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...     

multifit: an R function for multi-scale analysis in landscape ecology

Context

Multi-scale analyses are a common approach in landscape ecology. Their aim is to find the appropriate spatial scale for a particular landscape attribute in order to perform a correct interpretation of results and conclusions.

Objectives

I present an R function that performs statistical analysis relating a biological response with a landscape attribute at a set of specified spatial scales and extracts the statistical strength of the models through a specified criterion index. Also, it draws a plot with the value of these indexes, allowing the user to choose the most appropriate spatial scale. This paper introduces the usage of multifit and demonstrates its functionality through a case study.

Conclusions

The spatial scale at which ecologists conduct studies may change study outcomes and conclusions. Because of this, landscape ecologists commonly conduct multi-scale studies in order to establish an appropriate spatial scale for particular biological or ecological responses. The tool presented here allows ecologists to simultaneously run several statistical models for a response variable and a specified set of spatial scales, automating the process of multi-scale analysis.

     

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.     

A multi-scale spatial analysis method for point data

This paper presents a nearest neighbor method for the spatial analysis of data collected from discrete field sampling sites. The method was applied to point counts of birds at permanent survey sites in the Nicolet National Forest of northeastern Wisconsin. The spatial analysis method we developed uses a Monte Carlo randomization approach to test for non-randomness not only of the mean nearest neighbor distance between n points but also the mean second nearest, third nearest,..., to (n–1)th nearest distances to reveal spatial information at multiple scales. Because the bird survey sites are not randomly distributed throughout the forest, the survey sites at which a given species was recorded were compared with random samples drawn from the total survey sites rather than from all possible points within the forest. More refined analyses restricted the randomization by (a) habitat type, in order to separate the effects of non-randomly distributed habitat types on species' distributions; and (b) north-south regions of the forest, in order to account for regional gradients in distribution which were evident for some species. Spatial patterns among the sites at which the birds were detected reveal information about the scale at which the birds are distributed in their environment and provide a more complete picture of multi-scale bird population dynamics.     

Connectivity analysis as a tool for assessing restoration success

Context

Methods for measuring restoration success that include functional connectivity between species’ populations are rare in landscape ecology and restoration practices. We developed an approach that analyzes connectivity between populations of target species and their dispersal probabilities to assess restoration success based on easily accessible input data. Applying this method to landscape development scenarios can help optimize restoration planning.

Objectives

We developed an assessment for restoration success and restoration planning based on functional connectivity between species’ populations and spatially explicit scenarios. The method was used in a case study to test its applicability.

Methods

Based on data on available habitat, species’ occurrence and dispersal ranges, connectivity metrics and dispersal probabilities for target species are calculated using the software Conefor Sensinode. The metrics are calculated for scenarios that reflect possible changes in the landscape to provide a basis for future restoration planning. We applied this approach to floodplain meadows along the Upper Rhine for four plant species and three future scenarios.

Results

In the case study, habitats of the target species were poorly connected. Peucedanum officinale and Sanguisorba officinalis were more successful in recolonizing new habitats than Iris spuria and Serratula tinctoria. The scenarios showed that restoration of species-rich grassland was beneficial for dispersal of the target species. As expected in the agriculturally dominated study area, restoration of former arable land significantly increased dispersal probabilities.

Conclusions

In the case study, the developed approach was easily applicable and provided reasonable results. Its implementation will be helpful in decision-making for future restoration planning.

     

Path analysis of spatial predictors of front-yard landscape in an anthropogenic environment

