Fractal dimension as an indicator for quantifying the effects of changing spatial scales on landscape metrics

While geographers and ecologists are well aware of the scale effects of landscape patterns, there is still a need for quantifying these effects. This paper applies the fractal method to measure the scale (grain or cell size) sensitivity of landscape metrics at both landscape and class levels using the Gold Coast City in Southeast Queensland, Australia as a case study. By transforming the original land use polygon data into raster data at eleven aggregate scales, the fractal dimensions of 57 landscape metrics as defined in FRAGSTATS were assessed. A series of linear log–log regression models were constructed based on the power law to obtain the coefficient of determination (COD or R²) of the models and the fractal dimension (FD) of the landscape metrics. The results show that most landscape metrics in the area and edge, shape and the aggregation groups exhibit a fractal law that is consistent over a range of scales. The six variations of several landscape metrics that belong to both the area/edge and shape groups show different scale behaviours and effects. However, the metrics that belong to the diversity group are scale-independent and do not accord to fractal laws. In addition, the scale effects at the class level are more complex than those at the landscape level. The quantitative assessment of the scale effect using the fractal method provides a basis for investigating landscape patterns when upscaling or downscaling as well as creating any scale-free metric to understand landscape patterns.     

Empirical assessment of the relation between ecological connectivity and land complexity based on information-theoretic metrics

Habitat fragmentation and connectivity loss pose significant threats to biodiversity at both local and landscape levels. Strategies to increase ecological connectivity and preserve strong connectivity are important for dealing with the potential threat of habitat degradation. Various metrics have been used to measure (i.e., quantify) landscape composition and configuration in landscape ecology. However, their relationship with ecological connectivity must be understood to interpret landscape patterns comprehensively. In the present study, correlations between ecological connectivity and land complexity are examined based on information-theory metrics. Two primary questions are explored: (1) to what extent are landscape mosaic measures of entropy correlated with ecological connectivity, with landscape gradient-based measures, and with each other? (2) are landscape gradient-based entropy measures correlated with ecological connectivity more than discrete entropy measures? Results show that all information theoretic metrics are statistically significant (p < 0.05) for modelling ecological connectivity. Among categorically-based indices, the relationship between ECI and joint entropy was the most significant, while a generalized additive model indicated that Boltzmann entropy could predict the ecological connectivity index, explaining ∼60% of the variance. Therefore, configurational entropy can be used for improving ecological connectivity models.     

Integrating geo-biodiversity features in the analysis of landscape patterns

Spatial patterns are deeply linked to ecological processes and this relationship lies at the core of landscape ecology. In turn, landscape patterns are influenced by physical, biological and anthropogenic factors. The aim of this study was to explore how specific physical and biological factors, namely geo- and biodiversity features influence landscape patterns. The focus was on microscale relationships and we chose as our focus area a small scale study site covering 3091 ha characterized by vegetation mosaics with multiple patterns. We considered geology, soil and altitude (for geodiversity) and land cover classes (for biodiversity) as superposed layers and we aggregated their elements into a new combined mosaic. Several landscape metrics related to patterns such as landscape fragmentation, connectivity of habitats and ecotone properties were computed at the class level for the new mosaic and were used in multivariate statistical analyses. We determined the most important parameters by Principal Component Analysis. The first component was mainly linked to metrics related to size variability, while the second one was related to border complexity. In the reduced space, we delineated three clusters of objects that were characterized by different landscape patterns. We analyzed the underlying geology, soil structure and occurring land cover classes for each cluster. We then performed Redundancy Analysis using geo- and biodiversity features as predictor variables and metrics as response variables. While the land cover acted as explanatory variable for the first principal axis of variation, the geodiversity features (geology and soil) were related to the second one. Specifically, the occurrence of limestone yields more complex borders of patches; some phenomena are visible in situ, such as limestone appearing at the surface as outcrops (lapis) that induce irregular shapes of the patches. Overall, the analyses hinted that, besides the land cover class, the underlying geology plays an important role in defining landscape patterns, and this relationship can be revealed through the use of appropriate statistical tools. On the other hand, the study area is an agro-silvopastoral landscape, where local traditional management practices are also an important driver for the occurrence of specific patterns. Therefore, understanding the links between geo- and biodiversity characteristics and landscape features can contribute to developing appropriate management and planning strategies.     

