88 research outputs found

    Locating mesolithic hunter-gatherer camps in the Carpathian Basin

    Get PDF
    The Mesolithic in Eastern Europe was the last time that hunter-gatherer economies thrived there before the spread of agriculture in the second half of the seventh millennium BC. But the period, and the interactions between foragers and the first farmers, are poorly understood in the Carpathian Basin and surrounding areas because few sites are known, and even fewer have been excavated and published. How did site location differ between Mesolithic and Early Neolithic settlers? And where should we look for rare Mesolithic sites? Proximity analysis is seldom used for predictive modeling for hunter-gatherer sites at large scales, but in this paper, we argue that it can serve as an important starting point for prospection for rare and poorly understood sites. This study uses proximity analysis to provide quantitative landscape associations of known Mesolithic and Early Neolithic sites in the Carpathian Basin to show how Mesolithic people chose attributes of the landscape for camps, and how they differed from the farmers who later settled. We use elevation and slope, rivers, wetlands prior to the twentieth century, and the distribution of lithic raw materials foragers and farmers used for toolmaking to identify key proxies for preferred locations. We then build predictive models for the Mesolithic and Early Neolithic in the Pannonian region to highlight parts of the landscape that have relatively higher probabilities of having Mesolithic sites still undiscovered and contrast them with the settlement patterns of the first farmers in the area. We find that large parts of Pannonia conform to landforms preferred by Mesolithic foragers, but these areas have not been subject to investigation

    Physiological consequences of abnormal connectivity in a developmental epilepsy

    Get PDF
    Objective Many forms of epilepsy are associated with aberrant neuronal connections, but the relationship between such pathological connectivity and the underlying physiological predisposition to seizures is unclear. We sought to characterize the cortical excitability profile of a developmental form of epilepsy known to have structural and functional connectivity abnormalities. Methods We employed transcranial magnetic stimulation (TMS) with simultaneous electroencephalographic (EEG) recording in 8 patients with epilepsy from periventricular nodular heterotopia and matched healthy controls. We used connectivity imaging findings to guide TMS targeting and compared the evoked responses to single-pulse stimulation from different cortical regions. Results Heterotopia patients with active epilepsy demonstrated a relatively augmented late cortical response that was greater than that of matched controls. This abnormality was specific to cortical regions with connectivity to subcortical heterotopic gray matter. Topographic mapping of the late response differences showed distributed cortical networks that were not limited to the stimulation site, and source analysis in 1 subject revealed that the generator of abnormal TMS-evoked activity overlapped with the spike and seizure onset zone. Interpretation Our findings indicate that patients with epilepsy from gray matter heterotopia have altered cortical physiology consistent with hyperexcitability, and that this abnormality is specifically linked to the presence of aberrant connectivity. These results support the idea that TMS-EEG could be a useful biomarker in epilepsy in gray matter heterotopia, expand our understanding of circuit mechanisms of epileptogenesis, and have potential implications for therapeutic neuromodulation in similar epileptic conditions associated with deep lesions

    Body Size Evolution in Extant Oryzomyini Rodents: Cope's Rule or Miniaturization?

    Get PDF
    At the macroevolutionary level, one of the first and most important hypotheses that proposes an evolutionary tendency in the evolution of body sizes is “Cope's rule". This rule has considerable empirical support in the fossil record and predicts that the size of species within a lineage increases over evolutionary time. Nevertheless, there is also a large amount of evidence indicating the opposite pattern of miniaturization over evolutionary time. A recent analysis using a single phylogenetic tree approach and a Bayesian based model of evolution found no evidence for Cope's rule in extant mammal species. Here we utilize a likelihood-based phylogenetic method, to test the evolutionary trend in body size, which considers phylogenetic uncertainty, to discern between Cope's rule and miniaturization, using extant Oryzomyini rodents as a study model. We evaluated body size trends using two principal predictions: (a) phylogenetically related species are more similar in their body size, than expected by chance; (b) body size increased (Cope's rule)/decreased (miniaturization) over time. Consequently the distribution of forces and/or constraints that affect the tendency are homogenous and generate this directional process from a small/large sized ancestor. Results showed that body size in the Oryzomyini tribe evolved according to phylogenetic relationships, with a positive trend, from a small sized ancestor. Our results support that the high diversity and specialization currently observed in the Oryzomyini tribe is a consequence of the evolutionary trend of increased body size, following and supporting Cope's rule

