Forest edges are interfaces between forest interiors and adjacent land cover types. They are important elements in the landscape with almost 20 % of the global forest area located within 100 m of the edge.Edges are structurally different from forest interiors, which results in unique edge influences on microclimate, functioning and biodiversity. These edge influences have been studied for multiple decades, yet there is only limited information available on how forest edge structure varies at the continental scale, and which factors drive this potential structural diversity. Here we quantified the structural variation along 45 edge-to-interior transects situated along latitudinal, elevational and management gradients across Europe. We combined state-of-the-art terrestrial laser scanning and conventional forest inventory techniques to investigate how the forest edge structure (e.g. plant area index, stem density, canopy height and foliage height diversity) varies and which factors affect this forest edge structural variability. Macroclimate, management, distance to the forest edge and tree community composition all influenced the forest edge structural variability and interestingly we detected interactive effects of our predictors as well. We found more abrupt edge-to-interior gradients (i.e. steeper slopes) in the plant area index in regularly thinned forests. In addition, latitude, mean annual temperature and humidity all affected edge-to-interior gradients in stem density. We also detected a simultaneous impact of both humidity and management, and humidity and distance to the forest edge, on the canopy height and foliage height diversity. These results contribute to our understanding of how environmental conditions and management shape the forest edge structure. Our findings stress the need for site-specific recommendations on forest edge management instead of generalized recommendations as the macroclimate substantially influences the forest edge structure.Only then, the forest edge microclimate, functioning and biodiversity can be conserved at a local scale.
There is mounting empirical evidence that lianas affect the carbon cycle of tropical forests. However, no single vegetation model takes into account this growth form, although such efforts could greatly improve the predictions of carbon dynamics in tropical forests. In this study, we incorporated a novel mechanistic representation of lianas in a dynamic global vegetation model (the Ecosystem Demography Model). We developed a liana‐specific plant functional type and mechanisms representing liana–tree interactions (such as light competition, liana‐specific allometries, and attachment to host trees) and parameterized them according to a comprehensive literature meta‐analysis. We tested the model for an old‐growth forest (Paracou, French Guiana) and a secondary forest (Gigante Peninsula, Panama). The resulting model simulations captured many features of the two forests characterized by different levels of liana infestation as revealed by a systematic comparison of the model outputs with empirical data, including local census data from forest inventories, eddy flux tower data, and terrestrial laser scanner‐derived forest vertical structure. The inclusion of lianas in the simulations reduced the secondary forest net productivity by up to 0.46 tC ha−1 year−1, which corresponds to a limited relative reduction of 2.6% in comparison with a reference simulation without lianas. However, this resulted in significantly reduced accumulated above‐ground biomass after 70 years of regrowth by up to 20 tC/ha (19% of the reference simulation). Ultimately, the simulated negative impact of lianas on the total biomass was almost completely cancelled out when the forest reached an old‐growth successional stage. Our findings suggest that lianas negatively influence the forest potential carbon sink strength, especially for young, disturbed, liana‐rich sites. In light of the critical role that lianas play in the profound changes currently experienced by tropical forests, this new model provides a robust numerical tool to forecast the impact of lianas on tropical forest carbon sinks.
Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree-and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package.Index Terms-Leaf versus wood separation, LiDAR, machine learning, python package, tropical forests.
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