三江平原湿地鸟类丰富度的空间格局及热点地区保护

全球气候变化和人类的开垦开发活动使湿地生物多样性遭到严重的干扰和破坏,导致生物多样性空间分布格局及热点地区的保护成为研究的热点。在对三江平原湿地鸟类预测的基础上,利用空间自相关方法分析三江平原湿地鸟类丰富度的空间分布格局,并找出湿地鸟类多样性的热点地区及优先保护顺序。研究结果表明,三江平原湿地鸟类丰富度高高集聚区主要分布在保护区及周边地区、河流和湖泊沿岸,是新建和扩建自然保护区的最佳区域。湿地鸟类丰富度高低集聚区主要分布在农田景观中,将它们设立成微型保护地块对于区域景观生态安全具有重要意义;利用湿地鸟类物种丰富度、国家级保护湿地鸟类、生境类型和结构、距最近保护区距离、破碎度、干扰度等指标,在研究区内共找到13个热点地区,总面积为1018.7km²,占研究区总面积的8%;利用系统聚类分析,将13个热点地区划分成3种优先保护顺序。构建的小区域范围内寻找生物多样性热点地区的方法,为相关政府部门更有效地进行湿地生物多样性的保护和管理提供科学依据。     

香溪河流域河岸带植物群落物种丰富度格局

通过不同海拔高度的样带调查来研究香溪河流域河岸植物群落物种丰富度格局,并探讨河岸带中生物多样性维持的生态学机制。结果表明：河岸植物群落总的物种丰富度、乔木层物种丰富度和草本层物种丰富度沿海拔梯度均表现出相似的格局特征,利用抛物线方程进行拟合,物种丰富度与海拔之间有显著的相关性。灌木层物种丰富度和藤本植物物种丰富度格局特征不明显,且物种丰富度与坡度相关,在流域尺度上,海拔对物种丰富度有着重要的控制作用;在局部尺度上,季节性洪水干扰导致的空间异质性和小地形对群落的生物多样性有着重要的影响,研究结果支在于总的物种多样性在原始河流的中间河段将达到最大值的预测。     

公别拉河流域三类湿地水化学特征研究

选择公别拉河流域有代表性的丛苔草湿地、臌囊苔草-细叶沼柳湿地和沼泽皱蒴藓-柴桦湿地为研究对象,分别对其水化学特征进行研究和分析.结果表明,三类湿地水中的阴离子以HCO3-为主,占阴离子总量的81.91%～85.46%,阳离子以Ca²⁺为主,占阳离子总量的56.80%～69.32%,其水化学类型为重碳酸盐类钙型水.三类湿地水pH为6.2～7.1,矿化度为112.5～461.23 mg·L^-1,总硬度为14.31～148.53 mg·L^-1.三类湿地水的各项指标基本符合国家Ⅰ、Ⅱ类水质标准,但Fe、Mn含量超标,在一定程度上影响本区水资源质量.从时间和空间角度分析了三类湿地水的水化学特征变化规律,并对湿地水微量元素含量进行了分析.     

霍林河流域湿地土壤碳氮空间分布特征及生态效应

对霍林河流域湿地土壤有机碳及全氮空问分布特征及其生态效应的研究表明,有机碳和全氮的水平分异和垂直分异都十分显著,干湿交替周期是引起分异的关键因子;表层土壤有机碳与全氮含量显著相关(r=0．977),土壤碳氮比基本沿湿度梯度变化;土壤pH值对土壤表层碳氮含量及碳氮比值影响显著;流域湿地土壤与流域草原土壤碳氮比与土壤碳氮含量的相关性差异显著;其生态效应主要表现在生产效应和净化效应两方面．     

次生林不同类型森林边缘的鸟类物种丰富度及个体多度比较

边缘效应对动物的分布及行为会产生一定的影响,在鸟类生态学研究中已证实某些鸟类在森林内部和森林边缘区域存在着物种丰富度和个体多度的差异。于1999至2001年的春夏季,在吉林省左家自然保护区对阔叶林/农田边缘、阔叶林/灌丛边缘及阔叶林/针叶林边缘3种不同类型边缘地带的鸟类物种丰富度及个体多度进行了比较研究。结果表明,不同年间鸟类物种丰富度无显著变化,但个体多度存在着一定的波动。不同类型森林边缘的鸟类物种丰富度存在着一定的差异,阔叶林/灌丛边缘的鸟类物种丰富度最高,而阔叶林/针叶林边缘的鸟类物种丰富度最低。鸟类个体多度的总体趋势在3种不同类型的边缘差异不显著,但存在种间差异,灰椋鸟、灰头啄木鸟和喜鹊在阔叶林/农田边缘的个体多度最高,斑啄木鸟、黄胸、三道眉草和日本树莺在阔叶林/灌丛边缘的个体多度最高,而沼泽山雀、冕柳莺和山在阔叶林/针叶林边缘的个体多度最高。     