    Successful Cognitive Aging in Rats: A Role for mGluR5 Glutamate Receptors, Homer 1 Proteins and Downstream Signaling Pathways

    Get PDF
    Normal aging is associated with impairments in cognition, especially learning and memory. However, major individual differences are known to exist. Using the classical Morris Water Maze (MWM) task, we discriminated a population of 24-months old Long Evans aged rats in two groups - memory-impaired (AI) and memory-unimpaired (AU) in comparison with 6-months old adult animals. AI rats presented deficits in learning, reverse memory and retention. At the molecular level, an increase in metabotropic glutamate receptors 5 (mGluR5) was observed in post-synaptic densities (PSD) in the hippocampus of AU rats after training. Scaffolding Homer 1b/c proteins binding to group 1 mGluR facilitate coupling with its signaling effectors while Homer 1a reduces it. Both Homer 1a and 1b/c levels were up-regulated in the hippocampus PSD of AU animals following MWM task. Using immunohistochemistry we further demonstrated that mGluR5 as well as Homer 1b/c stainings were enhanced in the CA1 hippocampus sub-field of AU animals. In fact mGluR5 and Homer 1 isoforms were more abundant and co-localized in the hippocampal dendrites in AU rats. However, the ratio of Homer 1a/Homer 1b/c bound to mGluR5 in the PSD was four times lower for AU animals compared to AI rats. Consequently, AU animals presented higher PKCγ, ERK, p70S6K, mTOR and CREB activation. Finally the expression of immediate early gene Arc/Arg3.1 was shown to be higher in AU rats in accordance with its role in spatial memory consolidation. On the basis of these results, a model of successful cognitive aging with a critical role for mGluR5, Homer 1 proteins and downstream signalling pathways is proposed here

    Glaciation Effects on the Phylogeographic Structure of Oligoryzomys longicaudatus (Rodentia: Sigmodontinae) in the Southern Andes

    Get PDF
    The long-tailed pygmy rice rat Oligoryzomys longicaudatus (Sigmodontinae), the major reservoir of Hantavirus in Chile and Patagonian Argentina, is widely distributed in the Mediterranean, Temperate and Patagonian Forests of Chile, as well as in adjacent areas in southern Argentina. We used molecular data to evaluate the effects of the last glacial event on the phylogeographic structure of this species. We examined if historical Pleistocene events had affected genetic variation and spatial distribution of this species along its distributional range. We sampled 223 individuals representing 47 localities along the species range, and sequenced the hypervariable domain I of the mtDNA control region. Aligned sequences were analyzed using haplotype network, Bayesian population structure and demographic analyses. Analysis of population structure and the haplotype network inferred three genetic clusters along the distribution of O. longicaudatus that mostly agreed with the three major ecogeographic regions in Chile: Mediterranean, Temperate Forests and Patagonian Forests. Bayesian Skyline Plots showed constant population sizes through time in all three clusters followed by an increase after and during the Last Glacial Maximum (LGM; between 26,000–13,000 years ago). Neutrality tests and the “g” parameter also suggest that populations of O. longicaudatus experienced demographic expansion across the species entire range. Past climate shifts have influenced population structure and lineage variation of O. longicaudatus. This species remained in refugia areas during Pleistocene times in southern Temperate Forests (and adjacent areas in Patagonia). From these refugia, O. longicaudatus experienced demographic expansions into Patagonian Forests and central Mediterranean Chile using glacial retreats

    The RESET project: constructing a European tephra lattice for refined synchronisation of environmental and archaeological events during the last c. 100 ka

