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.     

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.     

Identifying important species that amplify or mitigate the interactive effects of human impacts on marine food webs

Some species may have a larger role than others in the transfer of complex effects of multiple human stressors, such as changes in biomass, through marine food webs. We devised a novel approach to identify such species. We constructed annual interaction-effect networks (IENs) of the simulated changes in biomass between species of the southeastern Australian marine system. Each annual IEN was composed of the species linked by either an additive (sum of the individual stressor response), synergistic (lower biomass compared with additive effects), or antagonistic (greater biomass compared with additive effects) response to the interaction effect of ocean warming, ocean acidification, and fisheries. Structurally, over the simulation period, the number of species and links in the synergistic IENs increased and the network structure became more stable. The stability of the antagonistic IENs decreased and became more vulnerable to the loss of species. In contrast, there was no change in the structural attributes of species linked by an additive response. Using indices common in food-web and network theory, we identified the species in each IEN for which a change in biomass from stressor effects would disproportionately affect the biomass of other species via direct and indirect local, intermediate, and global predator–prey feeding interactions. Knowing the species that transfer the most synergistic or antagonistic responses in a food-web may inform conservation under increasing multiple-stressor impacts.     

Applying artificial neural network methodology to ocean color remote sensing

Artificial neural networks (ANN) are widely used as continuous models to fit non-linear transfer functions. In this study we used ANN to retrieve chlorophyll pigments in the near-surface of oceans from Ocean Color measurements. This bio-optical inversion is established by analyzing concomitant sun-light spectral reflectances over the ocean surface and pigment concentration. The relationships are complex, non-linear, and their biological nature implies a significant variability. Moreover, the sun-light reflectances are usually measured by satellite radiometers flying at 800 km over the ocean surface, which affect the data by adding radiometric noise and atmospheric correction errors. By comparison with the polynomial fit usually employed to treat this problem, we show the advantages of neural function approximation like the association of non-linear complexity and noise filtering.     

典型云南高原湖泊叶绿素a与影响因子的定量关系及对比分析

湖泊富营养化的影响因子涉及水文、物化、生物等多方面,具有复合性和非线性特征,定量化其与影响因素间的相关关系有助于识别影响湖泊营养状态的关键因子,可以用较低的成本、较短的时间达到理想的控制效果.云南高原湖泊具有易发生富营养化的自然和气候特征,对其富营养化发生条件及影响因子的分析可为科学的控制决策提供参考.本文选取云南滇池、程海、抚仙湖和异龙湖4个高原湖泊,比较湖泊自然特征与流域社会经济条件的异同;构建包括绝对主成分多元线性回归分析(APCS-MLR)、结构方程模型(SEM)及人工神经网络模型(ANN)的综合分析方法,重点关注并确定浮游初级生产力的代表指标(叶绿素a,Chla)与相关影响因子间的定量相关关系.研究发现:14个湖泊中,对Chla浓度变化影响最大的均为理化因子,但在各湖中该影响的正、负性及不同理化因子的贡献权重有较大差异;2流域污染源构成的不同在一定程度上影响了入湖的氮、磷负荷,使4个湖泊表现出不同的营养盐限制性特征;3流域面积、湖泊形态及湖体水动力条件影响着营养盐在湖体中的迁移转换,造成4个湖泊富营养化的差异性特征;4对Chla与影响因子间因果关联的识别须结合深入的机理过程分析.     

