What Makes A Bank A Bank? Differences and Commonalities in Credit Calculation, Application, and Risks in Mitigation Banks Targeting Freshwater Fish Species and Associated Ecosystems

Mitigation banking is part of the ever-expanding global environmental market framework that aims to balance negative approved anthropogenic impacts versus third-party provided ecosystem benefits, sold in the form of credits. Given the need to conserve freshwater biodiversity and habitat, banking has received great traction for freshwater species and systems. While extensive reviews and studies have been conducted on evaluating if equivalency between impacts and offset can be achieved, there is almost no research being done on the way credits are being generated and banks are managed to inform future best practice and policy. Synthesizing banking data through cluster analyzes from 26 banks in the United States generating credits for freshwater species and associated systems, we show two generalizable approaches: removing barriers and targeting whole communities. Both address crucial freshwater conservation needs but come with their risks and caveats. Using common characteristics and management practices founded in federal and district level guidance within these two groups, we showcase and conclude that credit generation via barrier removal can be at risk of granting credit generation for too large of an area, leading to over-crediting. Banks targeting whole freshwater communities and accounting for landscape-level interactions and influences can potentially be detrimental for species on an individual level and large-scale credit availability as well as transfer can incentivize non-compliance with the mitigation hierarchy.

Assessment of the viable effects of structural reconstruction of habitats in a mountain stream: A long-term study

The Carpathians are biodiversity hotspots in Europe. However, transformations resulting from human activity have affected stream catchments where forest management is carried out. Numerous anti-rubble weirs and other low-head barriers disrupt the continuity of many streams. New requirements for human interference in stream channels were formulated. Measures are urgently needed to restore the ecological continuity and mosaicism of semi-natural habitats in mountain streams. Recently, small streams have been restored in mountainous areas. To assess the viable effects of stream restoration, analyses of in-stream habitats and fish communities, which are excellent bioindicators, were performed. This study aimed to assess the effects of structural reconstruction of habitats in a mountain stream (Eastern Carpathians, Poland). The analyses were based on a long-term assessment of fish responses to barrier removal and habitat changes following stream restoration measures. The research was conducted from 2009 to 2022. Our study indicates numerous positive and viable effects of using boulder ramps as technical solutions for restoring small mountain streams. Restored ecological continuity and in-stream habitats provide fish with adequate feeding grounds, spawning grounds, and fry-growth places. On the other hand, we indicated that the assessment of the effects of mountain stream restoration should be carried out over a long-term period, in three stages after the completion of work: early, medium, and late. This may allow for the most reliable assessment of the effects of restoration measures and the early detection of the need for corrections. It is also necessary to monitor possible changes that may occur in new hydrotechnical objects, such as boulder ramps, due to natural hydro-morphodynamic processes in the stream bed.

A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams

Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA, and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80-94%. Accounting for previously unmapped dams resulted in a 62-90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.

Prioritizing native migratory fish passage restoration while limiting the spread of invasive species: A case study in the Upper Mississippi River

Despite increasing efforts globally to remove dams and construct fish passage structures, broad-scale analyses balancing tradeoffs between cost and habitat gains from these mitigations infrequently consider invasive species. We present an optimization-based approach for prioritizing dam mitigations to restore habitat connectivity for native fish species, while limiting invasive species spread. Our methodology is tested with a case study involving 240 dams in the Upper Mississippi River, USA. We integrate six native migratory fish species distribution models, distributions of two invasive fishes, and estimated costs for dam removal and construction of fish passes. Varying budgets and post-mitigation fish passage rates are analyzed for two scenarios: 'no invasives' where non-selective mitigations (e.g., dam removal) are used irrespective of potential invasive species habitat gains and 'invasives' where a mixture of selective (e.g., lift-and-sort fish passage) and non-selective mitigations are deployed to limit invasive species range expansion. To achieve the same overall habitat connectivity gains, we find that prioritizations accounting for invasive species are 3 to 6 times more costly than those that do not. Habitat gains among native fish species were highly variable based on potential habitat overlap with invasive species and post-mitigation passabilities, ranging from 0.4-58.9% ('invasives') and 7.9-95.6% ('no invasives') for a $50M USD budget. Despite challenges associated with ongoing nonnative fish invasions, opportunities still exist to restore connectivity for native species as indicated by individual dams being frequently selected in both scenarios across varying passabilities and budgets, however increased restoration costs associated with invasive species control indicates the importance of limiting their further spread within the basin. Given tradeoffs in managing for native vs. invasive species in river systems worldwide, our approach demonstrates strategies for identifying a portfolio of candidate barriers that can be investigated further for their potential to enhance native fish habitat connectivity while concurrently limiting invasive species dispersal.

