Ecological Buffer Capacity Research Articles

Operationalizing food system resilience: An indicator-based assessment in agroindustrial, smallholder farming, and agroecological contexts in Bolivia and Kenya

Resilience is often considered a precondition for sustainable social-ecological systems. But how can this understanding of resilience be applied to food systems? We operationalized the concept by subdividing it into different resilience dimensions, namely: buffer capacity, self-organization, and capacity for learning and adaptation. Specific indicators were defined for each dimension: (1) agrobiodiversity and livelihood assets for buffer capacity; (2) decentralization and independence, local consumption of production, interest groups, ecological self-regulation, and connectivity for self-organization; and (3) knowledge of threats and opportunities, reflective and shared learning, feedback mechanisms, existence and use of local-traditional knowledge, and a shared food system vision for capacity for learning and adaptation. Next, we applied the resilience indicators to different food systems (agroindustrial, local, and agroecological) in Kenya and Bolivia, including assessment of the interaction and coexistence of food systems. While the contexts in the two countries differ greatly, we identified several common trends that appear to be undermining food system resilience in both settings. These trends include low ecological buffer capacity and self-regulation in agroindustrial food systems; strong disparities in income and access to productive resources; competition for water, land, and labor; exclusion from markets; and low human capital and feedback mechanisms in locally based, traditional food systems. Taken together, these trends cast doubt on food system coexistence narratives. Finally, the results enable us to identify leverage points in the food systems – e.g., regarding learning and feedback mechanisms – that could be used to foster food system transformations linked to goals of sustainability and justice.

Nutrient recycling and soil fertility management in the course of the industrial transition of traditional, organic agriculture: The case of Bruck estate, 1787–1906

Abstract Late 18th century agriculture was under social and economic pressures, emanating from unprecedented population growth and establishing world markets. Farmers met these challenges successfully and largely without external energy subsidies and mineral fertilizers. Our research asks (1) if and how performance gains were achieved by improving traditional nutrient management through advanced, organic farming and (2) whether this early agrarian intensification compromised the natural capital of agro-ecosystems. We present a long-term socio-ecological perspective on nutrient cycling in the historical agro-ecosystem of the estate Bruck in eastern Austria, from 1787 to 1906. Focal point is the development of different patterns in nutrient cycling, tracing the transition from traditional, organic to industrial farming. We used nutrient balances and input-output analysis for a quantitative assessment of nitrogen and phosphorus flows. To investigate the correlation between growing system performance and improved internal nutrient cycling, we applied the Finn cycling index (FCI), which represents the fraction of nutrients recycled on-farm before leaving the system. Improving nutrient cycling entailed the operational integration of livestock, the utilization of ecological nutrient replenishment processes through legume intercropping and optimizing nutrient transfers from extensive land-use systems. Nitrogen recycling grew steadily from 20% in 1787 to 36% in 1906. Phosphorus recycled on farm grew accordingly from 33% in 1787 to 46% in 1840, before dropping to 31% in 1906 due to increased application of mineral fertilizers. We show, that cereal yields more than doubled through improved traditional nutrient management. In the long-run, however, advanced organic farming induced an unsustainable use of phosphorus and the loss of ecological buffer capacity from traditional mixed land-use systems.

Using eutrophication and ecological indicators to assess ecosystem condition in Poyang Lake, a Yangtze-connected lake

Poyang Lake, the largest freshwater lake in China, has been facing shrinkage and environmental problems in recent years, making it imperative to assess the status of its ecosystem. In this study, such an assessment was conducted based on the trophic status index, phytoplankton biomass, the ratio of zooplankton biomass to phytoplankton biomass, eco-exergy, structural eco-exergy, and ecological buffer capacity to total phosphorus for phytoplankton. First, the spatial and temporal variations of the ecosystem status in Poyang Lake were assessed based on these six indicators. Then, cluster analysis was utilized to classify the 15 sites based on the various indicators and to identify the most influential indicators. The results showed that the ecological status of Poyang Lake was affected not only by eutrophication but also by hydrological characteristics. All of the 15 sites had deteriorated to a certain degree. An analysis of the relative spatial ecological status showed that the status of the water in the rivers was superior to the status of water in the central lake. Furthermore, the relative ecological status was found to be best in summer and second best in autumn with ecological status deteriorating in winter. There was no significant correlation between eco-exergy and trophic status index, but a very strong relationship was found between eco-exergy and macrozoobenthos biomass with an R2 of 0.92, indicating that macrozoobenthos contributed greatly to the eco-exergy.

