Great uncertainty exists in the global exchange of carbon between the atmosphere and the terrestrial biosphere. An important source of this uncertainty lies in the dependency of photosynthesis on the maximum rate of carboxylation (V_cmax) and the maximum rate of electron transport (J_max). Understanding and making accurate prediction of C fluxes thus requires accurate characterization of these rates and their relationship with plant nutrient status over large geographic scales. Plant nutrient status is indicated by the traits: leaf nitrogen (N), leaf phosphorus (P), and specific leaf area (SLA). Correlations between V_cmax and J_max and leaf nitrogen (N) are typically derived from local to global scales, while correlations with leaf phosphorus (P) and specific leaf area (SLA) have typically been derived at a local scale. Thus, there is no global‐scale relationship between V_cmax and J_max and P or SLA limiting the ability of global‐scale carbon flux models do not account for P or SLA. We gathered published data from 24 studies to reveal global relationships of V_cmax and J_max with leaf N, P, and SLA. V_cmax was strongly related to leaf N, and increasing leaf P substantially increased the sensitivity of V_cmax to leaf N. J_max was strongly related to V_cmax, and neither leaf N, P, or SLA had a substantial impact on the relationship. Although more data are needed to expand the applicability of the relationship, we show leaf P is a globally important determinant of photosynthetic rates. In a model of photosynthesis, we showed that at high leaf N (3 gm⁻²), increasing leaf P from 0.05 to 0.22 gm⁻² nearly doubled assimilation rates. Finally, we show that plants may employ a conservative strategy of J_max to V_cmax coordination that restricts photoinhibition when carboxylation is limiting at the expense of maximizing photosynthetic rates when light is limiting.

Understanding what structures ecological communities is vital to answering questions about extinctions, environmental change, trophic cascades, and ecosystem functioning. Optimal foraging theory was conceived to increase such understanding by providing a framework with which to predict species interactions and resulting community structure. Here, we use an optimal foraging model and allometries of foraging variables to predict the structure of real food webs. The qualitative structure of the resulting model provides a more mechanistic basis for the phenomenological rules of previous models. Quantitative analyses show that the model predicts up to 65% of the links in real food webs. The deterministic nature of the model allows analysis of the model's successes and failures in predicting particular interactions. Predacious and herbivorous feeding interactions are better predicted than pathogenic, parasitoid, and parasitic interactions. Results also indicate that accurate prediction and modeling of some food webs will require incorporating traits other than body size and diet choice models specific to different types of feeding interaction. The model results support the hypothesis that individual behavior, subject to natural selection, determines individual diets and that food web structure is the sum of these individual decisions.

Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development.

Predators of herbivorous animals can affect plant populations by altering herbivore density, behavior, or both. To test whether the indirect effect of predators on plants arises from density or behavioral responses in a herbivore population, we experimentally examined the dynamics of terrestrial food chains comprised of old field plants, leaf-chewing grasshoppers, and spider predators in Northeast Connecticut. To separate the effects of predators on herbivore density from the effects on herbivore behavior, we created two classes of spiders: (i) risk spiders that had their feeding mouth parts glued to render them incapable of killing prey and (ii) predator spiders that remained unmanipulated. We found that the effect of predators on plants resulted from predator-induced changes in herbivore behavior (shifts in activity time and diet selection) rather than from predator-induced changes in grasshopper density. Neither predator nor risk spiders had a significant effect on grasshopper density relative to a control. This demonstrates that the behavioral response of prey to predators can have a strong impact on the dynamics of terrestrial food chains. The results make a compelling case to examine behavioral as well as density effects in theoretical and empirical research on food chain dynamics.