    Get PDF
    This paper introduces the aims and scope of the RESET project (. RESponse of humans to abrupt Environmental Transitions), a programme of research funded by the Natural Environment Research Council (UK) between 2008 and 2013; it also provides the context and rationale for papers included in a special volume of Quaternary Science Reviews that report some of the project's findings. RESET examined the chronological and correlation methods employed to establish causal links between the timing of abrupt environmental transitions (AETs) on the one hand, and of human dispersal and development on the other, with a focus on the Middle and Upper Palaeolithic periods. The period of interest is the Last Glacial cycle and the early Holocene (c. 100-8 ka), during which time a number of pronounced AETs occurred. A long-running topic of debate is the degree to which human history in Europe and the Mediterranean region during the Palaeolithic was shaped by these AETs, but this has proved difficult to assess because of poor dating control. In an attempt to move the science forward, RESET examined the potential that tephra isochrons, and in particular non-visible ash layers (cryptotephras), might offer for synchronising palaeo-records with a greater degree of finesse. New tephrostratigraphical data generated by the project augment previously-established tephra frameworks for the region, and underpin a more evolved tephra 'lattice' that links palaeo-records between Greenland, the European mainland, sub-marine sequences in the Mediterranean and North Africa. The paper also outlines the significance of other contributions to this special volume: collectively, these illustrate how the lattice was constructed, how it links with cognate tephra research in Europe and elsewhere, and how the evidence of tephra isochrons is beginning to challenge long-held views about the impacts of environmental change on humans during the Palaeolithic. © 2015 Elsevier Ltd.RESET was funded through Consortium Grants awarded by the Natural Environment Research Council, UK, to a collaborating team drawn from four institutions: Royal Holloway University of London (grant reference NE/E015905/1), the Natural History Museum, London (NE/E015913/1), Oxford University (NE/E015670/1) and the University of Southampton, including the National Oceanography Centre (NE/01531X/1). The authors also wish to record their deep gratitude to four members of the scientific community who formed a consultative advisory panel during the lifetime of the RESET project: Professor Barbara Wohlfarth (Stockholm University), Professor Jørgen Peder Steffensen (Niels Bohr Institute, Copenhagen), Dr. Martin Street (Romisch-Germanisches Zentralmuseum, Neuwied) and Professor Clive Oppenheimer (Cambridge University). They provided excellent advice at key stages of the work, which we greatly valued. We also thank Jenny Kynaston (Geography Department, Royal Holloway) for construction of several of the figures in this paper, and Debbie Barrett (Elsevier) and Colin Murray Wallace (Editor-in-Chief, QSR) for their considerable assistance in the production of this special volume.Peer Reviewe

    Stable population structure in Europe since the Iron Age, despite high mobility

    Get PDF
    Ancient DNA research in the past decade has revealed that European population structure changed dramatically in the prehistoric period (14,000–3000 years before present, YBP), reflecting the widespread introduction of Neolithic farmer and Bronze Age Steppe ancestries. However, little is known about how population structure changed from the historical period onward (3000 YBP - present). To address this, we collected whole genomes from 204 individuals from Europe and the Mediterranean, many of which are the first historical period genomes from their region (e.g. Armenia and France). We found that most regions show remarkable inter-individual heterogeneity. At least 7% of historical individuals carry ancestry uncommon in the region where they were sampled, some indicating cross-Mediterranean contacts. Despite this high level of mobility, overall population structure across western Eurasia is relatively stable through the historical period up to the present, mirroring geography. We show that, under standard population genetics models with local panmixia, the observed level of dispersal would lead to a collapse of population structure. Persistent population structure thus suggests a lower effective migration rate than indicated by the observed dispersal. We hypothesize that this phenomenon can be explained by extensive transient dispersal arising from drastically improved transportation networks and the Roman Empire’s mobilization of people for trade, labor, and military. This work highlights the utility of ancient DNA in elucidating finer scale human population dynamics in recent history

    Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae

    Full text link
    A mechanistic, dynamic model was developed to predict infection of loquat fruit by conidia of Fusicladium eriobotryae, the causal agent of loquat scab. The model simulates scab infection periods and their severity through the sub-processes of spore dispersal, infection, and latency (i.e., the state variables); change from one state to the following one depends on environmental conditions and on processes described by mathematical equations. Equations were developed using published data on F. eriobotryae mycelium growth, conidial germination, infection, and conidial dispersion pattern. The model was then validated by comparing model output with three independent data sets. The model accurately predicts the occurrence and severity of infection periods as well as the progress of loquat scab incidence on fruit (with concordance correlation coefficients .0.95). Model output agreed with expert assessment of the disease severity in seven loquatgrowing seasons. Use of the model for scheduling fungicide applications in loquat orchards may help optimise scab management and reduce fungicide applications.This work was funded by Cooperativa Agricola de Callosa d'En Sarria (Alicante, Spain). Three months' stay of E. Gonzalez-Dominguez at the Universita Cattolica del Sacro Cuore (Piacenza, Italy) was supported by the Programa de Apoyo a la Investigacion y Desarrollo (PAID-00-12) de la Universidad Politecnica de Valencia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.González Domínguez, E.; Armengol Fortí, J.; Rossi, V. (2014). Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae. PLoS ONE. 9(9):1-12. https://doi.org/10.1371/journal.pone.0107547S11299Sánchez-Torres, P., Hinarejos, R., & Tuset, J. J. (2009). Characterization and Pathogenicity ofFusicladium eriobotryae, the Fungal Pathogen Responsible for Loquat Scab. Plant Disease, 93(11), 1151-1157. doi:10.1094/pdis-93-11-1151Gladieux, P., Caffier, V., Devaux, M., & Le Cam, B. (2010). Host-specific differentiation among populations of Venturia inaequalis causing scab on apple, pyracantha and loquat. Fungal Genetics and Biology, 47(6), 511-521. doi:10.1016/j.fgb.2009.12.007González-Domínguez, E., Rossi, V., Armengol, J., & García-Jiménez, J. (2013). Effect of Environmental Factors on Mycelial Growth and Conidial Germination ofFusicladium eriobotryae, and the Infection of Loquat Leaves. Plant Disease, 97(10), 1331-1338. doi:10.1094/pdis-02-13-0131-reGonzález-Domínguez, E., Rossi, V., Michereff, S. J., García-Jiménez, J., & Armengol, J. (2014). Dispersal of conidia of Fusicladium eriobotryae and spatial patterns of scab in loquat orchards in Spain. European Journal of Plant Pathology, 139(4), 849-861. doi:10.1007/s10658-014-0439-0Becker, C. M. (1994). Discontinuous Wetting and Survival of Conidia ofVenturia inaequalison Apple Leaves. Phytopathology, 84(4), 372. doi:10.1094/phyto-84-372Hartman, J. R., Parisi, L., & Bautrais, P. (1999). Effect of Leaf Wetness Duration, Temperature, and Conidial Inoculum Dose on Apple Scab Infections. Plant Disease, 83(6), 531-534. doi:10.1094/pdis.1999.83.6.531Holb, I. J., Heijne, B., Withagen, J. C. M., & Jeger, M. J. (2004). Dispersal of Venturia inaequalis Ascospores and Disease Gradients from a Defined Inoculum Source. Journal of Phytopathology, 152(11-12), 639-646. doi:10.1111/j.1439-0434.2004.00910.xRossi, V., Giosue, S., & Bugiani, R. (2003). Influence of Air Temperature on the Release of Ascospores of Venturia inaequalis. Journal of Phytopathology, 151(1), 50-58. doi:10.1046/j.1439-0434.2003.00680.xStensvand, A., Gadoury, D. M., Amundsen, T., Semb, L., & Seem, R. C. (1997). Ascospore Release and Infection of Apple Leaves by Conidia and Ascospores ofVenturia inaequalisat Low Temperatures. Phytopathology, 87(10), 1046-1053. doi:10.1094/phyto.1997.87.10.1046Machardy WE (1996) Apple scab. Biology, epidemiology and management. St. Paul: APS Press. 