Assessing and managing freshwater ecosystems vulnerable to environmental change

Freshwater ecosystems are important for global biodiversity and provide essential ecosystem services. There is consensus in the scientific literature that freshwater ecosystems are vulnerable to the impacts of environmental change, which may trigger irreversible regime shifts upon which biodiversity and ecosystem services may be lost. There are profound uncertainties regarding the management and assessment of the vulnerability of freshwater ecosystems to environmental change. Quantitative approaches are needed to reduce this uncertainty. We describe available statistical and modeling approaches along with case studies that demonstrate how resilience theory can be applied to aid decision-making in natural resources management. We highlight especially how long-term monitoring efforts combined with ecological theory can provide a novel nexus between ecological impact assessment and management, and the quantification of systemic vulnerability and thus the resilience of ecosystems to environmental change.     

Towards a definition of climate science

The intrinsic difficulties in building realistic climate models and in providing complete, reliable and meaningful observational datasets, and the conceptual impossibility of testing theories against data imply that the usual Galilean scientific validation criteria do not apply to climate science. The different epistemology pertaining to climate science implies that its answers cannot be singular and deterministic; they must be plural and stated in probabilistic terms. Therefore, in order to extract meaningful estimates of future climate change from a model, it is necessary to explore the model's uncertainties. In terms of societal impacts of scientific knowledge, it is necessary to accept that any political choice in a matter involving complex systems is made under unavoidable conditions of uncertainty. Nevertheless, detailed probabilistic results in science can provide a baseline for a sensible process of decision-making.     

Conflicting demands and shifts between policy and intra-scientific orientation during conservation research programmes

Conservation scientists must meet the sometimes conflicting demands of policy and science, but not necessarily at the same time. We analysed the policy and intra-scientific orientations of research projects on effects of stump extraction on biodiversity, and found shifts over time associated with these demands. Our results indicate that uncertainties related to both factual issues and human decisions are often ignored in policy-oriented reports and syntheses, which could give misleading indications of the reliability or feasibility of any conclusions. The policy versus intra-scientific orientation of the scientific papers generated from the surveyed projects varied substantially, although we argue that in applied research, societal relevance is generally more important than intra-scientific relevance. To make conservation science more socially relevant, there is a need for giving societal relevance higher priority, paying attention to uncertainties and increasing the awareness of the value of cross-disciplinary research considering human decisions and values.     

Uncertainties and recommendations

An assessment of the impacts of changes in climate and UV-B radiation on Arctic terrestrial ecosystems, made within the Arctic Climate Impacts Assessment (ACIA), highlighted the profound implications of projected warming in particular for future ecosystem services, biodiversity and feedbacks to climate. However, although our current understanding of ecological processes and changes driven by climate and UV-B is strong in some geographical areas and in some disciplines, it is weak in others. Even though recently the strength of our predictions has increased dramatically with increased research effort in the Arctic and the introduction of new technologies, our current understanding is still constrained by various uncertainties. The assessment is based on a range of approaches that each have uncertainties, and on data sets that are often far from complete. Uncertainties arise from methodologies and conceptual frameworks, from unpredictable surprises, from lack of validation of models, and from the use of particular scenarios, rather than predictions, of future greenhouse gas emissions and climates. Recommendations to reduce the uncertainties are wide-ranging and relate to all disciplines within the assessment. However, a repeated theme is the critical importance of achieving an adequate spatial and long-term coverage of experiments, observations and monitoring of environmental changes and their impacts throughout the sparsely populated and remote region that is the Arctic.     

Integrated assessment: rhetoric of models and perceptions of world futures

Integrated assessment of climate change implies the construction of models describing the very complex interactions among economic processes, geochemical or geophysical phenomena and ecological processes at the Earth level. This attempt is unprecedented in the history of science. To build models of a unique, very large and extremely complex object, the whole Earth, supposes the definition of concepts allowing the comparison of these models through their structures and not only comparisons of their numerical results. The purpose of this paper is to give some preliminary elements of such a structural comparison.     