Towards more predictable and consistent landscape metrics across spatial scales

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

Indicating landscape fragmentation using L–Z complexity

Landscape metrics have been widely applied to measure landscape fragmentation in recent years. However, those metrics or indicators have their own specific application. Searching better performance metric or method to quantify the landscape fragmentation is still challenging issue. In this paper an L–Z complexity method for the measurement of landscape fragmentation was presented. After introducing the algorithm of L–Z complexity and converting landscape structure data to symbolic sequence, the implementation of this methodology was demonstrated using synthetic data generated by SIMMAP software and real urban land use data. The results show that the L–Z complexity can efficiently detect differentiation of landscape fragmentation controlled by SIMMAP parameters m, n and p. As to the application in real landscape, L–Z complexity is also a good indicator to identify the landscape fragmentation, which integrates some metrics information of landscape composition, shape and configuration. L–Z complexity provides an alternative approach to measuring the landscape fragmentation.     

Comparative Landscape Genetics of Three Closely Related Sympatric Hesperid Butterflies with Diverging Ecological Traits

To understand how landscape characteristics affect gene flow in species with diverging ecological traits, it is important to analyze taxonomically related sympatric species in the same landscape using identical methods. Here, we present such a comparative landscape genetic study involving three closely related Hesperid butterflies of the genus Thymelicus that represent a gradient of diverging ecological traits. We analyzed landscape effects on their gene flow by deriving inter-population connectivity estimates based on different species distribution models (SDMs), which were calculated from multiple landscape parameters. We then used SDM output maps to calculate circuit-theoretic connectivity estimates and statistically compared these estimates to actual genetic differentiation in each species. We based our inferences on two different analytical methods and two metrics of genetic differentiation. Results indicate that land use patterns influence population connectivity in the least mobile specialist T. acteon. In contrast, populations of the highly mobile generalist T. lineola were panmictic, lacking any landscape related effect on genetic differentiation. In the species with ecological traits in between those of the congeners, T. sylvestris, climate has a strong impact on inter-population connectivity. However, the relative importance of different landscape factors for connectivity varies when using different metrics of genetic differentiation in this species. Our results show that closely related species representing a gradient of ecological traits also show genetic structures and landscape genetic relationships that gradually change from a geographical macro- to micro-scale. Thus, the type and magnitude of landscape effects on gene flow can differ strongly even among closely related species inhabiting the same landscape, and depend on their relative degree of specialization. In addition, the use of different genetic differentiation metrics makes it possible to detect recent changes in the relative importance of landscape factors affecting gene flow, which likely change as a result of contemporary habitat alterations.     

Ecological metrics predict connectivity better than geographic distance

We use microsatellite loci to examine genetic structure of the Florida scrub lizard (Sceloporus woodi) and test for the effects of landscape variables at the scale of neighboring patches. We evaluate ecological metrics of connectivity with genetics data, which to our knowledge is the first application of these particular metrics to landscape-level genetics studies in Florida scrub. Florida scrub is a highly threatened ecosystem in which habitat patches are remnants of a previously widespread xeric landscape. Analysis of mitochondrial DNA (mtDNA) has shown that landscape structure influenced the evolutionary history of the Florida scrub lizard (S. woodi) across its range. Our results concur with these mtDNA studies in documenting divergence between xeric ridge systems and also demonstrate divergence at very local scales. Both least-cost distance and pairwise isolation (a metric used in ecological studies that includes patch size, quality and a modified isolation index) were better predictors of genetic distance than Euclidean distance, indicating that mesic and hydric habitat influence spatial patterns in genetic variation. Our results support the need for focusing on spatial distribution of scrub habitat at the scale of neighboring patches, as well as regionally, in conservation management and restoration. Also, our study points to the value of integrating landscape ecology metrics into landscape genetics.     

