Incorporating ecological principles into statistical models for the prediction of species’ distribution and abundance

Understanding the determinants of species’ distributions and abundances is a central theme in ecology. The development of statistical models to achieve this has a long history and the notion that the model should closely reflect underlying scientific understanding has encouraged ecologists to adopt complex statistical methods as they arise. In this paper we describe a Bayesian hierarchical model that reflects a conceptual ecological model of multi‐scaled environmental determinants of riverine fish species’ distributions and abundances. We illustrate this with distribution and abundance data of a small‐bodied fish species, the Empire gudgeon Hypseleotris galii, in the Mary and Albert Rivers, Queensland, Australia. Specifically, the model sought to address; 1) the extent that landscape‐scale abiotic variables can explain the species’ distribution compared to local‐scale variables, 2) how local‐scale abiotic variables can explain species’ abundances, and 3) how are these local‐scale relationships mediated by landscape‐scale variables. Overall, the model accounted for around 60% of variation in the distribution and abundance of H. galii. The findings show that the landscape‐scale variables explain much of the distribution of the species; however, there was considerable improvement in estimating the species’ distribution with the addition of local‐scale variables. There were many strong relationships between abundance and local‐scale abiotic variables; however, several of these relationships were mediated by some of the landscape‐scale variables. The extent of spatial autocorrelation in the data was relatively low compared to the distances among sampling reaches. Our findings exemplify that Bayesian statistical modelling provides a robust framework for statistical modelling that reflects our ecological understanding. This allows ecologists to address a range of ecological questions with a single unified probability model rather than a series of disconnected analyses.     

Landscape heterogeneity metrics as indicators of bird diversity: Determining the optimal spatial scales in different landscapes

Species distribution models are often used to study the biodiversity of ecosystems. The modelling process uses a number of parameters to predict others, such as the occurrence of determinate species, population size, habitat suitability or biodiversity. It is well known that the heterogeneity of landscapes can lead to changes in species’ abundance and biodiversity. However, landscape metrics depend on maps and spatial scales when it comes to undertaking a GIS analysis.We explored the goodness of fit of several models using the metrics of landscape heterogeneity and altitude as predictors of bird diversity in different landscapes and spatial scales. Two variables were used to describe biodiversity: bird richness and trophic level diversity, both of which were obtained from a breeding bird survey by means of point counts. The relationships between biodiversity and landscape metrics were compared using multiple linear regressions. All of the analyses were repeated for 14 different spatial scales and for cultivated, forest and grassland environments to determine the optimal spatial scale for each landscape typology.Our results revealed that the relationships between species’ richness and landscape heterogeneity using 1:10,000 land cover maps were strongest when working on a spatial scale up to a radius of 125–250 m around the sampled point (circa 4.9–19.6 ha). Furthermore, the correlation between measures of landscape heterogeneity and bird diversity was greater in grasslands than in cultivated or forested areas. The multi-spatial scale approach is useful for (a) assessing the accuracy of surrogates of bird diversity in different landscapes and (b) optimizing spatial model procedures for biodiversity mapping, mainly over extensive areas.     

Statistical modelling of the spatial distribution of prevalence of Calicophoron daubneyi infection in sheep from central Italy

Statistical modelling for Disease Mapping and Ecological Analysis is of particular importance in veterinary parasitology because environmental characteristics can affect parasite distribution. However, the main difficulties relate to the concentration of animal populations within farms, which contrasts to the study of wild animal populations. In the present paper we report the results of a cross-sectional coprological survey designed to study the presence and distribution of the rumen fluke Calicophoron daubneyi--which causes paramphistomosis, a snail borne disease--in pastured sheep living in the Latina province of central Italy. We show how techniques derived from human epidemiology can be used to study the spatial distribution of parasite infection in animals. We proposed a hierarchical Bayesian model with random terms for unstructured variability (heterogeneity) to account for local farm characteristics and spatially structure terms (clustering) to cope with medium-large scale environmental characteristics.     

