Validating graph-based connectivity models with independent presence–absence and genetic data sets

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]). These graphs are often built based on expert opinion or species distribution models (SDMs) and therefore lack empirical validation from data more closely reflecting functional connectivity. Accordingly, we tested whether landscape graphs reflect how habitat connectivity influences gene flow, which is one of the main ecoevolutionary processes. To that purpose, we modeled the habitat network of a forest bird (plumbeous warbler [Setophaga plumbea]) on Guadeloupe with graphs based on expert opinion, Jacobs’ specialization indices, and an SDM. We used genetic data (712 birds from 27 populations) to compute local genetic indices and pairwise genetic distances. Finally, we assessed the relationships between genetic distances or indices and cost distances or connectivity metrics with maximum-likelihood population-effects distance models and Spearman correlations between metrics. Overall, the landscape graphs reliably reflected the influence of connectivity on population genetic structure; validation R² was up to 0.30 and correlation coefficients were up to 0.71. Yet, the relationship among graph ecological relevance, data requirements, and construction and analysis methods was not straightforward because the graph based on the most complex construction method (species distribution modeling) sometimes had less ecological relevance than the others. Cross-validation methods and sensitivity analyzes allowed us to make the advantages and limitations of each construction method spatially explicit. We confirmed the relevance of landscape graphs for conservation modeling but recommend a case-specific consideration of the cost-effectiveness of their construction methods. We hope the replication of independent validation approaches across species and landscapes will strengthen the ecological relevance of connectivity models.     

Boosting large-scale river connectivity restoration by planning for the presence of unrecorded barriers

Conservation decisions are invariably made with incomplete data on species’ distributions, habitats, and threats, but frameworks for allocating conservation investments rarely account for missing data. We examined how explicit consideration of missing data can boost return on investment in ecosystem restoration, focusing on the challenge of restoring aquatic ecosystem connectivity by removing dams and road crossings from rivers. A novel way of integrating the presence of unmapped barriers into a barrier optimization model was developed and applied to the U.S. state of Maine to maximize expected habitat gain for migratory fish. Failing to account for unmapped barriers during prioritization led to nearly 50% lower habitat gain than was anticipated using a conventional barrier optimization approach. Explicitly acknowledging that data are incomplete during project selection, however, boosted expected habitat gains by 20–273% on average, depending on the true number of unmapped barriers. Importantly, these gains occurred without additional data. Simply acknowledging that some barriers were unmapped, regardless of their precise number and location, improved conservation outcomes. Given incomplete data on ecosystems worldwide, our results demonstrate the value of accounting for data shortcomings during project selection.     

Emission control for precursors causing acid rain (V): Improvement of acid soil with the bio-briquette combustion ash

The bio-briquette technique which mixes coal, biomass and sulfur fixation agent and bio-briquettes under 3-5 t/cm^2 line pressure has aroused people‘s attention in view of controlling the air pollution and the acid rain. In this paper, the physicochemical properties of bio-briquette and its ash were investigated. And the acid soil was improved by the bio-briquette combustion ash, which contained nutritive substances such as P, N, K and had the acid-neutralizing capacity(ANC). The pH, EC, effective nutrient elements(Ca, Mg, K, P and N), heavy metal elements(AI, Cu, Cd, Cr, Zn and Mn) and acid-neutralizing capacity change of ash-added soils within the range of 0-10%, were also studied. Specially, when 5% bio-briquette combustion ash was added to the tested soil, the content of the effective elements such as Ca, Mg and K rose by 100 times, ? times and twice, respectively. The total nitrogen also increased by about twice. The results showed the oxyanions such as that of AI, Cu, Cd, Cr, Zn and Mn were not potentially dangerous, because they were about the same as the averages of them in Chinese soil. It is shown that the ANC became stronger, though the ANC hardly increases in the ash-added soil. On the basis of the evaluation indices, it is concluded that the best mixture ratio is to add 2.5%--8% of the bio-briquette combustion ash to the tested soil.     

推行清洁生产 企业扭亏为盈

巨化集团公司锦纶厂逐步推行清洁生产，从原材料的选择、工艺改造、设备更新、综合利用、节约用水、加强管理等各方面采取了一系列措施，经两年努力，使企业扭亏为盈。实践证明，推行清洁生产是化工企业减亏增盈和防治污染的有效途径。     

企业系统化安全管理的主要问题及成因分析

针对系统化安全管理实践中存在着安全管理体系建立的模板化、安全管理体系运行的形式化、安全管理体系持续改进的无效化等问题,提出要提高安全管理绩效,首先纠正企业对系统化安全管理的认知偏差,统一企业不同管理层次的认识;其次是用正确的安全管理方法论作指导,开展系统化安全管理实践;三是匹配企业安全管理实践与系统化要求,发挥和协调不同管理主体的参与,对危险源进行动态化管理,有效实施绩效监测与改进。总之,企业系统化安全管理不能作为单纯的一项工作或任务来完成,必须以解决企业的具体安全生产问题为目标;系统化安全管理是一个持续优化的动态过程,既要把握安全管理系统化的要求,也要有效开展安全管理系统化实践。     

流化床垃圾焚烧炉工艺改进的探讨

对流化床垃圾焚烧炉的工艺改进进行了分析 ,在焚烧炉内更有利于脱除酸性气体 ,当垃圾热值达 4 5 0 0kJ/kg时 ,部分吹入氧气可使燃烧温度达 1 30 0℃以上。用空心球代替流动砂可以取消流动砂的循环环节     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

改善深圳河水质的补水方案及生态影响初步分析

深圳河旱季天然径流量小而污染负荷高,为了改善河流水质,在削减入河污染物的同时有必要采取补水措施。利用水质模型,计算了不同污水处理率下,分别以污水资源化再生水、珠江口海水和大鹏湾海水为补水水源时,深圳河达到基本不黑臭所需的补水量。讨论了补水方案对河流水质、盐度、水动力条件的改变及其对生态系统的影响。研究表明,引海水对河流生态系统的冲击不容忽视。而污水资源化是较优的补水方案。     

COD测定方法的发展与研究动态

本文较为全面地论述了COD测定方法的发展与研究现状。在论述了国标中COD标准方法不足之处的基础上,本文就标准方法的众多改进措施分类进行了比较与探讨,包括消解方法的改进、替代催化剂的研究以及氯离子干扰的消除,然后介绍了相关系数法、电化学法、分光光度法和流动注射法等COD测定替代技术。本文认为,研究试剂用量少、无污染的绿色COD测定方法具有重要而积极的意义,而研究适应性强、运行可靠、性价比高的COD在线监测仪则是COD监测的发展趋势。     

某铁矿安全避险六大系统建设实例研究与分析

对所负责参与的某铁矿安全避险六大系统建设的实例进行研究与分析。针对系统运行期间发现的问题和不足,提出相关的应对改进措施。