TY - CONF TI - Heat and nutritional interactions in bentgrass AU - Seth Carley, D. AU - Cappy, J. AU - Tredway, L. AU - Rufty, T. T2 - Agronomy Society Meeting C2 - 2007/11// C3 - Agronomy Society Meeting Abstracts CY - New Orleans, LA DA - 2007/11// PY - 2007/11// ER - TY - RPRT TI - User manual for NAPPFAST AU - Borchert, D. AU - Magarey, R. DA - 2007/// PY - 2007/// ER - TY - ER - TY - JOUR TI - Does size matter for dispersal distance? AU - Jenkins, David G. AU - Brescacin, Camille R. AU - Duxbury, Craig V. AU - Elliott, Jennifer A. AU - Evans, Jennafer A. AU - Grablow, Katherine R. AU - Hillegass, Melissa AU - Lyon, Boyd N. AU - Metzger, Genevieve A. AU - Olandese, Michelle L. AU - Pepe, Diana AU - Silvers, Greg A. AU - Suresch, Hiliary N. AU - Thompson, Tracy N. AU - Trexler, Christina M. AU - Williams, Graham E. AU - Williams, Natalie C. AU - Williams, Sara E. T2 - Global Ecology and Biogeography AB - ABSTRACT Aim The aim of this study is to answer the questions: (1) do small organisms disperse farther than large, or vice versa; and (2) does the observed pattern differ for passive and active dispersers? These questions are central to several themes in biogeography (including microbial biogeography), macroecology, metacommunity ecology and conservation biology. Location The meta‐analysis was conducted using published data collected worldwide. Methods We collected and analysed 795 data values in the peer‐reviewed literature for direct observations of both maximal dispersal distance and mass of the dispersing organisms (e.g. seeds, not trees). Analysed taxa ranged in size from bacteria to whales. We applied macroecology analyses based on null models (using Monte Carlo randomizations) to test patterns relative to specific hypotheses. Results Collected dispersal distance and mass data spanned 9 and 21 orders of magnitude, respectively. Active dispersers dispersed significantly farther ( P < 0.001) and were significantly greater in mass ( P < 0.001) than passive dispersers. Overall, size matters: larger active dispersers attained greater maximum observed dispersal distances than smaller active dispersers. In contrast, passive‐disperser distances were random with respect to propagule mass, but not uniformly random, in part due to sparse data available for tiny propagules. Conclusions Size is important to maximal dispersal distance for active dispersers, but not for passive dispersers. Claims that microbes disperse widely cannot be tested by current data based on direct observations of dispersal: indirect approaches will need to be applied. Distance–mass relationships should contribute to a resolution of neutral and niche‐based metacommunity theories by helping scale expectations for dispersal limitation. Also, distance–mass relationships should inform analyses of latitudinal species richness and conservation biology topics such as fragmentation, umbrella species and taxonomic homogenization. DA - 2007/7// PY - 2007/7// DO - 10.1111/j.1466-8238.2007.00312.x VL - 16 IS - 4 SP - 415-425 KW - active dispersal KW - allometry KW - fragmentation KW - latitudinal species richness KW - mass KW - metacommunity KW - microbial biogeography KW - neutral theory KW - passive dispersal ER - TY - JOUR TI - A developmental database to support insect phenology models AU - Nietschke, Brett S. AU - Magarey, Roger D. AU - Borchert, Daniel M. AU - Calvin, Dennis D. AU - Jones, Edward T2 - CROP PROTECTION AB - Insect phenology models are widely used for decision support in pest management and more recently in phytosanitary risk assessments. The development of generic and flexible modeling tools means that phenology models can be quickly created using an insect's developmental threshold temperatures and degree-day requirements. To assist scientists and field practioners in the rapid development and deployment of phenology models, an Insect Development Database containing the developmental requirements for over 500 insect species was created. DA - 2007/9// PY - 2007/9// DO - 10.1016/j.cropro.2006.12.006 VL - 26 IS - 9 SP - 1444-1448 SN - 1873-6904 KW - insect development KW - temperature KW - degree-day KW - development thresholds KW - phenology model KW - decision support system ER - TY - JOUR TI - Leaf wetness duration in sunflower (Helianthus annuus): Analysis of observations, measurements and simulations AU - Dalla Marta, Anna AU - Magarey, Roger D. AU - Martinelli, Luca AU - Orlandini, Simone T2 - EUROPEAN JOURNAL OF AGRONOMY AB - The presence of free water on a leaf surface caused by dew is the result of the radiative balance determined by the interactions between the canopy