Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)

Developing a robust algorithm for automatic individual tree crown (ITC) detection from airborne laser scanning datasets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here we describe a Multi-Class Graph Cut (MCGC) approach to tree crown delineation. This uses local three-dimensional geometry and density information, alongside knowledge of crown allometries, to segment individual tree crowns from airborne LiDAR point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognise small trees. From these three-dimensional crowns, we are able to measure individual tree biomass. Comparing these estimates to those from permanent inventory plots, our algorithm is able to produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of three-dimensional data, such as structure from motion datasets.Jonathan Williams holds a NERC studentship [NE/N008952/1] which is a CASE partnership with support from Royal Society for the Protection of Birds (RSPB). David Coomes was supported by an International Academic Fellowship from the Leverhulme Trust. Carola-Bibiane Schoenlieb was supported by the RISE projects CHiPS and NoMADS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Quadro P6000 GPU used for this research

Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)

Obtaining low vegetation data is important in order to quantify the structural characteristics of a forest. Dense three-dimensional (3D) laser scanning data can provide information on the vertical profile of a forest. However, most studies have focused on the dominant and subdominant layers of the forest, while few studies have tried to delineate the low vegetation. To address this issue, we propose a framework for individual tree crown (ITC) segmentation from laser data that focuses on both overstory and understory trees. The framework includes 1) a new algorithm (SSD) for 3D ITC segmentation of dominant trees, by detecting the symmetrical structure of the trees, and 2) removing points of dominant trees and mean shift clustering of the low vegetation. The framework was tested on a boreal forest in Sweden and the performance was compared 1) between plots with different stem density levels, vertical complexities, and tree species composition, and 2) using airborne laser scanning (ALS) data, terrestrial laser scanning (TLS) data, and merged ALS and TLS data (ALS + TLS data). The proposed framework achieved detection rates of 0.87 (ALS + TLS), 0.86 (TLS), and 0.76 (ALS) when validated with field inventory data (of trees with a diameter at breast height >= 4 cm). When validating the estimated number of understory trees by visual interpretation, the framework achieved 19%, 21%, and 39% root-mean-square error values with ALS + TLS, TLS, and ALS data, respectively. These results show that the SSD algorithm can successfully separate laser points of overstory and understory trees, ensuring the detection and segmentation of low vegetation in forest. The proposed framework can be used with both ALS and TLS data, and achieve ITC segmentation for forests with various structural attributes. The results also illustrate the potential of using ALS data to delineate low vegetation

Topics on Machine Learning under Imperfect Supervision

This dissertation comprises several studies addressing supervised learning problems where the supervision is imperfect. Firstly, we investigate the margin conditions in active learning. Active learning is characterized by its special mechanism where the learner can sample freely over the feature space and exploit mostly the limited labeling budget by querying the most informative labels. Our primary focus is to discern critical conditions under which certain active learning algorithms can outperform the optimal passive learning minimax rate. Within a non-parametric multi-class classification framework,our results reveal that the uniqueness of Bayes labels across the feature space serves as the pivotal determinant for the superiority of active