Lacunarity analysis of spatial pattern: A comparison

Lacunarity analysis has been proposed as a general method for the analysis of spatial pattern, in particular for patterns of the dispersion of points. The method is clearly an improvement over the variance:mean ratio approach based on quadrat counts, because it examines dispersion at a range of spatial scales. This paper examines the properties of lacunarity analysis and compares it with other methods of pattern analysis. Lacunarity analysis gives different results for complementary patterns, which may be an advantage depending on circumstances. The method, however, is not precise in determining the scale or the patch size in pattern with known properties. A modification that improves the interpretability of the results of the analysis is introduced but a weakness of this approach is that it does provide clear indications of the characteristics of cases that exhibit more than one scale of pattern. Because different methods react to different features in data, it is recommended that data be analysed by more than one method and the results compared for greater insight into their characteristics.     

Zonal lacunarity analysis: a new spatial analysis tool for geographic information systems

Landscape Ecology - Lacunarity as a scale-dependent measure of spatial heterogeneity has received great attention in landscape ecology. Most lacunarity measures have been obtained from greyscale or...     

Movement behavior in response to landscape structure: the role of functional grain

Landscape structure can influence the fine-scale movement behavior of dispersing animals, which ultimately may influence ecological patterns and processes at broader scales. Functional grain refers to the finest scale at which an organism responds to spatial heterogeneity among patches and extends to the limits of its perceptual range. To determine the functional grain of a model insect, red flour beetle (Tribolium castaneum), we examined its movement behavior in response to experimental flour landscapes. Landscape structure was varied by manipulating habitat abundance (0%, 10%, 30%, and 100%) and grain size of patches (fine-2 × 2 cm, intermediate-5 × 5 cm, and coarse-10 × 10 cm) in 50 × 50 cm landscapes. Pathway metrics indicated that beetles used a similar proportion of all landscape types. Several pathway metrics indicated a graded response from the fine to the coarse grain landscape. Lacunarity analysis of beetle pathways indicated a non-linear change in space use between the fine and intermediate landscapes and the coarse-grained landscape. Beetles moved more slowly and tortuously (with many turns), and remained longer in both the overall landscape and individual patches, in fine-grained compared to coarse-grained landscapes. Our research demonstrates how detailed examination of movement pathways and measures of lacunarity can be useful in determining functional grain. Spatially explicit, organism-centered studies focusing on behavioral responses to different habitat configurations can serve as an important first step to identify behavioral rules of movement that may ultimately lead to more accurate predictions of space use in landscapes.     

Lacunarity as a tool for assessing landscape configuration over time and informing long-term monitoring: an example using seagrass

Context

Seagrasses are submerged marine plants that have been declining globally at increasing rates. Natural resource managers rely on monitoring programs to detect and understand changes in these ecosystems. Technological advancements are allowing for the development of patch-level seagrass maps, which can be used to explore seagrass meadow spatial patterns.

Objectives

Our research questions involved comparing lacunarity, a measure of landscape configuration, for seagrass to assess cross-site differences in areal coverage and spatial patterns through time. We also discussed how lacunarity could help natural resource managers with monitoring program development and restoration decisions and evaluation.

Methods

We assessed lacunarity of seagrass meadows for various box sizes (0.0001 ha to 400.4 ha) around Cat Island and Ship Island, Mississippi (USA). For Cat Island, we used seagrass data from 2011 to 2014. For Ship Island, we used seagrass data for seven dates between 1963 and 2014.

Results

Cat Island, which had more continuous seagrass meadows, had lower lacunarity (i.e., denser coverage) compared to Ship Island, which had patchier seagrass beds. For Ship Island, we found a signal of disturbance and path toward recovery from Hurricane Camille in 1969. Finally, we highlighted how lacunarity curves could be used as one of multiple considerations for designing monitoring programs, which are commonly used for seagrass monitoring.

Conclusions

Lacunarity can help quantify spatial pattern dynamics, but more importantly, it can assist with natural resource management by defining fragmentation and potential scales for monitoring. This approach could be applied to other environments, especially other coastal ecosystems.

     

A comparison of quantitative methods for examining landscape pattern and scale

Ecologists have long recognized the importance of spatial and temporal patterns that characterize heterogeneity in landscapes. However, despite the realization that inferences about ecological phenomena are scale dependent, little attention has been paid to determining appropriate scales of measurement (e.g., plot or grain size) in studies of landscape dynamics or ecosystem change. This paper compares the results from three data sets using several quantitative methods available for characterizing landscape heterogeneity and/or for determining scale of measurement. Methods evaluated include tests of non-randomness, estimation of patch size, spectral analysis, fractals, variance ratio analysis, and correlation analysis. The results showed that no one method provides consistently good estimates of scale. Thus, sampling strategies for landscape studies should be derived from estimates of patch size and/or scale of pattern obtained from more than one of these methods.     

