Bioclimatic and vegetation mapping of a topographically complex oceanic island applying different interpolation techniques

Different spatial interpolation techniques have been applied to construct objective bioclimatic maps of La Palma, Canary Islands. Interpolation of climatic data on this topographically complex island with strong elevation and climatic gradients represents a challenge. Furthermore, meteorological stations are not evenly distributed over the island, with few stations at high elevations. We carried out spatial interpolations of the compensated thermicity index (Itc) and the annual ombrothermic Index (Io), in order to obtain appropriate bioclimatic maps by using automatic interpolation procedures, and to establish their relation to potential vegetation units for constructing a climatophilous potential natural vegetation map (CPNV). For this purpose, we used five interpolation techniques implemented in a GIS: inverse distance weighting (IDW), ordinary kriging (OK), ordinary cokriging (OCK), multiple linear regression (MLR) and MLR followed by ordinary kriging of the regression residuals. Two topographic variables (elevation and aspect), derived from a high-resolution digital elevation model (DEM), were included in OCK and MLR. The accuracy of the interpolation techniques was examined by the results of the error statistics of test data derived from comparison of the predicted and measured values. Best results for both bioclimatic indices were obtained with the MLR method with interpolation of the residuals showing the highest R ² of the regression between observed and predicted values and lowest values of root mean square errors. MLR with correction of interpolated residuals is an attractive interpolation method for bioclimatic mapping on this oceanic island since it permits one to fully account for easily available geographic information but also takes into account local variation of climatic data.     

A review and a framework for the integration of biodiversity monitoring at the habitat level

The monitoring of biodiversity at the level of habitats is becoming widespread in Europe and elsewhere as countries establish national habitat monitoring systems and various organisations initiate regional and local schemes. Parallel to this growth, it is increasingly important to address biodiversity changes on large spatial (e.g. continental) and temporal (e.g. decade-long) scales, which requires the integration of currently ongoing monitoring efforts. Here we review habitat monitoring and develop a framework for integrating data or activities across habitat monitoring schemes. We first identify three basic properties of monitoring activities: spatial aspect (explicitly spatial vs. non-spatial), documentation of spatial variation (field mapping vs. remote sensing) and coverage of habitats (all habitats or specific habitats in an area), and six classes of monitoring schemes based on these properties. Then we explore tasks essential for integrating schemes both within and across the major classes. Finally, we evaluate the need and potential for integration of currently existing schemes by drawing on data collected on European habitat monitoring in the EuMon project. Our results suggest a dire need for integration if we are to measure biodiversity changes across large spatial and temporal scales regarding the 2010 target and beyond. We also make recommendations for an integrated pan-European habitat monitoring scheme. Such a scheme should be based on remote sensing to record changes in land cover and habitat types over large scales, with complementary field mapping using unified methodology to provide ground truthing and to monitor small-scale changes, at least in habitat types of conservation importance.     

The effects of minimum map unit in coral reefs maps generated from high resolution side scan sonar mosaics

Changes in coral habitats from benthic maps at two minimum mapping units (4 and 400 m²) were examined across 600 ha at the Puerto Rico insular shelf. The maps were produced by visually interpreting high resolution side scan sonar seafloor mosaics. Comparisons were performed by using published spatial indices based on patch size and abundance. At the fine scale, small coral patches were dominant and had more complex shapes, while coarse scale maps lost information on small or rare habitats, but included new mixed habitat categories that helped conserve estimates of total habitat area. Variations in spatial indices occurred across spatial scales, but trends were not necessarily predictable. For instance, habitat diversity increased at the coarse scale maps, but habitat richness remained unchanged. Differences were related as much to the peculiarities of the abundance, shape and arrangement of habitats at the two sites as to changes in grain size.     

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.     

Three-Dimensional Mapping of Soil Chemical Characteristics at Micrometric Scale by Combining 2D SEM-EDX Data and 3D X-Ray CT Images

