利用分布有/无数据预测物种空间分布的研究方法综述

详细的物种地理分布信息是生态学研究和制定保护策略的基础。相比较于直接估测种群数量,获取物种分布的有/无数据更为实用。因此,利用分布有/无数据并结合环境变量建立模型预测物种空间分布的方法在近年来得到了长足发展,并被广泛应用。利用分布有/无数据预测物种分布,关键的步骤包括：1)构建总体概念模型,2)收集物种分布有/无数据,并准备环境变量图层;3)选择合适的统计模型和算法,以及4)对模型进行评估。概念模型提出研究假设,并确定数据收集及模型方法。收集物种分布数据有系统调查及非系统调查方法。筛选并准备与物种分布相关的环境变量,利用GIS工具处理,使之成为符合模型条件的具有合适的空间尺度的数字化图层。利用环境变量和物种分布有/无的数据,选择合适的方法及软件建立模型,并对模型进行检验和评估。我们总结了用于构建物种分布模型的不同算法和软件。本文将针对以上各个环节,阐述利用物种分布有/无数据进行研究所需要的技术细节,以期望为读者提供借鉴。     

The spatial distribution of zooplankton populations in the presence and absence of fish

The spatial distribution patterns of the main zooplankton groups in two small experimental ponds, one which contained fish and one which did not, were measured over a twelve month period from March 1983. Populations of all zooplankton groups were generally distributed non-randomly and with varying degrees of aggregation. The aggregation ofDaphnia longispina populations was inversely related to ambient wind speed, indicating a breakdown of behavioural clumping by wind-induced water currents. Evidence that populations ofDaphnia aggregate in response to fish predation was inconclusive. In contrast cyclopoid nauplii showed no evidence of behavioural clumping and the degree of aggregation was not related to wind speed. Nauplii aggregations were however greater in the absence than in the presence of fish.     

Modelling and predicting fungal distribution patterns using herbarium data

Aim The main aims of this study are: (1) to test if temperature and related parameters are the primary determinants of the regional distribution of macrofungi (as is commonly recognized for plants); (2) to test if the success of modelling fungal distribution patterns depends on species and distribution characteristics; and (3) to explore the potential of using herbarium data for modelling and predicting fungal species’ distributions. Location The study area, Norway, spans 58–71° N latitude and 4–32° E longitude, and embraces extensive ecological gradients in a small area. Methods The study is based on 1020 herbarium collections of nine selected species of macrofungi and a set of 75 environmental predictor variables, all recorded in a 5 × 5‐km grid covering Norway. Primarily, generalized linear model (GLM; logistic regression) analyses were used to identify the environmental variables that best accounted for the species’ recorded distributions in Norway. Second, Maxent analyses (using variables identified by GLM) were used to produce predictive potential distribution maps for these species. Results Variables relating to temperature and radiation were most frequently included in the GLMs, and between 24.8% and 59.8% of the variation in single‐species occurrence was accounted for. The fraction of variation explained by the GLMs ranged from 41.6% to 59.8% for species with restricted distributions, and from 24.8% to 39.3% for species with widespread/scattered and intermediate distributions. The two‐step procedure of GLM followed by Maxent gave predictions with very high values for the area under the curve (0.927–0.997), and maps of potential distribution were generally credible. Main conclusions We show that temperature is a key factor governing the distribution of macrofungi in Norway, indicating that fungi may respond strongly to global warming. We confirm that modelling success depends partly on species and distribution characteristics, notably on how the distribution relates to the extent of the study area. Our study demonstrates that the combination of GLM and Maxent may be a fruitful approach for biogeography. We conclude that herbarium data improve insight into factors that control the distributions of fungi, of particular value for research on fleshy fungi (mushrooms), which have largely cryptic life cycles.     