基于移动窗口法的岷江干旱河谷景观格局梯度分析

以岷江干旱河谷为研究区,基于GIS技术和移动窗口法对其景观格局梯度变化进行分析,以期为区域的景观格局优化和管理提供支持。根据研究区的形状特征,分别沿干流和支流设置4条样带;选取景观水平下的景观指数,利用FRAGSTATS3.3软件分别采取标准法和移动窗口法获得不同尺度下的景观指数值;综合利用景观指数粒度效应分析、区域面积信息守恒评价方法和景观指数幅度效应评价曲线确定研究区景观格局梯度分析移动窗口尺寸,并计算了此窗口尺度下4条样带上的景观指数,得到沿样带方向的景观梯度格局。结果表明:岷江干旱河谷的景观基质是灌木林地,面积占73.82%。有林地和草地的景观异质性低,居民地和耕地斑块破碎化程度较大。确定了50m的栅格大小,250m的移动窗口尺寸为研究区景观格局梯度分析的窗口尺度;4条样带上各景观指数均随景观类型变化出现不同幅度的上下波动特征,梯度特征明显。处于景观类型过渡地带的区域,景观多样性和异质性增加,破碎化程度高;干旱河谷景观格局梯度变化主要受地形、水热因子,以及堤坝建设和土地开发利用影响。研究作为一种有益的尝试,更精细地分析了研究区的景观格局,能够为河谷地区景观格局量化分析提供参考,但也存在一定不足,需要在今后工作中继续深入研究。     

The distribution of pond snail communities across a landscape: separating out the influence of spatial position from local habitat quality for ponds in south-east Northumberland,UK

Ponds support a rich biodiversity because the heterogeneity of individual ponds creates, at the landscape scale, a diversity of habitats for wildlife. The distribution of pond animals and plants will be influenced by both the local conditions within a pond and the spatial distribution of ponds across the landscape. Separating out the local from the spatial is difficult because the two are often linked. Pond snails are likely to be affected by both local conditions, e.g. water hardness, and spatial patterns, e.g. distance between ponds, but studies of snail communities struggle distinguishing between the two. In this study, communities of snails were recorded from 52 ponds in a biogeographically coherent landscape in north-east England. The distribution of snail communities was compared to local environments characterised by the macrophyte communities within each pond and to the spatial pattern of ponds throughout the landscape. Mantel tests were used to partial out the local versus the landscape respective influences. Snail communities became more similar in ponds that were closer together and in ponds with similar macrophyte communities as both the local and the landscape scale were important for this group of animals. Data were collected from several types of ponds, including those created on nature reserves specifically for wildlife, old field ponds (at least 150 years old) primarily created for watering livestock and subsidence ponds outside protected areas or amongst coastal dunes. No one pond type supported all the species. Larger, deeper ponds on nature reserves had the highest numbers of species within individual ponds but shallow, temporary sites on farm land supported a distinct temporary water fauna. The conservation of pond snails in this region requires a diversity of pond types rather than one idealised type and ponds scattered throughout the area at a variety of sites, not just concentrated on nature reserves. Handling editor: B. Oertli     

Characterizing the spatial distribution of giant pandas (Ailuropoda melanoleuca) in fragmented forest landscapes