喀斯特山区植物物种丰富度的空间变异分析

应用地统计学对地处滇黔桂连片喀斯特腹地的贵州省毕节地区植物物种丰富度的海拔空间变异进行分析。结果表明,乔木物种丰富度的半变异函数最佳理论模型为球状模型,灌木、草本为线性有基台模型。乔木物种丰富度的空间异质比为0．0052,具有强烈的海拔空间相关性,主要受随海拔梯度变化的自然性控制因素的影响;灌木、草本物种丰富度的空间异质比分别为3．15、34．55,海拔梯度的空间相关性很弱,受随机因素作用较大。乔木物种丰富度的变程为177．37m受因素影响的海拔范围较宽;灌木和草本物种丰富度的变程分别为73．02m和49．97m,受因素影响的海拔范围较窄。灌木、草本物种丰富度的Moran’s I系数随海拔梯度变化的趋势相类似,但乔木的差别较大。     

华北平原及黄土高原啮齿动物物种丰富度的空间格局及其分异

在采用网格法对华北平原及黄土高原啮齿动物调查的基础上,分析了啮齿动物物种丰富度空间格局。华北平原物种丰富度最低,其次为晋、翼山地和汾、渭谷地,而南蒙高原和黄土高原的丰富度较高;物种丰富度纬向变化不明显,而经向变化显著,由东向西,物种丰富度呈递增趋势;丰富度在海拔上的变化并不存在相关的地理模式;丰富度与山地面积呈正相关,与平原面积呈负相关,而与丘陵面积相关不显著,丰富度与各地地貌类型面积的总和呈明显的正相关;丰富度与温度相关不明显,而与降雨量呈负相关。在华北平原及黄土高原,生境结构类型愈复杂的地区,啮齿动物物种丰富度愈高。     

植物居群遗传变异的空间自相关分析

本文介绍了植物遗传变异空间自相关分析的理论、方法与应用 ,包括将基因型作为绝对型数据与等位基因频率作为连续型数据进行自相关分析的基本方法等。并对影响植物居群遗传变异空间结构的因素以及研究居群内遗传结构的重要意义作了评述     

植物居群遗传变异的空间自相关分析

本文介绍了植物遗传变异空间自相关分析的理论、方法与应用,包括将基因型作为绝对型数据与等位基因频率作为连续型数据进行自相关分析的基本方法等。并对影响植物居群遗传变异空间结构的因素以及研究居群内遗传结构的重要意义作了评述。     

郑州市景观多样性的空间自相关格局分析

景观多样性的研究在土地管理与规划、生态景观评价和自然保护区建设等方面起着重要的指导作用。随着城市经济的快速发展, 城市景观多样性也同样发生着剧烈的变化。利用四期Landsat TM 影像, 分析郑州市2000—2015年土地类型的时空变化, 并结合景观多样性指数和空间自相关构建方法, 在对景观多样性时空分布变化分析的基础上,进一步对地理空间单元上景观多样性的空间自相关关系进行了深入探讨。研究结果表明: 研究区整体景观多样性增加,景观破碎化加重; 城市边缘地带的变化符合初期地类转化强烈且景观多样性高值聚集, 随着时间推移最终下降的统一规律, 反映出人类活动与景观多样性分布密切相关; 研究区内区域经济带动性强, 导致研究区整体景观多样性单元之间关联性高, 尤其是郑州东部地区, 经济发展比较突出, 其高值聚集区显著性明显较高。该研究结果表明经济发展是引起城市景观多样性空间分布变化以及空间地理单元自相关强弱的主导因素。     

Effects of incorporating spatial autocorrelation into the analysis of species distribution data

Aim  Spatial autocorrelation (SAC) in data, i.e. the higher similarity of closer samples, is a common phenomenon in ecology. SAC is starting to be considered in the analysis of species distribution data, and over the last 10 years several studies have incorporated SAC into statistical models (here termed 'spatial models'). Here, I address the question of whether incorporating SAC affects estimates of model coefficients and inference from statistical models.
Methods  I review ecological studies that compare spatial and non-spatial models.
Results  In all cases coefficient estimates for environmental correlates of species distributions were affected by SAC, leading to a mis-estimation of on average c . 25%. Model fit was also improved by incorporating SAC.
Main conclusions  These biased estimates and incorrect model specifications have implications for predicting species occurrences under changing environmental conditions. Spatial models are therefore required to estimate correctly the effects of environmental drivers on species present distributions, for a statistically unbiased identification of the drivers of distribution, and hence for more accurate forecasts of future distributions.     

