Integration of Satellite Imagery, Topography and Human Disturbance Factors Based on Canonical Correspondence Analysis Ordination for Mountain Vegetation Mapping: A Case Study in Yunnan, China

The distribution pattern of epilithic diatoms in the Gui River basin in relation to water quality,land use and topography was investigated by principal component analysis(PCA),corresponding analysis(CA),canonical correspondence analysis(CCA) and partial CCA.Twenty-four sites were sampled throughout the basin,ranging from the mainstream to first order streams.The data indicated that electrical conductivity(EC) increased from upstream to downstream,while other water quality parameters varied across wide ranges.A total of 112 diatom taxa were found in the basin,but only 37 taxa or more were observed in more than 5% of the samples.The most abundant species were Achnanthidium minutissimum,A.pusilla,A.tropica and Cymbella laevis,in order of abundance.Three different groups of taxa were identified,located in the headwater,middle and lowland zones of basin.Nitzschia recta was the dominant species in the lowland zone,A.lanceolata,Amphora montan,and Planothidium frequentissimum were more abundant in the middle zone,while high species diversity was typical of the headwaters.Biological Diatom Index(IBD) and specific PolluoSensitivity Index(IPS) were significantly related to many parameters including Chl.a,NH4-N,altitude,sub-basin catchment area and land use.The first two Principal component analysis axes explained 56.20% of the water quality variance,with the first axis significantly related to NH4-N,NO3-N and TN,and the second axis significantly related to water temperature,pH,EC and dissolved oxygen(DO).The first two Canonical correspondence analysis(CCA) axes collectively explained 28.60% of the species-environment variation.The first CCA axis was significantly positively related to water quality(EC,temperature,NH4-N,NO3-N,and TN),significantly negatively related to land use(areas of urban,agriculture and forest),and significantly positively related to topography(basin areas,altitude and slope).The second two CCA axes were significantly positively related to turbidity.Partial CCA analyses showed that water quality explained a high proportion(48.50%) of the variance,while land use and topographic factors explained 7.20% and 17.50% of the variance respectively.The results indicated that the distribution of diatom assemblages in the Gui River basin was strongly related to water quality parameters,as expected,but was also sensitive to land use and topography.

Soil erosion in the agricultural area of a hill slope is a fundamental issue for crop productivity and environmental sustainability. Building terrace is a very popular way to control soil erosion, and accurate assessment of the soil erosion rate is important for sustainable agriculture and environmental management. Currently, many soil erosion estimations are mainly based on the freely available medium or coarse resolution digital elevation model (DEM) data that neglect micro topographic modification of the agriculture terraces. The development of unmanned aerial vehicle (UAV) technology enables the development of high-resolution (centimeter level) DEM to present accurate topographic features. To demonstrate the sensitivity of soil erosion estimates to DEM resolution at this high-resolution level, this study tries to evaluate soil erosion estimation in the Middle Hill agriculture terraces in Nepal based on UAV derived high-resolution (5 × 5 cm) DEM data and make a comparative study for the estimates by using the DEM data aggregated into different spatial resolutions (5 × 5 cm to 10 × 10 m). Firstly, slope gradient, slope length, and topographic factors were calculated at different resolutions. Then, the revised universal soil loss estimation (RUSLE) model was applied to estimate soil erosion rates with the derived LS factor at different resolutions. The results indicated that there was higher change rate in slope gradient, slope length, LS factor, and soil erosion rate when using DEM data with resolution from 5 × 5 cm to 2 × 2 m than using coarser DEM data. A power trend line was effectively used to present the relationship between soil erosion rate and DEM resolution. The findings indicated that soil erosion estimates are highly sensitive to DEM resolution (from 5 × 5 cm to 2 × 2 m), and the changes become relatively stable from 2 × 2 m. The use of DEM data with pixel size larger than 2 × 2 m cannot detect the micro topography. With the insights about the influencing mechanism of DEM resolution on soil erosion estimates, this study provides important suggestions for appropriate DEM data selection that should be investigated first for accurate soil erosion estimation.

