Future Representation of Species’ Climatic Niches in Protected Areas: A Case Study With Austrian Endemics

Climate driven species’ range shifts may interfere with existing protected area (PA) networks, resulting in a mismatch between places where species are currently protected and places where they can thrive in the future. Here, we assess the climate-smartness of the Austrian PA network by focusing on endemic species’ climatic niches and their future representation within PAs. We calculated endemic species’ climatic niches and climate space available in PAs within their dispersal reach under current and future climates, with the latter represented by three climate change scenarios and three time-steps (2030, 2050, and 2080). Niches were derived from the area of occupancy of species and the extent of PAs, respectively, and calculated as bivariate density kernels on gradients of mean annual temperature and annual precipitation. We then computed climatic representation of species’ niches in PAs as the proportion of the species’ kernel covered by the PA kernel. We found that under both a medium (RCP 4.5) and severe (RCP 8.5) climate change scenario, representation of endemic species’ climatic niches by PAs will decrease to a sixth for animals and to a third for plants, on average, toward the end of the century. Twenty to thirty percent of Austrian endemic species will then have no representation of their climatic niches in PAs anymore. Species with larger geographical and wider elevational ranges will lose less climatic niche representation. The declining representation of climatic niches in PAs implies that, even if PAs may secure the persistence of a part of these endemics, only a small portion of intraspecific diversity of many species may be represented in PAs in the future. We discuss our findings in the context of the varied elevational gradients found in Austria and suggest that the most promising strategies for safeguarding endemic species’ evolutionary potential are to limit the magnitude of climate change and to reduce other pressures that additionally threaten their survival.

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures and symmetries in user activities as well as density estimations. In the current study, different density estimation techniques are utilized to analyze the check-in frequency of users in more detail from location-based social network dataset acquired from Sina-Weibo, also referred as Weibo, over a specific period in 10 different districts of Shanghai, China. The aim of this study is to analyze the density of users in Shanghai city from geolocation data of Weibo as well as to compare their density through univariate and bivariate density estimation techniques; i.e., point density and kernel density estimation (KDE) respectively. The main findings of the study include the following: (i) characteristics of users’ spatial behavior, the center of activity based on their check-ins, (ii) the feasibility of check-in data to explain the relationship between users and social media, and (iii) the presentation of evident results for regulatory or managing authorities for urban planning. The current study shows that the point density and kernel density estimation. KDE methods provide useful insights for modeling spatial patterns using geo-spatial dataset. Finally, we can conclude that, by utilizing the KDE technique, we can examine the check-in behavior in more detail for an individual as well as broader patterns in the population as a whole for the development of smart city. The purpose of this article is to figure out the denser places so that the authorities can divide the mobility of people from the same routes or at least they can control the situation from any further inconvenience.

Local dependence in bivariate copulae with Beta marginals

The local dependence function (LDF) describes changes in the correlation structure of continuous bivariate random variables along their range. Bivariate density functions with Beta marginals can be used to model jointly a wide variety of data with bounded outcomes in the (0,1) range, e.g. proportions. In this paper we obtain expressions for the LDF of bivariate densities constructed using three different copula models (Frank, Gumbel and Joe) with Beta marginal distributions, present examples for each, and discuss an application of these models to analyse data collected in a study of marks obtained on a statistics exam by postgraduate students.