Spatiotemporal dynamics of complex ecological networks: Power-law scaling and implications for conservation planning

Spatial constraints on the topology of complex networks are just beginning to be appreciated, both theoretically and in concrete examples like the Internet and global air transportation network. Ecological networks, composed of habitat patches connected by species dispersal, are intrinsically spatial and show promise as a tool for conservation planning; but while habitat-loss effects on ecological networks have been simulated, such effects have not been directly measured in ecological networks varying over time. In this study, I used satellite remote sensing to study ecological networks composed of wetland habitat in the Prairie Pothole Region (PPR) of North America. I find power-law scaling of important topological properties as a function of dispersal ability and as wetland density varies with climate. Prairie wetland networks are 'meso-worlds' with mean topological distance increasing faster with network size than small-world networks, but slower than regular lattices. While similar dynamics have been shown in random spatial networks, these results emphasize the importance of processes determining locations of nodes in a spatial network, with possible implications in other areas like wireless communication networks or disease transmission networks. Wetland networks establish a climate envelope for landscape connectivity in the PPR, and I show that wetland-dependent species face a 'crisis of connectivity' with climate change. The global biodiversity crisis requires that conservation planners act quickly over large areas using limited resources; a network-based approach to coarse-filter conservation planning in dynamic landscapes should be broadly applicable to this problem

Estimation of dynamic networks for high-dimensional nonstationary time series

This paper is concerned with the estimation of time-varying networks for high-dimensional nonstationary time series. Two types of dynamic behaviors are considered: structural breaks (i.e., abrupt change points) and smooth changes. To simultaneously handle these two types of time-varying features, a two-step approach is proposed: multiple change point locations are first identified based on comparing the difference between the localized averages on sample covariance matrices, and then graph supports are recovered based on a kernelized time-varying constrained $L_1$-minimization for inverse matrix estimation (CLIME) estimator on each segment. We derive the rates of convergence for estimating the change points and precision matrices under mild moment and dependence conditions. In particular, we show that this two-step approach is consistent in estimating the change points and the piecewise smooth precision matrix function, under certain high-dimensional scaling limit. The method is applied to the analysis of network structure of the S\&P 500 index between 2003 and 2008

A Multi-dimensional Real World Spectrum Occupancy Data Measurement and Analysis for Spectrum Inference in Cognitive Radio Network

Spectrum Inference in contrast to Spectrum Sensing is an active technique for dynamically inferring radio spectrum state in Cognitive Radio Networks. Efficient spectrum inference demands real world multi-dimensional spectral data with distinct features. Spectrum bands exhibit varying noise floors; an effective band wise noise thresholding guarantees an accurate occupancy data. In this work, we have done an extensive real world spectrum occupancy data measurement in frequency range 0.7 GHz to 3 GHz for tele density wise varying locations at Pune, Solapur and Kalaburagi with time diversity ranging from 2 to 7 days. We have applied maximum noise (Max Noise), m-dB and probability of false alarm (PFA) noise thresholding for spectrum occupancy calculations in all bands and across all locations. Overall occupancy across these locations is 37.89 %, 18.90 % and 13.69 % respectively. We have studied signal to noise ratio (SNR), channel vacancy length durations (CVLD) and service congestion rates (SCR) as characteristic features of measured multi-dimensional spectrum data. The results reveal strong time, spectral and spatial correlations of these features across all locations. These features can be used for a multi-dimensional spectrum inference in cognitive radio based on machine learning

Estimating Continental and Terrestrial Precipitation Averages from Raingauge Networks

© 1994 by the Royal Meteorological Society. Available from the publisher's website at http://dx.doi.org/10.1002/joc.3370140405Influences of varying rain-gauge networks on continental and terrestrial precipitation averages (derived from data observed on those networks) are evaluated. Unsystematically and systematically designed station networks are considered, the latter being represented by the NCAR World Monthly Surface Station Climatology, which contains hand-picked but time-varying networks that date back to the 1800s. Biases arising from spatially uneven and temporally variable precipitation-observing networks can be significant. For all the continents, except South America, sparse rain-gauge networks produce overestimates of continental mean precipitation. Mean precipitation for South America, in contrast, is underestimated substantially by low densities of observing stations. Sampling errors tend to be large in areas of high precipitation and in regions with strong spatial precipitation gradients (e.g. in the Sahel). These patterns occur whether the station network has been selected systematically (as in the NCAR network) or unsystematically. Systematic sampling of mean precipitation (at the NCAR station locations), however, suggests that many yearly NCAR station networks are adequate for estimating continental average precipitation. As early as 1890, NCAR networks for Australia resolve continental average precipitation accurately. Not until 1960, however, do NCAR networks for South America begin to resolve continental mean precipitation adequately. Regional and continental NCAR network errors also tend to cancel one another, often giving accurate yearly estimates of terrestrial mean precipitation

Congestion Control for Network-Aware Telehaptic Communication

Telehaptic applications involve delay-sensitive multimedia communication between remote locations with distinct Quality of Service (QoS) requirements for different media components. These QoS constraints pose a variety of challenges, especially when the communication occurs over a shared network, with unknown and time-varying cross-traffic. In this work, we propose a transport layer congestion control protocol for telehaptic applications operating over shared networks, termed as dynamic packetization module (DPM). DPM is a lossless, network-aware protocol which tunes the telehaptic packetization rate based on the level of congestion in the network. To monitor the network congestion, we devise a novel network feedback module, which communicates the end-to-end delays encountered by the telehaptic packets to the respective transmitters with negligible overhead. Via extensive simulations, we show that DPM meets the QoS requirements of telehaptic applications over a wide range of network cross-traffic conditions. We also report qualitative results of a real-time telepottery experiment with several human subjects, which reveal that DPM preserves the quality of telehaptic activity even under heavily congested network scenarios. Finally, we compare the performance of DPM with several previously proposed telehaptic communication protocols and demonstrate that DPM outperforms these protocols.Comment: 25 pages, 19 figure