Mountainous areas in synthetic aperture radar (SAR) images suffer severe geometric distortions caused by different look directions. Consequently, ground control point (GCP) extraction hardly obtains accurate results for aircraft positioning. Based on the digital elevation model (DEM) and polarimetric SAR (PolSAR) data, we propose a method for extracting GCPs in mountainous areas by introducing the polarization orientation angle shift (POAS) to minimize geometric distortions. In this method, DEM data are used as the reference map by providing POASs at arbitrary look directions to make up for the look-direction sensitivity of POASs transformed from PolSAR data. The geometric distortions between POASs transformed from DEM and PolSAR data are effectively reduced by calculating the POASs from DEM data at the same look direction and look angles of the PolSAR data. In contrast to the SAR data, which have a large dynamic range, the values of POAS are limited to a small interval. Therefore, the illumination distortions induced by visualization can be reduced. Finally, the GCP extraction between the POAS images is conducted by bilateral filter scale-invariant feature transform. Experiments using various data at different look directions demonstrate that the proposed method obtains better-quality GCPs but less invalid keypoints than the method using only intensity images for mountainous areas.

Aims Many past practices in afforestation and forest management were instrumented for addressing the issues of tree species selection, planting regimes and development of overstory structure, but neglected understory vegetation. Our objective was to determine the controlling factors of herb-layer plant distribution and the importance of topography in determining local-scale spatial patterns of herbaceous plants. Methods The occurrence and distribution of herb-layer plants were investigated on 26 plots in the Xiaoshegou catchment of Lingkong Mountain, Shanxi Province, China. Community types were classified using two-way indicator species analysis (TWINSPAN) and the relationship between the distribution and abundance of herb-layer species and environmental gradients was analyzed using the canonical correspondence analysis (CCA) ordination method. Forward selection and Monte Carlo permutation test were used to select the factors important in determining the herb-layer plant distribution. Partial CCA (PCCA) was also performed to partition the variance that was explainable by categorical habitat and biotic factors studied. Important findings The 26 survey plots were classified into six groups characterized by the dominant overstory tree species. The results of CCA ordination reflected the relationship between herb-layer community structure and selective environmental variables. The classification of 26 plots in CCA ordination was consistent with the result of TWINSPAN. Forward selection and Monte Carlo test suggested that stand type, soil nutrients and slope position were the most important factors determining understory plant distribution. PCCA revealed that habitat and biotic factors together explained 42.9% of variance in the distribution of understory herbaceous plants. Habitat factors alone explained 31.8% of the variance, biotic factors alone explained 7.8% of the variance and interactionbetween habitat and biotic factors explained 3.2% of the variance. The partitioning of variance using the PCCA helped with identifying the important habitat factors regulating understory herbaceous plant distribution at the study site. However, the fact that more than half of the variance was unaccounted for by the factors studied suggests that other factors we did not measure could also play a role in determining the occurrence and distribution of herbaceous plants on the forest floor, e.g., human activities and random events. Our study demonstrates the importance of both topography and overstory tree species in determining the occurrence and distribution of herb-layer plant species in temperate forest of mountainous areas.

The accelerated pace of urbanization has significant effects on the community composition, structure, regional distribution, and diversity characteristics of vegetation within urban river corridors. Spontaneous plants have strong environmental adaptability, high plasticity, and shorter life cycles; they also operate largely independently of human control. As a result, they are widely distributed throughout urban river corridors, and their ability to respond rapidly to heterogeneous habitats within these corridors makes them an ideal subject for studying the reciprocal mechanisms between rapid urbanization and riverine biodiversity. Based on a survey of 208 plots across six river corridors in Changchun, China, we found that the hydrological gradient was the strongest predictor of spontaneous herbaceous community distribution among the environmental factors examined. A total of 181 native herbaceous plant species, belonging to 55 families and 140 genera, were recorded. The Asteraceae, Poaceae, Fabaceae, Lamiaceae, and Polygonaceae families dominated. TWINSPAN classification divided the native herbaceous plant communities into 11 types, with the dominant species being predominantly low-growing perennial herbaceous plants. Canonical correspondence analysis (CCA) ordination confirmed this pattern, showing that the community distribution from aquatic to terrestrial habitats primarily aligned along the first CCA axis (defined by water depth and canopy cover), while the second axis reflected gradients in anthropogenic disturbance and slope. Thus, even in intensively managed urban rivers, natural hydrological processes remain pivotal in shaping riparian plant community composition and enhancing biodiversity. This study provides a scientific foundation for the conservation and sustainable utilization of plant resources in urban river corridors.

