Given that few ecosystems on the Earth have been unaffected by humans, restoring them holds great promise for stemming the biodiversity crisis and ensuring ecosystem services are provided to humanity. Nonetheless, few studies have documented the recovery of ecosystems globally or the rates at which ecosystems recover. Even fewer have addressed the added benefit of actively restoring ecosystems versus allowing them to recover without human intervention following the cessation of a disturbance. Our meta-analysis of 400 studies worldwide that document recovery from large-scale disturbances, such as oil spills, agriculture and logging, suggests that though ecosystems are progressing towards recovery following disturbances, they rarely recover completely. This result reinforces conservation of intact ecosystems as a key strategy for protecting biodiversity. Recovery rates slowed down with time since the disturbance ended, suggesting that the final stages of recovery are the most challenging to achieve. Active restoration did not result in faster or more complete recovery than simply ending the disturbances ecosystems face. Our results on the added benefit of restoration must be interpreted cautiously, because few studies directly compared different restoration actions in the same location after the same disturbance. The lack of consistent value added of active restoration following disturbance suggests that passive recovery should be considered as a first option; if recovery is slow, then active restoration actions should be better tailored to overcome specific obstacles to recovery and achieve restoration goals. We call for a more strategic investment of limited restoration resources into innovative collaborative efforts between scientists, local communities and practitioners to develop restoration techniques that are ecologically, economically and socially viable.

A primary goal of ecological restoration is to increase biodiversity in degraded ecosystems. However, the success of restoration ecology is often assessed by measuring the response of a single functional group or trophic level to restoration, without considering how restoration affects multitrophic interactions that shape biodiversity. An ecosystem-wide approach to restoration is therefore necessary to understand whether animal responses to restoration, such as changes in biodiversity, are facilitated by changes in plant communities (plant-driven effects) or disturbance and succession resulting from restoration activities (management-driven effects). Furthermore, most restoration ecology studies focus on how restoration alters taxonomic diversity, while less attention is paid to the response of functional and phylogenetic diversity in restored ecosystems. Here, we compared the strength of plant-driven and management-driven effects of restoration on four animal communities (ground beetles, dung beetles, snakes, and small mammals) in a chronosequence of restored tallgrass prairie, where sites varied in management history (prescribed fire and bison reintroduction). Our analyses indicate that management-driven effects on animal communities were six-times stronger than effects mediated through changes in plant biodiversity. Additionally, we demonstrate that restoration can simultaneously have positive and negative effects on biodiversity through different pathways, which may help reconcile variation in restoration outcomes. Furthermore, animal taxonomic and phylogenetic diversity responded differently to restoration, suggesting that restoration plans might benefit from considering multiple dimensions of animal biodiversity. We conclude that metrics of plant diversity alone may not be adequate to assess the success of restoration in reassembling functional ecosystems.

Phylogenetic and species‐based taxonomic descriptions of community structure may provide complementary information about the mechanisms driving community assembly across different environments. Environmental filtering may have similar effects on taxonomic and phylogenetic diversity under the assumption of niche conservatism, whereas competitive exclusion could produce contrasting patterns in these diversity metrics. In grassland restorations, these diversity patterns might then reveal potential assembly mechanisms underlying the impacts of restoration and management conditions on community structure. We compared plant community structure (alpha diversity, composition, and within‐site beta diversity) from both phylogenetic and taxonomic perspectives. Using surveys from 120 tallgrass prairie restorations in four regions of the Midwestern United States, we examined the effects of four potential drivers or environmental gradients: precipitation in the first year of restoration, seed mix richness, time since last prescribed fire, and restoration age, and included soil conditions as a covariate. First‐year precipitation influenced taxonomic community structure, but had weak effects on phylogenetic diversity and composition. Similarly, greater seed mix richness increased taxonomic diversity but did not influence phylogenetic diversity. Taxonomic, but not phylogenetic, diversity generally was lower in older restorations and those with a longer time since the last prescribed fire. These drivers consistently explained more variation in taxonomic than phylogenetic diversity and composition, perhaps in part because species turnover was largely among related species, producing weak impacts on phylogenetic community measures. An impact of precipitation on taxonomic but not phylogenetic diversity suggests that there may not be large differences in drought tolerance among clades that would cause phylogenetic patterns to arise from this environmental filter. Declining taxonomic diversity but not phylogenetic diversity is consistent with competitive exclusion as an assembly mechanism when competition is strongest between related species. Synthesis. This research shows how studying taxonomic and phylogenetic diversity of ecosystem restorations can inform plant community ecology and help natural resource managers better predict the outcomes of restoration actions and management.

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

The most common method for modeling forest attributes with airborne lidar, the area-based approach, involves summarizing the point cloud of individual plots and relating this to attributes of interest. Tree- and voxel-based approaches have been considered as alternatives to the area-based approach but are rarely considered in an area-based context. We estimated three forest attributes: basal area, overstory biomass, and volume, across 1,680 field plots in Arizona and New Mexico. Variables from the three lidar approaches (area, tree, voxel) were created for each plot. Random forests were estimated using subsets of variables based on each individual lidar approach and mixtures of each approach. Boruta feature selection was performed on variable subsets, including the mixture of all lidar-approach predictors (KS-Boruta). A corrected paired t-test was utilized to compare six validated models (area-Boruta, tree-Boruta, voxel-Boruta, KS-Boruta, KS-all, ridge-all) for each forest attribute. Based on significant reductions in error (SMdAPE), basal area and biomass were best modeled with KS-Boruta while volume was best modeled with KS-all. Analysis of variable importance shows voxel-based predictors are critical for the prediction of the three forest attributes. This study highlights the importance of multi-resolution voxel-based variables for modeling forest attributes in an area-based context.