Surface metrics: scaling relationships and downscaling behavior

Context

Considerable research has examined scale effects for patch-based metrics with the ultimate goal of predicting values at finer resolutions (i.e., downscaling), but results have been inconsistent. Surface metrics have been suggested as an alternative to patch-based metrics, although far less is known about their scaling relationships and downscaling potential. If successful, downscaling would enable integration of disparate datasets and comparison of landscapes using different resolution datasets.

Objectives

(1) Determine how surface metrics scale as resolution changes and how consistent those scaling relationships are across landscapes. (2) Test whether these scaling relationships can be accurately downscaled to predict metric values for finer resolutions.

Methods

Various scaling functions were fit to 16 surface metrics computed for multiple resolutions for a set of landscapes. Best-fitting functions were then extrapolated to test downscaling behavior (i.e., predict metric value for a finer resolution) for an independent set of validation landscapes. Relative error was assessed between the predicted and true values to determine downscaling robustness.

Results

Seven surface metrics (Sa, Sq, S10z, Sdq, Sds, Sdr, Srwi) fit consistently well (R² > 0.99) with a 3rd order polynomial or power law. Of those, the scaling functions for Sa, Sq, and S10z were able to predict metric values at a finer resolution within 5 %. Three metrics, (Ssk, Sku, Sfd) were also notable in terms of fit and downscaling.

Conclusions

Many metrics exhibit consistent scaling relations across resolution, and several are able to accurately predict values at finer resolutions. However, prediction accuracy is likely related to the amount of information lost during aggregation.

     

Linking greenhouse gas emissions to urban landscape structure: the relevance of spatial and thematic resolutions of land use/cover data

Context

Emissions of greenhouse gases in urban areas play an important role in climate change. Increasing attention has been given to urban landscape structure–emission relationships (SERs). However, it remains unknown if and to what extent SERs are dependent on observational scale.

Objective

To assess how changing observational scales (in terms of spatial and thematic resolutions) of urban landscape structure affect SERs.

Methods

We examined correlations between 16 landscape metrics and greenhouse gas emissions across 52 European cities, through (1) systematic manipulation of spatial and thematic resolutions of the urban land use/cover (ULUC) dataset, and (2) comparison between available standard ULUC datasets with different spatial resolutions.

Results

Our analyses showed that the observed SERs significantly depend on both thematic and spatial resolutions of the ULUC data. For the 16 landscape metrics, we found diverse spatial/thematic scaling relations exhibiting monotonic, hump-shaped or scale-invariant trends. For different landscape metrics, the SERs were strongest at different spatial scales, suggesting that there is no consistent scaling relation over those observational scales.

Conclusions

SERs are highly sensitive to spatial and thematic resolutions of landscape data. To avoid the problem of ‘ecological fallacy,’ important caveats should be taken for interpretations based on single landscape metrics. Particular consideration should be paid to metrics that are easily interpretable, predictable in scaling behaviors, and important for shaping SERs, such as PLAND, ED, and LPI. Systematic investigations on scaling behaviors of SERs over well-defined scale domains are encouraged in future studies linking greenhouse gas emissions and urban landscape structure.

     

Empirical patterns of the effects of changing scale on landscape metrics

While ecologists are well aware that spatial heterogeneity is scale-dependent, a general understanding of scaling relationships of spatial pattern is still lacking. One way to improve this understanding is to systematically examine how pattern indices change with scale in real landscapes of different kinds. This study, therefore, was designed to investigate how a suite of commonly used landscape metrics respond to changing grain size, extent, and the direction of analysis (or sampling) using several different landscapes in North America. Our results showed that the responses of the 19 landscape metrics fell into three general categories: Type I metrics showed predictable responses with changing scale, and their scaling relations could be represented by simple scaling equations (linear, power-law, or logarithmic functions); Type II metrics exhibited staircase-like responses that were less predictable; and Type III metrics behaved erratically in response to changing scale, suggesting no consistent scaling relations. In general, the effect of changing grain size was more predictable than that of changing extent. Type I metrics represent those landscape features that can be readily and accurately extrapolated or interpolated across spatial scales, whereas Type II and III metrics represent those that require more explicit consideration of idiosyncratic details for successful scaling. To adequately quantify spatial heterogeneity, the metric-scalograms (the response curves of metrics to changing scale), instead of single-scale measures, seem necessary.This revised version was published online in May 2005 with corrections to the Cover Date.     

