Description: Human activities have differentially altered biogeochemical cycling at local, regional and global scales. We propose that a stoichiometric approach, examining the fluxes of multiple elements and the ratio between them, may be a useful tool for better understanding human effects on ecosystem processes and services. The different scale of impacts of the elements carbon, nitrogen and phosphorus and the different nature of their biogeochemical cycles, imply a large variation of their stoichiometric ratios in space and time and thus divergent impacts on biota. In this paper, we examine the effects of anthropogenic perturbations on nutrient ratios in ecosystems in two examples and one case study. Altered stoichiometry in agricultural systems (example 1) can affect not only crop yield and quality but also the interactions between plants and their pollinators, pests and pathogens. Human activities have also altered stoichiometry in coastal ecosystems (example 2). Increased N loading has especially lead to increased N:P and reduced Si:N ratios, with detrimental effects on ecosystem services derived from coastal pelagic food webs, such as fish yield and water quality. The terrestrial–aquatic linkage in stoichiometric alterations is illustrated with a case study, the Mississippi River watershed, where anthropogenic activities have caused stoichiometric changes that have propagated through the watershed into the northern Gulf of Mexico. Coupled with altered stoichiometric nutrient inputs are the inherent differences in variation and sensitivity of different ecosystems to anthropogenic disturbance. Furthermore, the connections among the components of a watershed may result in downstream cascades of disrupted functioning. Applying a multiple element perspective to understanding and addressing societal needs is a new direction for both ecological stoichiometry and sustainability.

Description: A Bayesian network model was developed to assess the combined influence of nutrient conditions and climate on the occurrence of cyanobacterial blooms within lakes of diverse hydrology and nutrient supply. Physicochemical, biological, and meteorological observations were collated from 20 lakes located at different latitudes and characterized by a range of sizes and trophic states. Using these data, we built a Bayesian network to (1) analyze the sensitivity of cyanobacterial bloom development to different environmental factors and (2) determine the probability that cyanobacterial blooms would occur. Blooms were classified in three categories of hazard (low, moderate, and high) based on cell abundances. The most important factors determining cyanobacterial bloom occurrence were water temperature, nutrient availability, and the ratio of mixing depth to euphotic depth. The probability of cyanobacterial blooms was evaluated under different combinations of total phosphorus and water temperature. The Bayesian network was then applied to quantify the probability of blooms under a future climate warming scenario. The probability of the ?high hazardous? category of cyanobacterial blooms increased 5% in response to either an increase in water temperature of 0.8°C (initial water temperature above 24°C) or an increase in total phosphorus from 0.01 mg/L to 0.02 mg/L. Mesotrophic lakes were particularly vulnerable to warming. Reducing nutrient concentrations counteracts the increased cyanobacterial risk associated with higher temperatures. # doi:10.1890/13-1677.1