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.

Riverine threat indices to assess watershed condition and identify primary management capacity of agriculture natural resource management agencies.

Managers can improve conservation of lotic systems over large geographies if they have tools to assess total watershed conditions for individual stream segments and can identify segments where conservation practices are most likely to be successful (i.e., primary management capacity). The goal of this research was to develop a suite of threat indices to help agriculture resource management agencies select and prioritize watersheds across Missouri River basin in which to implement agriculture conservation practices. We quantified watershed percentages or densities of 17 threat metrics that represent major sources of ecological stress to stream communities into five threat indices: agriculture, urban, point-source pollution, infrastructure, and all non-agriculture threats. We identified stream segments where agriculture management agencies had primary management capacity. Agriculture watershed condition differed by ecoregion and considerable local variation was observed among stream segments in ecoregions of high agriculture threats. Stream segments with high non-agriculture threats were most concentrated near urban areas, but showed high local variability. 60 % of stream segments in the basin were classified as under U.S. Department of Agriculture's Natural Resources Conservation Service (NRCS) primary management capacity and most segments were in regions of high agricultural threats. NRCS primary management capacity was locally variable which highlights the importance of assessing total watershed condition for multiple threats. Our threat indices can be used by agriculture resource management agencies to prioritize conservation actions and investments based on: (a) relative severity of all threats, (b) relative severity of agricultural threats, and (c) and degree of primary management capacity.

Linking biological integrity and watershed models to assess the impacts of historical land use and climate changes on stream health.

Land use change and other human disturbances have significant impacts on physicochemical and biological conditions of stream systems. Meanwhile, linking these disturbances with hydrology and water quality conditions is challenged due to the lack of high-resolution datasets and the selection of modeling techniques that can adequately deal with the complex and nonlinear relationships of natural systems. This study addresses the above concerns by employing a watershed model to obtain stream flow and water quality data and fill a critical gap in data collection. The data were then used to estimate fish index of biological integrity (IBI) within the Saginaw Bay basin in Michigan. Three methods were used in connecting hydrology and water quality variables to fish measures including stepwise linear regression, partial least squares regression, and fuzzy logic. The IBI predictive model developed using fuzzy logic showed the best performance with the R (2) = 0.48. The variables that identified as most correlated to IBI were average annual flow, average annual organic phosphorus, average seasonal nitrite, average seasonal nitrate, and stream gradient. Next, the predictions were extended to pre-settlement (mid-1800s) land use and climate conditions. Results showed overall significantly higher IBI scores under the pre-settlement land use scenario for the entire watershed. However, at the fish sampling locations, there was no significant difference in IBI. Results also showed that including historical climate data have strong influences on stream flow and water quality measures that interactively affect stream health; therefore, should be considered in developing baseline ecological conditions.

Projecting the effects of agricultural conservation practices on stream fish communities in a changing climate.

How anticipated climate change might affect long-term outcomes of present-day agricultural conservation practices remains a key uncertainty that could benefit water quality and biodiversity conservation planning. To explore this issue, we forecasted how the stream fish communities in the Western Lake Erie Basin (WLEB) would respond to increasing amounts of agricultural conservation practice (ACP) implementation under two IPCC future greenhouse gas emission scenarios (RCP4.5: moderate reductions; RCP8.5: business-as-usual conditions) during 2020-2065. We used output from 19 General Circulation Models to drive linked agricultural land use (APEX), watershed hydrology (SWAT), and stream fish distribution (boosted regression tree) models, subsequently analyzing how projected changes in habitat would influence fish community composition and functional trait diversity. Our models predicted both positive and negative effects of climate change and ACP implementation on WLEB stream fishes. For most species, climate and ACPs influenced species in the same direction, with climate effects outweighing those of ACP implementation. Functional trait analysis helped clarify the varied responses among species, indicating that more extreme climate change would reduce available habitat for large-bodied, cool-water species with equilibrium life-histories, many of which also are of importance to recreational fishing (e.g., northern pike, smallmouth bass). By contrast, available habitat for warm-water, benthic species with more periodic or opportunistic life-histories (e.g., northern hogsucker, greater redhorse, greenside darter) was predicted to increase. Further, ACP implementation was projected to hasten these shifts, suggesting that efforts to improve water quality could come with costs to other ecosystem services (e.g., recreational fishing opportunities). Collectively, our findings demonstrate the need to consider biological outcomes when developing strategies to mitigate water quality impairment and highlight the value of physical-biological modeling approaches to agricultural and biological conservation planning in a changing climate.

Riparian shading and groundwater enhance growth potential for smallmouth bass in Ozark streams.

Moderation of stream temperatures by riparian shading and groundwater are known to promote growth and survival of salmonid fishes, but effects of riparian shade and groundwater on to be growth of warmwater stream fishes are poorly understood or assumed to be negligible. We used stream temperature models to relate shading from riparian vegetation and groundwater inflow to summer water temperatures in Missouri Ozark streams and evaluated effects of summer water temperatures on smallmouth bass, Micropterus dolomieu, growth using a bioenergetics model. Bioenergetics model simulations revealed that adult smallmouth bass in non-spring-fed streams have lower growth potential during summer than fish in spring-fed streams, are subject to mass loss when stream temperatures exceed 27 degrees C, and will likely exhibit greater interannual variation in growth during summer if all growth-influencing factors, other than temperature, are identical between the two stream types. Temperature models indicated that increased riparian shading will expand the longitudinal extent of thermal habitat capable of supporting adult smallmouth bass growth in spring-fed stream reaches when mean daily air temperatures exceed 27 degrees C. Optimum growth temperature (22 degrees C) will be present only in spring-fed streams under these conditions. Potential for increasing shade through riparian restoration is greatest for streams <5 m wide and along north-south reaches of larger streams. However, temperature models also indicated that restoring riparian shading to maximum levels throughout a watershed would increase the total stream mileage capable of supporting positive growth of adult smallmouth bass by only 1-6% when air temperatures are at or near average summer maxima; increases in suitable thermal habitat would be greatest in watersheds with higher spring densities. Riparian management for maintenance or restoration of the thermal habitat of adult smallmouth bass during summer should be focused in areas strongly influenced by groundwater. Restoring riparian shading along spring-fed warmwater streams will likely benefit adult smallmouth bass growth and may ultimately influence population sizes.

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 model's 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.

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.

Influence of targets and assessment region size on perceived conservation priorities.

We used an existing conservation opportunity area (OA) data layer for four contiguous ecological subsections within the Ozark Highlands to quantitatively evaluate the influence of conservation targets and assessment region size on conservation priorities. OAs are natural and seminatural land-cover patches that are away from roads and away from patch edges. To evaluate the influence of targets, we assigned a priority score to each OA polygon for each of five different conservation targets, including land-cover patch size, landform representation, target vertebrate richness, target breeding bird richness, and target land cover. The top-scoring OAs for each target were added to an OA selection set for that target until 50% of the study area was chosen. These five OA selection sets were overlain to quantify overlap in priorities. Only 1.6% of the study area, or 2.1% of all OA polygons, was selected by all five targets. To evaluate the influence of assessment region size, we compared results of priority ranking of OAs relative to the entire study area against a merged set of priority rankings established separately relative to each of the four subsections within the study area. When high-priority OAs were added until 25% of the region was within the selection set for each of the five targets, the sets based on the whole study area versus each subsection evaluated separately overlapped from 45.4% to 81.9%. Thus, perceived priorities of conservation assessments are strongly influenced both by the targets that are evaluated and by the size of the assessment region.