Development of risk-based air quality management strategies under impacts of climate change

Climate change is forecast to adversely affect air quality through perturbations in meteorological conditions, photochemical reactions, and precursor emissions. To protect the environment and human health from air pollution, there is an increasing recognition of the necessity of developing effective air quality management strategies under the impacts of climate change. This paper presents a framework for developing risk-based air quality management strategies that can help policy makers improve their decision-making processes in response to current and future climate change about 30-50 years from now. Development of air quality management strategies under the impacts of climate change is fundamentally a risk assessment and risk management process involving four steps: (1) assessment of the impacts of climate change and associated uncertainties; (2) determination of air quality targets; (3) selections of potential air quality management options; and (4) identification of preferred air quality management strategies that minimize control costs, maximize benefits, or limit the adverse effects of climate change on air quality when considering the scarcity of resources. The main challenge relates to the level of uncertainties associated with climate change forecasts and advancements in future control measures, since they will significantly affect the risk assessment results and development of effective air quality management plans. The concept presented in this paper can help decision makers make appropriate responses to climate change, since it provides an integrated approach for climate risk assessment and management when developing air quality management strategies. Implications: Development of climate-responsive air quality management strategies is fundamentally a risk assessment and risk management process. The risk assessment process includes quantification of climate change impacts on air quality and associated uncertainties. Risk management for air quality under the impacts of climate change includes determination of air quality targets, selections of potential management options, and identification of effective air quality management strategies through decision-making models. The risk-based decision-making framework can also be applied to develop climate-responsive management strategies for the other environmental dimensions and assess costs and benefits of future environmental management policies.     

An ecologically relevant exposure assessment for a polluted river using an integrated multivariate PLS approach

A case study is presented where an integrated, ecologically relevant exposure assessment is presented for a polluted lowland river. Using partial least squares regression of latent structures (PLS), an analysis of the impact of two effluents on physico-chemical water quality measures, macroinvertebrate and diatom communities, and in situ bioassay responses with four different test species are combined into an integrative exposure assessment. Bioassays focussed on growth and condition related endpoints, because they are key functional processes of organisms and populations. Integrating these multiple lines of evidence, we were able to discriminate among the impact of both effluents, link changes in physico-chemical water quality with bioassay endpoints and ecological quality of the ecosystem, and address the importance of integrating all information into one exposure assessment framework. The bioassays under field conditions indicated that most endpoints measured are a reflection of ecological effects rather than pollution related effects, or at least a combination of both ecological and toxicological effects. Factors such as food availability clearly influenced the outcome of in situ bioassays and ecological information was essential to explain observed discrepancies when trying to extrapolate bioassay results from the laboratory to the field.     

Uncertainties in a Baltic Sea Food-Web Model Reveal Challenges for Future Projections

Models that can project ecosystem dynamics under changing environmental conditions are in high demand. The application of such models, however, requires model validation together with analyses of model uncertainties, which are both often overlooked. We carried out a simplified model uncertainty and sensitivity analysis on an Ecopath with Ecosim food-web model of the Baltic Proper (BaltProWeb) and found the model sensitive to both variations in the input data of pre-identified key groups and environmental forcing. Model uncertainties grew particularly high in future climate change scenarios. For example, cod fishery recommendations that resulted in viable stocks in the original model failed after data uncertainties were introduced. In addition, addressing the trophic control dynamics produced by the food-web model proved as a useful tool for both model validation, and for studying the food-web function. These results indicate that presenting model uncertainties is necessary to alleviate ecological surprises in marine ecosystem management.     

Photochemical oxidants: state of the science

Atmospheric photochemical processes resulting in the production of tropospheric ozone (O(3)) and other oxidants are described. The spatial and temporal variabilities in the occurrence of surface level oxidants and their relationships to air pollution meteorology are discussed. Models of photooxidant formation are reviewed in the context of control strategies and comparisons are provided of the air concentrations of O(3) at select geographic locations around the world. This overall oxidant (O(3)) climatology is coupled to human health and ecological effects. The discussion of the effects includes both acute and chronic responses, mechanisms of action, human epidemiological and plant population studies and briefly, efforts to establish cause-effect relationships through numerical modeling. A short synopsis is provided of the interactive effects of O(3) with other abiotic and biotic factors. The overall emphasis of the paper is on identifying the current uncertainties and gaps in our understanding of the state of the science and some suggestions as to how they may be addressed.     