海湾生态系统健康评价方法构建及在大亚湾的应用

Bay ecosystem health,recently,has faced a growing interference and disturbance by anthropogenic activities,which has also caused challenges to ecosystem management.Ecosystem health assessment,a new methodology of ecosystem assessment,could obtain the current situation both of ecosystem structure and function and provide a great of necessary information for ecosystem management.An integrated assessment methodology for bay ecosystem health was established based on geographical information system(GIS) technology,which was on basis of a pressure-structure-response frame model for marine ecosystem and an ecosystem health assessment indicator system constructed by external pressure,ecosystem structure and response parameters.This paper also presented a case study of Daya bay in spring by employing GIS technology. The results showed that the average ecosystem health integrated index was 0.57 in Daya Bay ecosystem in spring.Detailedly,organic pollutant index ranged from 0.36 to 1,with the average value at 0.87,was at very level of Daya Bay ecosystem.Eutrophic level index value ranged from 0.17 to 1,with the average of 0.77,indicated better level of the ecosystem.Furthermore,phytoplankton abundance index was at a worse level because of the ranges from 0.33 to 1 and the average of 0.21.Zooplankton biomass index ranged from 0.16 to 1,with the average of 0.88,suggested the ecosystem was at a very level.Zoobenthos biomass index was at critical state,as the index value ranged from 0 to 1 and the average value at 0.59.Moreover,phytoplankton diversity index was also critical state level,as the index ranged from 0.39 to 0.75 and the average of 0.55.Primary productivity index,ranged from 0.21 to 1 and the average of 0.77,illustrated the better level of the ecosystem.However,ecological buffer capacity index was at a worst level,as the index value ranged from 0 to 0.39 and the average value of 0.06.Resumptively,spatial distribution of the assessed sea area could be divided into three zones according to the grade of integrated assessment index,namely health zone,critical health zone and bad health zone.The percentages of three zones were 46.56%,47.97% and 5.47%,with the average integrated health index of 0.64,0.54 and 0.35,respectively. In general,the status of the Daya Bay ecosystem health was at a better level according to the above indexes.However,the health status of the ecosystem was potential to change to critical state by the negative influences by factors including phytoplankton abundance,phytoplankton diversity and buffer capacities of the ecosystem.The bad health zone in the Daya Bay ecosystem distributed at or near the area with the intensive anthropogenic activity.Consequently,the special distribution of Daya Bay ecosystem health status was mostly affected by anthropogenic activity around the bay.The established methodology has been ultimately proved to be a valuable quantitative assessment method and to be worth widely popularizing in marine ecology due to the characteristics of wide applicability,high accuracy and good expression.

