Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan / Previsão de produção florestal em diferentes cenários de mudanças climáticas usando modelos inteligentes de dados no Paquistão

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabe's (LM), Willmott's index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

Resumo Este estudo teve como objetivo desenvolver e avaliar modelos baseados em dados para previsão da produção florestal em diferentes cenários de mudanças climáticas na divisão florestal Gallies do distrito de Abbottabad, Paquistão. Os modelos Random Forest (RF) e Kernel Ridge Regression (KRR) foram desenvolvidos e avaliados usando dados de produção de duas espécies (pinheiro-azul e abeto-prateado) como uma variável objetiva e dados climáticos (temperatura, umidade, precipitação e velocidade do vento) como preditivos variáveis. A precisão da previsão de ambos os modelos foi avaliada por meio de erro quadrático médio (RMSE), erro absoluto médio (MAE), coeficiente de correlação (r), erro quadrático médio relativo (RRMSE), Legates-McCabe's (LM), índice de Willmott (WI) e métricas Nash-Sutcliffe (NSE). No geral, o modelo RF superou o modelo KRR devido à sua maior precisão na previsão do rendimento florestal. O estudo recomenda fortemente que o modelo RF seja aplicado em outras regiões do país para previsão do crescimento e produtividade florestal, o que pode ajudar no manejo e planejamento futuro da produtividade florestal no Paquistão.

Agrobiological evaluations of newly introduced grapes varieties under climatic conditions of the south of Kazakhstan / Avaliações agrobiológicas de variedades de uvas recém-introduzidas sob as condições climáticas do sul do Cazaquistão

Variety assortment enhancement is a normal task that involves enhancing the assortment's quality and upgrading varieties. The findings of a research of imported grape varieties in the southeast of Kazakhstan, namely in the Almaty region's bottom-mountain zone, are presented in this article. The onset and conclusion of the main phenological phases of a grape plant throughout the vegetative period were directly influenced by the climatic and meteorological conditions of this district. In comparison to the recognized variety Almaty early-maturing, it has been proven that types Priusadebny, Iyulsky, and Kuibyshevsk early-maturing have a high degree of eyes wintering buds and may provide a high-quality crop in this location.

O aprimoramento da variedade é uma tarefa normal que envolve o aprimoramento da qualidade da variedade e o aprimoramento das variedades. As descobertas de uma pesquisa de variedades de uvas importadas no sudeste do Cazaquistão, ou seja, na zona de base da montanha da região de Almaty, são apresentadas neste artigo. O início e a conclusão das principais fases fenológicas de uma videira ao longo do período vegetativo foram diretamente influenciados pelas condições climáticas e meteorológicas deste distrito. Em comparação com a variedade reconhecida Almaty de maturação precoce, foi comprovado que os tipos Priusadebny, Iyulsky e Kuibyshevsk de maturação precoce têm um alto grau de gomos de inverno de olhos e podem fornecer uma safra de alta qualidade neste local.

Analysis of rainfall dynamics in the three main cities of northern Cameroon.

The northern zone of Cameroon, which depends mainly on agriculture, is considered one of the most vulnerable regions in the country to climate change. Few studies based on field data have examined the changes in climatic conditions that affect agriculture. This research focuses on fluctuations in precipitation that determine dry and wet seasons. From 1973 to 2020, data were collected from weather stations located in three major cities in northern Cameroon: Ngaoundere, Garoua and Maroua. Data were tested for homogeneity using the Pettitt and Buishand tests. Trends were analyzed using the Mann-Kendall test, Sen's slope estimator and the regression line, while drought severity was assessed using the standardized rainfall index method. These data homogeneity tests were performed using two statistical tools, SPSS and XLSTA software. According to Pettitt's test, rainfall increased by 29.6% in Ngaoundere from 1997 to 2020 compared to the previous years of 1973-1996; in Garoua, rainfall increased by 36.2% from 1988 to 2020 compared to the previous years of 1973-1987. However, from 1973 to 2020, the average rainfall in Maroua remained stable at approximately 716.5 mm, with a decreasing trend according to the Mann-Kendall test. In conclusion, this study shows that rainfall has increased significantly in the cities of Ngaoundere and Garoua, making these areas favorable for seasonal and market gardening. However, in Maroua, caution is advised, as rainfall is reportedly decreasing in this locality, increasing the risk of food insecurity. A credible climate warning system must be implemented on a large scale to guide farmers.

[Cholera and Climate: What do we know?] / Choléra et climat : que savons-nous ?