There is currently a significant need to improve our understanding of the factors that control a number of critical soil processes by integrating physical, chemical and biological measurements on soils at microscopic scales to help produce 3D maps of the related properties. Because of technological limitations, most chemical and biological measurements can be carried out only on exposed soil surfaces or 2-dimensional cuts through soil samples. Methods need to be developed to produce 3D maps of soil properties based on spatial sequences of 2D maps. In this general context, the objective of the research described here was to develop a method to generate 3D maps of soil chemical properties at the microscale by combining 2D SEM-EDX data with 3D X-ray computed tomography images. A statistical approach using the regression tree method and ordinary kriging applied to the residuals was developed and applied to predict the 3D spatial distribution of carbon, silicon, iron, and oxygen at the microscale. The spatial correlation between the X-ray grayscale intensities and the chemical maps made it possible to use a regression-tree model as an initial step to predict the 3D chemical composition. For chemical elements, e.g., iron, that are sparsely distributed in a soil sample, the regression-tree model provides a good prediction, explaining as much as 90% of the variability in some of the data. However, for chemical elements that are more homogenously distributed, such as carbon, silicon, or oxygen, the additional kriging of the regression tree residuals improved significantly the prediction with an increase in the R² value from 0.221 to 0.324 for carbon, 0.312 to 0.423 for silicon, and 0.218 to 0.374 for oxygen, respectively. The present research develops for the first time an integrated experimental and theoretical framework, which combines geostatistical methods with imaging techniques to unveil the 3-D chemical structure of soil at very fine scales. The methodology presented in this study can be easily adapted and applied to other types of data such as bacterial or fungal population densities for the 3D characterization of microbial distribution.     

Remote sensing variables as predictors of habitat suitability of the viscacha rat (<Emphasis Type="Italic">Octomys mimax</Emphasis>), a rock-dwelling mammal living in a desert environment

Identifying high-quality habitats across large areas is a central goal in biodiversity conservation. Remotely sensed data provide the opportunity to study different habitat characteristics (e.g., landscape topography, soil, vegetation cover, climatic factors) that are difficult to identify at high spatial and temporal resolution on the basis of field studies. Our goal was to evaluate the applicability of remotely sensed information as a potential tool for modeling habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling species that lives in a desert ecosystem. We fitted models considering raw indices (i.e., green indices, Brightness Index (BI) and temperature) and their derived texture measures on locations used by and available for the viscacha rat. The habitat preferences identified in our models are consistent with results of field studies of landscape use by the viscacha rat. Rocky habitats were well differentiated by the second-order contrast of BI, instead of BI only, making an important contribution to the global model by capturing the heterogeneity of the substratum. Furthermore, rocky habitats are able to maintain more vegetation than much of the surrounding desert; hence, their availability might be estimated using SATVI (Soil Adjusted Total Vegetation Index) and its derived texture measures: second-order contrast and entropy. This is the first study that evaluates the usefulness of remotely sensed data for predicting and mapping habitat suitability for a small-bodied rock dwelling species in a desert environment. Our results may contribute to conservation efforts focused on these habitat specialist species by using good predictors of habitat quality.     

Geostatistical modelling of regional bird species richness: exploring environmental proxies for conservation purpose

Identifying spatial patterns in species diversity represents an essential task to be accounted for when establishing conservation strategies or monitoring programs. Predicting patterns of species richness by a model-based approach has recently been recognised as a significant component of conservation planning. Finding those environmental predictors which are related to these patterns is crucial since they may represent surrogates of biodiversity, indicating in a fast and cheap way the spatial location of biodiversity hotspots and, consequently, where conservation efforts should be addressed. Predictive models based on classical multiple linear regression or generalised linear models crowded the recent ecological literature. However, very often, problems related with spatial autocorrelation in observed data were not adequately considered. Here, a spatially-explicit data-set on birds presence and distribution across the whole Tuscany region was analysed. Species richness was calculated within 1 × 1 km grid cells and 10 environmental predictors (e.g. altitude, habitat diversity and satellite-derived landscape heterogeneity indices) were included in the analysis. Integrating spatial components of variation with predictive ecological factors, i.e. using geostatistical models, a general model of bird species richness was developed and used to obtain predictive regional maps of bird diversity hotspots. A meaningful subset of environmental predictors, namely habitat productivity, habitat heterogeneity, combined with topographic and geographic information, were included in the final geostatistical model. Conservation strategies based on the predicted hotspots as well as directions for increasing sampling effort efficiency could be extrapolated by the proposed model.     

