132 research outputs found
Estimation of biomass density and carbon storage in the forests of Andhra Pradesh, India, with emphasis on their deforestation and degradation conditions
The current study evaluates the growing stock, biomass and carbon content of Andhra Pradesh state’s forest (India) along with its current status of forest degradation and loss. For this purpose, the study used the growing stock data collected by state forest department in 2010 for the calculation of biomass and carbon storage using the standard conversion and expansion factors given by IPCC. The analysis shows low biomass and carbon values for the state’s forest in comparison to the mean values recorded in different studies made for Andhra Pradesh. It is also observed to be lower when compared with the average carbon and biomass for Indian forests. Overall, the analysis showed degradation and loss of forest in the state, coupled with reduction in biomass and
carbon sink
Is current forest landscape research approaches providing the right insights? Observations from India context
One of the major challenges in the current scenario for ecological conservation is to quantify the forest landscape in its spatio-temporal domain and understand further implications of those. While the detailed study of the forest ecosystems may provide insights into biodiversity, carrying capacity and productive nature, most of the studies are restricted to single time/event inventory and focused on assessment of tree diversity patterns. Through the adoption of geospatial technologies like remote sensing and Geographical Information System (GIS), though forest monitoring has been possible, the linkages to the biodiversity distribution and its patterns are still at an empirical level, thus supporting broad measures of protection and preservation without accounting for the local/regional variability.Towards this the paper discusses the lacuna in the current landscape research approaches in Indian scenario. Presents a framework to analyze the landscape structure at the, micro, meso and macro levels. Emphasize the need for the collection of spatio-temporal field data to analyze the change in biodiversity and their linked entities. The paper suggests the need for development of long term ecological area networks to understand the ecological processes, making the data open and improve collaborations among the organizations working in the similar domain to enhance the impact of the research works
The global abundance of tree palms
Aim Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location Tropical and subtropical moist forests. Time period Current. Major taxa studied Palms (Arecaceae). Methods We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co‐occurring non‐palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long‐term climate stability. Life‐form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non‐tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above‐ground biomass, but the magnitude and direction of the effect require additional work. Conclusions Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests
Phylogenetic classification of the world's tropical forests
Knowledge about the biogeographic affinities of the world’s tropical forests helps to better understand regional differences in forest structure, diversity, composition, and dynamics. Such understanding will enable anticipation of region-specific responses to global environmental change. Modern phylogenies, in combination with broad coverage of species inventory data, now allow for global biogeographic analyses that take species evolutionary distance into account. Here we present a classification of the world’s tropical forests based on their phylogenetic similarity. We identify five principal floristic regions and their floristic relationships: (i) Indo-Pacific, (ii) Subtropical, (iii) African, (iv) American, and (v) Dry forests. Our results do not support the traditional neo- versus paleotropical forest division but instead separate the combined American and African forests from their Indo-Pacific counterparts. We also find indications for the existence of a global dry forest region, with representatives in America, Africa, Madagascar, and India. Additionally, a northern-hemisphere Subtropical forest region was identified with representatives in Asia and America, providing support for a link between Asian and American northern-hemisphere forests.</p
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Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation
Determining species expansion and extinction possibilities using probabilistic and graphical models
Survival of plant species is governed by a number of functions. The participation of each function in species survival and the impact of the contrary behaviour of the species vary from function to function. The probability of extinction of species varies in all such scenarios and has to be calculated separately. Secondly, species follow different patterns of dispersal and localisation at different stages of occupancy state of the site, therefore, the scenarios of competition for resources with climatic shifts leading to deterioration and loss of biodiversity resulting in extinction needs to be studied. Furthermore, most possible deviations of species from climax community states needs to be calculated before species become extinct due to sudden environmental disruption. Globally, various types of anthropogenic disturbances threaten the diversity of biological systems. The impact of these anthropogenic activities needs to be analysed to identify extinction patterns with respect to these activities. All the analyses mentioned above have been tried to be achieved through probabilistic or graphical models in this study
Semi-empirical model for upscaling leaf spectra (SEMULS): a novel approach for modeling canopy spectra from in situ leaf reflectance spectra
The use of in situ hyperspectral reflectance and bio-physical measurements has been increasing in forestry. Due to limited physical accessibility in a forest environment, most of the reflectance measurements of trees are acquired at a leaf or bunch of leaves level. A few radiative transfer models are available for upscaling leaf spectra to canopy level. While these models are sophisticated, they retrieve canopy spectra based on certain assumptions. We propose ‘semi-empirical model for upscaling leaf spectra (SEMULS)’ which upscales in situ leaf spectra to canopy level based on the relationship between leaf spectra and its bio-physical parameters. The performance of the model has been quantitatively validated by comparing the upscaled canopy spectra with spectra from – CHRIS hyperspectral imagery acquired concurrently and from the PROSAIL model. Results indicate that the SEMULS retrievals are comparable with image spectra and PROSAIL with additional advantages of not requiring scene-dependent geometric-radiometric parameters and assumptions
Geospatial technology perspectives for mining vis-a-vis sustainable forest ecosystems
Forests, the backbone of biogeochemical cycles and life supporting systems, are under severe pressure due to varied anthropogenic activities. Mining activities are one among the major reasons for forest destruction questioning the survivability and sustainability of flora and fauna existing in that area. Thus, monitoring and managing the impact of mining activities on natural resources at regular intervals is necessary to check the status of their depleted conditions, and to take up restoration and conservative measurements. Geospatial technology provides means to identify the impact of different mining operations on forest ecosystems and helps in proposing initiatives for safeguarding the forest environment. In this context, the present study highlights the problems related to mining in forest ecosystems and elucidates how geospatial technology can be employed at various stages of mining activities to achieve a sustainable forest ecosystem. The study collates information from various sources and highlights the role of geospatial technology in mining industries and reclamation process
Competitive Exclusion of Parthenium hysterophorus by Other Invasive Species - A Case Study from Andhra Pradesh, India
The abundance, dominance and growth performance of Parthenium hysterophorus in relation to its field associates in extensively large areas was investigated. The preliminary analysis of the data revealed that P. hysterophorus is a weak or poor competitor and hence it fails to grow in the company of any aggressive species. Senna uniflora and a few other plants were identified for the control of this pernicious weed. The ability of other species to control P. hysterophorus was attributed to allelopathy. In order to understand how Hyptis suaveolens and Senna uniflora are capable of arresting the growth of P. hysterophorus, pot culture experiments in de Wit replacement series, field experiments in experimental plots and experimental manipulation of the competitive species under natural conditions during different seasons were carried out for two years in 2004 and 2005. The results clearly revealed that both H. suaveolens and S. uniflora were highly effective in the management of P. hysterophorus. The results further showed that the physical dominance and the ability of the competitive species to deprive P. hysterophorus of light are mainly responsible for the decline of P. hysterophorus. Allelopathy doesn't seem to play any effective role under natural conditions
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