Operational risk assessment: A case of the Bhopal disaster

Accidental releases of hazardous chemicals from process facilities can cause catastrophic consequences. The Bhopal disaster resulting from a combination of inherently unsafe designs and poorly managed operations is a well-known case. Effective risk modeling approaches that provide early warnings are helpful to prevent and control such rare but catastrophic events. Probability estimation of these events is a constant challenge due to the scarcity of directly relevant data. Therefore, precursor-based methods that adopt the Bayesian theorem to update prior judgments on event probabilities using empirical data have been proposed. The updated probabilities are then integrated with consequences of varying severity to produce the risk profile.This paper proposes an operational risk assessment framework, in which a precursor-based Bayesian network approach is used for probability estimation, and loss functions are applied for consequence assessment. The estimated risk profile can be updated continuously given real-time operational data. As process facilities operate, this method integrates a failure-updating mechanism with potential consequences to generate a real-time operational risk profile. The real time risk profile is valuable in activating accident prevention and control strategies. The approach is applied to the Bhopal accident to demonstrate its applicability and effectiveness.     

Fast method for simulation of radionuclide chain migration in dual porosity fracture rocks

In fractured rocks with a porous rock matrix such as granites, radionuclides will flow with the water in the fracture network. The nuclides will diffuse in and out the rock matrix where they can sorb and be considerably retarded compared to the water velocity. A water parcel entering the network will mix and split at the fracture intersections and parts of the original parcel will traverse a multitude of different fractures. The flowrates, velocities, sizes and apertures of the fractures can vary widely. Normally one must solve the transport equations for every fracture and use the effluent concentration as inlet condition to the next fracture and so on. It is shown that under some weakly simplified conditions it suffices to determine one single parameter group containing information on the flow wetted surface that a water parcel contacts along the entire path. It is also shown how this can be obtained. Then, solving the transport equations only once for time and location along the path gives the concentration and nuclide flux of every nuclide in the chain everywhere along a path. The same solution actually is valid for every path in the network. This dramatically reduces the computation effort. The same approach can be used for models based on streamtubes.     

Individual-based modelling of fishermen search behaviour with neural networks and reinforcement learning

A model to mimic the search behaviour of fishermen is built with two neural networks to cope with two separate decision-making processes in fishing activities. One neural network deals with decisions to stay or move to new fishing grounds and the other is constructed for the purpose of finding prey within the fishing areas. Some similarities with the behaviour of real fishermen are found: concentrated local search once a prey has been located to increase the probability of remaining near a prey patch and the straightforward movement to other fishing grounds. The artificial fisherman prefers areas near the port when conditions in different fishing grounds are similar or when there is high uncertainty in its world. In the latter case a reluctance to navigate to other areas is observed. The artificial fisherman selects areas with higher concentration of prey, even if they are far from the port of departure, unless a high uncertainty is related to the fishing ground. Connected areas are preferred and followed in orderly fashion if a higher catch is expected. The observed behaviour of the artificial fisherman in uncertain scenarios can be described as a risk-averse attitude. The approach seems appropriate for an individual-based modelling of fishery systems, focusing on the learning and adaptive characteristics of fishermen and on interactions that take place at a fine scale.     

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.     

Use of Community‐Composition Data to Predict the Fecundity and Abundance of Species

Abstract: Species distribution models are critical tools for the prediction of invasive species spread and conservation of biodiversity. The majority of species distribution models have been built with environmental data. Community ecology theory suggests that species co‐occurrence data could also be used to predict current and potential distributions of species. Species assemblages are the products of biotic and environmental constraints on the distribution of individual species and as a result may contain valuable information for niche modeling. We compared the predictive ability of distribution models of annual grassland plants derived from either environmental or community‐composition data. Composition‐based models were built with the presence or absence of species at a site as predictors of site quality, whereas environment‐based models were built with soil chemistry, moisture content, above‐ground biomass, and solar radiation as predictors. The reproductive output of experimentally seeded individuals of 4 species and the abundance of 100 species were used to evaluate the resulting models. Community‐composition data were the best predictors of both the site‐specific reproductive output of sown individuals and the site‐specific abundance of existing populations. Successful community‐based models were robust to omission of data on the occurrence of rare species, which suggests that even very basic survey data on the occurrence of common species may be adequate for generating such models. Our results highlight the need for increased public availability of ecological survey data to facilitate community‐based modeling at scales relevant to conservation.