learning over passive learning. Secondly, we study the estimation of central mean subspace (CMS), and its application in transfer learning. We show that a fast parametric convergence rate is achievable via estimating the expected smoothed gradient outer product, for a general class of covariate distribution that admits Gaussian or heavier distributions. When the link function is a polynomial with a degree of at most r and the covariates follow the standard Gaussian, we show that the prefactor depends on the ambient dimension d as d^r. Furthermore, we show that under a transfer learning setting, an oracle rate of prediction error as if the CMS is known is achievable, when the source training data is abundant. Finally, we present an innovative application involving the utilization of weak (noisy) labels for addressing an Individual Tree Crown (ITC) segmentation challenge. Here, the objective is to delineate individual tree crowns within a 3D LiDAR scan of tropical forests, with only 2D noisy manual delineations of crowns on RGB images available as a source of weak supervision. We propose a refinement algorithm designed to enhance the performance of existing unsupervised learning methodologies for the ITC segmentation problem

Amélioration de l’inventaire forestier à l’aide de nuages de points à haute densité acquis par drone lidar et lidar mobile : étude de cas en forêts feuillues tempérées

Les exigences en matière d'inventaire forestier évoluent rapidement pour répondre à un ensemble de normes économiques, sociales et environnementales de plus en plus complexes en matière de gestion durable des ressources forestières. Le manque d'informations détaillées sur l'approvisionnement, c'est-à-dire la quantité et les caractéristiques des ressources forestières, constitue un obstacle important à la satisfaction de ces exigences. Avec le développement continu et la démocratisation des capteurs de lidar sur drone (ULS) et de lidar mobile (MLS), de nouveaux types de nuages de points sont de plus en plus accessibles pour appuyer le niveau opérationnel de l’inventaire. Dans la présente thèse, le potentiel et les limites de l’utilisation de nuages de points ULS et MLS pour la numérisation des arbres feuillus en amont de la chaine d’approvisionnement ont été évalués. Des méthodes de traitement ont été développées pour l’estimation d’attributs structuraux clefs tels que le diamètre à hauteur de poitrine (DHP), la hauteur de l'arbre, les dimensions de la couronne et le volume de bois marchand. Dans le premier article, nous nous sommes concentrés sur le développement et l'évaluation de chaînes de traitement automatiques pour la détection et la segmentation des arbres individuels (ITD : Individual Tree Dectection and Delineation) et l'estimation de leurs attributs structuraux. Ceci, à partir de données ULS acquises avec et sans feuilles dans un peuplement naturel hétérogène de feuillus nordiques. Des comparaisons fines avec des nuages de points de lidar aérien (ALS) et terrestre (TLS) ont été réalisées pour mieux comprendre la configuration des données ULS et pour valider l'extraction d’attributs d’inventaire dérivés de l’ULS. Les meilleurs résultats pour la segmentation des arbres et l’estimation de leurs attributs structuraux ont été obtenus hors feuilles via l’utilisation d’une approche de segmentation dite ascendante (« bottom-up »). Les performances globales des capteurs ULS, en termes d'ajustement cylindrique des tiges et de précision géométrique des points le long de la tige, ne sont toutefois pas comparables à celles du TLS. Les incertitudes sont encore trop élevées au niveau de l'arbre individuel pour respecter les normes de l’inventaire terrain. L’acquisition hors feuilles de données ULS à haute densité pourrait toutefois jouer un rôle important dans le développement de modèles allométriques locaux qui font généralement défaut dans les peuplements complexes de feuillus, ainsi que pour la caractérisation des ressources et le soutien des opérations de foresterie de