Sensitivity and effectiveness and of landscape metric scalograms in determining the characteristic scale of a hierarchically structured landscape

Landscape metric scalograms (the response curves of landscape metrics to changing grain size) have been used to illustrate the scale effects of metrics for real landscapes. However, whether they detect the characteristic scale of hierarchically structured landscapes remains uncertain. To address this question, the scalograms of 26 class-level metrics were systematically examined for a simple random landscape, seven hierarchical neutral landscapes, and the real landscape of the Xilin River Basin of Inner Mongolia, China. The results show that when the fraction of the focal patch type (P) is below a critical value (P _c), most metric scalograms are sensitive to change in single-scale and lower-level hierarchical structure and insensitive to change in higher-level hierarchical structure. The scalograms of only a few metrics measuring spatial aggregation and connectedness are sensitive to change in intermediate-level hierarchical structure. Most metric scalograms explicitly identify the characteristic scale of a single-scale landscape and fine or intermediate characteristic scales of a multi-scale landscape for both simulated and real landscapes. When P exceeds P _c, only some metrics detect scale and change in structure. The scalograms of total class area and Euclidean nearest-neighbor distance cannot detect scale or change in structure in either case. Landscape metric scalograms are useful for addressing scale issues, including illustrating the scale effects of spatial patterns, detecting multi-scale patterns, and developing possible scaling relations.

     

The transiogram as a graphic metric for characterizing the spatial patterns of landscapes

Context

Landscape metrics play an important role in measurement, analysis, and interpretation of spatial patterns of landscapes. There are a variety of different landscape metrics widely used in landscape ecology. However, existing landscape metrics are mostly non-graphic and single-value indices, which may not be sufficient to describe the complex spatial correlation and interclass relationships of various landscapes. As a transition probability diagram over the lag distance, the transiogram, which emerged in recent years, essentially provides a new graphic metric for measuring and visualizing the auto and cross correlations of landscape categories.

Objectives

To explore the capability of the transiogram for measuring spatial patterns of categorical landscape maps and compare it with existing landscape metrics.

Methods

Sixteen commonly-used landscape metrics and transiograms (including auto- and cross-transiograms) were estimated and compared for land cover/use classes in four areas with different landscapes.

Results

Results show that (1) these transiograms can provide visual information about the proportions, aggregation levels, interclass adjacencies, and intra-class/interclass correlation ranges of landscape classes; (2) sills and auto-correlation ranges of transiograms are correlated with the values of some landscape metrics; and (3) the peak height ratios of idealized transiograms can effectively represent the juxtaposition strength of neighboring class pairs.

Conclusions

The transiogram can be an effective graphic metric for characterizing the auto-correlation of single classes (through auto-transiograms) and the complex interclass relationships, such as interdependency and juxtaposition, between different landscape classes (through cross-transiograms).

     

Evaluation of landscape connectivity at community level using satellite-derived NDVI

Landscape connectivity, defined as the degree to which the landscape facilitates or impedes movement among resource patches, has been considered to be a key issue for biodiversity conservation. However, the use of landscape connectivity measurements has been strongly criticised due to uncertainties in the methods used and the lack of validation. Moreover, measurements are typically restricted to the population level, whereas management is generally carried out at the community level. Here, we used satellite imagery and network metrics to predict the landscape connectivity at community level for semi-natural herbaceous patches in an urban area near Paris (France). We tested different measurement methods, both taking into account and ignoring the spatial heterogeneity of matrix resistance estimated by the normalised difference vegetation index (NDVI), and quantifying the link strength between patches with the shortest path and flow metrics. We assessed the fit of these connectivity predictions with empirical data on plant communities embedded in an urban matrix. Our results indicate that the best fit with the empirical data is obtained when the connectivity is estimated with the flow metric and takes into account the matrix heterogeneity. Overall, our study helps to estimate the landscape connectivity of urban areas and makes recommendations for ways in which we might optimise landscape planning with respect to conservation of urban biodiversity.     

