<p id="p00010">The world is currently experiencing a biodiversity crisis, and climate and land-cover changes are now recognized as two major threats to biodiversity. China is one of the mega-biodiversity countries and the threatened species list of China’s higher plants was reported in 2017. This list provided important data for biological conservation and protected area planning from regional to global scales. However, it was mainly based on the past and current status of species population and distribution, while future responses of species to climate and land-cover changes were rarely considered. This will lead to an underestimation of future local extinction risks. Using high-resolution species distribution data of woody plants and species distribution models, we evaluated the impacts of climate and land-cover changes on woody species distributions and estimated changes in the extent of occurrence (EOA) for each species. Our results indicate that 12.9%-40.5% of woody species will be threatened under different climate and dispersal scenarios. Based on these results, we updated the list of threatened woody species in China based on the IUCN Red List Criteria. This new list of threatened woody species provides important data for assessing the conservation priorities of woody plants, for the planning of future nature reserve extension, for improving the performance of nature reserves under future global change scenarios, and for updating the list of threatened species of other taxa.</p><table-wrap id="T2"><label/><caption xml:lang="en"><p id="p00020"><strong>Database/Dataset Profile</strong></p></caption><table><thead></thead><tbody><tr><td valign="top" align="justify" style="border-top:1px solid #000;border-bottom:1px solid #000;">Title</td><td valign="top" align="justify" style="border-top:1px solid #000;border-bottom:1px solid #000;">A new list of threatened woody species in China under future global change scenarios</td></tr><tr><td valign="top" align="justify" style="border-top:1px solid #000;" width="230">Authors</td><td valign="top" align="justify" style="border-top:1px solid #000;">Shijia Peng, Yuan Luo, Hongyu Cai, Xiaoling Zhang, Zhiheng Wang</td></tr><tr><td valign="top" align="justify">Corresponding author</td><td valign="top" align="justify">Zhiheng Wang (zhiheng.wang@pku.edu.cn)</td></tr><tr><td valign="top" align="justify">Time range</td><td valign="top" align="justify">Current-2070</td></tr><tr><td valign="top" align="justify">Geographical scope</td><td valign="top" align="justify">China</td></tr><tr><td valign="top" align="justify">File size</td><td valign="top" align="justify">2.06 MB</td></tr><tr><td valign="top" align="justify">Data format</td><td valign="top" align="justify">*.xlsx</td></tr><tr><td valign="top" align="justify">Data link</td><td valign="top" align="justify"><a href="http://dataopen.info/home/datafile/index/id/256">http://dataopen.info/home/datafile/index/id/256</a><br><a href="http://doi.org/10.24899/do.202205002">http://doi.org/10.24899/do.202205002</a><br><a href="https://www.biodiversity-science.net/fileup/1005-0094/DATA/2021459.zip">https://www.biodiversity-science.net/fileup/1005-0094/DATA/2021459.zip</a></td></tr><tr><td valign="top" align="justify" style="border-bottom:1px solid #000;">Database/Dataset <br>composition</td><td valign="top" align="justify" style="border-bottom:1px solid #000;">The dataset consists of two data files: (1) The classification of 11,405 woody plant species as threatened or non-threatened under current (Qin et al, <xref ref-type="bibr" rid="b17">2017</xref>) and future climate and dispersal change scenarios in China (1: Threatened species; 0: Non-threatened species); (2) The conservation status of 11,405 woody species under current (Qin et al, <xref ref-type="bibr" rid="b17">2017</xref>) and future climate and dispersal change scenarios (EX: Extinction; CR: Critically Endangered; EN: Endangered; VU: Vulnerable; LC: Least Concern).</td></tr></tbody></table></table-wrap>
Nitrogen (N) is a major ingredient of the atmosphere, but a trace component in the silicate Earth. Its initial inventory in these reservoirs during Earth's early differentiation requires knowledge of N speciation in magmas, for example, whether it outgasses as N 2 or is sequestered in silicate melts as N 3− , which remains largely unconstrained over the entire mantle regime. Here we examine N species in anhydrous and hydrous pyrolitic melts at varying P‐T‐redox conditions by ab‐initio calculations, and find N‐N bonding under oxidizing conditions from ambient to lower mantle pressures. Under reducing conditions, N interacts with the silicate network or forms N‐H bonds, depending on the availability of hydrogen. Redox control of N speciation is demonstrated valid over a P‐T space encompassing probable magma ocean depths. Finally, if the Earth accreted from increasingly oxidized materials toward the end of its accretion, an N‐enriched secondary atmosphere might be produced and persist until later impacts.
