How Kentish plovers,<Emphasis Type="Italic"> Charadrius alexandrinus</Emphasis>, cope with heat stress during incubation

Biparental incubation is frequent among shorebirds and is expected when the survival prospects of offspring increase relative to uniparental incubation. To understand why this occurs, it is important to identify the factors that constrain uniparental incubation. It is assumed that birds choose nesting sites that provide an appropriate microclimate for incubation. Many shorebirds nest in sites with no or little cover, where ambient temperatures at ground level might be >50°C during very hot days. Shorebirds nest in exposed sites because predation risk on incubating adults is higher in covered sites. In hot environments, incubating shorebirds might experience heat stress in exposed sites, and this may compromise nesting success if adults are unable to attend their nests continuously, limiting the possibilities of uniparental incubation and thus the expression of a sexual conflict over incubation. The operative temperatures of Kentish plovers (Charadrius alexandrinus) were recorded in exposed and covered sites, and the thermal behaviour and incubating tactics of pair members were studied in a hot environment. During the hottest part of the day, there was a difference of 10–15°C in the operative temperatures of plovers between covered and exposed sites. Plovers in covered sites did not exhibit any thermoregulatory behaviour indicative of thermal stress, probably because the thermal range encountered by them in such places during most of the daytime was close to the thermo-neutral zone. The frequency with which plovers in exposed sites exhibited thermoregulatory behaviour was related to ambient temperature. Under very hot conditions, incubating birds were probably unable to maintain homeostasis for long periods and pair members resorted to shortening incubation bouts. Female Kentish plovers mainly incubate in the daytime and males during the night. However, the probability of diurnal incubation by males increased with ambient temperature in exposed nests, but not in covered ones. In fact, the frequency of participation in diurnal incubation by males was greater in exposed than in covered sites, suggesting that the participation of males in diurnal incubation may be related to the inability of females to stay at the nest during long periods when the ambient temperature is high. Even after resorting to shortened incubation bouts, the plovers may be unable to attend their nests continuously during heat waves, and the nests may be deserted. The propensity of plovers to desert their nests was affected by proximity to water, with nests located close to water being deserted less frequently. It seems likely that susceptibility to thermal stress changed in relation to proximity to water because in sites close to water it was possible to belly-soak, which would allow a more continuous nest attendance. Therefore, despite the adoption of behavioural solutions to face heavy heat loads, nesting success was vulnerable to these solutions because heat stress during extended periods may constrain parental nest attendance, and this may limit the opportunities for sexual conflicts over incubation.Communicated by J. Graves     

Identifying key needs for the integration of social–ecological outcomes in arctic wildlife monitoring

For effective monitoring in social–ecological systems to meet needs for biodiversity, science, and humans, desired outcomes must be clearly defined and routes from direct to derived outcomes understood. The Arctic is undergoing rapid climatic, ecological, social, and economic changes and requires effective wildlife monitoring to meet diverse stakeholder needs. To identify stakeholder priorities concerning desired outcomes of arctic wildlife monitoring, we conducted in-depth interviews with 29 arctic scientists, policy and decision makers, and representatives of indigenous organizations and nongovernmental organizations. Using qualitative content analysis, we identified and defined desired outcomes and documented links between outcomes. Using network analysis, we investigated the structure of perceived links between desired outcomes. We identified 18 desired outcomes from monitoring and classified them as either driven by monitoring information, monitoring process, or a combination of both. Highly cited outcomes were make decisions, conserve, detect change, disseminate, and secure food. These reflect key foci of arctic monitoring. Infrequently cited outcomes (e.g., govern) were emerging themes. Three modules comprised our outcome network. The modularity highlighted the low strength of perceived links between outcomes that were primarily information driven or more derived (e.g., detect change, make decisions, conserve, or secure food) and outcomes that were primarily process driven or more derived (e.g., cooperate, learn, educate). The outcomes expand monitoring community and disseminate created connections between these modules. Key desired outcomes are widely applicable to social–ecological systems within and outside the Arctic, particularly those with wildlife subsistence economies. Attributes and motivations associated with outcomes can guide development of integrated monitoring goals for biodiversity conservation and human needs. Our results demonstrated the disconnect between information- and process-driven goals and how expansion of the monitoring community and improved integration of monitoring stakeholders will help connect information- and process-derived outcomes for effective ecosystem stewardship.     

