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...     

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...     

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.

     

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...     

Uncertainty analysis as a tool for refining land dynamics modelling on changing landscapes: a case study in a Spanish Natural Park

In this study we developed a methodology aimed at improving the assessment of inter-annual land cover dynamics from hard classified remotely sensed data in heterogeneous and resilient landscapes. The methodology is implemented for the Spanish Natural Park of Sierra de Ancares, where human interference during the last century has resulted in the destruction and fragmentation of the original land cover. We ran supervised classifications, with a maximum likelihood algorithm (Maxlike), on a temporal series of Landsat images (1991–2005), followed by an uncertainty assessment using fuzzy classifications and confusion indices (CIs). This allowed us to show how much (and where) of the resulting maps contained a substantial amount of error, distinguishing data that might be useful to measure land change from data that are not particularly useful when applying a post-classification comparison methodology. In this way, we can detect true changes not skewed by the effects of uncertainty. Even if patterns of change were always coherent amongst years, they were more realistic after reducing uncertainty, in spite of a substantial decrease in the number of available pixels (i.e. unmasked by the method). We then computed land cover dynamics by means of a model specifically designed to determine the frequency of disturbances (mainly fire events) and the vegetation recovery time during the study period. Model outputs showed correlated landscape patterns at a broad scale and provided useful results to explore land cover change from pattern to process.     

Uncertainty analysis of a spatial habitat suitability model and implications for ecological management of water bodies

Habitat suitability index (HSI) models have been generally accepted in ecological management as a means to predict effects of pressures and restoration measures on habitats and populations. HSI-models estimate habitat suitability from relevant habitat variables. Because outcomes of HSI-studies may have significant consequences, it is crucial to have insight into the uncertainties of the predictions. In this study a method for uncertainty analysis, using Monte Carlo simulations, was developed and applied for a HSI-model of pondweed (Potamogeton pectinatus) in Lake IJsselmeer, The Netherlands. Uncertainties in both habitat model functions and in input data were considered. The magnitude of the uncertainties in model functions were estimated by a panel of experts, and the uncertainty was highest at intermediate values of the suitability index (0.4–0.6). Uncertainty in the predicted habitat suitability is spatially correlated with variations in environmental habitat variables such as water quality and substrate. The estimated uncertainty may be considered acceptable for the purposes of water management, namely directing ecological rehabilitation and conservation activities. However, the uncertainties may be too high to meet the accuracy requirements of legislation such as the EU Bird and Habitat directive.     

Environmental justice and park quality in an intermountain west gateway community: assessing the spatial autocorrelation

Context

Research on environmental justice issues, particularly unequal park distribution and quality, has found that communities’ minority density and socioeconomic status (SES) are often correlated with disparate park qualities. However, most studies of spatial relationships between park quality and socioeconomic factors employ simple statistical analyses, which do not account for potential spatial autocorrelations and their effects on validity.

Objectives

This study determines whether the distribution of park quality is spatially autocorrelated and assesses the associations among multiple indicators of environmental justice and both separate park features and overall park quality.

Methods

This study evaluates spatial relationships between park quality and multiple environmental justice indicators in Cache County, Utah following the spatial regression process conducted in R programming language. Both overall park quality and separate feature qualities were audited by the PARK (Parks, Activity, and Recreation among Kids) tool. Environmental justice indicators included minority density, poverty, unemployment, low-education, renter rate, and yard size.

Results

Results illustrate a spatial autocorrelation existing in park quality distribution, detecting the dependence of the variable for quantitative research. They also show significant correlations between park quality and environmental justice indicators.

Conclusions

The study’s spatial regression model is a model for analyzing the spatial data and avoids the autocorrelation which is overlooked by the normal statistical approaches. Also, variances of park quality can be accounted for by different environmental justice indicators, such as minority density, poverty, and yard size. This disclosure of disparate public resource quality treatment among different groups of individuals could inspire policy makers and city planners to correct these disparities.

     

A spatial analysis of land cover patterns and its implications for urban avifauna persistence under climate change

Context

Although biodiversity in cities is essential to ensure the healthy functioning of ecosystems and biosecurity over time, biodiversity loss resulting from human interventions in land cover patterns is widespread in urban landscapes. In the Southern Hemisphere, climate change is likely to accelerate the process of landscape upheavals, and consequently biodiversity loss.

Objectives & Methods

The aim of this research is to test the potentials of landscape pattern composition and configuration in safeguarding indigenous avifauna against the local impacts of climate change in urban landscapes, with reference to New Zealand. To build up a platform for landscape pattern interpretation, the literature was reviewed and semi-structured interviews with six subject-matter experts were conducted to provide information about the most important avifauna in the study area, key information on their ecological traits and niches, possible impacts of climate change on their primary habitats, and spatial requirements for ongoing species survival as the climate continues to change. A spatial analysis of land cover patterns was undertaken in Wellington, New Zealand using GIS and FRAGSTATS.

Results

Although there are still opportunities for biodiversity conservation in the study area, the current land cover patterns are unlikely to safeguard the selected species against climate change impacts.

Conclusions

Eight implications for avifauna persistence under climate change are discussed for the first time in relation to a New Zealand context. These implications can give rise to a higher level of informed decision-making on a wide range of practices for biodiversity conservation related to uncertainties associated with climate change.

     

Land ownership and landscape structure: a spatial analysis of sixty-six Oregon (USA) Coast Range watersheds

Patterns of land ownership and forest cover are related in complex and ecologically significant ways. Using a Geographic Information System and regression analysis, we tested for spatial relationships between the structure of land ownership and forest cover across 66 watersheds in the state of Oregon (USA), Coast Range mountains. We found that in these watersheds (1) forest cover diversity increased with land ownership diversity, (2) size of forest patches increased with size of land ownership patches, and (3) connectivity of forest cover increased with connectivity of land ownership. Land ownership structure explained between 29% and 40% of the variability of forest cover structure across these watersheds. Driving this relationship are unique associations among particular ownership classes and various forest cover classes. The USDA Forest Service and the USDI Bureau of Land Management were associated with mature forest cover; private industry was associated with young forest cover; nonindustrial private forest owners were associated with a wide diversity of cover classes. Watersheds with mixed ownership appear to provide greater forest cover diversity, whereas watersheds with concentrated ownership provide less diverse but more connected forest cover. Results suggest that land ownership patterns are strongly correlated with forest cover patterns. Therefore, understanding landscape structure requires consideration of land ownership institutions, dynamics, and patterns.This revised version was published online in May 2005 with corrections to the Cover Date.     

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.     

Consequences of spatial heterogeneity for ecosystem services in changing forest landscapes: priorities for future research

Changes in key drivers (e.g., climate, disturbance regimes and land use) may affect the sustainability of forest landscapes and set the stage for increased tension among competing ecosystem services. We addressed two questions about a suite of supporting, regulating and provisioning ecosystem services in each of two well-studied forest landscapes in the western US: (1) How might the provision of ecosystem services change in the future given anticipated trajectories of climate, disturbance regimes, and land use? (2) What is the role of spatial heterogeneity in sustaining future ecosystem services? We determined that future changes in each region are likely to be distinct, but spatial heterogeneity (e.g., the amount and arrangement of surviving forest patches or legacy trees after disturbance) will be important in both landscapes for sustaining forest regeneration, primary production, carbon storage, natural hazard regulation, insect and pathogen regulation, timber production and wildlife habitat. The paper closes by highlighting five general priorities for future research. The science of landscape ecology has much to contribute toward understanding ecosystem services and how land management can enhance—or threaten—the sustainability of ecosystem services in changing landscapes.