Aim To examine the effects of forest fragmentation on the distribution of the entire wild giant panda (Ailuropoda melanoleuca) population, and to propose a modelling approach for monitoring the spatial distribution and habitat of pandas at the landscape scale using Moderate Resolution Imaging Spectro‐radiometer (MODIS) enhanced vegetation index (EVI) time‐series data. Location Five mountain ranges in south‐western China (Qinling, Minshan, Qionglai, Xiangling and Liangshan). Methods Giant panda pseudo‐absence data were generated from data on panda occurrences obtained from the third national giant panda survey. To quantify the fragmentation of forests, 26 fragmentation metrics were derived from 16‐day composite MODIS 250‐m EVI multi‐temporal data and eight of these metrics were selected following factor analysis. The differences between panda presence and panda absence were examined by applying significance testing. A forward stepwise logistic regression was then applied to explore the relationship between panda distribution and forest fragmentation. Results Forest patch size, edge density and patch aggregation were found to have significant roles in determining the distribution of pandas. Patches of dense forest occupied by giant pandas were significantly larger, closer together and more contiguous than patches where giant pandas were not recorded. Forest fragmentation is least in the Qinling Mountains, while the Xiangling and Liangshan regions have most fragmentation. Using the selected landscape metrics, the logistic regression model predicted the distribution of giant pandas with an overall accuracy of 72.5% (κ = 0.45). However, when a knowledge‐based control for elevation and slope was applied to the regression, the overall accuracy of the model improved to 77.6% (κ = 0.55). Main conclusions Giant pandas appear sensitive to patch size and isolation effects associated with fragmentation of dense forest, implying that the design of effective conservation areas for wild giant pandas must include large and dense forest patches that are adjacent to other similar patches. The approach developed here is applicable for analysing the spatial distribution of the giant panda from multi‐temporal MODIS 250‐m EVI data and landscape metrics at the landscape scale.     

广州市主城区树冠覆盖景观格局梯度

城市树冠覆盖是城市森林在小尺度上的景观表达,是衡量城市森林生态功能服务质量的重要指标.通过在ArcGIS9.2中对广州市中心城区的高分辨率航片进行目视解译,对形成的树冠覆盖专题图分析并生成栅格数据,利用Fragstats3.3软件分别选择标准方法和移动窗口方法分析研究区景观格局特征.其中基于移动窗口法形成了一系列基于所选择格局特征的连续栅格表面,每一个栅格单元代表的是设定的移动窗口半径尺度下景观类型的格局特征.分析表明,研究区景观格局呈现明显的空间异质性梯度特征.基于样带区尺度效应分析的结果表明,粒度3-5m和移动窗口半径400-600m可适合于研究区的景观格局梯度分析,选择的景观指数能够形成较为平滑的栅格表面.基于移动窗口的分析结果以连续变量图的形式对景观格局空间异质性进行可视化表达,能够为小尺度上的城市森林景观格局优化提供重要的参考.     

景观格局-土壤侵蚀研究中景观指数的意义解释及局限性

景观格局分析是景观生态学研究的重要组成部分。景观指数是景观格局分析的有力工具。近年来,景观格局与土壤侵蚀关系的相关研究增多,常规景观格局指数得到应用。但针对土壤侵蚀过程的景观指数意义解释不足,景观指数在刻画景观格局-土壤侵蚀过程关系存在局限。选择了连接性、多样性、边界/斑块密度、形状4个方面的12个常用景观指数,对这些指数在景观格局-土壤侵蚀过程关系研究中的意义进行阐述,对指数应用的局限性及其原因进行了分析。景观数据属性、景观指数本身性质和土壤侵蚀过程的复杂性使得常规景观格局指数在景观格局-土壤侵蚀关系研究中存在不足。这3方面的影响使得常规景观格局指数与土壤侵蚀表征变量之间不存在确定的关系,从而难以通过景观指数来表征景观土壤侵蚀特征。缺乏土壤侵蚀过程基础是常规景观指数在土壤侵蚀研究应用中存在局限的主要原因。因此,构建基于土壤侵蚀过程的景观指数是景观格局-土壤侵蚀关系研究的需要和新的发展方向。     

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。     

Local environmental effects and spatial effects in macroecological studies using mapped abundance classes: the case of the rook Corvus frugilegus in Scotland