Due to the influences of local topographical factors and terrain inter-shielding, calculation of direct solar radiation (DSR) quantity of rugged terrain is very complex. Based on digital elevation model (DEM) data and meteorological observations, a distributed model for calculating DSR over rugged terrain is developed. This model gives an all-sided consideration on factors influencing DSR. Using the developed model, normals of annual DSR quantity with a resolution of 1 km × 1 km for the Yellow River Basin was generated, with DEM data as the general characterization of terrain. Characteristics of DSR quantity influenced by geographic and topographic factors over rugged terrain were analyzed thoroughly. Results suggest that: influenced by local topographic factors, i.e. azimuth, slope and so on, and annual DSR quantity over mountainous area has a clear spatial difference; annual DSR quantity of sunny slope (or southern slope) of mountains is obviously larger than that of shady slope (or northern slope). The calculated DSR quantity of the Yellow River Basin is provided in the same way as other kinds of spatial information and can be employed as basic geographic data for relevant studies as well.

To assess how the Exxon Valdez oil spill affected habitat occupancy by communities of marine-oriented birds in Prince William Sound, Alaska, we conducted a canonical correspondence analysis (CCA) using data collected between 1989 and 1996 in 10 bays that had been exposed to different levels of oiling. CCA creates a multivariate space defined by a combination of environmental variables and measures of the abundances of the bird species present in the bays. The locations of individual sites (site scores) in this multivariate space indicate how bird-community composition at the sites varies in relation to the combination of environmental variables, while the locations of individual bird species (species scores) indicate the mean values of the response curves (abundance variations) of species on the CCA axes. Relationships among the site scores of sites exposed to different levels of oiling may therefore be used to evaluate the effects of the spill on the bird communities occupying these sites, and the relationship of a species score to the site scores indicates the conditions under which the species is, on average, most abundant. In surveys conducted the summer after the oil spill, the site scores for unoiled or lightly oiled (“unoiled”) bays occupied a region of the CCA space that was largely separate from that occupied by the moderately or heavily oiled (“oiled”) bays, indicating consistent differences in bird-community composition. Although the structure of the CCA ordination and site locations in that space varied among seasons and years, the separation of the unoiled and oiled site scores in the CCA space continued. Rather than indicating continuing spill effects, however, the separation of site scores was probably related to environmental differences among the bays and chance differences among bays in the reassembly of bird communities following the spill. Consideration of the locations of species scores in the CCA space clarified the patterns of spill impacts and subsequent recovery. The increase in the number of bird species present in early summer surveys between 1989 and 1990 was associated with the occupancy of previously oiled bays by species that were absent in 1989 or that previous analyses had shown to be initially impacted by the spill. Species scores for birds for which the previous analyses showed continuing spill impacts in early summer 1990 were associated most closely with the site scores of the unoiled bays. The species scores of several species that occurred too infrequently to be evaluated in the previous studies were associated with the site scores of the unoiled bays in 1989, suggesting initial spill impacts. In subsequent years, these species scores were associated with the site scores of the previously oiled bays, suggesting reoccupancy of those bays. Patterns for surveys conducted in midsummer generally were similar to those in early summer. Surveys conducted in midsummer 1996 suggested continuing recovery in the use of initially oiled habitats by birds, including some species that did not show clear evidence of recovery in 1991. Overall, these analyses showed that the Exxon Valdez oil spill had clear initial impacts on bird species and communities in the study bays in Prince William Sound, but they also provided clear evidence of increasing occupancy of previously oiled sites. Multivariate analyses such as CCA can provide valuable insights into the complex responses of environments and biological communities to large-scale contamination events when precontamination data are lacking, especially when the analyses are repeated over time and the changing relationships of both species and site scores in the multivariate space are considered.

