Prediction of response of zooplankton biomass to climatic and oceanic changes

This paper examines the long-term variation in zooplankton biomass in response to climatic and oceanic changes, using a neural network as a nonlinear multivariate analysis method. Zooplankton data collected from 1951 to 1990 off the shore of northeastern Japan were analyzed. We considered patterns of the Kuroshio and the Oyashio, sea surface temperature, and meteorological parameters as environmental factors that affect zooplankton biomass. Back propagation neural networks were trained to generate mapping functions between environmental variables and zooplankton biomass. The performance of the network models was tested by varying the numbers of input and hidden units. Changes in zooplankton biomass could be predicted from environmental conditions. The neural network yielded predictions with smaller errors than those of predictions determined by linear multiple regression. The sensitivity analysis of networks was used to extract predictive knowledge. The air pressure, sea surface temperature, and some indices of atmospheric circulation were the primary factors for predictions. The patterns of the Kuroshio and the Oyashio demonstrated different effects among sea areas.     

USING ARTIFICIAL NEURAL NETWORKS TO ESTIMATE MISSING RAINFALL DATA1

ABSTRACT: Missing rainfall data from a time series or a spatial field of observations can present a serious obstacle to data analysis, modeling studies and operational forecasting in hydrology. Numerous schemes for replacing missing data have been proposed, ranging from simple weighted averages of data points that are nearby in time and space to complex statistically-based interpolation methods and function fitting schemes. This paper presents a technique for replacing missing spatial data using a backpropagation neural network applied to concurrent data from nearby gauges. Tests performed on a sample of gauges in the Middle Atlantic region of the United States show that this technique produces results that compare favorably to simple techniques such as arithmetic and distance-weighted averages of the values from nearby gauges, and also to linear optimization methods such as regression.     

Field‐Scale Application of Three Types of Neural Networks to Predict Ground‐Water Levels1

Abstract: In this paper, a field‐scale applicability of three forms of artificial neural network algorithms in forecasting short‐term ground‐water levels at specific control points is presented. These algorithms are the feed‐forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground‐water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground‐water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field‐scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored.     

Evaluating and expanding the European Union's protected-area network toward potential post-2020 coverage targets

The Convention on Biological Diversity's (CBD) strategic plan will expire in 2020, but biodiversity loss is ongoing. Scientists call for more ambitious targets in the next agreement. The nature-needs-half movement, for example, has advocated conserving half of Earth to solve the biodiversity crisis, which has been translated to protecting 50% of each ecoregion. We evaluated current protection levels of ecoregions in the territory of one of the CBD's signatories, the European Union (EU). We also explored the possible enlargement of the Natura 2000 network to implement 30% or 50% ecoregion coverage in the EU member states’ protected area (PA) network. Based on the most recent land-use data, we examined whether ecoregions have enough natural area left to reach such high coverage targets. We used a spatially explicit mixed integer programing model to estimate the least-cost expansion of the PA network based on 3 scenarios that put different emphasis on total conservation cost, ecological representation of ecosystems, or emphasize an equal share of the burden among member states. To realize 30% and 50% ecoregion coverage, the EU would need to add 6.6% and 24.2%, respectively, of its terrestrial area to its PA network. For all 3 scenarios, the EU would need to designate most recommended new PAs in seminatural forests and other semi- or natural ecosystems. Because 15 ecoregions did not have enough natural area left to implement the ecoregion-coverage targets, some member states would also need to establish new PAs on productive land, allocating the largest share to arable land. Thirty percent ecoregion coverage was met by protecting remaining natural areas in all ecoregions except 3, where productive land would also need to be included. Our results support discussions of higher ecoregions protection targets for post-2020 biodiversity frameworks.     

Attack vulnerability of self-organizing networks

There are several self-organizing networks in the real world, and these networks severely affect the development of the modern society. This paper investigates the vulnerability of self-organizing networks subject to malicious attacks according to a new framework. Assuming the initial load of node i as L_i=αk_i+(1-α)∑_j∈Γik_j with k_i and Γ_i being the degree and the set of neighbor nodes of the node i, where α is a tunable parameter and control the strength of the initial loads of nodes. The node with the maximum degree is considered as the attacked node, and with the changes of the parameter α, cascading failures will be investigated in this paper. Local redistribution rule has been adopted to study the cascading breakdowns of the US power grid and IEEE-118 networks. Additionally, the capacity of the node i is defined as C_i = (1 + β)L_i, the critical threshold β_c of the US power grid and IEEE-118 networks will be obtained from the evolutional process of cascading failures. Finally, an optimal design of US power grid network is given in this paper.     

