Kinetic factors of physiology and the dynamic light environment influence the economic landscape of short-term hydraulic risk.

Optimality-based models of stomatal conductance unify biophysical and evolutionary constraints and can improve predictions of land-atmosphere carbon and water exchange. Recent models incorporate hydraulic constraints by penalizing excessive stomatal opening in relation to hydraulic damage caused by low water potentials. We used simulation models to test whether penalties based solely on vulnerability curves adequately represent the optimality hypothesis, given that they exclude the effects of kinetic factors on stomatal behavior and integrated carbon balance. To quantify the effects of nonsteady-state phenomena on the landscape of short-term hydraulic risk, we simulated diurnal dynamics of leaf physiology for 10 000 patches of leaf in a canopy and used a ray-tracing model, Helios, to simulate realistic variation in sunfleck dynamics. Our simulations demonstrated that kinetic parameters of leaf physiology and sunfleck properties influence the economic landscape of short-term hydraulic risk, as characterized by the effect of stomatal strategy (gauged by the water potential causing a 50% hydraulic penalty) on both aggregated carbon gain and the aggregated carbon cost of short-term hydraulic risk. Hydraulic penalties in optimization models should be generalized to allow their parameters to account for kinetic factors, in addition to parameters of hydraulic vulnerability.

Succession of bacterial communities on carrion is independent of vertebrate scavengers.

The decomposition of carrion is carried out by a suite of macro- and micro-organisms who interact with each other in a variety of ecological contexts. The ultimate result of carrion decomposition is the recycling of carbon and nutrients from the carrion back into the ecosystem. Exploring these ecological interactions among animals and microbes is a critical aspect of understanding the nutrient cycling of an ecosystem. Here we investigate the potential impacts that vertebrate scavenging may have on the microbial community of carrion. In this study, we placed seven juvenile domestic cow carcasses in the Grassy Mountain region of Utah, USA and collected tissue samples at periodic intervals. Using high-depth environmental sequencing of the 16S rRNA gene and camera trap data, we documented the microbial community shifts associated with decomposition and with vertebrate scavenger visitation. The remarkable scarcity of animals at our study site enabled us to examine natural carrion decomposition in the near absence of animal scavengers. Our results indicate that the microbial communities of carcasses that experienced large amounts of scavenging activity were not significantly different than those carcasses that observed very little scavenging activity. Rather, the microbial community shifts reflected changes in the stage of decomposition similar to other studies documenting the successional changes of carrion microbial communities. Our study suggests that microbial community succession on carrion follows consistent patterns that are largely unaffected by vertebrate scavenging.

A stomatal control model based on optimization of carbon gain versus hydraulic risk predicts aspen sapling responses to drought.

Empirical models of plant drought responses rely on parameters that are difficult to specify a priori. We test a trait- and process-based model to predict environmental responses from an optimization of carbon gain vs hydraulic risk. We applied four drought treatments to aspen (Populus tremuloides) saplings in a research garden. First we tested the optimization algorithm by using predawn xylem pressure as an input. We then tested the full model which calculates root-zone water budget and xylem pressure hourly throughout the growing season. The optimization algorithm performed well when run from measured predawn pressures. The per cent mean absolute error (MAE) averaged 27.7% for midday xylem pressure, transpiration, net assimilation, leaf temperature, sapflow, diffusive conductance and soil-canopy hydraulic conductance. Average MAE was 31.2% for the same observations when the full model was run from irrigation and rain data. Saplings that died were projected to exceed 85% loss in soil-canopy hydraulic conductance, whereas surviving plants never reached this threshold. The model fit was equivalent to that of an empirical model, but with the advantage that all inputs are specific traits. Prediction is empowered because knowing these traits allows knowing the response to climatic stress.