Spatial analysis of marine categorical information using indicator kriging applied to georeferenced video mosaics of the deep-sea Håkon Mosby Mud Volcano

The exact area calculation of irregularly distributed data is in the focus of all territorial geochemical balancing methods or definition of protection zones. Especially in the deep-sea environment the interpolation of measurements into surfaces represents an important gain of information, because of cost- and time-intensive data acquisition. The geostatistical interpolation method indicator kriging therefore is applied for an accurate mapping of the spatial distribution of benthic communities following a categorical classification scheme at the deep-sea submarine Håkon Mosby Mud Volcano. Georeferenced video mosaics were obtained during several dives by the Remotely Operated Vehicle Victor6000 in a water depth of 1260 m. Mud volcanoes are considered as significant source locations for methane indicated by unique chemoautotrophic communities as Beggiatoa mats and pogonophoran tube worms. For the detection and quantification of their spatial distribution 2840 georeferenced video mosaics were analysed by visual inspection. Polygons, digitised on the georeferenced images within a GIS, build the data basis for geostatistically interpolated mono-parametric surface maps. Indicator kriging is applied to the centroids of the polygons calculating surface maps. The quality assessment of the surface maps is conducted by leave-one-out cross-validation evaluating the fit of the indicator kriging variograms by using statistical mean values. Furthermore, the estimate was evaluated by a validation dataset of the visual inspection of 530 video mosaics not included within the interpolation, thus, proving the interpolated surfaces independently. With regard to both validating mechanisms, we attained satisfying results and we provided each category applied for the identification of biogeochemical habitats with a percentage probability value of occurrence.     

Habitat monitoring in Europe: a description of current practices

Monitoring of biodiversity at the level of habitats is becoming increasingly common. Here we describe current practices in habitat monitoring based on 150 schemes in Europe. Most schemes were initiated after 1990 in response to EU nature directives or habitat management/restoration actions, with funding mostly from European or national sources. Schemes usually monitor both the spatial distribution and the quality of the habitats, and they frequently collect data on environmental parameters and potential causes of changes. Many schemes are local or regional rather than national or international in scope, and sampling effort varies greatly across spatial and temporal scales. Experimental design is used in half of the schemes, however, data are rarely analysed by advanced statistics. Most schemes require two months or less per year in manpower and are typically run by professionals rather than by volunteers. Estimated salaries plus equipment costs average 650,000 Euro per year per scheme, and add up to 80 million Euros annually. Costs are particularly high for schemes based on European or international law and for schemes funded by European or national sources. Costs are also high in schemes in which sampling sites are selected subjectively rather than based on sampling theory, and in schemes that do not use field mapping or remote sensing to document spatial variation in habitats. Our survey demonstrates promising developments in European habitat monitoring but also underlines the need for better spatial coverage, documentation of spatial variaton, improved sampling design and advanced data analysis. Such improvements are essential if we are to judge progress towards the 2010 biodiversity targets.     

Assessing regional scale habitat area with a three dimensional measure

A method of measuring the three-dimensional (3D) area of habitats across a regionally complex cultural landscape is described. It is based on overlaying field mapped habitats from sample grid squares onto a digital terrain model of topography constructed using triangulated irregular networks to calculate a terrain corrected area (3D area). The method is suitable for regional-scale ecological studies and has the potential to be used to determine habitat metrics other than area that are sensitive to dimensionality. It is pragmatic with currently available software. The extent to which the area of habitats is underestimated by a two-dimensional (2D) area measure, compared with a 3D measure, is assessed. There was a significantly greater 3D area of all habitat types. The 2D to 3D area increase was especially large in specific seminatural habitats associated with topographic complexity and agricultural land use constraints. They included European Priority Habitats of high conservation value such as dry heath and calcareous grassland. This is of direct relevance for assessing area-based biodiversity evaluation criteria. The economic consequences of 3D area adjustments to production subsidies for agricultural habitats and agri-environment scheme payments were large if applied at a regional scale, especially in marginal upland and upland farms.     

Null models and spatial patterns of species richness in South American birds of prey

In this paper, we used geostatistical approaches to describe bi-dimensional spatial patterns in species richness of South American birds of prey (Falconiformes and Strigiformes). They indicated strong spatial patterns both across latitude and longitude, for the two groups. These patterns were then correlated with those expected by a bi-dimensional null model constructed to take into account South America continental edges. As considerable departures from the null model were observed, there may be other ecological or evolutionary explanations for spatial patterns in species richness. Variation seems to be related to habitat heterogeneity across the continent, especially when considering differences between habitats in the central and south-eastern portion of the continent and in the Andean region. This supports previous conclusions that habitat type and heterogeneity affect species richness and abundance at different spatial scales.     