Uncertainty can be apprehended as lack of knowledge about a certain phenomenon. Decisions about whether and how to react to this uncertainty depend on a number of factors. These factors include the ability to estimate the amount of uncertainty and thus estimate the involved risk, available options to decrease either the uncertainty or its relevance, and the costs for responding or ignoring uncertainty.
\nIn GIScience, the modelling of processes is subject to uncertainties from a number of sources. Above all, the abstraction inherent in any model results in uncertainty,
\ncreated from the assumptions made to simplify complex processes and interrelations in order to formalise and model them. Additionally, uncertainty in any input data
\npropagates through a model into the results. For topography-based models, i.e. models characterising and detecting topographic form, or models simulating processes that act upon this topography, digital elevation models (DEMs) are a potential source of uncertainty. DEMs consist of measured or digitised elevation values, and as such are
\nsubject to any error in the data capturing process. Widespread DEMs such as GLOBE or SRTM are distributed with accuracy figures that only give global measures such as
\nroot mean square error (RMSE) lacking any information on the spatial distribution of error. Where uncertainty from DEM accuracy has to be modelled to assess its impact on the results of associated topographic models, assumptions have to be made about the spatial distribution of uncertainty. Within this dissertation it has been shown that these assumptions influence the impact of uncertainty on modelled ice sheets. Besides DEM accuracy, a number of factors in handling DEM data introduce additional uncertainty. These factors include the choice of data model, processing such as projecting and resampling of a DEM data, as well as algorithms used to extract and process elevation based information.
\nWithin this dissertation, the influence of resampling on uncertainty in topography has been explored. This was done by assessing the variation in resampled DEMs introduced
\nby changing the source and target resolution, choice of resampling algorithms and resampling origin. When these uncertainties were modelled and added to input topographies for the GLIMMER ice sheet model, they had noticeable influence on modelled ice sheet configurations. Where higher accuracy reference data for a DEM is available, error can be derived and analysed to provide information about spatial autocorrelation and possible dependencies of error with topographic attributes such as elevation, slope or roughness. Within the course of this dissertation, an uncertainty model was developed which allows modelling of GLOBE DEM uncertainty for areas without higher accuracy reference data such as Scandinavia. The model is based on derived dependencies of GLOBE error with topographic attributes, derived from areas where SRTM data was available to be used as a reference. The model includes both deterministic and stochastic components and reproduces GLOBE DEM uncertainty well for different test areas.
\nThe developed uncertainty model was applied to investigate the impact of DEM uncertainty on different types of models in three case studies. The first case study applied a geomorphologic and hydrologic model (TARDEM), the second case study used two snow melt models, and in the third case study the GLIMMER ice sheet model was employed. Results showed the impact of uncertainty to be depending on a number of facts. Generally, modelled DEM uncertainty had less impact on derived global topographic variables such as mean slope length or the number of derived watersheds when
\napplied to a hydrological model. Higher impacts were recorded where the model focus was on local processes, such as the delineation of a certain watershed and calculation
\nof associated parameters such as hypsometry. For process models like the ice sheet model, factors such as terrain configuration (smooth vs. rough topography, abundant
\nridges or valleys) influenced the impact of DEM uncertainty on ice sheet model (ISM)results.
\nAdditionally, the amount of uncertainty and its spatial correlation, as well as the relative influence of topography within a model were found to play key roles. This implies that for process models, the impact of uncertainty can vary over time. In the case of the ice sheet model, uncertainty had the greatest impact on ice sheet configuration during phases of inception and retreat, and its impact was shown to be dependent on the overall size of the ice masses.
\nIn another set of experiments, a range of sensitivity tests using different ISM parameters and input data were conducted, and the results of these tests were used to
\nconduct a full parametric uncertainty analysis (PUA) for a steady-state climate scenario on Fennoscandia. Results from this analysis allowed the comparison of the influence
\nof uncertainty in other parameters to that of DEM uncertainty, which was found to be equivalent to a 1degC change in climate. The impact of DEM uncertainty was found to be comparable to that of various ‘internal’ ISM parameters. However modelled DEM uncertainty resulted in significantly different ice sheet configurations. This underlines the importance of DEM uncertainty to be considered in ice sheet modelling.
\nUsing different temperature index models (TIM) to model potential snow melt across different resolutions revealed significant impact of scale and resampling on modelled melt rates. This effect was substantially decreased by the use of subgrid model approaches. While it was shown that these subgrid approaches are subject to an increased susceptibility to DEM uncertainty, this effect was more than compensated for by an increased performance in terms of modelled melt rates.
\nIn summary, the results of this dissertation underline the necessity of detailed information on the statistical and spatial distribution of DEM uncertainty to be included
\nwith the data. Additionally, in topographic modelling, uncertainty from other sources such as resampling have shown to be of importance, and modellers and end-users
\nshould account for these uncertainties introduced into model results.

