Multiple stressor effects on biological quality elements in the Ebro River: Present diagnosis and predicted responses

Water quality deterioration remains a major stressor for macroinvertebrate, diatom and fish communities in German rivers

About 60 % of Europe's rivers fail to meet ecological quality standards derived from biological criteria. The causes are manifold, but recent reports suggest a dominant role of hydro-morphological and water quality-related stressors. Yet, in particular micropollutants and hydrological stressors often tend to be underrepresented in multiple-stressor studies. Using monitoring data from four Federal States in Germany, this study investigated the effects of 19 stressor variables from six stressor groups (nutrients, salt ions, dissolved oxygen/water temperature, mixture toxicity of 51 micropollutants, hydrological alteration and morphological habitat quality) on three biological assemblages (fishes, macroinvertebrates, benthic diatoms). Biological effects were analyzed for 35 community metrics and quantified using Random Forest (RF) analyses to put the stressor groups into a hierarchical context. To compare metric responses, metrics were grouped into categories reflecting important characteristics of biological communities, such as sensitivity, functional traits, diversity and community composition as well as composite indices that integrate several metrics into one single index (e.g., ecological quality class). Water quality-related stressors - but not micropollutants - turned out to dominate the responses of all assemblages. In contrast, the effects of hydro-morphological stressors were less pronounced and stronger for hydrological stressors than for morphological stressors. Explained variances of RF models ranged 23-64 % for macroinvertebrates, 16-40 % for benthic diatoms and 18-48 % for fishes. Despite a high variability of responses across assemblages and stressor groups, sensitivity metrics tended to reveal stronger responses to individual stressors and a higher explained variance in RF models than composite indices. The results of this study suggest that (physico-chemical) water quality deterioration continues to impact biological assemblages in many German rivers, despite the extensive progress in wastewater treatment during the past decades. To detect water quality deterioration, monitoring schemes need to target relevant physico-chemical stressors and micropollutants. Furthermore, monitoring needs to integrate measures of hydrological alteration (e.g., flow magnitude and dynamics). At present, hydro-morphological surveys rarely address the degree of hydrological alteration. In order to achieve a good ecological status, river restoration and management needs to address both water quality-related and hydro-morphological stressors. Restricting analyses to just one single organism group (e.g., macroinvertebrates) or only selected metrics (e.g., ecological quality class) may hamper stressor identification and its hierarchical classification and, thus may mislead river management.

Non-interactive effects drive multiple stressor impacts on the taxonomic and functional diversity of atlantic stream macroinvertebrates

Freshwaters are considered among the most endangered ecosystems globally due to multiple stressors, which coincide in time and space. These local stressors typically result from land-use intensification or hydroclimatic alterations, among others. Despite recent advances on multiple stressor effects, current knowledge is still limited to manipulative approaches minimizing biological and abiotic variability. Thus, the assessment of multiple stressor effects in real-world ecosystems is required. Using an extensive survey of 50 stream reaches across North Portugal, we evaluated taxonomic and functional macroinvertebrate responses to multiple stressors, including marked gradients of nutrient enrichment, flow reduction, riparian vegetation structure, thermal stress and dissolved oxygen depletion. We analyzed multiple stressor effects on two taxonomic (taxon richness, Shannon-diversity) and two trait-based diversity indices (functional richness, functional dispersion), as well as changes in trait composition. We found that multiple stressors had additive effects on all diversity metrics, with nutrient enrichment identified as the most important stressor in three out of four metrics, followed by dissolved oxygen depletion and thermal stress. Taxon richness, Shannon-diversity and functional richness responded similarly, whereas functional dispersion was driven by changes in flow velocity and thermal stress. Functional trait composition changed along a major stress gradient determined by nutrient enrichment and oxygen depletion, which was positively correlated with organisms possessing fast-living strategies, aerial respiration, adult phases, and gathering-collector feeding habits. Overall, our results reinforce the need to consider complementary facets of biodiversity to better identify assembly processes in response to multiple stressors. Our data suggest that stressor interactions may be less frequent in real-word streams than predicted by manipulative experiments, which can facilitate mitigation strategies. By combining an extensive field survey with an integrative consideration of multiple biodiversity facets, our study provides new insights that can help to better assess and manage rivers in a global change context.

