UAV-Lidar reveals that canopy structure mediates the influence of edge effects on forest diversity, function and microclimate

1. Widespread forest loss and fragmentation dramatically increases the proportion of forest areas located close to edges. Although detrimental, the precise extent and mechanisms by which edge proximity impacts remnant forests remain to be ascertained. 2. By combining unmanned aerial vehicle laser scanning (UAV-LS) with field data from 46 plots distributed at varying distances from the edge to the forest interior in a fragmented forest of New-Caledonia, we investigated edge influence on forest structure, composition, function, above-ground biomass (AGB) and microclimate. Using simple linear regressions, structural equation modelling and variance partitioning, we analysed the direct and indirect relationships between distance to edge, UAV-LS-derived canopy structural metrics, understorey microclimate, AGB, taxonomic and functional composition while accounting for the potential influence of fine-scale variation in topography. 3. We found that the distance to the closest forest edge was strongly correlated with canopy structure and that canopy structure was better correlated to forest composition, function, biomass and microclimate than distance to the closest forest edge. This suggests that the influence of edge is mediated by changes in canopy structure. Plots located near the edge exhibited a lower canopy with more gaps, higher microclimate extremes, lower biomass, lower taxonomic and functional diversity as well as denser wood and lower specific leaf area. UAV-LS-derived canopy structural metrics were relevant predictors of understorey microclimate, biomass and taxonomic and functional composition. Overall, the influence of topography was marginal compared to edge effects. 4. Synthesis. Accounting for fine-scale variation in canopy structure captured by UAV-LS provides insights on the multiple edge impacts on key forest properties related to structure, diversity, function, biomass and microenvironmental conditions. Integrating UAV-LS-derived data can foster our understanding of cascading and interacting impacts of anthropogenic influence on tropical forest ecosystems and should help to improve conservation strategies and landscape management policies.