景观生态学原理在城市土地利用分类中的应用

根据城市相同土地利用类型具有相似景观格局特征的原理,探讨了融合景观格局特征指数和遥感技术的城市土地利用信息提取的新方法。以北京市五环内建城区为例,研究表明,在斑块类型水平和景观水平上,居住用地和非居住用地内景观斑块的大小、形状、边缘特征、空间连接度、核心区面积特征、多样性、均匀性等特征都有极显著的差异。进一步融合TM遥感影像和这些景观格局特征指数,提取了居民用地和非居民用地类型,总分类精度是79.7%,Kappa系数达到59.8%。研究揭示,景观生态学原理的引入,为传统的遥感技术应用提供了新的思路,在格局复杂的城市土地利用信息提取中有很大的应用发展潜力。     

Metrics for macroscale invasion and dispersal patterns

Aim The ability to quantitatively measure the continuum of macroscale patterns of species invasion is a first step toward deeper understanding of their causal factors. We took advantage of two centuries worth of herbarium data, to evaluate a set of metrics to measure macroscale patterns, allowing cross-species comparisons of invasive expansion across large geographic areas.Methods We used herbarium specimens to reconstruct county-level invasion histories for two non-native plants (Alliaria petiolata and Lonicera japonica), with distinct spatiotemporal distribution patterns over the past two centuries. Using county centroids from species' initial occurrences, we quantified point pattern metrics from multiple disciplines (e.g. urban crime analysis, landscape ecology etc.) that are historically used at smaller spatial scales, to evaluate their ability to detect macroscale spatial diffusion and amount of directional expansion. Metrics were further assessed for their ease of use, data requirements, independence from other metrics and intuitiveness of interpretation.Important findings We identified four suitable metrics for distinguishing differences in spatial patterns: (i) standard distance, (ii) number of patches, (iii) Euclidean nearest neighbor summary class statistic coefficient of variation and (iv) mean center that when applied to county-level presence data allowed us to determine the directions by which distributions expanded and if distributions increased via outward expansion, infilling and/or jump dispersal events. These metrics when compared during the same invasion phase are capable of quantifying macroscale variability among species in their distributional and dispersal patterns. Being able to quantify differences among species in these patterns is important in understanding the drivers of species dispersal patterns. These metrics therefore represent a simple yet thorough toolset for achieving this goal.     

<Emphasis Type="Italic">Maggengo</Emphasis> meadow patches enclosed by forests in the Italian Alps: evidence of landscape legacy on plant diversity

The maggenghi are mid slope meadows typical of all the southern and of great parts of the northern European Alps, for centuries managed with traditional and low intensity techniques. Usually, they are scattered patches in surrounding forests. The spontaneous expansion of trees and shrubs, favored by the recent decline of mountain agriculture, lead the maggengo patches patterns and shapes to change. Our objective was to analyze the effect of this change on current plant diversity of the remnant patches, as the adaptive response could be slow and possibly related more to historical than to current landscape patterns. We analyzed the trend of the size, shape, elongation, fractal dimension and connectivity of maggengo patches of a Central-Eastern Italian Alpine district, in four time steps, from 1973 to 2006, and in 1859, when mountain agriculture was still widespread. Then, we studied the relationships between those landscape metrics and two current patch-level plant diversity measures: interior species richness and species density. Aerial photographs were used to investigate that trend, while a historical cadastral map was used to assess the landscape metrics in 1859. As expected, in the last 30 years, the total size of maggenghi has been reduced by 57% while their shapes have been progressively simplified. Interior species richness was positively related to size, both in 2006 and over the past 30 years, but not to any 1859 measures. Conversely, species density was positively correlated only with 1859 size, shape index and connectivity. We conclude that the historical shape, size and connectivity are some of the key variables affecting the plant species density of maggengo patches, but not of their interior plant species richness.     

Effects of vegetation structure and landscape complexity on insect parasitism across an agricultural frontier in Argentina

Insect parasitism patterns are influenced by vegetation structure and landscape complexity. Our objective was to examine the effects of vegetation structure and landscape complexity on parasitism based on direct measurements of structure and diversity indices as well as on metrics based on remote sensing using Quickbird images. We collected 2266 lepidopteran larvae and pupae, including different families and habits, to estimate parasitism, and recorded vegetation characteristics in five 100-m² transects and 18 1 ha-plots in the dry Chaco, Northwest Argentina. We calculated landscape metrics and semivariograms in the plots from the image. The plots represented four “complexity groups”: agricultural, riparian/hedgerow, bare ground, and forest plots. Mean parasitism in the study sites was 10.7% (min: 0%, max: 23%). Parasitism was highest in agricultural plots, lowest in forest plots, and intermediate in riparian/hedgerow and bare ground plots. The landscape model explained parasitism more than the vegetation model. The landscape final model included Normalized Difference Vegetation Index (NDVI) Range, a measure of landscape heterogeneity, and Mean Shape Index, a measure of patch shape irregularity, and their interaction. The vegetation model included basal area and the Coefficient of Variation of tree density among transects, a measure of tree spatial distribution within a plot. Our results agree with previous studies that found higher parasitism in agricultural vs. non-agricultural environments in the subtropics, while riparian/hedgerow plots were important for conserving parasitism, as reported for temperate environments. We showed that under-explored tools such as the semivariogram and satellite band combinations were useful for the assessment of parasitism and that studying vegetation and landscape complexity simultaneously can help us examine mechanisms in detail. The identified variables related to high parasitism should be used for image classifications with a functional approach.     

