Projected increases in cyclonic storm intensity under a warming climate will have profound effects on forests, potentially changing these ecosystems from carbon sinks to sources. forecasting storm impacts on these ecosystems requires consideration of risk factors associated with storm meteorology, landscape structure, and forest attributes. Here we evaluate risk factors associated with damage severity caused by Hurricanes María and Irma across Puerto Rican forests. Using field and remote sensing data, total forest aboveground biomass (AGB) lost to the storms was estimated at 10.44 (±2.33) Tg, ca. 23% of island-wide pre-hurricane forest AGB. Storm-related rainfall was a stronger predictor of forest damage than maximum wind speeds. Soil water storage capacity was also an important risk factor, corroborating the influence of rainfall on forest damage. Expected increases of 20% in hurricane-associated rainfall in the North Atlantic highlight the need to consider how such shifts, together with high speed winds, will affect terrestrial ecosystems. Cyclonic storms (hurricanes, typhoons, and cyclones) represent the dominant natural disturbance for many coastal forests 1-4. Multiple lines of evidence indicate that atmospheric warming will lead to more intense tropical cyclones 5. Sea surface temperature increases in most regions of tropical cyclone formation suggest that maximum wind speeds will rise and storms are likely to intensify more rapidly 6. Anthropogenic warming will also lead to higher atmosphere moisture content and increases in tropical-cyclone rainfall rates 6-9. Increases in the intensity and frequency of tropical cyclones may reduce the ability of tropical forests to sequester carbon 10. Tropical forests account for ~70% of the gross carbon sink in the world forests (~4.0 Pg C year −1) 11. Although land use change is the predominant driver of change in the tropical forest carbon sink, natural disturbance (e.g., fires, cyclonic storms) can also have important effects 10,12. Models and empirical evidence agree that ecosystems are generally carbon sources immediately following disturbance, but are likely to shift to carbon sinks as vegetation recovers 10,13,14. Forecasting the impacts of a greater number of more severe storms on the ability of tropical forests to act as a carbon sink requires consideration of myriad risk factors that determine the magnitude of storm impacts on vegetation across landscapes. Observational and modelling studies suggest that forests growing at high elevations or on windward slopes are more exposed to high wind speeds, and experience greater damage and tree mortality from severe storms 1,15-18. Associations between topography and tree damage may also be mediated by geology and soil characteristics 19. Restricted root growth of trees growing on ridges, in shallow soils, or soils with poor drainage, may make trees more vulnerable to wind-throw and stem break 1,15,16 , particularly when extreme winds are accompanied by large amounts of rainfall and flooding 20. Forest stand attribute...

Scientific communication relies on clear presentation of data. Logarithmic scales are used frequently for data presentation in many scientific disciplines, including ecology, but the degree to which they are correctly interpreted by readers is unclear. Analysing the extent of log scales in the literature, we show that 22% of papers published in the journal Ecology in 2015 included at least one log-scaled axis, of which 21% were log-log displays. We conducted a survey that asked members of the Ecological Society of America (988 responses, and 623 completed surveys) to interpret graphs that were randomly displayed with linear-linear or log-log axes. Many more respondents interpreted graphs correctly when the graphs had linear-linear axes than when they had log-log axes: 93% versus 56% for our all-around metric, although some of the individual item comparisons were even more skewed (for example, 86% versus 9% and 88% versus 12%). These results suggest that misconceptions about log-scaled data are rampant. We recommend that ecology curricula include explicit instruction on how to interpret log-scaled axes and equations, and we also recommend that authors take the potential for misconceptions into account when deciding how to visualize data.

Tropical cyclones (for example, hurricanes, typhoons) are expected to intensify under a warming climate, with uncertain effects on tropical forests. These ecosystems contribute disproportionately to greenhouse gas (GHG; carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O)) fluxes globally but there is high uncertainty in how these fluxes will respond to the projected increase in the frequency of severe tropical cyclones. To examine how these natural disturbance events may alter ecosystem processes in tropical forests, we studied the effects of Hurricane Marı ´a (2017), a category 4 storm, on soil GHG fluxes from a forest in Puerto

1. Studies of spatial point patterns (SPPs) are often used to examine the role that density-dependence (DD) and environmental filtering (EF) play in community assembly and species coexistence in forest communities. However, SPP analyses often struggle to distinguish the opposing effects that DD and EF may have on the distribution of tree species.2. We tested percolation threshold analysis on simulated tree communities as a method to distinguish the importance of thinning from DD EF on SPPs. We then compared the performance of percolation threshold analysis results and a Gibbs point process model in detecting environmental associations as well as clustering patterns or overdispersion. Finally, we applied percolation threshold analysis and the Gibbs point process model to observed SPPs of 12 dominant tree species in a Puerto Rican forest to detect evidence of DD and EF.3. Percolation threshold analysis using simulated SPPs detected a decrease in clustering due to DD and an increase in clustering from EF. In contrast, the Gibbs point process model clearly detected the effects of EF but only identified DD thinning in two of the four types of simulated SPPs. Percolation threshold analysis on the 12 observed tree species' SPPs found that the SPPs for two species were consistent with thinning from DD processes only, four species had SPPs consistent with EF only and SPP for five reflected a combination of both processes. Gibbs models of observed SPPs of living trees detected significant environmental associations for 11 species and clustering consistent with DD processes for seven species. 4. Percolation threshold analysis is a robust method for detecting community assembly processes in simulated SPPs. By applying percolation threshold analysis to natural communities, we found that tree SPPs were consistent with thinning from both DD and EF. Percolation threshold analysis was better suited to detect DD thinning than Gibbs models for clustered simulated communities. Percolation threshold analysis improves our understanding of forest community assembly processes by quantifying the relative importance of DD and EF in forest communities.

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