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Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood. [ABSTRACT FROM AUTHOR]

The cloud forest (CF), a hugely biodiverse ecosystem, is a hotspot of unexplored plants with potential for discovering pharmacologically active compounds. Without sufficient ethnopharmacological information, developing strategies for rationally selecting plants for experimental studies is crucial. With this goal, a CF metabolites library was created, and a ligand-based virtual screening was conducted to identify molecules with potential hypoglycemic activity. From the most promising botanical families, plants were collected, methanolic extracts were prepared, and hypoglycemic activity was evaluated through in vitro enzyme inhibition assays on α-amylase, α-glucosidase, and dipeptidyl peptidase IV (DPP-IV). Metabolomic analyses were performed to identify the dominant metabolites in the species with the best inhibitory activity profile, and their affinity for the molecular targets was evaluated using ensemble molecular docking. This strategy led to the identification of twelve plants (in four botanical families) with hypoglycemic activity. Sida rhombifolia (Malvaceae) stood out for its DPP-IV selective inhibition versus S. glabra. A comparison of chemical profiles led to the annotation of twenty-seven metabolites over-accumulated in S. rhombifolia compared to S. glabra, among which acanthoside D and cis-tiliroside were noteworthy for their potential selective inhibition due to their specific intermolecular interactions with relevant amino acids of DPP-IV. The workflow used in this study presents a novel targeting strategy for identifying novel bioactive natural sources, which can complement the conventional selection criteria used in Natural Product Chemistry. [ABSTRACT FROM AUTHOR]

Leguminous Inga trees are thought to enhance soil carbon (C) accumulation following reforestation, through mostly unknown mechanisms. This study amplified soil DNA using the ITS1F and ITS4 primers for PCR and Illumina MiSeq methods to identify fungal taxa, and traditional C analysis methods to evaluate how planted 4-, 8-, and 11-year-old Inga punctata trees affected soil fungal community compositions and C utilization patterns compared to old-growth I. punctata trees and an adjacent unplanted pasture within the same reforestation zone in Monteverde, Costa Rica. Along the tree age gradient, the planted I. punctata trees enhanced the tree soil C capture capacity, as indicated by increased levels of soil biomass C, Respiration, and efficiency of organic C use (with lower qCO2 values), and development of increasingly more abundant, stable, and successionally developed fungal communities, including those associated with the decomposition of complex organic C compounds. The level and strength of differences coincided with differences in the time of separation between the pasture and tree age or between the different tree ages. Fungal taxa were also identified as potential indicators of the early and late stages of soil recovery. Thus, planting I. punctata should be part of future reforestation strategies used in this region of the Monteverde Cloud Forest in Costa Rica. [ABSTRACT FROM AUTHOR]

Deforestation in the tropics causes shifts in adult odonate community structure, from forests dominated by thermoconforming zygopteran specialists to open areas with higher representations of heliothermic anisopterans. We tested for these shifts in the Maquina and Cuecha rivers in Monteverde, Costa Rica. We compared adult odonate communities in 100 m plots (subdivided into twenty 5 m subplots) located in disturbed, partially open areas with those in 100 m plots located in intact forest and used general linear models to describe how odonate abundance, species richness, species diversity, and the Anisoptera/Zygoptera ratio varied among plots, subplots, habitat type (disturbed/forested), rivers, and as functions of percent canopy cover and light levels. Plots varied in light levels and percent canopy cover, but there were no significant differences in species richness or diversity. Community composition, however, varied across plots and subplots in NMDS and PERMANOVA analyses, largely as a consequence of the preference of Hetaerina cruentata and Paltothemis lineatipes for high light subplots and H. majuscula for low light subplots. NMDS axes were significantly correlated with percent canopy cover and light level in subplots, and the Anisoptera/Zygoptera ratio correlated with NMDS axes at both the plot and subplot scales, indicating that the relative abundance of anisopterans did increase with increasing light and decreasing canopy cover. Differences among plots and habitats can largely be attributed to species-specific differences in habitat selection at a small spatial scale, causing predicted shifts in the Anisoptera/Zygoptera ratio as dominance shifts from endemic forest species to wide-ranging generalists. This is one of the first studies that confirms these patterns for a cloud forest community. [ABSTRACT FROM AUTHOR]

Pleione formosana Hayata is an endemic orchid that was once widely distributed across the mid-elevations of Taiwan. However, populations of this orchid have steadily shrunk due to orchid poaching in most of its habitats. By correlating data from micrometeorological stations that we installed in the cloud forest canopy at the study site, Yuanyang Lake (YYL) from 2017, we discovered the critical role of spring rainfall in triggering flowering and summer rainfall in promoting the growth of new bulbs. We found that rising temperatures and frequent drought events threaten orchid growth, potentially leading to pathogen infections. We climbed old-growth yellow cypresses to collect seed capsules of P. formosana for in vitro germination at YYL beginning in the autumn of 2018. Orchid plantlets were reintroduced to the study site in mid-August of 2022. However, the seedlings failed to survive the summer of 2023. This study is the first persistent monitoring of this rare orchid in the forest canopy of this old-growth cloud forest. Based on the result, we propose conservation strategies and directions for protecting this orchid on a regional scale. Our study highlights the mounting challenge to conservation efforts posed by global climate change. [ABSTRACT FROM AUTHOR]

Multiplatform fusion point clouds can effectively compensate for the disadvantages of individual platform point clouds in forest parameter extraction, maximizing the potential of LiDAR technology. However, existing registration algorithms often suffer from insufficient feature extraction and limited registration accuracy. To address these issues, we propose a ULS (Unmanned Aerial Vehicle Laser Scanning)-TLS (Terrestrial Laser Scanning) point cloud data registration method based on Similar Distance Search (SDS). This method enhances coarse registration by accurately retrieving points with similar features, leading to high overlap in the rough registration stage and further improving fine registration precision. (1) The proposed method was tested on four natural forest plots, including Pinus densata Mast., Pinus yunnanensis Franch., Pices asperata Mast., Abies fabri (Mast.) Craib, and demonstrated high registration accuracy. Both coarse and fine registration achieved superior results, significantly outperforming existing algorithms, with notable improvements over the TR algorithm. (2) In addition, the study evaluated the accuracy of individual tree parameter extraction from fusion point clouds versus single-platform point clouds. While ULS point clouds performed slightly better in some metrics, the fused point clouds offered more consistent and reliable results across varying conditions. Overall, the proposed SDS method and the resulting fusion point clouds provide strong technical support for efficient and accurate forest resource management, with significant scientific implications. [ABSTRACT FROM AUTHOR]

Canopy volume is a crucial biological parameter for assessing tree growth, accurately estimating forest Above-Ground Biomass (AGB), and evaluating ecosystem stability. Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) are advanced precision mapping technologies that capture highly accurate point clouds for forest digitization studies. Despite advances in calculating canopy volume, challenges remain in accurately extracting the canopy and removing gaps. This study proposes a canopy volume extraction method based on an improved PointNeXt model, fusing ALS and TLS point cloud data. In this work, improved PointNeXt is first utilized to extract the canopy, enhancing extraction accuracy and mitigating under-segmentation and over-segmentation issues. To effectively calculate canopy volume, the canopy is divided into multiple levels, each projected into the xOy plane. Then, an improved Mean Shift algorithm, combined with KdTree, is employed to remove gaps and obtain parts of the real canopy. Subsequently, a convex hull algorithm is utilized to calculate the area of each part, and the sum of the areas of all parts multiplied by their heights yields the canopy volume. The proposed method's performance is tested on a dataset comprising poplar, willow, and cherry trees. As a result, the improved PointNeXt model achieves a mean intersection over union (mIoU) of 98.19% on the test set, outperforming the original PointNeXt by 1%. Regarding canopy volume, the algorithm's Root Mean Square Error (RMSE) is 0.18 m3, and a high correlation is observed between predicted canopy volumes, with an R-Square (R2) value of 0.92. Therefore, the proposed method effectively and efficiently acquires canopy volume, providing a stable and accurate technical reference for forest biomass statistics. [ABSTRACT FROM AUTHOR]

