Recovery of Forest Structure Following Large-Scale Windthrows in the Northwestern Amazon

The dynamics of forest recovery after windthrows (i.e., broken or uprooted trees by wind) are poorly understood in tropical forests. The Northwestern Amazon (NWA) is characterized by a higher occurrence of windthrows, greater rainfall, and higher annual tree mortality rates (~2%) than the Central Amazon (CA). We combined forest inventory data from three sites in the Iquitos region of Peru, with recovery periods spanning 2, 12, and 22 years following windthrow events. Study sites and sampling areas were selected by assessing the windthrow severity using remote sensing. At each site, we recorded all trees with a diameter at breast height (DBH) ≥ 10 cm along transects, capturing the range of windthrow severity from old-growth to highly disturbed (mortality > 60%) forest. Across all damage classes, tree density and basal area recovered to >90% of the old-growth values after 20 years. Aboveground biomass (AGB) in old-growth forest was 380 (±156) Mg ha−1. In extremely disturbed areas, AGB was still reduced to 163 (±68) Mg ha−1 after 2 years and 323 (± 139) Mg ha−1 after 12 years. This recovery rate is ~50% faster than that reported for Central Amazon forests. The faster recovery of forest structure in our study region may be a function of its higher productivity and adaptability to more frequent and severe windthrows. These varying rates of recovery highlight the importance of extreme wind and rainfall on shaping gradients of forest structure in the Amazon, and the different vulnerabilities of these forests to natural disturbances whose severity and frequency are being altered by climate change

Preserving empirical data utility in k-anonymous microaggregation via linear discriminant analysis

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Today’s countless benefits of exploiting data come with a hefty price in terms of privacy. -Anonymous microaggregation is a powerful technique devoted to revealing useful demographic information of microgroups of people, whilst protecting the privacy of individuals therein. Evidently, the inherent distortion of data results in the degradation of its utility. This work proposes and analyzes an anonymization method that draws upon the technique of linear discriminant analysis (LDA), with the aim of preserving the empirical utility of data. Further, this utility is measured as the accuracy of a machine learning model trained on the microaggregated data. By transforming the original data records to a different data space, LDA enables -anonymous microaggregation to build microcells more tailored to an intrinsic classification threshold. To do this, first, data is rotated (projected) towards the direction of maximum discrimination and, second, scaled in this direction by a factor that penalizes distortion across the classification threshold. The upshot is that thinner cells are built along the threshold, which ends up preserving data utility in terms of the accuracy of machine learned models for a number of standardized data sets.We gratefully acknowledge the invaluable assistance of Irene Carrión-Barberà, M.D., in the preparation of the medical example in Fig. 2. This work is supported by the Escuela Politécnica Nacional through the project ‘‘Privacidad Sintáctica Funcional: Análisis y adaptación de mecanismos de anonimato con enfoque en la preservación de utilidad de los datos’’, ref. PII-DETRI-2019-01. This work is partly supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the project ‘‘MAGOS’’, ref. TEC2017-84197-C4-3-R. Ana Rodríguez-Hoyos and José Estrada-Jiménez acknowledge the support from Escuela Politécnica Nacional (EPN), Republic of Ecuador for their doctoral studies at Universitat Politècnica de Catalunya (UPC).Peer ReviewedPostprint (author's final draft

Windthrows Inventory Data Base (WInD) v1

<p>In total 1403 windthrow swere identified, of which 1343 were processed, identified, and Classify.</p&gt

Coherent and automatic address resolution for vehicular ad hoc networks

The interest in vehicular communications has increased notably. In this paper, the use of the address resolution (AR) procedures is studied for vehicular ad hoc networks (VANETs).We analyse the poor performance of AR transactions in such networks and we present a new proposal called coherent, automatic address resolution (CAAR). Our approach inhibits the use of AR transactions and instead increases the usefulness of routing signalling to automatically match the IP and MAC addresses. Through extensive simulations in realistic VANET scenarios using the Estinet simulator, we compare our proposal CAAR to classical AR and to another of our proposals that enhances AR for mobile wireless networks, called AR+. In addition, we present a performance evaluation of the behaviour of CAAR, AR and AR+ with unicast traffic of a reporting service for VANETs. Results show that CAAR outperforms the other two solutions in terms of packet losses and furthermore, it does not introduce additional overhead

Identifying fatigue crack growth by AE analysis

SIGLELD:8019.3153(PNR--90075). / BLDSC - British Library Document Supply CentreGBUnited Kingdo