Spatial autocorrelation and red herrings in geographical ecology

Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates ‘red herrings’, such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro‐scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence. Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East. Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least‐squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared. Results Bird richness is characterized by a quadratic north–south gradient. Spatial correlograms usually had positive autocorrelation up to c. 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non‐detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de‐emphasized predictors with strong autocorrelation and long‐distance clinal structures, giving more importance to variables acting at smaller geographical scales. Conclusion Although spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation.     

Effects of flooding on a wetland bird community

The Lake Izunuma–Uchinuma was extensively flooded in 1998 and vast areas (approximately 200 ha) of rice fields were submerged for the first time since the land was reclaimed in 1963. When the numbers of birds in 1998 were compared with those in normal years (1995–1997), the numbers of black-crowned night herons (Nycticorax nycticorax L.), cattle egrets (Bubulcus ibis L.) and black kites (Milvus migrans Boddaert) increased during the flooding, and the numbers of ducks, especially dabbling ducks (seven species of 11 Anas spp.) were high even after the flooding, while the numbers of the little grebe Tachybaputus ruficollis Pallas were diminished by the flooding.     

Spatial models to account for variation in observer effort in bird atlases

To assess the importance of variation in observer effort between and within bird atlas projects and demonstrate the use of relatively simple conditional autoregressive (CAR) models for analyzing grid‐based atlas data with varying effort. Pennsylvania and West Virginia, United States of America. We used varying proportions of randomly selected training data to assess whether variations in observer effort can be accounted for using CAR models and whether such models would still be useful for atlases with incomplete data. We then evaluated whether the application of these models influenced our assessment of distribution change between two atlas projects separated by twenty years (Pennsylvania), and tested our modeling methodology on a state bird atlas with incomplete coverage (West Virginia). Conditional Autoregressive models which included observer effort and landscape covariates were able to make robust predictions of species distributions in cases of sparse data coverage. Further, we found that CAR models without landscape covariates performed favorably. These models also account for variation in observer effort between atlas projects and can have a profound effect on the overall assessment of distribution change. Accounting for variation in observer effort in atlas projects is critically important. CAR models provide a useful modeling framework for accounting for variation in observer effort in bird atlas data because they are relatively simple to apply, and quick to run.     

Spatial autocorrelation and the selection of simultaneous autoregressive models

Aim Spatial autocorrelation is a frequent phenomenon in ecological data and can affect estimates of model coefficients and inference from statistical models. Here, we test the performance of three different simultaneous autoregressive (SAR) model types (spatial error = SAR_err, lagged = SAR_lag and mixed = SAR_mix) and common ordinary least squares (OLS) regression when accounting for spatial autocorrelation in species distribution data using four artificial data sets with known (but different) spatial autocorrelation structures. Methods We evaluate the performance of SAR models by examining spatial patterns in model residuals (with correlograms and residual maps), by comparing model parameter estimates with true values, and by assessing their type I error control with calibration curves. We calculate a total of 3240 SAR models and illustrate how the best models [in terms of minimum residual spatial autocorrelation (minRSA), maximum model fit (R²), or Akaike information criterion (AIC)] can be identified using model selection procedures. Results Our study shows that the performance of SAR models depends on model specification (i.e. model type, neighbourhood distance, coding styles of spatial weights matrices) and on the kind of spatial autocorrelation present. SAR model parameter estimates might not be more precise than those from OLS regressions in all cases. SAR_err models were the most reliable SAR models and performed well in all cases (independent of the kind of spatial autocorrelation induced and whether models were selected by minRSA, R² or AIC), whereas OLS, SAR_lag and SAR_mix models showed weak type I error control and/or unpredictable biases in parameter estimates. Main conclusions SAR_err models are recommended for use when dealing with spatially autocorrelated species distribution data. SAR_lag and SAR_mix might not always give better estimates of model coefficients than OLS, and can thus generate bias. Other spatial modelling techniques should be assessed comprehensively to test their predictive performance and accuracy for biogeographical and macroecological research.     