吸气式火灾探测系统采样管网优化设计

对吸气式火灾探测报警系统中采样管网进行优化设计是提高其探测性能的重要手段。基于流体动力学原理,建立管网内气体运动数学模型,给出各状态参数之间的关系式。分析常见管网布置方式的优缺点,提出吸气式采样管网的优化设计方案:视每个采样孔为1个点型感烟探测器,采样孔间距基本一致,调整孔径使各采样孔进气量相同,孔数较多时,可增设末端孔,以缩短气体样本传输时间。从理论上分析管网参数对探测性能的影响,并利用Fluent 6.3进行相应的数值模拟。结果表明,样本传输时间与管长、管径、开孔数成正比,与总流量和末端孔孔径成反比。     

矿井通风网络的社团结构及灾害控制研究

为研究矿井通风网络的社团结构特征和有效控制灾害在通风网络中的传播范围,根据通风网络的连接特点对传统的GN算法进行改进,以淮南某矿井的通风网络结构为例进行说明,发现矿井通风网络具有较强的社团结构特征;引入强弱社团结构和外内连接边数比后,社团结构的划分更符合实际。根据矿井通风网络社团间的连接关系,就会找到灾害传播的关键分支与节点并加以控制,降低灾害对相邻社团危害程度,控制灾害的蔓延范围。     

Patterns of preference and practice: bridging actors in wildfire response networks in the American Northwest

The roles of bridging actors in emergency response networks can be important to disaster response outcomes. This paper is based on an evaluation of wildfire preparedness and response networks in 21 large‐scale wildfire events in the wildland—urban interface near national forests in the American Northwest. The study investigated how key individuals in responder networks anticipated seeking out specific people in perceived bridging roles prior to the occurrence of wildfires, and then captured who in fact assumed these roles during actual large‐scale events. It examines two plausible, but contradictory, bodies of theory—similarity and dissimilarity—that suggest who people might seek out as bridgers and who they would really go to during a disaster. Roughly one‐half of all pre‐fire nominations were consistent with similarity. Yet, while similarity is a reliable indicator of how people expect to organise, it does not hold up for how they organise during the real incident.     

Implications of network particle tracking (NPT) for ecological model interpretation

Network particle tracking (NPT), building on the foundation of network environ analysis (NEA), is a new development in the definition of coherence relations within and between connected systems. This paper evaluates three ecosystem models in a comparison of throughflow- and storage-based NEA and NPT. Compartments in models with high indirect effects and Finn cycling showed low correlation of NEA storage and throughflow with particle repeat visits and numbers of particles in compartments at steady state. Conversely, the correlation between NEA and NPT results was high with two models having lower indirect effects and Finn cycling. Analysis of ecological orientors associated with NEA showed NPT to fully support conventional NEA results when the common conditions of donor control and steady state are satisfied. Particle trajectories are recorded in the new concept of a particle “passport”. Ability to track and record particle in-system histories enables views of multiple scales and opens the possibility of making pathway-dependent modeling decisions. NPT may also enable modeling of time, allowing integration of Newtonian, organismal and stochastic modeling perspectives in a single comprehensive analysis.     

Protection of Genetic Diversity and Maintenance of Connectivity among Reef Corals within Marine Protected Areas

Abstract: High‐latitude coral reefs (HLRs) are potentially vulnerable marine ecosystems facing well‐documented threats to tropical reefs and exposure to suboptimal temperatures and insolation. In addition, because of their geographic isolation, HLRs may have poor or erratic larval connections to tropical reefs and a reduced genetic diversity and capacity to respond to environmental change. On Australia's east coast, a system of marine protected areas (MPAs) has been established with the aim of conserving HLRs in part by providing sources of colonizing larvae. To examine the effectiveness of existing MPAs as networks for dispersal, we compared genetic diversity within and among the HLRs in MPAs and between these HLRs and tropical reefs on the southern Great Barrier Reef (GBR). The 2 coral species best represented on Australian HLRs (the brooding Pocillopora damicornis and the broadcast‐spawning Goniastrea australensis) exhibited sharply contrasting patterns of diversity and connectedness. For P. damicornis, the 8‐locus genetic and genotypic diversity declined dramatically with increasing latitude (N_a= 3.6–1.2, H_e= 0.3–0.03, N_g:N = 0.87–0.06), although population structure was consistent with recruitment derived largely from sexual reproduction (G_o:G_e= 1.28–0.55). Genetic differentiation was high among the HLRs (F_ST[SD]= 0.32 [0.08], p < 0.05) and between the GBR and the HLRs (F_ST= 0.24 [0.06], p < 0.05), which indicates these temperate populations are effectively closed. In contrast for G. australensis, 9‐locus genetic diversity was more consistent across reefs (N_a= 4.2–3.9, H_e= 0.3–0.26, N_g:N = 1–0.61), and there was no differentiation among regions (F_ST= 0.00 [0.004], p > 0.05), which implies the HLRs and the southern GBR are strongly interconnected. Our results demonstrate that although the current MPAs appear to capture most of the genetic diversity present within the HLR systems for these 2 species, their sharply contrasting patterns of connectivity indicate some taxa, such as P. damicornis, will be more vulnerable than others, and this disparity will provide challenges for future management.