Surface metrics: an alternative to patch metrics for the quantification of landscape structure

Modern landscape ecology is based on the patch mosaic paradigm, in which landscapes are conceptualized and analyzed as mosaics of discrete patches. While this model has been widely successful, there are many situations where it is more meaningful to model landscape structure based on continuous rather than discrete spatial heterogeneity. The growing field of surface metrology offers a variety of surface metrics for quantifying landscape gradients, yet these metrics are largely unknown and/or unused by landscape ecologists. In this paper, we describe a suite of surface metrics with potential for landscape ecological application. We assessed the redundancy among metrics and sought to find groups of similarly behaved metrics by examining metric performance across 264 sample landscapes in western Turkey. For comparative purposes and to evaluate the robustness of the observed patterns, we examined 16 different patch mosaic models and 18 different landscape gradient models of landscape structure. Surface metrics were highly redundant, but less so than patch metrics, and consistently aggregated into four cohesive clusters of similarly behaved metrics representing surface roughness, shape of the surface height distribution, and angular and radial surface texture. While the surface roughness metrics have strong analogs among the patch metrics, the other surface components are largely unique to landscape gradients. We contend that the surface properties we identified are nearly universal and have potential to offer new insights into landscape pattern–process relationships. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Spatial and semantic dimensions of landscape heterogeneity

This paper addresses the challenge of measuring spatial heterogeneity in categorical map data. Spatial heterogeneity is a complex notion that involves both spatial variability and attribute variability, and metrics to capture this are a product of their developers’ simplifying assumptions about both spatial and attribute dimensions. We argue that the predominantly binary treatment of categorical data is frequently an unnecessary oversimplification that can be replaced by ordered measures based on semantic similarity evaluations. We develop a typology of autocorrelation metrics for categorical data that identifies a critical gap: existing measures are limited in their ability to capture variability of both spatial and attribute dimensions simultaneously. We demonstrate an approach to formally characterize the semantic similarity between pairs of categorical data classes as a continuous numeric variable. A series of experiments on synthetic and actual land cover data contrasts the information content provided by metrics representative of all portions of the typology: the recently proposed semantic variogram, the indicator variogram, the contagion index, and the edge contrast index. Experimental results suggest that the typology captures essential qualities of metric information richness. Among our findings is that the commonly used contagion index is directly correlated with Moran’s I for 2-class maps but it fails to distinguish between negatively and positively autocorrelated patterns. We identify the semantic variogram as the only metric that can simultaneously detect both spatial and semantic attribute aspects of categorical autocorrelation. The semantic variogram is also relatively robust to attribute scale changes and therefore less sensitive to class aggregation than the other metrics.     

Scaling properties in landscape patterns: New Zealand experience

In this paper we present a case study of spatial structure in landscape patterns for the North and South Islands of New Zealand. The aim was to characterise quantitatively landscape heterogeneity and investigate its possible scaling properties. The study examines spatial heterogeneity, in particular patchiness, at a range of spatial scales, to help build understanding on the effects of landscape heterogeneity on water movement in particular, and landscape ecology in general.We used spatial information on various landscape properties (soils, hydrogeology, vegetation, topography) generated from the New Zealand Land Resource Inventory. To analyse this data set we applied various methods of fractal analyses following the hypothesis that patchiness in selected landscape properties demonstrates fractal scaling behaviour at two structural levels: (1) individual patches; and (2) mosaics (sets) of patches.Individual patches revealed scaling behaviour for both patch shape and boundary. We found self-affinity in patch shape with Hurst exponent H from 0.75 to 0.95. We also showed that patch boundaries in most cases were self-similar and in a few cases of large patches were self-affine. The degree of self-affinity was lower for finer patches. Similarly, when patch scale decreases the orientation of patches tends to be uniformly distributed, though patch orientation on average is clearly correlated with broad scale geological structures. These results reflect a tendency to isotropic behaviour of individual patches from broad to finer scales. Mosaics of patches also revealed fractal scaling in the total patch boundaries, patch centers of mass, and in patch area distribution. All these reflect a special organisation in patchiness represented in fractal patch clustering. General relationships which interconnect fractal scaling exponents were derived and tested. These relationships show how scaling properties of individual patches affect those for mosaics of patches and vice-versa. To explain similarity in scaling behaviour in patchiness of different types we suggest that the Self-Organised Criticality concept should be used. Also, potential applications of our results in landscape ecology are discussed, especially in relation to improved neutral landscape models.     

