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African savanna elephants (Loxodonta africana) can substantially modify their habitat through their interactions with woody vegetation. Nonetheless, the scale, intensity and characteristics of these relations are not yet fully understood. Consequently, it is unclear how vegetation-megafauna interactions can be disrupted by external factors, such as land management. This study attempted to quantify and characterize structural changes in vegetation caused by elephants, from landscape to tree level scales. We applied multi-scale geospatial tools, including airborne (ALS) and terrestrial laser scanning (TLS), to address the following questions: (1) How do elephants shape landscape level vegetation structure in conservation areas? (2) Are the impacts of elephants evident on individual tree architecture? Our study area was located at the Taita Hills Wildlife Sanctuary in South-eastern Kenya. The occurrence of elephants was estimated using elephant observation records and proximity to elephant tracks. Landscape level structure was assessed using tree density maps calculated based on individually detected treetops from ALS data. Next, TLS measurements of 72 trees were processed using quantitative structural modelling to characterize their architecture. Our results demonstrate a widespread influence of elephants on both landscape and tree level structural characteristics. This influence was strongly mediated by management, as we observed differences in vegetation structure inside and outside conservation areas. Tree density was up to 42% lower (5.84 trees/ha) in conservation areas than in non-conservation areas (10.17 trees/ha). Trees were relatively larger with closer proximity to elephant tracks, while smaller trees were more often observed in areas further away from elephants. At an architectural level, trees closer to elephant tracks had lower ratio between the crown length and the tree height, demonstrating a substantial influence of elephants on the morphological characteristics of trees. Our results highlight the importance of accounting for vegetation fauna interactions when planning conservation areas in African savannahs. • We quantified structural changes in vegetation caused by elephants at multiple scales. • The impacts of elephants were assessed using airborne and terrestrial LiDAR. • We demonstrate the influence of elephants on landscape and individual tree levels. • Tree density was up to 42% lower in conservation areas than in non-conservation areas. • Trees consumed by elephants had reduced crown length-to-height ratio. [ABSTRACT FROM AUTHOR]

Buttressed trees provide mechanical support for themselves and offer essential ecological functions, such as nutrient acquisition, while being one of the largest sources of volume or biomass estimation variation in tropical forests. In this study, we collected 51 buttressed trees from (33) Democratic Republic of Congo, (12) Indonesia, and (6) Costa Rica, including (48) point clouds, and (3) destructive measurement. Specifically, we compared the performance of the Alpha Shape Algorithm (ASA) and the Slice Triangulation (ST) method on buttress volume estimation based on 30 point clouds with two species. Six point clouds from Costa Rica were used to validate the 3D surface reconstruction method. Meanwhile, we developed three allometric models based on 36 point clouds: a diameter above the buttress-based (DAB, 39 to 203 cm) model, a diameter computed from the non-convex area (D area130) model, and the convex hull perimeter (D convex130) of the breast height model. The developed models were validated with independent data, including (6) point clouds and (3) destructive measurements, to highlight the broader contextualization and application of these methods. Volume estimated by the ASA and ST showed a high agreement with the reference volume acquired using the Smalian formula (relative RMSE of 0.07 and 0.11, respectively, regardless of species effect). ASA was also robust when modeling trees with more and shallower horizontal buttresses. D area130 was the most accurate predictor to estimate buttress volume, with a lower Akaike information criterion (−66.25) than DAB (−59.55) and D convex130 (30.56); however, DAB and D area130 (relative RMSE of 0.21 and 0.23, respectively) showed similar performance when validated with independent datasets. Our results indicate that the ASA approach performs better than both the ST and allometric models used in this study. Furthermore, the ASA method can help correct the bias in the present and past estimates of volume and biomass of large trees, which are foundational components for understanding biomass allocation and dynamics in tropical forests contemporary fields. • The alpha shape and slice triangulation methods show similar performance on buttress volume estimation. • The alpha shape algorithm overestimates volume when the tree presents more and shallower horizontal buttresses. • Concerning allometric models, D area130 is the most robust predictor to estimate buttress volume. • The 3D surface reconstruction methods outperform traditional allometric models to estimate buttress volume. [ABSTRACT FROM AUTHOR]

• Current diameter-based allometric models for liana biomass are highly uncertain. • Terrestrial LiDARs (TLS) measure 3D forest structure with high spatial accuracy. • We developed a method for estimating liana stem biomass non-destructively using TLS. • The method shows good agreement with reference liana biomass estimates. • The method facilitates consistent long-term monitoring of plot-level liana biomass. Lianas are important and yet understudied components of tropical forests. Recent studies have shown that lianas are increasing in abundance and biomass in neotropical forests. However, aboveground biomass estimates of lianas are highly uncertain when calculated from allometric relations. This is mainly because of the limited sample size, especially for large lianas, used to construct the allometric models. Furthermore, the allometry of lianas can be weakly constrained mechanically throughout its development from sapling to mature form. In this study, we propose to extract liana stem biomass from terrestrial laser scanning (TLS) data of tropical forests. We show good agreement with a concordance correlation coefficient (CCC) of 0.94 between the TLS-derived volume to reference volume from eleven synthetic lianas. We also compare the TLS-derived biomass for ten real lianas in Nouragues, French Guiana, with the biomass derived from all existing allometric equations for lianas. Our results show relatively low CCC values for all the allometric models with the most commonly used pantropical model overestimating the total biomass by up to 133% compared to the TLS-derived biomass. Our study not only facilitates the testing of allometric equations but also enables non-destructive estimation of liana stem biomass. Since lianas are disturbance-adapted plants, liana abundance is likely to increase with increased forest disturbance. Our method will facilitate the long-term monitoring of liana biomass change in regenerating forests after disturbance, which is critical for developing effective forest management strategies. [ABSTRACT FROM AUTHOR]