précision. Dans le second article, nous proposons une méthode innovante pour extraire le volume de bois marchand à partir des données MLS-SLAM (localisation et cartographie simultanées). Les approches actuelles pour prédire le volume de bois marchand reposent sur des équations allométriques qui sont indépendantes de la forme et de la géométrie de l'arbre. Il existe des biais et des erreurs connus associés à cette simplification, en particulier pour les arbres feuillus. L'utilisation d'algorithmes de modèles structurels quantitatifs (QSM : Quantitative Structural Model) pour estimer le volume de bois à partir de nuages de points 3D représente une alternative prometteuse aux mesures destructives et un fort potentiel pour améliorer les modèles allométriques. Les résultats ont montré une grande similitude entre les données TLS et MLS pour l'estimation de la hauteur des arbres, des dimensions de la couronne et du DHP. L'application de QSMs sur des nuages de points MLS filtrés pour extraire le volume marchand du tronc principal des arbres feuillus n'a montré aucun biais significatif par rapport aux estimations TLS. Néanmoins, les données MLS sont plus bruitées que les données TLS, ce qui a entraîné une surestimation du volume de bois des branches qui augmente avec l'ordre de ramification. Toutefois, ces erreurs ont été limitées du fait que les branches de 2ème et de 3ème ordre de ramification ne représentaient qu'une faible proportion du volume marchand total. Ces résultats constituent une étape importante vers la prochaine génération d'inventaires forestiers améliorés par lidar mobiles au sol. Compte tenu de l'utilisation accrue des systèmes ULS et MLS dans la gestion forestière, nos développements constituent des étapes importantes pour les futurs inventaires lidar à l’échelle de l’arbre individuel. Nos résultats démontrent des avancées significatives dans l'utilisation des configurations ULS et MLS pour l’estimation des paramètres biophysiques forestiers.Abstract : Forest inventory requirements are rapidly evolving to meet an increasingly complex set of economic, social and environmental standards for sustainable forest resource management. A significant obstacle to support this requirement is the lack of detailed information on the supply, i.e., the quantity and characteristics of forest resources. In recent decades, a substantial effort has been made to reduce the costs of forest inventories by minimizing labor-intensive field surveys and developing inventory systems enhanced by remote sensing. As such, the use of lidar technology in various aerial and terrestrial platforms, such as airborne laser scanning (ALS) and terrestrial laser scanning (TLS) has considerably increased to the point of becoming essential to improve the forest inventories beyond the existing photo-interpretation techniques. With the continuous development and the democratization of UAV-borne laser scanning (ULS) and mobile laser scanning (MLS) sensors, new types of point cloud are increasingly accessible for forest investigations. The level of detail of ULS and MLS point cloud is becoming comparable to that of TLS, decreasing the boundaries between ALS and TLS systems and providing new opportunities to characterize forest resources at the tree level. In the present thesis, the baselines of ULS and MLS point clouds in digitizing hardwood trees up the supply chain were benchmarked and methods were developed to extract critical structural attributes such as diameter at breast height (DBH), tree height, crown dimensions and merchantable wood volume. In the first article, we emphasized on the development and the evaluation of automatic workflows for the detection, the delineation and the estimation of tree structural attributes from leaf-on and leaf-off ULS data collection. These analyses were conducted in a complex heterogeneous natural stand of northern hardwoods. Co-registration process with ALS and TLS point clouds was achieved for a better understanding of ULS data configuration and to validate ULS