1. The study of the spatial pattern of species abundance is complicated by statistical problems, such as spatial autocorrelation of the abundance data, which lead to the confusion of environmental effects and dispersal. 2. Atlas-derived data for the rook in Scotland are used as a case study to propose an approach for assessing the likely contribution of dispersal and local environmental effects, based on a Bayesian Conditional Autoregressive (CAR) approach. 3. The availability of moist grasslands is a key factor explaining the spatial pattern of abundance. This is influenced by a combination of climatic and soil-related factors. A direct link to soil properties is for the first time reported for the wide-scale distribution of a bird species. In addition, for this species, dispersal seems to contribute significantly to the spatial pattern and produces a smoother than expected decline in abundance at the north-western edge of its distribution range. Areas where dispersal is most likely to be important are highlighted. 4. The approach described can help ecologists make more efficient use of atlas data for the investigation of the structure of species abundance, and can highlight potential sink areas at the landscape and regional scale. 5. Bayesian spatial models can deal with data autocorrelation in atlas-type data, while clearly communicating uncertainty through the estimation of the full posterior probability distribution of all parameters.     

Does local habitat fragmentation affect large‐scale distributions? The case of a specialist grassland bird

Aim

Although the negative effects of habitat fragmentation have been widely documented at the landscape scale, much less is known about its impacts on species distributions at the biogeographical scale. We hypothesize that fragmentation influences the large‐scale distribution of area‐ and edge‐sensitive species by limiting their occurrence in regions with fragmented habitats , despite otherwise favourable environmental conditions. We test this hypothesis by assessing the interplay of climate and landscape factors influencing the distribution of the calandra lark, a grassland specialist that is highly sensitive to habitat fragmentation.

Location

Iberia Peninsula, Europe.

Methods

Ecological niche modelling was used to investigate the relative influence of climate/topography, landscape fragmentation and spatial structure on calandra lark distribution. Modelling assumed explicitly a hierarchically structured effect among explanatory variables, with climate/topography operating at broader spatial scales than landscape variables. An eigenvector‐based spatial filtering approach was used to cancel bias introduced by spatial autocorrelation. The information theoretic approach was used in model selection, and variation partitioning was used to isolate the unique and shared effects of sets of explanatory variables.

Results

Climate and topography were the most influential variables shaping the distribution of calandra lark, but incorporating landscape metrics contributed significantly to model improvement. The probability of calandra lark occurrence increased with total habitat area and declined with the number of patches and edge density. Variation partitioning showed a strong overlap between variation explained by climate/topography and landscape variables. After accounting for spatial structure in species distribution, the explanatory power of environmental variables remained largely unchanged.

Main conclusions

We have shown here that landscape fragmentation can influence species distributions at the biogeographical scale. Incorporating fragmentation metrics into large‐scale ecological niche models may contribute for a better understanding of mechanism driving species distributions and for improving predictive modelling of range shifts associated with land use and climate changes.

     

Complex effects of scale on the relationships of landscape pattern versus avian species richness and community structure in a woodland savanna mosaic

Landscape pattern metrics are widely used for predicting habitat and species diversity. However, the relationship between landscape pattern and species diversity is typically measured at a single spatial scale, even though both landscape pattern, and species occurrence and community composition are scale‐dependent. While the effects of scale on landscape pattern are well documented, the effects of scale on the relationships between spatial pattern and species richness and composition are not well known. Here, our main goal was to quantify the effects of cartographic scale (spatial resolution and extent) on the relationships between spatial pattern and avian richness and community structure in a mosaic of grassland, woodland, and savanna in central Wisconsin. Our secondary goal was to evaluate the effectiveness of a newly developed tool for spatial pattern analysis, multiscale contextual spatial pattern analysis (MCSPA), compared to existing landscape metrics. Landscape metrics and avian species richness had quadratic, exponential, or logarithmic relationships, and these patterns were generally consistent across two spatial resolutions and six spatial extents. However, the magnitude of the relationships was affected by both resolution and extent. At the finer resolution (10‐m), edge density was consistently the best predictor of species richness, followed by an MCSPA metric that measures the standard deviation of woody cover across extents. At the coarser resolution (30‐m), NDVI was the best predictor of species richness by far, regardless of spatial extent. Another MCSPA metric that denotes the average woody cover across extents, together with percent of woody cover, were always the best predictors of variation in avian community structure. Spatial resolution and extent had varying effects on the relationships between spatial pattern and avian community structure. We therefore conclude that cartographic scale not only affects measures of landscape pattern per se, but also the relationships among spatial pattern, species richness, and community structure, often in complex ways, which reduces the efficacy of landscape metrics for predicting the richness and diversity of organisms.     