The TROPOMI XCH4 data product requires rigorous cloud filtering to achieve a product accuracy of <1%. To this end, operational XCH4 data processing has been based on SUOMI-NPP VIIRS cloud observations. However, SUOMI-NPP is nearing the end of its operational life and has encountered malfunctions in 2022 and 2023. In this study, we introduce a novel machine learning cloud-clearing approach based on a random forest classifier (RFC). The RFC is trained on collocated TROPOMI and SUOMI-NPP VIIRS data to emulate VIIRS-like cloud clearing. After training, cloud masking requires only TROPOMI data, and so becomes operationally independent of SUOMI-NPP. We demonstrate the RFC approach by applying cloud clearing to operational TROPOMI XCH4 data for August 2022, a period in which VIIRS was not operational. For validation, we analyze the TROPOMI XCH4 data at 12 TCCON stations. Comparison of cloud clearing using the RFC and the original VIIRS method reveals excellent agreement with a similar station-to-station bias (−7.4 ppb versus −5.6 ppb), a similar standard deviation of the station-to-station bias (11.6 ppb versus 12 ppb), and the same Pearson correlation coefficient of 0.9. Remarkably, the RFC cloud clearing provides a slightly higher volume of data (2182 versus 2035 daily means) and appears to have fewer outliers. Since 21 November 2023, the RFC approach is part of the operational processing chain of the European Space Agency (ESA). For now, the default practice is to utilize SNPP-VIIRS when accessible. Only in cases where VIIRS data are unavailable do we resort to the RFC cloud mask. [ABSTRACT FROM AUTHOR]

The Australian Wet Tropics World Heritage Area (WTWHA) in northeast Queensland is home to approximately 18 percent of the nation's total vascular plant species. Over the past century, human activity and industrial development have caused global climate changes, posing a severe and irreversible danger to the entire land-based ecosystem, and the WTWHA is no exception. The current average annual temperature of WTWHA in northeast Queensland is 24 °C. However, in the coming years (by 2030), the average annual temperature increase is estimated to be between 0.5 and 1.4 °C compared to the climate observed between 1986 and 2005. Looking further ahead to 2070, the anticipated temperature rise is projected to be between 1.0 and 3.2 °C, with the exact range depending on future emissions. We identified 84 plant species, endemic to tropical montane cloud forests (TMCF) within the WTWHA, which are already experiencing climate change threats. Some of these plants are used in herbal medicines. This study comprehensively reviewed the metabolomics studies conducted on these 84 plant species until now toward understanding their physiological and metabolomics responses to global climate change. This review also discusses the following: (i) recent developments in plant metabolomics studies that can be applied to study and better understand the interactions of wet tropics plants with climatic stress, (ii) medicinal plants and isolated phytochemicals with structural diversity, and (iii) reported biological activities of crude extracts and isolated compounds. [ABSTRACT FROM AUTHOR]

Impatiens mexicana is a native balsam restricted to the cloud forests of central and southwest Mexico, which is currently known to exist in four states (Zacatecas, Veracruz, Puebla, and Oaxaca), and is probably threatened by geographic restriction. Morphological and distributional information about this species is still scarce, with only a couple of investigations since its first description in 1910, which means the phylogenetic position of this species has yet to be placed. In order to contribute to knowledge of I. mexicana, morphological and molecular studies were carried out with material collected in the localities of Hidalgo, Veracruz, and Oaxaca, during expeditions from July to October 2021 and in 2022. The specimens selected were measured, photographed, and compared with morphological information from the six American balsams. At the same time, phylogenetic studies were carried out by using two regions of the cpDNA and one of the nrDNA. We report I. mexicana for the first time in Hidalgo state, and identify new registers in the Veracruz and Oaxaca localities, thus increasing the knowledge in its geographic distribution. We also redescribe the Mexican balsam by using new and complementary traits: we note that while the species is morphologically similar to I. capensis, it is distinguished by differences in flower color, the distribution of spot patterns on the upper petal, lower sepal, and lateral petals, and geographic information such as endemic geographic distribution in Mexico's Tropical Montane Cloud Forest (TMCF). We also carried out phylogenetic analyses by using ITS and ITS + atpb-rbcL, which showed the Mexican balsam was independent from its Asian-American congeners. Interestingly, our genetic distance analyses reveal differences of 0.01–0.16% between I. mexicana and the other North American congeners that use ITS, atpb-rbcL and trnL-F, both independently and as concatenated genes: this low divergence most likely occurred because of the recent diversification of the group. However, both future redescriptions and phylogenetic studies in American species are imperative, as this will enable better discrimination. [ABSTRACT FROM AUTHOR]

The genus Cinchona L. has important medicinal, cultural, and economic value and is the emblematic tree of Peru. The genus is mainly found in the cloud forests of the Andes. However, the expansion of agriculture and livestock farming in the department of Amazonas is degrading these ecosystems and has reduced the size of the genus's populations. In this work, we model the potential distribution under current conditions of three Cinchona species (C. capuli L. Anderson, C. macrocalyx Pav. Ex DC., and C. pubescens Vahl.) to identify areas with a high likelihood of species presence and their key conservation and reforestation zones. We fitted a maximum entropy (MaxEnt) model using nineteen bioclimatic variables, three topographic variables, nine edaphic variables, and solar radiation. Under current conditions, the potential distribution of C. capuli covers 17.22% (7243.98 km2); C. macrocalyx, 29.11% (12,238.91 km2); and C. pubescens, 22.94% (9647.63 km2) of the study area, which was mostly located in central and southern Amazonas. Only 24.29% (25.51% of C. capuli, 21.02% of C. macrocalyx, and 26.35% of C. pubescens) of the potential distributions are within protected areas, while 10,987.22 km2 of the surface area of the department of Amazonas is degraded, of which 29.80% covers the area of probable occurrence of C. capuli, 38.72% of C. macrocalyx, and 34.82% of C. pubescens. Consequently, it is necessary to promote additional conservation strategies for Cinchona, including the establishment of new protected areas and the recovery of degraded habitats, in order to protect this species. [ABSTRACT FROM AUTHOR]

Seed germination is among the most crucial and vulnerable stages in plant life cycles. Quercus marlipoensis is a critically endangered sclerophyllous oak. Only one population has ever been found worldwide in the tropical montane cloud forests of southeastern Yunnan, China, and it has shown difficulties with regeneration. However, its seed biological traits and key restrictive germination factors remain unknown. We investigate the impacts of scarification, temperature, and water potential on the seed germination of Q. marlipoensis. Results show that the seeds show typical epicotyl dormancy. The seed germination increased when removing part or all of the pericarp and part of the cotyledon (one-third and two-thirds). The seeds can germinate at 5 to 30 °C, but the highest T50 was achieved at 25 °C. When the water potential decreased from 0 to −1.0 MPa, the germination rate decreased but the germination time increased. Q. marlipoensis seeds are typically recalcitrant and highly sensitive to moisture loss, but the species can tolerate animal predation and low germination temperatures. The more frequent climatic extremes and droughts in the Indo-China region will severely degrade its natural habitats. Therefore, ex situ conservation to preserve its germplasm and introduce seedlings into a suitable habitat are essential for its conservation management. [ABSTRACT FROM AUTHOR]

