Data analyses: species size effects on — and responses to — light penetration
Because there is no standard definition of “small species” we compared two approaches: 1) small species were regarded as those smaller than the first quartile of species height for all species in the focal community (‘1st quartile’, <53.25 cm, N=15), 2) small species were regarded as those smaller than the median of species height in the community (‘median’, <83 cm, N=29; Fig. 2). Comparing results across these two definitions allowed us to test whether any significant patterns were sensitive to our method of defining small species.
We tested whether plots with larger species had lower light penetration. Specifically, we fit linear models with mean plot height (mean plot-level maximum height weighted by species plot-level abundance) and large species abundance (including all species which are not considered “small” by either definition, >83 cm) as predictors and mean light penetration as the response variable. We also tested whether small species abundance and richness responded to variation among plots in light penetration. We fit linear models with small species abundance and richness (for both ‘1st quartile’ and ‘median’ defined small species) as response variables. Light penetration and mean intraplot variation in light penetration (calculated for each plot at each sampling event and averaged across months) were our predictor variables. We checked the variance inflation factor to ensure that there was not a high degree of multicollinearity between our variables.
We used the package ‘stats’ from R.4.0.3 (R Core Team 2020) for modeling, and checked statistical assumptions using residual vs. fitted, normal quantile-quantile, scale location, and constant leverage plots (‘ggfortify’ v.0.4.11; Tang 2016). We log10-transformed variables where necessary to meet assumptions. For our multiple linear regressions, we used the Akaike Information Criterion corrected for small sample sizes for model selection (AICc). Where multiple models were within two units of the lowest AICc score, we used the full model average (via the ‘model.avg’ function from the ‘MuMIn’ v1.43.17 package; Bartoń 2020) to determine the significance of predictors.
Finally, to assess the collective response of small species to light availability, we performed distance-based redundancy analyses (dbRDA) using Bray-Curtis dissimilarities. Additionally, we used the ‘decorana’ function from the package ‘vegan’ v.2.5-6 (Oksanen 2019) to confirm linearity of responses. We conducted RDA with mean light penetration and mean intraplot variation as possible predictors using the ‘capscale’ function from ‘vegan’ v.2.5-6 (Oksanen 2019). We compared models using AICc scores, selecting the model with the lowest score.