545.James, J. R. (1982). Environmental Factors Influencing Pseudothecial Development and Ascospore Maturation ofVenturia inaequalis. Phytopathology, 72(8), 1073. doi:10.1094/phyto-72-1073Li, B., Zhao, H., Li, B., & Xu, X.-M. (2003). Effects of temperature, relative humidity and duration of wetness period on germination and infection by conidia of the pear scab pathogen (Venturia nashicola). Plant Pathology, 52(5), 546-552. doi:10.1046/j.1365-3059.2003.00887.xLi, B.-H., Xu, X.-M., Li, J.-T., & Li, B.-D. (2005). Effects of temperature and continuous and interrupted wetness on the infection of pear leaves by conidia of Venturia nashicola. Plant Pathology, 54(3), 357-363. doi:10.1111/j.1365-3059.2005.01207.xUMEMOTO, S. (1990). Dispersion of ascospores and conidia of causal fungus of Japanese pear scab, Venturia nashicola. Japanese Journal of Phytopathology, 56(4), 468-473. doi:10.3186/jjphytopath.56.468Rossi, V., Salinari, F., Pattori, E., Giosuè,, S., & Bugiani, R. (2009). Predicting the Dynamics of Ascospore Maturation ofVenturia pirinaBased on Environmental Factors. Phytopathology, 99(4), 453-461. doi:10.1094/phyto-99-4-0453Spotts, R. A. (1991). Effect of Temperature and Wetness on Infection of Pear byVenturia pirinaand the Relationship Between Preharvest Inoculation and Storage Scab. Plant Disease, 75(12), 1204. doi:10.1094/pd-75-1204Spotts, R. A. (1994). Factors Affecting Maturation and Release of Ascospores ofVenturia pirinain Oregon. Phytopathology, 84(3), 260. doi:10.1094/phyto-84-260Villalta, O., Washington, W. S., Rimmington, G. M., & Taylor, P. A. (2000). Australasian Plant Pathology, 29(4), 255. doi:10.1071/ap00048Villalta, O. N., Washington, W. S., Rimmington, G. M., & Taylor, P. A. (2000). Effects of temperature and leaf wetness duration on infection of pear leaves by Venturia pirina. Australian Journal of Agricultural Research, 51(1), 97. doi:10.1071/ar99068Lan, Z., & Scherm, H. (2003). Moisture Sources in Relation to Conidial Dissemination and Infection byCladosporium carpophilumWithin Peach Canopies. Phytopathology, 93(12), 1581-1586. doi:10.1094/phyto.2003.93.12.1581Lawrence, Jr., E. G. (1982). Environmental Effects on the Development and Dissemination ofCladosporium carpophilumon Peach. Phytopathology, 72(7), 773. doi:10.1094/phyto-72-773Gottwald, T. R. (1985). Influence of Temperature, Leaf Wetness Period, Leaf Age, and Spore Concentration on Infection of Pecan Leaves by Conidia ofCladosporium caryigenum. Phytopathology, 75(2), 190. doi:10.1094/phyto-75-190Latham, A. J. (1982). Effects of Some Weather Factors andFusicladium effusumConidium Dispersal on Pecan Scab Occurrence. Phytopathology, 72(10), 1339. doi:10.1094/phyto-72-1339MARZO, L., FRISULLO, S., LOPS, F., & ROSSI, V. (1993). Possible dissemination of Spilocaea oleagina conidia by insects (Ectopsocus briggsi). EPPO Bulletin, 23(3), 389-391. doi:10.1111/j.1365-2338.1993.tb01341.xLOPS, F., FRISULLO, S., & ROSSI, V. (1993). Studies on the spread of the olive scab pathogen, Spilocaea oleagina. EPPO Bulletin, 23(3), 385-387. doi:10.1111/j.1365-2338.1993.tb01340.xObanor, F. O., Walter, M., Jones, E. E., & Jaspers, M. V. (2007). Effect of temperature, relative humidity, leaf wetness and leaf age on Spilocaea oleagina conidium germination on olive leaves. European Journal of Plant Pathology, 120(3), 211-222. doi:10.1007/s10658-007-9209-6Obanor, F. O., Walter, M., Jones, E. E., & Jaspers, M. V. (2010). Effects of temperature, inoculum concentration, leaf age, and