Drought and cold spells trigger dieback of temperate oak and beech forests in northern Spain

Dieback in temperate forests is understudied, despite this biome is predicted to be increasingly affected by more extreme climate events in a warmer world. To evaluate the potential drivers of dieback we reconstructed changes in radial growth and intrinsic water-use efficiency (iWUE) from stable isotopes in tree rings. Particularly, we compared tree size, radial-growth trends, growth responses to climate (temperature, precipitation, cloudiness, number of foggy days) and drought, and changes in iWUE of declining and non-declining trees showing contrasting canopy dieback and defoliation. This comparison was done in six temperate forests located in northern Spain and based on three broadleaved tree species (Quercus robur, Quercus humilis, Fagus sylvatica). Declining trees presented lower radial-growth rates than their non-declining counterparts and tended to show lower growth variability, but not in all sites. The growth divergence between declining and non-declining trees was significant and lasted more in Q. robur (15–30 years) than in F. sylvatica (5–10 years) sites. Dieback was linked to summer drought and associated atmospheric patterns, but in the wettest Q. robur sites cold spells contributed to the growth decline. In contrast, F. sylvatica was the species most responsive to summer drought in terms of growth reduction followed by Q. humilis which showed coupled changes in growth and iWUE as a function of tree vigour. Low growth rates and higher iWUE characterized declining Q. robur and F. sylvatica trees. However, declining F. sylvatica trees became less water-use efficient close to the dieback onset, which could indicate impending tree death. In temperate forests, dieback and growth decline can be triggered by climate extremes such as dry and cold spells, and amplified by climate warming and rising drought stress.     

Detecting early-warning signals for influenza A pandemic based on protein dynamical network biomarkers

The outbreak of influenza A comes from a relatively stable state is a critical phenomenon on epidemic. In this paper, influenza A varying from different states is studied in the method of dynamical network biomarkers (DNB). Through studying DNB of influenza A virus protein, we can detect the warning signals of outbreak for influenza A and obtain a composite index. The composite index varies along with the state of pandemic influenza, which gives a clue showing the turn point of outbreak. The low value (<1) steady state of the composite index means influenza A is normally in the relatively steady stage. Meanwhile, if the composite index of a certain year increases by more than 0.8 relative to the previous year and it is less than 1 and it increases sharply and reaches a peak being larger than 1 in next year, it means the year is normal in the critical state before outbreak and the next year is normally in the outbreak state. Therefore, we can predict the outbreak of influenza A and identify the critical state before influenza A outbreak or outbreak state by observing the variation of index value.     

食蟹猴心血管病风险因素与心血管病相关基因在外周血白细胞表达的相关性

研究心血管病风险因素和心血管病相关基因表达的相关性有助于心血管病风险预警和早期诊断研究。该文采用温和的动脉粥样硬化膳食(0.053mg胆固醇/千焦、40%的能量来源于脂肪)喂养中老年雄性食蟹猴(Macaca fascicularis)(12个月),根据传统心血管病风险因素筛选低、高风险食蟹猴,然后采用荧光定量PCR技术检测113个心血管病相关基因在正常组、低风险和高风险组食蟹猴外周血白细胞内的表达差异。结果在食蟹猴外周血白细胞中共检测到65个心血管病相关基因,其中低、高风险组有16个基因相对于正常组表达上调(P<0.05),19个基因表达下调(P<0.05),另外,还有15个基因表达模式特异(P<0.05)。此外,还检测到42个心血管病相关基因在人和食蟹猴外周血白细胞内均有效表达,其中22个基因在两者之间表达模式一致。上述结果为进一步研究心血管病风险预警和早期诊断指标,缩小了基因遴选范围。     

Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Decline of seagrasses has been documented in many parts of the world. Reduction in water clarity, through increased turbidity and increased nutrient concentrations, is considered to be the primary cause of seagrass loss. Recent studies have indicated the need for new methods that will enable early detection of decline in seagrass extent and productivity, over large areas. In this review of current literature on coastal remote sensing, we examine the ability of remote sensing to serve as an information provider for seagrass monitoring. Remote sensing offers the potential to map the extent of seagrass cover and monitor changes in these with high accuracy for shallow waters. The accuracy of mapping seagrasses in deeper waters is unclear. Recent advances in sensor technology and radiometric transfer modelling have resulted in the ability to map suspended sediment, sea surface temperature and below-surface irradiance. It is therefore potentially possible to monitor the factors that cause the decline in seagrass status. When the latest products in remote sensing are linked to seagrass production models, it may serve as an early-warning system for seagrass decline and ultimately allow a better management of these susceptible ecosystems.     

外来植物西亚大蒜芥在云南出现并定居

报道了云南省一新记录的外来植物——西亚大蒜芥(Sisymbrium orientaleL.)。该物种在澳大利亚入侵农田、草场,在我国虽已有分布记录,但未见到相关预警性研究报道,目前为止尚未引起重视。西亚大蒜芥种子细小,易随风传播,具有形成大面积扩散的潜力。本次调查发现该物种在云南昆明有扩大分布的趋势。建议对该物种在我国的分布进行全面调查,查清其分布状况和入侵途径,并进行种群动态监测,适时清除,避免不必要的入侵发生。     

Measuring complexity to infer changes in the dynamics of ecological systems under stress

Despite advances in our mechanistic understanding of ecological processes, the inherent complexity of real-world ecosystems still limits our ability in predicting ecological dynamics especially in the face of on-going environmental stress. Developing a model is frequently challenged by structure uncertainty, unknown parameters, and limited data for exploring out-of-sample predictions. One way to address this challenge is to look for patterns in the data themselves in order to infer the underlying processes of an ecological system rather than to build system-specific models. For example, it has been recently suggested that statistical changes in ecological dynamics can be used to infer changes in the stability of ecosystems as they approach tipping points. For computer scientists such inference is similar to the notion of a Turing machine: a computational device that could execute a program (the process) to produce the observed data (the pattern). Here, we make use of such basic computational ideas introduced by Alan Turing to recognize changing patterns in ecological dynamics in ecosystems under stress. To do this, we use the concept of Kolmogorov algorithmic complexity that is a measure of randomness. In particular, we estimate an approximation to Kolmogorov complexity based on the Block Decomposition Method (BDM). We apply BDM to identify changes in complexity in simulated time-series and spatial datasets from ecosystems that experience different types of ecological transitions. We find that in all cases, K_BDM complexity decreased before all ecological transitions both in time-series and spatial datasets. These trends indicate that loss of stability in the ecological models we explored is characterized by loss of complexity and the emergence of a regular and computable underlying structure. Our results suggest that Kolmogorov complexity may serve as tool for revealing changes in the dynamics of ecosystems close to ecological transitions.