基于景观及微地形特征的丘陵区土壤属性预测

为探讨小流域尺度丘陵区的高分辨率数字土壤制图方法,通过对景观相分类的探索,配合应用不同尺度的Geomorphons(GM)微地形特征数据构成分类变量组参与高分辨率土壤pH、黏粒含量和阳离子交换量的预测制图,并与传统数字高程模型衍生变量和遥感变量进行组合与比较分析。此外,采用支持向量机、偏最小二乘回归和随机森林3种机器学习模型择优与残差回归克里金复合参与预测模型的构建与评价。结果表明: 景观及多尺度微地形分类变量组的应用分别提高小流域尺度丘陵地貌区pH、黏粒含量和阳离子交换量预测精度的18.8%、8.2%和8.7%。包含植被信息的景观相分类图相比土地利用数据有更高的模型贡献度;5 m分辨率的GM微地形分类图相比低分辨率的分类图更适宜高精度的预测制图。黏粒含量使用随机森林复合模型有最高的预测精度,而pH和阳离子交换量则不适宜在随机森林模型的基础上加入残差回归克里金模型。景观-多尺度微地形分类变量、数字高程模型衍生变量和遥感变量三者结合的模型预测表现最佳,表明多元变量在起伏地形区域相比单一数据源能够包含更多的土壤有效信息。由GM数据和地表景观数据组成的景观分类变量组作为主要变量能够解释小流域丘陵区部分土壤属性约40%的空间变异。在同类型土壤预测制图研究中,多分辨率GM及景观分类数据有潜力作为环境变量参与预测模型的构建。     

Habitat selection by nocturnal passerine migrants en route: mechanisms and results

Habitat use and habitat selection are essential for successful stopovers. Passerine migrants use habitats in a clearly non-random manner, even if many species utilise a broader range of habitats during passage than during breeding or wintering. Habitat selection proceeds as a sequence of events: landfall; search/settling, including redistribution across habitats if necessary; and habitat exploitation, with all stages probably condition-dependent. This review is aimed at studying this sequence and also the factors which govern decision-making in nocturnal passerine migrants at all levels. In most cases, habitats are (pre)selected by migrants already at landfall using both visual and acoustic cues. After landfall, migrants start to perform direct sampling of habitats during which they may move constantly and perform fine-tuning of their habitat choice. Some species subsequently occupy a small home range in a particular (micro)habitats, whereas others continue to move broadly during the whole stopover period. An interaction of several factors shapes the use of habitats after landing, among which are migrants’ innate preferences and functional morphology, foraging strategies and food resource distribution, habitat carrying capacity and exposure to predators. The large-scale spatial context probably also plays a role which might be currently underestimated.     

Combining geostatistical models and remotely sensed data to improve tropical tree richness mapping

Information on the spatial distribution and composition of biological communities is essential in designing effective strategies for biodiversity conservation and management. Reliable maps of species richness across the landscape can be useful tools for these purposes. Acquiring such information through traditional survey techniques is costly and logistically difficult. The kriging interpolation method has been widely used as an alternative to predict spatial distributions of species richness, as long as the data are spatially dependent. However, even when this requirement is met, researchers often have few sampled sites in relation to the area to be mapped. Remote sensing provides an inexpensive means to derive complete spatial coverage for large areas and can be extremely useful for estimating biodiversity. The aim of this study was to combine remotely sensed data with kriging estimates (hybrid procedures) to evaluate the possibility of improving the accuracy of tree species richness maps. We did this through the comparison of the predictive performance of three hybrid geostatistical procedures, based on tree species density recorded in 141 sampling quadrats: co-kriging (COK), kriging with external drift (KED), and regression kriging (RK). Reflectance values of spectral bands, computed NDVI and texture measurements of Landsat 7 TM imagery were used as ancillary variables in all methods. The R² values of the models increased from 0.35 for ordinary kriging to 0.41 for COK, and from 0.39 for simple regression estimates to 0.52 and 0.53 when using simple KED and RK, respectively. The R² values of the models also increased from 0.60 for multiple regression estimates to 0.62 and 0.66 when using multiple KED and RK, respectively. Overall, our results demonstrate that these procedures are capable of greatly improving estimation accuracy, with multivariate RK being clearly superior, because it produces the most accurate predictions, and because of its flexibility in modeling multivariate relationships between tree richness and remotely sensed data. We conclude that this is a valuable tool for guiding future efforts aimed at conservation and management of highly diverse tropical forests.     