Quantifying Spatiotemporal Patterns of Urban Land-use Change in Four Cities of China with Time Series Landscape Metrics

This paper provides a dynamic inter- and intra-city analysis of spatial and temporal patterns of urban land-use change. It is the first comparative analysis of a system of rapidly developing cities with landscape pattern metrics. Using ten classified Landsat Thematic Mapper images acquired from 1988 to 1999, we quantify the annual rate of urban land-use change for four cities in southern China. The classified images were used to generate annual maps of urban extent, and landscape metrics were calculated and analyzed spatiotemporally across three buffer zones for each city for each year. The study shows that for comprehensive understanding of the shapes and trajectories of urban expansion, a spatiotemporal landscape metrics analysis across buffer zones is an improvement over using only urban growth rates. This type of analysis can also be used to infer underlying social, economic, and political processes that drive the observed urban forms. The results indicate that urban form can be quite malleable over relatively short periods of time. Despite different economic development and policy histories, the four cities exhibit common patterns in their shape, size, and growth rates, suggesting a convergence toward a standard urban form.     

Information theory as a consistent framework for quantification and classification of landscape patterns

Context

Quantitative grouping of similar landscape patterns is an important part of landscape ecology due to the relationship between a pattern and an underlying ecological process. One of the priorities in landscape ecology is a development of the theoretically consistent framework for quantifying, ordering and classifying landscape patterns.

Objective

To demonstrate that the information theory as applied to a bivariate random variable provides a consistent framework for quantifying, ordering, and classifying landscape patterns.

Methods

After presenting information theory in the context of landscapes, information-theoretical metrics were calculated for an exemplar set of landscapes embodying all feasible configurations of land cover patterns. Sequences and 2D parametrization of patterns in this set were performed to demonstrate the feasibility of information theory for the analysis of landscape patterns.

Results

Universal classification of landscape into pattern configuration types was achieved by transforming landscapes into a 2D space of weakly correlated information-theoretical metrics. An ordering of landscapes by any single metric cannot produce a sequence of continuously changing patterns. In real-life patterns, diversity induces complexity—increasingly diverse patterns are increasingly complex.

Conclusions

Information theory provides a consistent, theory-based framework for the analysis of landscape patterns. Information-theoretical parametrization of landscapes offers a method for their classification.

     

Gradient modeling of conifer species using random forests

Landscape ecology often adopts a patch mosaic model of ecological patterns. However, many ecological attributes are inherently continuous and classification of species composition into vegetation communities and discrete patches provides an overly simplistic view of the landscape. If one adopts a niche-based, individualistic concept of biotic communities then it may often be more appropriate to represent vegetation patterns as continuous measures of site suitability or probability of occupancy, rather than the traditional abstraction into categorical community types represented in a mosaic of discrete patches. The goal of this paper is to demonstrate the high effectiveness of species-level, pixel scale prediction of species occupancy as a continuous landscape variable, as an alternative to traditional classified community type vegetation maps. We use a Random Forests ensemble learning approach to predict site-level probability of occurrence for four conifer species based on climatic, topographic and spectral predictor variables across a 3,883 km² landscape in northern Idaho, USA. Our method uses a new permutated sample-downscaling approach to equalize sample sizes in the presence and absence classes, a model selection method to optimize parsimony, and independent validation using prediction to 10% bootstrap data withhold. The models exhibited very high accuracy, with AUC and kappa values over 0.86 and 0.95, respectively, for all four species. The spatial predictions produced by the models will be of great use to managers and scientists, as they provide vastly more accurate spatial depiction of vegetation structure across this landscape than has previously been provided by traditional categorical classified community type maps.     

Landscape patterns simulation with a modified random clusters method

A new modified random clusters method for the simulation of landscape the matic spatial patterns is presented. It produces more realistic and general results than landscape models that have been commonly used to date in the field of landscape ecology. Simulated patterns are said to be realistic, apart from their patchy and irregular appearance, because the values of the spatial indices as a function of habitat abundance measured in real landscape patterns (number of patches, edge length and patch cohesion index) can be replicated with the proposed landscape model. It allows a wide range of spatial patterns to be obtained, in which fragmentation and habitat abundance can be systematically and independently varied. Furthermore, a degree of control over the irregularity of the shapes of the simulated landscapes can be achieved, and it is also possible to simulate patterns with anisotropy. The proposed method is easy to implement and requires little computation time, which enhances the practical possibilities of this method in different areas of landscape ecology.     