Background. With the advancement of "digital forestry" and "intelligent forestry", point cloud data have emerged as a powerful tool for accurately capturing three-dimensional forest scenes. It enables the creation and presentation of digital forest systems, facilitates the monitoring of dynamic changes such as forest growth and logging processes, and facilitates the evaluation of forest resource fluctuations. However, forestry point cloud data are characterized by its large volume and the need for time-consuming and labor-intensive manual processing. Deep learning, with its exceptional learning capabilities, holds tremendous potential for processing forestry environment point cloud data. This potential is attributed to the availability of accurately annotated forestry point cloud data and the development of deep learning models specifically designed for forestry applications. Nonetheless, in practical scenarios, conventional direct annotation methods prove to be inefficient and time-consuming due to the complex terrain, dense foliage occlusion, and uneven sparsity of forestry point clouds. Furthermore, directly applying deep learning frameworks to forestry point clouds results in subpar accuracy and performance due to the large size, occlusion, sparsity, and unstructured nature of these scenes. Therefore, the proposal of accurately annotated forestry point cloud datasets and the establishment of semantic segmentation methods tailored for forestry environments hold paramount importance. Methods. A point cloud data annotation method based on single-tree positioning to enhance annotation efficiency was proposed and challenges such as occlusions and sparse distribution in forestry environments were addressed. This method facilitated the construction of a forestry point cloud semantic segmentation dataset, consisting of 1259 scenes and 214.4 billion points, encompassing four distinct categories. The pointDMM framework was introduced, a semantic segmentation framework specifically designed for forestry point clouds. The proposed method first integrates tree features using the DMM module and constructs key segmentation graphs utilizing energy segmentation functions. Subsequently, the cutpursuit algorithm is employed to solve the graph and achieve the pre-segmentation of semantics. The locally extracted forestry point cloud features from the pre-segmentation are comprehensively inputted into the network. Feature fusion is performed using the MLP method of multi-layer features, and ultimately, the point cloud is segmented using the lightweight PointNet. Result. Remarkable segmentation results are demonstrated on the DMM dataset, achieving an accuracy rate of 93% on a large-scale forest environment point cloud dataset known as DMM-3. Compared to other algorithms, the proposed method improves the accuracy of standing tree recognition by 21%. This method exhibits significant advantages in extracting feature information from artificially planted forest point clouds obtained from TLS. It establishes a solid foundation for the automation, intelligence, and informatization of forestry, thereby possessing substantial scientific significance. [ABSTRACT FROM AUTHOR]

Among Boletales, the family Boletaceae has the highest diversity worldwide. Additionally, this fungal group has great ecological relevance because it not only includes mainly ectomycorrhizal but also saprotrophic species. Furthermore, some species are used as food and have sociocultural and economic importance worldwide. In Mexico, the Boletaceae family boasts a substantial number of species, yet our understanding of these species remains far from comprehensive. In this work, by using macro- and micromorphological and phylogenetic analyses of DNA sequences from multi-gene analyses based on ITS, nrLSU, rpb1, rpb2, and tef1, we report five new species belonging to the genera Aureoboletus and Chalciporus: A. ayuukii and A. elvirae from a Quercus scytophylla forest, A. readii from a mixed forest, C. perezsilvae from cloud forest, and C. piedracanteadensis from both a mixed coniferous forest and a Quercus-Pinus forest. In Mexico, four species of Aureoboletus are used as a food source, and in this work, we add another one, A. readii, which is traditionally consumed by members of the Tlahuica-Pjiekakjoo culture, who are located in the central part of the country. This work contributes to our knowledge of two genera of Boletaceae in a geographical area that is scarcely studied, and thus, our understanding of its biocultural relevance is enriched. [ABSTRACT FROM AUTHOR]

The tropical montane cloud forest is the most diverse and threatened vegetation type in Mexico. In the last decade, the number of described Ascomycetes species has notably increased, reaching more than 1300 species. This study describes six new species based on their molecular and morphological characteristics. Our results suggest that Mexico has the highest number of described species in the Neotropics. However, many other Mexican lineages still need to be described. [ABSTRACT FROM AUTHOR]

The lulo plant (Solanum quitoense Lamarck) is native to South America. In Mexico, this species shows potential for the conversion of agroecosystems. It is used as food and pharmaceutical sources for metabolites. However, there are few papers related to how this species can grow under conditions outside of the Andean countries (Bolivia, Colombia, Ecuador, and Peru). The objective of this research was to evaluate the development of lulo under cloud forest conditions and the effect of inoculating the plant with mycorrhizae (Funneliformis mosseae (T. H. Nicolson and Gerd.) C. Walker and A. Schüssler, and Entrophospora colombiana Spain and N. C. Schenck) and diammonium phosphate (DAP: NPK 18-46-00) fertilization. The plant growth, leaf area, mycorrhizal colonization, and leaf mineral content were evaluated from transplant to fruit formation. The experiment was conducted under field conditions in volcanic soils (clayey Vertisol) in a cloud forest. The inoculation of E. colombiana was 86.19% of the colonization, and the content of N, K, Ca, Mg, Mn, Cu, Zn, and Fe in the leaves was the higher in these plants. The highest P content was obtained from the DAP treatment and the height of the plant was 11.8% and 12.5% in the treatments using DAP and E. colombiana, respectively. The plant growth was significantly higher in the plants inoculated with E. colombiana followed by DAP. The plants inoculated with F. mosseae registered lower values than the control. Lulo plants grow in the climate and soils of volcanic origin of the cloud forest. The results showed that AMF colonization was beneficial and outperformed the native strains. The results are new for the introduced lulo plants in Mexico and can help reduce the learning path for commercial cultivation. [ABSTRACT FROM AUTHOR]

As the three-dimensional (3D) laser scanner is widely used for forest inventory, analyzing and processing point cloud data captured with a 3D laser scanner have become an important research topic in recent years. The extraction of single trees from point cloud data is essential for further investigation at the individual tree level, such as counting trees and trunk analysis, and many developments related to this topic have been published. However, constructing an accurate and automated method to obtain the tree crown silhouette from the point cloud data is challenging because the tree crowns often overlap between adjacent trees. A soft segmentation method that uses K-Nearest Neighbor (KNN) and contour shape constraints at the overlap region is proposed to solve this task. Experimental results show that the visual effect of the tree crown shape and the precision of point cloud segmentation have improved. It is concluded that the proposed method works well for tree crown segmentation and silhouette reconstruction from the terrestrial laser scanning point cloud data of the forest. [ABSTRACT FROM AUTHOR]

Deep learning, neural networks and other data-driven processing techniques are increasingly used in the analysis of LiDAR point cloud data in forest environments due to the benefits offered in accuracy and adaptability to new environments. One of the downsides of these techniques in practical applications is the requirement for manually annotated data necessary for training neural networks, which can be time consuming and costly to attain. We develop an approach to training neural networks for forest tree stem segmentation from point clouds that uses synthetic data from a custom tree simulator, which can generate large quantities of training examples without manual human effort. Our tree simulator captures the geometric characteristics of tree stems and foliage, from which automatically-labelled synthetic point clouds can be generated for training a semantic segmentation algorithm based on the PointNet++ architecture. Using evaluations on real aerial and terrestrial LiDAR point clouds from a range of different forest sites, we demonstrate our synthetic data-trained models can out-perform, or provide comparable performance with models trained on real data from other sites or when available real training data is limited (increases in IoU from 1–7%). Our simulation code is open-source and made available to the research community. [ABSTRACT FROM AUTHOR]

During a survey of Phytophthora diversity in Panama, fast-growing oomycete isolates were obtained from naturally fallen leaves of an unidentified tree species in a tropical cloud forest. Phylogenetic analyses of sequences from the nuclear ITS, LSU and ßtub loci and the mitochondrial cox1 and cox2 genes revealed that they belong to a new species of a new genus, officially described here as Synchrospora gen. nov., which resided as a basal genus within the Peronosporaceae. The type species S. medusiformis has unique morphological characteristics. The sporangiophores show determinate growth, multifurcating at the end, forming a stunted, candelabra-like apex from which multiple (8 to >100) long, curved pedicels are growing simultaneously in a medusa-like way. The caducous papillate sporangia mature and are shed synchronously. The breeding system is homothallic, hence more inbreeding than outcrossing, with smooth-walled oogonia, plerotic oospores and paragynous antheridia. Optimum and maximum temperatures for growth are 22.5 and 25–27.5 °C, consistent with its natural cloud forest habitat. It is concluded that S. medusiformis as adapted to a lifestyle as a canopy-dwelling leaf pathogen in tropical cloud forests. More oomycete explorations in the canopies of tropical rainforests and cloud forests are needed to elucidate the diversity, host associations and ecological roles of oomycetes and, in particular, S. medusiformis and possibly other Synchrospora taxa in this as yet under-explored habitat. [ABSTRACT FROM AUTHOR]