白洋淀生态系统健康评价

PDF HTML阅读 XML下载 导出引用 引用提醒 白洋淀生态系统健康评价 DOI: 10.5846/stxb201207050937 作者: 作者单位: 北京师范大学环境学院,北京师范大学环境学院,北京师范大学环境学院,北京师范大学环境学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金创新研究群体科学基金资助项目（51121003）；国家水体污染控制与治理科技重大专项资助项目（2008ZX07209-009）；国家自然科学基金资助项目（51279009） Ecosystem health assessment in Baiyangdian Lake Author: Affiliation: State Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University,State Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University,State Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University,State Key Laboratory of Water Environment Simulation,School of Environment,Beijing Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:涵盖热力学指标的指标体系可全面表征湖泊生态系统在受扰条件下的健康状态。在实地监测的基础上，应用浮游植物生物量（BA）、浮游动物生物量/浮游植物生物量（BZ/BA）浮游植物群落初级生产量（P）、能质（Ex）、结构能质（Exst）和浮游植物对TP的缓冲能力（βp）表征白洋淀的健康状态，并通过集成健康综合指数，确定了白洋淀14个水域的健康等级。结果表明，白洋淀大部分水域健康受损较为严重。枣林庄和采蒲台水域处于亚健康状态，烧车淀、光淀张庄和东田庄水域处于不健康状态，其余9个水域均处于病态。结合各水域人类干扰活动的分布可知，白洋淀健康退化的程度与人类干扰强度密切相关。府河和淀内居民生活及水产养殖污水排放是造成白洋淀健康受损的主要原因。该结果可为白洋淀生态系统的管理和修复提供一定的科学依据。 Abstract:Index system covering thermodynamic indicators specialized by eco-exergy and structural eco-exergy could comprehensively characterize the ecological health condition of lake ecosystems under human disturbances. This system has been widely used for assessing lake ecosystem health in recent decades. For comparing ecological health of one lake across time scale or that of different lakes, integration of comprehensive health index is needed to identify the degree of lake health and to better reflect the discrepancies of the ecological health conditions. In this paper, an integrated index system including indicators of phytoplankton biomass (BA), ratio of zooplankton to phytoplankton biomass (BZ/BA), phytoplankton primary production (P), eco-exergy (Ex), structural eco-exergy (Exst) and ecological buffer capacity to total phosphorus for phytoplankton (βp) collected from field survey was structured to assess the ecological health condition of Baiyangdian Lake which is located in North China. Health classes of fourteen water areas in Baiyangdian Lake were identified using comprehensive health index, which was computed through the sum of normalized indices multiplied by the weights. The results indicate that the ecological health of most water areas in Baiyangdian Lake is deteriorated severely. The water areas of Zaolinzhuang and Caiputai are sub-healthy, those of Shaochedian, GuangdianZhangzhuang and Dongtianzhuang are less healthy, and those of the remaining nine are unhealthy. The spatial variations of ecological health condition of the all water areas across Baiyangdian Lake are notable. The water areas in the northeast and southeast are under relatively better ecological health conditions. The ecological health conditions in the northern, central and southern water areas are deteriorated to some extent while those in the western water area, which is close to Fu River, the ecological health conditions are seriously degraded. The ecological health conditions from good to bad in spatial scale are in the following order: water areas in the northeast and southeast > water areas in the north, center and south > water areas in the west. There is close relationship between intensity of human disturbances and extent of lake health degradation. The water areas in the northeast and southeast with little human disturbances can maintain relatively natural state of the lake ecosystem with good health conditions. The dense non-point source pollution associated with aquaculture and villages is the key factor which leads to the ecological health deterioration in the northern, central and southern water areas. The western water areas have been greatly affected greatly by large amounts of pollutants from Fu River, leading to relatively worse ecological health condition. It can be seen that sewages discharged from the villages and aquaculture into the reservoir are the dominant factors resulting in ecological health degradation in Baiyangdian Lake. In comparison with the pollutants from the aquaculture and villages, there are more impacts of pollutants discharged from Fu River on the ecological health condition of Baiyangdian Lake. The results in the paper could offer foundation for further study of early warning and control of lake ecosystem health. In addition, they can provide scientific evidence for ecosystem restoration and management of Baiyangdian Lake. 参考文献 相似文献 引证文献