Neutral models for the analysis of broad-scale landscape pattern

The relationship between a landscape process and observed patterns can be rigorously tested only if the expected pattern in the absence of the process is known. We used methods derived from percolation theory to construct neutral landscape models,i.e., models lacking effects due to topography, contagion, disturbance history, and related ecological processes. This paper analyzes the patterns generated by these models, and compares the results with observed landscape patterns. The analysis shows that number, size, and shape of patches changes as a function of p, the fraction of the landscape occupied by the habitat type of interest, and m, the linear dimension of the map. The adaptation of percolation theory to finite scales provides a baseline for statistical comparison with landscape data. When USGS land use data (LUDA) maps are compared to random maps produced by percolation models, significant differences in the number, size distribution, and the area/perimeter (fractal dimension) indices of patches were found. These results make it possible to define the appropriate scales at which disturbance and landscape processes interact to affect landscape patterns.     

Multiscale comparison of spatial patterns using two-dimensional cross-spectral analysis: application to a semi-arid (gapped) landscape

Spectral analysis allows the characterization of temporal (1D) or spatial (2D) patterns in terms of their scale (frequency) distribution. Cross-spectral analysis can also be used to conduct independent correlation analyses at different scales between two variables, even in the presence of a complex superposition of structures, such as structures that are shifted, have different scales or have different levels of anisotropy. These well-grounded approaches have rarely been applied to two-dimensional ecological datasets. In this contribution, we illustrate the potential of the method. We start by providing a basic methodological introduction, and we clarify some technical points concerning the computation of two-dimensional coherency and phase spectra and associated confidence intervals. First, we illustrate the method using a simple theoretical model. Next, we present a real world application: the case of patterned (gapped) vegetation in SW Niger. In this example, we investigate the functional relationships between topography and the spatial distribution of two shrub species, Combretum micranthum G. Don. and Guiera senegalensis J.F. Gmel. We show that both the global vegetation pattern and the distribution of C. micranthum are independent at all analyzable scales (i.e., from 10 to 50 m) from possible relief-induced determinisms. Additionally, the two dominant shrub species form distinct patches, thus suggesting separate niches.     

Relationships between students’ demographic characteristics,perceived naturalness and patterns of use associated with campus green space,and self-rated restoration and health

There is increasing awareness of the benefits of campus green space in promoting student's health. However, information on how campus green spaces benefit students’ health is insufficient or limited to guide the planning or management process. As a result, the present study collected 897 valid responses to examine the differences in campus green space usage patterns among students with varying individual characteristics and to assess the interrelationships between students’ socio-demographic characteristics, perceived naturalness, patterns of use, and self-rated restoration and health using a structural equation model. The findings indicated that there were gender disparities in the presence of companions and frequency of use of campus green space, as well as discipline differences in companion presence. Additionally, gender, age, and discipline had distinct associations with perceived naturalness, patterns of use and self-rated restoration and health. Perceived naturalness positively contribute to patterns of use and self-rated restoration and health, while the frequency of use positively contributed to self-rated restoration and health. Moreover, students’ perceived health can be improved in part through the mediating effect of the self-rated restoration. The study findings demonstrated how campus green spaces benefit students’ health and provided valuable information for campus green space managers and designers. Therefore, we propose that presenting diverse natural elements, manipulating them in natural forms, providing feelings of wildness or friendliness, and eatablishing vast, high-quality, and diverse green spaces to promote students’ health.     

A new approach for rescaling land cover data

The resolution of satellite imagery must often be increased or decreased to fill data gaps or match preexisting project requirements. It is well known that a change in resolution introduces systematic errors of size, shape, location and amount of contiguous land cover types. Nevertheless, robust methods for rescaling landscape data are frequently required to assess patterns of landscape change through time and over large areas. We developed a new method for rescaling spatial data that allows map resolution (grain size) to be either increased or decreased while holding the total proportion of land cover types constant. The method uses a weighted sampling net of variable resolution to sample an existing map and then randomly selects from the frequency of cover types derived from this sample to assign the cover type for the corresponding location in the rescaled map. The properties of the sampling net had a variable effect on measures of landscape pattern with the characteristic patch size (S) the most robust metric and the number of clusters (A) the most variable. A comparison of up-scaled and down-scaled maps showed that this process is not symmetrical, producing different errors for increases versus decreases in grain size. Rescaling Landsat (30 m) imagery to the 10 m resolution of SPOT imagery for four National Park units within Maryland and Virginia resulted in errors due to rescaling that were small (1–2%) relative to the total error (∼11%) associated with these images. The new rescaling method is general because it provides a single method for increasing or decreasing resolution, can be applied to maps with multiple land cover types, allows grid geometry to be transformed (i.e., square to hexagonal grids), and provide a more consistent basis for landscape comparisons when maps must be derived from multiple sources of classified imagery.     