Cholera is an acute diarrheal disease caused by the bacteria Vibrio cholerae. Each year, 100'000 people die from cholera. The links between cholera, weather and climate are visible in the seasonality of cholera globally, but evidence to date illustrates that the relationships between them are highly heterogeneous across settings, with differences in both the direction and strength of the associations. Before we can devise evidence-based scenarios on how climate change may influence cholera burden in the future, more detailed case studies, using more robust climate and epidemiological data from across the globe, are needed. In the meantime, provision of sustainable water and sanitation is of the highest priority to offset potential impacts of climate change on cholera.

Le choléra est une maladie diarrhéique aiguë causée par la bactérie Vibrio cholerae. Chaque année, 100 000 personnes meurent du choléra. Les liens entre choléra, météorologie et climat sont évidents dans la saisonnalité de la maladie, mais les données disponibles à ce jour montrent que ces relations sont très hétérogènes selon les endroits, avec des différences dans la direction et l'ampleur des associations. Avant de pouvoir élaborer des scénarios basés sur des preuves décrivant la manière dont le changement climatique pourrait influencer le fardeau du choléra à l'avenir, il est nécessaire de réaliser des études de cas plus détaillées à travers le monde. Dans l'intervalle, fournir l'accès à l'eau et à un assainissement durable est une priorité absolue pour limiter les effets potentiels du changement climatique sur le choléra.

[Spatial-temporal Variation in Net Primary Productivity in Terrestrial Vegetation Ecosystems and Its Driving Forces in Southwest China].

Studying the spatial-temporal variation in net primary productivity (NPP) in terrestrial vegetation ecosystems and its driving forces in southwest China is of great importance for regional eco-environmental protection. The spatial and temporal changes in net primary productivity (NPP) in terrestrial vegetation ecosystems and its responding characteristics to climate change and human activities were explored in this study on the basis of the Moderate Resolution Imaging Spectroradiometer (MODIS) NPP from 2000 to 2021, in situ meteorological data from 1999 to 2021, and land use type datasets from 2000 to 2020 using principal component analysis, residual analysis, Theil-Sen Median analysis, and partial correlation analysis. The results showed that on a temporal scale, the vegetation NPP showed a fluctuating upward trend, with a rate of 3.54 g·(m2·a)-1in southwest China from 2000 to 2021. Meanwhile, under the influence of climate change and human activities, NPP of farmland, grassland, and forests all showed an upward trend, but the magnitude of the increasing trends of farmland NPP was the most significant. On the spatial scale, the areas with an upward trend in vegetation NPP accounted for 89.06% in southwest China, and the areas with significant and extremely significant increases were mainly distributed in southern Guangxi, eastern Sichuan, western Chongqing, and the junction areas of Yunnan and Guizhou. Climate change and human activities had dual effects on vegetation growth in southwest China, and the proportions of the areas with upward trends in farmland NPP were higher than that of grassland and forests both under the influences of climate change and human activities. The correlations between vegetation NPP and climate factors showed obvious regional differences in southwest China. On the regional scale, the areas with a positive correlation between vegetation NPP and temperature, precipitation, and sunshine duration were greater than that of the areas with a negative correlation. However, an opposite relationship could be found between vegetation NPP and biological aridity/humidity index. Among them, the areas with a positive correlation between vegetation NPP and temperature were greater than that with other climate factors. In terms of different vegetation ecosystems, temperature, precipitation, and sunshine duration had a stronger role in promoting NPP variation in the grassland ecosystem than in farmland and forest ecosystems. The transformation of other land use types to forest land had contributed to vegetation improvement in southwest China.

[Impacts of Climate Change and Human Activities on Vegetation Restoration in Typical Grasslands of China].

Grasslands, as one of the key ecosystems relevant to the terrestrial ecosystem carbon and water cycles as well as the ecological security in China, are very sensitive to climate change and human activities. However, the relative contributions of climate change and human activities on the vegetation restoration in those regions are still controversial. Using ecosystem net primary production (NPP) as an ecological indicator, this study quantified the relative roles of climate change and human activities on vegetation restoration in Chinese typical grasslands (northern temperate grasslands and Qinghai-Tibet Plateau alpine grasslands) by comparing the trends of actual NPP derived from MODIS and potential NPP estimated by the Thornthwaite Memorial model during 2000-2020. The results showed that approximately 93% of the grasslands in the study area experienced a recovering tendency, with an average increase of NPP (carbon) by 2.12 g·(m2·a)-1(P<0.01). Therein, nearly half of the vegetation-restored areas were jointly-dominated by climate change and human activities, whereas approximately 36% and 10% of the restored areas were controlled individually by climate change and human activities, respectively. In addition, the share of climate-change dominated areas differed greatly by grassland types, characterized by a much larger area percentage in the alpine grasslands than that in the temperate grasslands and an increasing area share with a drying background climate. This study suggested that human activities were not primarily responsible for the vegetation restoration in northern temperate grasslands and Qinghai-Tibet Plateau alpine grasslands, but they could decrease and even cancel the possible vegetation degeneration caused by worsening climate in a few regions. Long-term monitoring of vegetation dynamics and a multi-method comparison are needed in future studies.

Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems.

Vegetation phenology can profoundly modulate the climate-biosphere interactions and thus plays a crucial role in regulating the terrestrial carbon cycle and the climate. However, most previous phenology studies rely on traditional vegetation indices, which are inadequate to characterize the seasonal activity of photosynthesis. Here, we generated an annual vegetation photosynthetic phenology dataset with a spatial resolution of 0.05 degrees from 2001 to 2020, using the latest gross primary productivity product based on solar-induced chlorophyll fluorescence (GOSIF-GPP). We combined smoothing splines with multiple change-point detection to retrieve the phenology metrics: start of the growing season (SOS), end of the growing season (EOS), and length of growing season (LOS) for terrestrial ecosystems above 30° N latitude (Northern Biomes). Our phenology product can be used to validate and develop phenology or carbon cycle models and monitor the climate change impacts on terrestrial ecosystems.

Quantifying spatially varying impacts of public transport on NO[Formula: see text] concentrations with big geo-data.

Anthropogenic NO[Formula: see text] concentrations cause climate change and human health issues. Previous studies have focused on the contribution of traffic factors to NO[Formula: see text] emissions but have ignored the spatially varying impact of public transport supply and demand on high-resolution NO[Formula: see text] concentrations. This study first applies a two-stage interpolation model to generate a high-resolution urban NO[Formula: see text] concentration map originating from satellite measurement products. Then, we formulate 12 explanatory indicators derived from a fusion of massive big geo-data including smart card data and point of interest information, to represent the specific degree of public transport supply and citizens' demand. Furthermore, a geographically weighted regression is applied to quantify the spatial variation in the effect of these indicators on the urban NO[Formula: see text] concentrations. The result shows that public transportation coverage, frequency, and capabilities as public transport supply indicators in metropolitan and suburban areas have a two-way influence on the NO[Formula: see text] emissions. However, among public transport demand indicators, the economic level has a significant positive impact in most areas. Our findings can provide policy implications for public transportation system optimization and air quality improvement.

Projected landscape-scale repercussions of global action for climate and biodiversity protection.

Land conservation and increased carbon uptake on land are fundamental to achieving the ambitious targets of the climate and biodiversity conventions. Yet, it remains largely unknown how such ambitions, along with an increasing demand for agricultural products, could drive landscape-scale changes and affect other key regulating nature's contributions to people (NCP) that sustain land productivity outside conservation priority areas. By using an integrated, globally consistent modelling approach, we show that ambitious carbon-focused land restoration action and the enlargement of protected areas alone may be insufficient to reverse negative trends in landscape heterogeneity, pollination supply, and soil loss. However, we also find that these actions could be combined with dedicated interventions that support critical NCP and biodiversity conservation outside of protected areas. In particular, our models indicate that conserving at least 20% semi-natural habitat within farmed landscapes could primarily be achieved by spatially relocating cropland outside conservation priority areas, without additional carbon losses from land-use change, primary land conversion or reductions in agricultural productivity.

Predicting the potential geographical distribution of onion thrips, Thrips tabaci in India based on climate change projections using MaxEnt.