Climate change: potential impact on plant diseases

Global climate has changed since pre-industrial times. Atmospheric CO(2), a major greenhouse gas, has increased by nearly 30% and temperature has risen by 0.3 to 0.6 degrees C. The intergovernmental panel on climate change predicts that with the current emission scenario, global mean temperature would rise between 0.9 and 3.5 degrees C by the year 2100. There are, however, many uncertainties that influence these predictions. Despite the significance of weather on plant diseases, comprehensive analysis of how climate change will influence plant diseases that impact primary production in agricultural systems is presently unavailable. Evaluation of the limited literature in this area suggests that the most likely impact of climate change will be felt in three areas: in losses from plant diseases, in the efficacy of disease management strategies and in the geographical distribution of plant diseases. Climate change could have positive, negative or no impact on individual plant diseases. More research is needed to obtain base-line information on different disease systems. Most plant disease models use different climatic variables and operate at a different spatial and temporal scale than do the global climate models. Improvements in methodology are necessary to realistically assess disease impacts at a global scale.     

Opportunities for Increasing Resilience and Sustainability of Urban Social–Ecological Systems: Insights from the URBES and the Cities and Biodiversity Outlook Projects

Urban futures that are more resilient and sustainable require an integrated social–ecological system approach to urban policymaking, planning, management, and governance. In this article, we introduce the Urban Biodiversity and Ecosystem Services (URBES) and the Cities and Biodiversity Outlook (CBO) Projects as new social–ecological contributions to research and practice on emerging urban resilience and ecosystem services. We provide an overview of the projects and present global urbanization trends and their effects on ecosystems and biodiversity, as a context for new knowledge generated in the URBES case-study cities, including Berlin, New York, Rotterdam, Barcelona, and Stockholm. The cities represent contrasting urbanization trends and examples of emerging science–policy linkages for improving urban landscapes for human health and well-being. In addition, we highlight 10 key messages of the global CBO assessment as a knowledge platform for urban leaders to incorporate state-of-the-art science on URBES into decision-making for sustainable and resilient urban development.     

Future climate scenarios and rainfall--runoff modelling in the Upper Gallego catchment (Spain)

Global climate change may have large impacts on water supplies, drought or flood frequencies and magnitudes in local and regional hydrologic systems. Water authorities therefore rely on computer models for quantitative impact prediction. In this study we present kernel-based learning machine river flow models for the Upper Gallego catchment of the Ebro basin. Different learning machines were calibrated using daily gauge data. The models posed two major challenges: (1) estimation of the rainfall-runoff transfer function from the available time series is complicated by anthropogenic regulation and mountainous terrain and (2) the river flow model is weak when only climate data are used, but additional antecedent flow data seemed to lead to delayed peak flow estimation. These types of models, together with the presented downscaled climate scenarios, can be used for climate change impact assessment in the Gallego, which is important for the future management of the system.     

Impact of Climate Change on Fish Population Dynamics in the Baltic Sea: A Dynamical Downscaling Investigation

Understanding how climate change, exploitation and eutrophication will affect populations and ecosystems of the Baltic Sea can be facilitated with models which realistically combine these forcings into common frameworks. Here, we evaluate sensitivity of fish recruitment and population dynamics to past and future environmental forcings provided by three ocean-biogeochemical models of the Baltic Sea. Modeled temperature explained nearly as much variability in reproductive success of sprat (Sprattus sprattus; Clupeidae) as measured temperatures during 1973-2005, and both the spawner biomass and the temperature have influenced recruitment for at least 50 years. The three Baltic Sea models estimate relatively similar developments (increases) in biomass and fishery yield during twenty-first century climate change (ca. 28 % range among models). However, this uncertainty is exceeded by the one associated with the fish population model, and by the source of global climate data used by regional models. Knowledge of processes and biases could reduce these uncertainties.     

