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Fractals have been widely used to determine bifurcation patterns in trees or to analyse the homeostasis of the development of plants to different environments. In a few instances, fractals have been used to predict tree or stand metrics. Here, we explore the use of fractal geometry based on the voxel‐counting method (VC) to predict tree and stands metrics on point clouds derived from terrestrial laser scanning. This was explored using 189 leaf‐on and leaf‐off point clouds from seven databases around the world.Four metrics were estimated at the tree level: height, diameter at breast height, crown area and tree volume. At the stand level, artificial stands were created by adding trees to a given plot, and then the basal area, stand volume and area coverage by crowns were estimated. The VC was applied to trees or stands creating voxels of different volumes (S) while counting the number of voxels (N) required to fill it. Log–log relationships between N and 1/S were used to estimate the fractal dimension (dMB) and the interceptMB.At the tree level, the interceptMB shows a stronger relationship with metrics for leaf‐on (r2 = 0.26‒0.90) and leaf‐off point clouds (r2 = 0.18‒0.87) than dMB (r2 < 0.34); however, dMB seems to describe better the complexity embedded within leaf‐on/leaf‐off point clouds. The predictions by the interceptMB are affected by the presence/absence of leaves, but less affected by the random effects of the databases. At the stand level, both fractal geometry parameters (interceptMB and dMB) tend to predict the variability of stand metrics (r2 = 0.61‒0.98).The estimation of tree and stand metrics based on fractal geometry equations can be considered a fast approach for predicting irregular structures. Using fractals on point clouds also allows us to understand the structural complexity of how trees or stands occupy their 3D space. This complexity can be further used as a structural trait of trees or forest ecosystems. Fractal geometry equations can also help towards the development of large‐scale biomass maps at different ecosystems. [ABSTRACT FROM AUTHOR]

Limited information on greenhouse gas emissions from tropical dry forest soils still hinders the assessment of the sources/sinks from this ecosystem and their contribution at global scales. Particularly, rewetting events after the dry season can have a significant effect on soil biogeochemical processes and associated exchange of greenhouse gases. This study evaluated the temporal variation and annual fluxes of CO2, N2O, and CH4 from soils in a tropical dry forest successional gradient. After a prolonged drought of 5 months, large emissions pulses of CO2 and N2O were observed at all sites following first rain events, caused by the "Birch effect," with a significant effect on the net ecosystem exchange and the annual emissions budget. Annual CO2 emissions were greatest for the young forest (8,556 kg C ha−1 yr−1) followed by the older forest (7,420 kg C ha−1 yr−1) and the abandoned pasture (7,224 kg C ha−1 yr−1). Annual emissions of N2O were greatest for the forest sites (0.39 and 0.43 kg N ha−1 yr−1) and least in the abandoned pasture (0.09 kg N ha−1 yr−1). CH4 uptake was greatest in the older forest (−2.61 kg C ha−1 yr−1) followed by the abandoned pasture (−0.69 kg C ha−1 yr−1) and the young forest (−0.58 kg C ha−1 yr−1). Fluxes were mainly influenced by soil moisture, microbial biomass, and soil nitrate and ammonium concentrations. Annual CO2 and N2O soil fluxes of tropical dry forests in this study and others from the literature were much lower than the annual fluxes in wetter tropical forests. Conversely, tropical dry forests and abandoned pastures are on average stronger sinks for CH4 than wetter tropical forests. Key Points: Soil greenhouse gas emissions and NEE of a tropical dry ecosystem were evaluatedPulse emissions at the start of the rainy season contribute highly to annual ecosystem CO2 and N2O budgetsSoil emissions in tropical dry forests are lower than in wetter tropical forests [ABSTRACT FROM AUTHOR]

