基于源-库互反馈的温室青椒坐果时空动态模拟

基于源-库互反馈的温室青椒坐果时空动态模拟     

Assessment of the stream invertebrate β‐diversity along an elevation gradient using a bidimensional null model analysis

β‐Diversity, commonly defined as the compositional variation among localities that links local diversity (α‐diversity) and regional diversity (γ‐diversity), can arise from two different ecological phenomena, namely the spatial species turnover (i.e., species replacement) and the nestedness of assemblages (i.e., species loss). However, any assessment that does not account for stochasticity in community assembly could be biased and misinform conservation management. In this study, we aimed to provide a better understanding of the overall ecological phenomena underlying stream β‐diversity along elevation gradients and to contribute to the rich debate on null model approaches to identify nonrandom patterns in the distribution of taxa. Based on presence‐absence data of 78 stream invertebrate families from 309 sites located in the Swiss Alpine region, we analyzed the effect size of nonrandom spatial distribution of stream invertebrates on the β‐diversity and its two components (i.e., turnover and nestedness). We used a modeling framework that allows exploring the complete range of existing algorithms used in null model analysis and assessing how distribution patterns vary according to an array of possible ecological assumptions. Overall, the turnover of stream invertebrates and the nestedness of assemblages were significantly lower and higher, respectively, than the ones expected by chance. This pattern increased with elevation, and the consistent trend observed along the altitudinal gradient, even in the most conservative analysis, strengthened our findings. Our study suggests that deterministic distribution of stream invertebrates in the Swiss Alpine region is significantly driven by differential dispersal capacity and environmental stress gradients. As long as the ecological assumptions for constructing the null models and their implications are acknowledged, we believe that they still represent useful tools to measure the effect size of nonrandom spatial distribution of taxa on β‐diversity.     

Allometric Models for Predicting Aboveground Biomass in Two Widespread Woody Plants in Hawaii

Allometric models are important for quantifying biomass and carbon storage in terrestrial ecosystems. Generalized allometry exists for tropical trees, but species‐ and site‐specific models are more accurate. We developed species‐specific models to predict aboveground biomass in two of the most ubiquitous natives in Hawaiian forests and shrublands, Metrosideros polymorpha and Dodonaea viscosa. The utility of the M. polymorpha allometry for predicting biomass across a range of sites was explored by comparing size structure (diameter at breast height vs. tree height) of the trees used to develop the models against trees from four M. polymorpha‐dominated forests along a precipitation gradient (1630–2380 mm). We also compared individual tree biomass estimated with the M. polymorpha model against existing generalized equations, and the D. viscosa model with an existing species‐specific model. Our models were highly significant and displayed minimal bias. Metrosideros polymorpha size structures from the three highest precipitation sites fell well within the 95% confidence intervals for the harvested trees, indicating that the models are applicable at these sites. However, size structure in the area with the lowest precipitation differed from those in the higher rainfall sites, emphasizing that care should be taken in applying the models too widely. Existing generalized allometry differed from the M. polymorpha model by up to 88 percent, particularly at the extremes of the data range examined, underestimating biomass in small trees and overestimating in large trees. The existing D. viscosa model underestimated biomass across all sizes by a mean of 43 percent compared to our model. The species‐specific models presented here should enable more accurate estimates of biomass and carbon sequestration in Hawaiian forests and shrublands.     

黄河三角洲垦利县夏季土壤水盐空间变异及土壤盐分微域特征

黄河三角洲作为我国重要的后备土地资源区,土壤盐渍化问题突出,切实掌握季节性土壤水盐状况及其微域特征是该区土壤盐渍化防控和土地资源高效利用的重要基础。选择黄河三角洲垦利县,通过野外调查实测与室内化验分析获取土壤水盐含量数据,利用统计分析、GIS空间插值、实地观测与数据分析对比等方法,分析了研究区夏季土壤水盐状况及其微域变异规律。结果显示:研究区夏季土壤水盐含量总体较高,含盐量以中度盐渍化为主,随着土层深度的增加含盐量呈上升趋势,且各层土壤含盐量呈显著正相关性;含盐量较高的地区主要分布在该区东北部和中东部,含盐量较低的地区主要分布在西南部和中部;土壤含盐量从大到小的植被类型依次为光板地→碱蓬→高粱→芦苇→茅草→水稻→棉花→玉米;土壤盐分微域变化特征明显,含盐量受距路边远近、不同耕作措施、地形部位、植被群落等因素影响较大,表现出微域规律性和复杂性。该研究基本摸清了研究区夏季时相的土壤水盐状况及其微域特征,为黄河三角洲农作物栽培管理及土壤资源可持续利用提供了科学依据。     

基因组规模代谢网络模型构建及其应用

微生物制造产业的发展迫切需要进一步提高认识、设计和改造微生物细胞代谢的能力,以推动工业生物技术快速发展。随着微生物全基因组序列等高通量数据的不断积聚和生物信息学策略的持续涌现,使全局性、系统化地解析、设计、调控微生物生理代谢功能成为可能。而基于基因组序列注释和详细生化信息整合的基因组规模代谢网络模型(GSMM)构建为全局理解和理性调控微生物生理代谢功能提供了最佳平台。以下在详述GSMM的应用基础上,描述了如何构建一个高精确度的GSMM,并展望了未来的发展方向。     

京东板栗主产区土壤氮磷钾的空间变异

采用经典统计学和地统计学相结合的方法,对京东板栗主产区迁西县土壤氮、磷、钾的空间变异进行了分析.结果表明：迁西县土壤全量氮、磷、钾和速效氮、钾的含量普遍较低,但表层土壤速效磷含量较高;氮、磷、钾的空间分布均表现为中等变异,且以磷的变异最大.表层土壤全氮和碱解氮的分布符合高斯模型,其空间变异主要由结构性因素决定;全钾和速效钾的分布分别符合球状模型和高斯模型,前者空间变异受结构性因素和随机性因素的共同影响,空间相关性为中等;后者主要受结构性因素的影响,空间相关性强烈.氮、钾在全县范围内的空间分布特征相似,高值区均出现在县域南部和西北部,而中部和东北部的含量较低.全磷的高值区主要分布在该县北部,速效磷高值区则分布在县区南部.氮、钾全量和有效态含量之间存在极显著正相关关系,但全磷和速效磷含量的相关性不显著.     

Testing for density dependence

Summary The robustness and sensitivity of a test for density dependence in an animal population against departures from the assumed null and alternative model is assessed via simulation. The test is shown to be nonrobust and insensitive to departures from the assumed models.     

京津冀城市群区域产业协同的政策格局及评价

产业协同发展是京津冀区域协同发展的三个重点领域之一,科学合理的产业协同政策是加快实现这一发展目标的重要保障。通过对京津冀城市群区域产业协同政策事件的系统梳理,从区域、省际和城际3个尺度解读了产业协同政策的格局及其演进过程,并从新区域主义的管治视角进行了评价。研究认为:(1)京津冀产业协同发展进入了快速发展的机遇期,协同政策极大地推动了区域内跨地产业合作和城市间产业联系,有助于提高城市群整体竞争力;(2)不同空间尺度的产业协同政策关注的重点、发展的方向、演化的特征不同,当前城市间的产业协同政策与区域协同发展目标的关系不明确,产业协同对生态环境的改善尚不显著,亟需加强研究和统筹协调;(3)受城市行政区等级关系和竞争关系的影响,京津冀产业协同政策与市场调节无法实现高度契合统一,与新区域主义的管治理念并不相符,影响科学合理协同政策的制定与实施。研究对当前京津冀区域产业协同发展推进及其政策制定有重要的参考意义。     

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.