Current scenario and challenges of plastic pollution in Bangladesh: a focus on farmlands and terrestrial ecosystems

● A global snapshot of plastic waste generation and disposal is analysed. ● Effect of plastic pollution on environment and terrestrial ecosystem is reviewed. ● Ecotoxicity and food security from plastic pollution is discussed. Plastic is considered one of the most indispensable commodities in our daily life. At the end of life, the huge ever-growing pile of plastic waste (PW) causes serious concerns for our environment, including agricultural farmlands, groundwater quality, marine and land ecosystems, food toxicity and human health hazards. Lack of proper infrastructure, financial backup, and technological advancement turn this hazardous waste plastic management into a serious threat to developing countries, especially for Bangladesh. A comprehensive review of PW generation and its consequences on environment in both global and Bangladesh contexts is presented. The dispersion routes of PW from different sources in different forms (microplastic, macroplastic, nanoplastic) and its adverse effect on agriculture, marine life and terrestrial ecosystems are illustrated in this work. The key challenges to mitigate PW pollution and tackle down the climate change issue is discussed in this work. Moreover, way forward toward the design and implementation of proper PW management strategies are highlighted in this study.     

气候变化背景下中国陆地生态系统碳储量及碳通量研究进展

全球气候变化对全球生态系统的结构、功能和过程产生了重要影响,成为各国政府、社会公众以及科学界共同关心的焦点问题。陆地生态系统碳循环又是当前气候变化和区域可持续发展研究的核心内容之一,影响到经济和社会发展的各个方面。因此,开展陆地生态系统碳储量和碳通量的研究仍将是气候变化研究中的重点内容。总结了近年来国内森林、土壤、草地、农田四种陆地生态系统在碳储量、碳通量方面取得的研究成果和不足：随着遥感、GIS及模型的发展和应用,森林、草地生态系统碳储量的研究精度和范围要高于农田和土壤,而农田和土壤生态系统碳储量的研究多基于典型性样地和大量实验数据,结果受制于样点布设和采样密度;目前,土壤生态系统碳储量结果多基于上世纪80年代全国二次土壤普查数据计算所得,且总有机碳库的估算存在较大差异,土壤有机碳的组分研究中易氧化有机碳库研究滞后于总有机碳,迫切需要对我国现有土壤有机碳进行研究;农田生态系统受人类活动干扰强烈,从一个或几个站点到全国尺度都有对农田土壤有机碳贮量的研究成果,与国外相比,我国试验田的设置时间短,资料积累较少,更多侧重不同施肥方式下农作物产量和农田合理的施肥培肥模式研究,农田土壤有机碳含量关系我国农业生产和粮食安全,对农田土壤固碳机理的研究仍将是今后关注的焦点。各生态系统碳通量的监测取得了一定成果,近年来涡度相关系统在森林、土壤、草地、农田生态系统中得到了广泛的应用。并从气候、人类活动两个因素分析了其对生态系统碳储量、碳通量的影响。针对目前存在的问题,进一步指出了目前国内不同生态系统中碳循环在现状研究、有机碳变化机制、模型建立及气候变化和人类活动影响下的碳库时空格局方面得到加强。     

Modelling carbon and nitrogen dynamics in forest ecosystems of Central Russia under different climate change scenarios and forest management regimes

The individual-based stand-level model EFIMOD was used for large-scale simulations using standard data on forest inventories as model inputs. The model was verified for the case-study of field observations, and possible sources of uncertainties were analysed. The approach developed kept the ability for fine-tuning to account for spatial discontinuity in the simulated area. Several forest management regimes were simulated as well as forest wildfires and climate changes. The greatest carbon and nitrogen accumulations were observed for the regime without cuttings. It was shown that cuttings and wildfires strongly influence the processes of carbon and nitrogen accumulations in both soil and forest vegetation. Modelling also showed that the increase in annual average temperatures resulted in the partial relocation of carbon and nitrogen stocks from soil to plant biomass. However, forest management, particularly harvesting, has a greater effect on the dynamics of forest ecosystems than the prescribed climate change.     