Riverine Landscape Patch Heterogeneity Drives Riparian Ant Assemblages in the Scioto River Basin,USA

Although the principles of landscape ecology are increasingly extended to include riverine landscapes, explicit applications are few. We investigated associations between patch heterogeneity and riparian ant assemblages at 12 riverine landscapes of the Scioto River, Ohio, USA, that represent urban/developed, agricultural, and mixed (primarily forested, but also wetland, grassland/fallow, and exurban) land-use settings. Using remotely-sensed and ground-collected data, we delineated riverine landscape patch types (crop, grass/herbaceous, gravel, lawn, mudflat, open water, shrub, swamp, and woody vegetation), computed patch metrics (area, density, edge, richness, and shape), and conducted coordinated sampling of surface-active Formicidae assemblages. Ant density and species richness was lower in agricultural riverine landscapes than at mixed or developed reaches (measured using S [total number of species], but not using Menhinick’s Index [D _M]), whereas ant diversity (using the Berger-Park Index [D_BP]) was highest in agricultural reaches. We found no differences in ant density, richness, or diversity among internal riverine landscape patches. However, certain characteristics of patches influenced ant communities. Patch shape and density were significant predictors of richness (S: R ² = 0.72; D _M: R ²=0.57). Patch area, edge, and shape emerged as important predictors of D_BP (R ² = 0.62) whereas patch area, edge, and density were strongly related to ant density (R ² = 0.65). Non-metric multidimensional scaling and analysis of similarities distinguished ant assemblage composition in grass and swamp patches from crop, gravel, lawn, and shrub as well as ant assemblages in woody vegetation patches from crop, lawn, and gravel (stress = 0.18, R ² = 0.64). These findings lend insight into the utility of landscape ecology to river science by providing evidence that spatial habitat patterns within riverine landscapes can influence assemblage characteristics of riparian arthropods.     

Habitat quality of source patches and connectivity in fragmented landscapes

Because spatial connectivity is critical to dispersal success and persistence of species in highly fragmented landscapes, the way that we envision and measure connectivity is consequential for biodiversity conservation. Connectivity metrics used for predictive modeling of spatial turnover and patch occupancy for metapopulations, such as with Incidence Function Models (IFM), incorporate distances to and sizes of possible source populations. Here, our focus is on whether habitat quality of source patches also is considered in these connectivity metrics. We propose that effective areas (weighted by habitat quality) of source patches should be better surrogates for population size and dispersal potential compared to unadjusted patch areas. Our review of a representative sample of the literature revealed that only 12.5% of studies incorporated habitat quality of source patches into IFM-type connectivity metrics. Quality of source patches generally was not taken into account in studies even if habitat quality of focal patches was included in analyses. We provide an empirical example for a metapopulation of a rare wetland species, the round-tailed muskrat (Neofiber alleni), demonstrating that a connectivity metric based on effective areas of source patches better predicts patch colonization and occupancy than a metric that used simple patch areas. The ongoing integration of landscape ecology and metapopulation dynamics could be hastened by incorporating habitat quality of source patches into spatial connectivity metrics applied to species conservation in fragmented landscapes.     

