The relationship between British ground beetles (Coleoptera, Carabidae) and land cover

Aims and methods Ground beetle and satellite‐derived land cover data from 1687 United Kingdom 10 km national grid squares were used to assess the relationship between species pool and cover data in Great Britain using fuzzy classification and constrained ordination. Results Ground beetle species pools classified into nine groups which were related to land cover variables using constrained ordination. There was a strong relationship between upland land cover and three ground beetle groups. Deciduous woodland, coastal and tilled land were associated with three other groups. Three further groups did not appear to be strongly associated with any particular cover, but differed in geographical position. Conclusion The distribution of species pools derived from the British national recording scheme at the 10 km scale was strongly related to satellite‐derived land cover data. There appears to be considerable potential for the use of a synthesis of land cover and ground beetle data in the monitoring of environmental change over a large, countrywide, area.     

An investigation into the relationship between water beetle (Coleoptera) distribution and land cover in Scotland and northeast England

Aims To determine the relationship between satellite‐derived land cover data and distribution patterns of water beetle species pools. Location A total of 687 British national grid 10 km squares in Scotland and 588 1 km grid squares in 99 10 km grid squares in northeast England. Methods Multivariate classification and constrained ordination analyses were used with water beetle species and land cover data. Results The major variation in both the Scotland and northeast England 10 km species pools was from squares with upland acid water habitats to squares with lowland fens and ponds whilst for the 1 km northeast England data it was from squares dominated by fast‐flowing streams and rivers to those with fens and ponds. The secondary variations in the 10 and 1 km analyses were the primary ones juxtaposed. Constrained ordination of the Scottish 10 km data showed upland land cover types to be most influential in determining species pool distribution whilst lowland covers were more important with both the 10 and 1 km northeast England pools. The three analyses showed that coastal cover had a relatively high influence on species pool distribution but no species pool group was dominated by coastal species. Main conclusions There were strong relationships between water beetle species pool distributions and the satellite‐derived land cover types dominating upland and lowland areas. The major variation in the northeast England species pool data differed at the two scales analysed. Results indicated that there is considerable potential for the synthesis of water beetle distribution and land cover data for use in environmental and conservation monitoring at both the regional and national scales.     

Numeric classification as an aid to spectral mapping of vegetation communities

This study demonstrates a vegetation mapping methodology that relates the reflectance information contained in multispectral imagery to traditionally accepted ecological classifications. Key elements of the approach used are (a) the use of cover rather than density or presence/absence to quantify the vegetation, (b) the inclusion of physical components as well as vegetation cover to describe and classify field sites, (c) development of an objective land cover classification from this quantitative data, (d) use of the field sample sites as training areas for the spectral classification, and (e) the use of a discriminant function to effectively tie the two classifications together. Land cover over 39000 ha of Australian chenopod shrubland was classified into nine groups using agglomerative hierarchical clustering, a discriminant function developed to relate cover and spectral classes, and the vegetation mapped using a maximum likelihood classification of multi-date Landsat TM imagery. The accuracy of the mapping was assessed with an independent set of field samples and by comparison with a map of land systems previously interpreted from aerial photography. Overall agreement between the digital classification and the land system map was good. The units that have been mapped are those derived from numeric vegetation classification, demonstrating that accepted ecological methods and sound image analysis can be successfully combined.     

Assessing the application of a geographic presence-only model for land suitability mapping

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the MaxEnt model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results.     

Climate and landscape explain richness patterns depending on the type of species’ distribution data