DEM Explorer: An online interoperable DEM data sharing and analysis system

Spatial variability of aquatic macroinvertebrates was examined in riffles of second to fourth order streams in Onondaga Creek, in central New York, USA. Even with only small differences in stream sizes, aquatic macroinvertebrates were distributed primarily by a headwater-to-valley gradient as defined by mean stream width and water surface slope. Secondary and tertiary gradients were based on hydraulic character. Direct gradient analysis using canonical correspondence analysis (CCA) examined the common and rare (respectively, < 5—0.5% and 0.5—0.001% of total) macroinvertebrate assemblage among sites. The variables used in the analysis were mean wetted width, water surface slope, mean Froude number and Froude number variance. The first CCA axis explained 43.1% of the variability. Froude number variance and Froude number affected the second and third CCA axes most strongly. The gradients revealed by the second and third CCA axes did not influence lower gradient valley sites, but did separate the headwater sites based on hydraulic character. Taxa were also distributed by functional feeding groups (i.e., collector-gatherer). The headwater-to-valley gradient did separate taxa slightly by functional feeding groups; however, the hydraulic gradient clearly separated scrapers and shredders from collector filterers.

Aim The aim of this study was to determine whether the Casiquiare River functions as a free dispersal corridor or as a partial barrier (i.e. filter) for the interchange of fish species of the Orinoco and Negro/Amazon basins using species assemblage patterns according to geographical location and environmental features.Location The Casiquiare, Upper Orinoco and Upper Negro rivers in southern Venezuela, South America.Methods Our study was based on an analysis of species presence/absence data and environmental information (11 habitat characteristics) collected by the authors and colleagues between the years 1984 and 1999. The data set consisted of 269 sampled sites and 452 fish species (&gt; 50,000 specimens). A wide range of habitat types was included in the samples, and the collection sites were located at various points along the entire length of the Casiquiare main channel, at multiple sites on its tributary streams, as well as at various nearby sites outside the Casiquiare drainage, within the Upper Orinoco and Upper Rio Negro river systems. Most specimens and field data used in this analysis are archived in the Museo de Ciencias Naturales in Guanare, Venezuela. We performed canonical correspondence analysis (CCA) based on species presence/absence using two versions of the data set: one that eliminated sites having &lt; 5 species and species occurring at &lt; 5 sites; and another that eliminated sites having &lt; 10 species and species occurring at &lt; 10 sites. Cluster analysis was performed on sites based on species assemblage similarity, and a separate analysis was performed on species based on CCA loadings.Results The CCA results for the two versions of the data set were qualitatively the same. The dominant environmental axis contrasted assemblages and sites associated with blackwater vs. clearwater conditions. Longitudinal position on the Casiquiare River was correlated (r2 = 0.33) with CCA axis‐1 scores, reflecting clearwater conditions nearer to its origin (bifurcation of the Orinoco) and blackwater conditions nearer to its mouth (junction with the Rio Negro). The second CCA axis was most strongly associated with habitat size and structural complexity. Species associations derived from the unweighted pair‐group average clustering method and pair‐wise squared Euclidean distances calculated from species loadings on CCA axes 1 and 2 showed seven ecological groupings. Cluster analysis of species assemblages according to watershed revealed a stronger influence of local environmental conditions than of geographical proximity.Main conclusions Fish assemblage composition is more consistently associated with local environmental conditions than with geographical position within the river drainages. Nonetheless, the results support the hypothesis that the mainstem Casiquiare represents a hydrochemical gradient between clearwaters at its origin and blackwaters at its mouth, and as such appears to function as a semi‐permeable barrier (environmental filter) to dispersal and faunal exchanges between the partially vicariant fish faunas of the Upper Orinoco and Upper Negro rivers.

High-resolution Digital Elevation Model (DEM) data provides essential information for pluvial flood simulation. Although the increased accessibility and quality of publicly available DEM datasets can facilitate geospatial analysis at various scales, existing DEM datasets with global coverage mostly lack sufficient spatial resolution for pluvial flood simulations, which require detailed topographic information to be included in the simulation. Simulating flood scenarios with low-resolution DEMs (&gt;30m) can result in substantial deviations from real cases. This issue becomes even more severe for flood-prone areas in data-scarce developing countries.Image super-resolution is a technique for reconstructing low-resolution information into high-resolution data. Various deep-learning models have been employed for this task, primarily focusing on generating high-resolution natural-colour images. However, the effects of these deep learning models on enhancing the resolution of DEM data have not been extensively investigated. One of the state-of-the-art super-resolution models, the Residual Channel Attention Network (RCAN), has gained popularity due to its accuracy and efficiency. Leveraging publicly available low-resolution global DEM data and high-resolution regional DEM data, this study assesses the performance of RCAN models in a DEM super-resolution task. The experimental results suggest that, compared to conventional interpolation methods, the tested RCAN model exhibits superior performance in constructing high-resolution DEM data. The generated super-resolution DEM data were then tested in pluvial flood simulations and achieved substantially higher realism in modelling floodwater distribution. The proposed method for constructing super-resolution DEMs opens up the possibility of simulating flooding at hyper-resolution globally.