continuous and interrupted wetness on infection of olive plants by Spilocaea oleagina. Plant Pathology, 60(2), 190-199. doi:10.1111/j.1365-3059.2010.02370.xViruega, J. R., Moral, J., Roca, L. F., Navarro, N., & Trapero, A. (2013). Spilocaea oleaginain Olive Groves of Southern Spain: Survival, Inoculum Production, and Dispersal. Plant Disease, 97(12), 1549-1556. doi:10.1094/pdis-12-12-1206-reViruega, J. R., Roca, L. F., Moral, J., & Trapero, A. (2011). Factors Affecting Infection and Disease Development on Olive Leaves Inoculated withFusicladium oleagineum. Plant Disease, 95(9), 1139-1146. doi:10.1094/pdis-02-11-0126Eikemo, H., Gadoury, D. M., Spotts, R. A., Villalta, O., Creemers, P., Seem, R. C., & Stensvand, A. (2011). Evaluation of Six Models to Estimate Ascospore Maturation in Venturia pyrina. Plant Disease, 95(3), 279-284. doi:10.1094/pdis-02-10-0125Li, B.-H., Yang, J.-R., Dong, X.-L., Li, B.-D., & Xu, X.-M. (2007). A dynamic model forecasting infection of pear leaves by conidia of Venturia nashicola and its evaluation in unsprayed orchards. European Journal of Plant Pathology, 118(3), 227-238. doi:10.1007/s10658-007-9138-4Rossi, V., Giosuè, S., & Bugiani, R. (2007). A-scab (Apple-scab), a simulation model for estimating risk of Venturia inaequalis primary infections. EPPO Bulletin, 37(2), 300-308. doi:10.1111/j.1365-2338.2007.01125.xXU, X.-M., BUTT, D. J., & SANTEN, G. (1995). A dynamic model simulating infection of apple leaves by Venturia inaequalis. Plant Pathology, 44(5), 865-876. doi:10.1111/j.1365-3059.1995.tb02746.xRoubal, C., Regis, S., & Nicot, P. C. (2012). Field models for the prediction of leaf infection and latent period ofFusicladium oleagineumon olive based on rain, temperature and relative humidity. Plant Pathology, 62(3), 657-666. doi:10.1111/j.1365-3059.2012.02666.xPayne, A. F., & Smith, D. L. (2012). Development and Evaluation of Two Pecan Scab Prediction Models. Plant Disease, 96(9), 1358-1364. doi:10.1094/pdis-03-11-0202-reTrapman M, Jansonius PJ (2008) Disease management in organic apple orchards is more than applying the right product at the correct time. Ecofruit-13th International Conference on Cultivation Technique and Phytopathological Problems in Organic Fruit-Growing: Proceedings to the Conference from 18th February to 20th February 2008 at Weinsberg/Germany. 16–22.HOLB, I. J., JONG, P. F., & HEIJNE, B. (2003). Efficacy and phytotoxicity of lime sulphur in organic apple production. Annals of Applied Biology, 142(2), 225-233. doi:10.1111/j.1744-7348.2003.tb00245.xGent, D. H., Mahaffee, W. F., McRoberts, N., & Pfender, W. F. (2013). The Use and Role of Predictive Systems in Disease Management. Annual Review of Phytopathology, 51(1), 267-289. doi:10.1146/annurev-phyto-082712-102356Alavanja, M. C. R., Hoppin, J. A., & Kamel, F. (2004). Health Effects of Chronic Pesticide Exposure: Cancer and Neurotoxicity. Annual Review of Public Health, 25(1), 155-197. doi:10.1146/annurev.publhealth.25.101802.123020Brent KJ, Hollomon DW (2007) Fungicide resistance in crop pathogens: How can it be managed? FRAC Monog 2. Fungicide Resistance Action Committee.Shtienberg, D. (2013). Will Decision-Support Systems Be Widely Used for the Management of Plant Diseases? Annual Review of Phytopathology, 51(1), 1-16. doi:10.1146/annurev-phyto-082712-102244Leffelaar P (1993) On Systems Analysis and Simulation of Ecological Processes. Kluwer. London.Rossi V, Giosuè S, Caffi T (2010) Modelling plant diseases for decision making in crop protection. In: Oerke E-C, Gerhards R, Menz G, Sikora RA, editors. Precision Crop Protection-the Challenge and Use of Heterogeneity.Hui, C. (2006). Carrying capacity, population equilibrium, and environment’s maximal load. Ecological