and the atmosphere. Leaf wetness duration (LWD) depends on leaf and canopy characteristics (exposure, position on the plant, etc.). To study the LWD in a horizontal canopy, a set of sensors were placed inside a sunflower (Helianthus annuus) experimental field and the records were compared with visual observations. An agrometeorological station was installed outside the crop to collect the variables necessary to feed a LWD model. The simulations were compared both to sensor measurements and to visual observations. The main aims were the verification of the sensor performances in the detection of dew and the analysis of LWD spatial variability inside the crop canopy. The potential of simulation models as an alternative to field instruments was also investigated. Our results showed that in sunflower canopy, organised in different layers, the LWD increases with height in the canopy but the sensors are not always able to reproduce the observed variability. The use of models can be considered as an operational procedure but attention should be paid to their calibration. DA - 2007/4// PY - 2007/4// DO - 10.1016/j.eja.2006.11.002 VL - 26 IS - 3 SP - 310-316 SN - 1161-0301 KW - agrometeorology KW - model KW - horizontal canopy KW - energy balance KW - dew ER - TY - JOUR TI - NAPPFAST: An internet system for the weather-based mapping of plant pathogens AU - Magarey, R. D. AU - Fowler, G. A. AU - Borchert, D. M. AU - Sutton, T. B. AU - Colunga-Garcia, M. T2 - PLANT DISEASE AB - HomePlant DiseaseVol. 91, No. 4NAPPFAST: An Internet System for the Weather-Based Mapping of Plant Pathogens PreviousNext OPENOpen Access licenseNAPPFAST: An Internet System for the Weather-Based Mapping of Plant PathogensR. D. Magarey, G. A. Fowler, D. M. Borchert, T. B. Sutton, M. Colunga-Garcia, and J. A. SimpsonR. D. MagareyCorresponding author: R. D. Magarey, Center for Plant Health Science and Technology, Plant Epidemiology and Risk Analysis Laboratory, 1730 Varsity Drive, Suite 300, Raleigh, NC 27603; E-mail: E-mail Address: [email protected]Search for more papers by this author, G. A. FowlerSearch for more papers by this author, D. M. BorchertSearch for more papers by this author, T. B. SuttonSearch for more papers by this author, M. Colunga-GarciaSearch for more papers by this author, and J. A. SimpsonSearch for more papers by this authorAffiliationsAuthors and Affiliations R. D. Magarey , Center for Integrated Pest Management, North Carolina State University, Raleigh G. A. Fowler D. M. Borchert , Animal and Plant Health Inspection Service-Plant Protection and Quarantine-Center for Plant Health Science and Technology, Plant Epidemiology and Risk Analysis Laboratory, Raleigh T. B. Sutton , Department of Plant Pathology, North Carolina State University, Raleigh M. Colunga-Garcia , Department of Entomology, Michigan State University, East Lansing J. A. Simpson , Department of Agriculture Food and Forestry, Canberra, Australia Published Online:14 Mar 2007https://doi.org/10.1094/PDIS-91-4-0336AboutSectionsPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmailWechat DetailsFiguresLiterature CitedRelated Vol. 91, No. 4 April 2007SubscribeISSN:0191-2917e-ISSN:1943-7692 Metrics Article History Issue Date: 25 Jan 2008Published: 14 Mar 2007 Pages: 336-345 Information© 2007 The American Phytopathological SocietyPDF downloadCited byImproved methods for estimating effective accumulated temperature for pest forecastingJournal of Agricultural Meteorology, Vol. 79, No. 2modelación y simulación matemáticas: una herramienta para la protección de cultivos24 October 2022 | Revista Mexicana de Ciencias Agrícolas, Vol. 13, No. 6Potential Distribution of Invasive Boxwood Blight Pathogen (Calonectriapseudonaviculata) as Predicted by Process-Based and Correlative Models1 June 2022 | Biology, Vol. 11, No. 6Austropuccinia psidii (myrtle rust)CABI Compendium, Vol. CABI CompendiumProbabilistic risk-based model for the assessment of Phyllosticta citricarpa-infected citrus fruit and illicit plant material as pathways for pathogen introduction and establishmentCrop Protection, Vol. 142Early Intervention Strategies for Invasive Species Management: Connections Between Risk Assessment, Prevention Efforts, Eradication, and Other Rapid Responses2 February 2021PPMaP: Reproducible and Extensible Open-Source Software for Plant Phenological Phase Duration Prediction and Mapping in Sub-Saharan Africa30 October 2020 | Agriculture, Vol. 10, No. 11Performance and Profitability of Rain-Based Thresholds for Timing Fungicide Applications in Soybean Rust ControlGustavo C. 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