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Catalina rated it it was ok Jun 19, Paco added it Jan 15, Omar Essam marked it as to-read Mar 20, Gaby Ge added it Nov 25, Priscila Paladino marked it as to-read Feb 06, Rasha added it Mar 20, Franziskaner marked it as to-read Oct 08, Emine Erzin marked it as to-read Oct 23, Fabio Avilez joyo marked it as to-read Nov 11, Necla Alptekin marked it as to-read Nov 20, Elisa Comanducci marked it as to-read Nov 20, Keshiv Kaushal marked it as to-read Nov 27, K11 marked it as to-read Dec 01, Jacklougheed marked it as to-read Dec 02, Didem marked it as to-read Dec 10, Aislinn marked it as to-read Dec 17, Javier Velazquez martin added it Dec 24, Kesia marked it as to-read Dec 31, Swift added it Jan 08, Yosuf Mohamed added it Jan 09, Jean Vargas marked it as to-read Jan 21, Quentin Vanderper marked it as to-read Jan 23, Carmen Alvarez Barba marked it as to-read Jan 25, Nani Fer marked it as to-read Jan 26, Beyond any ethical or deontological issues, they constitute an important part of our archaeological and historical heritage.
Their dating is often inexact and imprecise, and nevertheless this parameter is very interesting, mostly if it is correlated or associated with other analytical or typological data, e. The present study has been carried out in the context of a multidisciplinary scientific program on a set of Coptic mummies found in the site of Antinoe Egypt , deposited in the Louvre Museum or sent by the State to several French museums.
To minimize the sample size, we have developed a new method for the pretreatment of hair samples before AMS-radiocarbon dating. Thus, we have taken samples from about thirty mummies, distributed in eight museums around France, and also from different textiles or plants near or on the bodies.
The results and conclusions show the importance of dating mummies in a museum context. When the time between two drops has reached 10 seconds for the first time, the weighing process is performed with a laboratory scale.
For the determination of the biomass, precision scales and analytical scales from Mettler company were used with an accuracy of at least 0.
In a series of 84 weightings of four different samples repeating this measurement procedure, an average deviation from the mean value of the measurement results of 0.
Climate change is a well-known factor responsible for insect declines [ 15 , 18 , 21 , 37 ]. To test if weather variation could explain the observed decline, we included mean daily temperature, precipitation and wind speed in our analysis, integrating data from weather stations [ 38 ] located within km to the trap locations.
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We examined temporal trends in each weather variable over the course of the study period to assess changes in climatic conditions, as a plausible explanation for insect decline. Estimates of each weather variable at the trap locations were obtained by interpolation of each variable from the climate stations.
We initially considered mean daily air temperature, precipitation, cloud cover, relative air moisture content, wind speed, and sunshine duration. Additionally, we calculated the number of frost days and the sum of precipitation in the months November- February preceding a sampling season.
We used spatio-temporal geostatistical models [ 39 , 40 ] to predict daily values for each weather variable to each trap location.
Amongst other methods, the geostatistical approach is considered a superior interpolation method in order to derive weather variables to trap locations [ 41 ]. Uncertainty in interpolated variables such as wind speed is usually associated with altitude differences. However, as our trap locations are all situated in lowland areas with little altitude variation, we do not expect a large error in our interpolations at trap locations.
We decomposed the daily values of each weather variable into a long-term average trend between years , a mean seasonal trend, and a yearly seasonal anomaly component S2 Fig , modeled using regression splines [ 42 ] while controlling for altitude of weather stations.
The remaining residual daily values of each station were further modeled using a spatio-temporal covariance structure.
For example, temperature T, on given day t, of a given year k at a given trap location s is modeled as: 1 where fk k is the long-term trend over the years a thin plate regression spline , ft t the mean seasonal trend within years a penalized cyclic cubic regression spline , r k, t the mean residual seasonal component, which measures annual anomaly in mean daily values across selected stations, and a is the linear coefficient for the altitude h effect.
The spatio-temporal covariance structure Cs, t, fitted independently to the residuals of each weather variable model, allowed us to deal with lack of independence between daily weather data within and between stations, as well as to interpolate to trap locations using kriging.
Altitude of trap locations was extracted from a digital elevation models at 90m resolution [ 43 ]. Land use data.Leo Palma rated it it was amazing Nov 14, For each trap location, aerial photographs were manually processed, polygons extracted and categorized, and their surface area calculated with a radius of meter.
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For example, radiocarbon dates permit the exclusion of the assumed relationship of a woman with a child. Enlarge cover.
Priscila Paladino marked it as to-read Feb 06, We initially considered mean daily air temperature, precipitation, cloud cover, relative air moisture content, wind speed, and sunshine duration.