Effects of Watershed Characteristics on Stream Vulnerability to Urbanization: Implications of Future Land Use on Streams in Maine, USA

Catchment urbanization has deleterious effects on freshwater resources and aquatic communities in small stream ecosystems. In the State of Maine, many streams have been negatively affected by urbanization and are in need of management and restoration. Impervious cover (IC), i.e., any surface that impedes water infiltration into the ground, can serve as a measure of watershed urbanization. Recent studies conducted in Maine have indicated that stream biotic community structure and function begin to decline at impervious cover levels of approximately 1 to 15%. This wide range presents a challenge to regulatory agencies and watershed managers charged with protecting stream quality to avoid costly restoration efforts. In this research, we employed three statistical analyses to identify spatially-explicit watershed characteristics associated with climate, geology, and land use/land cover that affect stream vulnerability to urbanization. First, a Kruskal-Wallis one-way analysis of variance was used to discriminate watershed characteristics associated with macroinvertebrate and algal sample data classified into high and low vulnerability categories. Next, a logistic regression analysis was applied to predict attainment of stream regulatory standards based on macroinvertebrate and algal sample data combined with watershed biophysical parameters. Finally, a Bayesian network was developed to predict stream vulnerability to urbanization using an expert-informed model structure. Results from the three approaches identified a number of watershed parameters that are associated with the vulnerability of streams to impairment from urbanization stress. The Kruskal-Wallis analysis indicated that watersheds with higher amounts of well-draining soils, deeper water tables, and fewer wetlands are less likely to become impaired at a given value of IC. The logistic regression models provided evidence that watersheds with an intact riparian buffer, a shallow aquifer, soils resistant to erosion, few wetlands, and shallower soils are more likely to attain their regulatory standards and are thus less vulnerable to urbanization. The Bayesian network shared a number of similarities with the two statistical analyses in terms of important watershed parameters. Overall, results of the three analyses indicated that stream vulnerability tends to increase with a higher percentage of agriculture and wetlands in the watershed and to decrease with a higher percentage of forested or natural buffers and percent resistant surfaces in the watershed. The ultimate goal of this research was to identify specific streams that are at risk of becoming impaired by future development. This goal was achieved by integrating the results of the three-step vulnerability analysis with earlier work that created spatially-explicit development suitability indices for two major watersheds in Maine. Areas likely to face future degradation were identified as watersheds in the top quartile of vulnerability that coincide with areas highly suitable for development are likely to face future degradation. We highlighted the locations of these “at-risk” streams and provided resource managers and policy makes with a tool that can be used to prioritize and guide the protection of vulnerable streams in the Maine landscape

