Rodriguez Hernandez, D. I.

bioRxiv2026

Laser scanning identifies large trees as a major source of uncertainty in mangrove carbon accounting

BackgroundMangrove forests are crucial ecosystems which support biodiversity, protect coastlines and store vast amounts of carbon. Mangrove conservation and protection rely on accurate carbon accounting to unlock investment. However, the allometric equations underpinning these carbon estimates remain poorly constrained, particularly for the large trees. MethodsWe used terrestrial laser scanning (TLS) to estimate the biomass of 187 mangrove stems across Suriname, Panama, Colombia and Jamaica, including 84 stems >20 cm DBH. TLS-derived biomass estimates were used to evaluate local, regional and pantropical allometric equations. ResultsMost diameter-based allometric equations underestimated biomass by 8-65%. Equations additionally incorporating tree height performed better, but still underestimated biomass by 12-16% on average. Applying alternative allometries to a representative mangrove inventory from Panama produced biomass estimates ranging from 80 to 200 Mg ha-{superscript 1}, demonstrating that allometric uncertainty alone can generate more than a two-fold difference in estimated carbon stocks. ConclusionsCurrent allometric equations systematically underestimate the biomass of large mangrove trees and are therefore likely to underestimate mangrove carbon stocks. TLS provides a practical, non-destructive approach for expanding biomass datasets and improving allometric equations. Reducing allometric uncertainty should be a priority for strengthening blue carbon accounting and mangrove conservation.

ecology↗

bioRxiv2026

Limited predictability of tree-level responses to drought across European forests

Climate change is increasing the frequency, duration and severity of extreme events such as heatwaves and droughts, pushing trees near or beyond their ecophysiological limits. Understanding what governs variability in how trees respond to drought - such as intrinsic factors related to their size, age, and species, or extrinsic factors shaped by their local competitive environment - is critical for predicting long-term forest resilience to climate change and developing climate-smart forest management strategies. Here, we use tree ring data from 2909 trees belonging to sixteen species distributed across Europes major forest types to comprehensively assess what factors contribute most to a trees ability to withstand and recover from extreme drought events. We found that trees with larger living crowns generally exhibited higher post-drought growth recovery and resilience, while trees exposed to lower drought intensities showed greater resistance. Conversely, neither the density nor the diversity of a trees local competitive neighbourhood had any clear influence on its response to drought. More generally, we found that our ability to predict whether a tree would exhibit resilience to drought was low (R2 = 13-21) and was largely driven by species-specific responses and topographic variation across forest types, rather than by tree- and stand-level attributes. These findings highlight that drought responses are inherently complex and strongly influenced by forest type and by heterogeneous responses among species. Integrating tree-ring, physiological, and remote-sensing data with mechanistic models represents a promising avenue for improving forecasts of future forest resilience to climate change.

ecology↗