Integrated ecosystem health assessment of a macrophyte-dominated lake

A set of ecological indicators were proposed for the lake ecosystem health assessment via the application of structural, functional, and system-level responses of whole-lake ecosystems to land-use types, chemical stressors including nutrients and organic pollution, and physical stressors including water depth (WD) and grain size (GR). The structural metrics incorporated Chlorophyll c/Chlorophyll a (Chl c/a), Shannon diversity index (H), and proportion of Bacillariophyta (BAC). The functional metrics encompassed leucine amino peptide enzymes (LEU) and general primary productivity (GPP). The ecosystem level indicators consisted of eco-exergy (Ex), structural eco-exergy (Exst), and ecological buffer capacity (βTP-A). Using these indicators, we developed an integrated ecological health modeling method (IEHMM) for macrophyte-dominated lake health assessment. The IEHMM was designed to: (1) identify the relevant questions related to the health of the ecosystem; (2) analyze the lake's ecosystem structure including the pelagic and benthic pathways and use these results to determine the model's structure and complexity; (3) establish a whole-lake ecological conceptual model; (4) calculate the lake's ecosystem health indicators; (5) assess lake ecosystem health using these different indicators; and (6) compare the assessment results with different calculation methods. The IEHMM conceptual model included 5 sub-models and 15 state variables. The results of the case study in Baiyangdian Lake (China) demonstrated that the IEHMM provided comprehensive assessment results that corresponded with the lake's actual health state.

Ecosystem health assessment of the plant-dominated Baiyangdian Lake based on eco-exergy

Thermodynamic indicators, including eco-exergy and structural eco-exergy, have been widely applied to lake ecosystem health assessment in recent decades. In this paper, the index system, including phytoplankton biomass, microzooplankton biomass, trophic state index ( TSI), eco-exergy, structural eco-exergy and ecological buffer capacity to total phosphorus for phytoplankton, were applied to assess the health states of fourteen typical water areas in the plant-dominated Baiyangdian Lake. Different types of organisms, including zooplankton, benthonic animals and macrophytes with various denotative meanings, were differentiated to some extent and the latest weighting factors, were used in the eco-exergy and structural eco-exergy calculations for a better characterization of health state. The results showed that only the water area in site 4, with relatively high eco-exergy, structural eco-exergy, ecological buffer capacity and low phytoplankton biomass, microzooplankton biomass and TSI, is in healthy state. Health states of the other water areas were all deteriorated at various degrees due to the human disturbances. The health states of the northeast and southeast water areas were better than those of the northern, central and southern water areas with aquaculture and villages and western water areas close to the Fu River. For the plant-dominated Baiyangdian Lake, combining eco-exergy with structural eco-exergy could better characterize the health state of ecosystem with macrophytes hypermorphosis and meanwhile based on the distinguishing of various organism components, specifically benthonic animals, to some extent for eco-exergy and structural eco-exergy calculations, spatial variations of health state obtained better description, which indicated eco-exergy and structural eco-exergy were the robust indicators for ecosystem health assessment of the plant-dominated lake.

Comparison of exergy found by a classical thermodynamic approach and by the use of the information stored in the genome

It is possible to calculate the exergy for organisms based on classic thermodynamics as already demonstrated by Mejer and Jorgensen [Mejer, H., Jorgensen, S.E., 1979. Exergy and ecological buffer capacity. State-of-the-art in Ecol. Model. 7, 829–846]. The calculation of exergy as eco-exergy, which is based on the information stored in the genome, has lately been proposed by Jørgensen and co-workers. Recently, Ludovisi [Ludovisi, A., 2009. Exergy vs information in ecological successions: interpreting community changes by a classical thermodynamic approach. Ecol. Model. 220, 1566–1577] has put forward a method based on classical thermodynamics, which leads to the calculation of “virtual” values of concentration at equilibrium for a number of organic compounds ( VEC) and freshwater organisms ( VECE). This paper compares the two approaches by analysing the correlation existing between the VECE- and the β-values derived by Jørgensen et al. [Jørgensen, S.E., Ladegaard, N., Debeljak, M., Marques, J.C., 2005. Calculations of exergy for organisms. Ecol. Model. 185, 165–175]. It was found that there was a good correlation, which can be useful for estimating β-values for organisms whose genome is not known in a sufficient detail. The relationship between VECE- and β-values suggests that two proposed thermodynamic orientors based on these quantities – the eco-exergy index and the structural information – should lead to coherent results when applied to the evaluation of the development state of ecosystems. A numerical simulation shows that this expectation is verified in a major case, but also that different, even opposite, responses can arise, depending on the biological composition of the biocoenosis investigated.

