Application of the Logistic Growth Model: Estimation of Livestock Population in Mongolia

We propose the logistic growth model to predict number of livestock in Mongolia. For this purpose, we estimate its parameters based on data retrieved from National Statistical Office of Mongolia for period of 2004-2013. Once parameter estimates are made, we apply those parameters into the model

USING SATELLITE DATA FOR ENVIRONMENTAL IMPACT ANALYSIS IN ECONOMIC GROWTH: THE CASE OF MONGOLIA

The Mongolian economy is based on the primary and secondary economic sectors of agriculture and industry. In addition, minerals and mining become a key sector of its economy. The main mining resources are gold, copper, coal, fluorspar and steel. However, the environment and green economy is one of the big problems among most of the countries and especially for countries like Mongolia where the mining is major part of economy; it is a number one problem. The research of the work tested how environmental elements effect to current Mongolian economic growth, which is growing economy because of mining sector. The study of economic growth but the starting point for any study of economic growth is the neoclassical growth model emphasizing the role of capital accumulation. The growth is analysed either in terms of models with exogenous saving rates (the Solow-Swan model), or models where consumption and hence savings are determined by optimizing individuals. These are the so-called optimal growth or Ramsey-Cass-Koopmans. The study extends the Solow model and the Ramsey-Cass-Koopmans model, including environmental elements which are satellite data determine to degraded land and vegetation value from 1995 to 2013. In contrast, we can see the degraded land area increases from 1995 (4856 m2) to 2013 (10478 m2) and vegetation value decrease at same time. A description of the methodology of the study conducted follows together with the data collected and econometric estimations and calibration with environmental elements

Co-Produced Research Supports Pastoralists to Pursue Transformative Social and Ecological Change in Rangelands

Over the last two decades, pastoralists and researchers have formed powerful alliances to transform how we think about and do research-with-action in rangelands. These alliances promote faster learning about problems and their potential solutions by bringing together diverse partners and their different ways of understanding important issues. They also ensure research is fully relevant to real problems, so it supports pastoralists to act on both old and new issues that they face. While these approaches can be contentious when perspectives and experiences do not align, team members are finding them transformative, if they commit to working together over the long term. Based on a long history of participatory research approaches in the social sciences, these alliances are now inter- and trans-disciplinary, spread throughout the sciences. This paper uses six case studies to explore the experience of teams who have used this research-with-action approach in the rangelands of Kenya, Tanzania, Mongolia, Tajikistan, Afghanistan, Spain and the US. These teams developed and implemented this approach not in halls of academia, but in equal pastoralist-researcher partnerships by creating full co-learning and democratized processes together. These teams then purposely built the capacity of all stakeholders to act together to promote desired change. The case studies integrate diverse knowledges at multiple scales into collective ‘learning and doing’ teams composed of pastoral peoples, policy makers, scientists, business people, and others. This process ensures a broad range of understandings and interpretations form the foundation of the actions and adaptations taken by actors across landscapes and scales. The approach contributes to the resilience of place-based social-ecological systems in rangelands by avoiding top-down, one-size-fits-all approaches. Uniting these ideas and practices has allowed research-with-action to become truly transformative, by accelerating the capacity of all stakeholders to learn and act more effectively

Not a melting pot: Plant species aggregate in their non-native range

Aim: Plant species continue to be moved outside of their native range by human activities. Here, we aim to determine whether, once introduced, plants assimilate into native communities or whether they aggregate, thus forming mosaics of native- and alien-rich communities. Alien species might aggregate in their non-native range owing to shared habitat preferences, such as their tendency to establish in high-biomass, species-poor areas. Location: Twenty-two herbaceous grasslands in 14 countries, mainly in the temperate zone. Time period: 2012–2016. Major taxa studied: Plants. Methods: We used a globally coordinated survey. Within this survey, we found 46 plant species, predominantly from Eurasia, for which we had co-occurrence data in their native and non-native ranges. We tested for differences in co-occurrence patterns of 46 species between their native (home) and non-native (away) range. We also tested whether species had similar habitat preferences, by testing for differences in total biomass and species richness of the patches that species occupy in their native and non-native ranges. Results: We found the same species to show different patterns of association depending on whether they were in their native or non-native range. Alien species were negatively associated with native species; instead, they aggregated with other alien species in species-poor, high-biomass communities in their non-native range compared with their native range. Main conclusions: The strong differences between the native (home) and non-native (away) range in species co-occurrence patterns are evidence that the way in which species associate with resident communities in their non-native range is not species dependent, but is instead a property of being away from their native range. These results thus highlight that species might undergo important ecological changes when introduced away from their native range. Overall, we show origin-dependent associations that result in novel communities, in which alien-rich patches exist within a mosaic of native-dominated communities