Predicting water quality and ecological responses

Abstract Changes to climate are predicted to have effects on freshwater streams. Stream flows are likely to change, with implications for freshwater ecosystems and water quality. Other stressors such as population growth, community preferences and management policies can be expected to interact in various ways with climate change and stream flows, and outcomes for freshwater ecosystems and water quality are uncertain. Managers of freshwater ecosystems and water supplies could benefit from being able to predict the scales of likely changes. This project has developed and applied a linked modelling framework to assess climate change impacts on water quality regimes and ecological responses. The framework is designed to inform water planning and climate adaptation activities. It integrates quantitative tools, and predicts relationships between future climate, human activities, water quality and ecology, thereby filling a gap left by the considerable research effort so far invested in predicting stream flows. The modelling framework allows managers to explore potential changes in the water quality and ecology of freshwater systems in response to plausible scenarios for climate change and management adaptations. Although set up for the Upper Murrumbidgee River catchment in southern NSW and ACT, the framework was planned to be transferable to other regions where suitable data are available. The approach and learning from the project appear to have the potential to be broadly applicable. We selected six climate scenarios representing minor, moderate and major changes in flow characteristics for 1oC and 2oC temperature increases. These were combined with four plausible alternative management adaptations that might be used to modify water supply, urban water demand and stream flow regimes in the Upper Murrumbidgee catchment. The Bayesian Network (BN) model structure we used was developed using both a ‘top down’ and ‘bottom up’ approach. From analyses combined with expert advice, we identified the causal structure linking climate variables to stream flow, water quality attributes, land management and ecological responses (top down). The ‘bottom up’ approach focused on key ecological outcomes and key drivers, and helped produce efficient models. The result was six models for macroinvertebrates, and one for fish. In the macroinvertebrate BN models, nodes were discretised using statistical/empirical derived thresholds using new techniques. The framework made it possible to explore how ecological communities respond to changes in climate and management activities. Particularly, we focused on the effects of water quality and quantity on ecological responses. The models showed a strong regional response reflecting differences across 18 regions in the catchment. In two regions the management alternatives were predicted to have stronger effects than climate change. In three other regions the predicted response to climate change was stronger. Analyses of water quality suggested minor changes in the probability of water quality exceeding thresholds designed to protect aquatic ecosystems. The ‘bottom up’ approach limited the framework’s transferability by being specific to the Upper Murrumbidgee catchment data. Indeed, to meet stakeholder questions models need to be specifically tailored. Therefore the report proposes a general model-building framework for transferring the approach, rather than the models, to other regions.  Please cite this report as: Dyer, F, El Sawah, S, Lucena-Moya, P, Harrison, E, Croke, B, Tschierschke, A, Griffiths, R, Brawata, R, Kath, J, Reynoldson, T, Jakeman, T 2013 Predicting water quality and ecological responses, National Climate Change Adaptation Research Facility, Gold Coast, pp. 110 Changes to climate are predicted to have effects on freshwater streams. Stream flows are likely to change, with implications for freshwater ecosystems and water quality. Other stressors such as population growth, community preferences and management policies can be expected to interact in various ways with climate change and stream flows, and outcomes for freshwater ecosystems and water quality are uncertain. Managers of freshwater ecosystems and water supplies could benefit from being able to predict the scales of likely changes. This project has developed and applied a linked modelling framework to assess climate change impacts on water quality regimes and ecological responses. The framework is designed to inform water planning and climate adaptation activities. It integrates quantitative tools, and predicts relationships between future climate, human activities, water quality and ecology, thereby filling a gap left by the considerable research effort so far invested in predicting stream flows. The modelling framework allows managers to explore potential changes in the water quality and ecology of freshwater systems in response to plausible scenarios for climate change and management adaptations. Although set up for the Upper Murrumbidgee River catchment in southern NSW and ACT, the framework was planned to be transferable to other regions where suitable data are available. The approach and learning from the project appear to have the potential to be broadly applicable. We selected six climate scenarios representing minor, moderate and major changes in flow characteristics for 1oC and 2oC temperature increases. These were combined with four plausible alternative management adaptations that might be used to modify water supply, urban water demand and stream flow regimes in the Upper Murrumbidgee catchment. The Bayesian Network (BN) model structure we used was developed using both a ‘top down’ and ‘bottom up’ approach. From analyses combined with expert advice, we identified the causal structure linking climate variables to stream flow, water quality attributes, land management and ecological responses (top down). The ‘bottom up’ approach focused on key ecological outcomes and key drivers, and helped produce efficient models. The result was six models for macroinvertebrates, and one for fish. In the macroinvertebrate BN models, nodes were discretised using statistical/empirical derived thresholds using new techniques. The framework made it possible to explore how ecological communities respond to changes in climate and management activities. Particularly, we focused on the effects of water quality and quantity on ecological responses. The models showed a strong regional response reflecting differences across 18 regions in the catchment. In two regions the management alternatives were predicted to have stronger effects than climate change. In three other regions the predicted response to climate change was stronger. Analyses of water quality suggested minor changes in the probability of water quality exceeding thresholds designed to protect aquatic ecosystems. The ‘bottom up’ approach limited the framework’s transferability by being specific to the Upper Murrumbidgee catchment data. Indeed, to meet stakeholder questions models need to be specifically tailored. Therefore the report proposes a general model-building framework for transferring the approach, rather than the models, to other regions.&nbsp

