Predicting spatial and temporal variations: maps
We mapped the effect of future climate change on the magnitude of the effect of ecosystem engineers on arthropods using the effect sizes (Hedges’ d) obtained at the experiment scale.
The magnitudes of the engineering effects on arthropod communities observed at each site and their association with climatic variables (see Table S1 for variable definitions) were tested using linear models (Gaussian error distribution). We used the function predict.lm to obtain the predicted relationship between independent variables and the Hedges’ d effect size (abundance, richness and biomass) (n = 52). Temperature (bio1, bio2, bio4, bio7) and precipitation (bio12, bio14, bio15) were included as independent continuous variables in separate models (see Table S1 for variable definitions). Separate climatic variables rather than the principal component scores identified in our meta-analyses were used because the correlations structure among variables may shift with climate change, and thus parameter estimation may be biased in different models (Hadi and Ling, 1998). In addition, using the climatic variables in the predictive modelling allowed us to inspect the reliability of the predict.lm function. However, for both temperature and precipitation climatic models most variables were collinear (see Figs. S2 and S3). We therefore first eliminated the influence of collinearity using the variance inflation factor (VIF) (Zuur et al., 2010). We then reduced the model through backward selection, retaining only the significant variables and those that improved the model fit (i.e., higher R2 values). We modelled the current climate scenario and those projected to 2070 using rcp8.5 (MIROC5) as a general circulation model. We checked normality, homoscedasticity and outliers through graphical inspections (e.g., qq-plots, Cook’s d, and influence), and residuals of the response variables were log transformed when necessary. Previous studies have used this approach to modelling distribution maps to provide a geographical interpretation of global ecological patterns (Tallavaaraet al ., 2018, Gusmão et al ., 2020).
Model performance was tested using a k -fold cross-validation approach (see Appendix S1).