Trophic field overlap: A new approach to quantify keystone species

It is a current challenge to better understand the relative importance of species in ecosystems, and the network perspective is able to offer quantitative tools for this. It is plausible to assume, in general, that well-linked species, being key interactors, are also more important for the community. Recently a number of methods have been suggested for quantifying the network position of species in ecological networks (like the topological importance metric, TI). Most of them are based on node centrality indices and it may happen that the two most important species in a food web have very similar interaction structure and they can essentially replace each other if one becomes extinct. For conservation considerations it is a challenge to identify species that are richly connected and, at the same time, have a relatively unique and irreplaceable interaction pattern. We present a new method and illustrate our approach by using the Kuosheng Bay trophic network in Taiwan. The new method is based on the interaction matrix, where the strength of the interaction between nodes i and j depends only on topology. By defining a threshold separating weak and strong interactors, we define the effective range of interactions for each graph node. If the overlaps between pairs of these ranges are quantified, we gain a metric expressing how unique is the interaction pattern of a focal node (TO). The combination of centrality (TI) and uniqueness (TO) is called topological functionality (TF). We compare the nodal importance rank provided by this metric to others based on a variety of centrality measures. The main conclusion is that shrimps seem to have the most unique interaction pattern despite that their structural importance has been underestimated by all conventional centrality indices. Also, our network analysis suggests that fisheries disturb the ecosystem in a more critical network position than the impingement by the local power plant.     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.     

A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China

Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSE_r = 39.88% for the training set, and RMSE_r = 42.36% for the testing set) than MLR (RMSE_r = 49.51% for the training, and RMSE_r = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m², respectively.     

长江三角洲河网地区典型城镇街尘中多环芳烃的污染特征

街尘是一种重要的环境介质,其携带的污染物在城镇化过程中对河网地区的水环境构成了一定的威胁.为初步探讨长江三角洲河网地区典型街尘中多环芳烃(PAHs)的污染特征,以杭嘉湖平原河网地区饮用水水源河流上游典型小城镇的街尘为研究对象,对工业区、交通干道、旧居区、新居区和商业区5种土地利用类型的15个采样点进行街尘样品采集和粒径分级,测试街尘的密度、有机质含量以及16种U.S.EPA优控PAHs含量等指标.结果表明,粒径为<63、63～125、125～250和250～900μm街尘中总多环芳烃(∑PAHs)的平均含量分别为7261、5835、4660和2909μg·kg-1,粒径越小,PAHs含量越高,其生态风险越大.不同土地利用类型的街尘中PAHs的含量顺序依次为:工业区>交通干道>旧居区>新居区>商业区.街尘中PAHs和有机质含量存在显著正相关关系,且街尘粒径越小,有机质和PAHs的相关性越强.源解析结果表明,街尘中的PAHs多为燃烧源.     

Social network analysis and valid Markov chain Monte Carlo tests of null models

Analyses of animal social networks derived from group-based associations often rely on randomisation methods developed in ecology (Manly, Ecology 76:1109–1115, 1995) and made available to the animal behaviour community through implementation of a pair-wise swapping algorithm by Bejder et al. (Anim Behav 56:719–725, 1998). We report a correctable flaw in this method and point the reader to a wider literature on the subject of null models in the ecology literature. We illustrate the importance of correcting the method using a toy network and use it to make a preliminary analysis of a network of associations among eagle rays.

OPAQ系统两种模式对O 3预报准确率的探讨

以江苏省宿迁环境监测中心OPAQ系统为例,基于人工神经网络算法的OPAQ空气质量预报系统的2种模式对O 3预报准确率的进行了分析,结果表明,趋势最优模式(RMSE模式)对预报当天及未来3 d的预报值与监测值的相关性系数均>0.78,相对误差在25%以下,在预测当天及未来24、48及72 h优-良天的预测准确率较高,分别为88.8%、87.2%、86.3%及84.7%,在预测轻度污染-重度污染的准确率较低;极值最优模式(SI模式)对预报当天及未来3 d的预报值与监测值的相关性系数(R)均>0.76,相对误差<32%,预测未来24和48 h的轻度污染-中度污染的级别准确率>60%。OPAQ系统的极值最优模式(SI模式)更适合作为夏季ρ(O 3)较高时的预测工具。     

新标准下环境空气质量监控网络完善实践 ——以珠三角地区佛山市为例

介绍珠三角及佛山市新标准监测网络结构,从拓宽网络有效覆盖范围、提升网络对污染研究的支撑能力、以及巩固数据实时发布工作基础等多角度分析了网络完善需求,提出了下一阶段网络建设的重点措施建议,并介绍了佛山市目前计划进行的网络完善工作。     

新疆城市环境空气PM_(10)自动监测仪器质控巡检工作探析

随着城市环境空气质量自动监测业务现代化的飞速发展,新疆城市共安装38套PM10环境空气自动监测系统。本文通过对新疆19个城市空气自动监测系统PM10的巡检和质控考核,发现近几年来该系统运行维护中的诸多问题,从而提出仪器维护注意事项,以期提高新疆城市PM10监测数据质量,准确反映环境空气质量状况,为环境管理和政府决策提供更加科学、准确、可靠的基础监测数据。