Beavers have the ability to modify ecosystems profoundly to meet their ecological needs, with significant associated hydrological, geomorphological, ecological, and societal impacts. To bring together understanding of the role that beavers may play in the management of water resources, freshwater, and terrestrial ecosystems, this article reviews the state‐of‐the‐art scientific understanding of the beaver as the quintessential ecosystem engineer. This review has a European focus but examines key research considering both Castor fiber—the Eurasian beaver and Castor canadensis—its North American counterpart. In recent decades species reintroductions across Europe, concurrent with natural expansion of refugia populations has led to the return of C. fiber to much of its European range with recent reviews estimating that the C. fiber population in Europe numbers over 1.5 million individuals. As such, there is an increasing need for understanding of the impacts of beaver in intensively populated and managed, contemporary European landscapes. This review summarizes how beaver impact: (a) ecosystem structure and geomorphology, (b) hydrology and water resources, (c) water quality, (d) freshwater ecology, and (e) humans and society. It concludes by examining future considerations that may need to be resolved as beavers further expand in the northern hemisphere with an emphasis upon the ecosystem services that they can provide and the associated management that will be necessary to maximize the benefits and minimize conflicts.This article is categorized under: Water and Life > Nature of Freshwater Ecosystems

Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R² of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha⁻¹. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe.

Beavers influence hydrology by constructing woody dams. Using a Before After Control Impact experimental design, we quantified the effects of a beaver dam sequence on the flow regime of a stream in SW England and consider the mechanisms that underpin flow attenuation in beaver wetlands. Rainfall‐driven hydrological events were extracted between 2009 and 2020, for the impacted (n = 612) and control (n = 634) catchments, capturing events 7 years before and 3 years after beaver occupancy, at the impacted site. General additive models were used to describe average hydrograph geometry across all events. After beaver occupancy, Lag times increased by 55.9% in the impacted site and declined by 17.5% in the control catchment. Flow duration curve analysis showed a larger reduction in frequency of high flows, following beaver dam construction, with declines of Q5 exceedance levels of 33% for the impacted catchment and 15% for the control catchment. Using event total rainfall to predict peak flow, five generalized linear models were fitted to test the hypothesis that beaver dams attenuate flow, to a greater degree, with larger storm magnitude. The best performing model showed, with high confidence, that beaver dams attenuated peak flows, with increasing magnitude, up to between 0.5 and 2.5 m³ s⁻¹ for the 94th percentile of event total rainfall; but attenuation beyond the 97th percentile cannot be confidently detected. Increasing flow attenuation, with event magnitude, is attributed to transient floodplain storage in low gradient/profile floodplain valleys that results from an increase in active area of the floodplain. These findings support the assertion that beaver dams attenuate flows. However, with long‐term datasets of extreme hydrological events lacking, it is challenging to predict the effect of beaver dams during extreme events with high precision. Beaver dams will have spatially variable impacts on hydrological processes, requiring further investigation to quantify responses to dams across differing landscapes and scales.

Eurasian beaver (Castor fiber) populations are expanding across Europe. Depending on location, beaver dams bring multiple benefits and/or require management. Using nationally available data, we developed: a Beaver Forage Index (BFI), identifying beaver foraging habitat, and a Beaver Dam Capacity (BDC) model, classifying suitability of river reaches for dam construction, to estimate location and number of dams at catchment scales. Models were executed across three catchments, in Great Britain (GB), containing beaver. An area of 6747 km² was analysed for BFI and 16,739 km of stream for BDC. Field surveys identified 258 km of channel containing beaver activity and 89 dams, providing data to test predictions. Models were evaluated using a categorical binomial Bayesian framework to calculate probability of foraging and dam construction. BFI and BDC models successfully categorised the use of reaches for foraging and damming, with higher scoring reaches being preferred. Highest scoring categories were ca. 31 and 79 times more likely to be used than the lowest for foraging and damming respectively. Zero-inflated negative binomial regression showed that modelled dam capacity was significantly related (p = 0.01) to observed damming and was used to predict numbers of dams that may occur. Estimated densities of dams, averaged across each catchment, ranged from 0.4 to 1.6 dams/km, though local densities may be up to 30 dams/km. These models provide fundamental information describing the distribution of beaver foraging habitat, where dams may be constructed and how many may occur. This supports the development of policy and management concerning the reintroduction and recolonisation of beaver.