Understanding the patterns of species richness and their environmental drivers, remains a central theme in ecological research and especially in the continental scales where many conservation decisions are made. Here, we analyzed the patterns of species richness from amphibians, reptiles and mammals at the EU level. We used two different data sources for each taxon: expert-drawn species range maps, and presence/absence atlases. As environmental drivers, we considered climate and land cover. Land cover is increasingly the focus of research, but there still is no consensus on how to classify land cover to distinct habitat classes, so we analyzed the CORINE land cover data with three different levels of thematic resolution (resolution of classification scheme ˗ less to more detailed). We found that the two types of species richness data explored in this study yielded different richness maps. Although, we expected expert-drawn range based estimates of species richness to exceed those from atlas data (due to the assumption that species are present in all locations throughout their region), we found that in many cases the opposite is true (the extreme case is the reptiles where more than half of the atlas based estimates were greater than the expert-drawn range based estimates). Also, we detected contrasting information on the richness drivers of biodiversity patterns depending on the dataset used. For atlas based richness estimates, landscape attributes played more important role than climate while for expert-drawn range based richness estimates climatic variables were more important (for the ectothermic amphibians and reptiles). Finally we found that the thematic resolution of the land cover classification scheme, also played a role in quantifying the effect of land cover diversity, with more detailed thematic resolution increasing the relative contribution of landscape attributes in predicting species richness.     

Investigating spatial structure in specific tree species in ancient semi-natural woodland using remote sensing and marked point pattern analysis

Remote sensing classification has the potential to provide important information, such as tree species distribution maps, to ecologists, at a range of spatial and temporal scales. However, standard classification procedures often fail to provide the high accuracies required for many ecological applications. Previously, a modified remote sensing classification technique was used to provide very high classification accuracies for one or two classes (e.g. species) of interest. The aim of this paper was to demonstrate that the output from the method can be suitable for spatial ecological analyses, and to provide a generic simulation framework for assessing the adequacy of any given remote sensing classification for such analyses. Marked point pattern analysis (MPPA) was applied to tree species distribution data obtained for sycamore Acer pseudoplatanus and ash Fraxinus excelsior from a 400 ha ancient semi-natural woodland in southern England using the modified remote sensing classification method to test several hypotheses of ecological interest relating to the spatial distribution and interaction of these species. Monte Carlo simulation methods were then used to evaluate the data and data quality requirements of the MPPA to check that the classified tree species maps for sycamore and ash were adequate. Using the combined method the spatial distributions for sycamore and ash were found to be aggregated and inter-dependent at a range of spatial scales. Together, the remote sensing classification and simulation approaches provide the basis for exploiting more fully the potential of remote sensing to provide information of value to ecologists.     

Locating eutrophication effects across British vegetation between 1990 and 1998

Large‐scale ecological surveillance data were analysed to determine the locations of apparent eutrophication effects across common British vegetation types between 1990 and 1998. Plant species composition was recorded from a total of 9514 fixed plots located in a stratified, random sample of 501 1 km squares across Britain. Changes in plant species composition along a gradient of substrate fertility were inferred from statistical tests of change in mean Ellenberg fertility value calculated for each fixed plot. Plots were grouped by eight vegetation types, five landscape features (hedges, road verges, watercourse banks, small biotopes, larger units in fields and unenclosed land) and six environmental zones. Tests of change in mean Ellenberg value were carried out on all combinations of these strata. Decision tree modelling was used to identify groups of test outcomes sharing the same direction of change and where each group was defined by a minimum number of strata. Post hoc power analysis was used to select statistically non‐significant test outcomes that could be used to infer stability in the sampled sub‐population. Out of a total of 142 tests of change in fertility value, 67% were increases, 8% were reductions and 25% indicated stability. The best overall predictor of increases in fertility value, and therefore of shifts in favour of plants suited to higher substrate fertility, was vegetation type. Irrespective of landscape feature and environmental zone, increased means were associated with infertile grasslands, moorland, upland woodlands and heath/bog. Already highly fertile grasslands and woodland assemblages in lowland Britain remained largely stable. The small number of decreasing test outcomes were associated with arable land in Scotland, Wales and western England. These patterns of change are hypothesized to reflect pervasive land‐use drivers combined with the inherent responsiveness of the vegetation.     

Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system

Monitoring land cover and habitat change is a key issue for conservation managers because of its potential negative impact on biodiversity. The Land Cover Classification System (LCCS) and the General Habitat Categories (GHC) System have been proposed by the remote sensing and ecological research community, respectively, for the classification of land covers and habitats across various scales. Linking the two systems can be a major step forward towards biodiversity monitoring using remote sensing. The translation between the two systems has proved to be challenging, largely because of differences in definitions and related difficulties in creating one-to-one relationships between the two systems. This paper proposes a system of rules for linking the two systems and additionally identifies requirements for site-specific contextual and environmental information to enable the translation. As an illustration, the LCCS classification of the Le Cesine protected area in Italy is used to show rules for translating the LCCS classes to GHCs. This study demonstrates the benefits of a translation system for biodiversity monitoring using remote sensing data but also shows that a successful translation is often depending on the degree of ecological knowledge of the habitats and its relationship with land cover and contextual information.     

Contrasting trends of butterfly species preferring semi-natural grasslands,field margins and forest edges in northern Europe

Indicator classifications help us to focus on the most relevant groups of species in monitoring the effects of land use changes on biodiversity. We studied changes in distribution area of 74 butterfly species preferring one of the three common habitats of boreal agricultural landscapes: semi-natural grasslands (35 species), arable field margins (7) and forest edges (32). Using extensive atlas data from four time periods during the last 50 years in Finland, we quantified trends in the occupancy of the species in 10 km grid squares, and classified them into four classes: declining (23), stable (17), increasing (27) and fluctuating (7) species. Trends among the species favouring three habitats were different: 60% of the species of semi-natural grasslands had declined, whereas 86% of the species typical of open field margins had increased. An increase also predominated in species associated with forest edges. Declining and increasing species differed in three ecological characteristics: increasing species were more mobile, utilized a wider range of habitats and, based on their larval host plants, lived in more eutrophic habitats than declining species. Species overwintering as adults showed more positive trends in occupancy than species overwintering as eggs, larvae or pupae. Observed trends in occupancy are in good agreement with long-term changes in land use and habitat availability in Finland: a long-continued decrease in the area of semi-natural grasslands and an increased amount of open forest edges and clearings due to modern forestry during the past 50 years.     

Assessing land cover change in Kenya's Mau Forest region using remotely sensed data

Kenya's Rift Valley has been undergoing rapid land cover change for the past two decades, which has resulted in ecological and hydrological changes. An effort is under way to quantify the timing and rate of these changes in and around the River Njoro watershed located near the towns of Njoro and Nakuru using remote sensing and geographic information system (GIS) methods. Three Landsat TM images, representing a 17‐year period from 1986 to 2003 in which the area underwent a significant land cover transition, were classified and compared with one another. Vegetation diversity and temporal variability, common to tropical and sub‐tropical areas, posed several challenges in disaggregating classified data into sub‐classes. An iterative approach for the resolving challenges is presented that incorporates unsupervised and supervised classification routines in coordination with knowledge‐based spatial analyses. Changes are analysed at three spatial scales ranging from the highly impacted and deforested uplands to the watershed and landscape scales. Land cover transitions primarily occurred after 1995, and included large forest losses coupled with increases in mixed small‐scale agriculture and managed pastures and degraded areas. These changes in cover type are highly spatially variable and are theorized to have significant impacts on ecological and hydrologic systems with implications for environmental sustainability.     

样本量与研究范围变化对MaxEnt模型准确度的影响——以黑白仰鼻猴为例

MaxEnt模型是过去几年最为流行的物种分布预测模型之一。针对一些濒危物种、入侵种和模拟数据的研究表明,MaxEnt模型均能在小样本的分布数据下得到较准确的预测结果。此外,研究范围的变化也会影响MaxEnt模型的构建。 然而,基于动物的实际分布数据来评估MaxEnt模型的研究甚少。 我们以黑白仰鼻猴 (Rhinopithecus bieti)为例,以11个猴群的分布数据为训练数据（样本量从1到10个猴群）,在不同研究范围内构建MaxEnt模型,通过其它5个的猴群分布数据验证,分析样本量和研究范围变化对模型准确度产生的影响。 结果表明,随样本量和研究范围增大,MaxEnt模型准确度及稳定性都有增加。 此外,研究范围变化对模型准确度有一定影响。 应用Maxent进行物种分布预测时,训练数据应尽可能涵盖该物种可能出现的全部环境梯度。构建模型所需的背景数据点选择,应与建模使用的物种出现点形成有效对照。     