Onion thrips, Thrips tabaci Lindeman, an economically important onion pest in India, poses a severe threat to the domestic and export supply of onions. Therefore, it is important to study the distribution of this pest in order to assess the possible crop loss, which it may inflict if not managed in time. In this study, MaxEnt was used to analyze the potential distribution of T. tabaci in India and predict the changes in the suitable areas for onion thrips under two scenarios, SSP126 and SSP585. The area under the receiver operating characteristic curve values of 0.993 and 0.989 for training and testing demonstrated excellent model accuracy. The true skill statistic value of 0.944 and 0.921, and the continuous Boyce index of 0.964 and 0.889 for training and testing, also showed higher model accuracy. Annual Mean Temperature (bio1), Annual Precipitation (bio12) and Precipitation Seasonality (bio15) are the main variables that determined the potential distribution of T. tabaci, with the suitable range of 22-28 °C; 300-1000 mm and 70-160, respectively. T. tabaci is distributed mainly in India's central and southern states, with 1.17 × 106 km2, covering 36.4% of land area under the current scenario. Multimodal ensembles show that under a low emission scenario (SSP126), low, moderate and optimum suitable areas of T. tabaci is likely to increase, while highly suitable areas would decrease by 17.4% in 2050 20.9% in 2070. Whereas, under the high emission scenario (SSP585), the high suitability is likely to contract by 24.2% and 51.7% for 2050 and 2070, respectively. According to the prediction of the BCC-CSM2-MR, CanESM5, CNRM-CM6-1 and MIROC6 model, the highly suitable area for T. tabaci would likely contract under both SSP126 and SSP585. This study detailed the potential future habitable area for T. tabaci in India, which could help monitor and devise efficient management strategies for this destructive pest.

Climate change and infectious disease: a review of evidence and research trends.

BACKGROUND: Climate change presents an imminent threat to almost all biological systems across the globe. In recent years there have been a series of studies showing how changes in climate can impact infectious disease transmission. Many of these publications focus on simulations based on in silico data, shadowing empirical research based on field and laboratory data. A synthesis work of empirical climate change and infectious disease research is still lacking. METHODS: We conducted a systemic review of research from 2015 to 2020 period on climate change and infectious diseases to identify major trends and current gaps of research. Literature was sourced from Web of Science and PubMed literary repositories using a key word search, and was reviewed using a delineated inclusion criteria by a team of reviewers. RESULTS: Our review revealed that both taxonomic and geographic biases are present in climate and infectious disease research, specifically with regard to types of disease transmission and localities studied. Empirical investigations on vector-borne diseases associated with mosquitoes comprised the majority of research on the climate change and infectious disease literature. Furthermore, demographic trends in the institutions and individuals published revealed research bias towards research conducted across temperate, high-income countries. We also identified key trends in funding sources for most resent literature and a discrepancy in the gender identities of publishing authors which may reflect current systemic inequities in the scientific field. CONCLUSIONS: Future research lines on climate change and infectious diseases should considered diseases of direct transmission (non-vector-borne) and more research effort in the tropics. Inclusion of local research in low- and middle-income countries was generally neglected. Research on climate change and infectious disease has failed to be socially inclusive, geographically balanced, and broad in terms of the disease systems studied, limiting our capacities to better understand the actual effects of climate change on health.

Life-history responses to temperature and seasonality mediate ectotherm consumer-resource dynamics under climate warming.

Climate warming is altering life cycles of ectotherms by advancing phenology and decreasing generation times. Theoretical models provide powerful tools to investigate these effects of climate warming on consumer-resource population dynamics. Yet, existing theory primarily considers organisms with simplified life histories in constant temperature environments, making it difficult to predict how warming will affect organisms with complex life cycles in seasonal environments. We develop a size-structured consumer-resource model with seasonal temperature dependence, parameterized for a freshwater insect consuming zooplankton. We simulate how climate warming in a seasonal environment could alter a key life-history trait of the consumer, number of generations per year, mediating responses of consumer-resource population sizes and consumer persistence. We find that, with warming, consumer population sizes increase through multiple mechanisms. First, warming decreases generation times by increasing rates of resource ingestion and growth and/or lengthening the growing season. Second, these life-history changes shorten the juvenile stage, increasing the number of emerging adults and population-level reproduction. Unstructured models with similar assumptions found that warming destabilized consumer-resource dynamics. By contrast, our size-structured model predicts stability and consumer persistence. Our study suggests that, in seasonal environments experiencing climate warming, life-history changes that lead to shorter generation times could delay population extinctions.

Climate variables are not the dominant predictor of Arctic shorebird distributions.

Competing theoretical perspectives about whether or not climate is the dominant factor influencing species' distributions at large spatial scales have important consequences when habitat suitability models are used to address conservation problems. In this study, we tested how much variables in addition to climate help to explain habitat suitability for Arctic-breeding shorebirds. To do this we model species occupancy using path analyses, which allow us to estimate the indirect effects of climate on other predictor variables, such as land cover. We also use deviance partitioning to quantify the total relative importance of climate versus additional predictors in explaining species occupancy. We found that individual land cover variables are often stronger predictors than the direct and indirect effects of climate combined. In models with both climate and additional variables, on average the additional variables accounted for 57% of the explained deviance, independent of shared effects with the climate variables. Our results support the idea that climate-only models may offer incomplete descriptions of current and future habitat suitability and can lead to incorrect conclusions about the size and location of suitable habitat. These conclusions could have important management implications for designating protected areas and assessing threats like climate change and human development.

