The effective use of spatial patterns to infer underlying ecological and physical drivers of forest dynamics is a key multi-disciplinary challenge for plant ecology. Recent technological advances have improved our ability to analyse and integrate the wealth of spatial information generated by studies on global forest plots. However, it is unclear what the most common pattern is, empirically (aggregated, randomness or regular) and the degree to which studies are cross-comparable. We undertook a meta-synthesis of spatially explicit forest-plot data from six continents, based on 87 studies that reported explicit pattern statistics. We focused on 264 unique forest plots spanning over 1,000 different species occurrences.
Results/Conclusions
Our analysis demonstrates, for the first time, that aggregation is the dominant pattern (60-70% of communities and species) described worldwide. However, our ability to synthesize pattern data and generalize across studies was constrained by strong geographical biases and methodological inconsistencies (in plot size and size of tree measured). As methods for spatial point-pattern analysis become increasingly sophisticated, future research on forest dynamics and structure must work to improve cross-study comparability and account for scale dependencies, by designing and reporting plot-data using consistent field designs and modes of analysis, coupled with larger plot sizes.