Subtropical montane moist evergreen broadleaf forests (MMEBFs) have a unique environmental envelope harboring a high percentage of endemic biota. This ecosystem is highly vulnerable, and little is known about its possible response to future climate change. In this study, we used Lithocarpus hancei (Fagaceae), a dominant tree in East Asian subtropical MMEBFs, as a proxy to investigate MMEBF distribution dynamics and key distribution restriction factors. In total, 607 L. hancei occurrence points were obtained after being gathered and filtered. The random forest model was selected as the best model. Eight environmental variables were selected to simulate the potential suitable areas of L. hancei under the Last Glacial Maximum (LGM), present, and future (2041–2060, 2061–2080, 2081–2100) climate change scenarios, including four shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Our results showed that precipitation of the warmest quarter, the precipitation of the driest month, the mean diurnal range, and temperature seasonality are the key factors affecting the spatial range of L. hancei with 11.2%, 10.9%, 8.1%, and 7.6% contributions, respectively. The current distribution of L. hancei is mainly within East and South China, with a scattered distribution in North Indo-China and the Southeast Himalayas. The highly suitable area only accounts for 9.7% of the total distribution area. The distribution area of the current suitability area is the smallest compared to that under LGM and future scenarios. In all the future climatic scenarios, the highly suitable areas of L. hancei would decrease or even disappear, whereas the medium- and low-suitability areas might increase with the centroid of the total suitable area northern. Its distribution in Central China, the Southern Himalayas, and Northern Indo-China will be lost in the future. Overall, our study predicted a prominent degradation of East Asian MMEBFs in the future. In situ and ex situ conservation on East Asian MMEBFs should be prioritized and enforced. [ABSTRACT FROM AUTHOR]

The Cloud Ocean phenomenon in the Hyrcanian Forests, listed in the UNESCO World Heritage List, is one of the most stunning yet lesser-known ecotourism destinations that has recently attracted the attention of adventure tours. However, the increasing number of ecotourists visiting these sites is expected to threaten the sensitive ecosystem of the Cloud Ocean sites. Therefore, this study aims to conceptualize sustainable behaviors for off-road forest tours in the Cloud Ocean sites of the Hyrcanian Forests. A combined qualitative-quantitative approach was adopted to achieve this aim, using the meta-synthesis method to cover the qualitative part and Smart PLS software for the quantitative data analysis. The qualitative assessment involved 19 participants, while the quantitative study included 240 experts who helped monitor and evaluate the indicators as components of the model. Previous literature has given little attention to the Cloud Ocean sites. Therefore, the conceptualization of sustainable behavior for forest adventure tours in these sites is considered the most significant contribution of this study. Paying attention to the dimensions of the proposed model can significantly reduce damage to these sites. [ABSTRACT FROM AUTHOR]

The Oostvaardersplassen nature reserve in the Netherlands is grazed by large herbivores. Due to their increasing numbers, the area became dominated by short grazed grasslands and biodiversity decreased. From 2018, the numbers are controlled to create a diverse landscape. Fine-scale mapping and monitoring of the aboveground biomass is a tool to evaluate management efforts to restore a heterogeneous and biodiverse area. We developed a random forest model that describes the correlation between field-based samples of aboveground biomass and fifteen height-related vegetation metrics that were calculated from high-density point clouds collected with a handheld LiDAR. We found that two height-related metrics (maximum and 75th percentile of all height points) produced the best correlation with an R2 of 0.79 and a root-mean-square error of 0.073 kg/m2. Grassland segments were mapped by applying a segmentation routine on the normalized grassland's digital surface model. For each grassland segment, the aboveground biomass was mapped using the point cloud and the random forest AGB model. Visual inspection of video recordings of the scanned trajectories and field observations of grassland patterns suggest that drift and stretch effects of the point cloud influence the map. We recommend optimizing data collection using looped trajectories during scanning to avoid point cloud drift and stretch, test horizontal vegetation metrics in the model development and include seasonal influence of the vegetation status. We conclude that handheld LiDAR is a promising technique to retrieve detailed height-related metrics in grasslands that can be used as input for semi-automated spatio-temporal modelling of grassland aboveground biomass for supporting management decisions in nature reserves. [ABSTRACT FROM AUTHOR]

The tropical montane cloud forest in Mexico is the most diverse and threatened ecosystem. Mexican macrofungi numbers more than 1408 species. This study described four new species of Agaricomycetes (Bondarzewia, Gymnopilus, Serpula, Sparassis) based on molecular and morphological characteristics. Our results support that Mexico is among the most biodiverse countries in terms of macrofungi in the Neotropics. [ABSTRACT FROM AUTHOR]

Tropical cloud forests are characterized by abundant and biodiverse mosses which grow epiphytically as well as on the ground. Nitrogen (N)-fixing cyanobacteria live in association with most mosses, and contribute greatly to the N pool via biological nitrogen fixation (BNF). However, the availability of nutrients, especially N and phosphorus (P), can influence BNF rates drastically. To evaluate the effects of increased N and P availability on BNF in mosses, we conducted a laboratory experiment where we added N and P, in isolation and combined, to three mosses (Campylopus sp., Dicranum sp. and Thuidium peruvianum) collected from a cloud forest in Peru. Our results show that N addition almost completely inhibited BNF within a day, whereas P addition caused variable results across moss species. Low N2 fixation rates were observed in Campylopus sp. across the experiment. BNF in Dicranum sp. was decreased by all nutrients, while P additions seemed to promote BNF in T. peruvianum. Hence, each of the three mosses contributes distinctively to the ecosystem N pool depending on nutrient availability. Moreover, increased N input will likely significantly decrease BNF associated with mosses also in tropical cloud forests, thereby limiting N input to these ecosystems via the moss-cyanobacteria pathway. [ABSTRACT FROM AUTHOR]

The water balance is the volume of water flowing through the hydrological cycle, and one of its main components is fog. Fog is considered a type of low-lying cloud and is heavily influenced by water bodies, topography, and wind conditions. Fog incorporates water from the atmosphere to the terrestrial surface and for some ecosystems (e.g., cloud forests) represents a great water contribution. In this work, we aimed to answer the following questions: (1) What is the fog-water contribution to the water balance? (2) How does the presence of vegetation affect the water supply to the ecosystem? We took as a case study the Central Mountain Region of Veracruz, in eastern Mexico, and measured components of the water balance; this included precipitation (gross and net), fog water, interception, transpiration, evapotranspiration, and condensation, and we estimated water gain and loss of the water balance. We registered 510 precipitation events distributed throughout the year with three peaks (October, May, and January). Of these, 386 were fog events, 41 were rain events, and 83 were events combining fog and rain. Fog had a substantial contribution of water to the system, with a volume 22 times greater than that of rain (4311.14 mm vs. 197.5 mm). From the total water gain, the highest proportion (91%) was contributed by fog interception. Fog was considered a constant source of moisture throughout the year; however, water intercepted during the dry season was higher (56%) compared with the wet season (36%). Our results highlight the importance of the fog as a source of water for the region. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) has been a tool of choice for 3D dense point cloud reconstructions of forest canopy over the past two decades, but advances in computer vision techniques, such as structure from motion (SfM) photogrammetry, have transformed 2D digital aerial imagery into a powerful, inexpensive and highly available alternative. Canopy modelling is complex and affected by a wide range of inputs. While studies have found dense point cloud reconstructions to be accurate, there is no standard approach to comparing outputs or assessing accuracy. Modelling is particularly challenging in native eucalypt forests, where the canopy displays abrupt vertical changes and highly varied relief. This study first investigated whether a remotely sensed LiDAR dense point cloud reconstruction of a native eucalypt forest completely reproduced canopy cover and accurately predicted tree heights. A further comparison was made with a photogrammetric reconstruction based solely on near-infrared (NIR) imagery to gain some insight into the contribution of the NIR spectral band to the 3D SfM reconstruction of native dry eucalypt open forest. The reconstructions did not produce comparable canopy height models and neither reconstruction completely reproduced canopy cover nor accurately predicted tree heights. Nonetheless, the LiDAR product was more representative of the eucalypt canopy than SfM-NIR. The SfM-NIR results were strongly affected by an absence of data in many locations, which was related to low canopy penetration by the passive optical sensor and sub-optimal feature matching in the photogrammetric pre-processing pipeline. To further investigate the contribution of NIR, future studies could combine NIR imagery captured at multiple solar elevations. A variety of photogrammetric pre-processing settings should continue to be explored in an effort to optimise image feature matching. [ABSTRACT FROM AUTHOR]