A Scientometric Analysis of the Use Indices for Water Quality Biomonitoring

Water resources are supplied under strong anthropic pressures and result in several problems of water contamination in the world, which necessitates the use of methodologies for their evaluation. Thus, this research aimed to identify the biomonitoring indices based on macroinvertebrates in the evaluation of the quality of the water used and to identify its application trends. The study was developed through a scientometric review over a 20-year period (2000–2020). The search consisted of articles indexed in the Scielo, ScienceDirect, and Scopus databases, based on the keywords “biotic index” * OR “aquatic macroinvertebrates” * OR “benthic macroinvertebrates” * OR “Biomonitoring” * AND “water quality.” Selection, such as inclusion and exclusion, was applied in the Start program (State of the Art through Systematic Review-Start). The results showed that the EPT index (Ephemeroptera, Plecoptera, and Trichoptera) was the most used among all researched databases (20%), and among the Ecological Indicators journals from which the largest number of publications were obtained (11%). Regarding the indices used in biomonitoring research, the ASPT index (average score per taxon) was the only one with a tendency to increase in use over the years (R² = 0.29; p < 0.05). Although the biomonitoring indices are commonly used worldwide denoting that it is still an alternative tool, this literature review showed that among the indices only one has a trend of use, which must be considered for further research.Os recursos hídricos são abastecidos sob forte pressão antrópica e dão origem a diversos problemas de poluição hídrica no mundo, que exigem o uso de metodologias para sua avaliação. Assim, esta pesquisa teve como objetivo identificar índices de biomonitoramento baseados em macroinvertebrados na avaliação da qualidade da água utilizada e identificar suas tendências de aplicação. O estudo foi desenvolvido através de uma revisão cienciométrica durante um período de 20 anos (2000-2020). A busca consiste em artigos indexados nas bases de dados Scielo, ScienceDirect e Scopus, com base nas palavras-chave "índice biótico" * OR "macroinvertebrados aquáticos" * OR "macroinvertebrados bentônicos" * OR "Biomonitoring" * AND "qualidade da água". A seleção, assim como a inclusão e a exclusão, foi aplicada no programa Start (estado da arte por meio de revisão sistemática-start). Os resultados mostraram que o índice EPT (Ephemeroptera, Plecoptera e Trichoptera) foi o mais utilizado entre todas as bases investigadas (20%) e entre os periódicos, obteve-se o maior número de Indicadores Ecológicos (11%). Em relação aos índices utilizados nas pesquisas de biomonitoramento, o índice ASPT (pontuação média por táxon) foi o único com tendência a aumentar sua utilização ao longo dos anos (R² = 0,29; p < 0,05). Embora os índices de biomonitoramento sejam comumente usados ​​em todo o mundo, indicando que ainda é uma ferramenta alternativa, esta revisão de literatura mostra que dentre os índices apenas um apresenta tendência de uso, o que deve ser considerado para pesquisas futuras

Incorporating Human Effects in Quantifying Mechanisms of Stream Fish Community Structure Using Metacommunity Theory

Metacommunity theory incorporates local and regional factors to understand how biotic communities are structured across the landscape. Despite established knowledge of how humans impact aquatic systems, inclusion of anthropogenic factors in metacommunity studies have been largely ignored. Additionally, alpha, beta, and gamma diversity can all be explored at the metacommunity level to investigate mechanistic drivers of community structure. Beta diversity can be further partitioned into turnover and richness difference components, each with different mechanistic drivers. Streams provide an excellent study system for metacommunity research because of the dendritic structure of watersheds and the natural delineation that watershed boundaries provide. Large-extent datasets provide the ability to create multiple metacommunities serving as replicates for robust statistical analyses. As such, the overall goal of this dissertation was to use large datasets of stream fish community structure to investigate how anthropogenic variables affect stream fish beta diversity and metacommunity structure in conjunction with ‘traditionally investigated’ factors including natural landscape features and spatial distance among communities. This research uses two large extent datasets. The first covers 13 states on the eastern coast of the United States, and the second covers 350 sites throughout South Carolina. Three different approaches were taken to understand the factors affecting stream fish metacommunities across the landscape. First, we created a spatial scale continuum using nested watersheds identified by hydrologic unit codes (HUCs) to explore how beta diversity and its components change over three spatial scales, and (a) how land use, (b) climatic, and (c) anthropogenic factors affect beta diversity within and between spatial scales. We found increasing beta diversity with increasing spatial scale, and equal contribution between turnover and richness difference components. All three factors were related to beta diversity or its components depending on the spatial scale, but few scale-dependent relationships were found. These results suggest that while a diversity of factors affect beta diversity at a given spatial scale their effects on beta diversity do not change across spatial scales. These effects may be scale-invariant, although other cross-scale effects may arise at finer spatial scales. Second, we investigated how environmental and anthropogenic factors and aspects of study design affect coherence, turnover, and boundary clumping—the elements of metacommunity structure (EMS) at a single spatial scale. The EMS were affected by temperature, density of dams, and percentage of developed land, but also gamma diversity and number of sites sampled in a metacommunity. These results suggest that anthropogenic factors affect the elements of metacommunity structure and thus set the context for assigning metacommunities into archetypical processes across the landscape. Moreover, the EMS were affected by study design aspects such as the number of communities sampled and the distance between them within metacommunities. These results have important implications for both existing and future studies because they show that inference on spatial processes is contextualized by aspects of datasets that are inherent to datasets and are rarely considered in analyses. Including these factors in future analyses will allow researchers to better focus on the signal of key processes by accounting for the variability caused by aspects of study design. Third, we used variation partitioning to parse out the relative effects of anthropogenic, natural, and spatial factors on beta diversity as measured in three key dimensions: taxonomic, functional, and phylogenetic. These analyses were done at three spatial delineations representing artificial, geomorphic, and natural watershed metacommunities. These are commonly used spatial delineations in metacommunity analyses, but are rarely included in the same study. We explained 25-81% of beta diversity where different spatial, natural, and anthropogenic factors structured these metacommunities depending on the spatial delineation and diversity dimension. Geomorphic metacommunities had very different results compared to other spatial delineations suggesting that accounting for geomorphic differences leads to stronger anthropogenic signals. By conducting this work in different spatial delineations within the same dataset, we show for the first time that defining metacommunities has bearing on results of analyses—an issue that is rarely considered in metacommunity studies. Overall, this body of work suggests that anthropogenic factors have pervasive effects on stream fish beta diversity and metacommunity structure across the landscape—an aspect of metacommunity ecology that has until recently been largely ignored. This work suggests that considering anthropogenic effects in metacommunity studies will improve inference. Researchers must also consider important aspects of study design, including how metacommunities are defined and delineated, as well as how intensely and densely communities are sampled within those metacommunities. In all, this dissertation adds an important practical dimension to the field of metacommunity ecology, which up to this point has been largely theoretical. Considering these practicalities may improve our overall understanding of metacommunities in a variety of taxa and systems