Measuring temporal patterns in ecology: The case of mast seeding

1. Properly assessing temporal patterns is a central issue in ecology in order to understand ecosystem processes and their mechanisms. Mast seeding has traditionally been described as a reproductive behavior consisting of highly variable and synchronized reproductive events. The most common metric used to measure temporal variability and thus infer masting behavior, the coefficient of variation (CV), however, has been repeatedly suggested to improperly estimate temporal variability. Biases of CV estimates are especially problematic for non‐normally distributed data and/or data sets with a high number of zeros.
2. Some recent studies have already adopted new metrics to measure temporal variability, but most continue to use CV. This controversy has started a strong debate about what metrics to use.
3. We here summarize the problems of CV when assessing temporal variability, particularly across data sets containing a large number of zeros, and highlight the benefits of using other metrics of temporal variability, such as proportional variability (PV) and consecutive disparity (D). We also suggest a new way to look at reproductive behavior, by separating temporal variability from frequency of reproduction, to allow better comparison of data sets with different characteristics.
4. We suggest future studies to properly describe the temporal patterns in fully scientific and measurable terms that do not lead to confusion, such as variability and frequency of reproduction, using robust and fully comparable metrics.

     

一种新的景观扩张指数的定义与实现

景观格局动态信息的定量表达始终是景观生态学研究的一个重要科学问题,景观格局指数是其中的一种重要方法,但其多是静态指数,难以有效定量表达景观格局的动态信息.因此,针对景观扩张过程以斑块扩张面积为基础提出了一种新的景观扩张指数,来表达景观格局的动态信息.并以妫水河流域1998-2009年的景观农田化过程为例,验证该指数的适用性,结果表明:该指数不仅能够定量表达斑块的空间扩张规模,而且可以准确识别斑块的空间扩张模式.根据扩张斑块与原斑块的空间位置关系,将景观的空间扩张模式划分为邻接扩张式和外部扩张式两种.提出的景观扩张指数在技术方法上计算简便,易于实现,完善了景观格局动态的量化表征科学方法.     

景观指数耦合景观格局与土壤侵蚀的有效性

景观格局分析是景观生态学中揭示景观变化及其生态效应的主要方法,而景观指数是景观格局分析中广泛使用的工具。土壤侵蚀是土壤物质在景观中的迁移和再分配过程,受地形、植被和人类活动及其空间格局的调控。运用景观格局分析揭示景观格局变化特别是土地利用/覆被格局变化对土壤侵蚀的影响是土壤侵蚀研究中应用景观生态学原理和方法的典型。在当前的研究中,斑块-廊道-基质范式下建立的景观指数对侵蚀过程的解释能力不断受到质疑,建立筛选适用的景观指数的原则和方法十分必要。以延河流域碾庄沟小流域为例,利用WATEM/SEDEM模型模拟多个年份流域侵蚀产沙和输沙量;基于土地利用/覆被数据,利用Fragstat4.2软件,计算了相应年份流域斑块、边界密度、形状、集聚与分散和斑块类型多样性4个方面的代表性景观指数。在此基础上,分析了景观指数与流域侵蚀产沙和输沙之间的关系,讨论了景观指数在土壤侵蚀研究中的有效性,在景观和斑块类型水平上分析了景观指数表达"源"、"汇"两大类景观类型的空间格局与侵蚀产沙和输沙之间的关系的一致性。结果表明:斑块-廊道-基底范式下发展的景观指数在指示景观格局的土壤侵蚀效应时存在局限。相对而言,斑块类型尺度的景观指数更能有效表达景观格局与土壤侵蚀的关系。基于景观类型在土壤侵蚀过程中的"源"、"汇"功能,提出了在土壤侵蚀研究中筛选适用的景观指数的原则:(1)对"源"、"汇"两类景观类型,景观指数与土壤侵蚀状况表征变量的相关系数符号相反;(2)对同为"源"或"汇"景观类型的多个景观类型,景观指数与土壤侵蚀表征变量的相关系数应具有符号一致性。尽管景观指数在斑块类型水平上具有一定的有效性,但用其预测景观格局变化的侵蚀效应有很大的不确定性。因此,基于土壤侵蚀过程与景观格局的作用机制发展新型的景观指数是增强景观格局分析预测土壤侵蚀过程的能力的途径。     