Ecosystem services of regulation and support in Amazonian pioneer fronts: searching for landscape drivers

Landscape dynamics result from forestry and farming practices, both of which are expected to have diverse impacts on ecosystem services (ES). In this study, we investigated this general statement for regulating and supporting services via an assessment of ecosystem functions: climate regulation via carbon sequestration in soil and plant biomass, water cycle and soil erosion regulation via water infiltration in soil, and support for primary production via soil chemical quality and water storage. We tested the hypothesis that patterns of land-cover composition and structure significantly alter ES metrics at two different scales. We surveyed 54 farms in two Amazonian regions of Brazil and Colombia and assessed land-cover composition and structure from remote sensing data (farm scale) from 1990 to 2007. Simple and well-established methods were used to characterize soil and vegetation from five points in each farm (plot scale). Most ES metrics were significantly correlated with land-use (plot scale) and land-cover (farm scale) classifications; however, spatial variability in inherent soil properties, alone or in interaction with land-use or land-cover changes, contributed greatly to variability in ES metrics. Carbon stock in above-ground plant biomass and water infiltration rate decreased from forest to pasture land covers, whereas soil chemical quality and plant-available water storage capacity increased. Land-cover classifications based on structure metrics explained significantly less ES metric variation than those based on composition metrics. Land-cover composition dynamics explained 45 % (P < 0.001) of ES metric variance, 15 % by itself and 30 % in interaction with inherent soil properties. This study describes how ES evolve with landscape changes, specifying the contribution of spatial variability in the physical environment and highlighting trade-offs and synergies among ES.     

Effects of increasing landscape heterogeneity on local plant species richness: how much is enough?

Contemporary landscape ecology continues to explore the causes and consequences of landscape heterogeneity across a range of scales, and demands for the scientific underpinnings of landscape planning and management still remains high. The spatial distribution of resources can be a key element in determining habitat quality, and that in turn is directly related to the level of heterogeneity in the system. In this sense, forest habitat mosaics may be more affected by lack of heterogeneity than by structural fragmentation. Nonetheless, increasing spatial heterogeneity at a given spatial scale can also decrease habitat patch size, with potential negative consequences for specialist species. Such dual effect may lead to hump-backed shape relationships between species diversity and heterogeneity, leading to three related assumptions: (i) at low levels of heterogeneity, an increase in heterogeneity favours local and regional species richness, (ii) there is an optimum heterogeneity level at which a maximum number of species is reached, (iii) further increase in spatial heterogeneity has a negative effect on local and regional species richness, due to increasing adverse effects of habitat fragmentation. In this study, we investigated the existence of a hump-shaped relationship between local plant species richness and increasing forest landscape heterogeneity on a complex mosaic in the French Alps. Forest landscape heterogeneity was quantified with five independent criteria. We found significant quadratic relationships between local forest species richness and two heterogeneity criteria indicators, showing a slight decrease of forest species richness at very high heterogeneity levels. Species richness–landscape heterogeneity relationships varied according to the heterogeneity metrics involved and the type of species richness considered. Our results support the assumption that intermediate levels of heterogeneity may support more species than very high levels of heterogeneity, although we were not able to conclude for a systematic negative effect of very high levels of heterogeneity on local plant species richness.     

Quantifying the effect of grazing and shrub-clearing on small scale spatial pattern of vegetation

Disturbances such as grazing, invading species, and clear-cutting, often act at small spatial scales, and means for quantifying their impact on fine scale vegetation patterns are generally lacking. Here we adopt a set of landscape metrics, commonly used for quantifying coarse scale fragmentation, to quantify fine scale fragmentation, namely the fine scale vegetation structure. At this scale, patches often consist of individual plants smaller than 1 m², requiring the grain of the analysis to be much smaller. We used balloon aerial photographs to map fine details of Mediterranean vegetation (pixel size <0.04 m) in experimental plots subjected to grazing and clear-cutting and in undisturbed plots. Landscape metrics are sensitive to scale. Therefore, we aggregated the vegetation map into four coarser scales, up to a resolution of 1 m, and analyzed the effect of scale on the metrics and their ability to distinguish between different disturbances. At the finest scale, six of the seven landscape metrics we evaluated revealed significant differences between treated and undisturbed plots. Four metrics revealed differences between grazed and control plots, and six metrics revealed differences between cleared and control plots. The majority of metrics exhibited scaling relations. Aggregation had mixed effects on the differences between metric values for different disturbances. The control plots were the most sensitive to scale, followed by grazing and clearing. We conclude that landscape metrics are useful for quantifying the very fine scale impact of disturbance on woody vegetation, assuming that the analysis is based on sufficiently high spatial resolution data.     

