Development of a field method to assess root parasites of woody plants - ecological analyses of the grapevine-phylloxera interaction

The development and the growth of plants is strongly affected by the interactions between roots, rootrnassociated organisms and rhizosphere communities. Methods to assess such interactions are hardly torndevelop particularly in perennial and woody plants, due to their complex root system structure and theirrntemporal change in physiology patterns. In this respect, grape root systems are not investigated veryrnwell. The aim of the present work was the development of a method to assess and predict interactionsrnat the root system of rootstocks (Vitis berlandieri x Vitis riparia) in field. To achieve this aim, grapernphylloxera (Daktulosphaira vitifoliae Fitch, Hemiptera, Aphidoidea) was used as a graperoot parasitizingrnmodel.rnTo develop the methodical approach, a longt-term trial (2006-2009) was arranged on a commercial usedrnvineyard in Geisenheim/Rheingau. All 2 to 8 weeks the top most 20 cm of soil under the foliage wallrnwere investigated and root material was extracted (n=8-10). To include temporal, spatial and cultivarrnspecific root system dynamics, the extracted root material was analyzed digitally on the morphologicalrnproperties. The grape phylloxera population was quantified and characterized visually on base of theirrnlarvalstages (oviparous, non oviparous and winged preliminary stages). Infection patches (nodosities)rnwere characterized visually as well, partly supported by digital root color analyses. Due to the knownrneffects of fungal endophytes on the vitality of grape phylloxera infested grapevines, fungal endophytesrnwere isolated from nodosity and root tissue and characterized (morphotypes) afterwards. Further abioticrnand biotic soil conditions of the vineyards were assessed. The temporal, spatial and cultivar specificrnsensitivity of single parameters were analyzed by omnibus tests (ANOVAs) and adjacent post-hoc tests.rnThe relations between different parameters were analyzed by multiple regression models.rnQuantitative parameters to assess the degeneration of nodosity, the development nodosity attachedrnroots and to differentiate between nodosities and other root swellings in field were developed. Significantrndifferences were shown between root dynamic including parameters and root dynamic ignoringrnparameters. Regarding the description of grape phylloxera population and root system dynamic, thernmethod showed a high temporal, spatial and cultivar specific sensitivity. Further, specific differencesrncould be shown in the frequency of endophyte morphotypes between root and nodosity tissue as wellrnas between cultivars. Degeneration of nodosities as well as nodosity occupation rates could be relatedrnto the calculated abundances of grape phylloxera population. Further ecological questions consideringrngrape root development (e.g. relation between moisture and root development) and grape phylloxerarnpopulation development (e.g. relation between temperature and population structure) could be answeredrnfor field conditions.rnGenerally, the presented work provides an approach to evaluate vitality of grape root systems. Thisrnapproach can be useful, considering the development of control strategies against soilborne pests inrnviticulture (e.g. grape phylloxera, Sorospheara viticola, Roesleria subterranea (Weinm.) Redhaed) as well as considering the evaluation of integrated management systems in viticulture.

Enhanced Atlantic subpolar gyre variability through baroclinic threshold in a coarse resolution model

Direct observations, satellite measurements and paleo records reveal strong variability in the Atlantic subpolar gyre on various time scales. Here we show that variations of comparable amplitude can only be simulated in a coupled climate model in the proximity of a dynamical threshold. The threshold and the associated dynamic response is due to a positive feedback involving increased salt transport in the subpolar gyre and enhanced deep convection in its centre. A series of sensitivity experiments is performed with a coarse resolution ocean general circulation model coupled to a statistical-dynamical atmosphere model which in itself does not produce atmospheric variability. To simulate the impact of atmospheric variability, the model system is perturbed with freshwater forcing of varying, but small amplitude and multi-decadal to centennial periodicities and observational variations in wind stress. While both freshwater and wind-stress-forcing have a small direct effect on the strength of the subpolar gyre, the magnitude of the gyre's response is strongly increased in the vicinity of the threshold. Our results indicate that baroclinic self-amplification in the North Atlantic ocean can play an important role in presently observed SPG variability and thereby North Atlantic climate variability on multi-decadal scales.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.