Search

Aim To develop an integrative framework to evaluate variation in aboveground carbon storage ( AGC). A model that can be applied to understand and predict how global-change drivers influence tropical carbon sinks. Location Old-growth tropical forests world-wide. Methods Using structural equation modelling ( SEM), we propose an a priori model to evaluate the direct and indirect effects of climate, stand variables (basal area, tree diameter and wood density at plot level) and liana abundance on AGC. Our model indicated that stand variables increased AGC while liana abundance decreased AGC indirectly via negative effects on stand variables. We used a multigroup SEM to test the generality of our framework using a standardized dataset of 145 plots (0.1 ha) in dry, moist and wet tropical forests. Results Our model explained over 85% variation in AGC and showed a positive and consistent relationship between stand variables and AGC across forests types. The effects of climate on AGC were indirect rather than direct, with negative effects of temperature in all forests. Liana abundance reduced tree diameter and basal area in moist forests, but did not affect AGC in wet or dry forests. Main conclusions Our results suggest that climate affects AGC indirectly, via its direct influence on stand variables and liana abundance. The effects of lianas on AGC result from reductions in stand variables and are as important as climate for moist forests, which harbour the greatest tropical carbon pools. Our model was consistent across forest types. This highlights the usefulness of an integrative framework to improve predictions of the effects of drivers of global change on tropical carbon sinks. [ABSTRACT FROM AUTHOR]

[Display omitted] • Low-cloud cover in cloud forests is declining. • Low-cloud trends over 23 years in 521 cloud forests were evaluated. • Seventy percent of the cloud forests experienced a decrease in low-clouds. • Cloud forests experience steeper declines in low-clouds than in tropical landmasses. • Low-cloud trends indicate potential threats to species and ecosystem services. Clouds are critical to the biodiversity and function of Tropical Montane Cloud Forests (TMCF) as they control water regimes and sunlight that can be perceived by plants. These ecosystems provide a key role in ecosystem services to humanity and are considered hotspots of endemism, given that the number of species is restricted to their microclimates. The cloudiness in these ecosystems is projected to decline owing to global warming, but recent temporal trends remain unclear. Here, we evaluated trends in low-cloud fractions (CF) (e.g., the proportion of an area covered by low-cloud) and other Essential Climatic Variables (ECV) (e.g., surface temperature, pressure, soil moisture, and precipitation) for 521 sites worldwide with TMFCs from 1997 to 2020. We hypothesize that recent traces of global warming over the last few decades have led to decreases in CF on TMCFs. The previous was also assessed globally and among biogeographic realms to identify regional trends. We calculated trends by aggregating hourly observations from ERA5 reanalysis and CHIRPS into annual averages and then using linear regressions to calculate slopes (i.e., rate of change) (Δ, year−1). Our results suggest that CF trends at the TMCFs range between −64.7×10−4 and 51.4×10−4 CF year−1, revealing that 70 % of the assessed sites have experienced reductions in CF. Declines in low-clouds in these ecosystems are 253 % more severe than tropical landmasses when peak values of density distribution are compared (TMCFs: −7.8×10−4 CF year−1; tropical landmasses −2.3×10−4 CF year−1). Despite this, CF trends differ among biogeographic realms, as those TMCFs from the Neotropics and Indomalayan realms have the most pronounced declines. Decreases in CF were also associated with increases in surface temperature and pressure and decreases in soil moisture, revealing that the TMCF's climate is changing to warmer environments. These climatic shifts may represent imprints of global change on TMCFs, highlighting a current threat to species and essential ecosystem services that these ecosystems provide. [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]