Plant species leave a chemical signature in the soils below them, generating fine-scale spatial variation that drives ecological processes. Since the publication of a seminal paper on plant-mediated soil heterogeneity by Paul Zinke in 1962, a robust literature has developed examining effects of individual plants on their local environments (individual plant effects). Here, we synthesize this work using meta-analysis to show that plant effects are strong and pervasive across ecosystems on six continents. Overall, soil properties beneath individual plants differ from those of neighbours by an average of 41%. Although the magnitudes of individual plant effects exhibit weak relationships with climate and latitude, they are significantly stronger in deserts and tundra than forests, and weaker in intensively managed ecosystems. The ubiquitous effects of plant individuals and species on local soil properties imply that individual plant effects have a role in plant -soil feedbacks, linking individual plants with biogeochemical processes at the ecosystem scale.

Abstract. Lianas are an important component of tropical forests, where they reduce tree growth, fecundity, and survival. Competition for light from lianas may be intense; however, the amount of light that lianas intercept is poorly understood. We used a large-scale liana-removal experiment to quantify light interception by lianas in a Panamanian secondary forest. We measured the change in plant area index (PAI) and forest structure before and after cutting lianas (for 4 yr) in eight 80 m × 80 m plots and eight control plots (16 plots total). We used ground-based LiDAR to measure the 3-dimensional canopy structure before cutting lianas, and then annually for 2 yr afterwards. Six weeks after cutting lianas, mean plot PAI was 20% higher in control vs. liana removal plots. One yr after cutting lianas, mean plot PAI was ~17% higher in control plots. The differences between treatments diminished significantly 2 yr after liana cutting and, after 4 yr, trees had fully compensated for liana removal. Ground-based LiDAR revealed that lianas attenuated light in the upper-and middle-forest canopy layers, and not only in the upper canopy as was previously suspected. Thus, lianas compete with trees by intercepting light in the upper-and mid-canopy of this forest.

1. Lianas (woody vines) are a key component of tropical forests, known to reduce forest carbon storage and sequestration and to be increasing in abundance.Analysing how and why lianas are distributed in forest canopies at landscape scales will help us determine the mechanisms driving changes in lianas over time. This will improve our understanding of liana ecology and projections of tropical forest carbon storage now and into the future. Despite competing hypotheses on the mechanisms driving spatial patterning of lianas, few studies have integrated multiple tree-level biotic and abiotic factors in an analytical framework.None have done so in the Palaeotropics, which are biogeographically and evolutionarily distinct from the Neotropics, where most research on lianas has been conducted.2. We used an unoccupied aerial system (UAS; drone) to assess liana load in 50ha of Palaeotropical forest canopy in Southeast Asia. We obtained data on hypothesised drivers of liana spatial distribution in the forest canopy, including disturbance, tree characteristics, soil chemistry and topography, from the UAS, from airborne LiDAR and from ground surveys. We integrated these in a comprehensive analytical framework to extract variables at an individual-tree level and evaluated the relative strengths of the hypothesised drivers and their ability to predict liana distributions through boosted regression tree (BRT) modelling.3. Tree height and distance to canopy gaps were the two most important predictors of liana load, with relative contribution values in BRT models of 34.60%-45.39% and 7.93%-10.19%, respectively. Our results suggest that taller trees were less often and less heavily infested by lianas than shorter trees, opposite to

Below-canopy UAVs hold promise for automated forest surveys because their sensors can provide detailed information on below-canopy forest structures, especially in dense forests, which may be inaccessible to above-canopy UAVs, aircraft, and satellites. We present an end-to-end autonomous system for estimating tree diameters using a below-canopy UAV in parklands. We used simultaneous localization and mapping (SLAM) and LiDAR data produced at flight time as inputs to diameter-estimation algorithms in post-processing. The SLAM path was used for initial compilation of horizontal LiDAR scans into a 2D cross-sectional map, and then optimization algorithms aligned the scans for each tree within the 2D map to achieve a precision suitable for diameter measurement. The algorithms successfully identified 12 objects, 11 of which were trees and one a lamppost. For these, the estimated diameters from the autonomous survey were highly correlated with manual ground-truthed diameters (R2=0.92, root mean squared error = 30.6%, bias = 18.4%). Autonomous measurement was most effective for larger trees (>300 mm diameter) within 10 m of the UAV flight path, for medium trees (200–300 mm diameter) within 5 m, and for trees with regular cross sections. We conclude that fully automated below-canopy forest surveys are a promising, but still nascent, technology and suggest directions for future research.

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