Comparison of phenology trends by land cover class: a case study in the Great Basin, USA

Direct impacts of human land use and indirect impacts of anthropogenic climate change may alter land cover and associated ecosystem function, affecting ecological goods and services. Considerable work has been done to identify long‐term global trends in vegetation greenness, which is associated with primary productivity, using remote sensing. Trend analysis of satellite observations is subject to error, and ecosystem change can be confused with interannual variability. However, the relative trends of land cover classes may hold clues about differential ecosystem response to environmental forcing. Our aim was to identify phenological variability and 10‐year trends for the major land cover classes in the Great Basin. This case study involved two steps: a regional, phenology‐based land cover classification and an identification of phenological variability and 10‐year trends stratified by land cover class. The analysis used a 10‐year time series of Advanced Very High Resolution Radiometer satellite data to assess regional scale land cover variability and identify change. The phenology‐based regional classification was more detailed and accurate than national or global products. Phenological variability over the 10‐year period was high, with substantial shifts in timing of start of season of up to 9 weeks. The mean long‐term trends of montane land cover classes were significantly different from valley land cover classes due to a poor response of montane shrubland and pinyon‐juniper woodland to the early 1990s drought. The differential response during the 1990s suggests that valley ecosystems may be more resilient and montane ecosystems more susceptible to prolonged drought. This type of regional‐scale land cover analysis is necessary to characterize current patterns of land cover phenology, distinguish between anthropogenically driven land cover change and interannual variability, and identify ecosystems potentially susceptible to regional and global change.     

区域生态用地的概念及分类

随着自然资源持续利用的思想、理论逐步深入和发展,生态用地分类及规划研究具有重要意义,成为土地资源利用研究和生态建设工作的重点.总结了国内外生态用地的研究进展,在土地生态系统服务的基础上,提出了生态用地指的是区域或城镇土地中以提供生态系统服务为主的土地利用类型,可将区域土地分为"生态用地"、"生产用地"和"生活用地"三大类型,生态用地按照不同生态系统服务分为自然用地、保护区用地、休养与休闲用地和废弃与纳污用地4个二级类型,进一步分为20个三级类型.生态用地的分类可为建立合理的土地利用生态分类体系和区划提供理论基础.     

Narrowing Historical Uncertainty: Probabilistic Classification of Ambiguously Identified TreeSpecies in Historical ForestSurvey Data

Historical data have increasingly become appreciated for insight into the past conditions of ecosystems. Uses of such data include assessing the extent of ecosystem change; deriving ecological baselines for management, restoration, and modeling; and assessing the importance of past conditions on the composition and function of current systems. One historical data set of this type is the Public Land Survey (PLS) of the United States General Land Office, which contains data on multiple tree species, sizes, and distances recorded at each survey point, located at half-mile (0.8-km) intervals on a 1-mi (1.6 km) grid. This survey method was begun in the 1790s on US federal lands extending westward from Ohio. Thus, the data have the potential of providing a view of much of the US landscape from the mid-1800s, and they have been used extensively for this purpose. However, historical data sources, such as those describing the species composition of forests, can often be limited in the detail recorded and the reliability of the data, since the information was often not originally recorded for ecological purposes. Forest trees are sometimes recorded ambiguously, using generic or obscure common names. For the PLS data of northern Wisconsin, USA, we developed a method to classify ambiguously identified tree species using logistic regression analysis, using data on trees that were clearly identified to species and a set of independent predictor variables to build the models. The models were first created on partial data sets for each species and then tested for fit against the remaining data. Validations were conducted using repeated, random subsets of the data. Model prediction accuracy ranged from 81% to 96% in differentiating congeneric species among oak, pine, ash, maple, birch, and elm. Major predictor variables were tree size, associated species, landscape classes indicative of soil type, and spatial location within the study region. Results help to clarify ambiguities formerly present in maps of historic ecosystems for the region and can be applied to PLS datasets elsewhere, as well as other sources of ambiguous historical data. Mapping the newly classified data with ecological land units provides additional information on the distribution, abundance, and associations of tree species, as well as their relationships to environmental gradients before the industrial period, and clarifies the identities of species formerly mapped only to genus. We offer some caveats on the appropriate use of data derived in this way, as well as describing their potential.     