Geostatistical approaches and optimal additional sampling schemes for spatial patterns and future sampling of bird diversity

Aim To evaluate geostatistical approaches, namely kriging, co‐kriging and geostatistical simulation, and to develop an optimal sampling design for mapping the spatial patterns of bird diversity, estimating their spatial autocorrelations and selecting additional samples of bird diversity in a 2450 km² basin. Location Taiwan. Methods Kriging, co‐kriging and simulated annealing are applied to estimate and simulate the spatial patterns of bird diversity. In addition, kriging and co‐kriging with a genetic algorithm are used to optimally select further samples to improve the kriging and co‐kriging estimations. The association between bird diversity and elevation, and bird diversity and land cover, is analysed with estimated and simulated maps. Results The Simpson index correlates spatially with the normalized difference vegetation index (NDVI) within the micro‐scale and the macro‐scale in the study basin, but the Shannon diversity index only correlates spatially with NDVI within the micro‐scale. Co‐kriging and simulated annealing simulation accurately simulate the statistical and spatial patterns of bird diversity. The mean estimated diversity and the simulated diversity increase with elevation and decrease with increasing urbanization. The proposed optimal sampling approach selects 43 additional sampling sites with a high spatial estimation variance in bird diversity. Main conclusions Small‐scale variations dominate the total spatial variation of the observed diversity due to a lack of spatial information and insufficient sampling. However, simulations of bird diversity consistently capture the sampling statistics and spatial patterns of the observed bird diversity. The data thus accumulated can be used to understand the spatial patterns of bird diversity associated with different types of land cover and elevation, and to optimize sample selection. Co‐kriging combined with a genetic algorithm yields additional optimal sampling sites, which can be used to augment existing sampling points in future studies of bird diversity.     

太湖流域宜兴片河流生境质量评价

建立包含河道生境、河岸生境和滨岸带生境3方面共10项的河流生境质量评价指标体系,对太湖流域宜兴片42个样点进行生境评价及空间差异比较,分析不同土地利用类型下河流生境各参数的差异性,探讨河流生境质量指数与富营养化综合指数的相关关系。结果表明:河流生境质量指数分值介于29～79,31.0%样点生境质量处于较差和很差等级,57.1%样点为一般等级,表明整体上生境退化明显;南部丘陵山区河流的生境质量状况好于广大的平原河网区;不同土地利用类型下河流生境质量差异显著,林地区域的生境质量明显好于耕地和建设用地;河流生境质量指数与富营养化综合指数显著负相关,说明水质状况对河流生境质量具有较大贡献。     

Developing landscape habitat models for rare amphibians with small geographic ranges: a case study of Siskiyou Mountains salamanders in the western USA

To advance the development of conservation planning for rare species with small geographic ranges, we determined habitat associations of Siskiyou Mountains salamanders (Plethodon stormi) and developed habitat suitability models at fine (10 ha), medium (40 ha), and broad (202 ha) spatial scales using available Geographic Information Systems data and logistic regression analysis with an information theoretic approach. Across spatial scales, there was very little support for models with structural habitat features, such as tree canopy cover and conifer diameter. Model-averaged 95% confidence intervals for regression coefficients and associated odds ratios indicated that the occurrence of Siskiyou Mountains salamanders was positively associated with rocky soils and Pacific madrone (Abutus menziesii) and negatively associated with elevation and white fir (Abies concolor); these associations were consistent across 3 spatial scales. The occurrence of this species also was positively associated with hardwood density at the medium spatial scale. Odds ratios projected that a 10% decrease in white fir abundance would increase the odds of salamander occurrence 3.02–4.47 times, depending on spatial scale. We selected the model with rocky soils, white fir, and Oregon white oak (Quercus garryana) as the best model across 3 spatial scales and created habitat suitability maps for Siskiyou Mountains salamanders by projecting habitat suitability scores across the landscape. Our habitat suitability models and maps are applicable to selection of priority conservation areas for Siskiyou Mountains salamanders, and our approach can be easily adapted to conservation of other rare species in any geographical location.     

基于河段尺度的太湖流域城市河流生境评价

快速城市化背景下,太湖流域城市河流水环境与生态功能退化问题日益突出,系统开展城市河流生境评价尤为重要。本研究参考和修正了英国城市河流调查评价体系,基于太湖流域城市河流特点,构建了太湖流域城市河段生境质量指数(SHQI)评价体系,分析太湖流域城市河流生境现状及空间差异。结果表明: 50 个河段的SHQI值介于8～21,3个河段的SHQI值为好,6个为较好,27个为一般,9个为较差,5个为差。太湖流域城市河流生境中植被指数情况较好,物理生境、材质指数情况较差。生境总体状况在空间上表现为镇江>湖州>杭州>嘉兴>苏州>无锡>常州,各城市的物理生境、材质指数与污染指数存在显著差异。干流河段与支流河段的SHQI值无显著差异,但干流河段与支流河段的材质指数和污染指数差异显著。本研究构建的评价体系较好地反映了太湖流域城市河流生境现状,可为城市河流生态修复工作提供参考。