红松存在/不存在数据的多尺度空间分布格局

红松属小兴安岭地区地带性植被优势种,该地区也是其分布的北缘。在景观尺度上开展红松的分布格局研究有利于进一步了解红松分布机理、未来迁移过程等问题,对其经营和保护有重要意义。将景观指数法与点格局分析法结合,设定8个空间尺度,利用红松存在/不存在数据,通过计算各空间尺度上红松聚集程度和景观指数,分析小兴安岭地区红松种群在多尺度上的分布格局。研究结果表明,小尺度上红松聚集分布明显,随机分布区多处于其聚集分布区的边缘,均匀分布区则散布在其聚集分布区内。景观指数研究表明,通过景观指数可判断红松聚集分布格局趋势,而不能判断均匀分布、随机分布格局趋势,因为它们在多尺度下景观指数波动大,不能用景观指数来描述分布格局。研究得出如下结论:1)红松主要分布在其分布区的核心区域内,在分布区边缘和过渡带上呈随机分布,2)存在/不存在数据能够用来分析种群的多尺度空间分布格局,3)空间尺度的变化会引起树种分布格局的变化,随机分布随尺度增加,边缘化程度加强,4)单一尺度上,景观格局指数不能完全描述种群分布格局;而在多尺度上,变化趋势稳定的景观指数表明聚集分布存在,而波动剧烈的景观指数常与随机分布和均匀分布联系在一起,5)地形因子中,红松对坡度和海拔两个因子变化敏感。     

Modelling spatial patterns in harbour porpoise satellite telemetry data using maximum entropy

The distribution of harbour porpoises in EU waters is poorly understood, and modelled predictions of their distributions could inform the strategic spatial planning of future exploitation of the marine environment to avoid potential conflicts. We analysed satellite telemetry data from 39 harbour porpoises Phocoena phocoena in inner Danish waters using a modelling tool rooted in maximum entropy: Maxent. Maxent does not require absence data and has been shown to be effective for data characterised by small sample size, sampling bias and locational errors. For each season we used an iterative bootstrapping procedure to randomly select among the most precise records from each of the 39 tagged individuals, and ran Maxent on pooled records based on explanatory environmental variables hypothesised to serve as good proxies for harbour porpoise prey abundance. Among our environmental variables, distance to coast and bottom salinity had the most explanatory power, and their response shapes were relatively consistent across most seasons. The predictive power of the models (assessed by ROC‐AUC) ranged from 0.70 to 0.86 within seasons. The southern Kattegat, the Belt Seas, most western part of the Baltic Sea and the Sound were predicted to have relatively high probabilities of occurrence across seasons. In contrast, the central part of Kattegat and the Baltic Sea south and east of Limhamn and Darss Ridge consistently showed low probabilities of occurrence. Areas with the lowest probabilities of occurrence were generally characterised by high predictive uncertainty. Our methods have implications for the analyses of satellite tagged animals in terrestrial and marine environments. By coupling a bootstrapping procedure with Maxent we circumvented some of the statistical challenges presented by satellite telemetry data to generate spatial predictions within the inner Danish waters.     

Modelling the spatial distribution of linear landscape elements in Europe

Linear landscape elements, such as ditches, hedgerows, lines of trees and field margins, provide important habitats and ecosystem services and function as ecological infrastructure for species within agricultural landscapes. Spatial maps of the distribution of these elements are needed to better represent landscape structure within regional scale environmental assessments. We present wall-to-wall maps for green lines, ditches and grass margins for Europe, using spatial modelling of ground observations on linear features from the 2009 LUCAS (land use/cover area frame statistical survey) database. We compare different spatial interpolation methods, ranging from spatial autocorrelation-based methods to methods that explain the occurrence of elements based on biophysical and socio-economic information. Our results are 1 km² resolution maps of the occurrence of linear landscape elements for Europe. Independent validation of green lines based on aerial photographs showed the best results for interpolation based on regionally estimated regressions relating the occurrence of landscape elements to environmental and socio-economic location factors. The results confirm the importance of the underlying biophysical and socio-economic factors on the presence and abundance of linear landscape elements. However, the total explanatory strength of the considered factors is moderate and a considerable uncertainty in the exact distribution remains.     

Modelling spatial landscape complexity using the Levenshtein algorithm

A new measure (C_L) of spatial/structural landscape complexity is developed in this paper, based on the Levenshtein algorithm used in Computer Science and Bioinformatics for string comparisons. The Levenshtein distance (or edit distance) between two strings of symbols is the minimum of all possible replacements, deletions and insertions necessary to convert one string into the other. In this paper, it is shown how this measure can be applicable on raster landscape maps of any size or shape. Calculations and applications are shown on model and real landscapes. The main advantages of this measure for structural (spatial) landscape analysis are the following: it is easily applicable; it can be compared to its maximum value (depending on the grid resolution); it can be used to compare structural/spatial complexities between landscapes; it is applicable to raster landscape maps of any shape; and it can be used to calculate changes in landscape complexity over time. At the level of ecological practice, it may aid in landscape monitoring, management and planning, by identifying areas of higher structural landscape complexity, which may deserve greater attention in the process of landscape conservation.     