Predicting nutrient and sediment loadings to streams from landscape metrics: A multiple watershed study from the United States Mid-Atlantic Region

There has been an increasing interest in evaluating the relative condition or health of water resources at regional and national scales. Of particular interest is an ability to identify those areas where surface and ground waters have the greatest potential for high levels of nutrient and sediment loadings. High levels of nutrient and sediment loadings can have adverse effects on both humans and aquatic ecosystems. We analyzed the ability of landscape metrics generated from readily available, spatial data to predict nutrient and sediment yield to streams in the Mid-Atlantic Region in the United States. We used landscape metric coverages generated from a previous assessment of the entire Mid-Atlantic Region, and a set of stream sample data from the U.S. Geological Survey. Landscape metrics consistently explained high amounts of variation in nitrogen yields to streams (65 to 86% of the total variation). They also explained 73 and 79% of the variability in dissolved phosphorus and suspended sediment. Although there were differences in the nitrogen, phosphorus, and sediment models, the amount of agriculture, riparian forests, and atmospheric nitrate deposition (nitrogen only) consistently explained a high proportion of the variation in these models. Differences in the models also suggest potential differences in landscape-stream relationships between ecoregions or biophysical settings. The results of the study suggest that readily available, spatial data can be used to assess potential nutrient and sediment loadings to streams, but that it will be important to develop and test landscape models in different biophysical settings.     

Landscape characteristics affecting streams in urbanizing regions of the Delaware River Basin (New Jersey,New York,and Pennsylvania,U.S.)

Widespread and increasing urbanization has resulted in the need to assess, monitor, and understand its effects on stream water quality. Identifying relations between stream ecological condition and urban intensity indicators such as impervious surface provides important, but insufficient information to effectively address planning and management needs in such areas. In this study we investigate those specific landscape metrics which are functionally linked to indicators of stream ecological condition, and in particular, identify those characteristics that exacerbate or mitigate changes in ecological condition over and above impervious surface. The approach used addresses challenges associated with redundancy of landscape metrics, and links landscape pattern and composition to an indicator of stream ecological condition across a broad area of the eastern United States. Macroinvertebrate samples were collected during 2000–2001 from forty-two sites in the Delaware River Basin, and landscape data of high spatial and thematic resolution were obtained from photointerpretation of 1999 imagery. An ordination-derived ‘biotic score’ was positively correlated with assemblage tolerance, and with urban-related chemical characteristics such as chloride concentration and an index of potential pesticide toxicity. Impervious surface explained 56% of the variation in biotic score, but the variation explained increased to as high as 83% with the incorporation of a second land use, cover, or configuration metric at catchment or riparian scales. These include land use class-specific cover metrics such as percent of urban land with tree cover, forest fragmentation metrics such as aggregation index, riparian metrics such as percent tree cover, and metrics related to urban aggregation. Study results indicate that these metrics will be important to monitor in urbanizing areas in addition to impervious surface.     

Categorical, class-focused map patterns: characterization and comparison

We present a rigorous and simple approach for the comparison of binary landscapes by class-focused metric values that complements the ease of computing these metrics for landscape ecology research. First, we assess whether a class-focused pattern metric value could have emerged due to random chance. Second, we compare two landscapes and assess whether class-focused pattern metrics computed for each landscape are significantly different or not. Our frameworks are based on conditional autoregressive simulations to derive empirical distributions for each metric where composition and configuration parameters are controlled. Our method permits the computation of probabilities that an observed metric value is either greater than or less than a given level of expectation. We also provide means for situating any landscape on a selected pattern metric-surface defined by parameters of composition and configuration. These surfaces illustrate which parameter would be most easily adjusted to effect a desired change in a selected class-focused pattern metric’s value. Implementation is fully within the R statistical computing environment.     

