Search

Vegetation structural complexity is an important component of forest ecosystems, influencing biodiversity and functioning. Due to the heterogeneous distribution of vegetation elements, structural complexity underpins ecological dynamics, species composition, microclimate, and habitat diversity. Field measurements and Light Detection and Ranging (LiDAR) data, such as airborne (ALS) and terrestrial (TLS), can assess structural characteristics of forest and agroforestry systems at various spatial scales. This assessment is urgently needed for monitoring ecosystem restoration in degraded lands (e.g., in oil palm landscapes), where it is not well-known how structural measures derived from these different approaches relate to each other. Here, we compared the degree of correlation between individual and multivariate datasets of vegetation structural complexity metrics derived from ALS, TLS, and ground-based inventory approaches. The study was conducted in a 140 ha oil palm monoculture, enriched with 52 plots in the form of tree islands representing agroforestry systems of varying sizes and planted diversity levels in Sumatra, Indonesia. Our datasets comprised 25 ALS, five TLS, and nine ground-based inventory metrics. We studied correlations among metrics related to traditional stand summary, heterogeneity, and vertical and horizontal stand structure. We used principal component analysis for data dimensionality reduction, correlation analysis to quantify the strength of relationships between metrics, and Procrustes analysis to investigate the agreement between datasets. Significant correlations were found between ALS and TLS metrics for canopy density (r = 0.79) and maximum tree height (r = 0.58) and between ALS and ground-based inventory measures of stand heterogeneity and height diversity (r between 0.60 and −0.63). Further, we observed significant agreements between the ordinations of multivariate datasets (r = 0.56 for ALS − TLS; and r = 0.46 for ALS – ground-based inventory). Our findings underline the ability of ALS to capture structural complexity patterns, especially for canopy gap dynamics and vegetation height metrics, as captured by TLS, and for measures of heterogeneity and vertical structure as captured by ground-based inventories. Our study highlights the strength of each approach and underscores the potential of integrating ALS and TLS with ground-based inventories for a comprehensive characterization of vegetation structure in complex agroforestry systems, which can provide guidance for their management and support ecosystem restoration monitoring efforts.

Abstract Enhancing biodiversity in monoculture-dominated landscapes is a key sustainability challenge that requires considering the spatial organization of ecological communities (beta diversity). Here, we tested whether increasing landscape heterogeneity, through establishing 52 tree islands in an oil-palm landscape, is a suitable restoration strategy to enhance the diversity of six taxa (multi-taxa diversity). Further, we elucidated whether patterns in the spatial distribution of above- and below-ground taxa are related, and their role in shaping multi-taxa beta diversity. After five years, islands enhanced diversity at the landscape scale by fostering unique species (turnover). Partial correlation networks revealed that dissimilarity, in vegetation structural complexity and soil conditions, impacts multi-taxa beta diversity and turnover. In addition, soil fauna, bacteria, and fungi were more strongly associated with the overall community than aboveground taxa. Thus, strategies aiming to enhance multi-taxa diversity should consider the central role of landscape heterogeneity and soil biota.

Here, we describe BIOVERA-Tree, a database on tree diversity, community composition, forest structure, and functional traits collected in 120 forest plots distributed along an extensive elevational gradient in Veracruz State, Mexico. BIOVERA-Tree includes information on forest structure from three levels of forest-use intensity, namely old-growth, degraded, and secondary forest, replicated across eight elevations from sea-level to near the tree line at 3500 m and on size and location of 4549 tree individuals with a diameter at breast height ≥ 5 cm belonging to 216 species, 154 genera, and 80 families. We also report measurements of eight functional traits, namely wood density for 143 species, maximum height for 216 species and leaf traits including: specific leaf area, lamina density, leaf thickness, chlorophyll content, and leaf area for 148 species and leaf dry matter content for 145 species.BIOVERA-Tree is a new database comprising data collected in a rigorous sampling design along forest-use intensity and elevational gradients, contributing to our understanding of how interactive effects of forest-use intensity and elevation affect tree diversity, community composition, and functional traits in tropical forests.

Soil organism biodiversity contributes to ecosystem function, but biodiversity and function have not been equivalently studied across the globe. Here the authors identify locations, environment types, and taxonomic groups for which there is currently a lack of biodiversity and ecosystem function data in the existing literature.