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Influence of some climatic factors on Ixodes ricinus ticks studied along altitudinal gradients in two geographic regions in Switzerland.

Lise Gern — 2008-04-10T08:01:37-00:00

International journal of medical microbiology : IJMM (14 March 2008)

In the context of climate change, the seasonal activity of questing Ixodes ricinus and their infection with Borrelia burgdorferi sensu lato (s.l.) were examined in relation to some climatic data along altitudinal gradients in Switzerland. The first study took place in an Alpine area (Valais) from 750 to 1020m above sea level. The other gradient was located on a mountain in the foothills of the Jura chain (Neuchâtel) from 620 to 1070m above sea level. In the Alpine area, the highest questing tick density was observed at the highest altitude. At the lowest altitudes (750 and 880m), very high saturation deficits, >10mmHg, were present during most of the tick activity season and they seem to have impaired the thriving of tick populations. The second study in Neuchâtel (2003-2005) was a follow-up of a previous study (1999-2001) in which it was observed that tick density decreased with increasing altitude. During the follow-up study, substantial differences in questing tick density and phenology of ticks were observed: At high elevations, questing tick densities were 2.25 and 3.5 times higher for nymphs and adults, respectively, than during 1999-2001. As observed during 1999-2001, questing tick density decreased with increasing altitude in this site in 2003-2005. Tick questing density remained higher at the lowest altitude. Increased temperatures during summer months, more favorable for ticks, reaching values similar to those registered in the first study at the lowest elevations are probably responsible for the higher tick questing density at high altitudes. B. burgdorferi s.l. infection prevalence in ticks decreased with increasing altitudes along both altitudinal gradients. Long-lasting high saturation deficit values may limit the development of tick populations as too high a moisture stress has a negative effect on tick survival. This factor may have a permanent impact, as it is probably the case at the lowest altitudes in the Alpine area or a more transient effect like in the Neuchâtel gradient.

Ixodes ricinus density and infection prevalence of Borrelia burgdorferi sensu lato along a North-facing altitudinal gradient in the Rhône Valley (Switzerland).

C Burri — 2008-01-11T09:28:45-00:00

Vector Borne Zoonotic Dis, Vol. 7, No. 1. (2007), pp. 50-58.

Questing Ixodes ricinus ticks were sampled monthly along a north-facing altitudinal gradient in the canton of Valais, Switzerland, from March 2004 to February 2005. Tick density and infection with Borrelia burgdorferi sensu lato were monitored. Ticks were collected by flagging vegetation at three different altitudes (750 m, 880 m, and 1020 m above sea level). Ticks were examined for Borrelia by polymerase chain reaction (PCR) followed by reverse line blot. At the three altitudes, questing tick activity was not observed under 10 degrees C and was reduced when saturation deficit was higher than 5 mm Hg, most questing tick activity was occurred between 2 mm Hg and 7 mm Hg. Tick density and peak tick density were highest at 1020 m. High saturation deficits at the lowest altitudes appear to impair the tick population. The prevalence of B. burgdorferi infection in nymphs and adults decreased with altitude. The prevalence of infection was higher in adult ticks (47%) than in nymphs (29%). Four B. burgdorferi sensu lato genospecies were detected: B. afzelii (40%), B. garinii (22%), B. valaisiana (12%) and B. burgdorferi sensu stricto (6%). Mixed infections were detected in 13% of infected ticks.

Effect of error in the DEM on environmental variables for predictive vegetation modelling

KP Van Niel — 2006-01-28T15:27:12-00:00

Journal of Vegetation Science, Vol. 15, No. 6. (2006), pp. 747-756.

Question: Predictive vegetation modelling relies on the use of environmental variables, which are usually derived from a base data set with some level of error, and this error is propagated to any subsequently derived environmental variables. The question for this study is: What is the level of error and uncertainty in environmental variables based on the error propagated from a Digital Elevation Model (DEM) and how does it vary for both direct and indirect variables? Location: Kioloa region, New South Wales, Australia. Methods: The level of error in a DEM is assessed and used to develop an error model for analysing error propagation to derived environmental variables. We tested both indirect (elevation, slope, aspect, topographic position) and direct (average air temperature, net solar radiation, and topographic wetness index) variables for their robustness to propagated error from the DEM. Results: It is shown that the direct environmental variable net solar radiation is less affected by error in the DEM than the indirect variables aspect and slope, but that regional conditions such as slope steepness and cloudiness can influence this outcome. However, the indirect environmental variable topographic position was less affected by error in the DEM than topographic wetness index. Interestingly, the results disagreed with the current assumption that indirect variables are necessarily less sensitive to propagated error because they are less derived. Conclusions: The results indicate that variables exhibit both systematic bias and instability under uncertainty. There is a clear need to consider the sensitivity of variables to error in their base data sets in addition to the question of whether to use direct or indirect variables.

Digital Terrain Model Computation from Contour Lines: How to Derive Quality Information from Artifact Analysis

Olivier Bonin — 2005-10-25T07:41:29-00:00

GeoInformatica, Vol. 9 (2005), pp. 253-268.

Digital Terrain Models constructed from contour lines often contain artifacts originating from their construction from irregularly spaced height measurements. We evaluate and illustrate these artifacts and their impact on terrain parameters. We provide algorithms to correct them as much as possible, and propose a methodology to predict areas where uncertainty still remains because of these artifacts. The analysis of these artifacts enables us to derive qualitative parameters such as “likely over-estimated”, “likely underestimated”, and “with no significant bias” on the DTM. Our method provides a qualitative description of local uncertainty, which is often more relevant for geographical applications than global quality parameters.