Finding the functional grain: comparing methods for scaling resistance surfaces

The influence of landscape features on the movement of an organism between two point locations is often measured as an effective distance. Typically, raster models of landscape resistance are used to calculate effective distance. Because organisms may experience landscape heterogeneity at different scales (i.e. functional grains), using a raster with too fine or too coarse a spatial grain (i.e. analysis grain) may lead to inaccurate estimates of effective distance. We adopted a simulation approach where the true functional grain and effective distance for a theoretical organism were defined and the analysis grains of landscape connectivity models were systematically changed. We used moving windows and grains of connectivity, a recently introduced landscape graph method that uses an irregular tessellation of the resistance surface to coarsen the landscape data. We then used least-cost path metrics to measure effective distance and found that matching the functional and analysis grain sizes was most accurate at recovering the expected effective distance, affirming the importance of multi-scale analysis. Moving window scaling with a maximum function (win.max) performed well when the majority of landscape structure influencing connectivity consisted of high resistance features. Moving window scaling with a minimum function (win.min) performed well when the relevant landscape structure consisted of low resistance regions. The grains of connectivity method performed well under all scenarios, avoiding an a priori choice of window function, which may be challenging in complex landscapes. Appendices are provided that demonstrate the use of grains of connectivity models.     

Habitat connectivity for pollinator beetles using surface metrics

Measuring habitat connectivity in complex landscapes is a major focus of landscape ecology and conservation research. Most studies use a binary landscape or patch mosaic model for describing spatial heterogeneity and understanding pattern-process relationships. While the value of landscape gradient approaches proposed by McGarigal and Cushman are recognized, applications of these newly proposed three dimensional surface metrics remain under-used. We created a gradient map of habitat quality from several GIS layers and applied three dimensional surface metrics to measure connectivity between 67 locations in Indiana, USA surveyed for one group of ecosystem service providers, flower longicorn beetles (Cerambycidae: Lepturinae). The three dimensional surface metrics applied to the landscape gradient model showed great potential to explain the differences of lepturine assemblages among the 2,211 studied landscapes (between site pairs). Surface kurtosis and its interaction with geographic distance were among the most important metrics. This approach provided unique information about the landscape through four configuration metrics. There were some uniform trends of the responses of many species to some of surface metrics, however some species responded differently to other metrics. We suggest that three dimensional surface metrics applied to a habitat surface map created with insight into species requirements is a valuable approach to understanding the spatial dynamics of species, guilds, and ecosystem services.     

The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing

Statistical analyses provide a means for assessing relationships between landscape spatial pattern and errors in the estimates of cover-type proportions as land-cover data are aggregated to coarser scales. Results from a multiple-linear regression model suggest that as patch sizes, variance/mean ratio, and initial proportions of cover types increase, the proportion error moves in a positive direction and is governed by the interaction of the spatial characteristics and the scale of aggregation. However, the standard linear model does not account for the different directions of scale-dependent proportion error since some classes become larger and others become smaller as the scene is aggregated. Addition of indicator variables representing class-type significantly improves the performance by allowing the model to respond differently to different classes. A regression tree model provides a much simpler fit to the complex scaling behavior through an interaction between patch size and aggregation scale. An understanding of the relationships between landscape pattern, scale, and proportion error may advance methods for correcting land-cover area estimates. Such methods could also facilitate high-resolution calibration and validation of coarse-scale remote-sensing-based land-cover mapping algorithms. Ongoing initiatives to produce global land-cover datasets from remote sensing, such as efforts within the IGBP and the EOS MODIS Land-Team, include significant emphasis on high level calibration and validation activities of this nature.     

Influences of scale on bat habitat relationships in a forested landscape in Nicaragua

Context

Scale dependence of bat habitat selection is poorly known with few studies evaluating relationships among landscape metrics such as class versus landscape, or metrics that measure composition or configuration. This knowledge can inform conservation approaches to mitigate habitat loss and fragmentation.

Objectives

We evaluated scale dependence of habitat associations and scaling patterns of landscape metrics in relation to bat occurrence or capture rate in forests of southwestern Nicaragua.

Methods

We captured 1537 bats at 35 locations and measured landscape and class metrics across 10 spatial scales (100–1000 m) surrounding capture locations. We conducted univariate scaling across the 10 scales and identified scales and variables most related to bat occurrence or capture rate.

Results

Edge and patch density, at both landscape and class levels, were the most important variables across species. Feeding guilds varied in their response to metrics. Certain landscape and configuration metrics were most influential at fine (100 m) and/or broad (1000 m) spatial scales while most class and composition metrics were influential at intermediate scales.

Conclusions

These results provide insight into the scale dependence of habitat associations of bat species and the influence of fine and broad scales on habitat associations. The effects of scale, examined in our study and others from fine (100 m) to broad (5 km) indicate habitat relationships for bats may be more informative at larger scales. Our results suggest there could be general differences in scale relationships for different groups of landscape metrics, which deserves further evaluation in other taxonomic groups.