LiDAR point clouds are characterized by high geometric and radiometric resolution and are therefore of great use for large-scale forest analysis. Although the analysis of 3D geometries and shapes has improved at different resolutions, processing large-scale 3D LiDAR point clouds is difficult due to their enormous volume. From the perspective of using LiDAR point clouds for forests, the challenge lies in learning local and global features, as the number of points in a typical 3D LiDAR point cloud is in the range of millions. In this research, we present a novel end-to-end deep learning framework called ADCoSNet, capable of adaptively reconstructing 3D LiDAR point clouds from a few sparse measurements. ADCoSNet uses empirical mode decomposition (EMD), a data-driven signal processing approach with Deep Learning, to decompose input signals into intrinsic mode functions (IMFs). These IMFs capture hierarchical implicit features in the form of decreasing spatial frequency. This research proposes using the last IMF (least varying component), also known as the Residual function, as a statistical prior for capturing local features, followed by fusing with the hierarchical convolutional features from the deep compressive sensing (CS) network. The central idea is that the Residue approximately represents the overall forest structure considering it is relatively homogenous due to the presence of vegetation. ADCoSNet utilizes this last IMF for generating sparse representation based on a set of CS measurement ratios. The research presents extensive experiments for reconstructing 3D LiDAR point clouds with high fidelity for various CS measurement ratios. Our approach achieves a maximum peak signal-to-noise ratio (PSNR) of 48.96 dB (approx. 8 dB better than reconstruction without data-dependent transforms) with reconstruction root mean square error (RMSE) of 7.21. It is envisaged that the proposed framework finds high potential as an end-to-end learning framework for generating adaptive and sparse representations to capture geometrical features for the 3D reconstruction of forests. [ABSTRACT FROM AUTHOR]

Investigating the three-dimensional structure of mangrove forests is critical for their conservation and restoration. However, mangrove forests are difficult to survey in the field, and their 3D structure is poorly understood. Light detection and ranging (LiDAR) is considered an accurate and dependable method of measuring the 3D structure of mangrove forests. This study aimed to find a suitable LiDAR platform for obtaining attributes such as breast height diameter and canopy area, as well as for measuring a digital terrain model (DTM), the base data for hydrological analysis. A mangrove forest near the mouth of the Oura River in Aza-Oura, Nago City, Okinawa Prefecture, Japan, was studied. We used data from terrestrial LiDAR scanning "TLS" and unmanned aerial vehicle (UAV) LiDAR scanning "ULS" as well as data merged from TLS and ULS "Merge". By interpolating point clouds of the ground surface, DTMs of 5 cm × 5 cm were created. DTMs obtained from ULS could not reproduce the heaps of Thalassina anomala or forest floor microtopography compared with those obtained from TLS. Considering that ULS had a few point clouds in the forest, automatic trunk identification could not be used to segment trees. TLS could segment trees by automatically identifying trunks, but the number of trees identified roughly doubled that of the visual identification results. The number of tree crowns identified using TLS and ULS was approximately one quarter of those identified visually, and many of them were larger in area than the visually traced crowns. The accuracy of tree segmentation using the canopy height model (CHM) was low. The number of canopy trees identified using Merge produced the best results, accounting for 61% of the visual identification results. Results of tree segmentation by CHM suggest that combining TLS and ULS measurements may improve tree canopy identification. Although ULS is a promising new technology, its applications are clearly limited, at least in mangrove forests such as the Oura River, where Bruguiera gymnorhiza is dominant. Depending on the application, using different LiDAR platforms, such as airborne LiDAR scanning, UAV LiDAR scanning, and TLS, is important. Merging 3D point clouds acquired by different platforms, as proposed in this study, is an important option in this case. [ABSTRACT FROM AUTHOR]

To identify the determining microclimatic factors affecting epiphyte distributions, we comprehensively investigated the epiphyte community of montane cloud forests in Chilan. Four sites of adjacent old-growth forests were chosen based on the degree of prevailing wind exposure. At each site, a micrometeorological station was installed about 20 m in the forest canopy. Five emergent trees (with heights of 19~46 m and DBHs of 73~230 cm) were selected at each site for tree structure measurement and epiphyte inventory. In total, 2043 occurrences of 61 epiphytic species were recorded. Epiphytes were most abundant and diverse at the Lake site, followed by the Lee, Wind, and WindN sites. The epiphyte assemblages greatly differed across these sites, which was likely determined by the range variation in vapor pressure deficit (VPD). At least 14 site-exclusive species were identified across the four sites. This is the first study comparing epiphyte distributions of old-growth forests at a fine topographic scale. We found that small changes in microclimate, especially the range variance of VPD, dramatically affect epiphyte communities. Our results reinforce the understanding that epiphytes are a highly sensitive functional group of the forest ecosystem and serve as a good indicator of climate change. [ABSTRACT FROM AUTHOR]

In this study, we document the distribution, current knowledge, and conservation of twenty-six tree species of gymnosperms (four species) and angiosperms (twenty-two species) characteristic of the Mexican cloud forests and most endemic to Mexico. Many species are threatened and included in international and national Red Data List, such as the IUCN, and the Mexican Official Norm (NOM-059-SEMARNAT-2010). Distribution maps of these 26 species were generated based on information from herbarium specimens, specialized literature, web databases, and our own field surveys. All records were displayed on a map of the Mexican territory divided into grid cells of 15 × 20 min of latitude/longitude (a spatial resolution of approximately 27.75 km × 36.75 km) to obtain the richness patterns. Additionally, these records were displayed on the map of the current Mexican System of Natural Protected Areas (NPAs) to evaluate their representativeness in these areas of in situ conservation. We also include information on populations and the habitat status of these tree species in some Mexican locations. Most species studied here require particular policies for their conservation due to the problems affecting their natural populations and habitat. Our results indicate that three species are not represented in the Mexican System of NPAs and that some are underrepresented. [ABSTRACT FROM AUTHOR]

In the ongoing series of Nova Interviews, Challenges Advisory Board member Alan C. Logan meets with thought leaders, scientists, scholars, healthcare professionals, artisans and visionaries concerned about health at the scales of persons, places, and the planet. In this interview, Dr. Nalini M. Nadkarni, of the University of Utah, responds to a set of questions posed by Nova for Challenges. For over forty years, Dr. Nadkarni has been studying the fragility and resiliency of rainforest ecosystems. During this time, Dr. Nadkarni has prioritized science communication with an emphasis on highlighting the interdisciplinary relevancy of research findings. Dr. Nadkarni has worked to promote an integrative way of thinking about the various branches of science and medicine, with an eye toward shifting public policy. Her research career has taken her where only a small minority of scientists have traveled—from remote cloud forests to segregated housing within prison facilities. Dr. Nadkarni successfully challenged the Mattel Corporation to update their globally-recognized toy, Barbie, with women in science in mind. Here, Dr. Nadkarni reflects on the early influences that shaped her career, updates Challenges on the latest directions of her work, and discusses the ways in which the canopy ecosystem can help us understand the complex interconnected challenges of our time. [ABSTRACT FROM AUTHOR]