Digital elevation model (DEM) data describe the information of ground elevation. So it is important to protect the copyright of digital elevation model data. A lossless visible 3-D watermarking algorithm to protect DEM data is proposed in this paper. The copyright watermarking is embedded in the DEM data by an visible way. The original data, blocked by 3-D visible watermarking, are hidden in the watermarked DEM data by a generalized histogram algorithm. Because the visible watermark blocks a part of DEM data, illegal users are restricted to retrieve. At the same time, 3-D visible watermark can identify the copyright. Without original 3-D watermark data, authorized users can eliminate the 3-D visible watermark and restore the original DEM data lossless by applying the proposed algorithm. It is a blind watermarking algorithm. Experiments demonstrate that the proposed algorithm has satisfactory security and can effectively protect the copyright of DEM data.

Soil particle-size distribution (PSD) is one of the most fundamental physical attributes of soil due to its strong influence on other soil properties related to water movement, productivity, and soil erosion. Characterizing variation of PSD in soils is an important issue in environmental research. Using ordination methods to characterize particle size distributions (PSDs) on a small-scale is very limited. In this paper, we selected the Cele River Basin on the north slope of the Middle Kunlun Mountains as a study area and investigated vegetation and soil conditions from 1960 to 4070 m a.s.l. Soil particle-size distributions obtained by laser diffractometry were used as a source data matrix. The Canonical Correspondence Analysis (CCA) ordination was applied to analyse the variation characteristics of PSDs and the relationships between PSDs and environmental factors. Moreover, single fractal dimensions were calculated to support the interpretation of the ordination results. Our results indicate that a differentiation of 16 particle fractions can sufficiently characterize the PSDs in CCA biplots. Elevation has the greatest effect on PSDs: the soil fine fractions increase gradually with increasing elevation. In addition, soil pH, water and total salt content are significantly correlated with PSDs. CCA ordination biplots show that soil and vegetation patterns correspond with one another, indicating a tight link between soil PSDs and plant communities on a small scale in arid regions. The results of fractal dimensions analysis were rather similar to CCA ordination results, but they yielded less detailed information about PSDs. Our study shows that ordination methods can be beneficially used in research into PSDs and, combined with fractal measures, can provide comprehensive information about PSDs.

Frequency data for epiphytic bryophytes in systematically sampled 2 x 2 km grid squares within a belt transect aligned S. W. to N. E. across southern Britain were analysed by canonical correspondence analysis (CCA). The environmental data used in the analysis included variables representing climate, atmospheric pollutants, forest cover, geology, altitude and presence of water courses. The two most important canonical gradients obtained expressed the effects of moisture availability and atmospheric pollution/geology on the epiphytic flora. Frullania tamarisci, Neckera pumila, Metzgeria temperata, Microlejeunea ulicina and Hypnum andoi were restricted to tetrads with high moisture availability, whereas Syntrichia ruralis, Grimmia pulvinata, Tortula muralis and Aulacomnium androgynum only occurred as epiphytes in tetrads with low moisture status. At one end of the second CCA axis were species characteristic of acid woodland, including Dicranum tauricum and D. montanum, which may be indicative of elevated deposition of sulphur and nitrogen from atmospheric pollution. Calcicole bryophytes and taxa that avoid acid substrata (e.g. Syntrichia laevipila, S. papillosa and Ulota phyllantha) were positioned on the other end of this axis. The CCA axes were used to generate log-linear regression models for individual epiphyte species. For 12 species, fitted distributions for the transect tetrads were compared with the observed distributions and used to predict epiphyte distributions for a wide area of southern Britain based on an independent set of environmental data. A separate analysis was made of the associations between particular epiphytes and their phorophytes by detrended correspondence analysis (DCA). The primary DCA axis represented a sequence of epiphytes from species more commonly associated with calcareous masonry (e.g. Syntrichia ruralis and Grimmia pulvinata) to calcifuges such as Tetraphis pellucida, Hypnum jutlandicum, Dicranum montanum and Orthodontium lineare. The corresponding ranking of host trees was from Sambucus nigra, Malus sp., Ulmus spp., Salix spp. and Acer campestre, all species characterized by nearly neutral or nutrient-rich bark, to those with strongly acidic bark including Carpinus betulus, Betula spp. and Castanea sativa.