Contagious spatial patterns were shown to exist in the landscape of front-yards in street sections of Hochelaga-Maisonneuve, Montréal. Neighbour mimicry was hypothesized as the mechanism behind this pattern (Zmyslony and Gagnon 1998). To assess the role of spatial environmental factors in structuring this pattern, we carried out a path analysis on the front-yard landscape with five spatial factors: relative distance, street side, width, depth and type of front-yard. We removed all non-significant factors from our model with simple Mantel tests and untangled the common spatial component from the relationship between spatial factors and front-yard landscape with partial Mantel tests. We then used path analysis to evaluate the relative importance of all significant spatial factors in structuring front-yard landscape and to determine the r ² (% of landscape variation explained by spatial factors). Results showed that (1) among all spatial environmental factors, distance (proximity) remained the best predictor of front-yard vegetation – distance alone explained an average of 20% of the landscape variation of a street section, (2) depth, width and type of front-yard also structured the front-yard landscape independently of distance, (3) front-yard landscape expresses greater similarity within the same side of a street section, and (4) in two street sections of Hochelaga-Maisonneuve, spatial factors predicted over 40% of the landscape variation. This suggests (1) that landscape contagion exists also in highly humanized environments and (2) that the mimicry phenomenon was induced not only by proximity, but also by similar environmental conditions in same side street sections and whole street sections. Finally, we suggest that street sections are a very useful and appropriate unit of analysis of urban ecosystems.     

Emerging trajectories for spatial pattern analysis in landscape ecology

Landscape Ecology - Landscape ecology is an interdisciplinary field, drawing on theories and methods from across the physical, natural, and social sciences. Spatial pattern analysis was built on...     

‘Scalescape’: an R package for estimating distance-weighted landscape effects on an environmental response

Landscape Ecology - Landscape studies often focus on determining how the landscape around a discrete set of field sites affects an abiotic or biotic response measured at those sites. To run this...     

Blue-native PAGE in plants: a tool in analysis of protein-protein interactions

Intact protein complexes can be separated by apparent molecular mass using a standard polyacrylamide gel electrophoresis system combining mild detergents and the dye Coomassie Blue. Referring to the blue coloured gel and the gentle method of solubilization yielding native and enzymatically active protein complexes, this technique has been named Blue-Native Polyacrylamide Gel-Electrophoresis (BN-PAGE). BN-PAGE has become the method of choice for the investigation of the respiratory protein complexes of the electron transfer chains of a range of organisms, including bacteria, yeasts, animals and plants. It allows the separation in two dimensions of extremely hydrophobic protein sets for analysis and also provides information on their native interactions. In this review we discuss the capabilities of BN-PAGE in proteomics and the wider investigation of protein:protein interactions with a focus on its use and potential in plant science.     

Blue-native PAGE in plants: a tool in analysis of protein-protein interactions

Plant Methods is a new journal for plant biologists, specialising in the rapid publication of peer-reviewed articles with a focus on technological innovation in the plant sciences. The aim of Plant Methods is to stimulate the development and adoption of new and improved techniques and research tools in plant biology. We hope to promote more consistent standards in the plant sciences, and make readily accessible laboratory and computer-based research tools available to the whole community. This will be achieved by publishing Research articles, Methodology papers and Reviews using the BioMed Central Open Access publishing model. The journal is supported by a prestigious editorial board, whose members all recognise the importance of technological innovation as a driver for basic science.     

Underestimating the effects of spatial heterogeneity due to individual movement and spatial scale: infectious disease as an example

Many ecological and epidemiological studies occur in systems with mobile individuals and heterogeneous landscapes. Using a simulation model, we show that the accuracy of inferring an underlying biological process from observational data depends on movement and spatial scale of the analysis. As an example, we focused on estimating the relationship between host density and pathogen transmission. Observational data can result in highly biased inference about the underlying process when individuals move among sampling areas. Even without sampling error, the effect of host density on disease transmission is underestimated by approximately 50 % when one in ten hosts move among sampling areas per lifetime. Aggregating data across larger regions causes minimal bias when host movement is low, and results in less biased inference when movement rates are high. However, increasing data aggregation reduces the observed spatial variation, which would lead to the misperception that a spatially targeted control effort may not be very effective. In addition, averaging over the local heterogeneity will result in underestimating the importance of spatial covariates. Minimizing the bias due to movement is not just about choosing the best spatial scale for analysis, but also about reducing the error associated with using the sampling location as a proxy for an individual’s spatial history. This error associated with the exposure covariate can be reduced by choosing sampling regions with less movement, including longitudinal information of individuals’ movements, or reducing the window of exposure by using repeated sampling or younger individuals.

