It is virtually impossible to reliably assess water quality with target chemical analyses only. Therefore, a complementary effect-based risk assessment by bioanalyses on mixtures of bioavailable micropollutants is proposed: the Smart Integrated Monitoring (SIMONI) strategy. The goal of this strategy is to obtain more reliable information on the water quality to select optimum measures for improvement. The SIMONI strategy is 2-tiered. Tier 1 is a bioanalytical hazard identification of sites. A tier 2 ecological risk assessment is carried out only at a limited number of sites where increased hazards are detected in tier 1. Tier 2 will be customized, based on tier 1 evaluation and additional knowledge of the aquatic system. The present study focuses on the tier 1 bioanalytical hazard identification to distinguish "hot spots" of chemical pollution. First, a selection was made of relevant and cost-effective bioanalytical endpoints to cover a wide spectrum of micropollutant modes of action. Specific endpoints may indicate which classes of chemicals might cause adverse effects. Second, effect-based trigger values (EBT) were derived for these bioassays to indicate potential ecological risks. Comparison of EBT with bioassay responses should discriminate sites exhibiting different chemical hazards. Third, a model was designed to estimate the overall risks for aquatic ecosystems. The associated follow-up for risk management is a "toxicity traffic light" system: green, low hazard (no action required); orange, potential risk (further research needed); and red, high risk (mitigation measures). Thanks to cost-effectiveness, flexibility, and relevance, the SIMONI strategy has the potential to become the first bioanalytical tool to be applied in regular water quality monitoring programs. Environ Toxicol Chem 2017;36:2385-2399. © 2017 SETAC.

Global stores of important resources such as phosphorus (P) are being rapidly depleted, while the excessive use of nutrients has led to the enrichment of surface waters worldwide. Ideally, nutrients would be recovered from wastewater, which will not only prevent eutrophication but also provide access to alternative nutrient stores. Current state-of-the-art wastewater treatment technologies are effective in removing these nutrients from wastewater, yet they can only recover P and often in an insufficient way. Microalgae, however, can effectively assimilate P and nitrogen (N), as well as other macro- and micronutrients, allowing these nutrients to be recovered into valuable products that can be used to close nutrient cycles (e.g., fertilizer, bioplastics, color dyes, and bulk chemicals). Here, we show that the green alga Chlorella sorokiniana is able to remove all inorganic N and P present in concentrated toilet wastewater (i.e., black water) with N:P ratios ranging between 15 and 26. However, the N and P uptake by the algae is imbalanced relative to the wastewater N:P stoichiometry, resulting in a rapid removal of P but relatively slower removal of N. Here, we discuss how ecological principles such as ecological stoichiometry and resource-ratio theory may help optimize N:P removal and allow for more effective recovery of N and P from black water.

Human-driven changes affect nutrient inputs, oxygen solubility and the hydrodynamics of lakes, which affect biogeochemical cycles mediated by microbial communities. However, information on the succession of microbes involved in nitrogen cycling in seasonally stratified lakes is still incomplete. Here, we investigated the succession of nitrogen-transforming microorganisms in Lake Vechten over 19 months, combining 16S rRNA gene amplicon sequencing and quantification of functional genes. Ammonia-oxidizing archaea (AOA) and bacteria (AOB) and anammox bacteria were abundant in the sediment during winter, accompanied by nitrate in the water column. Nitrogen-fixing bacteria and denitrifying bacteria emerged in the water column in spring when nitrate was gradually depleted. Denitrifying bacteria containing nirS genes were exclusively present in the anoxic hypolimnion. During summer stratification, abundances of AOA, AOB and anammox bacteria decreased sharply in the sediment, and ammonium accumulated in hypolimnion. After lake mixing during fall turnover, abundances of AOA, AOB and anammox bacteria increased and ammonium was oxidized to nitrate. Hence, nitrogen-transforming microorganisms in Lake Vechten displayed a pronounced seasonal succession, which was strongly determined by the seasonal stratification pattern. These results imply that changes in stratification and vertical mixing induced by global warming are likely to alter the nitrogen cycle of seasonally stratified lakes.

The use of spatially interactive forest landscape models has increased in recent years. These models are valuable tools to assess our knowledge about the functioning and provisioning of ecosystems as well as essential allies when predicting future changes. However, developing the necessary inputs and preparing them for research studies require substantial initial investments in terms of time. Although model initialization and calibration often take the largest amount of modelers’ efforts, such processes are rarely reported thoroughly in application studies. Our study documents the process of calibrating and setting up an ecophysiologically based forest landscape model (LANDIS-II with PnET-Succession) in a biogeographical region where such a model has never been applied to date (southwestern Mediterranean mountains in Europe). We describe the methodological process necessary to produce the required spatial inputs expressing initial vegetation and site conditions. We test model behaviour on single-cell simulations and calibrate species parameters using local biomass estimations and literature information. Finally, we test how different initialization data—with and without shrub communities—influence the simulation of forest dynamics by applying the calibrated model at landscape level. Combination of plot-level data with vegetation maps allowed us to generate a detailed map of initial tree and shrub communities. Single-cell simulations revealed that the model was able to reproduce realistic biomass estimates and competitive effects for different forest types included in the landscape, as well as plausible monthly growth patterns of species growing in Mediterranean mountains. Our results highlight the importance of considering shrub communities in forest landscape models, as they influence the temporal dynamics of tree species. Besides, our results show that, in the absence of natural disturbances, harvesting or climate change, landscape-level simulations projected a general increase of biomass of several species over the next decades but with distinct spatio-temporal patterns due to competitive effects and landscape heterogeneity. Providing a step-by-step workflow to initialize and calibrate a forest landscape model, our study encourages new users to use such tools in forestry and climate change applications. Thus, we advocate for documenting initialization processes in a transparent and reproducible manner in forest landscape modelling.

This dataset provides information about infestation caused by the pine processionary moth (Thaumetopoeapityocampa ([Denis & Schiffermüller], 1775)) in pure or mixed pine woodlands and plantations in Andalusia. It represents a long-term series (1993–2015) containing 81,908 records that describe the occurrence and incidence of this species. Data were collected within a monitoring programme known as COPLAS, developed by the Regional Ministry of Environment and Territorial Planning of the Andalusian Regional Government within the frame of the Plan de Lucha Integrada contra la Procesionaria del Pino (Plan for Integrated Control Against the Pine Processionary Moth). In particular, this dataset includes 4,386 monitoring stands which, together with the campaign year, define the dataset events in Darwin Core Archive. Events are related with occurrence data which show if the species is present or absent. In turn, the event data have a measurement associated: degree of infestation.