Managing coextinction of insects in a changing climate

AbstractApproximately a quarter of global terrestrial biodiversity is represented by plant dwelling insects and the potential for thousands of species to be extinguished through widespread disturbances such as a changing climate is high. From a large database of 1,019 insect species on 104 plant species, we identified that 70 species were of immediate conservation concern due to their reliance on threatened plant species. A further 15 insect species were of lesser conservation concern because they rely on several threatened plant species for survival. Of those insects that feed from non-threatened plant species, 178 host-specific species are likely to be at risk in the event that climate change or synergistic factors reduces their host plant’s range size. Insect groups that appear most prone to extinction are sessile feeders and highly host specific groups such as whiteflies, scales, mealybugs. Many weevils are also host specific and at higher risk, possibly as they are dispersal inhibited, such as through brachyptery. More surprisingly, mobile plant louse groups (Psylloidea) were also at high risk. Endophagous insects are predicted to be at high risk, but were under-studied here.Regions such as gullies and mountains provide refugia for some species. The fluctuations in temperature (less within refugia), and average humidity (higher in refugia) appear particularly important in these habitats. Particular vegetation types are associated with refugial regions, with a recognised Threatened Ecological Community (TEC) of flora species associated with the highest peaks of the Eastern Mastif, and there is evidence of insect species restricted to these peaks. For the majority of plant species that are not restricted to certain areas, their insect assemblages differ significantly between plant populations, particularly across different mountains.With the assistance of end-users, we have developed an adaptation management framework. The framework assists with conserving plant-dwelling insect species, after they are identified as in need of conservation action. Whilst the primary reason for the development of the framework was to provide adaptation actions in the face of climate change, the framework can be used when insects require conservation action to ameliorate impacts of other threatening processes. Previously published frameworks can be utilized to determine whether an insect is threatened with extinction. Despite the availability of such tools, a survey of end-users still indicted that lack of expertise is the most important factor inhibiting considering plant-dwelling insects.Land managers currently struggle to determine which insect species inhabit their lands, let alone knowing which are in need of conservation. To assist land managers with these problems, we suggest the employment of dedicated conservation entomologists by the Federal and State governments. Their role would be to bridge the interface between taxonomists, government conservation bodies, land managers and disturbance ecologists. The conservation entomologist’s principle tasks would be to identify insects most at risk of extinction, nominate them for listing, and develop management plans to ensure their survival

Infection Spreading and Source Identification: A Hide and Seek Game

The goal of an infection source node (e.g., a rumor or computer virus source) in a network is to spread its infection to as many nodes as possible, while remaining hidden from the network administrator. On the other hand, the network administrator aims to identify the source node based on knowledge of which nodes have been infected. We model the infection spreading and source identification problem as a strategic game, where the infection source and the network administrator are the two players. As the Jordan center estimator is a minimax source estimator that has been shown to be robust in recent works, we assume that the network administrator utilizes a source estimation strategy that can probe any nodes within a given radius of the Jordan center. Given any estimation strategy, we design a best-response infection strategy for the source. Given any infection strategy, we design a best-response estimation strategy for the network administrator. We derive conditions under which a Nash equilibrium of the strategic game exists. Simulations in both synthetic and real-world networks demonstrate that our proposed infection strategy infects more nodes while maintaining the same safety margin between the true source node and the Jordan center source estimator

Identifying Infection Sources and Regions in Large Networks

Identifying the infection sources in a network, including the index cases that introduce a contagious disease into a population network, the servers that inject a computer virus into a computer network, or the individuals who started a rumor in a social network, plays a critical role in limiting the damage caused by the infection through timely quarantine of the sources. We consider the problem of estimating the infection sources and the infection regions (subsets of nodes infected by each source) in a network, based only on knowledge of which nodes are infected and their connections, and when the number of sources is unknown a priori. We derive estimators for the infection sources and their infection regions based on approximations of the infection sequences count. We prove that if there are at most two infection sources in a geometric tree, our estimator identifies the true source or sources with probability going to one as the number of infected nodes increases. When there are more than two infection sources, and when the maximum possible number of infection sources is known, we propose an algorithm with quadratic complexity to estimate the actual number and identities of the infection sources. Simulations on various kinds of networks, including tree networks, small-world networks and real world power grid networks, and tests on two real data sets are provided to verify the performance of our estimators

