Sharing FAIR monitoring program data improves discoverability and reuse.

Data resulting from environmental monitoring programs are valuable assets for natural resource managers, decision-makers, and researchers. These data are often collected to inform specific reporting needs or decisions with a specific timeframe. While program-oriented data and related publications are effective for meeting program goals, sharing well-documented data and metadata allows users to research aspects outside initial program intentions. As part of an effort to integrate data from four long-term large-scale US aquatic monitoring programs, we evaluated the original datasets against the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and offer recommendations and lessons learned. Differences in data governance across these programs resulted in considerable effort to access and reuse the original datasets. Requirements, guidance, and resources available to support data publishing and documentation are inconsistent across agencies and monitoring programs, resulting in various data formats and storage locations that are not easily found, accessed, or reused. Making monitoring data FAIR will reduce barriers to data discovery and reuse. Programs are continuously striving to improve data management, data products, and metadata; however, provision of related tools, consistent guidelines and standards, and more resources to do this work is needed. Given the value of these data and the significant effort required to access and reuse them, actions and steps intended on improving data documentation and accessibility are described.

Did changes in western federal land management policies improve salmonid habitat in streams on public lands within the Interior Columbia River Basin?

Historic management actions authorized or allowed by federal land management agencies have had a profound negative effect on salmon, trout, and char populations and their habitats. To rectify past failings, in the 1990s, federal agencies in the Interior Columbia River Basin modified how they conducted land management activities to foster the conservation of aquatic species. The primary policy changes were to provide additional protection and restoration of lands near streams, lakes, and wetlands. What remains uncertain was whether these changes have altered the trajectory of stream habitat conditions. To address this question, we evaluate the status and trends of ten stream habitat attributes; wood frequency, wood volume, residual pool depth, percent pool, pool frequency, pool tail fines (< 6 mm), median particle size, percent undercut banks, bank angle, and streambank stability in managed and reference catchments following changes in management policies. Our review of these data support the hypothesis that changes made in management standards and guidelines in the 1990s are related to improved stream conditions. Determining the precise magnitude of changes in stream conditions that resulted from the modification of land management policies is difficult due to the shifting environmental baseline. By understanding and accounting for how changes in stream conditions reflect improved land management policies and broader environmental trends, federal agencies will be better situated to make project level decisions that benefit aquatic resources.

Corrigendum: Ecosystem experiment reveals benefits of natural and simulated beaver dams to a threatened population of steelhead (Oncorhynchus mykiss).

This corrects the article DOI: 10.1038/srep28581.

Ecosystem experiment reveals benefits of natural and simulated beaver dams to a threatened population of steelhead (Oncorhynchus mykiss).

Beaver have been referred to as ecosystem engineers because of the large impacts their dam building activities have on the landscape; however, the benefits they may provide to fluvial fish species has been debated. We conducted a watershed-scale experiment to test how increasing beaver dam and colony persistence in a highly degraded incised stream affects the freshwater production of steelhead (Oncorhynchus mykiss). Following the installation of beaver dam analogs (BDAs), we observed significant increases in the density, survival, and production of juvenile steelhead without impacting upstream and downstream migrations. The steelhead response occurred as the quantity and complexity of their habitat increased. This study is the first large-scale experiment to quantify the benefits of beavers and BDAs to a fish population and its habitat. Beaver mediated restoration may be a viable and efficient strategy to recover ecosystem function of previously incised streams and to increase the production of imperiled fish populations.

Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration.

Before-after-control-impact (BACI) designs are an effective method to evaluate natural and human-induced perturbations on ecological variables when treatment sites cannot be randomly chosen. While effect sizes of interest can be tested with frequentist methods, using Bayesian Markov chain Monte Carlo (MCMC) sampling methods, probabilities of effect sizes, such as a ≥20 % increase in density after restoration, can be directly estimated. Although BACI and Bayesian methods are used widely for assessing natural and human-induced impacts for field experiments, the application of hierarchal Bayesian modeling with MCMC sampling to BACI designs is less common. Here, we combine these approaches and extend the typical presentation of results with an easy to interpret ratio, which provides an answer to the main study question-"How much impact did a management action or natural perturbation have?" As an example of this approach, we evaluate the impact of a restoration project, which implemented beaver dam analogs, on survival and density of juvenile steelhead. Results indicated the probabilities of a ≥30 % increase were high for survival and density after the dams were installed, 0.88 and 0.99, respectively, while probabilities for a higher increase of ≥50 % were variable, 0.17 and 0.82, respectively. This approach demonstrates a useful extension of Bayesian methods that can easily be generalized to other study designs from simple (e.g., single factor ANOVA, paired t test) to more complicated block designs (e.g., crossover, split-plot). This approach is valuable for estimating the probabilities of restoration impacts or other management actions.