Prediction of ecological status of surface water bodies with supervised machine learning classifiers

Ensuring a good ecological status of water bodies is one of the key challenges of communities and one of the objectives of the European Water Framework Directive. Although recent works identified the most significant stressors affecting the ecological quality of rivers, the ability to predict the overall ecological status of rivers based on a limited amount of easily accessible geospatial data has not been investigated so far. Most of the analyses focus on detailed local modelling and measurements which cannot be systematically applied at regional scales for the purposes of water resources management. The aim of this work is to understand the capabilities of five supervised machine learning classifiers of predicting the ecological status of rivers based on land use, climate, morphology, and water management parameters extracted over the river catchments corresponding to the ecological monitoring stations. Moreover, the performances of machine learning classifiers are compared to the results of the canonical correlation analysis. The method is applied to 360 catchments in Tuscany (central Italy) with a median size of 33.6 km² and a Mediterranean climate. The results show (i) a significant correlation of ecological status with summer climate (i.e., maximum temperatures and minimum precipitation), land use and water exploitation, (ii) an 80 % precision of Random Forest algorithm to predict ecological status and (iii) higher capability of all classifiers to predict at least good ecological status. In perspective, such predictive capabilities can support decision making in the land and water resources management and highlight strategies for river eco-hydrological conservation.

Natural and anthropogenic factors and their interactions drive stream community integrity in a North American river basin at a large spatial scale

Urbanization, agriculture, and other human activities can exert considerable influence on the health and integrity of stream ecosystems. These influences vary greatly over space, time, and scale. We investigated trends in stream biotic integrity over 19 years (1997-2016) in relation to natural and anthropogenic factors in their spatial context using data from a stream biomonitoring program in a region dominated by agricultural land use. Macroinvertebrate and fish diversity and abundance data were used to calculate four multimetric indices (MMIs) that described biotic integrity of streams from 1997 to 2016. Boosted regression trees (BRT), a machine learning technique, were used to model how stream integrity responded to catchment-level natural and anthropogenic drivers including land use, human population density, road density, runoff potential, and natural factors such as latitude and elevation. Neither natural nor anthropogenic factors were consistently more influential on the MMIs. Macroinvertebrate indices were most responsive to time, latitude, elevation, and road density. Fish indices were driven mostly by latitude and longitude, with agricultural land cover among the most influential anthropogenic factors. We concluded that 1) stream biotic integrity was mostly stable in the study region from 1997 to 2016, although macroinvertebrate MMIs had decreased approximately 10% since 2010; 2) stream biotic integrity was driven by a mix of factors including geography, human activity, and variability over yearly time intervals; 3) MMI responses to environmental drivers were nonlinear and often nonmonotonic; 4) MMI composition could influence causal inferences; and 5) although our findings were mostly consistent with the literature on drivers of stream integrity, some commonly seen patterns were not evident. Our findings highlight the utility of large-scale, publicly available spatial data for understanding drivers of stream biodiversity and illustrate some potential pitfalls of large scale, integrative analyses.

Diatoms as indicators in running waters: trends of studies on biological assessment and monitoring