Spatial autocorrelation in biology 1. Methodology

Spatial autocorrelation analysis tests whether the observed value of a nominal, ordinal, or interval variable at one locality is independent of values of the variable at neighbouring localities. The computation of autocorrelation coefficients for nominal, ordinal, and for interval data is illustrated, together with appropriate significance tests. The method is extended to include the computation of correlograms for spatial autocorrelation. These show the autocorrelation coefficient as a function of distance between pairs of localities being considered, and summarize the patterns of geographic variation exhibited by the response surface of any given variable.
Autocorrelation analysis is applied to microgeographic variation of allozyme frequencies in the snail Helix aspersa. Differences in variational patterns in two city blocks are interpreted.
The inferences that can be drawn from correlograms are discussed and illustrated with the aid of some artificially generated patterns. Computational formulae, expected values and standard errors are furnished in two appendices.     

Anthropogenic noise reduces bird species richness and diversity in urban parks

Anthropogenic noise is becoming more prevalent in the world and has been shown to affect many animal species, including birds. The impact of such noise was measured in Neotropical urban parks to assess how the noise affects avifauna diversity and species richness. We sampled bird species, and concurrently measured sound pressure (noise) levels (L_eq, equivalent noise levels) in eight urban green areas or parks located in a large city (Belo Horizonte) in south‐eastern Brazil over a 1‐year period. The diversity of sampled points was measured by means of total species richness, Fisher's alpha and Shannon–Wiener diversity indices. Noise levels within all parks were greater than those in natural areas. We found that an increase in noise levels and the area of open habitats surrounding sampling points were negatively related to species richness. Social factors reflecting increased urbanization, such as higher incomes, were also negatively correlated with bird species richness. However, noise was the factor that explained most of the variance. These results suggest that anthropogenic noise can have a significant negative impact on the conservation value of urban parks for bird species.     

Incorporating spatial autocorrelation may invert observed patterns

Though still often neglected, spatial autocorrelation can be a serious issue in ecology because the presence of spatial autocorrelation may alter the parameter estimates and error probabilities of linear models. Here I re-analysed data from a previous study on the relationship between plant species richness and environmental correlates in Germany. While there was a positive relationship between native plant species richness and an altitudinal gradient when ignoring the presence of spatial autocorrelation, the use of a spatial simultaneous liner error model revealed a negative relationship. This most dramatic effect where the observed pattern was inverted may be explained by the environmental situation in Germany. There the highest altitudes are in the south and the lowlands in the north that result in some locally or regionally inverted patterns of the large-scale environmental gradients from the equator to the north. This study therefore shows the necessity to consider spatial autocorrelation in spatial analyses.     

Spatial autocorrelation patterns of stream invertebrates: exogenous and endogenous factors

Aim To investigate spatial autocorrelation of taxonomic stream invertebrate groups (richness and composition) at a large geographical scale and to analyse the importance of exogenous and endogenous factors. Location The Mediterranean Basin. Methods For exogenous factors, we used large‐scale factors related to climate, geology and river zonation; for endogenous factors, we used the dispersal mode of each taxonomic group. After describing and analysing spatial patterns of genus richness and genus composition of stream invertebrate groups in the Mediterranean Basin, we computed Moran’s I before and after accounting for the exogenous factors and related it to the endogenous factors. Results In relation to genus richness, most of the taxonomic groups did not show significant spatial autocorrelation, suggesting that no main large‐scale exogenous or endogenous factors were important and that local‐scale factors were probably controlling taxonomic richness. In contrast, for genus composition, all taxonomic groups except Odonata had significant spatial autocorrelation before accounting for the environment. After accounting for the environment, most taxonomic groups still had a significant spatial autocorrelation, but it decreased with their increasing dispersal ability (from Crustacea to Coleoptera). Thus, spatial taxonomic composition of groups with the strongest dispersal potential is mainly related to exogenous factors, whereas that of groups with weaker dispersal potential is related to a combination of exogenous and endogenous factors. Main conclusions Our results illustrate the importance of dispersal as an endogenous factor causing spatial autocorrelation and suggest that ignoring endogenous factors can lead to misunderstandings when explaining large‐scale community patterns.