Do plant–pollinator interaction networks result from stochastic processes?

Plant–pollinator interaction networks are characterized by several features that cannot be obtained from a totally random network (e.g. nestedness, power law distribution of degree specialization, temporal turnover). One reason is that both plants and pollinators are active for only a part of the year, and so a plant species flowering in spring cannot interact with a pollinator species that is active only in autumn. In this paper we build a stochastic model to simulate the plant–pollinator interaction network, taking into account the duration of activity of each species. To build the model we used an empirical plant–pollinator network from a Mediterranean scrub community surveyed over four years. In our simulated annual cycle we know which plant and pollinator species are active, and thus available to interact. We can obtain simulated plant–pollinator interaction networks with properties similar to the real ones in two different ways: (i) by assuming that the frequency distribution of both plant and pollinator duration of activity follow an exponential function, and that interaction among temporally coexisting species are totally random, and (ii) by assuming more realistic frequency distributions (exponential for pollinators, lognormal for plants) and that the interaction among coexisting species is occurring on a per capita basis. In the latter case we assume that there is a positive relationship between abundance and duration of activity. In our model the starting date of the species activity had little influence on the network structure. We conclude that the observed plant–pollinator network properties can be produced stochastically, and the mechanism shaping the network is not necessarily related to size constraints. Under such conditions co-evolutionary explanations should be given with caution.     

Are network indices robust indicators of food web functioning? A Monte Carlo approach

Indices based on network theory are often used to describe food web functioning. These indices take as input food web flows that are estimated based on merging of (scarce) data with linear inverse methods (LIMs). Due to under sampling, most food webs are highly uncertain and can only be quantified within a specific uncertainty range. The linear inverse method (LIM) can estimate food web flows using a variety of techniques, e.g. the parsimonious or minimum norm (MN) solution, which selects one food web, based on a quadratic minimization technique or the Monte Carlo solution where a finitely many random solutions are generated which are then averaged. We use the Monte Carlo approach (MCA) to estimate the values of several indices from four published food webs, the Gulf of Riga for the autumn, summer and spring seasons, and the Takapoto atoll system. We first show that network indices are much better constrained than the uncertain food webs from which they are calculated. Therefore, even in the face of food web uncertainty, they are robust estimators of food web functioning. We then use the MCA-derived network indices to generate cumulative density functions for each index. These serve to compute the probabilities of the MN indices estimates being an extreme solution as compared to the median values. Our findings show that 82% of the MN solutions are smaller than the MCA solutions, and 63% of the network indices are significantly under-estimated.     

The effects of aggregation on the performance of the inverse method and indicators of network analysis

Food webs are usually aggregated into a manageable size for their interpretation and analysis. The aggregation of food web components in trophic or other guilds is often at the choice of the modeler as there is little guidance in the literature as to what biases might be introduced by aggregation decisions. We examined the impacts of the choice of the a priori model on the subsequent estimation of missing flows using the inverse method and on the indices derived from ecological network analysis of both inverse method-derived flows and on the actual values of flows, using the fully determined Sylt-Rømø Bight food web model. We used the inverse method, with the least squares minimization goal function, to estimate ‘missing’ values in the food web flows on 14 aggregation schemes varying in number of compartments and in methods of aggregation. The resultant flows were compared to known values; the performance of the inverse method improved with increasing number of compartments and with aggregation based on both habitat and feeding habits rather than diet similarity. Comparison of network analysis indices of inverse method-derived flows with that of actual flows and the original value for the unaggregated food web showed that the use of both the inverse method and the aggregation scheme affected indices derived from ecological network analysis. The inverse method tended to underestimate the size and complexity of food webs, while an aggregation scheme explained as much variability in some network indices as the difference between inverse-derived and actual flows. However, topological network indices tended to be most robust to both the method of determining flows and to the inverse method. These results suggest that a goal function other than minimization of flows should be used when applying the inverse method to food web models. Comparison of food web models should be done with extreme care when different methodologies are used to estimate unknown flows and to aggregate system components. However, we propose that indices such as relative ascendency and relative redundancy are most valuable for comparing ecosystem models constructed using different methodologies for determining missing flows or for aggregating system components.     