Despite the biodiversity and ecosystem services provided by lotic ecosystems, they are strongly affected by anthropogenic activities. Therefore, biological monitoring and assessment strategies are crucial in helping maintain these ecosystems and developing mitigation policies. We provide a global overview of the use of benthic diatoms as bioindicators in lotic environments, by analyzing 764 articles published in the past 20 years. We analyzed the influence of substrate type on samplings, which species have been highlighted as indicators and for which type of impacts, which anthropogenic impacts have been most commonly evaluated, and which metrics have been commonly used in studies using diatoms to assess and monitor the quality of lotic environments. We found that the most studied anthropogenic impact is artificial eutrophication and that some species, especially Nitzschia palea, have been thoroughly mentioned as indicators of this impact. Indicator species related to other types of impact are less common, demonstrating the need for studies on this issue. Moreover, we verified that traditional taxonomic metrics, such as diversity and diatom indices, have been widely used. Some alternative metrics have been used recently, such as those based on teratological valves, lipid bodies, valve size, and DNA metabarcoding. The number of biomonitoring and assessment studies based on diatoms has increased considerably in the past 20 years. Nonetheless, the demand for natural resources and consequently the degradation of lotic ecosystems have accelerated significantly. Thus, the development of low-cost and time-efficient biological assessment and monitoring strategies is essential for evaluating the health of lotic environments.

What drives the ecological quality of surface waters? A review of 11 predictive modeling tools

What policy is needed to ensure that good-quality water is available for both people's needs and the environment? The EU Water Framework Directive (WFD), which came into force in 2000, established a framework for the assessment, management, protection and improvement of the status of water bodies across the European Union. However, recent reviews show that the ecological status of the majority of surface waters in the EU does not meet the requirement of good status. Thus, it is an important question what measures water management authorities should take to improve the ecological status of their water bodies. To find concrete answers, several institutes in the Netherlands cooperated to develop a software tool, the WFD Explorer, to assist water managers in selecting efficient measures. This article deals with the development of prediction tools that allow one to calculate the effect of restoration and mitigation measures on the biological quality, expressed in terms of Ecological Quality Ratios (EQRs). To find the ideal modeling tool we give a review of 11 predictive models: 10 models from the field of Machine Learning and, additionally, the Multiple Regression model. We present our results in terms of a 'prediction-interpretation competition'. All these models were tested in a multiple-stressor setting: the values of 15 stressors (or steering factors) are available to predict the EQR values of four biological quality elements (phytoplankton, other aquatic flora, benthic invertebrates and fish). Analyses are based on 29 data sets from various water clusters (streams, ditches, lakes, channels). All 11 models were ranked by their predictive performance and their level of model transparency. Our review shows a trade-off between these two aspects. Models that have the best EQR prediction performance show non-transparent model structures. These are Random Forest and Boosting. However, models with low prediction accuracies show transparent response relationships between EQRs on the one hand and individual steering factors on the other hand. These models are Multiple Regression, Regression Trees and Product Unit Neural Networks. To acknowledge both aspects of model quality - predictive power and transparency - we recommend that models from both groups are implemented in the WFD Explorer software.

How diatom-, invertebrate- and fish-based diagnostic tools can support the ecological assessment of rivers in a multi-pressure context: Temporal trends over the past two decades in France

The degradation of aquatic ecosystems, induced by worldwide intensification in the use of both land and aquatic resources, has highlighted the critical need for innovative methods allowing an objective quantification and ranking of anthropogenic pressure effects on aquatic organisms. Such diagnostic tools have a great potential for defining robust management responses to anthropogenic pressures. Our objective was to explore how the outputs of three diagnostic tools (based on benthic diatoms, macroinvertebrates and fishes) could be combined to (i) disentangle the temporal effects of multiple pressures over two decades and (ii) provide policy-relevant information for stream managers and decision makers. The diagnostic tools estimated, using taxonomy- and trait-based metrics, the impairment probabilities of biotic assemblages over time by different pressure categories, describing the alteration of water quality, hydromorphology and land use related to anthropogenic activities, in French streams (number of sites = 312). The main result shows that a large proportion of the time series exhibited no significant temporal patterns over the two decades (61.5% to 87.8%, depending on the used tests). Among time series exhibiting significant change, positive trends in impairment probabilities (i.e., degradation) were less frequent than negative ones, indicating a modest improvement in water quality at national scale over the study period. However, trends can be substantially different according to hydroecoregion and pressure category. The three biological compartments displayed convergent temporal responses according to the pressure category and regional context (e.g., lowland plains vs. mountains, pristine vs. agricultural regions). Altogether, this study proposes a unifying approach to integrate a vast amount of information in a single ecological diagnosis using an unparalleled database on natural and anthropized environments. Strengthening the synthesis of biological information provided by various biological compartments should be a priority before implementing evidence-based sustainable conservation and restoration actions.