Studies have shown that deforestation can cause changes in energy, moisture, and precipitation flows, with implications for local and regional climate. These studies generally focus on understanding how the hydrological cycle is impacted by deforestation, but few studies have investigated these impacts on cloud microphysics in tropical forest regions. The objective of this study was to quantitatively evaluate the impacts of deforestation on the microphysical parameters of clouds, based on data extracted from active and passive orbital sensors from the TRMM satellite. The study area comprised the state of Rondônia, Brazil. The analyses of the microphysical parameters extracted from the Microwave Imager (TMI) and Precipitation Radar (PR) sensors of the 2A-CLIM and 2A25 products were performed considering a period of 14 years. The parameters analyzed were Rain Water Path (RWP), Ice Water Path (IWP), Surface Precipitation (SP), Freezing Level Height (FH), and Rainfall Type (RT). Land cover type data were extracted from the Project to Monitor Deforestation in the Legal Amazon (PMDA). Our results showed that local deforestation significantly altered the microphysical parameters of the study region. In general, the values of the microphysical parameters of the clouds in the transition areas (locations where forest pixels are neighbors to deforested pixels) were about 5–25% higher compared to forested and deforested areas associated with a higher frequency of episodes of convective rainfall possibly driven by mesoscale circulations. Correspondingly, forested areas had higher rainfall rates compared to deforested areas. Meanwhile, deforested areas had higher amounts for IWP, of around 1–16%, and FH, of around 2–8%, in relation to forested areas. Conversely, the RWP showed a decrease of around 2–20%. These results suggest that the microphysical structure of clouds has different characteristics when related to forested and deforested areas in the Amazon. This is useful for evaluation of simulations of cloud microphysical parameters in numerical models of weather and climate. [ABSTRACT FROM AUTHOR]

In order to reduce the impact of global warming, forestry carbon sink trading is an effective approach to achieving carbon neutrality, while carbon storage estimation plays an important role as the basis of the whole carbon sink trading. Therefore, an accurate estimation of carbon storage is conducive to the sustainable development of carbon sink trading. In this paper, we use laser SLAM technology to model an artificial forest in three dimensions, extract the tree parameters by the point cloud processing software, and calculate the carbon storage according to the allometric growth equation of the tree species. The experimental results show that the loop path is the best among the three-path planning of ZEB-HORIZON scanner data acquisition. For large-scale plantations, the fusion data acquisition of linear and loop paths by Livox Mid-40 and ZEB-HORIZON LIDAR can be adopted with a highly precise and a complete 3D point cloud obtained. The Lidar360 software is used for single wood segmentation and parameter extraction, and the manual measurement is taken as the quasi-true value. After the measurement accuracy analysis, the carbon storage estimation is met. Using the volume source biomass method in the sample plot inventory method, the carbon storages of camphor and cypress in the experimental area were estimated through the allometric growth equation of camphor and cypress and the international conversion rate. [ABSTRACT FROM AUTHOR]

Litter weevil communities were investigated in two different types of montane forest in Ecuador: foothill evergreen forest, present in the Otongachi Integral Reserve between 800 and 1000 m, and tropical montane cloud forest (TMCF), present in the Otonga Integral Reserve between 1600 and 2300 m. Sampling was conducted along elevation gradients, applying the entomological sifting method to 19 sampling transects (11 in Otonga and 8 in Otongachi). The taxa collected were identified as morphospecies, since the majority of them are still undescribed. A total of 510 specimens were sampled, belonging to 100 different morphospecies, 85 of which were found in Otonga and 15 in Otongachi. No species in common between the two areas were found, despite the fact that the distance is extremely small (approximately 12 km). The Otonga area, regardless of primary or secondary forest habitats, had higher mean species richness. In both areas, the majority of species were found in a small number of stations and were sampled in a small number of specimens. A dominant and ubiquitous species was present only in Otongachi. The analysis of the community composition was carried out according to different categories of environmental variables (Otonga vs. Otongachi, forest type, elevation, litter and canopy coverage characteristics). Two well-differentiated coenoses were found, influenced firstly by the differences between the two reserves, secondarily by the elevation gradient and, to a lesser extent, by the forest type and other environmental variables. Some morphospecies characteristic of a specific type of biocoenosis could be identified. The study highlighted how most litter weevil species are strictly associated with a peculiar microhabitat and have a very narrow elevation and spatial range; the weevil communities can be strongly affected by heavy human impact. The results confirm that the tropical forests, in particular the TMCFs, host great biodiversity and that the majority of species are strictly associated with a single site; therefore, particular protection for these habitats should be granted. However, the non-significant variation between primary and secondary TMCFs indicates that, when correct management is carried out, a limited disturbance can be tolerated with only a limited loss of biodiversity. [ABSTRACT FROM AUTHOR]

There have been conflicting findings on hydrological dynamics in tropical montane cloud forests (TMCFs)—attributed to differences in climate, altitude, topography, and vegetation. We contribute another observation-based comparison between a TMCF (8.53 ha; 1906 m.a.s.l.) and a tropical lowland rainforest (TLRF) (5.33 ha; 484 m.a.s.l.) catchment in equatorial Sabah, Malaysian Borneo. In each catchment, a 90° v-notch weir was established at the stream's outlet and instrumented with a water-level datalogger that records data at 10-min intervals (converted to discharge). A nearby meteorological station records rainfall at the same 10-min intervals via a tipping bucket rain gauge connected to a datalogger. Over five years, 91 and 73 storm hydrographs from a TMCF and a TLRF, respectively, were extracted and compared. Various hydrograph metrices relating to discharge and flashiness were compared between the TMCF and TLRF while controlling for event rainfall, rainfall intensity, and antecedent moisture. Compared to the TLRF, storm-event runoff in the TMCF was up to 169% higher, reflecting the saturated conditions and tendency for direct runoff. Instantaneous peak discharge was also higher (up to 6.6x higher) in the TMCF. However, despite high moisture and steep topography, stream responsiveness towards rainfall input was lower in the TMCF, which we hypothesise was due to its wide and short catchment dimensions. Baseflow was significantly correlated with API20, API10, and API7. Overall, we found that the TMCF had higher runoff, but higher moisture condition alone may not be sufficient to govern flashiness. [ABSTRACT FROM AUTHOR]

Secondary forests, i.e., those arising after the clearance and abandonment of previously forested land, dominate tropical forest landscapes, rapidly sequester carbon, provide essential ecosystem services and are prone to re-clearance. Secondary cloud forests (SCF) play a particularly critical role for biodiversity and hydrological regulation. To promote their persistence, sustainable management is necessary; however, there is limited information regarding SCF potential for sustainable timber production. We estimated the revenue from selective timber harvesting in a pilot study in a 20-year-old SCF in Mexico. We explored the effect of the harvested timber volume, harvesting costs and price of forest products on the Net Present Value (NPV). Small landowners could only extract 17% of the harvestable standing volume due to a high number of small trees, a high (34%) volume of non-timber species, and their limited capacity to process timber. A third of the income derived from fuelwood, and overall financial returns were negative. A positive NPV may result from a 20% harvesting intensity, a 10% reduction in harvesting costs, or a 20% increase in stumpage price. Our results warrant the development of forest policy instruments and economic incentives for small-scale SCF landowners to alleviate poverty and meet national and global restoration and climate mitigation goals. [ABSTRACT FROM AUTHOR]

Agaricus is a genus of fungi in the family Agaricaceae, with several highly priced edible and medicinal species. Here we describe Agaricus macrochlamys, a new species, in A. sect. Arvenses, sympatric and morphologically cryptic with the edible and medicinally cultivated mushroom, A. subrufescens. Phylogenetic analyses showed that A. macrochlamys is closely related to A. subrufescens, and that A. fiardii is a new synonym of A. subrufescens. Despite being morphologically cryptic, A. macrochlamys can be distinguished from A. subrufescens by several ITS and tef1α species-specific markers and a 4-bp insertion in the tef1α sequence. Furthermore, A. subrufescens is a cosmopolitan species, while A. macrochlamys distribution is so far restricted to Mexico, the Dominican Republic, and the United States. [ABSTRACT FROM AUTHOR]