粒度变化对城市热岛空间格局分析的影响

尺度是景观格局和生态过程研究中的关键问题。综合目前城市热岛效应研究来看,景观格局指数的引入极大推进了热岛格局的定量研究,然而其尺度效应仍未得到重视。由于热岛空间格局与形成过程的复杂性和人类认识的局限性,对其尺度问题有待深入讨论。基于Landsat TM影像反演地表温度,采用均值-标准差分类方法划分热力等级,对珠三角城市热岛格局特征的粒度效应进行了研究。结果显示:随粒度增加,弱势热力斑块类型下降,向相邻斑块转移;景观指数在类型水平和景观水平均受空间粒度影响明显,"临界粒度"现象明显;总体而言,粒度150 m是城市热岛格局特征的临界粒度,对热岛格局进行景观指数计算的适宜粒度范围为30—150 m;不同景观指数粒度效应曲线有所差异,其中斑块密度和平均分维数在两个水平指数上都有较强的规律性;根据各景观指数变化特征,研究区尺度域主要在(30 m,150 m),表明在该尺度范围内构建的热岛效应预测模型可经简单推绎后使用。分析热岛格局随空间粒度变化特征对了解热岛格局的形成机制及进行尺度推绎具有重要意义。     

基于多源遥感数据的景观格局及预测研究

以TM、中巴资源卫星和环境与灾害监测预报小卫星等遥感影像为数据源,利用ENVI 4.7、ARCGIS 9.2、IDRISI 15等软件,研究了西安市辖区的景观特征与空间格局,预测了未来的景观变化,提出了景观格局预测的数据转化和多距离空间分析的精简步骤.结果表明:研究区的景观本底是一个由林地和耕地构成的复合景观基质,建设用地在研究时段内呈现持续增加趋势,且2004-2011年间的增加量高于2000-2004年间的增加量;林地面积略有降低,林地和草地总面积略呈增长趋势,水域和未利用地面积变化较小.研究时段内的景观破碎化程度在降低,林地景观的连通性增强了,耕地的降低了.各景观类型在所设定的最大预期研究尺度下均呈现显著的聚集空间格局;各年和各景观类型之间的聚集、随机和离散的临界阈值差别相对比较大;水域和未利用地的空间聚集强度明显高于耕地、林地、草地和城乡建设用地;耕地和草地空间分布存在一个异质性最大的特征尺度,且均出现了聚集分布、随机分布和离散分布3种分布格局,以2011年最为明显.利用景观指数法和多距离空间聚类分析方法研究景观格局特征的效果要比单一的景观指数法较理想.CA-Markov模型模拟的结果基本能够反映未来的景观格局状况.     

Determinants of Spatial Distribution in a Bee Community: Nesting Resources,Flower Resources,and Body Size