Future trends of dissolved inorganic nitrogen concentrations in Northwestern Mediterranean coastal waters under climate change

Coastal ecosystems are amongst the most vulnerable to climate change, due to their location at the land-sea interface. In coastal waters, the nitrogen cycle can be significantly altered by rising temperatures and other factors derived from climate change, affecting phytoplankton and higher trophic levels. This research analyzes the effect of meteorological variables on dissolved inorganic nitrogen (DIN) species in coastal inshore waters of a Northwestern Mediterranean region under climate change. We built simple mathematical schemes based on artificial neural networks (ANN), trained with field data. Then, we used regional climatic projections for the Spanish Mediterranean coast to provide inputs to the trained ANNs, and thus, allowing the estimation of future DIN trends throughout the 21st century. The results obtained indicate that nitrite and nitrate concentrations are expected to decrease mainly due to rising temperatures and decreasing continental inputs. Major changes are projected for the winter season, driven by a rise in minimum temperatures which decrease the nitrite and nitrate peaks observed at low temperatures. Ammonium concentrations are not expected to undergo a significant annual trend but may either increase or decrease during some months. These results entail a preliminary simplified approach to estimate the impact of meteorological changes on DIN concentrations in coastal waters under climate change.

A machine learning model to assess the ecosystem response to water policy measures in the Tagus River Basin (Spain)

Anthropogenic activities are seriously endangering the conservation of biodiversity worldwide, calling for urgent actions to mitigate their impact on ecosystems. We applied machine learning techniques to predict the response of freshwater ecosystems to multiple anthropogenic pressures, with the goal of informing the definition of water policy targets and management measures to recover and protect aquatic biodiversity. Random Forest and Gradient Boosted Regression Trees algorithms were used for the modelling of the biological indices of macroinvertebrates and diatoms in the Tagus river basin (Spain). Among the anthropogenic stressors considered as explanatory variables, the categories of land cover in the upstream catchment area and the nutrient concentrations showed the highest impact on biological communities. The model was then used to predict the biological response to different nutrient concentrations in river water, with the goal of exploring the effect of different regulatory thresholds on the ecosystem status. Specifically, we considered the maximum nutrient concentrations set by the Spanish legislation, as well as by the legislation of other European Union Member States. According to our model, the current nutrient thresholds in Spain ensure values of biological indices consistent with the good ecological status in only about 60% of the total number of water bodies. By applying more restrictive nutrient concentrations, the number of water bodies with biological indices in good status could increase by almost 40%. Moreover, coupling more restrictive nutrient thresholds with measures that improve the riparian habitat yields up to 85% of water bodies with biological indices in good status, thus proving to be a key approach to restore the status of the ecosystem.

Hierarchical response of littoral macroinvertebrates to altered hydromorphology and eutrophication

The composition of littoral macroinvertebrate communities in lakes is governed by multiple natural and anthropogenic environmental influences interacting at different spatial scales. Since ecological assessment methods using littoral macroinvertebrates should respond specifically to a single stressor, knowledge on the unique effects of a given stressor is necessary. To effectively disentangle the effects of hydromorphology and trophic state requires analysing macroinvertebrate communities at lake sites with the full range of both stressors. We used a dataset of 98 lakes encompassing the entire gradient of geographical locations, lake types, hydromorphological degradation and trophic states in Central European lakes. We studied the unique and joint effects of hydromorphology and trophic state on macroinvertebrate richness, community composition and the Littoral Invertebrate Multimetric Index based on Composite Sampling (LIMCO). Variation partitioning analyses were conducted to test the importance of hydromorphology relative to trophic state across and within hydromorphological states (natural shorelines, hard and soft shore modifications) and trophic states (oligotrophic to hypertrophic states). At natural, hard and soft modification sites, hydromorphology explained 10, 16 and 19%, respectively, of the average unique variation of diversity, community composition and the LIMCO index, whereas trophic state explained on average 2, 5 and 5%, respectively. Similarly, in low, medium and high trophic state lakes, hydromorphology explained 10, 15 and 7%, respectively, of the average unique variation of diversity, community composition and the LIMCO index, whereas trophic state explained on average 0.3, 3 and 6%, respectively. Our results demonstrate that littoral hydromorphology was a more important driver of macroinvertebrate diversity, community composition and LIMCO than trophic state across hydromorphological states and trophic states. This indicates that multiple stressors in lakes act hierarchically on littoral macroinvertebrate communities and that the hydromorphological degradation of littoral zones is the primary driver for altered communities.