Epidemiological risk assessment using linked network and grid based modelling: Phytophthora ramorum and Phytophthora kernoviae in the UK

We developed a stochastic simulation model incorporating most processes likely to be important in the spread of Phytophthora ramorum and similar diseases across the British landscape (covering Rhododendron ponticum in woodland and nurseries, and Vaccinium myrtillus in heathland). The simulation allows for movements of diseased plants within a realistically modelled trade network and long-distance natural dispersal. A series of simulation experiments were run with the model, representing an experiment varying the epidemic pressure and linkage between natural vegetation and horticultural trade, with or without disease spread in commercial trade, and with or without inspections-with-eradication, to give a 2 × 2 × 2 × 2 factorial started at 10 arbitrary locations spread across England. Fifty replicate simulations were made at each set of parameter values. Individual epidemics varied dramatically in size due to stochastic effects throughout the model. Across a range of epidemic pressures, the size of the epidemic was 5–13 times larger when commercial movement of plants was included. A key unknown factor in the system is the area of susceptible habitat outside the nursery system. Inspections, with a probability of detection and efficiency of infected-plant removal of 80% and made at 90-day intervals, reduced the size of epidemics by about 60% across the three sectors with a density of 1% susceptible plants in broadleaf woodland and heathland. Reducing this density to 0.1% largely isolated the trade network, so that inspections reduced the final epidemic size by over 90%, and most epidemics ended without escape into nature. Even in this case, however, major wild epidemics developed in a few percent of cases. Provided the number of new introductions remains low, the current inspection policy will control most epidemics. However, as the rate of introduction increases, it can overwhelm any reasonable inspection regime, largely due to spread prior to detection.     

应用径向基函数网络预测有机化合物的生物活性

为了构建径向基函数 ( RBF)神经网络模型 ,研究一批取代芳香族化合物结构与其大型蚤生物活性参数 log(1/EC₅₀)的关系 ,利用正交最小平方算法逐一选择非线性高斯函数的中心 ,并将归一化参数 σ和容差 ρ作为网络的系统参数 ,通过广程扫描确定其最佳值 .采用种子聚类分析与简单随机抽样相结合的方法将化合物合理地划分为训练集和预测集 .对构建的 RBF网络模型的预测质量进行了不确定性分析 .与回归模型相比 ,RBF网络具有较好的预测性能 ,模型预测的不确定性降低了 30 % .     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

The Kyoto protocol: Empirical evidence of a hidden success

How effective was the Kyoto protocol? International Environmental Agreements (IEA) have been on the rise over the past four decades; however, thus far their effectiveness is controversial. In view of the conflicting results found in the related literature, this paper addresses its effectiveness by utilizing for the first time the generalized synthetic control method (GSCM) to compare the emissions of the industrialized countries with a “No- Kyoto” counterfactual scenario that represents the expected emissions in the absence of the protocol. This method facilitates a robust comparison between treated and control countries as done by Almer and Winkler (2017) and account for the multiple treated units as done by Grunewald and Martinez-Zarzoso (2016), so as to capture the collective nature of the protocol. Results show that the protocol was successful in reducing the emissions of the ratifying countries approximately by 7% below the emissions expected under a “No-Kyoto” scenario, confirming the importance of accounting for the collective nature of the agreement.     

BP神经网络法预测唐山市需水量

需水量预测研究已成为当前水资源规划与管理研究中的重要课题之一.本文设定不同的神经网络运行次数,根据预测结果进行误差分析,BP神经网络在运行5 000次时,具有高度的可信度和可行性.应用5 000次运行次数的BP神经网络模型对唐山市规划水平年的需水量进行预测.最后引入人均综合用水量概念,结果表明,预测结果在理论上和实际上都具有可行性.     

The Power of Legitimation: The Role of Expert Networks in Global Environmental Governance

As part of the debate on the legitimacy of governance networks in global environmental politics, this article investigates the conditions under which policy solutions can be transferred worldwide as a result of a particular type of interaction within transnational expert networks and technical committees. To this end, the article hypothesises that policy solutions can be legitimised in governance networks meeting four cumulative criteria: participation, flexibility, horizontality and inclusiveness. This hypothesis is then tested by means of two heuristic case studies dedicated to the worldwide transfer of environmental standards via United Nations specialized agencies. The empirical work partly strengthens the validity of the hypothesis but also underscores the limits of legitimation strategies in the face of strong heterogeneity of interests. In such cases, environmental policy networks may adopt fairly “vertical” features and resort to classical bargaining and constraint strategies whereby compromises are exchanged and power asymmetries are mobilised.     

Hurricanes and hegemony: A qualitative analysis of micro-level climate change denial discourses

The climate change countermovement and its program of climate change denial have been well documented and studied. However, individual rationales for rejecting climate science remain under-studied. Twitter data related to Hurricane Sandy in 2012 are used to understand why individuals reject the orthodox climate consensus, using a summative content analysis of climate change denial discourses. Three major discourses are discovered: rejecting climate science because climate science is a conspiracy favoring growth of government; opposing renewable energy and energy taxation; and expressing fear of governmental abuse of power. Importantly, each discourse expressed certainty that climate science itself was a wholesale fraud; the denial discourses themselves focused far more on climate politics than on science.