Predictors of mammal species richness in KwaZulu-Natal,South Africa

The management of multi-functional landscapes warrants better knowledge of environment-richness associations at varying disturbance levels and habitat gradients. Intensive land-use patterns for agricultural purposes lead to fragmentation of natural habitat resulting in biodiversity loss that can be measured using landscape metrics to assess mammalian richness. Since carnivores and herbivores are likely to show different responses to disturbance, we calculated carnivore, non-carnivore, and total mammal species richness from camera surveys using a first order Jackknife Estimator. Richness was compared along a habitat gradient comprising coastal forest, Acacia thicket, and highland in KwaZulu-Natal, South Africa. We used standardized OLS regression models to identify climatic and disturbance variables, and landscape metrics as predictors of species richness. The estimated total and non-carnivore species richness were highest in coastal forest, while carnivore species richness was highest in highland followed by coastal forest and Acacia thicket. Average monthly maximum temperature was a significant predictor of all richness groups, and precipitation of the wettest month and isothermality determined total and non-carnivore species richness, respectively. These climatic variables possibly limit species distribution because of physiological tolerance of the species. Total mammal richness was determined by mean shape (+) and habitat division (−) while diversity (+) and patch richness (−) explained carnivore species richness. Mean shape index (+) influenced non-carnivore richness. However, habitat division and patch richness negatively influenced total mammal richness. Though habitat patch size and contiguity had a weak positive prediction, these metrics demonstrated the importance of habitat connectivity for maintaining mammal richness. The identification of these climatic and landscape patterns is important to facilitate future landscape management for mammal conservation in forest-mosaics.     

Lake connectivity and fish species richness in southern Brazilian coastal lakes

Connectivity is a key factor in metacommunity ecology, because it influences dispersal and colonization rates. However, it has received less attention in aquatic than in terrestrial ecology research. We investigated whether connectivity is a good predictor of species richness in functional fish communities (freshwater, FS; estuarine, ES and estuarine-freshwater, EFS) from 31 coastal lakes in southern Brazil. We used a model selection approach, including lake area and distance from the ocean as additional predictors of species richness and two connectivity metrics: primary connectivity (C _P) and estuarine connectivity (C _E), which measure connectivity to neighboring lakes and system-wide connectivity, respectively. Both metrics estimate functional connectivity and were calculated on habitat-based cost distances. Connectivity was more important for predicting richness of functional communities than for total richness, particularly C _E, which was distinctively related to each functional fish community richness (directly related to ES and EFS, and inversely related to FS; C _P was related only to ES). Remarkably, connectivity was more important than area for predicting ES and EFS richness. These results add support to dispersal limitation as an important mechanism influencing fish communities. We suggest that incorporating environmental filters (habitat type) to quantify connectivity is useful for accessing the patterns of species richness.     

Spatial metrics for detecting ecosystem degradation in the ridge-slough patterned landscape

Indicators of landscape condition should be selected based on their sensitivity to environmental changes and their capacity to provide early warning detection of those changes. We assessed the performance of a suite of spatial-pattern metrics selected to quantify the condition of the ridge-slough landscape in the Everglades (South Florida, USA). Spatial pattern metrics (n = 14) that describe landscape composition, geometry and hydrologic connectivity were enumerated from vegetation maps of twenty-five 2 × 2 km primary sampling units (PSUs) that span a gradient of hydrologic and ecological condition across the greater Everglades ecosystem. Metrics were assessed in comparison with field measurements from each PSU of landscape condition obtained from regional surveys of soil elevation, which have previously been shown to capture dramatic differences between conserved and degraded locations. Elevation-based measures of landscape condition included soil elevation bi-modality (BI_SE), a binary measure of landscape condition, and also the standard deviation of soil elevation (SD_SE), a continuous measure of condition. Metric performance was assessed based on the strength (sensitivity) and shape (leading vs. lagging) of the relationship between spatial pattern metrics and these elevation-based measures. We observed significant logistic regression slopes with BI_SE for only 4 metrics (slough width, ridge density, directional connectivity index – DCI, and least flow cost – LFC). More significant relationships (n = 8 metrics) were observed with SD_SE, with the strongest associations for slough density, mean ridge width, and the average length of straight flow, as well as for a suite of hydrologic connectivity metrics (DCI, LFC and landscape discharge competence – LDC). Leading vs. lagging performance, inferred from the curvature of the association obtained from the exponent of fitted power functions, suggest that only DCI was a leading metric of the loss of soil elevation variation; most metrics were indeterminate, though some were clearly lagging. Our findings support the contention that soil elevation changes from altered peat accretion dynamics precede changes in landscape pattern, and offer insights that will enable efficient monitoring of the ridge-slough landscape as part of the ongoing Everglades restoration effort.     

Interactions between landcover pattern and geospatial processing methods: Effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric “number of patches” than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty.