黄土沟壑区不同坡位白羊草群落物种多度分布研究

物种多度分析是研究群落结构和分析群落生态学机制的重要方法,以黄土沟壑区地带性白羊草群落为对象,研究了坡位对白羊草群落物种多度分布格局的影响。结果表明:(1)坡位对白羊草群落物种多样性有显著性影响,坡中的Shannon指数、Simpson指数、Patrick指数显著低于坡上和坡下(p<0.05),坡上和坡下上述指数无显著差异(p>0.05)。坡上、坡中和坡下的群落Pielou均匀度指数和Alatalo均匀度指数无显著差异(p>0.05);(2)采用Zipf、Zipf-Mandelbrot、几何序列、分割线段、优势优先5种生态位模型对不同坡位上白羊草群落的物种多度分布格局进行模拟,发现仅前两个模型能很好地拟合白羊草群落在坡上、坡中和坡下的多度分布格局。说明尽管在不同坡位上白羊群落的物种多样性不同,但不同坡位上群落的形成过程和机理基本一致。本研究支持了Zipf模型和Zipf-Mandel-brot模型能较好的描述演替后期群落分布格局的观点。     

不同阈值下土壤盐分的空间变异特征研究

指示克里格法(Indicator Kriging,IK)应用的关键是阈值的合理选择。本文以黄淮海平原盐渍土改良区典型县域禹城市为研究区域,在1.0 g kg-1、2.0 g kg-1、3.0 g kg-1等3个盐分阈值下,对0～20 cm耕地土壤盐分的变异函数、预测概率、预测概率空间分布的变化规律进行了研究。结果表明,(1)不同阈值下土壤盐分含量均具有中等强度的自相关性,随着阈值的减小,土壤盐分的空间结构性增强,变异函数的模型精度增大,因此从指示变异函数模型精度考虑,1.0 g kg-1盐分含量为研究区域盐渍化风险评价的最佳阈值;(2)土壤盐分的预测概率最大值和预测概率均值随盐分阈值的减小而增大,可为不同土壤盐渍化风险评价目标下的阈值选择提供参考;(3)不同阈值下土壤盐分的概率预测分布存在空间上的规律性与相似性,高概率区域主要集中在研究区域的西部,低概率区域主要集中在研究区域的东部。研究区域土壤盐分含量的概率分布与地形地貌特征和河流的分布状况有着密切的关系。     

自然植被分布预测研究进展

预测自然植被的空间分布格局,对于生物保护、气候变化、生境管理、生态恢复决策等具有重要的实践指导意义。在对现有研究深入分析的基础上,认为植被格局不仅取决于气候,而且其他环境条件如地形、土壤等对植被格局的形成也具有重要作用;随着空间分析理论与方法在生态学中的广泛应用,多因素综合分析植被分布格局是未来植被-环境关系研究的重要趋势;在预测精度方面,非参数模型的预测结果一般比参数模型较好,如神经网络系统、GAM模型等;而遥感及GIS的应用,则为在较大尺度综合研究气候、土壤、地形等因素与植被分布格局的关系提供了较为丰富的数据源和先进的分析手段,并促进一些较复杂模型的建立和应用,为理解大尺度生态系统提供基础;我国植被-环境关系研究已取得了较为丰富的研究成果,但在数据的时效性、研究方法、研究尺度与因子选择方面仍有较大的深入研究空间。     

冬小麦叶面积指数高光谱遥感反演方法对比

冬小麦叶面积指数(LAI,leafarea index)是评价其长势和预测产量的重要农学参数,高光谱遥感能够实现快速无损地监测叶面积指数。该文旨在将田间监测与高光谱遥感相结合,探索研究不同冬小麦叶面积指数高光谱反演方法的模拟精度及适应性。针对国际上普遍应用的2种高光谱遥感反演LAI模型方法,即回归分析法和BP神经网络法,在介绍2种LAI反演模型的基础上,选择位于黄淮海平原的山东省济南市长清区为研究区域,通过ASD地物光谱仪和SunScan冠层分析系统对冬小麦的冠层光谱及LAI变化进行田间观测,然后利用回归分析法和BP神经网络法构建冬小麦LAI反演模型,将模型估算LAI值和田间观测LAI值进行比对,分析评价2种方法的反演精度。结果表明,BP神经网络法较回归分析法估算冬小麦LAI的精度有较大提高,检验方程的决定系数(R2)为0.990、均方根误差(RMSE)为0.105。利用BP神经网络法构建反演模型能较好的对冬小麦LAI进行反演。研究结果可为不同冬小麦长势遥感监测提供理论和技术上的支持,并为大尺度传感器监测冬小麦长势和估产提供参考。     

基于Logistic-CA-Markov与InVEST模型对南京市土地利用与生物多样性功能模拟评价

针对目前国际上应用比较广泛的Logistic模型,在此基础上加入空间自相关变量,应用1985年、1995年、2005年、2015年4期土地利用数据,再结合CA-Markov模型模拟预测了南京市2025年3种不同情景(自然增长情景、生态保护情景和土地优化情景)下的土地利用发展方向;进一步结合InVEST模型,研究以上4年土地利用变化下的生物多样性服务功能分布和变化,以及2025年不同模拟情景下的生物多样性服务功能分布情况。结果表明:Logistic-CA-Markov模型精度Kappa值均在0.80以上,预测效果较好。在不同的情景设置下,土地利用存在明显的空间差异:自然增长情景按原有速率变化,则建设用地快速发展并占用大量耕地,生物多样性受到严重威胁,生态保护情景和土地优化情景对未来土地调控效果较好,生物多样性功能得到很好改善,可以为当地土地利用总体规划提供科学决策参考。     

邻域距离对细胞自动机模型模拟土地利用变化精度的影响

细胞自动机原理广泛应用于土地利用变化空间模拟。邻域距离是细胞自动机模型的主要参数,但在以往研究中讨论较少。该研究利用土地利用变化模型（CLUE-S）邻域模拟模块,以北京市密云县1991－2004年的土地利用变化模拟为例,研究了模型中不同邻域距离参数对土地利用空间变化模拟精度的影响。基于1991年真实土地利用数据,选择200、400、600、1 000和1 200 m的邻域距离在两种邻域形状下分别模拟了2004年土地利用空间分布。通过2004年实际土地利用空间数据和模拟数据比较,利用kappa系数评价模拟结果精度。结果表明,在两种邻域形状下,邻域距离的变化对模拟结果精度都有较大影响。环状邻域距离的增大会导致模拟精度的降低,面状邻域距离的增大则导致模拟精度先增后降。不同土地类型对邻域距离变化的敏感程度和反应皆不相同。今后在土地利用变化模型应用中,对细胞自动机模型的验证过程中需考虑对邻域距离的研究和讨论。     

根系水质模型中土壤与作物参数优化及其不确定性评价

农业系统模型参数优化存在很高的不确定性,是模型应用研究的重点和难点。该研究利用自动优化程序PEST(parameter estimation software)对根系水质模型(root zone water quality model,RZWQM)中土壤参数(土壤水力学参数和根系生长参数)和作物遗传参数进行了优化,结果表明PEST优化模拟结果明显优于传统试错法的校正结果,且具有较高的参数优化效率。模型参数优化不确定性评价表明校正数据和参数初始值的选择、土壤水力学参数估算方法、不同类型参数间的相互作用以及优化目标方程(误差来源计算)都明显影响模型模拟结果。以上过程中土壤水力学参数优化值差异较小,但其土壤水分特征曲线却明显不同。通过以上评价分析提高了RZWQM相关参数优化结果的可靠性及其模拟功能,降低了模型参数优化的不确定性,为PEST优化其他模型参数提供了重要支持。     

基于偏角光谱检索算法的油菜和水稻LAI反演研究

叶面积指数(LAI)是评价植被长势及产量预测的重要指标,对其进行精准快速估测有助于植被的生长状态诊断和管理。本研究以不同施氮水平、不同栽种方式下的油菜和不同品种水稻为试验对象,基于冠层高光谱曲线形态,引入偏角光谱检索算法(DABSR)提取光谱偏角,同时采用植被指数法和主成分分析法进行对比分析,探索适用于水稻、油菜LAI估算的统一模型构建方法。研究结果表明,估算油菜LAI时,DABSR反演精度较高,预测R~2、RMSEP分别为0.74、0.47,偏移量MNB为0.16;主成分分析法反演精度次之,预测R~2、RMSEP、MNB分别为0.73、0.48、-0.04;而植被指数法受不同生育期油菜株型、覆盖度影响反演精度普遍较低,精度较高模型的预测R~2、RMSEP、MNB分别为0.61、0.57、0.17。在估算水稻LAI时,DABSR反演精度最优,预测R~2、RMSEP、MNB可达0.70、0.80、0.05。综合考虑模型的验证精度、特征选择的合理性以及模型计算效率,DABSR偏角光谱检索法估算油菜和水稻LAI具有较高精度,且受施肥水平、栽种方式、生长期等因素影响较小,为构建精确的植被LAI统一估算模型提供了新思路。     

不同湍流模型在轴流泵性能预测中的应用

为了评价不同湍流模型在轴流泵性能预测中的精度,该文以南水北调工程轴流泵模型作为研究对象,分别选取了3种湍流模型标准k-ε湍流模型(standard k-ε)、重正化群k-ε湍流模型(renormalization group k-ε,RNG)和雷诺应力模型(reynolds stress model,RSM),基于SIMPLE算法(semi-implicit method for pressure-linked equations)和结构化网格,进行了轴流泵性能预测和全流场数值模拟,并以水利部天津同台测试的试验结果作为基准对预测扬程和效率进行了误差分析。研究结果表明,网格密度对模拟结果具有较大影响,较疏的网格导致性能预测精度降低,在大流量和小流量工况下预测的扬程和效率误差将达到3%以上;在最优工况下,Standard k-ε、RNG k-ε和RSM湍流模型的扬程预测误差分别为0.97%、1.12%和1.24%,效率预测误差分别为2.93%、2.49%和2.97%,可满足工程应用要求;但在非设计工况下,由于二次回流、空化等复杂流动的存在,内部流场复杂,3种湍流模型的扬程最大预测误差范围为9.40%～14.30%,效率最大预测误差范围为4.48%～8.30%。该结论将为轴流泵性能预测的可靠性提供依据。     

基于夏玉米冠层内辐射分布的不同层叶面积指数模拟

为了模拟夏玉米冠层内各层叶面积指数垂直分布,光合有效辐射（photosynthetically active radiation, PAR）是研究作物群体光合作用和长势的重要特征参数,阐明冠层内PAR的垂直分布规律与冠层结构等参数之间的相关关系,可为遥感定量反演冠层结构参数提供模型基础。该文基于PAR在冠层内的辐射传输规律结合冠层结构模拟不同太阳高度角的PAR透过率垂直分布模型,并用地面冠层分析仪测量值进行验证,结果表明模型对封垄前玉米抽雄期冠层内PAR透过率垂直分布模拟精度较高。通过不同太阳高度角PAR透过率的垂直分布模型结合消光系数运用不同算法分别反演层叶面积指数（leaf area index, LAI）,并与不同高度层LAI实测值进行比较。结果显示：Bonhomme& Chartier算法反演不同高度层LAI精度较高,上层均方根误差（root mean square error,RMSE）为0.18,中层RMSE为0.55,下层RMSE为0.09。不同太阳高度角反演结果存在差异,30°和45°高度角均能较好地反演下层LAI,RMSE分别为0.11与0.09;30°高度角反演中层LAI精度较高,RMSE为0.30;45°高度角反演上层LAI精度较高,RMSE为0.18。结果表明基于不同太阳高度角构建的层LAI反演模型更适于实现夏玉米不同高度层LAI的遥感估算。该研究可为模拟垄行结构冠层内LAI垂直分布提供参考。     

Overcoming the rare species modelling paradox: A novel hierarchical framework applied to an Iberian endemic plant

Rare species have restricted geographic ranges, habitat specialization, and/or small population sizes. Datasets on rare species distribution usually have few observations, limited spatial accuracy and lack of valid absences; conversely they provide comprehensive views of species distributions allowing to realistically capture most of their realized environmental niche. Rare species are the most in need of predictive distribution modelling but also the most difficult to model. We refer to this contrast as the “rare species modelling paradox” and propose as a solution developing modelling approaches that deal with a sufficiently large set of predictors, ensuring that statistical models are not over-fitted. Our novel approach fulfils this condition by fitting a large number of bivariate models and averaging them with a weighted ensemble approach. We further propose that this ensemble forecasting is conducted within a hierarchic multi-scale framework. We present two ensemble models for a test species, one at regional and one at local scale, each based on the combination of 630 models. In both cases, we obtained excellent spatial projections, unusual when modelling rare species. Model results highlight, from a statistically sound approach, the effects of multiple drivers in a same modelling framework and at two distinct scales. From this added information, regional models can support accurate forecasts of range dynamics under climate change scenarios, whereas local models allow the assessment of isolated or synergistic impacts of changes in multiple predictors. This novel framework provides a baseline for adaptive conservation, management and monitoring of rare species at distinct spatial and temporal scales.     

Using behavioral landscape ecology to predict species’ responses to land-use and climate change

Climate change and habitat destruction are widely recognized as major threats to species’ survival. As a result of these anthropogenic impacts, species are often forced into novel landscapes where their persistence is difficult to predict. Knowledge of how individuals move or disperse through the landscape, choose habitat in which to settle, and produce offspring which survive to repeat the process can greatly improve our ability to predict species’ persistence. The field of behavioral landscape ecology uses a strong theoretical base to explore, often experimentally, how the behavior of a particular species is affected by heterogeneous and rapidly changing landscapes and can offer valuable insight for managing species in the face of human-induced environmental changes. When interpreted by modelers, results of landscape-level behavioral experiments can be quantified for use in predictive models. To this end, we summarize the methods and results of research using direct experimental manipulation techniques broken into the following categories: translocations, playback experiments, food resource manipulations, manipulations of reproductive success, direct manipulations of the landscape, and manipulations of predation risk. We review and place in a theoretical framework the results from this emerging body of research regarding how organisms move in and respond to different types of landscapes, both natural and human-altered. We go onto highlight the potential of each experimental method to quantify different processes, which may be useful when interpreted by modelers attempting to parameterize predictive models. Finally, we suggest future directions for experimental research that will allow for greater integration of behavioral landscape ecology and predictive modeling.     

Impacts of climate change on Swiss biodiversity: An indicator taxa approach

We present a new indicator taxa approach to the prediction of climate change effects on biodiversity at the national level in Switzerland. As indicators, we select a set of the most widely distributed species that account for 95% of geographical variation in sampled species richness of birds, butterflies, and vascular plants. Species data come from a national program designed to monitor spatial and temporal trends in species richness. We examine some opportunities and limitations in using these data. We develop ecological niche models for the species as functions of both climate and land cover variables. We project these models to the future using climate predictions that correspond to two IPCC 3rd assessment scenarios for the development of ‘greenhouse’ gas emissions. We find that models that are calibrated with Swiss national monitoring data perform well in 10-fold cross-validation, but can fail to capture the hot-dry end of environmental gradients that constrain some species distributions. Models for indicator species in all three higher taxa predict that climate change will result in turnover in species composition even where there is little net change in predicted species richness. Indicator species from high elevations lose most areas of suitable climate even under the relatively mild B2 scenario. We project some areas to increase in the number of species for which climate conditions are suitable early in the current century, but these areas become less suitable for a majority of species by the end of the century. Selection of indicator species based on rank prevalence results in a set of models that predict observed species richness better than a similar set of species selected based on high rank of model AUC values. An indicator species approach based on selected species that are relatively common may facilitate the use of national monitoring data for predicting climate change effects on the distribution of biodiversity.     

Predicting soil micro-variables and the distribution of an endogeic earthworm species through a model based on large-scale variables

Studies on spatial patterns of distributions of soil dwelling animals have usually relied on soil micro-variables or statistical analyses based on presence/absence data. Geographic Information Systems (GIS) allow easy access to large-scale variables to build species distribution models. In this study, we used MaxEnt to model the distribution of the endogeic earthworm Hormogaster elisae. Significant differences were found between the predicted suitability values of localities where the species was present and those where it was absent, validating the predictive model. Most of the large-scale training variables showed significant correlation with soil micro-variables known to influence the biology of the species, proving the ability of the model to predict (to an extent) soil variables from environmental ones. The methodology could be extended to other soil fauna.     

网格大小选择对大尺度分布式水文模型水文过程模拟的影响

以都阳湖信江流域为研究区,分析了网格大小选择对大尺度分布式水文模型水文过程模拟的影响.研究结果显示,在同样参数条件下,1,2和4 km这3种不同尺寸网格对模拟的总径流量影响较小,但网格大小显著地改变着模拟水流在地表径流和地下径流间的分配;不同网格的模型计算的实际蒸发量的差别不显著;大网格的模型计算的地下水补给量大.经过率定后的3种不同网格的模型均能较好地模拟流域的径流过程,但2 km网格模型模拟的总体效果要好于1 km网格模型和4 km网格模型.研究表明,对于分布式水文模型,网格的精细并不一定提高模型的模拟效果,一定精度空间数据的翰人条件下,分布式水文模型存在一个合适的网格大小使得模型的模拟效果最佳.在流域水文模型的具体应用中,应考虑流域本身的尺度以及模拟的目的和精度要求,选择合适的网格大小,同时应结合模型机理,解释模拟结果.     

When the species is also a habitat: Comparing the predictively modelled distributions of Lophelia pertusa and the reef habitat it forms

Internationally there is political momentum to establish networks of marine protected areas for the conservation of threatened species and habitats. Practical implementation of such networks requires an understanding of the distribution of these species and habitats. Predictive modelling provides a method by which continuous distribution maps can be produced from limited sample data. This method is particularly useful in the deep sea where a number of biological communities have been identified as vulnerable ‘habitats’, including Lophelia pertusa reefs. Recent modelling efforts have focused on predicting the distribution of this species. However the species is widely distributed where as reef habitat is not. This study uses Maxent predictive modelling to investigate whether the distribution of the species acts as a suitable proxy for the reef habitat. Models of both species and habitat distribution across Hatton Bank and George Bligh Bank are constructed using multibeam bathymetry, interpreted substrate and geomorphology layers, and derived layers of bathymetric position index (BPI), rugosity, slope and aspect. Species and reef presence records were obtained from video observations. For both models performance is fair to excellent assessed using AUC and additional threshold dependant metrics. 7.17% of the study area is predicted as highly suitable for the species presence while only 0.56% is suitable for reef presence, using the sensitivity–specificity sum maximisation approach to determine the appropriate threshold. Substrate is the most important variable in the both models followed by geomorphology in the RD model and fine scale BPI in the SD model. The difference in the distributions of reef and species suggest that mapping efforts should focus on the habitat rather than the species at fine (100 m) scales.     