The Definition of British Water Beetle Species Pools (Coleoptera) and their Relationship to Altitude, Temperature, Precipitation and Land Cover Variables

Pooled water beetle species lists from 1826 British national grid 10-km squares were analysed using multivariate ordination and classification methods. The relationships of pool groups to the climate, altitude and land cover variables were assessed using constrained and partial ordinations. Ordination of the species pool data indicated a major trend between squares in the north-west of Scotland and those in southern England, illustrating differences in acid and basic water standing water. Secondary variation was from acid standing water to fast-flowing streams and rivers. Classification generated nine species pool groups. These showed a distinct north-west to south-east trend but there was no obvious coastal or brackish water effect on distribution. The climatic and land cover variables were all significantly related to each other, and to north-south variation in grid square location, but the constrained ordination results indicated that that the most important influence on water beetle species pool distribution was mean summer temperature. Although the amount of variation explained by the environmental variables was low, spatial variation in the environmental predictors was almost as important as the environmental variables themselves in determining species pool composition. Mean annual temperature was also strongly correlated with species pool distribution with two land cover variables slightly less important. Altitude and precipitation had the least influence. The water beetle national recording scheme database appears to be of sufficiently high quality for environmental investigations at the British scale. There is considerable potential for the synthesis of invertebrate species distribution, land cover and climate change predictions in the assessment of environmental change. The results, together with previous work on other invertebrate species, indicate that changing summer temperatures may have a considerable influence on the distribution British invertebrate species.     

Application of classification-tree models to characterize the mycobiota of grapes on the basis of origin

Classification-tree (CT) models are a simple and robust exploratory data analysis technique that can be used in classification, regressions and summaries of data. They distill complex ecological relationships into simplified rules and identify the species necessary for sample classification on the basis of detailed ecological inventories. The usefulness of this technique to characterize and represent differences in the grape mycobiota of distinct origins was evaluated. Grapes from four Portuguese winemaking regions were selected for a 3-year study: Alentejo, Douro, Ribatejo and Vinhos Verdes. The mycobiota of grapes was assessed with plating methods and the frequencies of isolations of the fungal taxa identified in 32 samples were used as a training dataset. The CT algorithm selected the fungal taxa and respective frequencies to classify grapes according to its region of origin. The ten-fold cross-validation technique was used for model evaluation. The success rate of the model was quantified and expressed in the number of correctly classified samples overall and into region. Furthermore, model refinement was performed using attribute selection algorithms and class redefinition. A simple tree model was generated that classified grapes into three regional origins: Douro, South (Alentejo and Ribatejo classes together) and Vinhos Verdes, on the basis of the incidence of Aspergillus niger aggregate and Penicillium thomii in samples with an accuracy of 82%. The merits and demerits of these models are discussed.     

Moist lower montane rainforest classification: a case study from Bwindi Impenetrable National Park,Uganda

Moist lower montane vegetation has rarely been classified beyond broad zonational belts over large altitudinal ranges due to highly diverse species composition and structure. This study shows it is possible to further classify such forest types within Bwindi‐Impenetrable National Park (BINP), and that these assemblages can be explained by a combination of environmental conditions and past management. Botanical and environmental data were collected along some 4000 m of linear transects from the area surrounding Mubwindi Swamp, BINP. Ordination using Nonmetric Multidimensional Scaling (NMDS) and classification using Two‐way Indicator Species Analysis (TWINSPAN) successfully identified four different species assemblages. These forest types were then named on the basis of the ecological characteristics of the species within the group, and the environmental conditions influencing the distribution and past disturbance of the forest. The techniques used were in agreement for three out of the four forest types identified. Analysis using an environmental overlay showed a significant association between forest type and altitude. The results of this study indicate that a regional classification of forest types within moist lower montane forest belt using only tree species is possible, and that the forest types identified can be explained by environmental conditions and past management.     

GIS-based niche models identify environmental correlates sustaining a contact zone between three species of European vipers

The current range of European vipers is mostly parapatric but local‐scale allopatric distribution is common and few cases of sympatry are known. In the High Course of Ebro River, northern Spain, there is a contact zone between Vipera aspis, V. latastei, and V. seoanei. Sympatry was detected between aspis and latastei and also specimens with intermediate morphological traits. Presence‐data at a local scale (1 × 1 km) and ecological niche‐based models manipulated in a GIS were used to (1) identify how environmental factors correlate with the distribution of the three vipers and with the location of the sympatry area, and (2) identify potential areas for viper occurrence and sympatry. Ensemble for casting with 10 Maximum Entropy models identified a mixture of topographical (altitude, slope), climatic (precipitation, evapotranspiration, and minimum and maximum temperature), and habitat factors (land cover) as predictors for viper occurrence. Similar predicted probabilities according to the variation of some environmental factors (indicating probable sympatry) were observed only for aspis‐latastei and aspis‐seoanei. In fact, areas of probable occurrence of vipers were generally allopatric but probable sympatry between vipers was identified for aspis‐latastei in 76 UTM 1 × 1 km squares, for aspis‐seoanei in 23 squares, and latastei‐seoanei in two squares. Environmental factors correlate with the location of this contact zone by shaping the species range: some enhance spatial exclusion and constrain distribution to spatially non‐overlapping ranges, while others allow contact between species. The distribution in the contact zone apparently results from the balance between the pressures exerted by the different environmental factors and in the sympatry area probably by interspecific competition. Further ecological and genetical data are needed to evaluate the dynamics of the probable hybrid zone. GIS and niche‐modelling tools proved to be powerful tools to identify environmental factors sustaining the location of contact zones.     

Exploring Spatial and Multitype Assemblages of Species Abundances

The ecological theory of the existence of multiple stable states between species, or the spatial heterogeneity of some unobserved environmental factor, supports the idea of multitype interactions between species. These multitype interactions can lead to different assemblages of species abundances. An exploratory tool for the detection of these species assemblages and for their spatial analysis is presented in this article. A two‐stage analysis is proposed. First, a classification into types of species assemblages using only the species abundances at each site, regardless of their spatial location, is performed. The clustering procedure is based on multivariate normal mixtures and provides a measure of the classification uncertainty. Second, some tools for the study of the spatial structure of these types of assemblages are presented. We transfer the classification uncertainty to the spatial analysis of the classes in order to draw more accurate conclusions. This classification and spatial analysis method is used to point out a spatial gradient of infection in a host–pathogen system in the Åland Islands in Finland. It can be a useful preliminary tool for ecological studies involving the spatial distributions of several species.     

Combining land cover mapping of coastal dunes with vegetation analysis

Question: Coastal dune systems are characterized by a natural mosaic that promotes species diversity. This heterogeneity often represents a severe problem for traditional mapping or ground survey techniques. The work presented here proposes to apply a very detailed CORINE land cover map as baseline information for plant community sampling and analysis in a coastal dune landscape. Location: Molise coast, Central Italy. Method: We analysed through an error matrix the coherence between land cover classes and vegetation types identified through a field survey. The CORINE land cover map (scale 1: 5000) of the Molise coast was used with the CORINE legend expanded to a fourth level of detail for natural and semi‐natural areas. Vegetation data were collected following a random stratified sampling design using the CORINE land cover classes as strata. An error matrix was used to compare, on a category‐by‐category basis, the relationship between vegetation types (obtained by cluster analyses of sampling plots) and land cover classes of the same area. Results: The coincidence between both classification approaches is quite good. Only one land cover class shows a very weak agreement with its corresponding vegetation type; this result was interpreted as being related to human disturbance. Conclusions: Since it is based on a standard land cover classification, the proposal has a potential for application to most European coastal systems. This method could represent a first step in the environmental planning of coastal systems.