     

Identifying biological landmarks using a novel cell measuring image analysis tool: Cell-o-Tape

Background

A large number of different plant lines are produced and maintained in a typical plant research laboratory, both as seed stocks and in active growth. These collections need careful and consistent management to track and maintain them properly, and this is a particularly pressing issue in laboratories undertaking research involving genetic manipulation due to regulatory requirements. Researchers and PIs need to access these data and collections, and therefore an easy-to-use plant-oriented laboratory information management system that implements, maintains and displays the information in a simple and visual format would be of great help in both the daily work in the lab and in ensuring regulatory compliance.

Results

Here, we introduce ??Phytotracker??, a laboratory management system designed specifically to organise and track plasmids, seeds and growing plants that can be used in mixed platform environments. Phytotracker is designed with simplicity of user operation and ease of installation and management as the major factor, whilst providing tracking tools that cover the full range of activities in molecular genetics labs. It utilises the cross-platform Filemaker relational database, which allows it to be run as a stand-alone or as a server-based networked solution available across all workstations in a lab that can be internet accessible if desired. It can also be readily modified or customised further. Phytotracker provides cataloguing and search functions for plasmids, seed batches, seed stocks and plants growing in pots or trays, and allows tracking of each plant from seed sowing, through harvest to the new seed batch and can print appropriate labels at each stage. The system enters seed information as it is transferred from the previous harvest data, and allows both selfing and hybridization (crossing) to be defined and tracked. Transgenic lines can be linked to their plasmid DNA source. This ease of use and flexibility helps users to reduce their time needed to organise their plants, seeds and plasmids and to maintain laboratory continuity involving multiple workers.

Conclusion

We have developed and used Phytotracker for over five years and have found it has been an intuitive, powerful and flexible research tool in organising our plasmid, seed and plant collections requiring minimal maintenance and training for users. It has been developed in an Arabidopsis molecular genetics environment, but can be readily adapted for almost any plant laboratory research.     

Testing assumptions of cost surface analysis—a tool for invasive species management

Applied ecology could benefit from new tools that identify potential movement pathways of invasive species, particularly where data are sparse. Cost surface analysis (CSA) estimates the permeability (friction) across a landscape and can be applied to dispersal modelling. Increasingly used in a diversity of applications, several fundamental assumptions that might influence the outputs of CSA (cost surfaces and least-cost pathways) have yet to be systematically examined. Thus, we explore two issues: the presumed relationship between habitat preferences and dispersal behaviour as well as the degree of landscape fragmentation through which an organism moves by modelling a total of 18 sensitivity and dispersal scenarios. We explored the effect of fragmentation by altering the friction values (generally assigned using expert opinion) associated with patch and linear features. We compared these sensitivity scenarios in two sites that differed in fragmentation. We also used eastern grey squirrels (Sciurus carolinensis) as an example invading species and compared diffusion models and two contrasting cost surface dispersal scenarios. The diffusion model underestimated spread because squirrels did not move randomly through the landscape. Despite contrasting assumptions regarding dispersal behaviour, the two cost surfaces were strikingly similar while the least-cost paths differed. Furthermore, while the cost surfaces were insensitive to changes in friction values for linear features, they were sensitive to assumptions made for patch features. Our results suggest that movement in fragmented landscapes may be more sensitive to assumptions regarding friction values than contiguous landscapes. Thus, the reliability of CSA may depend not only on the range of friction values used for patches but also the degree of contiguity in the landscape.     

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.

     

Planning for a green city: The Green Factor tool

In recent years, new planning tools have emerged to aid planners to achieve multiple goals to sustainability. The Green Factor tool has been adopted by some cities to increase the share and effectiveness of green areas. This short communication asks how useful the Green Factor tool is and how it fits with the existing planning procedures regarding green areas through a qualitative case study in the city of Helsinki. The results show that while the tool functions well, improvements could be made in relation to monitoring, for example. Also, an ambitious target set in the tool could encourage or force developers to aim higher with the planning of green areas and construction, but existing regulations challenge its use.     

