Weighted average regression and environmental calibration as a tool for quantifying climate-driven changes in vegetation

Aims Studies of the climatic responses of plant assemblages via vegetation-based environmental reconstructions by weighted averaging (WA) regression and calibration are a recent development in modern vegetation ecology. However, the performance of this technique for plot-based vegetation datasets has not been rigorously tested. We assess the estimation accuracy of the WA approach by comparing results, mainly the root mean square error of prediction (RMSEP) of WA regressions for six different vegetation datasets (total species, high-frequency species and low-frequency species as both abundance and incidence) each from two sites.     

Dynamic imaging and quantification of subcellular motion with eigen‐decomposition optical coherence tomography‐based variance analysis

The dynamic properties of subcellular organism are important biomarkers of the health. Imaging subcellular level dynamics provides effective solutions for evaluating cell metabolism and testing the responses of cells to pathogens and drugs in pharmaceutical engineering. In this paper, we demonstrate an innovative approach to contrast the subcellular motion by using eigen decomposition (ED)‐based variance analysis of time‐dependent complex optical coherence tomography signals. This method reveals a superior advantage of contrast to noise ratio when compared with the approach that employs intensity decorrelation. Furthermore, the eigen values derived from ED processing are calculated and applied to assess the power ratios of complex signal invariance that decreases exponentially along time dimension. The validation experiments are performed on the patterned samples of yeast powder mixed with gelatin/TiO2 water solution. Additionally, the proposed method is used to image mouse cerebral cortex in normal and pathological conditions, suggesting the practicality of variance power mapping in analyzing cortical neural activities. The technique promises efficient measurement of subcellular motions with high sensitivity and high throughput for in vivo and in situ applications.     

Activity-Dependent Nucleation of Dynamic Microtubules at Presynaptic Boutons Controls Neurotransmission

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Neuronal Correlates of Spatial Working Memory in the Endbrain of Crows

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辽宁省地表温度时空变化及影响因素

地温是评价气候变化的重要指标,对土壤的成分、结构以及形成和演化都具有很大影响。分析地温自身的时空变化规律以及与气候间的相互关系,对深入了解地气间相互作用的机理、明确气候变化的响应规律、预测未来地温的发展趋势等方面具有重要的科学意义。辽宁省位于内蒙古高原向渤海湾的过渡地带,地形地势复杂,此外辽宁省又处在季节性冻土区,地温的变化机制更具复杂性。利用辽宁省24个气象站点1960—2016年0cm地温(地表温度)数据和各气象要素(气温、日照时数、风速、降水量)数据资料,采用气候倾向率法、Mann-Kendall突变分析、小波周期分析等方法,系统的分析辽宁省地表温度的时空变化特征以及与气候要素间的关系。结果表明:辽宁省地表温度年际变化随时间向暖趋势发展,气候倾向率达0.36℃/10a,不同年代际存在不同的变化趋势,其中20世纪60—80年代低于多年平均地表温度,90年代至21世纪10年代高于多年平均地表温度,此外冬季地表温度增温幅度最大;突变分析显示在1995年发生突变,经检验其升高趋势显著;经周期分析显示辽宁省年地表温度具有30—46a和19—25a的两种时间尺度的周期变化;年地表温度呈西南向东北逐渐降低分布特征。相关分析表明,地表温度与气温的相关性最大,并且其大小在整个区域内呈西高东低的分布特点;在与降水的关系中,降水量高的年份地表温度均比较低,夏季受降水和日照时数的影响最显著,地表温度与风速均呈负相关,但夏季相关性较小,考虑夏季地表温度主要是受气温、日照和降水共同的作用,弱化了风速对地表温度的影响。     

基于生态系统服务价值重构的干旱内陆河流域生态安全格局优化——以张掖市甘州区为例

干旱区内陆河流域的生态环境极为脆弱,生态系统的微小变化也可能给流域生态安全带来巨大影响。甘州区位于干旱内陆河流域,生态问题突出,重视生态系统自身安全、构建合理的生态安全格局是保障该地区生态安全的有效措施。通过当量因子法估算2000、2005、2010年甘州区生态系统服务的静态价值,在充分考虑各项生态系统服务功能变化率及空间集聚程度对功能发挥影响的基础上,采用生态系统服务优先指数和空间丰富度指数,对甘州区2010年生态系统服务静态价值的估算结果进行动态重构。利用重构后的生态系统服务价值构建了甘州区3种不同安全水平情景下的生态安全格局。结果表明:1)甘州区生态系统服务价值总量逐步上升,各项服务多年变化率差异不大。2)重构后生态系统服务价值量为1438.21—390993元/hm~2,大部分地区为中低值。3)低、中、高安全水平生态安全格局中,生态源地面积分别为155.75 km~2,191.25 km~2和224.5 km~2。基于生态系统服务价值构建的生态安全格局是立足于生态系统服务现状提出的规划策略,可为区域生态保护规划和生态文明建设提供重要依据。     

Cryptic phenology in plants: Case studies,implications, and recommendations

Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant‐mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season‐initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are “cryptic”—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.     

Modeling optimal responses and fitness consequences in a changing Arctic

Animals must balance a series of costs and benefits while trying to maximize their fitness. For example, an individual may need to choose how much energy to allocate to reproduction versus growth, or how much time to spend on vigilance versus foraging. Their decisions depend on complex interactions between environmental conditions, behavioral plasticity, reproductive biology, and energetic demands. As animals respond to novel environmental conditions caused by climate change, the optimal decisions may shift. Stochastic dynamic programming provides a flexible modeling framework with which to explore these trade‐offs, but this method has not yet been used to study possible changes in optimal trade‐offs caused by climate change. We created a stochastic dynamic programming model capturing trade‐off decisions required by an individual adult female polar bear (Ursus maritimus) as well as the fitness consequences of her decisions. We predicted optimal foraging decisions throughout her lifetime as well as the energetic thresholds below which it is optimal for her to abandon a reproductive attempt. To explore the effects of climate change, we shortened the spring feeding period by up to 3 weeks, which led to predictions of riskier foraging behavior and higher reproductive thresholds. The resulting changes in fitness may be interpreted as a best‐case scenario, where bears adapt instantaneously and optimally to new environmental conditions. If the spring feeding period was reduced by 1 week, her expected fitness declined by 15%, and if reduced by 3 weeks, expected fitness declined by 68%. This demonstrates an effective way to explore a species' optimal response to a changing landscape of costs and benefits and highlights the fact that small annual effects can result in large cumulative changes in expected lifetime fitness.     

How are nitrogen availability,fine‐root mass,and nitrogen uptake related empirically? Implications for models and theory

Understanding the effects of global change in terrestrial communities requires an understanding of how limiting resources interact with plant traits to affect productivity. Here, we focus on nitrogen and ask whether plant community nitrogen uptake rate is determined (a) by nitrogen availability alone or (b) by the product of nitrogen availability and fine‐root mass. Surprisingly, this is not empirically resolved. We performed controlled microcosm experiments and reanalyzed published pot experiments and field data to determine the relationship between community‐level nitrogen uptake rate, nitrogen availability, and fine‐root mass for 46 unique combinations of species, nitrogen levels, and growing conditions. We found that plant community nitrogen uptake rate was unaffected by fine‐root mass in 63% of cases and saturated with fine‐root mass in 29% of cases (92% in total). In contrast, plant community nitrogen uptake rate was clearly affected by nitrogen availability. The results support the idea that although plants may over‐proliferate fine roots for individual‐level competition, it comes without an increase in community‐level nitrogen uptake. The results have implications for the mechanisms included in coupled carbon‐nitrogen terrestrial biosphere models (CN‐TBMs) and are consistent with CN‐TBMs that operate above the individual scale and omit fine‐root mass in equations of nitrogen uptake rate but inconsistent with the majority of CN‐TBMs, which operate above the individual scale and include fine‐root mass in equations of nitrogen uptake rate. For the much smaller number of CN‐TBMs that explicitly model individual‐based belowground competition for nitrogen, the results suggest that the relative (not absolute) fine‐root mass of competing individuals should be included in the equations that determine individual‐level nitrogen uptake rates. By providing empirical data to support the assumptions used in CN‐TBMs, we put their global climate change predictions on firmer ground.