Rhizobium as Biotechnological Tools for Green Solutions: An Environment-Friendly Approach for Sustainable Crop Production in the Modern Era of Climate Change.

Modern and industrialized agriculture enhanced farm output during the last few decades, but it became possible at the cost of agricultural sustainability. Industrialized agriculture focussed only on the increase in crop productivity and the technologies involved were supply-driven, where enough synthetic chemicals were applied and natural resources were overexploited with the erosion of genetic diversity and biodiversity. Nitrogen is an essential nutrient required for plant growth and development. Even though nitrogen is available in large quantities in the atmosphere, it cannot be utilized by plants directly with the only exception of legumes which have the unique ability to fix atmospheric nitrogen and the process is known as biological nitrogen fixation (BNF). Rhizobium, a group of gram-negative soil bacteria, helps in the formation of root nodules in legumes and takes part in the BNF. The BNF has great significance in agriculture as it acts as a fertility restorer in soil. Continuous cereal-cereal cropping system, which is predominant in a major part of the world, often results in a decline in soil fertility, while legumes add nitrogen and improve the availability of other nutrients too. In the present context of the declining trend of the yield of some important crops and cropping systems, it is the need of the hour for enriching soil health to achieve agricultural sustainability, where Rhizobium can play a magnificent role. Though the role of Rhizobium in biological nitrogen fixation is well documented, their behaviour and performance in different agricultural environments need to be studied further for a better understanding. In the article, an attempt has been made to give an insight into the behaviour, performance and mode of action of different Rhizobium species and strains under versatile conditions.

Once upon a time in Mexico: Holocene biogeography of the spotted bat (Euderma maculatum).

Holocene-era range expansions are relevant to understanding how a species might respond to the warming and drying climates of today. The harsh conditions of North American deserts have phylogenetically structured desert bat communities but differences in flight capabilities are expected to affect their ability to compete, locate, and use habitat in the face of modern climate change. A highly vagile but data-deficient bat species, the spotted bat (Euderma maculatum), is thought to have expanded its range from central Mexico to western Canada during the Holocene. With specimens spanning this latitudinal extent, we examined historical demography, and used ecological niche modeling (ENM) and phylogeography (mitochondrial DNA), to investigate historic biogeography from the rear to leading edges of the species' range. The ENM supported the notion that Mexico was largely the Pleistocene-era range, whereas haplotype pattern and Skyline plots indicated that populations expanded from the southwestern US throughout the Holocene. This era provided substantial gains in suitable climate space and likely facilitated access to roosting habitat throughout the US Intermountain West. Incongruent phylogenies among different methods prevented a precise understanding of colonization history. However, isolation at the southern-most margin of the range suggests a population was left behind in Mexico as climate space contracted and are currently of unknown status. The species appears historically suited to follow shifts in climate space but differences in flight behaviors between leading edge and core-range haplogroups suggest range expansions could be influenced by differences in habitat quality or climate (e.g., drought). Although its vagility could facilitate response to environmental change and thereby avoid extinction, anthropogenic pressures at the core range could still threaten the ability for beneficial alleles to expand into the leading edge.

Assessing the climate change mitigation potential from food waste composting.

Food waste is a dominant organic constituent of landfills, and a large global source of greenhouse gases. Composting food waste presents a potential opportunity for emissions reduction, but data on whole pile, commercial-scale emissions and the associated biogeochemical drivers are lacking. We used a non-invasive micrometeorological mass balance approach optimized for three-dimensional commercial-scale windrow compost piles to measure methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) emissions continuously during food waste composting. Greenhouse gas flux measurements were complemented with continuous oxygen (O2) and temperature sensors and intensive sampling for biogeochemical processes. Emission factors (EF) ranged from 6.6 to 8.8 kg CH4-C/Mg wet food waste and were driven primarily by low redox and watering events. Composting resulted in low N2O emissions (0.01 kg N2O-N/Mg wet food waste). The overall EF value (CH4 + N2O) for food waste composting was 926 kgCO2e/Mg of dry food waste. Composting emissions were 38-84% lower than equivalent landfilling fluxes with a potential net minimum savings of 1.4 MMT CO2e for California by year 2025. Our results suggest that food waste composting can help mitigate emissions. Increased turning during the thermophilic phase and less watering overall could potentially further lower emissions.