Modelling phytoclimatic versatility as a large scale indicator of adaptive capacity to climate change in forest ecosystems

CLIMPAIR is a new phytoclimatic model, correlative and niche-based, which simultaneously assesses non-linear, non-statistical and dual measurements of proximity/potentiality of a site with respect to a number of climatic ranges of species, defined by convex hulls, within a suitability space. This set of phytoclimatic distances makes it possible to evaluate the degree to which each species is suitable for that site. Considering not only the number of species compatible (expected species richness), but also all those compatible covers presenting a high level of suitability evenness and finally applying an indicator derived from Shannon's classic entropy index to the set of standardized phytoclimatic coordinates in the suitability hyperspace, we can evaluate the phytoclimatic entropy which may be considered as a means of estimating the phytoclimatic versatility of the site. A site with high phytoclimatic entropy would promise versatile future behaviour, characterized by a wide range of possibilities of adaptation to climate change, and hence versatility can be used as an index of resilience and ability of a forest ecosystem to adapt to climate change. The model has been applied to peninsular Spain for 18 forest tree species and 12 climatic variables between the current mean climate (period 1951-1999) and a future climatic scenario (period 2040-2069). The results generally point to a significant decrease in the versatility of forest tree formations in the area studied, which is not homogeneous owing to a dual altitudinal/latitudinal decoupling. The decrease in versatility is greater in Mediterranean biogeographical areas than in Euro-Siberian ones, where in some cases it actually increases. In altitudinal terms, areas at elevations of less than 1500 m tend to become less versatile than areas situated at higher elevations, where versatility increases partly as a result of enrichment of alpine conifer forests with broadleaf species.     

Evaluating the effects of future climate change and elevated CO2 on the water use efficiency in terrestrial ecosystems of China

Water use efficiency (WUE) is an important variable used in climate change and hydrological studies in relation to how it links ecosystem carbon cycles and hydrological cycles together. However, obtaining reliable WUE results based on site-level flux data remains a great challenge when scaling up to larger regional zones. Biophysical, process-based ecosystem models are powerful tools to study WUE at large spatial and temporal scales. The Integrated BIosphere Simulator (IBIS) was used to evaluate the effects of climate change and elevated CO₂ concentrations on ecosystem-level WUE (defined as the ratio of gross primary production (GPP) to evapotranspiration (ET)) in relation to terrestrial ecosystems in China for 2009-2099. Climate scenario data (IPCC SRES A2 and SRES B1) generated from the Third Generation Coupled Global Climate Model (CGCM3) was used in the simulations. Seven simulations were implemented according to the assemblage of different elevated CO₂ concentrations scenarios and different climate change scenarios. Analysis suggests that (1) further elevated CO₂ concentrations will significantly enhance the WUE over China by the end of the twenty-first century, especially in forest areas; (2) effects of climate change on WUE will vary for different geographical regions in China with negative effects occurring primarily in southern regions and positive effects occurring primarily in high latitude and altitude regions (Tibetan Plateau); (3) WUE will maintain the current levels for 2009-2099 under the constant climate scenario (i.e. using mean climate condition of 1951-2006 and CO₂ concentrations of the 2008 level); and (4) WUE will decrease with the increase of water resource restriction (expressed as evaporation ratio) among different ecosystems.     

Evaluating and managing wildlife impacts of climate change under uncertainty

Wildlife managers face the daunting task of managing wildlife in light of uncertainty about the nature and extent of future climate change and variability and its potential adverse impacts on wildlife. A conceptual framework is developed for managing wildlife under such uncertainty. The framework uses fuzzy logic to test hypotheses about the extent of the wildlife impacts of past climate change and variability, and fuzzy multiple attribute evaluation to determine best compensatory management actions for adaptively managing the potential adverse impacts of future climate change and variability on wildlife. A compensatory management action is one that can offset some of the potential adverse impacts of climate change and variability on wildlife. Implementation of the proposed framework requires wildlife managers to: (1) select climate impact states, hypotheses about climate impact states, possible management actions for alleviating adverse wildlife impacts of climate change and variability, and future climate change scenarios; (2) choose biological attributes or indicators of species integrity; (3) adjust those attributes for changes in non-climatic variables; (4) define linguistic variables and associated triangular fuzzy numbers for rating both the acceptability of biological conditions under alternative management actions and the relative importance of biological attributes; (5) select minimum or maximum acceptable levels of the attributes and reliability levels for chance constraints on the biological attributes; and (6) define fuzzy sets on the extent of species integrity and biological conditions and select a fuzzy relation between species integrity and biological conditions. A constructed example is used to illustrate a hypothetical application of the framework by a wildlife management team. An overall best compensatory management action across all climate change scenarios is determined using the minimax regret criterion, which is appropriate when the management team cannot assign or is unwilling to assign probabilities to the future climate change scenarios. Application of the framework can be simplified and expedited by incorporating it in a web-based, interactive, decision support tool.     

