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

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

Abstract: Variation in the leaf optical properties imposed by variation in genetics and location has been addressed in recent literature, but those stemming from forest seasonality and phenology have been less well explored. Here, we explore the effect of inter-seasonal spectral variation on the potential for automated classification methods to accurately discern species of trees and lianas from high-resolution spectral data collected at the leaf level at two tropical forest sites. Through the application of a set of data reduction techniques and classification methods to leaf-level spectral data collected at both rainforest and seasonally dry sites in Panama, we found that in all cases the structure and organization of spectrally-derived taxonomies varied substantially between seasons. Using principle component analysis and a non-parametric classifier, we found at both sites that species-level classification was possible with a high level of accuracy within a given season. Classification across season was not, however, with accuracy dropping on average by a factor of 10. This study represents one of the first systematic investigations of leaf-level spectro-temporal variability, an appreciation for which is crucial to the advancement of species classification methods, with broad applications within the environmental sciences. [Copyright &y& Elsevier]

Abstract: Research suggests that secondary forest structure and composition subsequent to human disturbance could be related to wind or vertebrate dependent seed dispersal mechanisms. This paper examines the capability of a waveform LiDAR system (LVIS: LiDAR Vegetation Imaging System) to detect the location of wind-dispersed or vertebrate-dispersed forest patches in a mosaic of a secondary Tropical Dry Forest in the Santa Rosa National Park, Guanacaste, Costa Rica. Canopy-height estimations derived from LVIS data were used to identify and locate two types of dispersal-dependent arrangements of canopy heights: flat-topped (windborne seed dispersed) or dome-shaped (vertebrate-dispersed) fragments. Following identification, a Ripley''s K indicator was used to compare the locations for each confirmed canopy type to those reported in existing maps. Results indicated that dome-shaped patches tended to be arranged in clusters at the middle of former pastures (far from older forests), and that flat-topped patches tended to be located adjacent to older forests, forming extensions from the old growth patches and at the downwind side of the slope. Such characteristics agree with the theoretical observation of the spatial configuration of vertebrate- and wind-dispersed fragments reported in the study site. Our findings demonstrate that processes controlling forest regeneration in the TDF can be successfully identified through LiDAR data. [Copyright &y& Elsevier]

Abstract: Increasing dominance of lianas in many tropical forests is considered a fingerprint of global environmental change. Despite the key role they play in ecosystem functioning, lianas remain one of the least studied life forms in tropical environments. This paper contrasts leaf traits and spectral properties (400–1100 nm) of liana and tree communities from a tropical dry forest and a tropical rainforest in Panama, Central America. Differences between lianas and tree leaf traits were analyzed using spectroscopy, leaf histology and pigment extractions. Results from this study indicate that many of the biochemical, structural, and optical properties of lianas and trees are different in the dry forest site but not in rainforest sites. In the dry forest site, liana leaves exhibited significantly lower chlorophyll and carotenoid contents and were thinner than the leaves of their host trees. Specific leaf area, dry to fresh mass ratio, and mean water content of liana leaves were significantly higher when compared with tree leaves. The differences observed in the tropical dry forest site indicate that lianas may have a higher rate of resource acquisition and usage, whereas trees tend to conserve acquired resources. We suggest that our results may be indicative of the presence of a liana syndrome related to water availability and thus best exhibited in tropical dry forests. Our findings have important implications for using remote sensing to accurately map the distribution of liana communities at regional scales and for the continued expansion of lianas in tropical environments as a result of global change. [Copyright &y& Elsevier]

Abstract: The transmission and interception of light through the canopy is an important indicator of forest productivity in tropical forest ecosystems, and the amount of light that eventually reaches the forest floor is influenced by its interactions with leaves, branches, fruits, and flowers among many different canopy elements. While most studies of forest canopy light interception focus on leaf area index (LAI), very few studies have examined wood area index (WAI), which may account for a substantial component of light interception in tropical forests. The influence of lianas on the interception of light and their overall contribution to WAI is a potentially important factor, but it is generally overlooked because of its difficulty to assess. In this paper we evaluate the relative contribution that lianas have to the overall WAI and canopy openness as function of successional stage via a latitudinal comparison of sites across the Americas (Mexico, Costa Rica and Brazil). Our results suggest that lianas significantly increase WAI and decreases canopy openness. However, lianas were absent at all of our study sites where canopy openness exceeded 60%. Our data are the first to explicitly document the role of lianas in the estimation of WAI and, overall, they will contribute to better estimations of ecosystem level LAI in tropical environments, where there is a lack of data on WAI. [Copyright &y& Elsevier]

Abstract: Tropical deforestation and habitat fragmentation in areas around conservation areas have been an important element for discussion in the conservation and policy-making community. In this paper, we evaluate land cover change processes around one of the most important tropical dry forest reserves in the Americas: the Chamela-Cuixmala Biosphere Reserve, Mexico. The analysis is conducted at three scales: regional scale (coast of the State of Jalisco), proximity to the reserve (via a 30-km buffer zone analysis), and inside the Biosphere Reserve. The proximity analysis is complemented by a unique data set that represents the extent and boundaries of Ejidos (local land management units) in the region. Results indicate that areas surrounding the reserve have low deforestation, with Ejidos and buffer areas having more forest cover as they get closer to the reserve. The nature of this important phenomenon is discussed in the context of the socio-economic conditions of the local communities. Our work suggests that the creation of a network of “Ejidos’ Conservation Areas”, in the context of an approach that will recognize payments for environmental services provided to local communities, could contribute to the conservation and maintenance of the rich biodiversity in a region that has one of the highest levels of endemism in the world. [Copyright &y& Elsevier]

