Scott P. Sowa

Joint analysis of stressors and ecosystem services to enhance restoration effectiveness

With increasing pressure placed on natural systems by growing human populations, both scientists and resource managers need a better understanding of the relationships between cumulative stress from human activities and valued ecosystem services. Societies often seek to mitigate threats to these services through large-scale, costly restoration projects, such as the over one billion dollar Great Lakes Restoration Initiative currently underway. To help inform these efforts, we merged high-resolution spatial analyses of environmental stressors with mapping of ecosystem services for all five Great Lakes. Cumulative ecosystem stress is highest in near-shore habitats, but also extends offshore in Lakes Erie, Ontario, and Michigan. Variation in cumulative stress is driven largely by spatial concordance among multiple stressors, indicating the importance of considering all stressors when planning restoration activities. In addition, highly stressed areas reflect numerous different combinations of stressors rather than a single suite of problems, suggesting that a detailed understanding of the stressors needing alleviation could improve restoration planning. We also find that many important areas for fisheries and recreation are subject to high stress, indicating that ecosystem degradation could be threatening key services. Current restoration efforts have targeted high-stress sites almost exclusively, but generally without knowledge of the full range of stressors affecting these locations or differences among sites in service provisioning. Our results demonstrate that joint spatial analysis of stressors and ecosystem services can provide a critical foundation for maximizing social and ecological benefits from restoration investments.

Modeling the effects of conservation practices on stream health.

Anthropogenic activities such as agricultural practices can have large effects on the ecological components and overall health of stream ecosystems. Therefore, having a better understanding of those effects and relationships allows for better design of mitigating strategies. The objectives of this study were to identify influential stream variables that correlate with macroinvertebrate indices using biophysical and statistical models. The models developed were later used to evaluate the impact of three agricultural management practices on stream integrity. Our study began with the development of a high-resolution watershed model for the Saginaw River watershed in Michigan for generating in-stream water quality and quantity data at stream reaches with biological sampling data. These in-stream data were then used to explain macroinvertebrate measures of stream health including family index of biological integrity (FamilyIBI), Hilsenhoff biotic index (HBI), and the number of Ephemeroptera, Plecoptera , and Trichoptera taxa (EPTtaxa). Two methods (stepwise linear regression and adaptive neuro-fuzzy inference systems (ANFIS)) were evaluated for developing predictive models for macroinvertebrate measures. The ANFIS method performed the best on average and the final models displayed the highest R(2) and lowest mean squared error (MSE) for FamilyIBI (R(2)=0.50, MSE=29.80), HBI (R(2)=0.57, MSE=0.20), and EPTtaxa (R(2)=0.54, MSE=6.60). Results suggest that nutrient concentrations have the strongest influence on all three macroinvertebrate measures. Consistently, average annual organic nitrogen showed the most significant association with EPTtaxa and HBI. Meanwhile, the best model for FamilyIBI included average annual ammonium and average seasonal organic phosphorus. The ANFIS models were then used in conjunction with the Soil and Water Assessment Tool to forecast and assess the potential effects of different best management practices (no-till, residual management, and native grass) on stream integrity. Based on the model predictions, native grass resulted in the largest improvement for all macroinvertebrate measures.

Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios.

Complex watershed simulation models are powerful tools that can help scientists and policy-makers address challenging topics, such as land use management and water security. In the Western Lake Erie Basin (WLEB), complex hydrological models have been applied at various scales to help describe relationships between land use and water, nutrient, and sediment dynamics. This manuscript evaluated the capacity of the current Soil and Water Assessment Tool (SWAT) to predict hydrological and water quality processes within WLEB at the finest resolution watershed boundary unit (NHDPlus) along with the current conditions and conservation scenarios. The process based SWAT model was capable of the fine-scale computation and complex routing used in this project, as indicated by measured data at five gaging stations. The level of detail required for fine-scale spatial simulation made the use of both hard and soft data necessary in model calibration, alongside other model adaptations. Limitations to the models predictive capacity were due to a paucity of data in the region at the NHDPlus scale rather than due to SWAT functionality. Results of treatment scenarios demonstrate variable effects of structural practices and nutrient management on sediment and nutrient loss dynamics. Targeting treatment to acres with critical outstanding conservation needs provides the largest return on investment in terms of nutrient loss reduction per dollar spent, relative to treating acres with lower inherent nutrient loss vulnerabilities. Importantly, this research raises considerations about use of models to guide land management decisions at very fine spatial scales. Decision makers using these results should be aware of data limitations that hinder fine-scale model interpretation.