Regulation of Noradrenaline Release From Rat Brain Tissue Chops by alpha2-Adrenoceptors

(1) Regulation of NA release from rat O.C. tissue chops is K+ and Ca2+ dependent

Cooperative and Distributed Localization for Wireless Sensor Networks in Multipath Environments

We consider the problem of sensor localization in a wireless network in a multipath environment, where time and angle of arrival information are available at each sensor. We propose a distributed algorithm based on belief propagation, which allows sensors to cooperatively self-localize with respect to one single anchor in a multihop network. The algorithm has low overhead and is scalable. Simulations show that although the network is loopy, the proposed algorithm converges, and achieves good localization accuracy

Distributed Local Linear Parameter Estimation using Gaussian SPAWN

We consider the problem of estimating local sensor parameters, where the local parameters and sensor observations are related through linear stochastic models. Sensors exchange messages and cooperate with each other to estimate their own local parameters iteratively. We study the Gaussian Sum-Product Algorithm over a Wireless Network (gSPAWN) procedure, which is based on belief propagation, but uses fixed size broadcast messages at each sensor instead. Compared with the popular diffusion strategies for performing network parameter estimation, whose communication cost at each sensor increases with increasing network density, the gSPAWN algorithm allows sensors to broadcast a message whose size does not depend on the network size or density, making it more suitable for applications in wireless sensor networks. We show that the gSPAWN algorithm converges in mean and has mean-square stability under some technical sufficient conditions, and we describe an application of the gSPAWN algorithm to a network localization problem in non-line-of-sight environments. Numerical results suggest that gSPAWN converges much faster in general than the diffusion method, and has lower communication costs, with comparable root mean square errors

Distributed localization of a RF target in NLOS environments

We propose a novel distributed expectation maximization (EM) method for non-cooperative RF device localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of non-line-of-sight signals, the signal path consists of a single reflection between the transmitter and receiver. Each sensor is able to measure the time difference of arrival of the target's signal with respect to a reference sensor, as well as the angle of arrival of the target's signal. We derive a distributed EM algorithm where each node makes use of its local information to compute summary statistics, and then shares these statistics with its neighbors to improve its estimate of the target localization. Since all the measurements need not be centralized at a single location, the spectrum usage can be significantly reduced. The distributed algorithm also allows for increased robustness of the sensor network in the case of node failures. We show that our distributed algorithm converges, and simulation results suggest that our method achieves an accuracy close to the centralized EM algorithm. We apply the distributed EM algorithm to a set of experimental measurements with a network of four nodes, which confirm that the algorithm is able to localize a RF target in a realistic non-line-of-sight scenario.Comment: 30 pages, 11 figure

On the Universality of Jordan Centers for Estimating Infection Sources in Tree Networks

Finding the infection sources in a network when we only know the network topology and infected nodes, but not the rates of infection, is a challenging combinatorial problem, and it is even more difficult in practice where the underlying infection spreading model is usually unknown a priori. In this paper, we are interested in finding a source estimator that is applicable to various spreading models, including the Susceptible-Infected (SI), Susceptible-Infected-Recovered (SIR), Susceptible-Infected-Recovered-Infected (SIRI), and Susceptible-Infected-Susceptible (SIS) models. We show that under the SI, SIR and SIRI spreading models and with mild technical assumptions, the Jordan center is the infection source associated with the most likely infection path in a tree network with a single infection source. This conclusion applies for a wide range of spreading parameters, while it holds for regular trees under the SIS model with homogeneous infection and recovery rates. Since the Jordan center does not depend on the infection, recovery and reinfection rates, it can be regarded as a universal source estimator. We also consider the case where there are k>1 infection sources, generalize the Jordan center definition to a k-Jordan center set, and show that this is an optimal infection source set estimator in a tree network for the SI model. Simulation results on various general synthetic networks and real world networks suggest that Jordan center-based estimators consistently outperform the betweenness, closeness, distance, degree, eigenvector, and pagerank centrality based heuristics, even if the network is not a tree