丹参DHAR家族基因的鉴定及表达模式分析

运用生物信息学方法,从丹参（Salvia miltiorrhiza）基因组数据库中鉴定得到5个脱氢抗坏血酸还原酶（Dehydroascorbate reductase,DHAR）家族成员SmDHAR1 ~ SmDHAR5,分析了基因结构特征、编码蛋白的理化性质、亚细胞定位、高级结构、系统进化关系以及启动子顺式作用元件,并考察了其在不同组织及胁迫下的表达模式。结果表明,SmDHAR基因家族成员长度在724 ~ 3 296 bp之间,含有1 ~ 5个外显子不等,编码218 ~ 470个氨基酸;其编码蛋白多为亲水性蛋白,且多不含跨膜结构域。SmDHAR家族成员分布在叶绿体和胞质外。系统进化分析结果显示丹参SmDHAR基因分为2个亚类。上游启动子区域分析发现SmDHAR基因家族成员均含有与植物发育、激素及胁迫响应相关的顺式作用元件。表达模式分析结果显示,SmDHAR5在丹参根中显著高表达,而在其余组织中表达量较低;SmDHAR1、SmDHAR3与SmDHAR4在根、茎、叶中表达量较高,在花组织中表达最低;SmDHAR2表现为组成型高表达。SmDHAR在茉莉酸甲酯、酵母提取物、脱落酸、赤霉素、水杨酸等处理时也表现出不同的响应模式。本研究的结果为深入探索丹参DHAR基因家族在抵抗逆境过程中的功能提供了参考。     

Spatial and temporal analysis of landscape patterns

A variety of ecological questions now require the study of large regions and the understanding of spatial heterogeneity. Methods for spatial-temporal analyses are becoming increasingly important for ecological studies. A grid cell based spatial analysis program (SPAN) is described and results of landscape pattern analysis using SPAN are presentedd. Several ecological topics in which geographic information systems (GIS) can play an important role (landscape pattern analysis, neutral models of pattern and process, and extrapolation across spatial scales) are reviewed. To study the relationship between observed landscape patterns and ecological processes, a neutral model approach is recommended. For example, the expected pattern (i.e., neutral model) of the spread of disturbance across a landscape can be generated and then tested using actual landscape data that are stored in a GIS. Observed spatial or temporal patterns in ecological data may also be influenced by scale. Creating a spatial data base frequently requires integrating data at different scales. Spatial is shown to influence landscape pattern analyses, but extrapolation of data across spatial scales may be possible if the grain and extent of the data are specified. The continued development and testing of new methods for spatial-temporal analysis will contribute to a general understanding of landscape dynamics.     

Plant growth analysis of discontinuous growth data: A modified Richards function

A method is presented for analyzing growth data of plants with discontinuous relative growth rates resulting from a sudden short-term change in the environment of the plants. This technique provides a quantification of a treatment effect on plant growth where the growth of the control plants can be represented by the Richards function. Growth data from phytotron-grown snap bean plants subjected to episodic ozone exposures are used to illustrate the method. Further applications of the approach are suggested, including the analysis of multiple and single, short-term and long-term episodic events on plant growth.     

板栗基因组AFLP银染反应体系的建立

为了建立准确、高效、实用的板栗品种鉴别体系和方法,采用改进的CTAB—DNA提取方法,从板栗的嫩叶和老叶中提取获得总DNA。所得DNA样品的D260nm/D280nm值为1.7～1.9,琼脂糖凝胶上主带清晰、较少降解、样品纯度高、蛋白去除干净且获得率高。该体系使用操作简便、快捷、高效,适合于板栗DNA提取,所提取的DNA样品不含PCR反应抑制剂及其他酶反应抑制剂,可被限制性内切酶MseI和EcoRI完全酶切,适合AFLP-PCR反应应用,得到清晰的指纹式样,适宜用作板栗品种鉴别的有效方法。     

Motif: an open-source R tool for pattern-based spatial analysis

Context

Pattern-based spatial analysis provides methods to describe and quantitatively compare spatial patterns for categorical raster datasets. It allows for spatial search, change detection, and clustering of areas with similar patterns.

Objectives

We developed an R package motif as a set of open-source tools for pattern-based spatial analysis.

Methods

This package provides most of the functionality of existing software (except spatial segmentation), but also extends the existing ideas through support for multi-layer raster datasets. It accepts larger-than-RAM datasets and works across all of the major operating systems.

Results

In this study, we describe the software design of the tool, its capabilities, and present four case studies. They include calculation of spatial signatures based on land cover data for regular and irregular areas, search for regions with similar patterns of geomorphons, detection of changes in land cover patterns, and clustering of areas with similar spatial patterns of land cover and landforms.

Conclusions

The methods implemented in motif should be useful in a wide range of applications, including land management, sustainable development, environmental protection, forest cover change and urban growth monitoring, and agriculture expansion studies. The motif package homepage is https://nowosad.github.io/motif.