Using summed individual species models and state-of-the-art modelling techniques to identify threatened plant species hotspots

Reliable identification of hotspot areas with high numbers of threatened plant species has a central role in conservation planning. We investigated the potentiality of identifying the distribution, richness and hotspots of threatened plant species at a 25 ha resolution using eight state-of-the-art modelling techniques (GLM, GAM, MARS, ANN, CTA, GBM, MDA and RF) in a taiga landscape in north-eastern Finland. First, the individual species models developed based on occurrence records of 28 species in the 1677 grid squares and derived from different statistical techniques were extrapolated to the whole study area of 41 750 km². Second, the projected presence/absence maps were then combined to create species richness maps, and the top 5% of grid cells ranked by species richness were classified as hotspots. Finally, we created an overall summary map by combining the individual hotspot maps from all eight modelling techniques and identified areas where the individual hotspots maps overlapped most. There were distinguishing differences in projections of the geographic patterns of species richness and hotspots between the modelling techniques. Most of the modelling techniques predicted several hotspot locations sporadically around the study area. However, the overall summary map showed the highest predictive performance based on Kappa statistics, indicating that the locations where the hotspot maps from the eight models coincided most harboured highest observed species richness. Moreover, the summary map filtered out the patchy structures of individual hotspot maps. The results show that the choice of modelling technique may affect the accuracy and prediction of hotspot patterns. Such differences may hamper the development of useful biodiversity model applications for conservation planning, and thus it is beneficial if the conservation decision-making can be based on sets of alternative maps and overlaying of predictions from multiple models.     

Rapid determination of carbohydrates, ash, and extractives contents of straw using attenuated total reflectance fourier transform mid-infrared spectroscopy

Analysis of the chemical components of lignocellulosic biomass is essential to understanding its potential for utilization. Mid-infrared spectroscopy and partial least-squares regression were used for rapid measurement of the carbohydrate (total glycans; glucan; xylan; galactan; arabinan; mannan), ash, and extractives content of triticale and wheat straws. Calibration models for total glycans, glucan, and extractives showed good and excellent predictive performance on the basis of slope, r2, RPD, and R/SEP criteria. The xylan model showed good and acceptable predictive performance. However, the ash model was evaluated as providing only approximate quantification and screening. The models for galactan, arabinan, and mannan indicated poor and insufficient prediction for application. Most models could predict both triticale and wheat straw samples with the same degree of accuracy. Mid-infrared spectroscopic techniques coupled with partial least-squares regression can be used for rapid prediction of total glycans, glucan, xylan, and extractives in triticale and wheat straw samples.     

Development and evaluation of predictive habitat models to assist the conservation planning of a threatened lucanid beetle, Hoplogonus simsoni, in north-east Tasmania

The use of predictive habitat distribution models by land managers in the conservation management of threatened species is increasing. Few models, however, are subsequently field-checked and evaluated. This study evaluates the statistical strength and usefulness for conservation purposes of three predictive habitat models developed for a threatened stag beetle, Hoplogonus simsoni, found in the wet eucalypt forests and mixed/rainforests of north-east Tasmania. The relationship between various environmental variables for which spatial (GIS) information was available and the density, frequency of occurrence and presence/absence of the species was investigated using generalised linear modelling. Models developed were coupled with the GIS data to develop maps of predicted occurrence within the species’ range, grouped into categories of habitat quality. The models found that altitude, aspect, slope, distance to nearest stream and overstorey tree height were significantly associated with the occurrence of the species. Evaluation of the statistical strength of the models with independent data of species’ occurrence collected at 95 sites found that the density model performed poorly with little correlation between predicted and observed densities of the species. The frequency of occurrence model, however, showed a moderate ability to predict both species’ abundance and presence/absence. The presence/absence model had a similar discriminatory ability in predicting presence or absence of H. simsoni, but also showed some potential as an indirect predictor of species’ abundance. Assuming a correlation between relative abundance and habitat quality, the frequency of occurrence predictive model appeared to be the better and more direct discriminator of high quality habitat relative to the other models. The value of species’ habitat models and the need to evaluate their utility in the development of conservation strategies are discussed.