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1.
AIM: To investigate a possible interaction between lolitrem B and ergovaline by comparing the incidence and severity of ryegrass staggers in sheep grazing ryegrass (Lolium perenne) containing lolitrem B or ryegrass containing both lolitrem B and ergovaline.

METHODS: Ninety lambs, aged approximately 6 months, were grazed on plots of perennial ryegrass infected with either AR98 endophyte (containing lolitrem B), standard endophyte (containing lolitrem B and ergovaline) or no endophyte, for up to 42 days from 2 February 2010. Ten lambs were grazed on three replicate plots per cultivar. Herbage samples were collected for alkaloid analysis and lambs were scored for ryegrass staggers (scores from 0–5) weekly during the study. Any animal which was scored ≥4 was removed from the study.

RESULTS: Concentrations of lolitrem B did not differ between AR98 and standard endophyte-infected pastures during the study period (p=0.26), and ergovaline was present only in standard endophyte pastures. Ryegrass staggers was observed in sheep grazing both the AR98 and standard endophyte plots, with median scores increasing in the third week of the study. Prior to the end of the 42-day grazing period, 22 and 17 animals were removed from the standard endophyte and AR98 plots, respectively, because their staggers scores were ≥4. The cumulative probability of lambs having scores ≥4 did not differ between animals grazing the two pasture types (p=0.41).

CONCLUSIONS AND CLINICAL RELEVANCE: There was no evidence for ergovaline increasing the severity of ryegrass staggers induced by lolitrem B. In situations where the severity of ryegrass staggers appears to be greater than that predicted on the basis of concentrations of lolitrem B, the presence of other tremorgenic alkaloids should be investigated.  相似文献   

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Spatial and temporal landscape patterns have long been recognized to influence biological processes, but these processes often operate at scales that are difficult to study by conventional means. Inferences from genetic markers can overcome some of these limitations. We used a landscape genetics approach to test hypotheses concerning landscape processes influencing the demography of Lahontan cutthroat trout in a complex stream network in the Great Basin desert of the western US. Predictions were tested with population- and individual-based analyses of microsatellite DNA variation, reflecting patterns of dispersal, population stability, and local effective population sizes. Complementary genetic inferences suggested samples from migratory corridors housed a mixture of fish from tributaries, as predicted based on assumed migratory life histories in those habitats. Also as predicted, populations presumed to have greater proportions of migratory fish or from physically connected, large, or high quality habitats had higher genetic variability and reduced genetic differentiation from other populations. Populations thought to contain largely non-migratory individuals generally showed the opposite pattern, suggesting behavioral isolation. Estimated effective sizes were small, and we identified significant and severe genetic bottlenecks in several populations that were isolated, recently founded, or that inhabit streams that desiccate frequently. Overall, this work suggested that Lahontan cutthroat trout populations in stream networks are affected by a combination of landscape and metapopulation processes. Results also demonstrated that genetic patterns can reveal unexpected processes, even within a system that is well studied from a conventional ecological perspective.  相似文献   
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Artificial neural networks are powerful predictive tools that have the ability to detect and approximate non‐linear relationships from the data. In an explorative analysis, artificial neural networks were used to predict the geographic distribution of groups of polyphagous plant pests. Using climate variables as predictors, artificial neural network models were compared with binary logistic models for predicting insect distribution. Using bootstrapping, artificial neural networks were shown to predict insect presence and absence significantly better than the binary logistic regression models. Results from the study suggest that artificial neural networks have the potential for application in many areas of plant protection and biosecurity.  相似文献   
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