Modelling, 192(1-2), 317-320. doi:10.1016/j.ecolmodel.2005.07.001Townsend C, Begon M, Harper J (2008) Essentials of ecology. John Wiley and Sons. New York. 510.Zadoks J, Schein R (1979) Epidemiology and plant disease management. Oxford University Press, New York. 427.Bennett, J. C., Diggle, A., Evans, F., & Renton, M. (2013). Assessing eradication strategies for rain-splashed and wind-dispersed crop diseases. Pest Management Science, 69(8), 955-963. doi:10.1002/ps.3459Ghanbarnia, K., Dilantha Fernando, W. G., & Crow, G. (2009). Developing Rainfall- and Temperature-Based Models to Describe Infection of Canola Under Field Conditions Caused by Pycnidiospores of Leptosphaeria maculans. Phytopathology, 99(7), 879-886. doi:10.1094/phyto-99-7-0879Gilligan, C. A., & van den Bosch, F. (2008). Epidemiological Models for Invasion and Persistence of Pathogens. Annual Review of Phytopathology, 46(1), 385-418. doi:10.1146/annurev.phyto.45.062806.094357Buck, A. L. (1981). New Equations for Computing Vapor Pressure and Enhancement Factor. Journal of Applied Meteorology, 20(12), 1527-1532. doi:10.1175/1520-0450(1981)0202.0.co;2Madden L V, Hughes G, van den Bosch F (2007) The study of plant disease epidemics. APS press. St. Paul. 421.González-Domínguez E, Rodríguez-Reina J, García-Jiménez J, Armengol J (2014) Evaluation of fungicides to control loquat scab caused by Fusicladium eriobotryae. Plant Heal Prog Accepted.De Wolf, E. D., & Isard, S. A. (2007). Disease Cycle Approach to Plant Disease Prediction. Annual Review of Phytopathology, 45(1), 203-220. doi:10.1146/annurev.phyto.44.070505.143329Krause, R. A., & Massie, L. B. (1975). Predictive Systems: Modern Approaches to Disease Control. Annual Review of Phytopathology, 13(1), 31-47. doi:10.1146/annurev.py.13.090175.000335Fourie, P., Schutte, T., Serfontein, S., & Swart, F. (2013). Modeling the Effect of Temperature and Wetness on Guignardia Pseudothecium Maturation and Ascospore Release in Citrus Orchards. Phytopathology, 103(3), 281-292. doi:10.1094/phyto-07-11-0194Gadoury, D. M. (1982). A Model to Estimate the Maturity of Ascospores ofVenturia inaequalis. Phytopathology, 72(7), 901. doi:10.1094/phyto-72-901Holtslag, Q. A., Remphrey, W. R., Fernando, W. G. D., St-Pierre, R. G., & Ash, G. H. B. (2004). The development of a dynamic diseaseforecasting model to controlEntomosporium mespilionAmelanchier alnifolia. Canadian Journal of Plant Pathology, 26(3), 304-313. doi:10.1080/07060660409507148Legler SEE, Caffi T, Rossi V (2013) A Model for the development of Erysiphe necator chasmothecia in vineyards. Plant Pathol. DOI:10.1111/ppa.12145.Luo, Y., & Michailides, T. J. (2001). Risk Analysis for Latent Infection of Prune by Monilinia fructicola in California. Phytopathology, 91(12), 1197-1208. doi:10.1094/phyto.2001.91.12.1197Gadoury, D. M. (1986). Forecasting Ascospore Dose of Venturia inaequalis in Commercial Apple Orchards. Phytopathology, 76(1), 112. doi:10.1094/phyto-76-112Gent, D. H., De Wolf, E., & Pethybridge, S. J. (2011). Perceptions of Risk, Risk Aversion, and Barriers to Adoption of Decision Support Systems and Integrated Pest Management: An Introduction. Phytopathology, 101(6), 640-643. doi:10.1094/phyto-04-10-0124Schut, M., Rodenburg, J., Klerkx, L., van Ast, A., & Bastiaans, L. (2014). Systems approaches to innovation in crop protection. A systematic literature review. Crop Protection, 56, 98-108. doi:10.1016/j.cropro.2013.11.017Mills W, Laplante A (1954) Diseases and insect in the orchard. Cornell Ext Bull 711.GVA (2013) Octubre-Noviembre 2013. Butlletí d’avisos 13.MacHardy, W. E. (1989). A Revision of Mills’s Criteria for Predicting Apple Scab Infection Periods. Phytopathology, 79(3), 304. doi:10.1094/phyto-79-30
    corecore