Quercus litseoides, an endangered montane cloud forest species, is endemic to southern China. To understand the genomic features, phylogenetic relationships, and molecular evolution of Q. litseoides, the complete chloroplast (cp) genome was analyzed and compared in Quercus section Cyclobalanopsis. The cp genome of Q. litseoides was 160,782 bp in length, with an overall guanine and cytosine (GC) content of 36.9%. It contained 131 genes, including 86 protein-coding genes, eight ribosomal RNA genes, and 37 transfer RNA genes. A total of 165 simple sequence repeats (SSRs) and 48 long sequence repeats with A/T bias were identified in the Q. litseoides cp genome, which were mainly distributed in the large single copy region (LSC) and intergenic spacer regions. The Q. litseoides cp genome was similar in size, gene composition, and linearity of the structural region to those of Quercus species. The non-coding regions were more divergent than the coding regions, and the LSC region and small single copy region (SSC) were more divergent than the inverted repeat regions (IRs). Among the 13 divergent regions, 11 were in the LSC region, and only two were in the SSC region. Moreover, the coding sequence (CDS) of the six protein-coding genes (rps12, matK, atpF, rpoC2, rpoC1, and ndhK) were subjected to positive selection pressure when pairwise comparison of 16 species of Quercus section Cyclobalanopsis. A close relationship between Q. litseoides and Quercus edithiae was found in the phylogenetic analysis of cp genomes. Our study provided highly effective molecular markers for subsequent phylogenetic analysis, species identification, and biogeographic analysis of Quercus. [ABSTRACT FROM AUTHOR]

Jalisco fir (Abies jaliscana) is an endemic tree with restricted distribution, and is considered a Pliocene relict inhabiting the cloud montane forests in the humid-temperate upper zone of western Jalisco Mountains. Despite its ecological importance, to date, there are no studies for this taxon aimed to understand its vulnerability to regional climate variability. We used dendrochronological techniques to investigate tree-ring patterns and the effects of climate variability on the radial growth of Jalisco fir through its elevational range in both southern and northern aspects. We developed tree-ring series that ranged in length from 79 to 152 years. High-elevation trees (2400 m.a.s.l.) showed higher annual radial growth compared to those present in low-elevation sites. Over the last 150 years, Abies jaliscana trees have had higher annual radial growth rates compared to those exhibited by other Abies species. Both winter and early spring precipitation are the main factors determining the growth rates at all the elevation sites for both aspects. In the cold season, mean maximum temperature negatively affected ring-width growth at most of the elevation zones except for two study sites. We conclude that the radial growth of A. jaliscana appears to be smaller during lower rainfall periods in the dry seasons (winter and early spring), and A. jaliscana annual radial growth responds differently to regional climate variability along the elevational gradient and mountain aspects. [ABSTRACT FROM AUTHOR]

Tree diversity inventories were undertaken. The goal of this study was to understand changes in tree community dynamics that may result from common anthropogenic disturbances at the Reserva Los Cedros, a tropical montane cloud forest reserve in northern Andean Ecuador. The reserve shows extremely high alpha and beta tree diversity. We found that all primary forest sites, regardless of age of natural gaps, are quite ecologically resilient, appearing to return to a primary-forest-type community of trees following gap formation. In contrast, forests regenerating from anthropogenic disturbance appear to have multiple possible ecological states. Where anthropogenic disturbance was intense, novel tree communities appear to be assembling, with no indication of return to a primary forest state. Even in ancient primary forests, new forest types may be forming, as we found that seedling community composition did not resemble adult tree communities. We also suggest small watersheds as a useful basic spatial unit for understanding biodiversity patterns in the tropical Andes that confound more traditional Euclidean distance as a basic proxy of dissimilarity. Finally, we highlight the conservation value of Reserva Los Cedros, which has managed to reverse deforestation within its boundaries despite a general trend of extensive deforestation in the surrounding region, to protect a large, contiguous area of highly endangered Andean primary cloud forest. [ABSTRACT FROM AUTHOR]

In tropical forests of southern Ecuador, artisanal gold mining releases heavy metals that become xenobiotic with indefinite circulation and eventual bioaccumulation. Restoration and rehabilitation of degraded mining sites represent a major ecological, technological and economic issue. In this study, we estimate the capacity of two native woody plants to accumulate cadmium (Cd), lead (Pb), zinc (Zn) and mercury (Hg), with the goal of developing effective strategies for phytoremediation of mining sites. Individuals of Erato polymnioides and Miconia sp., as well as their rhizospheric soils, were sampled from a natural zone (NZ) of montane cloud forest, used as a control, and a polluted zone (PZ) subjected to active gold mining. Concentrations of the four heavy metals were analyzed using atomic absorption spectrophotometry. Cd, Zn and Hg concentrations were higher in soils of PZ than NZ. Bioaccumulation (BCF) and translocation factors (TF) showed that Miconia sp. has potential for Cd and Zn phytostabilization, E. polymnioides has potential for Cd and Zn phytoextraction, and both species have potential for Hg phytoextraction. Despite the low productivity of these species, their adaptability to the edaphoclimatic conditions of the region and the possibility of using amendments to increase their biomass could compensate for the effectiveness of these species in reclaiming soils contaminated by mining. [ABSTRACT FROM AUTHOR]

The high-humidity mountain forest ecosystem (HHMF) of Jinyun Mountain in Chongqing is a fragile ecosystem that is sensitive to climate change and human activities. Because it is shrouded in fog year-round, illumination in the area is seriously insufficient. However, the flux (energy, water) exchanges (FEs) in this ecosystem and their influencing factors are not clear. Using one-year data from flux towers with a double-layer (25 m and 35 m) eddy covariance (EC) observation system, we proved the applicability of the EC method on rough underlying surfaces, quantified the FEs of HHMFs, and found that part of the fog might also be observed by the EC method. The observation time was separated from day and night, and then the environmental control of the FEs was determined by stepwise regression analysis. Through the water balance, it was proven that the negative value of evapotranspiration (ETN), which represented the water vapor input from the atmosphere to the ecosystem, could not be ignored and provided a new idea for the possible causes of the evaporation paradox. The results showed that the annual average daily sensible heat flux (H) and latent heat flux (LE) ranged from −126.56 to 131.27 W m−2 and from −106.7 to 222.27 W m−2, respectively. The annual evapotranspiration (ET), positive evapotranspiration (ETP), and negative evapotranspiration (ETN) values were 389.31, 1387.76, and −998.45 mm, respectively. The energy closure rate of the EC method in the ecosystems was 84%. Fog was the ETN observed by the EC method and an important water source of the HHMF. Therefore, the study area was divided into subtropical mountain cloud forests (STMCFs). Stepwise regression analysis showed that the H and LE during the day were mainly determined by radiation (Rn) and temperature (Tair), indicating that the energy of the ecosystem was limited, and future climate warming may enhance the FEs of the ecosystem. Additionally, ETN was controlled by wind speed (WS) in the whole period, and WS was mainly affected by altitude and temperature differences within the city. Therefore, fog is more likely to occur in the mountains near heat island cities in tropical and subtropical regions. This study emphasizes that fog, as an important water source, is easily ignored in most EC methods and that there will be a large amount of fog in ecosystems affected by future climate warming, which can explain the evaporation paradox. [ABSTRACT FROM AUTHOR]

Featured Application: The research results of this paper are mainly applied to a 3D reconstruction of a forest with a single-line lidar, obtaining tree distribution at low cost. In order to accurately obtain tree growth information from a forest at low cost, this paper proposes a forest point cloud real-time reconstruction method with a single-line lidar based on visual–IMU fusion. We build a collection device based on a monocular camera, inertial measurement unit (IMU), and single-line lidar. Firstly, pose information is obtained using the nonlinear optimization real-time location method. Then, lidar data are projected to the world coordinates and interpolated to form a dense spatial point cloud. Finally, an incremental iterative point cloud loopback detection algorithm based on visual key frames is utilized to optimize the global point cloud and further improve precision. Experiments are conducted in a real forest. Compared with a reconstruction based on the Kalman filter, the root mean square error of the point cloud map decreases by 4.65%, and the time of each frame is 903 μs; therefore, the proposed method can realize real-time scene reconstruction in large-scale forests. [ABSTRACT FROM AUTHOR]