Hydrological change--climate change impact simulations for Sweden

Climate change resulting from the enhanced greenhouse effect is expected to give rise to changes in hydrological systems. This hydrological change, as with the change in climate variables, will vary regionally around the globe. Impact studies at local and regional scales are needed to assess how different regions will be affected. This study focuses on assessment of hydrological impacts of climate change over a wide range of Swedish basins. Different methods of transferring the signal of climate change from climate models to hydrological models were used. Several hydrological model simulations using regional climate model scenarios from Swedish Regional Climate Modelling Programme (SWECLIM) are presented. A principal conclusion is that subregional impacts to river flow vary considerably according to whether a basin is in northern or southern Sweden. Furthermore, projected hydrological change is just as dependent on the choice of the global climate model used for regional climate model boundary conditions as the choice of anthropogenic emissions scenario.     

Shaping more resilient and just food systems: Lessons from the COVID-19 Pandemic

The COVID-19 pandemic has highlighted weaknesses in global food systems, as well as opening windows of opportunity for innovation and transformation. While the nature and extent of this crisis is rare, extreme climatic events will increase in magnitude and frequency, threatening similar societal impacts. It is therefore critical to identify mechanisms for developing food systems that are resilient to such impacts. We examine impacts of the crisis on UK food systems and how these further entrenched social inequalities. We present data on the experiences and actions of producers, consumers, and community organisers. The data were collected by adapting ongoing research to include surveys, interviews and online workshops focused on the pandemic. Actors’ responses to the pandemic foreshadow how enduring change to food systems can be achieved. We identify support required to enable these transformations and argue that it is vital that these opportunities are embedded in food justice principles which promote people-centred approaches to avoid exacerbating injustices prevalent pre-crisis. Learning from these experiences therefore provides insights for how to make food systems elsewhere more resilient and just.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

Sulfur Control In Coal Fired Power Plants: A Probabilistic Approach to Policy Analysis

The optimum level of sulfur pollution control for a coal fired power plant is the point where the sum of societal costs, due to pollution, and control costs is minimized. This basic microeconomic concept has been of limited practical value due to considerable uncertainty in estimating both costs. A probabilistic approach is used to characterize these uncertainties quantitatively for a hypothetical 1000 Mw_e plant located near Pittsburgh, Pennsylvania. Only mortality effects within a distance of 80 km of the plant have been included. The results allow explicit consideration of attitude toward risk and appropriate level of investment to prevent deaths. Limitations of the findings are discussed. Implications are described for policy based on alternative sets of values and assumptions.     

Global change and the boreal forest: thresholds, shifting states or gradual change?

Changes in boreal climate of the magnitude projected for the 21st century have always caused vegetation changes large enough to be societally important. However, the rates and patterns of vegetation change are difficult to predict. We review evidence suggesting that these vegetation changes may be gradual at the northern forest limit or where seed dispersal limits species distribution. However, forest composition may be quite resilient to climate change in the central portions of a species range until some threshold is surpassed. At this point, changes can be rapid and unexpected, often causing a switch to very different ecosystem types. Many of these triggers for change are amenable to management, suggesting that our choice of policies in the coming decades will substantially influence the ecological and societal consequences of current climatic change.     

A Reassessment of the Economic Effects of Ozone on U.S. Agriculture

The potential adverse effects of environmental change on agriculture have motivated considerable public research on this topic. Acid deposition, gaseous air pollutants, stratosphere ozone depletion and "green house" phenomena, individually and in combination, have been or are being evaluated in terms of effects on agricultural productivity. Assessments of the economic consequences of such effects have also been performed as input into the regulatory process. As with any applied bioeconomic analysis, the credibility of these economic assessments is dependent on the quality of the natural science and other data on the pollutant In question.

The ability of economists to assess the agricultural effects of one important pollutant, tropospheric ozone, has been Improved by the recently completed National Crop Loss Assessment Network (NCLAN). The structure, protocols and initial plant science findings of this U.S. Environmental Protection Agency program have been presented in this journal (see, for example, Heck et al).^1-2 In a related article,³ we reported the economic consequences of those preliminary ozone crop yield effects. Summary plant science findings have now been published.⁴

We provide here a more complete analysis of estimated benefits from reductions in troposphere ozone based on the final results of the NCLAN plant science research. In doing so, we concentrate on improvements in the modeling and underlying data which are reflected In this current assessment. While uncertainties still remain, these improvements should result in more defensible estimates of the magnitude of ozone’s effects on U.S. agriculture.