A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates

Riparian forest buffers have multiple benefits for biodiversity and ecosystem services in both freshwater and terrestrial habitats but are rarely implemented in water ecosystem management, partly reflecting the lack of information on the effectiveness of this measure. In this context, social learning is valuable to inform stakeholders of the efficacy of riparian vegetation in mitigating stream degradation. We aim to develop a Bayesian belief network (BBN) model for application as a learning tool to simulate and assess the reach-and segment-scale effects of riparian vegetation properties and land use on instream invertebrates. We surveyed reach-scale riparian conditions, extracted segment-scale riparian and subcatchment land use information from geographic information system data, and collected macroinvertebrate samples from four catchments in Europe (Belgium, Norway, Romania, and Sweden). We modelled the ecological condition based on the Average Score Per Taxon (ASPT) index, a macroinvertebrate-based index widely used in European bioassessment, as a function of different riparian variables using the BBN modelling approach. The results of the model simulations provided insights into the usefulness of riparian vegetation attributes in enhancing the ecological condition, with reach-scale riparian vegetation quality associated with the strongest improvements in ecological status. Specifically, reach-scale buffer vegetation of score 3 (i.e. moderate quality) generally results in the highest probability of a good ASPT score (99-100%). In contrast, a site with a narrow width of riparian trees and a small area of trees with reach-scale buffer vegetation of score 1 (i.e. low quality) predicts a high probability of a bad ASPT score (74%). The strengths of the BBN model are the ease of interpretation, fast simulation, ability to explicitly indicate uncertainty in model outcomes, and interactivity. These merits point to the potential use of the BBN model in workshop activities to stimulate key learning processes that help inform the management of riparian zones

Mining and residential development interact to produce highly impaired stream conditions in an intensively mined Appalachian watershed

Large scale surface mining in southern West Virginia causes significant alteration of headwater stream networks. It is unclear, however, the extent to which mining interacts with other stressors to determine physical, chemical, and biological conditions in aquatic systems downstream. Through a watershed scale assessment of Pigeon Creek, the specific objectives of this study were to: (1) quantify the direct and interactive effects of mining and residential development on in-stream conditions; and (2) identify landscape thresholds above which biological impairment occurs. Our results indicate high levels of impairment to habitat, water quality, and benthic invertebrate communities within this watershed. Statistical analyses indicate that degraded conditions were linked to both mining and residential development; however, residential development appeared to exhibit a stronger individual effect. Both mining and residential development resulted in a significant decrease in sensitive taxa. The impacts associated with residential development, however, also resulted in the proliferation of tolerant taxa. Both mining and residential development resulted in significant alterations to water chemistry, primarily through increases in dissolved ion concentrations and specific conductance. Changes in water quality resulting from mining, however, were more acute. Conversely, residential development resulted in more acute alterations to physical habitat, primarily through decreases in habitat complexity. Our results further suggest that the individual impacts associated with mining and residential development are additive, leading to highly degraded conditions downstream. The combined effects of mining and residential development were almost always worse than the individual effects of mining, but never worse than the individual effects of residential development. Thus, residential development appears to be the limiting factor in determining ecosystem impairment. Lastly, several community metrics exhibited potential threshold responses to relatively low levels of both total mining (∼25%) and parcel density (∼14 parcels/km 2). These change points corresponded to conductivities of approximately 100 uS/cm and 60 uS/cm, respectively. This study shows that effectively managing impacts from new mine development and watershed restoration efforts must address the prevalence of non-mining related impacts throughout this watershed