High Resolution Melt (HRM) analysis is an efficient tool to genotype EMS mutants in complex crop genomes

Background

Targeted Induced Loci Lesions IN Genomes (TILLING) is increasingly being used to generate and identify mutations in target genes of crop genomes. TILLING populations of several thousand lines have been generated in a number of crop species including Brassica rapa. Genetic analysis of mutants identified by TILLING requires an efficient, high-throughput and cost effective genotyping method to track the mutations through numerous generations. High resolution melt (HRM) analysis has been used in a number of systems to identify single nucleotide polymorphisms (SNPs) and insertion/deletions (IN/DELs) enabling the genotyping of different types of samples. HRM is ideally suited to high-throughput genotyping of multiple TILLING mutants in complex crop genomes. To date it has been used to identify mutants and genotype single mutations. The aim of this study was to determine if HRM can facilitate downstream analysis of multiple mutant lines identified by TILLING in order to characterise allelic series of EMS induced mutations in target genes across a number of generations in complex crop genomes.

Results

We demonstrate that HRM can be used to genotype allelic series of mutations in two genes, BraA.CAX1a and BraA.MET1.a in Brassica rapa. We analysed 12 mutations in BraA.CAX1.a and five in BraA.MET1.a over two generations including a back-cross to the wild-type. Using a commercially available HRM kit and the Lightscanner? system we were able to detect mutations in heterozygous and homozygous states for both genes.

Conclusions

Using HRM genotyping on TILLING derived mutants, it is possible to generate an allelic series of mutations within multiple target genes rapidly. Lines suitable for phenotypic analysis can be isolated approximately 8-9 months (3 generations) from receiving M₃ seed of Brassica rapa from the RevGenUK TILLING service.     

Modeling spatial distribution of European badger in arid landscapes: an ecosystem functioning approach

Understanding the factors determining the spatial distribution of species is a major challenge in ecology and conservation. This study tests the use of ecosystem functioning variables, derived from satellite imagery data, to explore their potential use in modeling the distribution of the European badger in Mediterranean arid environments. We found that the performance of distribution models was enhanced by the inclusion of variables derived from the Enhanced Vegetation Index (EVI), such as mean EVI (a proxy for primary production), the coefficient of variation of mean EVI (an indicator of seasonality), and the standard deviation of mean EVI (representing spatial heterogeneity of primary production). We also found that distributions predicted by remote sensing data were consistent with the ecological preferences of badger in those environments, which may be explained by the link between EVI-derived variables and the spatial and temporal variability of food resource availability. In conclusion, we suggest the incorporation of variables associated with ecosystem function into species modeling exercises as a useful tool for improving decision-making related to wildlife conservation and management.     

Graph theory as an invasive species management tool: case study in the Sonoran Desert

Context

Biodiversity in arid regions is usually concentrated around limited water resources, so natural resource managers have constructed artificial water catchments in many areas to supplement natural waters. Because invasive species may also use these waters, dispersing into previously inaccessible areas, the costs and benefits of artificial waters must be gauged and potential invasion- and climate change-management strategies assayed.

Objectives

We present a network analysis framework to identify waters that likely contribute to the spread of invasive species.

Methods

Using the Sonoran Desert waters network and the American bullfrog (Lithobates catesbeianus)—a known predator, competitor, and carrier of pathogens deadly to other amphibians—as an example, we quantified the structural connectivity of the network to predict regional invasion potential under current and two future scenarios (climate change and management reduction) to identify waters to manage and monitor for invasive species.

Results

We identified important and vulnerable waters based on connectivity metrics under scenarios representing current conditions, projected climate-limited conditions, and conditions based on removal of artificial waters. We identified 122,607 km² of land that could be used as a buffer against invasion and 67,745 km² of land that could be augmented by artificial water placement without facilitating invasive species spread.

Conclusions

Structural connectivity metrics can be used to evaluate alternative management strategies for invasive species and climate mitigation.

     

The scale effects of landscape variables on landscape experiences: a multi-scale spatial analysis of social media data in an urban nature park context

Landscape Ecology - The roles of landscape variables regarding the recreational services provided by nature parks have been widely studied. However, the potential scale effects of the relationships...