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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]

Abstract Increases in liana abundance in tropical forests are pervasive threats to the current and future forest carbon stocks. Never before has the need been more evident for new approaches to detect the presence of liana in ecosystems, given their significance as fingerprints of global environmental change. In this study, we explore the use of longwave infrared reflectance (LWIR, 8–11 μm) as a wavelength region for the classification of liana and tree leaves and compare classification results with those obtained using visible-near infrared reflectance data (VIS-NIR, 0.45–0.95 μm). Twenty sun leaves were collected from each of 14 liana species and 21 tree species located at the canopy or forest edge (n = 700) in Santa Rosa National Park, Costa Rica. LWIR and VIS-NIR reflectance measurements were performed on these leaves using a portable calibrated Fourier Transform Infrared Spectroscopy (FTIR) Agilent ExoScan 4100 and a UniSpec spectral analysis system, respectively. The VIS-NIR and LWIR data were first resampled. Then these two spectral libraries were pre-processed for noise reduction and spectral feature enhancement resulting in three datasets for each spectral region as follows: filtered only, filtered followed by extraction of the first derivative, and continuous wavelet transformation (CWT). Data reduction was then applied to these data sets using principal components analysis (PCA). The outputs obtained from the PCA were used to conduct the supervised classification of liana and tree leaves. In total, 21 classifiers were applied to datasets of training and testing to extract the classification accuracy and agreement for liana and tree leaves. The results suggest that the classification of leaves based on LWIR data can reach accuracy values between 66 and 96% and agreement values between 32 and 92%, regardless of the type of classifier. In contrast, the classification based on VIS-NIR data shows accuracy values between 50 and 70% and agreement values between 0.01 and 40%. The highest classification rates of liana and tree leaves were obtained from datasets pre-processed using the CWT or from the extraction of the first derivative and classified using either random forest, k -nearest neighbor, or support vector machine with radial kernel. The results using the LWIR reflectance highlight the potential of this spectral region for the accurate detection of liana extent in tropical ecosystems. Future studies should consider this potential and test the regional monitoring of lianas. Highlights • Life form classification using visible-near infrared or longwave infrared spectra. • Four pre-processing approaches and 21 classifiers were evaluated. • Best classification of life forms obtained with longwave infrared reflectance. • Best discrimination by pre-processing with continuous wavelet transformation. • Radial kernel SVM and random forest are of the most promising classifiers. [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]