Conserving stream fishes with changing climate: Assessing fish responses to changes in habitat over a large region.

Changes in climate are known to alter air temperature and precipitation and their associated thermal and hydrological regimes of freshwater systems, and such alterations in habitat are anticipated to modify fish composition in fluvial systems. Despite these expected changes, assessing climate change effects on habitat and fish over large regions has proven challenging. The goal of this study is to describe an approach to assess and identify stream reaches within a large region that are susceptible to climate changes based on responses of multiple fish species to changes in thermal and hydrological habitats occurring with changes in climate. We present a six-step approach to connect climate, habitat, and fish responses, demonstrated through an example to assess effects of climate change on fishes for all stream reaches in a large U.S. ecoregion (955,029 km2). Step 1 identified measures of air temperature and precipitation expected to change substantially in the future. Step 2 identified the climatic measures strongly associated with stream thermal and hydrologic metrics calculated from measured data from a subset of streams. Step 3 linked thermal and hydrologic metrics identified in Step 2 with abundances of fish species from the same stream reaches, and these fishes were combined into groups based on similar associations with specific thermal or hydrologic metrics. Step 4 used the linkages between fish groups and climatic measures and their associated thermal and hydrologic metrics to classify stream reaches. Step 5 assigned all stream reaches into classes based on the established classification under current climate measures and then re-assigned all stream reaches using projected climatic measures for three future time windows. Step 6 assessed changes in classes of stream reaches between current and future climate conditions. Stream reaches projected to change in stream classes were considered "vulnerable" to future climate change, as they would no longer support the same fish composition. The projected vulnerable streams for the years 2040, 2060, and 2090 were mapped and summarized to identify temporal patterns and identify their spatial distribution, along with underlying mechanisms leading to changes. Our results showed that 45.7% of the 320,000 reaches and 49.3% of the overall 650,000 km stream length in the study region were expected to change stream class by the year 2090, with spatially-explicit changes including streams' responding to changing air temperature or precipitation. This study provides critical guidance for integrating climate projections, landscape factors, stream habitat data, and fish data into a meaningful approach for understanding linkage. Outcomes greatly improve our ability to describe habitat changes at a stream reach scale throughout large regions, and they can aid in prioritizing management strategies to adapt to climate change at local and regional scales.

Landscape context and the biophysical response of rivers to dam removal in the United States.

Dams have been a fundamental part of the U.S. national agenda over the past two hundred years. Recently, however, dam removal has emerged as a strategy for addressing aging, obsolete infrastructure and more than 1,100 dams have been removed since the 1970s. However, only 130 of these removals had any ecological or geomorphic assessments, and fewer than half of those included before- and after-removal (BAR) studies. In addition, this growing, but limited collection of dam-removal studies is limited to distinct landscape settings. We conducted a meta-analysis to compare the landscape context of existing and removed dams and assessed the biophysical responses to dam removal for 63 BAR studies. The highest concentration of removed dams was in the Northeast and Upper Midwest, and most have been removed from 3rd and 4th order streams, in low-elevation (< 500 m) and low-slope (< 5%) watersheds that have small to moderate upstream watershed areas (10-1000 km2) with a low risk of habitat degradation. Many of the BAR-studied removals also have these characteristics, suggesting that our understanding of responses to dam removals is based on a limited range of landscape settings, which limits predictive capacity in other environmental settings. Biophysical responses to dam removal varied by landscape cluster, indicating that landscape features are likely to affect biophysical responses to dam removal. However, biophysical data were not equally distributed across variables or clusters, making it difficult to determine which landscape features have the strongest effect on dam-removal response. To address the inconsistencies across dam-removal studies, we provide suggestions for prioritizing and standardizing data collection associated with dam removal activities.

An approach for aggregating upstream catchment information to support research and management of fluvial systems across large landscapes.

The growing quality and availability of spatial map layers (e.g., climate, geology, and land use) allow stream studies, which historically have occurred over small areas like a single watershed or stream reach, to increasingly explore questions from a landscape perspective. This large-scale perspective for fluvial studies depends on the ability to characterize influences on streams resulting from throughout entire upstream networks or catchments. While acquiring upstream information for a single reach is relatively straight-forward, this process becomes demanding when attempting to obtain summaries for all streams throughout a stream network and across large basins. Additionally, the complex nature of stream networks, including braided streams, adds to the challenge of accurately generating upstream summaries. This paper outlines an approach to solve these challenges by building a database and applying an algorithm to gather upstream landscape information for digitized stream networks. This approach avoids the need to directly use spatial data files in computation, and efficiently and accurately acquires various types of upstream summaries of landscape information across large regions using tabular processing. In particular, this approach is not limited to the use of any specific database software or programming language, and its flexibility allows it to be adapted to any digitized stream network as long as it meets a few minimum requirements. This efficient approach facilitates the growing demand of acquiring upstream summaries at large geographic scales and helps to support the use of landscape information in assisting management and decision-making across large regions.