We analyze here how much carbon is being accumulated annually by secondary tropical dry forests (TDFs) and how structure, composition, time since abandonment, and climate can influence the dynamics of forest carbon accumulation. The study was carried out in Santa Rosa National Park in Guanacaste province, Costa Rica and Mata Seca State Park in Minas Gerais, Brazil. Total carbon storage and carbon accumulation were obtained for both sites from the sum of the aboveground carbon and belowground carbon gain plus the annual litterfall. Carbon accumulation of these TDFs varied from 2.6 Mg C ha−1 y−1 to 6.3 Mg C ha−1 y−1, depending on the age of the forest stands. Time since abandonment and number of stems per plot were the best predictors for carbon storage, annual carbon gains, and losses. Mortality rates and carbon losses were also associated with seasonal climate variability. We found significant correlations between tree mortality, carbon losses and mean seasonal temperature, mean seasonal precipitation, potential evapotranspiration, and the Oceanic Niño Index. Carbon dynamics in tropical dry forests are driven by time since abandonment and forest structure; however, rising temperature and El Niño Southern Oscillation (ENSO) events can have a significant impact on tree mortality and carbon losses. Depending on their location and land-use history, some dry forests are more impacted by climatic extremes than others, and differences between secondary stages are expected. [ABSTRACT FROM AUTHOR]

Leaf temperature (Tleaf) influences photosynthesis and respiration. Currently, there is a growing interest in including lianas in productivity models due to their increasing abundance and their detrimental effects in the carbon stock of tropical ecosystems. Therefore, understanding the differences of Tleaf between lianas and trees is important for future predictions of productivity. Here, we determined the displayed leaf temperature (Td = Tleaf − air temperature) of several species of lianas and their host trees during El Niño-Southern Oscillation (ENSO) and non-ENSO years to evaluate if the presence of lianas affects the Td of their host trees, and if leaves of lianas and their host trees exhibit differences in Td. Our results suggest that close to midday, the presence of lianas does not affect the Td of their host trees; however, lianas tend to have higher values of Td than their hosts across seasons, in both ENSO and non-ENSO years. Although lianas and trees tend to have similar physiological-temperature responses, differences in Td could lead to significant differences in rates of photosynthesis and respiration based on temperature response curves. Future models should thus consider differences in leaf temperature between these two life forms to achieve robust predictions of productivity. [ABSTRACT FROM AUTHOR]

• Corticolous lichen classification using visible-near infrared spectra. • Linear spectral mixing between lichen and bark was evaluated. • Overall lichen signatures tend to present low spectral bark mixing. • Lichen classification is more accurate at higher, rather than lower taxonomic ranks. • Linear discriminant analysis presents the highest performance classifying species. The optical properties of lichens have been traditionally explored in the context of geological mapping where the encrustation of lichens on rocks may influence the detection of minerals of interest. As of today, few studies have looked into the potential of using the optical properties of lichens to classify them; however, none has investigated the classification of tropical lichens using spectroscopy. Here we explore the use of the visible-near infrared reflectance (VNIR; 450–1000 nm) to discriminate Neotropical corticolous lichens; the most abundant lichens in tropical forests. Reflectance measurements on lichens and their bark substrate were performed on 282 lichens samples of 32 species attached to their host's bark. Using these measurements, we first explored the degree of spectral mixing of bark and lichens by linear unmixing each lichen spectrum with the corresponding average species spectrum and bark spectrum. Overall, the results reveal that the lichen signatures tend to mask the spectral contributions from bark; however, there are some specific groups of species with high bark mixing probably due to their nature and the similarities between the lichen and bark spectra. Next, we classified the lichen spectra based on growth forms and taxonomic ranks (i.e., family, genus, species) using five machine learning classifiers. This analysis was conducted on raw reflectance spectra and wavelet-transformed spectra to enhance the absorption features prior to classification. As expected, the classification of lichen spectra is less accurate at species-specific levels, rather than higher taxonomic ranks. The wavelet transformation was found to enhance the general performance of classification; however, the accuracy of the classification depends on the classifier. Of the classifiers used in this study, linear discrimination applied to reflectance spectra presents the highest performance at the species level. Our results reveal the potential of using the VNIR reflectance as a method to discriminate Neotropical lichens. The introduced methodology may be conducted in the field, thus allowing the monitoring of lichen communities in forests; thereby furthering the current understanding of the role of lichens in ecosystem functioning. [ABSTRACT FROM AUTHOR]