A family of functional dissimilarity measures for presence and absence data

Plot‐to‐plot dissimilarity measures are considered a valuable tool for understanding the complex ecological mechanisms that drive community composition. Traditional presence/absence coefficients are usually based on different combinations of the matching/mismatching components of the 2 × 2 contingency table. However, more recently, dissimilarity measures that incorporate information about the degree of functional differences between the species in both plots have received increasing attention. This is because such “functional dissimilarity measures” capture information on the species' functional traits, which is ignored by traditional coefficients. Therefore, functional dissimilarity measures tend to correlate more strongly with ecosystem‐level processes, as species influence these processes via their traits. In this study, we introduce a new family of dissimilarity measures for presence and absence data, which consider functional dissimilarities among species in the calculation of the matching/mismatching components of the 2 × 2 contingency table. Within this family, the behavior of the Jaccard coefficient, together with its additive components, species replacement, and richness difference, is examined by graphical comparisons and ordinations based on simulated data.     

Modelling the spatial distribution of beta diversity in Australian subtropical rainforest

Investigation of the spatial distribution of biodiversity among communities or across habitats (beta diversity) is often hampered by a scarcity of biological survey data. This is particularly the case in communities of high floristic diversity, such as the subtropical rainforests of eastern Australia. In contrast, there is excellent spatial coverage of environmental data for this region, such as geology, elevation and climate data. Generalized dissimilarity modelling was used in this study to combine biological survey data and environmental data grids for the investigation and prediction of floristic turnover among vegetation communities at a regional scale. Generalized dissimilarity modelling identified four environmental predictors of floristic turnover in the study region, all of which are linked with moisture stress: radiation of the driest quarter, precipitation of the driest period of the year, slope and aspect. Ten land classes representing largely homogeneous floristics and environment were identified and mapped for the region, allowing significantly greater discrimination than currently available mapping for this region. With increases in evapotranspiration and moisture stress predicted as a result of climate change, these results may allow future floristic shifts to be assessed in relation to regional‐scale gradients in floristic turnover.     

基于TM影像、森林资源清查数据和人工神经网络的森林碳空间分布模拟

森林是陆地生态系统中最大的碳库,在全球碳平衡和减缓全球气候变化方面发挥着不可替代的作用。当前主要利用森林资源清查数据和优势树种材积源-生物量的关系进行碳储量估算,在此基础上有效结合遥感影像数据将会更好的满足相关部门对国家和区域森林碳储量计算的需求。利用临安市2004年森林资源清查的930个样地数据和同年度Landsat TM影像数据,提取6个波段灰度值以及与碳储量相关性相对较大的3个波段组合,结合人工神经网络对研究区森林碳储量及其分布进行有效模拟。结果显示,用误差反向传播算法训练神经网络较好的重建了森林碳密度空间分布和变化,森林碳地上部分模拟结果与样地实测值之间的一致性好,全区域模拟结果森林碳平均值为0.98Mg(10.89Mg/hm²),总体森林碳密度模拟结果低于样地平均值约13%,进一步验证了人工神经网络在对大范围森林碳估算与模拟上具有较好的效果,为区域森林碳储量的估测研究提供有效的方法支持。     

Can we model the probability of presence of species without absence data?

In ecological studies, it is useful to estimate the probability that a species occurs at given locations. The probability of presence can be modeled by traditional statistical methods, if both presence and absence data are available. However, the challenge is that most species records contain only presence data, without reliable absence data. Previous presence‐only methods can estimate a relative index of habitat suitability, but cannot estimate the actual probability of presence. In this study, we develop a presence and background learning algorithm (PBL) that is successful in modeling the conditional probability of presence of a simulated species. The model is trained by two completely separate sets: observed presence and background data. Assuming that the probability of presence is one for ‘prototypical presence’ locations where the habitats are maximally suitable for a species, we can estimate a constant that can calibrate the trained model into the actual probability of presence. Experimental results show that the PBL method performs similarly to a presence‐absence method, and significantly better than the widely used maximum entropy method. The new algorithm enables us to model the probability that a species occurs conditional on environmental covariates without absence data. Hence, it has potential to improve modeling of the geographical distributions of species.     