An accessible optimisation method for barrier removal planning in stream networks

Barriers associated to human infrastructure are a widespread impact in freshwater ecosystems worldwide, disrupting connectivity along river networks and key processes. Restoration of connectivity has risen in the last decade, with thousands of dams, weirs and culverts removed. Spatial optimisation methods can help inform decision on what barriers to remove to maximise gain in connectivity under limited budgets. However, current optimisation approaches rely on programming skills that are not easily accessible to stakeholders, which restrict the use of these methods. We demonstrate how Marxan, a publicly available tool, can be used to prioritise the allocation of barrier removal projects. We mapped the distribution of >900 barriers in the Tagus River (Iberian Peninsula) and 29 freshwater fish species with different movement abilities and needs. We assessed the passability of each barrier by all species and relative removal cost. We then identified priority barriers for removal to increase connectivity of populations of all species simultaneously. We tested two alternative scenarios: i) locking out barriers assesses as non-removable for their high strategic value or removal cost and ii) making all barriers available for removal. We found that connectivity recovery targets could be achieved by removing a small proportion of barriers, and avoiding large infrastructure. However, for some species, large recovery targets could only be achieved by removing some of these large infrastructures at high increases in cost. We also found some spatial differences in the recovery value of particular barriers for improving upstream and downstream connectivity. Our study demonstrates how to use a robust optimisation approach in an accessible tool, to address the complexity of prioritisation exercises commonly faced by stakeholders when deciding where to invest in barrier removal projects. This will improve decision-making for river connectivity restoration through a transparent, reproducible, and better-informed approach than traditional opportunistic or ranking-based approaches.

Dam and reservoir removal projects: a mix of social-ecological trends and cost-cutting attitudes

The removal of dams and reservoirs may seem to be an unforeseen and sometimes controversial step in water management. The removal of barriers may be different for each country or region, as each differs greatly in terms of politics, economy and social and cultural awareness. This paper addresses the complex problem of removing dams on rivers and their connected reservoirs. We demonstrate the scales of the changes, including their major ecological, economic, and social impacts. Arguments and approaches to this problem vary across states and regions, depending on the political system, economy and culture, as confirmed by the qualitative and quantitative intensities of the dam removal process and its global geographical variation. The results indicate that the removal of dams on rivers and their connected reservoirs applies predominantly to smaller structures (< 2.5 m). The existing examples provide an important conclusion that dams and reservoirs should be considered with regard to the interrelations between people and the environment. Decisions to deconstruct hydraulic engineering structures (or, likewise, to construct them) have to be applied with scrutiny. Furthermore, all decision-making processes have to be consistent and unified and thus developed to improve the lack of strategies currently implemented across world.

Stochastic optimal switching model for migrating population dynamics

An optimal switching control formalism combined with the stochastic dynamic programming is, for the first time, applied to modelling life cycle of migrating population dynamics with non-overlapping generations. The migration behaviour between habitats is efficiently described as impulsive switching based on stochastic differential equations, which is a new standpoint for modelling the biological phenomenon. The population dynamics is assumed to occur so that the reproductive success is maximized under an expectation. Finding the optimal migration strategy ultimately reduces to solving an optimality equation of the quasi-variational type. We show an effective linkage between our optimality equation and the basic reproduction number. Our model is applied to numerical computation of optimal migration strategy and basic reproduction number of an amphidromous fish Plecoglossus altivelis altivelis in Japan as a target species.

Reducing greenhouse gas emissions of Amazon hydropower with strategic dam planning

Hundreds of dams have been proposed throughout the Amazon basin, one of the world's largest untapped hydropower frontiers. While hydropower is a potentially clean source of renewable energy, some projects produce high greenhouse gas (GHG) emissions per unit electricity generated (carbon intensity). Here we show how carbon intensities of proposed Amazon upland dams (median = 39 kg CO2eq MWh-1, 100-year horizon) are often comparable with solar and wind energy, whereas some lowland dams (median = 133 kg CO2eq MWh-1) may exceed carbon intensities of fossil-fuel power plants. Based on 158 existing and 351 proposed dams, we present a multi-objective optimization framework showing that low-carbon expansion of Amazon hydropower relies on strategic planning, which is generally linked to placing dams in higher elevations and smaller streams. Ultimately, basin-scale dam planning that considers GHG emissions along with social and ecological externalities will be decisive for sustainable energy development where new hydropower is contemplated.