to explore the correspondence between traits and performance, we performed Procrustean superimposition on the two PCA, using the axes that explained >75% of the variation in each PCA. Procrustes analysis tests the strength of association between two ordinations for a common object (species in our case) with each other (Peres-neto & Jackson 2001, Rüger et al. 2018), i.e., how well species positions in trait⁠ space matched their positions in performance space. Significance of the Procrustes correlation was assessed using permutation tests with 10,000 random iterations.
All analyses were performed using R v 3.4. We used packages nlme (Pinheiro et al. 2022)⁠ for mixed effects models with Gaussian error structure lme4 (Bates et al. 2015)⁠ for models with Bernoulli error, FactoMineR (Lê et al. 2008)⁠ for PCA and vegan (Oksanen et al. 2020)⁠ for Procrustes analysis.