Understanding biodiversity distribution is a primary goal of community ecology. At a landscape scale, bee communities are affected by habitat composition, anthropogenic land use, and fragmentation. However, little information is available on local-scale spatial distribution of bee communities within habitats that are uniform at the landscape scale. We studied a bee community along with floral and nesting resources over a 32 km² area of uninterrupted Mediterranean scrubland. Our objectives were (i) to analyze floral and nesting resource composition at the habitat scale. We ask whether these resources follow a geographical pattern across the scrubland at bee-foraging relevant distances; (ii) to analyze the distribution of bee composition across the scrubland. Bees being highly mobile organisms, we ask whether bee composition shows a homogeneous distribution or else varies spatially. If so, we ask whether this variation is irregular or follows a geographical pattern and whether bees respond primarily to flower or to nesting resources; and (iii) to establish whether body size influences the response to local resource availability and ultimately spatial distribution. We obtained 6580 specimens belonging to 98 species. Despite bee mobility and the absence of environmental barriers, our bee community shows a clear geographical pattern. This pattern is mostly attributable to heterogeneous distribution of small (<55 mg) species (with presumed smaller foraging ranges), and is mostly explained by flower resources rather than nesting substrates. Even then, a large proportion (54.8%) of spatial variability remains unexplained by flower or nesting resources. We conclude that bee communities are strongly conditioned by local effects and may exhibit spatial heterogeneity patterns at a scale as low as 500–1000 m in patches of homogeneous habitat. These results have important implications for local pollination dynamics and spatial variation of plant-pollinator networks.     

Towards more predictable and consistent landscape metrics across spatial scales

Habitat change and fragmentation are considered key drivers of environmental change and biodiversity loss. To understand and mitigate the effects of such spatial disturbances on biological systems, it is critical to quantify changes in landscape pattern. However, the characterization of spatial patterns remains complicated in part because most widely used landscape metrics vary with the amount of usable habitat available in the landscape, and vary with the scale of the spatial data used to calculate them. In this study, we investigate the nature of the relationship between intrinsic characteristics of spatial pattern and extrinsic scale-dependent factors that affect the characterization of landscape patterns. To do so, we used techniques from modern multivariate statistics to disentangle widely used landscape metrics with respect to four landscape components: extent (E), resolution (R), percentage of suitable habitat cover (P), and spatial autocorrelation level (H). Our results highlight those metrics that are less sensitive to change in spatial scale and those that are less correlated. We found, however, significant and complex interactions between intrinsic and extrinsic characteristics of landscape patterns that will always complicate researcher's ability to isolate purely landscape pattern driven effects from the effects of changing spatial scale. As such, our study illustrates the need for a more systematic investigation of the relationship between intrinsic characteristics and extrinsic properties to accurately characterize observed landscape patterns.     

黄土丘陵沟壑区景观格局对流域侵蚀产沙过程的影响——斑块类型水平

在黄土丘陵沟壑区,景观格局对侵蚀产沙过程有着复杂的影响,且与尺度密切相关。选取河口-龙门区间内42个水文站控制流域为研究对象,以侵蚀模数、输沙模数和泥沙输移比作为表征各流域单元内土壤侵蚀、产沙及泥沙输移过程的特征指标,运用景观指数和CCA排序,系统分析了斑块类型水平上景观格局对流域侵蚀产沙过程的影响。结果表明:流域侵蚀产沙及泥沙输移过程中,空间分异特征随景观类型不同而异;对于不同用地类型,影响"过程"空间分异的景观格局指标不同,显著影响流域侵蚀产沙及泥沙输移过程的景观指数有草地平均斑块面积(AREA_MN3)、居民建设用地景观面积百分比(PLAND5)、居民建设用地和其它类型用地景观的斑块密度(PD5和PD6),其中斑块密度(PD)是影响流域侵蚀产沙及泥沙输移过程的共性指标;草地、居民建设用地、其它类型用地的景观格局特征对"过程"变化的解释程度要高于其它景观类型。开展景观格局与生态过程关系研究时,不仅需要考虑景观格局的整体效应,更应关注单一景观类型及其格局特征对一些生态过程的指示意义。     

Assessing ecological habitat structure from local to landscape scales using synthetic aperture radar