Predicting the ecological status of rivers and streams under different climatic and socioeconomic scenarios using Bayesian Belief Networks

Freshwater systems have increasingly been subjected to a multitude of human pressures and the re-establishment of their ecological integrity is currently a major worldwide challenge. Expected future climate and socioeconomic changes will most probably further exacerbate such challenges. Modelling techniques may provide useful tools to help facing these demands, but their use is still limited within ecological quality assessment of water resources due to its technical complexity. We developed a Bayesian Belief Network (BBN) framework for modelling the ecological quality of rivers and streams in two European river basins located in two distinct European climatic regions: the Odense Fjord basin (Denmark) and the Sorraia basin (Portugal). This method enabled us to integrate different data sources into a single framework to model the effect of multiple stressors on several biological indicators of river water quality and, subsequently, on their ecological status. The BBN provided a simple interactive user interface with which we simulated combined climate and socioeconomic changes scenarios to assess their impacts on river ecological status. According to the resulting BBNs the scenarios demonstrated small impacts of climate and socioeconomic changes on the biological quality elements analysed. This yield a final ecological status similar to the baseline in the Odense case, and slightly worse in Sorraia. Since the present situation already depicts a high percentage of rivers and streams with moderate or worse ecological status in both basins, this means that many of them would not fulfil the Water Framework Directive target in the future. Results also showed that macrophytes and fish indices were mainly responsible for a non-desirable overall ecological status in Odense and Sorraia, respectively. The approach followed in this study is novel, since BBN modelling is used for the first time for assessing the ecological status of rivers and streams under future scenarios, using an ensemble of biological quality elements. An important advantage of this tool is that it may easily be updated with new knowledge on the nature of relationships already established in the BBN or even by introducing new causal links. By encompassing two case studies of very different characteristics, these BBN may be more easily adapted as decision-making tools for water management of other river basinsinfo:eu-repo/semantics/publishedVersio

Organic Matter Sources, Composition, and Quality in Rivers and Experimental Streams

Organic matter (OM) is often considered the “currency” for ecosystem processes, such as respiration and primary production. OM in aquatic ecosystems is derived from multiple sources, and is a complex mixture of thousands of different chemical constituents. Therefore, it is difficult to identify all the sources of OM that enter and exit aquatic ecosystems. As humans develop undisturbed land, the rate at which terrestrial OM (e.g.soil and plants) and associated nutrients (e.g.nitrogen) enters rivers has increased. Increased nutrients may lead to increased primary production from aquatic plants and algae, potentially causing eutrophication and harmful algal blooms. In this study, I identified and characterized different sources of OM in four watersheds of Northeastern Utah with multiple land covers such as cities, forests, and crops. I expected OM in watersheds with human-altered land cover would have more OM produced instream by algae and other primary producers, than OM in less disturbed watersheds, which typically have OM from terrestrial sources. I found that OM at river sites with high human impact had high amounts of OM from instream primary production, but there was also OM produced in-steam at sites with low human impact. The greatest differences in OM across watersheds was due to wastewater treatment effluent. I also measured microbial consumption rates of algal derived and terrestrially derived DOM in experimental streams to quantify how much faster algal derived OM was consumed than terrestrial OM. I found algal derived OM was consumed extremely fast, so fast that realistic measurements of its consumption in some river ecosystems may not be possible. It is important to identify and characterize sources of OM to rivers, so watershed manager scan devise effective OM reduction plans appropriate for the constituent of concern unique to that watershed or region. Constituents of concern associated with OM include pathogens affiliated with manure, toxins in harmful algal blooms, metals, and pharmaceuticals from wastewater treatment effluent. Each pollutant requires a unique mitigation strategy and therefore the first step to pollution mitigation is source identification