Exergy vs information in ecological successions: Interpreting community changes by a classical thermodynamic approach

This work proposes a methodology based on classical thermodynamics, which allows the variation in ecosystem composition to be interpreted within the framework of the exergy concept. The basic equation of exergy [Mejer, H., Jorgensen, S.E., 1979. Exergy and ecological buffer capacity. State-of-the-art in Ecological Modelling 7, 829–846] was decomposed into three terms – size ( C), structural information ( I) and concentration ( X) – and their significance as indicators of ecosystem state was evaluated by simulating different scenarios of development in a simplified freshwater ecosystem. In order to calculate the exergy terms, the most critical issue in using exergy in an ecological context, i.e. the estimate of reference equilibrium values for organic matter and organisms, had to be faced. With this aim, the equations of classical thermodynamics in solution were applied, and “virtual” values of concentration at equilibrium were calculated for a number of organic compounds ( VEC) and freshwater organisms ( VECE). The results of the simulation showed that, whereas exergy and the exergy terms inherently connected with the a-biotic component varied consistently with the incorporation of biomass into the ecosystem, the structural information of the biotic component followed different, even opposite, pathways of variation, which were dependent only on the change in the size spectrum of the community. Due to the strict dependence of the VECE values on organism size, the increase of structural information with increasing abundance of large and complex species is also consistent with the general pattern of succession delineated by the classical r– K model. S tructural information is therefore proposed as an indicator of the development state, as well as an ecological orientor, whose maximisation is expected during ecosystem development. However, since an increase in structural information is not necessarily accompanied by an increase in exergy, a sort of “antagonism” between these two related orientors emerges, whose resolution may contribute to shed light on the fundamental forces which drive ecosystem development.

Ecosystem theory, ecological buffer capacity, uncertainty and complexity

The paper discusses a wider application of models in ecosystem research and management. In particular, it is discussed how models are able to cope with the properties of ecosystems including their enormous complexity. An uncertainty principle in ecology similar to Heisenberg's uncertainty principles in quantum mechanics is proposed. The usually applied stability concepts in ecology presume that an ecosystem will return to the same state again after perturbation. As this is not the case, a new stability concept named ecological buffer capacity is introduced to account for real ecosystem reactions. Finally, the application of structural-dynamics models is discussed, and how they are able to predict changes in species composition. Exergy—a translation of Darwin's theory into thermodynamics—has been used as a goal function in these models. This recent development in ecosystem modelling seems very promising; several references are given to the literature which presents these results.

A holistic approach to ecological modelling

Models of different complexity were used to examine how the ecological buffer capacity, β (defined as the change in loading relative to the change in a considered state variable) varies when the loading, e.g. the input of phosphorus, is changed. It was found that while β = ΔP(total)/ΔP(soluble) increases with increasing complexity of the model at low P-loading, the β-value will — at medium P-loadings — have a maximum value at a certain degree of complexity, and will be a decreasing function of complexity at high phosphorus loadings. This might explain why very eutrophic lakes, rivers polluted with organic matter or other stressed ecosystems are stable although their complexity is low. The more complex ecosystems seem best able to cope with increasing variations in climatic factors. In the models considered the thermodynamic function exergy correlates well with the sum of relevant buffer capacities. High exergy levels mean that the structure is more able to meet changes in external factors.

Ecological buffer capacity

A new concept — the ecological buffer capacity — has been introduced to express the response of an ecosystem to changes in the loading. Either the relative or absolute stability gives this sort of information. By means of this concept, it has been attempted to show how complicated a model must be to give an acceptable description of the response to changes in the phosphorus loadings. It was found that the ecological buffer capacity increases with increasing model diversity, either expressed by means of the Shannon index or the number of state variables, but since it is of importance to include the most essential mass flows, the exergy is more suitably related to the buffer capacity than the diversity. Consequently, the exergy can be used as an expression for the buffer capacity, that is, as an expression for the response of an ecosystem to changes in the driving functions. Both the buffer capacity and the exergy can be used to select the required state variables for a model. Interchanging P j eq (= phosphorus concentration in box j at thermodynamic equilibrium) with P j 0 (= phosphorus concentration in box j at steady state) in the exergy expression, seems to give a useful Liapunov function for the considered set of models.