Yersinia pestis Lineages in Mongolia

BACKGROUND: Whole genome sequencing allowed the development of a number of high resolution sequence based typing tools for Yersinia (Y.) pestis. The application of these methods on isolates from most known foci worldwide and in particular from China and the Former Soviet Union has dramatically improved our understanding of the population structure of this species. In the current view, Y. pestis including the non or moderate human pathogen Y. pestis subspecies microtus emerged from Yersinia pseudotuberculosis about 2,600 to 28,600 years ago in central Asia. The majority of central Asia natural foci have been investigated. However these investigations included only few strains from Mongolia. METHODOLOGY/PRINCIPAL FINDINGS: Clustered Regularly Interspaced Short Prokaryotic Repeats (CRISPR) analysis and Multiple-locus variable number of tandem repeats (VNTR) analysis (MLVA) with 25 loci was performed on 100 Y. pestis strains, isolated from 37 sampling areas in Mongolia. The resulting data were compared with previously published data from more than 500 plague strains, 130 of which had also been previously genotyped by single nucleotide polymorphism (SNP) analysis. The comparison revealed six main clusters including the three microtus biovars Ulegeica, Altaica, and Xilingolensis. The largest cluster comprises 78 isolates, with unique and new genotypes seen so far in Mongolia only. Typing of selected isolates by key SNPs was used to robustly assign the corresponding clusters to previously defined SNP branches. CONCLUSIONS/SIGNIFICANCE: We show that Mongolia hosts the most recent microtus clade (Ulegeica). Interestingly no representatives of the ancestral Y. pestis subspecies pestis nodes previously identified in North-western China were identified in this study. This observation suggests that the subsequent evolution steps within Y. pestis pestis did not occur in Mongolia. Rather, Mongolia was most likely re-colonized by more recent clades coming back from China contemporary of the black death pandemic, or more recently in the past 600 years

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

USING SATELLITE DATA FOR ENVIRONMENTAL IMPACT ANALYSIS IN ECONOMIC GROWTH: THE CASE OF MONGOLIA

The Mongolian economy is based on the primary and secondary economic sectors of agriculture and industry. In addition, minerals and mining become a key sector of its economy. The main mining resources are gold, copper, coal, fluorspar and steel. However, the environment and green economy is one of the big problems among most of the countries and especially for countries like Mongolia where the mining is major part of economy; it is a number one problem. The research of the work tested how environmental elements effect to current Mongolian economic growth, which is growing economy because of mining sector. The study of economic growth but the starting point for any study of economic growth is the neoclassical growth model emphasizing the role of capital accumulation. The growth is analysed either in terms of models with exogenous saving rates (the Solow-Swan model), or models where consumption and hence savings are determined by optimizing individuals. These are the so-called optimal growth or Ramsey-Cass-Koopmans. The study extends the Solow model and the Ramsey-Cass-Koopmans model, including environmental elements which are satellite data determine to degraded land and vegetation value from 1995 to 2013. In contrast, we can see the degraded land area increases from 1995 (4856 m2) to 2013 (10478 m2) and vegetation value decrease at same time. A description of the methodology of the study conducted follows together with the data collected and econometric estimations and calibration with environmental elements

P A MAXIMIZATION PROPERTIES OF AVERAGE PRODUCTION FUNCTION

Abstract: It is well known that under classical economics assumptions production functions are concav

Carbon isotope discrimination of C3 vegetation in Central Asian grassland as related to long-term and short-term precipitation patterns

International audienceThe relationship between carbon isotope discrimination (13?) of C3 vegetation and long-term (30 years) and short-term (growing period) precipitation was investigated. Different species of Stipa, a dominant grass genus in the (semi-)arid Asian steppes, and other C3 species were collected along aridity gradients in Inner Mongolia in 2005 (11 sites, 71 samples) and in the Republic of Mongolia in 2006 (40 sites, 45 samples). The data set was expanded with published and unpublished data of Stipa and other C3 species (11 studies covering 8 years, including 64 observations of Stipa, and 103 observations of other C3 species) and C3 community bulk-samples (11 samples). Weather data were geostatistically interpolated for all sampling sites and years. 13? of Stipa followed different relationships for the individual years when related to mean annual precipitation due to large anomalies between annual and long-term average precipitation patterns. However, the 13? response to rainfall converged when the (long-term) mean annual precipitation was replaced by year-specific mean daily precipitation during the growing period (PG). Remarkably, the 13?-response to (PG) for C3 species as a whole (including herbaceous dicots, semi-shrubs and grasses) and also the C3 community-level response were virtually identical to that of Stipa. The relation was also valid outside the geographical and climatic range where it was developed, giving proof of its robustness