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Cyanobacterial harmful algal blooms (CyanoHABs), mainly composed of the genus Microcystis, occur frequently throughout the Laurentian Great Lakes. We used artificial neural networks (ANNs) involving 31 hydrological and meteorological predictors to model total phytoplankton (as chlorophyll a) and Microcystis biomass from 2009 to 2011 in western Lake Erie. Continuous ANNs provided modeled-measured correspondences (and modeling efficiencies) ranging from 0.87 to 0.97 (0.75 to 0.94) and 0.71 to 0.90 (0.45 to 0.88) for training-cross-validation and test data subsets of chlorophyll a concentrations and Microcystis biovolumes, respectively. Classification ANNs correctly assigned up to 94% of instances for Microcystis presenceabsence. The influences of select predictors on phytoplankton and CyanoHAB niches were visualized using biplots and threedimensional response surfaces. These then were used to generate mathematical expressions for the relationships between modeled CyanoHAB outcomes and the direct and interactive influences of environmental factors. Based on identified conditions (~40 to 50 µg total phosphorus (TP) x [L.sup.-1], 22 to 26°C, and prolonged wind speeds less than ~19 km x [h.sup.-1]) underlying the likelihood of occurrence and accumulation of phytoplankton and Microcystis, a 'target' concentration of 30 µg TP x [L.sup.- 1] appears appropriate for alleviating blooms. ANNs generated robust ecological niche models for Microcystis, providing a predictive framework for quantitative visualization of nonlinear CyanoHAB-environmental interactions. Les fleurs d'eau de cyanobacteries nuisibles (CyanoHAB), constituees principalement du genre Microcystis, sont frequentes dans tous les Grands Lacs laurentiens. Nous avons utilise des reseaux neuronaux artificiels (<< ANNs >>) incluant 31 predicteurs hydrologiques et meteorologiques pour modeliser la biomasse de phytoplancton totale (chlorophylle a) et de Microcystis, de 2009 a 2011, dans la partie occidentale du lac Erie. Des ANNs continus ont fourni des correspondances modele-mesures (et des valeurs d'efficacite de la modelisation) allant de 0,87 a 0,97 (0,75 a 0,94) et de 0,71 a 0,90 (0,45 a 0,88) pour des sous-ensembles de donnees d'entrainement-de contrevalidation et d'essai des concentrations de la chlorophylle a et des biovolumes de Microcystis, respectivement. Les ANNs de classification ont affecte correctement jusqu'il 94 % des cas d'absence ou de presence de Microcystis. Les influences de predicteurs selectionnes sur les niches de phytoplancton et de CyanoHAB ont ete visualisees a l'aide de diagrammes de double projection et de surfaces de reponse tridimensionnelles, qui ont ensuite ete utilises pour produire des expressions mathematiques pour les relations entre les CyanoHAB modelisees et les influences directes et interactives de facteurs environnementaux. A la lumiere des conditions cernees (~40 a 50 µg phosphore total (PT)x [L.sup.-1], de 22 a 26°C et des vitesses du vent soutenues inferieures a ~19 km x [h.sup.-1]) soustendant la probabilite de presence et d'accumulation de phytoplancton et de Microcystis, une concentration << cible >> de 30 µg PT x [L.sup.-1] semble adequate pour reduire les fleurs d'eau. Les ANNs ont produit des modeles de niche ecologique robustes pour Microcystis, fournissant ainsi un cadre previsionnel pour la visualisation quantitative d'interactions non lineaires entre le milieu ambiant et les CyanoHAB. [Traduit par la Redaction], Introduction The characterization of biotic responses to environmental controls, along with the dynamics of those responses, fuels ecological modeling on local scales and forms the foundation for regional forecasting efforts. [...]

Global amphibian declines have often been attributed to disease (1,3), but ignorance of the relative importance and mode of action of potential drivers of infection has made it difficult to [...]

Freshwater systems are critical to life on earth, yet they are threatened by the increasing rate of synthetic chemical pollution. Current predictions of the effects of synthetic chemicals on freshwater ecosystems are hampered by the sheer number of chemical contaminants entering aquatic systems, the diversity of organisms inhabiting these systems, the myriad possible direct and indirect effects resulting from these combinations, and uncertainties concerning how contaminants might alter ecosystem metabolism via changes in biodiversity. To address these knowledge gaps, we conducted a mesocosm experiment that elucidated the responses of ponds composed of phytoplankton and zooplankton to standardized concentrations of 12 pesticides, nested within four pesticide classes, and two pesticide types. We show that the effects of the pesticides on algae were consistent within herbicides and insecticides and that responses of over 70 phytoplankton species and genera were consistent within broad taxonomic groups. Insecticides generated top‐down effects on phytoplankton community composition and abundance, which were associated with persistent increases in ecosystem respiration. Insecticides had direct toxic effects on cladocerans, which led to competitive release of copepods. These changes in the zooplankton community led to a decrease in green algae and a modest increase in diatoms. Herbicides did not change phytoplankton composition but reduced total phytoplankton abundance. This reduction in phytoplankton led to short‐term decreases in ecosystem respiration. Given that ponds release atmospheric carbon and that worldwide pesticide pollution continues to increase exponentially, scientists and policy makers should pay more attention to the ways pesticides alter the carbon cycle in ponds via changes in communities, as demonstrated by our results. Our results show that these predictions can be simplified by grouping pesticides into types and species into functional groups. Adopting this approach provides an opportunity to improve the efficiency of risk assessment and mitigation responses to global change. [ABSTRACT FROM AUTHOR]