Abstract: Dry forests represent the most endangered ecosystem in tropical regions and continue to be one of the most sought after environments for human colonization, development and production. In spite of this, dry forests are one of the least well studied tropical habitats. This special issue is dedicated to reviewing much of the information that exists about tropical dry forest in the Americas. The introduction summarizes the articles presented herein, highlighting management strategies that are suggested throughout the special issue that may be useful for conserving tropical dry forest environments. [Copyright &y& Elsevier]

Abstract: The Beaver Hills region of central Alberta is located at the interface of the southern boreal mixedwood forest and the aspen parkland, an area now dominated by agriculture, urban and industrial development. Increasing anthropogenic land cover will eventually isolate remaining natural habitats currently protected in parks and reserves. This paper analyzes land cover and land cover change (LCC) in the Beaver Hills moraine and surrounding areas using a structured hierarchical satellite imagery classification applied to Landsat Multi Spectral Scanner and Thematic Mapper from 1977, 1987, and 1998. Our goal was to quantify deforestation and habitat fragmentation trends and then discuss how this information could be used to develop a conservation approach that will protect current areas against further habitat loss. We found that the rate of deforestation in the lands surrounding the moraine was similar to the broad trend at the southern periphery of the Canadian boreal forest region: annual rate of change in forest cover was −0.82%/year. However, in the Beaver Hills there was a net gain of +0.61%/year, due to regeneration of low quality agricultural lands. All fragmentation indices used indicated an increase in forest fragmentation. We designed a network of protected areas and remaining large forest patches, based on the UNESCO-MAB biosphere model. Our results underline concerns regarding the increasing isolation of national parks and biological reserves in Canada. [Copyright &y& Elsevier]

Initiated in 1984, the Committee Earth Observing Satellites' Working Group on Calibration and Validation (CEOS WGCV) pursues activities to coordinate, standardize and advance calibration and validation of civilian satellites and their data. One subgroup of CEOS WGCV, Land Product Validation (LPV), was established in 2000 to define standard validation guidelines and protocols and to foster data and information exchange relevant to the validation of land products. Since then, a number of leaf area index (LAI) products have become available to the science community at both global and regional extents. Having multiple global LAI products and multiple, disparate validation activities related to these products presents the opportunity to realize efficiency through international collaboration. So the LPV subgroup established an international LAI intercomparison validation activity. This paper describes the main components of this international validation effort. The paper documents the current participants, their ground LAI measurements and scaling techniques, and the metadata and infrastructure established to share data. The paper concludes by describing plans for sharing both field data and high-resolution LAI products from each site. Many considerations of this global LAI intercomparison can apply to other products, and this paper presents a framework for such collaboration. [ABSTRACT FROM AUTHOR]

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In this study, we investigated the use of Bayesian networks for inferring tropical dry forest leaf area index (LAI) from satellite imagery in dry and wet seasons. LAI was chosen as the variable of interest because leaf area is the exchange surface between the photosynthetically active component of the canopy and the atmosphere. Initial network estimates were obtained from ground truth plot data with known forest structure, LAI, and satellite reflectance in the red and near-infrared bands (as observed by the Landsat 7 Enhanced Thematic Mapper Plus sensor). We tested the performance of the Bayesian networks with scoring rules and also with confidence and surprise scores. We evaluated the networks on a per-pixel basis and created both LAI maps of the study area as well predicted the probability maps for the highest LAI states. Results not only demonstrate the predictive power of a Bayesian network but also its explanatory power which is far beyond what is typically available with current pixel classifier approaches such as spectral vegetation indices or other approaches such as neural networks. [ABSTRACT FROM AUTHOR]

Most estimates of deforestation are derived for areas that are not intrinsically related to the causes of deforestation, making it hard to link such estimates with strategies for improved land management. In this paper a method is presented to identify local areas of high rates of deforestation within larger areas, using remote sensing and geographic information systems (GIS). These deforestation hot spots are identified using a local analysis approach, considering for every location only land cover changes taking place in the immediate vicinity, and they thus reflect local deforestation. This method has potential application in improved land policy development, land management and it could supplement land use change modeling at the landscape level. Specifically, the method could be used to undertake a risk assessment of the effectiveness of ecological corridors. The method was applied to the province of Limo´n in Costa Rica, where commercial agriculture has caused large scale deforestation. The deforestation hot spots in the period 1986–1997 that were identified with this method represent a large proportion of the total deforestation having taken place in the province, and for each of the hot spots specific drivers of deforestation could be identified. [Copyright &y& Elsevier]

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

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

Alford et al. question the working model underlying our test for a link between global warming and amphibian disappearances, and Di Rosa et al. criticize our emphasis on a single proximate agent, the chytrid fungus Batrachochytrium dendrobatidis. Both teams report key pieces of the amphibian-decline puzzle and new evidence from different parts of the world that climate change is a factor in these losses. Here we show why our working model was appropriate and highlight the complexity of the imminent threat to species survival that results as global warming conspires with various other agents. [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]