Ecological studies need accurate environmental data such as vegetation characterization, landscape structure and organization, to predict and explain the spatial distribution of biodiversity. Few ecological studies use remote sensing data to assess the biophysical or structural properties of vegetation to understand species distribution. To date, synthetic aperture radar (SAR) data have seldom been used for ecological applications. However, these sensors provide data allowing access to the inner structure of vegetation which is a key information in ecology. The objective of this article is to compare the predictive power of ecological habitat structure variables derived from a TerraSAR-X image, an aerial photograph and a SPOT-5 image for species distribution. The test was run with a hedgerow network in Brittany and assessed the spatial distribution of the forest ground carabid beetles which inhabit these hedgerows. The results confirmed that radar and optical images can be indifferently used to extract hedgerow network and derived landscape metrics (hedgerow density, network grain) useful to explain the spatial distribution of forest carabid beetles. In comparison with passive optical remotely sensed data, VHSR SAR images provide new data to characterize vegetation structure and more particularly hedgerow canopy cover, a variable known to explain the spatial distribution of carabid beetles in an agricultural landscape, but not yet quantified at a fine scale. The hedgerow canopy cover derived from the SAR image is a strong predictor of the abundance of forest carabid beetles at two scales i.e., a local scale and a landscape scale.     

Towards indicators of butterfly biodiversity based on a multiscale landscape description

The conservation of biodiversity has gained prominence in ecological research for the last decades. Conservation actions require a measure of biodiversity such as species richness, but its assessment is very difficult, even for small areas and therefore the search for surrogates (i.e. indicators) of biodiversity has emerged as an active research topic. We investigated the relationships between butterfly species richness and landscape structure and composition in two pine plantation sites in Southwest France. We assessed the correlation between butterfly species richness and a set of 15 landscape metrics computed for 18 land-uses at 10 different spatial scales. Spatial scales were accounted for by computing landscape metrics for circular buffers with radius ranging from 100 to 1000 m. The joint use of the Partial Least Squares Regression (PLSR) and a stepwise regression procedure revealed strong correlations between butterfly species richness and various landscape metrics in both study sites. The selected landscape metrics differed from one site to another and mostly involved measures of landscape fragmentation. We found a very strong effect of the spatial scale of investigation upon the perception of the landscape–butterfly richness relationship. Our main conclusions are that (i) certain landscape attributes can potentially serve as indicators for butterfly species richness at the landscape scale; (ii) future indicators of biodiversity based on landscape features should consider various spatial scales.     

徐州城市景观格局对绿地植物多样性的多尺度影响

城市地表景观的变化导致城市生物栖息地的损坏、破碎和丧失,致使城市生物多样性下降.为探讨城市景观格局变化对植物多样性的影响,本研究以徐州市绕城高速公路内的城区为对象,采用布朗布伦奎特(Braun-Blanquet)盖度等级法对36块城市绿地植物群落进行调查,并对不同绿地植物群落的物种多样性进行了分析.同时,以研究区2005和2015年的Landsat ETM遥感影像作为基础数据源,在Erdas、GIS、Fragstats软件的支持下,以每块调查样地为中心,分别建立9个不同半径的缓冲区,计算各缓冲区内表征斑块形状、破碎度和连通性共3类39个景观指数,在此基础上,运用冗余分析(RDA)筛选出不同幅度下对城市绿地植物多样性有显著影响的景观格局指数.结果表明: 植物多样性对景观整体格局和景观要素的响应具有明显的时间和空间尺度效应.2005年,未利用地和农田的破碎度、形状指数在800 m半径的缓冲区内能更好地解释植物多样性,建筑用地的破碎度和连通性指数在800～2000 m范围内对植物多样性有重要影响;2015年,草地和林地景观在800 m缓冲区内可更好地解释植物多样性,而建筑用地和未利用地在1000～2000 m范围内对植物多样性有显著影响;研究区当前植物多样性受2005年类型水平景观指数的影响更显著,说明研究区植物多样性对周边景观格局变化的响应存在一定的时间滞后;景观水平的格局指数与类型水平指数特征较为一致,说明物种与空间尺度的相互作用更多依赖于景观类型.