Simplifying complexity: the reliability of lidar-derived forest structural complexity metrics for conservation management

Abstract

Abstract 1. Forest structural complexity is regarded as a driver of biodiversity. The restructuring of forests to increase structural complexity is now a common objective of forest restoration and conservation projects. Lidar provides an efficient means of monitoring forest structure; however, the reliability of lidar-derived structural complexity metrics remains uncertain. Most metrics require a choice of resolution (such as pixel or voxel size), and whilst metric outcomes are sensitive to this choice, an understanding of how this impacts subsequent ecological interpretations of forest restoration monitoring is missing. 2. We assess how metric resolution choices impact FSC estimates using terrestrial laser scanning data from 32 single-species forest plots in Cairngorms National Park, Scotland. These plots represent four contrasting structural categories: ancient woodland (AW), old plantation (OP), old plantation with restructuring management (OPM) and natural restoration (NR); with a hypothesised order of complexity: AW, OPM, OP, NR (from highest complexity to lowest). We computed 21 FSC metrics and calculated how each metric ranked our forest structural categories across a resolution gradient (0.02-1.5m). We evaluated the stability of metric rankings across resolution values, their ability to discriminate forest categories, and the consistency of ecological interpretation of forest management targets. 3. Nine metrics produced unstable rankings, indicating that ecological conclusions were sensitive to resolution selection. Stable metrics discriminated well between categories with obvious structural differences but failed to discriminate between categories with subtle differences, particularly OP and OPM. 4. AW did not consistently exhibit the highest structural complexity, instead occupying an intermediate position characterised by a combination of canopy occupancy, openness and height inequality, not captured by any single metric. 5. We conclude that methodological choices can influence FSC estimates and ecological conclusions drawn from them. 6. Synthesis and applications: Reliable assessment of forest restoration depends not only on the choice of metric but also on how that metric is parameterised. We recommend transparent reporting and justification of resolution parameter selection together with the use of stable metrics that capture structural characteristics relevant to management objectives being monitored, to improve reliability and comparability of lidar-based forest monitoring.