retrieval of tree structural attributes. In leaf-on condition, no significant differences were observed between ALS and ULS-R raster-based ITD results, where crown delineation errors led to a poor prediction of individual tree DBH using allometry. In contrast, results in leaf-off condition using point cloud-based individual tree detection and delineation (ITD) algorithm outperformed the raster based ITD in terms of tree detection and tree delineation accuracy, revealing the full potential of high-resolution ULS data. DBH estimation from the “bottom-up” point cloud-based ITD also provided accurate results for both methods, namely allometry and cylinder fitting. The latter showed to be more efficient in dealing with forked trees. The overall performance, in terms of stem cylinder fitting and geometric accuracy of stem points from ULS sensors are not yet comparable to TLS. Uncertainties are still too high at the individual tree level to reach the standard of field inventories, but one might expect to get closer to operational requirements with narrower beams and higher ranging accuracy ULS sensors. In leaf-off condition, the use of bottom-up tree segmentation approaches presents a strong potential to overcome ITD limits currently encountered in hardwood stands. Applications requiring accurate tree location and crown size data could greatly benefit from this innovative approach. Leaf-off acquisition of high-density ULS data could play an important role in developing local allometric models that are typically lacking in complex hardwood stands, as well as for resource characterization and supporting precision forestry operations. In the second article, we propose an innovative method to extract merchantable wood volume from MLS data. Current approaches to predict merchantable wood volume rely on allometric equations that are independent of tree form and the geometry of the tree. There are known biases and errors associated with this simplification, particularly for hardwood trees. The use of quantitative structural model (QSM) algorithms to estimate wood volume from 3D point clouds represent a promising alternative to destructive measurement and a strong potential to improve allometric models. However, so far, they were mainly used on TLS point clouds, which are time-consuming to acquire in the field and complex to process. With the rapid technological progress of SLAM-based (simultaneous localization and mapping) MLS systems, new types of ground-based lidar points clouds are available for QSM analysis. SLAM-based MLS systems open new possibilities to support field inventory. In this study, we collected SLAM-based MLS data from a 1 ha leaf-off northern hardwood site and investigated its use for estimating tree structural attributes. Validation was performed on 26 trees using destructive field measurements and multi-scans TLS data. Results showed high similitude of TLS and MLS data for the estimation of the tree height, crown dimensions and DBH. The application of QSM on filtered MLS point clouds to extract the merchantable stem volume of hardwood trees showed no significant bias compared to the TLS estimates. Nevertheless, the MLS data are noisier than the TLS data, primarily due to the propagation of positioning errors and the greater divergence of the sensor beam. This resulted in an overestimation of the branching volume that increases with the branching order. However, these errors were limited by the fact that branches from the 2nd and 3rd branching order represented a small proportion of the total merchantable volume. These findings are an important step towards next generation of forest inventories enhanced by ground-based lidar. Considering the increased use of ULS and MLS systems in forest management, our developments are important steps forward for future individual-tree-based lidar inventories. We believe that our results demonstrate significant advances in the use of ULS and MLS configuration for the retrieval of forest biophysical parameters

