Ecol Appl. 2026 Jan;36(1):e70183. doi: 10.1002/eap.70183.
ABSTRACT
Large old trees are widely recognized as ecologically important across forest landscapes and concern regarding the decline of these trees is well documented because of their role in maintaining biodiversity for a broad range of organisms. In response to a growing need to inventory such trees, we developed and present the methodology to map and quantify the occurrence of large trees based on height and dbh thresholds using airborne LiDAR data and associated canopy height models. The innovative, succinct, and flexible solution we offer is based on the integration and augmentation of several existing packages within the open-source statistical software R. We use local tree-height and crown diameter data to calibrate an algorithm to count individual trees above specified height thresholds, including supercanopy trees. To satisfy large-tree definitions based on dbh, we used individual-tree height and dbh data available from existing forest inventory plots to define height-dbh curves for dominant forest community types, which then allowed height thresholds to be used as a surrogate for specified dbh thresholds. We illustrate the use of these methods to efficiently map and quantify large tree distributions within 8 forest communities across a study area consisting of 1.65 million ha of productive, industrially managed forest in New Brunswick, Canada. Spatial maps are presented, along with large-tree frequency statistics for specific communities, according to the definitions outlined in New Brunswick’s provincial forest management guidelines. In excess of 37 million large trees are estimated to be broadly distributed across the study area. The methods developed identify patterns in the distribution of large trees across extensive areas (e.g., in millions of hectares) as one metric for maintenance of biodiversity at the landscape level. The methodology may be readily adapted to alternative forest-specific definitions of large trees based on tree height or dbh.
PMID:41582766 | DOI:10.1002/eap.70183