Quantile regression is a method of fundamental importance. How to efficiently conduct quantile regression for a large dataset on a distributed system is of great importance. We show that the popularly used one-shot estimation is statistically inefficient if data are not randomly distributed across different workers. To fix the problem, a novel one-step estimation method is developed with the following nice properties. First, the algorithm is communication efficient. That is the communication cost demanded is practically acceptable. Second, the resulting estimator is statistically efficient. That is its asymptotic covariance is the same as that of the global estimator. Third, the estimator is robust against data distribution. That is its consistency is guaranteed even if data are not randomly distributed across different workers. Numerical experiments are provided to corroborate our findings. A real example is also presented for illustration.
Nutrient scarcity is pervasive for natural microbial communities, affecting species reproduction and co-existence. However, it remains unclear whether there are general rules of how microbial species abundances are shaped by biotic and abiotic factors. Here we show that the ribosomal RNA gene operon (rrn) copy number, a genomic trait related to bacterial growth rate and nutrient demand, decreases from the abundant to the rare biosphere in the nutrient-rich coastal sediment but exhibits the opposite pattern in the nutrient-scarce pelagic zone of the global ocean. Both patterns are underlain by positive correlations between community-level rrn copy number and nutrients. Furthermore, inter-species co-exclusion inferred by negative network associations is observed more in coastal sediment than in ocean water samples. Nutrient manipulation experiments yield effects of nutrient availability on rrn copy numbers and network associations that are consistent with our field observations. Based on these results, we propose a “hunger games” hypothesis to define microbial species abundance rules using the rrn copy number, ecological interaction, and nutrient availability.
As a stereo odor cue, internostril odor influx could help us in many spatial tasks, including localization and navigation. Studies have also revealed that this benefit could be modulated by the asymmetric concentrations of both influxes (left nose vs right nose). The interaction between olfaction and vision, such as in object recognition and visual direction judgment, has been documented; however, little has been revealed about the impact of odor cues on sound localization. Here we adopted the ventriloquist paradigm in auditory-odor interactions and investigated sound localization with the concurrent unilateral odor influx. Specifically, we teased apart both the "nature" of the odors (pure olfactory stimulus vs. mixed olfactory/trigeminal stimulus) and the location of influx (left nose vs. right nose) and examined sound localization with the method of constant stimuli. Forty-one participants, who passed the Chinese Smell Identification Test, perceived sounds with different azimuths (0°, 5°, 10°, and 20° unilaterally deflected from the sagittal plane by head-related transfer function) and performed sound localization (leftward or rightward) tasks under concurrent, different unilateral odor influxes (10% v/v phenylethyl alcohol, PEA, as pure olfactory stimulus, 1% m/v menthol as mixed olfactory/trigeminal stimulus, and propylene glycol as the control). Meanwhile, they reported confidence levels of the judgments. Results suggested that unilateral PEA influx did not affect human sound localization judgments. However, unilateral menthol influx systematically biased the perceived sound localization, shifting toward the odor source. Our study provides evidence that unilateral odor influx could bias perceived sound localization only when the odor activates the trigeminal nerves.
This paper discusses a widely accepted emendation to an earlier version of IG X 2.1 137. Early draft copies of the Herennia announcement show that Antoninus Pius was hailed as Σωτήρ by the city of Thessalonike, a rare epithet for this emperor. This reading was later replaced due to an expert’s claim that σωτῆρος has to be read σωτηρίας. Since this seems to conform to a well-known salutary formula, the emendation was adopted from then on. This paper suggests that the reading of σωτῆρος is based on reliable and published reports instead, and ought to be preferred over the expert claim. Empirical evidence is given to support reading σωτῆρος.
Recently, recommendation based on causal inference has gained much attention in the industrial community. The introduction of causal techniques into recommender systems (RS) has brought great development to this field and has gradually become a trend. However, a unified causal analysis framework has not been established yet. On one hand, the existing causal methods in RS lack a clear causal and mathematical formalization on the scientific questions of interest. Many confusions need to be clarified: what exactly is being estimated, for what purpose, in which scenario, by which technique, and under what plausible assumptions. On the other hand, technically speaking, the existence of various biases is the main obstacle to drawing causal conclusions from observed data. Yet, formal definitions of the biases in RS are still not clear. Both of the limitations greatly hinder the development of RS.In this paper, we attempt to give a causal analysis framework to accommodate different scenarios in RS, thereby providing a principled and rigorous operational guideline for causal recommendation. We first propose a step-by-step guideline on how to clarify and investigate problems in RS using causal concepts. Then, we provide a new taxonomy and give a formal definition of various biases in RS from the perspective of violating what assumptions are adopted in standard causal analysis. Finally, we find that many problems in RS can be well formalized into a few scenarios using the proposed causal analysis framework.