ترغب بنشر مسار تعليمي؟ اضغط هنا

Environmental effects on the spread of the Neolithic

45   0   0.0 ( 0 )
 نشر من قبل Kate Davison
 تاريخ النشر 2005
  مجال البحث علم الأحياء
والبحث باللغة English
 تأليف K. Davison




اسأل ChatGPT حول البحث

The causes and implications of the regional variations in the spread of the incipient agriculture in Europe remain poorly understood. We apply population dynamics models to study the dispersal of the Neolithic in Europe from a localized area in the Near East, solving the two-dimensional reaction-diffusion equation on a spherical surface. We focus on the role of major river paths and coastlines in the advance of farming to model the rapid advances of the Linear Pottery (LBK) and the Impressed Ware traditions along the Danube-Rhine corridor and the Mediterranean coastline respectively. We argue that the random walk of individuals, which results in diffusion of the population, can be anisotropic in those areas. The standard reaction-diffusion equation is thus supplemented with advection-like terms confined to the proximity of major rivers and coastlines. The model allows for the spatial variation in both the human mobility (diffusivity) and the carrying capacity of landscapes, reflecting the local altitude and latitude. This approach can easily be generalised to include other environmental factors, such as the bioproductivity of landscapes. Our model successfully accounts for the regional variations in the spread of the Neolithic, consistent with the radiocarbon dated data, and reproduces a time delay in the spread of farming to the Eastern Europe and Scandinavia.


قيم البحث

اقرأ أيضاً

353 - Xinyi Shen 2020
The COVID-19 has caused more than three million infections and over two hundred thousand deaths by April 20201. Limiting socioeconomic activities (SA) is among the most adopted governmental mitigating efforts to combat the transmission of the virus, though the degree varies dramatically among different regimes2. This study aims to quantify the contribution from the SA and weather conditions to the transmission of COVID-19 at global scale. Ruling out the unobservable factors including medical facilities and other control policies (MOC) through region-by-time fixed effects3,4, we show that the limiting SA has a leading contribution to lower the reproductive number by 18.3%, while weather conditions, including ultraviolet, relative humidity, and wind explain a smaller amount of variation. Temperature might have a non-monotonic impact on the transmission. We further show that in developed countries5 and China, the SA effect is more pronounced whereas the weather effect is significantly downplayed possibly because people tend to stay indoors most of the time with a controlled climate. We finally estimate the reduced reproductive number and the population spared from infections due to restricting SA at 40,964, 180,336, 174,494, in China, United States, and Europe respectively. From late January to mid-April, all regions, except for China, Australia, and south Korea show a steep upward trend of spared infections due to restricting SA. US and Europe, in particular, show far steeper upward trends of spared infections in the analyzed timeframe, signaling a greater risk of reopening the economy too soon.
The course of an epidemic exhibits average growth dynamics determined by features of the pathogen and the population, yet also features significant variability reflecting the stochastic nature of disease spread. The interplay of biological, social, s tructural and random factors makes disease forecasting extraordinarily complex. In this work, we reframe a stochastic branching process analysis in terms of probability generating functions and compare it to continuous time epidemic simulations on networks. In doing so, we predict the diversity of emerging epidemic courses on both homogeneous and heterogeneous networks. We show how the challenge of inferring the early course of an epidemic falls on the randomness of disease spread more so than on the heterogeneity of contact patterns. We provide an analysis which helps quantify, in real time, the probability that an epidemic goes supercritical or conversely, dies stochastically. These probabilities are often assumed to be one and zero, respectively, if the basic reproduction number, or R0, is greater than 1, ignoring the heterogeneity and randomness inherent to disease spread. This framework can give more insight into early epidemic spread by weighting standard deterministic models with likelihood to inform pandemic preparedness with probabilistic forecasts.
Demographic change of human populations is one of the central questions for delving into the past of human beings. To identify major population expansions related to male lineages, we sequenced 78 East Asian Y chromosomes at 3.9 Mbp of the non-recomb ining region (NRY), discovered >4,000 new SNPs, and identified many new clades. The relative divergence dates can be estimated much more precisely using molecular clock. We found that all the Paleolithic divergences were binary; however, three strong star-like Neolithic expansions at ~6 kya (thousand years ago) (assuming a constant substitution rate of 1e-9/bp/year) indicates that ~40% of modern Chinese are patrilineal descendants of only three super-grandfathers at that time. This observation suggests that the main patrilineal expansion in China occurred in the Neolithic Era and might be related to the development of agriculture.
We analyze the spatial distributions of two groups of benthic foraminifera (Adelosina spp. + Quinqueloculina spp. and Elphidium spp.), along Sicilian coast, and their correlation with six different heavy metals, responsible for the pollution. Samples were collected inside the Gulf of Palermo, which has a high level of pollution due to heavy metals, and along the coast of Lampedusa island (Sicily Channel, Southern Mediterranean), which is characterized by unpolluted sea waters. Because of the environmental pollution we find: (i) an anticorrelated spatial behaviour between the two groups of benthic foraminifera analyzed; (ii) an anticorrelated (correlated) spatial behaviour between the first (second) group of benthic foraminifera with metal concentrations; (iii) an almost uncorrelated spatial behaviour between low concentrations of metals and the first group of foraminifera in clean sea water sites. We introduce a two-species model based on the generalized Lotka-Volterra equations in the presence of a multiplicative noise, which models the interaction between species and environmental pollution due to the presence in top-soft sediments of heavy metals. The interaction coefficients between the two species are kept constant with values in the coexistence regime. Using proper values for the initial conditions and the model parameters, we find for the two species a theoretical spatial distribution behaviour in a good agreement with the data obtained from the 63 sites analyzed in our study.
191 - Nilmani Mathur , Gargi Shaw 2020
We propose a mathematical model to analyze the time evolution of the total number of infected population with Covid-19 disease at a region in the ongoing pandemic. Using the available data of Covid-19 infected population on various countries we formu late a model which can successfully track the time evolution from early days to the saturation period in a given wave of this infectious disease. It involves a set of effective parameters which can be extracted from the available data. Using those parameters the future trajectories of the disease spread can also be projected. A set of differential equations is also proposed whose solutions are these time evolution trajectories. Using such a formalism we project the future time evolution trajectories of infection spread for a number of countries where the Covid-19 infection is still rapidly rising.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا