One strategy for plants to optimize stomatal function is to open and close their stomata quickly in response to environmental signals. It is generally assumed that small stomata can alter aperture faster than large stomata. We tested the hypothesis that species with small stomata close faster than species with larger stomata in response to darkness by comparing rate of stomatal closure across an evolutionary range of species including ferns, cycads, conifers, and angiosperms under controlled ambient conditions (380 ppm CO2; 20.9% O2). The two species with fastest half-closure time and the two species with slowest half-closure time had large stomata while the remaining three species had small stomata, implying that closing rate was not correlated with stomatal size in these species. Neither was response time correlated with stomatal density, phylogeny, functional group, or life strategy. Our results suggest that past atmospheric CO2 concentration during time of taxa diversification may influence stomatal response time. We show that species which last diversified under low or declining atmospheric CO2 concentration close stomata faster than species that last diversified in a high CO2 world. Low atmospheric [CO2] during taxa diversification may have placed a selection pressure on plants to accelerate stomatal closing to maintain adequate internal CO2 and optimize water use efficiency.

The results contradict the over-simplistic notion that global vegetation always responds with decreasing gs to elevated CO2, a finding that has important implications for predicting future vegetation feedbacks on the hydrological cycle at the regional level.

High energy weather events are often expected to play a substantial role in biotic community dynamics and large scale diversity patterns but their contribution is hard to prove. Currently, observations are limited to the documentation of accidental records after the passing of such events. A more comprehensive approach is synthesising weather events in a location over a long time period, ideally at a high spatial resolution and on a large geographic scale. We provide a detailed overview on how to generate hurricane exposure data at a meso-climate level for a specific region. As a case study we modelled landscape hurricane exposure in Cusuco National Park (CNP), Honduras with a resolution of 50 m×50 m patches. We calculated actual hurricane exposure vulnerability site scores (EVVS) through the combination of a wind pressure model, an exposure model that can incorporate simple wind dynamics within a 3-dimensional landscape and the integration of historical hurricanes data. The EVSS was calculated as a weighted function of sites exposure, hurricane frequency and maximum wind velocity. Eleven hurricanes were found to have affected CNP between 1995 and 2010. The highest EVSS’s were predicted to be on South and South-East facing sites of the park. Ground validation demonstrated that the South-solution (i.e. the South wind inflow direction) explained most of the observed tree damage (90% of the observed tree damage in the field). Incorporating historical data to the model to calculate actual hurricane exposure values, instead of potential exposure values, increased the model fit by 50%.