ENVIRONMENTAL REVIEWS AND CASE STUDIES: “Make No Little Plans”: Developing Biodiversity Conservation Strategies for the Great Lakes

The Laurentian Great Lakes represent the worlds largest freshwater ecosystem and contain irreplaceable biodiversity. Lakewide Action and Management Plans (LAMPs) hold the highest potential for ecosystem management in the Great Lakes but have not specifically addressed biodiversity status or strategies for conservation. For four Great Lakes, recently completed biodiversity conservation strategies (blueprints) have assessed the status and threats to biodiversity and recommended strategies for conservation and restoration; a blueprint is under way also for Lake Superior. Here, we compare the completed blueprints and explore challenges to conservation planning for large ecosystems. We also assess whether earlier blueprints are being adopted and offer suggestions for more effective implementation. All of the blueprints focus on biodiversity in the lakes and coastal areas, and some include tributaries and migratory species. Biodiversity status was rated as fair (out of desirable range but restorable) in each lake, with some exceptions and considerable spatial variability. Aquatic invasive species ranked as a top threat to biodiversity in all four blueprints. Other highly ranked threats included incompatible development, climate change, terrestrial invasive species, dams and barriers, and non-point-source pollutants. The recommended strategies are characterized by six themes: coastal conservation, invasive species, connectivity and hydrology, fish restoration, nearshore water quality, and climate change. Each blueprint highlights high-priority strategies, but successful protection and restoration of Great Lakes biodiversity require revisiting these priorities in an adaptive approach.

Exploring relationships between in-stream conditions and ecological health under landuse and climate scenarios using a watershed model

Land use and other human disturbances have significant impacts on physicochemical and biological conditions of stream systems. A good understanding of the relationships among those factors will help aquatic resource managers to make wise decisions in protecting un-impacted systems and rehabilitate degraded systems. The objectives of this study were to employ watershed model results to obtain in-stream flow and water quality data and fill a critical gap in data collection. This data was then used to describe and estimate fish index of biological integrity (IBI) and the percent of intolerant fish individuals representing ecological health at un-sampled stream reaches within the Saginaw Bay basin. Three methods were used in connecting in-stream variables to fish measures including stepwise linear regression, partial least squares regression, and fuzzy logic. The model developed using fuzzy logic showed the best performance based on the highest R2 for IBI (R2 = 0.48) and for percent intolerant fish individuals (R2 = 0.21) and the lowest mean square error for IBI (MSE=268) and for percent intolerant fish (MSE=275). Overall, average annual flow rate had the strongest influence on IBI, whereas nutrient concentration (average annual organic phosphorus) showed the largest influence on the percentage of intolerant individuals. Based on the best model identified from the previous section, predictions were made for pre-settlement landuse and climate conditions. Results showed overall significantly higher IBI and percent intolerant individuals under pre-settlement landuse scenario. This implies that landuse change from pre-settlement to current has profound negative impacts on stream health within the study area. Our results also showed that including pre-settlement climate factors in our models is ultimately important because climate factors have strong influences on stream flow and water quality measures that interactively affect stream health as indicated by fish measures. These results suggest that efforts to model historic baseline habitat conditions and to provide context for stream health assessments should include both pre-settlement land use and climate conditions.