An extension of presence/absence coefficients to abundance data: a new look at absence

Abstract. Alternative community analyses, based on quantitative and presence/absence data, are comparable logically if the data type is the only factor responsible for differences among results. For presence/absence indices that consider mutual absences, no quantitative alternatives are known. To facilitate such comparisons, a new family of similarity coefficients is proposed for abundance data. Formally, this extension is achieved by generalizing the four cells of the usual 2 × 2 contingency table to the quantitative case. This implies an expanded meaning of absence: for a given species at a given site it is understood as the difference between the actual value and the maximum detected in the entire study. The correspondence between 10 presence/absence coefficients and their quantitative counterparts is evaluated by graphical comparisons based on artificial data. The behaviour of the new functions is also examined using field data representing post‐fire regeneration processes in grasslands and a chronosequence pertaining to forest regeneration after clear‐cut. The examples suggest that the new coefficients are most informative for data sets with low beta‐diversity and temporal background changes.     

Modelling and predicting the spatial distribution of tree root density in heterogeneous forest ecosystems

Background and Aims In mountain ecosystems, predicting root density in three dimensions (3-D) is highly challenging due to the spatial heterogeneity of forest communities. This study presents a simple and semi-mechanistic model, named ChaMRoots, that predicts root interception density (RID, number of roots m^–2). ChaMRoots hypothesizes that RID at a given point is affected by the presence of roots from surrounding trees forming a polygon shape.Methods The model comprises three sub-models for predicting: (1) the spatial heterogeneity – RID of the finest roots in the top soil layer as a function of tree basal area at breast height, and the distance between the tree and a given point; (2) the diameter spectrum – the distribution of RID as a function of root diameter up to 50 mm thick; and (3) the vertical profile – the distribution of RID as a function of soil depth. The RID data used for fitting in the model were measured in two uneven-aged mountain forest ecosystems in the French Alps. These sites differ in tree density and species composition.Key Results In general, the validation of each sub-model indicated that all sub-models of ChaMRoots had good fits. The model achieved a highly satisfactory compromise between the number of aerial input parameters and the fit to the observed data.Conclusions The semi-mechanistic ChaMRoots model focuses on the spatial distribution of root density at the tree cluster scale, in contrast to the majority of published root models, which function at the level of the individual. Based on easy-to-measure characteristics, simple forest inventory protocols and three sub-models, it achieves a good compromise between the complexity of the case study area and that of the global model structure. ChaMRoots can be easily coupled with spatially explicit individual-based forest dynamics models and thus provides a highly transferable approach for modelling 3-D root spatial distribution in complex forest ecosystems.     

Testing quantitative traits for association and linkage in the presence or absence of parental data

Zhu and Elston developed a transmission disequilibrium test for quantitative traits by defining a linear transformation to condition out founder information. The method tests the null hypothesis of no linkage or association and can be applied to general pedigree structures. However, this method requires both genotype and phenotype parental information, which may be difficult to obtain. In this paper, we describe parametric and non-parametric methods to relax this requirement when only nuclear families are sampled. We show that neither method is affected by population stratification in the absence of linkage. The statistical power and validity of the tests are investigated by simulation. A simple simulation method to calculate the power of the nonparametric method is also discussed. In practice, the data may have some families with parental phenotype and genotype information available and some without. We briefly discuss how all the data may be analyzed jointly.     

重金属污染对土壤动物群落结构及空间分布的影响

2002年6月,通过对淮南煤矿和发电厂灰场周围等8个样地采样调查,共获得大型及中小型土壤动物3859个,计22类,隶属4门9纲,优势类群为弹尾类、螨类、线虫类,占全部捕获土壤动物的74%;其余为常见类群和稀有类群.重金属污染对土壤动物的影响非常明显.在洛河发电厂灰场大坝外围,土壤动物群落的个体数和类群数随着距灰场水平距离的缩小和污染的加重而减少.土壤动物群落个体数和类群数在土层的垂直分布出现了逆分布或变化和缓现象,在土壤表层的聚集性消失.随着重金属污染的加重,土壤动物群落多样性指数、均匀性指数、密度-类群指数均趋于减小,优势度指数趋于增大.