Eurasian beaver (Castor fiber) were nearly hunted to extinction but have recovered to occupy much of their former range. Beaver were extirpated from Great Britain c. 400 years ago but have recently been reintroduced. The River Otter catchment, Devon was the site of the first licensed wild release of beavers in England. With further releases being considered, there is a need to better understand population dynamics of this native, keystone species to inform conservation and management. Field signs were surveyed from 2015 to 2021. A semi‐automated territory detection method was adopted to estimate territory counts. A spatially explicit model was developed to estimate the ecological territory capacity of the catchment. Future territory expansion was modelled using logistic growth curves; initial growth rate was estimated from observed territory counts and the estimated territory capacity range was used to define the limiting value of the growth curve. Beaver territory removal was simulated, across a range of management intensities and start times, to determine potential impacts of translocation or lethal control upon population dynamics. Territory numbers increased from four to 18, inclusive of four additionally released individuals, during study period. In the absence of population management, the territory capacity of the catchment was estimated to range between 120 and 183; this may be reached between 2028 and 2057. Simulated territory removal, where territories were removed at a fixed rate from the sum of the estimated total population and the population increase for that year, demonstrated large uncertainties in predicted population responses. Simulations with territory removals >3/year all predicted potential population collapse. This finding emphasizes the need for caution when considering population management strategies; translocation of animals out of the catchment or culling should be considered only when populations are established and all alternatives have been considered. These results provide critical information for the expected rate and magnitude of beaver population change in the River Otter catchment. The methods provide a reproducible and generalizable approach for understanding beaver population change, which can inform policy on the reintroduction of beavers and the potential timing and intensity of future beaver population management.

Forest structure is key to understanding the resilience of tropical forests (their ability to recover from disturbance) and predicting how these ecosystems will respond to future environmental and climatic fluctuations. Current resilience studies in the Amazon rely on passive and active remote sensing forest cover metrics that offer limited insight into nuanced forest canopy structural changes associated with degradation. The Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar provides detailed information on canopy structure, a key factor in forest recovery, resilience, and ability to provide ecosystem services. We evaluate GEDI spaceborne lidar’s capability to characterise forest structure along a gradient of degradation (e.g. primary unburned (PU), secondary recovering, fire frequency), and investigate the potential of quantifying forest structure to advance understanding of forest responses to disturbance across the Amazon. We assess the correspondence of GEDI structural metrics such as relative height (RH) and canopy cover to airborne lidar metrics across the Brazilian Amazon using Lin’s concordance correlation coefficient (CCC). We evaluate GEDI forest structure variation along a gradient of forest degradation. We explore the potential of principal component (PC) analysis applied to GEDI data to derive a continuous descriptor of forest structural state that characterises the forest degradation continuum, and use a multinomial logistic regression model (MNLR) to further evaluate this descriptor. The strongest positive correspondence for all sampled GEDI and airborne lidar footprints occurs at RH96 (CCC; 0.57) of the canopy height profile, with strongest agreement in primary forest burned at least three times (CCC; 0.91). Whilst canopy cover showed significant recovery 15–25 years after disturbance, forest canopy height and aboveground biomass density had not fully recovered to pre-disturbance levels within 38 years. The PCA identified the importance of RH75, RH96, foliage height diversity index and canopy directional gap probability, with PC1 and PC2 explaining 80% and 15% of variance, respectively. The MNLR suggests that the ratio of these PCs effectively characterises the forest degradation gradient. We demonstrate the ability of GEDI 2A/B structural metrics to differentiate forest structure along a gradient of PU to secondary severely degraded forest in the Amazon rainforest, despite some overlap in structural characterisations between similar degradation classes. Our new method for deriving a forest structural state metric supports future research on forest monitoring, conservation, and the study of Amazon-wide ecosystem resilience.