The composition and diversity of rhizosphere microbial communities may be due to root–soil–microbial interactions. The fine root functional traits and rhizosphere soil environmental factors of 13 representative plants in the Bawangling tropical cloud forest of Hainan Island were measured, to assess the key factors driving plant rhizosphere microbial communities. Illumina MiSeq sequencing technology was used to sequence the v3-V4 region of the 16SrDNA gene of 13 plant rhizosphere soil bacteria and the ITS1 region of the fungal ITSrDNA gene. Results showed that there were 355 families, 638 genera, and 719 species of rhizosphere soil bacteria as well as 29 families, 31 genera, and 31 species of rhizosphere soil fungi in the tropical cloud forests. The fine root traits, such as root phosphorus content, the specific root length and specific root area, were significantly negatively correlated with the Faith-pd indices of the bacterial community but were not correlated with the diversity of fungi communities. The soil pH was significantly and positively correlated with the Chao1 index, OTUs, Faith-pd and Simpson indices of the bacteria and fungi communities. The soil available phosphorus content was significantly and negatively correlated with the bacteria Simpson and the fungus Faith-pd indices. ABT analysis showed that soil pH and soil available phosphorus were the most important environmental conditions contributing to the rhizosphere bacterial and fungi communities, respectively. Our findings demonstrate that the soil environments had more influence on rhizosphere soil microbial diversity than the fine root functional traits. [ABSTRACT FROM AUTHOR]

The protection of natural areas is considered an essential strategy for environment conservation. The objective of this work was to determine the level of vulnerability, considering the characterization and identification of the risk zones and ecological protection of the Pagaibamba Protection Forest (PPF, Peru). To determine the vulnerable areas, Landsat ETM satellite images, topographic, geological, ecological, and vegetation cover maps were used. Geological, physiographic, edaphological, vegetation cover, and land use potential characteristics, were analyzed. Three Ecological Protection and Risk Zones were identified, with the largest extension of the PPF corresponding to lands of very high and high vulnerability and high ecological risk, which include >85% of Protected Natural Areas (PNA) and 54% of the Buffer Zone (BZ). Moderate risk areas represent 30% of the Buffer Zone (BZ) and 13% of the PNA, and the low-risk areas (represent 15% of the BZ and 2% of the PNA). Biogeographically, the PPF was related to the Cloudy Montane Forests Ecoregion of the Andes Mountains, standing out the Tropical Montane Cloud Forest (TMCF) and the Tropical Lower Montane Cloud Forest (TLMCF). These forests are a global conservation priority due to their great biodiversity, high level of endemicity of flora and fauna, and the crucial hydrological function they fulfill. [ABSTRACT FROM AUTHOR]

Secondary cloud forests (SCFs), those that regenerate naturally following abandonment of human activities in previously deforested land, are of great value as refuges of high species diversity and for their critical role in hydrological regulation. This opinion paper analyzes the main environmental, socio-economic, and regulatory aspects that currently hamper the sustainable use and conservation of SCFs in Mexico for the provision of timber and ecosystem services. The main constraints identified include contradictory norms and policies and the marginalization of smallholders in timber production activities. Developing economic incentives for forest product harvesting and provision of ecosystem services derived from SCFs, while also addressing legal and normative aspects related to their sustainable use, is paramount. Given the high heterogeneity in floristic composition and stand structure of SCFs among localities, technical and social norms for sustainable use should be sufficiently flexible to allow adaptive management approaches. Future research areas should be focused on monitoring the response of SCFs to silvicultural interventions, documenting existing traditional practices as well as conducting socio-economic analyses of timber production and associated ecosystem services. This is essential for developing sound policies and approaches for the sustainable use and long-term management of SCFs in Mexico. [ABSTRACT FROM AUTHOR]

We describe a new species of Rhinella from montane forests between 1788 and 2305 m a.s.l. in the Departamentos Amazonas and San Martín, Peru. We tentatively assign the new species to the Rhinella festae species Group based on morphological similarities with its other 19 members. It is characterised by large size (maximum SVL 91.6 mm in females), a pointed and protruding snout that is posteroventrally inclined, absence of a visible tympanic annulus and tympanic membrane, long parotoid glands in contact with upper eyelid, presence of a dorsolateral row of enlarged tubercles, outer dorsolateral tarsus surface with a subconical ridge of fused tubercles, and absence of subgular vocal sac and vocal slits in males. One specimen from Departamento Amazonas tested positive for Batrachochytrium dendrobatidis. [ABSTRACT FROM AUTHOR]

The tropical montane cloud forests are recognized as one of the most biodiverse ecosystems. In spite of this, they are among the most threatened ecosystems in the world. This study integrates three ecological approaches generally studied separately: climate change scenery, ecological niche and population dynamics of Oreomunnea mexicana (an endangered and relict species), to understand how environmental change affects the population structure in the cloud forest that will allow its conservation. Potential distribution under future climatic scenarios of the species at national and regional levels was generated from the Maxent algorithm. Also, the current abundance, distribution and the ecological niche of the species were analyzed at the regional level. Changes in potential distribution under two climatic models suggest a habitat reduction from 36% to 55% nationally, and 2% to 9% at a regional level, for 2050 and 2070, respectively. The current distribution of the species is fragmented and consists of subpopulations that have spatial structures of aggregated populations and a size structure in reversed "J" form. The ecological niche of the species is highly specialized and sensitive to environmental changes. O. mexicana is a flagship species of biological and cultural importance to the region's inhabitants and could be fundamental to the conservation of tropical montane cloud forests. [ABSTRACT FROM AUTHOR]

Cloud forests harbor high levels of orchid diversity. However, due to the high fragmentation of these forests in the Andes, combined with the pressure for new agricultural land, orchid diversity is highly threatened. Despite this worrying scenario, few studies have assessed the effects of habitat loss specifically on orchid assemblages in the Andes. The aim of this study was to analyze the edge effect on orchids in cloud forest fragments of varying size. We measured forest structure, neighboring land cover and edge effect on orchid abundance, species richness and beta-diversity, by sampling assemblages along edge-to-interior transects in six different sized Andean (southwest Colombia) forest remnants. We recorded 11,127 stem-individuals of orchids in 141 species. Within the forest, edges sustained equal or more species than interior plots. Our results revealed neither patch metrics nor forest structure showed any significant association to orchid diversity at any scale. Nonetheless, from our observations in composition, the type of neighboring cover, particularly pastures, negatively influences interior species (richness and composition) in larger reserves. This might be due to the fact that some species found in interior plots tend to be confined, with sporadic appearances in regeneration forest and are very scarce or absent in pastures. Species richness differed significantly between matrix types. Our results suggest that (1) orchid diversity shows spatial variability in response to disturbances, but the response is independent from forest structure, patch size and patch geometry; (2) orchid communities are negatively affected by covers, and this pattern is reflected in reduced richness and high species turnover; (3) orchid richness edge effect across a pasture-interior gradient. Two forest management implications can be discerned from our results: (1) management strategies aiming to reduce edge effects may focus on improvement regeneration conditions around pasture lands; and (2) local scale management and conservation activities of natural forests in cloud forests will favor small reserves that harbor high levels of richness. [ABSTRACT FROM AUTHOR]

From an unprecedented experiment using airborne measurements performed over the rich forests of Réunion Island, this paper aims to present a methodology for the classification of diverse tropical forest biomes as retrieved from vertical profiles measured using a full-waveform LiDAR. This objective is met through the retrieval of both the canopy height and the Leaf Area Index (LAI), obtained as an integral of the foliage profile. The campaign involved sites ranging from coastal to rain forest, including tropical montane cloud forest, as found on the Bélouve plateau. The mean values of estimated LAI retrieved from the apparent foliage profile are between ~5 and 8 m²/m², and the mean canopy height values are ~15 m for both tropical montane cloud and rain forests. Good agreement is found between LiDAR- and MODIS-derived LAI for moderate LAI (~5 m²/m²), but the LAI retrieved from LiDAR is larger than MODIS on thick rain forest sites (~8 against ~6 m²/m² from MODIS). Regarding the characterization of tropical forest biomes, we show that the rain and montane tropical forests can be well distinguished from planted forests by the use of the parameters directly retrieved from LiDAR measurements. [ABSTRACT FROM AUTHOR]