Volume 98 Issue 5, pp. 899-1054

ECOLOGY-EPIDEMIOLOGY-BEHAVIOR Increased Surfacing Behavior in Longnose Killifish Infected by Brain-Encysting Trematode. B. L. FREDENSBORG and A. N. LONGORIA - 899 Spatial Structure of Helminth Communities in the Golden Grey Mullet, Liza aurata (Actinopterygii: Mugilidae), From the Western Mediterranean. RAUL MIGUEZ-LOZANO, TRINIDAD V. PARDO-CARRANZA, ISABEL BLASCO-COSTA, and JUAN ANTONIO BALBUENA - 904 Hepatozoon Infection Prevalence in Four Snake Genera: Influence of Diet, Prey Parasitemia Levels, or Parasite Type? BEATRIZ TOME, JOAD P. M. C. MAIA, and D. JAMES HARRIS - 913 ECTOPARASITOLOGY Molecular Identification and Description of the Female of Nothoaspis reddelli (Ixodida: Argasidae) From a Cave in Southeastern Mexico. CARMEN GUZMAN-CORNEJO, RICARDO PAREDES-LEON, MARCELO B. LABRUNA, SANTIAGO NAVA, and JOSE M. VENZAL - 918 Prevalence of Hemoproteus iwa in Galapagos Great Frigatebirds (Fregata minor) and Their Obligate Fly Ectoparasite (01- Jersia spiniJera). IRIS I. LEVIN and PATRICIA G. PARKER - 924 Variable Microsatellite Loci for Population Genetic Analysis of Old World Monkey Lice (Pedicinus sp.). KATLYN SCHOLL, JULIE M. ALLEN, FABIAN H. LEENDERTZ, COLIN A. CHAPMAN, and DAVID L. REED - 930 FUNCTIONAL MORPHOLOGY Ultrastructural Study of Vitellogenesis of Aphallus tubarium (Rudolphi, 1819) Poche, 1926 (Digenea: Cryptogonimidae), an Intestinal Parasite of Dentex dentex (Pisces: Teleostei). SAMUEL GREANI, YANN QUILICHINI, JOSEPHINE FOATA, and BERNARD MARCHAND - 938 IMMUNOLOGY Seroprevalence of Toxoplasma gondii Infection in Domestic Horses in Durango State, Mexico. C. ALVARADO-ESQUIVEL, S. RODRIGUEZ-PENA, I. VILLENA, and J. P. DUBEY - 944 INVERTEBRATE-PARASITE RELATIONSHIPS Excystation Signals Do Not Isolate Gregarine Gene Pools: Experimental Excystation of Blabericola migrator Among 11 Species of Cockroaches. SHELBY M. STEELE, DEBRA T. CLOPTON, and RICHARD E. CLOPTON - 946 LIFE CYCLES-SURVEY A New Sarcocystis Species (Apicomplexa: Sarcocystidae) From the Rock Gecko Bunopus tuberculatus in Saudi Arabi. A. S. ABDEL-BAKI, H. M. ABDEL-HALEEM, and S. AL-QURAISHY - 951 A Retrospective Study of Abattoir Condemnation Due to Parasitic Infections: Economic Importance in Ahwaz, Southwestern Iran. HASSAN BORJI, MOHAMMAD AZIZZADEH, and MEHRAB KAMELLI - 954 Prevalence of Eimeria Infection in Yaks on the Qinghai-Tibet Plateau of China. HUI DONG, CHUNHUA LI, QIPING ZHAO, JING LI, HONGYU HAN, LIANLIAN JIANG, SHUNHAI ZHU, TING LI, CHUNLIN KONG, BING HUANG, and JINZHONG CAI - 958 Prevalence of Coccidial Infection in Dairy Cattle in Shanghai, China. HUI DONG, QIPING ZHAO, HONGYU HAN, LIANLIAN JIANG, SHUNHAI ZHU, TING LI, CHUNLIN KONG, and BING HUANG - 963 Genetic Sequence Data Identifies the Cercaria of Drepanocephalus spathans (Digenea: Echinostomatidae), a Parasite of the Double-Crested Cormorant (Phalacrocorax auritus), with Notes on Its Pathology in Juvenile Channel Catfish (Ictalurus punctatus). MATT J. GRIFFIN, LESTER H. KHOO, SYLVIE M. QUINIOU, MARY M. O\u27HEAR, LINDA M. POTE, TERRENCE E. GREENWAY, and DAVID J. WISE - 967 SYSTEMATICS-PHYLOGENETICS A New Species of Megalobatrachonema (Nematoda: Kathlaniidae) in Fojia bumui (Squamata: Scincidae) From Papua New Guinea. CHARLES R. BURSEY, STEPHEN R. GOLDBERG, and FRED KRAUS - 973 Two New Species of Schizorchis (Cestoda: Anoplocephalidae) From Leporids (Lagomorpha: Leporidae) in China. KUIZHENG CAI, JIALIN BAI, and SHIEN CHEN - 977 The Genus Guerrerostrongylus (Nematoda: Heligmonellidae) in Cricetid Rodents From the Atlantic Rain Forest of Misiones, Argentina: Emended Description of Guerrerostrongylus zetta (Travassos, 1937) and Description of a New Species. MARIA CELINA DIGIANI, JULIANA NOTARNICOLA, and GRACIELA T. NAVONE - 985 A New Microphallid (Digenea) Species From Lontra provocax (Mammalia: Mustelidae) From Freshwater Environments of Northwestern Patagonia (Argentina). VERONICA R. FLORES, NORMA L. BRUGNI, and CARLA M. POZZI - 992 Description of Riouxgolvania kapapkamui sp. n. (Nematoda: Muspiceoidea: Muspiceidae), a Peculiar Intradermal Parasite of Bats in Hokkaido, Japan. HIDEO HASEGAWA, MASAHIKO SATO, KISHIO MAEDA, and YOSHIKO MURAYAMA - 995 A New Species of Choleoeimeria (Apicomplexa: Eimeriidae) From Meller\u27s Chameleon, Trioceros melleri (Sauria: Chamaeleonidae). CHRIS T. McALLISTER - 1001 A New Species of Eimeria (Apicomplexa: Eimeriidae) From the Northern Myotis, Myotis septentrionalis (Chiroptera: Vespertilionidae), in Oklahoma. CHRIS T. McALLISTER, R. SCOTT SEVILLE, and ZACHARY P. ROEHRS - 1003 A New Spirurid (Nematoda) Parasite From Mormoopid Bats in Mexico. JORGE LUIS PERALTA-RODRIGUEZ, JUAN MANUEL CASPETA-MANDUJANO, and JOSE ANTONIO GUERRERO - 1006 THERAPEUTICS-DIAGNOSTICS Resistance of Rhipicephalus microplus to Amitraz and Cypermethrin in Tropical Cattle Farms in Veracruz, Mexico. AGUSTIN FERNANDEZ-SALAS, ROGER IVAN RODRIGUEZ-VIVAS, and MIGUEL ANGEL ALONSO-DIAZ - 1010 RESEARCH NOTES Seroprevalence Study on Theileria equi and Babesia caballi Antibodies in Horses From Central Province of Saudi Arabia. A. D. ALANAZI, M. S. ALYOUSIF, and M. M. HASSIEB - 1015 Influence of Rangelia vitalii (Apicomplexa: Piroplasmorida) on Copper, Iron and Zinc Bloodstream Levels in Experimentally Infected Dogs, ALEKSANDRO S, DA SILVA, RAQUELI T. FRANC;:A, MARCIO M. COSTA, CARLOS B. V. PAIM, FRANCINE C. PAIM, CLARISSA M. M. SANTOS, ERICO M. M. FLORES, TIAGO L. EILERS, CINTHIA M. MAZZANTI, SILVIA G. MONTEIRO, CARLOS H. DO AMARAL, and SONIA T. A. LOPES - 1018 Plagiorchis elegans (Trematoda) Induces Immune Response in an Incompatible Snail Host Biomphalaria glabrata (Pulmonata: Planorbidae). S. P. DAOUST, M. E. RAU, and J. D. McLAUGHLIN - 1021 Prevalence and Intensity of Fish-Borne Zoonotic Trematodes in Cultured Freshwater Fish From Rural and Urban Areas of Northern Vietnam. NGUYEN VAN DE, THANH HOA LE, and K. D. MURRELL - 1023 Details of the Paranephridial System of a Species of Prohyptiasmus (Cyclocoelidae: Hyptiasminae) From an American Coot, Fulica americana (Rallidae) in Oklahoma. NORMAN O. DRONEN, F. AGUSTIN JIMENEZ, and SCOTT L. GARDNER - 1026 Surface Ultrastructure of the Eggs of Malacopsylla grossiventris and Phthiropsylla agenoris (Siphonaptera: Malacopsyllidae). M. C. EZQUIAGA and M. LARES CHI - 1029 Prevalence of Ancylostoma braziliense in Cats in Three Northern Counties of Florida, United States. JANICE L. LIOTTA, KHUANCHAI N. KOOMPAPONG, JOSEPH P. YAROS, JOSEPH PRULLAGE, and DWIGHT D. BOWMAN - 1032 Obtaining an Isolate of Ancylostoma braziliense From Dogs Without the Need for Necropsy. JANICE L. LIOTTA, ALICE C. Y. LEE, SARP AKSEL, IBRAHIM ALKHALIFE, ALEJANDRO CRUZ-REYES, HEEJEONG YOUN, STEPHEN E. BIENHOFF, and DWIGHT D. BOWMAN - 1034 Obtaining an Isolate of Ancylostoma braziliense From Cats Without the Need for Necropsy. JANICE L. LIOTTA, ALICE C. Y. LEE, KHUANCHAI N. KOOMPAPONG, JOSEPH P. YAROS, JOSEPH PRULLAGE, MICHAEL A. ULRICH, and DWIGHT D. BOWMAN - 1037 Prevalence of Ancylostoma braziliense in Dogs From Alachua and Marion Counties, Florida, United States. JANICE L. LIOTTA, HEEJEONG YOUN, SARP AKSEL, STEPHEN E. BIENHOFF, and DWIGHT D. BOWMAN - 1039 Morphological Differentiation of Eggs of Ancylostoma caninum, Ancylostoma tubaeforme, and Ancylostoma braziliense From Dogs and Cats in the United States. ARACELI LUCIO-FORSTER, JANICE L. LIOTTA, JOSEPH P. YAROS, KAITLYN R. BRIGGS, HUSSNI O. MOHAMMED, and DWIGHT D. BOWMAN - 1041 Molecular and Immunological Characterization of a Novel 32-kDa Secreted Protein of Babesia microti. HIDEO OOKA, MOHAMAD A. TERKAWI, SHINUO CAO, GABRIEL ABOGE, YO UN-KYOUNG GOO, YUZI LUO, YAN LI, YOSHIFUMI NISHIKAWA, IKUO IGARASHI, and XUENAN XUAN - 1045 DNA Barcoding of Schistosome Cercariae Reveals a Novel Sub-Lineage within Schistosoma rodhaini From Ngamba Island Chimpanzee Sanctuary, Lake Victoria. C. J. STANDLEY and J. R. STOTHARD - 1049 Host Susceptibility Is Altered by Light Intensity After Exposure to Parasites. MICHELLE L. STEINAUER and KAITLIN M. BONNER - 1052 ANNOUNCEMENT: Change in Editorship - 903 ERRATUM - 91