Search

Tropical rainforests of Latin America (LATAM) are one of the world's largest carbon sinks, with substantial future carbon sequestration potential and contributing a major proportion of the global supply of forest carbon credits. LATAM is poised to contribute predominantly towards high-quality forest carbon offset projects designed to reduce emissions from deforestation and forest degradation, halt biodiversity loss, and provide equitable conservation benefits to people. Thus, carbon markets, including compliance carbon markets and voluntary carbon markets continue to expand in LATAM. However, the extent of the growth and status of forest carbon markets, pricing initiatives, stakeholders, amongst others, are yet to be explored and extensively reviewed for the entire LATAM region. Against this backdrop, we reviewed a total of 299 articles, including peer-reviewed and non-scientific gray literature sources, from January 2010 to March 2023. Herein, based on the extensive literature review, we present the results and provide perspectives classified into five categories: (i) the status and recent trends of forest carbon markets (ii) the interested parties and their role in the forest carbon markets, (iii) the measurement, reporting and verification (MRV) approaches and role of remote sensing, (iv) the challenges, and (v) the benefits, opportunities, future directions and recommendations to enhance forest carbon markets in LATAM. Despite the substantial challenges, better governance structures for forest carbon markets can increase the number, quality and integrity of projects and support the carbon sequestration capacity of the rainforests of LATAM. Due to the complex and extensive nature of forest carbon projects in LATAM, emerging technologies like remote sensing can enable scale and reduce technical barriers to MRV, if properly benchmarked. The future directions and recommendations provided are intended to improve upon the existing infrastructure and governance mechanisms, and encourage further participation from the public and private sectors in forest carbon markets in LATAM.

LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. We show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g. canopy height models based on LiDAR data). By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.

Objective: To perform a systematic review with meta‐analysis to assess recent scientific evidence on the association between periodontitis and systemic parameters/conditions in individuals with chronic kidney disease (CKD). Materials and Methods: The search for studies was performed in MedLine/PubMed, Scopus, Web of Science, and BIREME databases. Reference lists of selected articles were also searched. Studies with different epidemiological designs evaluating the influence of exposure to periodontitis on serum markers and mortality in individuals with CKD were eligible for inclusion. Three independent reviewers performed the article selection and data extraction. The assessment of methodological quality used the adapted Newcastle Ottawa Scale. Random effects meta‐analysis was performed to calculate association measurements and 95% confidence intervals. Results: In total, 3053 records were identified in the database search, with only 25 studies meeting the eligibility criteria and, of these, 10 studies contributed data for meta‐analysis. Using a random‐effects model, periodontitis was associated with hypoalbuminemia (PRunadjusted = 2.47; 95%CI:1.43–4.26), with high levels of C‐reactive protein (PRunadjusted = 1.35; 95%CI%:1.12–1.64), death from cardiovascular disease (RRunadjusted = 2.29; 95%CI:1.67–3.15) and death from all causes (RRunadjusted = 1.73; 95%CI:1.32–2.27). Conclusions: The findings of this review validated a positive association between periodontitis and serum markers and mortality data in individuals with CKD. [ABSTRACT FROM AUTHOR]

Copyright of Intercontinental Journal of Sport Management / Revista Intercontinental de Gestão Desportiva is the property of Revista Intercontinental de Gestao Desportiva and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The soil solution is the compartment where plants uptake nutrients and this phase is in equilibrium with the soil solid phase. Changes in nutrient content and availability in the soil solution can vary among soil types in response to humic acid concentrations, thereby affecting Brachiaria growth. However, there are no studies demonstrating these effects of humic acid application on different soil types and how they affect Brachiaria growth. Thus, the aim of this study was to evaluate the effects of humic acid concentrations (0, 5, 10, 25, and 60 mg kg−1 carbon-humic acid) on Brachiaria brizantha growth and soil solution properties of contrasting tropical soils. Plants were grown for 35 days in greenhouse conditions in pots containing Sandy Entisol, Clayey (Red Oxisol), and Medium Texture (Red-Yellow Oxisol). Soil solution was assessed for pH, electrical conductivity (EC), carbon, and nutrient content. Shoot and root dry matter, as well as macro and micronutrients accumulation in the shoot, were determined. In a soil type-dependent effect, pH, EC, and concentrations of nutrients in solutions changed in response to carbon-humic acid concentration. In the less-buffered soils, Sandy Entisol and Red-Yellow Oxisol, the addition of 30–40 mg kg−1 carbon-humic acid increased root proliferation by 76–89%, while Brachiaria biomass produced in all soils increased by approximately 30%. Levels of carbon in solution were high (>580 mg L−1) and varied depending on the investigated soil type. Though solution carbon contents did not appear to be a driving factor controlling the positive effects of humic acid concentrations on Brachiaria dry matter, there was a direct relationship between other properties and nutrient content in the soil solution, and Brachiaria dry matter production. [ABSTRACT FROM AUTHOR]

BackgroundAccurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them.ResultsOur results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m-2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R2 ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha-1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha-1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R2 ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha-1 [between 0.69-0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha-1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R2 was between 0.58-0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha-1 for the echo-based model, whereas for the CHM R2 was between 0.37-0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha-1.ConclusionsMetrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m-2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m-2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m-2.