Xylem conduits have lignified walls to resist crushing pressures. The thicker the double-wall ( T) relative to its maximum diameter ( D), the greater the collapse/implosion resistance. Having xylem that is more resistant than necessary incurs high costs and reduced flow, while having xylem not resistant enough may lead to catastrophic collapse under drought. Despite the importance of xylem implosion safety in determining plant drought resistance, it is still unclear how leaves scale Tx D to trade-off among implosion safety, flow efficiency, mechanical support, and construction cost. We measured T and D in over 7,000 leaf xylem conduits of 122 ferns and angiosperms species to investigate how the Tx D scaling varies across species, clades, habitats, growth forms, and vein orders. Overall, leaf xylem conduits grow wider than thicker, potentially resulting in high flow efficiency and lower cost, but at the expense of high vulnerability to implosion. Conduits seem particularly vulnerable to implosion in monocots, aquatic species and in species from hydric habitats, as well as in major veins. The absence of strong trade-offs within the leaf functional traits examined suggests that implosion safety at the whole-leaf level cannot be easily predicted by the sum of the individual conduits’ resistance to collapse.

Community assembly provides the foundation for applications in biodiversity conservation, climate change, invasion ecology, restoration ecology, and synthetic ecology. Predicting and prioritizing community assembly outcomes remains challenging. We address this challenge via a mechanism-free LOVE (Learning Outcomes Via Experiments) approach suitable for cases where little data or knowledge exist: we carry out actions (randomly-sampled combinations of species additions), measure abundance outcomes, and then train a model to predict arbitrary outcomes of actions, or prioritize actions that would yield the most desirable outcomes. When trained on <100 randomly-selected actions, LOVE predicts outcomes with 2-5% error across datasets, and prioritizes actions for maximizing richness, maximizing abundance, or minimizing abundances of unwanted species, with 94-99% true positive rate and 12-83% true negative rate across tasks. LOVE complements existing approaches for community ecology by providing a foundation for additional mechanism-first study, and may help address numerous ecological applications.

Accumulating evidence suggests that ecological communities undergoing change in response to either anthropogenic or natural disturbance regimes exhibit macroecological patterns that differ from those observed in similar types of communities in relatively undisturbed sites. In contrast to such cross-site comparisons, however, there are few empirical studies of shifts over time in the shapes of macroecological patterns. Here we provide a dramatic example of a plant community in which the species-area relationship and the species-abundance distribution change markedly over a period of six years. These patterns increasingly deviate from the predictions of the Maximum Entropy Theory of Ecology (METE), which successfully predicts macroecological patterns in relatively static systems. Information on the dynamic state of an ecosystem inferred from snapshot measurements of macroecological community structure can assist in extending the domain of current theories and models to disturbed ecosystems.