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Drift or downstream dispersal is a fundamental process in the life cycle of many riverine organisms. In the face of rapidly declining freshwater biodiversity, there is a need to enhance our capacity to study the drift of riverine organisms, by overcoming the limitations of traditional labour‐intensive sampling methods that result in data of low temporal and spatial resolution.To address this need, we developed a new technology, the Riverine Organism Drift Imager (RODI), which combines in situ imaging with machine‐learning classification. This technique expands on the traditional methodology by replacing the collection cup of a drift net with a camera system that continuously images riverine organisms as they drift through the device. After being imaged, organisms are released into the environment unharmed. A machine‐learning classifier is used after field sampling to identify drifting organisms. Therefore, RODI provides a non‐invasive sampling method that can quantify organism drift at unprecedented temporal resolution.Multiple deployments have served to validate the performance of the technology in the field. In its current implementation, images are captured continuously for 1.5 h at 50 frames per second. We demonstrate that the quality of the resulting images enables a convolutional neural network classifier to identify organisms to the family level. The weighted F1 score, a metric for the performance of the classifier, was 94%, based on training and testing on a field‐collected dataset consisting of 4598 images of 285 organisms belonging to seven classes (one species, five families and one order).In conclusion, this work provides a proof of concept, demonstrating the viability of the deployment of RODI as an automated, in situ organism drift sampler. This novel approach offers the possibility to advance our fundamental understanding of the drift of riverine organisms and how this is affected by human impacts in natural streams while, at the same time, can serve as a cost‐effective tool for biodiversity monitoring. [ABSTRACT FROM AUTHOR]

In the recently declared United Nations Decade on Ecosystem Restoration (2021–2030), evidence‐based research has an essential role in summarizing restoration outcomes toward broad generalizations to advance restoration practice and science globally. However, to present trustworthy, high‐quality recommendations, evidence reviews should be based on rigorous methods that minimize bias and enhance systematicity, transparency, objectivity, comprehensiveness, and repeatability. To assess the current value of restoration reviews in terms of methodological repeatability at the searching stage, a fundamental review stage, we evaluated a sample of meta‐analyses and narrative syntheses (n = 79) and critically appraised how searches were conducted based on the information reported within each study. By assessing whether review methods were reported in sufficient detail to be repeated by an independent party, we found that repeatability varied substantially by the standard we used. Overall, reviews performed relatively poorly, with a median score of 3 out of 9 points available, and a mean score of 3.6. Most reviews (n = 76/79) failed in effectively reporting all necessary information to allow repeatability at this stage. We found no statistically significant differences considering review types, suggesting that the type of synthesis alone does not solely reflect the repeatability of a review. Toward increasingly repeatable, reliable reviews in restoration ecology, we recommend that authors incorporate and apply principles of systematic reviews and maps in their review process, and suggest that environmental journals should broadly emphasize ways of performing rigorous reviews, as well as increase efforts for the publication of open access review protocols. [ABSTRACT FROM AUTHOR]

Wildfires in many western North American forests are becoming more frequent, larger, and severe, with changed seasonal patterns. In response, coniferous forest ecosystems will transition toward dominance by fire‐adapted hardwoods, shrubs, meadows, and grasslands, which may benefit some faunal communities, but not others. We describe factors that limit and promote faunal resilience to shifting wildfire regimes for terrestrial and aquatic ecosystems. We highlight the potential value of interspersed nonforest patches to terrestrial wildlife. Similarly, we review watershed thresholds and factors that control the resilience of aquatic ecosystems to wildfire, mediated by thermal changes and chemical, debris, and sediment loadings. We present a 2‐dimensional life history framework to describe temporal and spatial life history traits that species use to resist wildfire effects or to recover after wildfire disturbance at a metapopulation scale. The role of fire refuge is explored for metapopulations of species. In aquatic systems, recovery of assemblages postfire may be faster for smaller fires where unburned tributary basins or instream structures provide refuge from debris and sediment flows. We envision that more‐frequent, lower‐severity fires will favor opportunistic species and that less‐frequent high‐severity fires will favor better competitors. Along the spatial dimension, we hypothesize that fire regimes that are predictable and generate burned patches in close proximity to refuge will favor species that move to refuges and later recolonize, whereas fire regimes that tend to generate less‐severely burned patches may favor species that shelter in place. Looking beyond the trees to forest fauna, we consider mitigation options to enhance resilience and buy time for species facing a no‐analog future. [ABSTRACT FROM AUTHOR]

Abstract: The construction of fishways for upstream and downstream connectivity is the preferred mitigation measure for hydropower dams and other riverine barriers. Yet empirical evidence for effective design criteria for many species is missing. We therefore assembled a group of international fishway designers and combined their knowledge with available empirical data using a formal expert elicitation protocol and Bayesian networks. The expert elicitation method we use minimizes biases typically associated with such approaches. Demonstrating our application with a case‐study on the temperate Southern Hemisphere, we use the resulting probabilistic models to predict the following, given alternative design parameters: (i) the effectiveness of technical fishways for upstream movement of migratory fish; (ii) habitat quality in nature‐like bypasses for resident fish; and (iii) rates of mortality during downstream passage of all fish through turbines and spillways. The Fish Passage Network (Fish‐Net) predicts that fishways for native species could be near 0% or near 100% efficient depending on their design, suggesting great scope for adequate mitigation. Sensitivity analyses revealed the most important parameters as follows: (i) design of attraction and entrance features of technical fishways for upstream migration; (ii) habitat preferences of resident fish in nature‐like bypasses; and (iii) susceptibility of fish to barotrauma and blade strike during turbine passage. Numerical modelling predicted that mortality rates of small‐bodied fish (50–100 mm TL) due to blade strike may be higher for Kaplan than Francis turbines. Our findings can be used to support environmentally sustainable decisions in the planning, design and monitoring stages of hydropower development. [ABSTRACT FROM AUTHOR]

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