Analyzing the Impacts of Dams on Riparian Ecosystems: A Review of Research Strategies and Their Relevance to the Snake River Through Hells Canyon

River damming provides a dominant human impact on river environments worldwide, and while local impacts of reservoir flooding are immediate, subsequent ecological impacts downstream can be extensive. In this article, we assess seven research strategies for analyzing the impacts of dams and river flow regulation on riparian ecosystems. These include spatial comparisons of (1) upstream versus downstream reaches, (2) progressive downstream patterns, or (3) the dammed river versus an adjacent free-flowing or differently regulated river(s). Temporal comparisons consider (4) pre- versus post-dam, or (5) sequential post-dam conditions. However, spatial comparisons are complicated by the fact that dams are not randomly located, and temporal comparisons are commonly limited by sparse historic information. As a result, comparative approaches are often correlative and vulnerable to confounding factors. To complement these analyses, (6) flow or sediment modifications can be implemented to test causal associations. Finally, (7) process-based modeling represents a predictive approach incorporating hydrogeomorphic processes and their biological consequences. In a case study of Hells Canyon, the upstream versus downstream comparison is confounded by a dramatic geomorphic transition. Comparison of the multiple reaches below the dams should be useful, and the comparison of Snake River with the adjacent free-flowing Salmon River may provide the strongest spatial comparison. A pre- versus post-dam comparison would provide the most direct study approach, but pre-dam information is limited to historic reports and archival photographs. We conclude that multiple study approaches are essential to provide confident interpretations of ecological impacts downstream from dams, and propose a comprehensive study for Hells Canyon that integrates multiple research strategies.     

Volunteer Macroinvertebrate Monitoring: Tensions Among Group Goals,Data Quality,and Outcomes

Volunteer monitoring of natural resources is promoted for its ability to increase public awareness, to provide valuable knowledge, and to encourage policy change that promotes ecosystem health. We used the case of volunteer macroinvertebrate monitoring (VMM) in streams to investigate whether the quality of data collected is correlated with data use and organizers' perception of whether they have achieved these outcomes. We examined the relation between site and group characteristics, data quality, data use, and perceived outcomes (education, social capital, and policy change). We found that group size and the degree to which citizen groups perform tasks on their own (rather than aided by professionals) positively correlated with the quality of data collected. Group size and number of years monitoring positively influenced whether a group used their data. While one might expect that groups committed to collecting good-quality data would be more likely to use it, there was no relation between data quality and data use, and no relation between data quality and perceived outcomes. More data use was, however, correlated with a group's feeling of connection to a network of engaged citizens and professionals. While VMM may hold promise for bringing citizens and scientists together to work on joint conservation agendas, our data illustrate that data quality does not correlate with a volunteer group's desire to use their data to promote regulatory change. Therefore, we encourage scientists and citizens alike to recognize this potential disconnect and strive to be explicit about the role of data in conservation efforts.     

Ecological scenarios analyzed and evaluated by a shallow lake model

We applied the complex ecosystem model EMMO, which was adopted to the shallow lake Müggelsee (Germany), in order to evaluate a large set of ecological scenarios. By means of EMMO, 33 scenarios and 17 indicators were defined to characterize their effects on the lake ecosystem. The indicators were based on model outputs of EMMO and can be separated into biological indicators, such as chlorophyll-a and cyanobacteria, and hydro-chemical indicators, such as phosphorus. The question to be solved was, what is the ranking of the scenarios based on their characterization by these 17 indicators? And how can we handle high quantities of complex data within evaluation procedures? The scenario evaluation was performed by partial order theory which, however, did not provide a clear result. By subsequently applying the hierarchical cluster analysis (complete linkage) it was possible to reduce the data matrix to indicator and scenario representatives. Even though this step implies losses of information, it simplifies the application of partial order theory and the post processing by METEOR. METEOR is derived from partial order theory and allows the stepwise aggregation of indicators, which subsequently leads to a distinct and clear decision. In the final evaluation result the best scenario was the one which defines a minimum nutrient input and no phosphorus release from the sediment while the worst scenario is characterized by a maximum nutrient input and extensive phosphorus release from the sediment. The reasonable and comprehensive results show that the combination of partial order, cluster analysis and METEOR can handle big amounts of data in a very clear and transparent way, and therefore is ideal in the context of complex ecosystem models, like that we applied.     

River-Stream Connectivity Affects Fish Bioassessment Performance

Stream fish bioassessment methods assume that fish assemblages observed in sample sites reflect responses to local stressors, but fish assemblages are influenced by local factors as well as regional dispersal to and from connected streams. We hypothesized that fish movement to and from refugia and source populations in connected rivers (i.e., riverine dispersal) would weaken or decouple relations between fish community metrics and local environmental conditions. We compared fish-environment relations between streams that flow into large rivers (mainstem tributaries) and streams that lack riverine confluences (headwater tributaries) at multiple spatial grains using data from the USEPA's Environmental Monitoring and Assessment Program in the mid-Atlantic highlands, USA (n = 157 sites). Headwater and mainstem tributaries were not different in local environmental conditions, but showed important differences in fish metric responses to environmental quality gradients. Stream sites flowing into mainstem channels within 10 fluvial km showed consistently weaker relations to local environmental conditions than stream sites that lacked such mainstem connections. Moreover, these patterns diminished at longer distances from riverine confluences, consistent with the hypothesis of riverine dispersal. Our results suggest that (1) the precision of fish bioassessment metrics may be improved by calibrating scoring criteria based on the spatial position of sites within stream networks and (2) the spatial grain of fish bioassessment studies may be manipulated to suit objectives by including or excluding fishes exhibiting riverine dispersal.     

Soil C and N models that integrate microbial diversity

Industrial agriculture is yearly responsible for the loss of 55–100 Pg of historical soil carbon and 9.9 Tg of reactive nitrogen worldwide. Therefore, management practices should be adapted to preserve ecological processes and reduce inputs and environmental impacts. In particular, the management of soil organic matter (SOM) is a key factor influencing C and N cycles. Soil microorganisms play a central role in SOM dynamics. For instance, microbial diversity may explain up to 77 % of carbon mineralisation activities. However, soil microbial diversity is actually rarely taken into account in models of C and N dynamics. Here, we review the influence of microbial diversity on C and N dynamics, and the integration of microbial diversity in soil C and N models. We found that a gain of microbial richness and evenness enhances soil C and N dynamics on the average, though the improvement of C and N dynamics depends on the composition of microbial community. We reviewed 50 models integrating soil microbial diversity. More than 90 % of models integrate microbial diversity with discrete compartments representing conceptual functional groups (64 %) or identified taxonomic groups interacting in a food web (28 %). Half of the models have not been tested against an empirical dataset while the other half mainly consider fixed parameters. This is due to the difficulty to link taxonomic and functional diversity.