Choice of threshold alters projections of species range shifts under climate change

One of the least explored sources of algorithmic uncertainty in bioclimatic envelope models (BEM) is the selection of thresholds to transform modelled probabilities of occurrence (or indices of suitability) into binary predictions of species presence and absence. We investigate the impacts of such thresholds in the specific context of climate change. BEM for European tree species were fitted combining 9 climatic models and emissions scenarios, 7 modelling techniques, and 14 threshold-setting techniques. We quantified sources of uncertainty in projections of turnover, and found that the choice of the modelling technique explained most of the variability (39%), while threshold choice explained 25% of the variability in the results, and their interaction an additional 19%. Choice of future climates explained 9% of total variability among projections. Estimated species range shifts obtained by applying different thresholds and models were grouped by IUCN-based categories of threat. Thresholds had a large impact on the inferred risks of extinction, producing 1.7- to 9.9-fold differences in the proportions of species projected to become threatened by climate change. Results demonstrate that threshold selection has large - albeit often unappreciated - consequences for estimating species range shifts under climate change.     

近50年三江源地表湿润指数变化特征及其影响因素

基于三江源区1959—2008年月平均气温、最高气温、最低气温、相对湿度、降水量、风速和日照百分率等气候要素资料,应用修订的Penman-Monteith（P-M）模型计算了最大潜在蒸散量和地表湿润指数,分析其空间分布、年际和年代际变化特征及其主要气象因子的影响。结果表明：1959—2008年间,研究区年降水量呈增加趋势,降水量变化曲线线性拟合倾向率为5.316~13.047 mm.（10a）-1,春夏季增幅较大;最大潜在蒸散量呈增加趋势,年最大潜在蒸散量变化曲线线性拟合倾向率在5.073~10.712 mm.（10a）-1,夏季增幅最大;地表湿润指数变化也呈增加趋势,年地表湿润指数变化曲线线性拟合倾向率0.011~0.026（10a）-1,冬季增幅最大,在15年周期附近,出现了3~5个干湿交替期,1984年之后为偏湿期,在中高频区,1998—2006年有偏干振荡;影响三江源区地表湿润指数的主要因子是降水量、相对湿度和平均最高气温。     

Modelling the impact of thermal adaptation of soil microorganisms and crop system on the dynamics of organic matter in a tropical soil under a climate change scenario

No consensus currently exists about how climate change should affect the status of soil organic matter (SOM) in the tropics. In this study, we analyse the impact of climate change on the underlying mechanisms controlling SOM dynamics in a ferralsol under two contrasting tropical crops: maize (C4 plant) and banana (C3 plant). We model the effect of microbial thermal adaptation on carbon (C) mineralisation at the crop system scale and introduce it in the model STICS, which was previously calibrated for the soil-crop systems tested in this study. Microbial thermal adaptation modelling is based on a reported theory for thermal acclimation of plant and soil respiration. The climate is simulated from 1950 to 2099 for the tropical humid conditions of Guadeloupe (French Antilles), using the ARPEGE model and the IPCC emission scenario A1B. The model predicts increases of 3.4 °C for air temperature and 1100 mm yr⁻¹ for rainfall as a response to an increase of 375 ppm for atmospheric carbon dioxide concentration in the 2090-2099 decade compared with the 1950-1959 decade. The results of the STICS model indicate that the crop affects the response of SOM to climate change by controlling the change in several variables involved in C dynamics: C input, soil temperature and soil moisture. SOM content varies little until 2020, and then it decreases faster for maize than for banana. The decrease is weakened under the hypothesis of thermal adaptation, and this effect is greater for maize (−180 kg C ha⁻¹ yr⁻¹ without adaptation and −140 kg C ha⁻¹ yr⁻¹ with adaptation) than for banana (−60 kg C ha⁻¹ yr⁻¹ and −40 kg C ha⁻¹ yr⁻¹, respectively). The greater SOM loss in maize is mainly due to the negative effect of warming on maize growth decreasing C input from residues. Climate change has a small effect on banana growth, and SOM loss is linked to its effect on C mineralisation. For both crops, annual C mineralisation increases until 2040, and then it decreases continuously. Thermal adaptation reduces the initial increase in mineralisation, but its effect is lower on the final decrease, which is mainly controlled by substrate limitation. No stabilisation in SOM status is attained at the end of the analysed period because C mineralisation is always greater than C input. Model predictions indicate that microbial thermal adaptation modifies, but does not fundamentally change the temporal pattern of SOM dynamics. The vegetation type (C3 or C4) plays a major role in SOM dynamics in this tropical soil because of the different impact of climate change on crop growth and then on C inputs.