Modeling the Ecological Impact of Phosphorus in Catchments with Multiple Environmental Stressors

The broken phosphorus (P) cycle has led to widespread eutrophication of freshwaters. Despite reductions in anthropogenic nutrient inputs that have led to improvement in the chemical status of running waters, corresponding improvements in their ecological status are often not observed. We tested a novel combination of complementary statistical modeling approaches, including random-effect regression trees and compositional and ordinary linear mixed models, to examine the potential reasons for this disparity, using low-frequency regulatory data available to catchment managers. A benthic Trophic Diatom Index (TDI) was linked to potential stressors, including nutrient concentrations, soluble reactive P (SRP) loads from different sources, land cover, and catchment hydrological characteristics. Modeling suggested that SRP, traditionally considered the bioavailable component, may not be the best indicator of ecological impacts of P, as shown by a stronger and spatially more variable negative relationship between total P (TP) concentrations and TDI. Nitrate-N ( < 0.001) and TP ( = 0.002) also showed negative relationship with TDI in models where land cover was not included. Land cover had the strongest influence on the ecological response. The positive effect of seminatural land cover ( < 0.001) and negative effect of urban land cover ( = 0.030) may be related to differentiated bioavailability of P fractions in catchments with different characteristics (e.g., P loads from point vs. diffuse sources) as well as resilience factors such as hydro-morphology and habitat condition, supporting the need for further research into factors affecting this stressor-response relationship in different catchment types. Advanced statistical modeling indicated that to achieve desired ecological status, future catchment-specific mitigation should target P impacts alongside multiple stressors.

Protecting and restoring Europe's waters: An analysis of the future development needs of the Water Framework Directive

The Water Framework Directive (WFD) is a pioneering piece of legislation that aims to protect and enhance aquatic ecosystems and promote sustainable water use across Europe. There is growing concern that the objective of good status, or higher, in all EU waters by 2027 is a long way from being achieved in many countries. Through questionnaire analysis of almost 100 experts, we provide recommendations to enhance WFD monitoring and assessment systems, improve programmes of measures and further integrate with other sectoral policies. Our analysis highlights that there is great potential to enhance assessment schemes through strategic design of monitoring networks and innovation, such as earth observation. New diagnostic tools that use existing WFD monitoring data, but incorporate novel statistical and trait-based approaches could be used more widely to diagnose the cause of deterioration under conditions of multiple pressures and deliver a hierarchy of solutions for more evidence-driven decisions in river basin management. There is also a growing recognition that measures undertaken in river basin management should deliver multiple benefits across sectors, such as reduced flood risk, and there needs to be robust demonstration studies that evaluate these. Continued efforts in 'mainstreaming' water policy into other policy sectors is clearly needed to deliver wider success with WFD goals, particularly with agricultural policy. Other key policy areas where a need for stronger integration with water policy was recognised included urban planning (waste water treatment), flooding, climate and energy (hydropower). Having a deadline for attaining the policy objective of good status is important, but even more essential is to have a permanent framework for river basin management that addresses the delays in implementation of measures. This requires a long-term perspective, far beyond the current deadline of 2027.