Landscape Graph Research Articles

Riparian habitat connectivity restoration in an anthropized landscape: A multi-species approach based on landscape graph and soil bioengineering structures.

In urbanized areas, rivers and riparian ecosystems are often the only ecological corridors available for wildlife movement. There, riverbanks are often stabilised by civil engineering structures (dykes, riprap). This can lead to habitat degradation and loss of landscape connectivity. Fascines (willow bundles tied together) could be an alternative to riprap, since they maintain the quality of the natural ecosystems by using native vegetal species instead of rocks, but their potential positive impact needs to be assessed. We proposed a landscape-scale decision-making method for river managers who want to restore banks by transforming riprap into fascines to improve landscape connectivity. We applied our methodology to a case study involving a 25 km-stretch of the Arve River, France. We selected four target vertebrate species based on biological traits to cover a wide range of dispersal capacities. For each species, we used landscape graphs to assess habitat connectivity under different contrasted riverbank scenarios. Scenarios included replacing all-natural banks with ripraps or replacing all ripraps with fascines. In addition, we systematically tested the effect of replacing individual 100 or 500 m sections of ripraps by fascines, to locate where riverbank restoration would maximize connectivity gain. The four species selected responded very differently to the scenarios (up to +14% and +46% change in Probability of Connectivity for common toads and Eurasian beavers, respectively, 0% for common sandpipers and barred grass snakes). The restoration of specific riverbank sections could result in important gains in PC (up to +33% for one single section for one species) but no section maximized connectivity gain for all the target species.

Role of green roofs in urban connectivity, an exploratory approach using landscape graphs in the city of Paris, France

Green roofs provide many ecosystem services, but little is known about the way they contribute to urban functional connectivity. This paper has the following four objectives: (1) to compare the potential green roofs’ role to connectivity in relation to other urban green spaces, (2) to specify the green roofs contribution’s type, (3) to explore the influence of building height integration method and finally (4) to assess the impact on connectivity of simulated greening new roofs. Using a landscape graph approach, we modeled ecological networks of three species groups with different dispersion capacities in the Paris region (France). Then, we computed several connectivity metrics to assess the potential contribution of green roofs to functional connectivity. At a large scale (metropole scale), our results show that green roofs can slightly improve the global connectivity largely through the connections rather than the addition of habitat area. More than a stepping stone function, green roofs would have a dispersion flux function at a local scale. Furthermore, when the difficulty of crossing movement is exponential to the height of buildings, green roofs over 20 m high are mostly disconnected from the ecological networks. In addition to the green roof’s height, our analysis highlights the very strong role played by buildings’ configuration. This study raises promising directions for the integration of building height into the analysis of urban connectivity. Detailed research and long-term biological data from green roofs and green spaces are needed to confirm our results.

Towards Ecological Management and Sustainable Urban Planning in Seoul, South Korea: Mapping Wild Pollinator Habitat Preferences and Corridors Using Citizen Science Data.

Simple SummaryUrban landscapes, though a primary contributor to habitat fragmentation, have the potential to facilitate habitat connectivity for native pollinator species, especially with strategic green space planning. Citizen science data have been proven to be useful to address conservation issues in urban areas, especially where knowledge is lacking about species richness and habitat preferences. In this study, we used data collected by a citizen science program between 2016 and 2018 to determine which families of pollinators were the most frequently observed in Seoul, with which habitats pollinators had the highest affinities, and what parts of the city facilitated habitat connectivity. We found that the most suitable habitats for multiple families were located in public parks, university campuses, and Cultural Heritage sites. These results are directly applicable in order to better understand urban planning stakes for pollinators and wildlife in general and provide avenues for improvement to recreate functional greenways in a dense city like Seoul.The preservation and restoration of habitats and ecological connectivity inside cities is crucial to ensure wildlife can find suitable areas to forage, rest and reproduce, as well as to disperse, thereby allowing metapopulation functioning. In this study, we used data collected by a citizen science program between 2016 and 2018 to determine which families of pollinators were the most frequently observed in Seoul and with which habitats pollinators had the highest affinities. Using species distribution modeling and landscape graph approaches, we located the main habitats and corridors to reinforce connectivity for six pollinator families. Finally, we identified habitats and corridors where conservation actions should be prioritized. In total, 178 species belonging to 128 genera and 60 families were observed. Hymenopterans were the most recorded, followed by dipterans and lepidopterans. The most suitable habitats for pollinators were constituted of public parks, university campuses, and Cultural Heritage sites. In a dense city like Seoul, most of the conservation corridors are located in built-up areas. Innovative urban planning and architecture are therefore required as well as the setting-up of ecological management practices to lead to a more sustainable urbanism for pollinators and wildlife in general.

Implementation of multispecies ecological networks at the regional scale: analysis and multi-temporal assessment

Today, major landscape changes affect ecological connectivity exerting adverse effects on ecosystems. Connectivity is a critical element of landscape structure and supports ecosystem functionality. Landscape connectivity can be efficiently increased in landscape ecology by building ecological networks (EN) through models mimicking the interaction between animal and vegetal species and their environment. ENs are important in sustainable landscape planning, where they need to be studied both by applying landscape metrics and by performing multi-temporal analyses. This paper presents theoretical and practical evidence of an analysis of a multispecies ecological network in Calabria (Italy) and its changes over three decades. Landscape connectivity was modeled basing on 66 focal faunal species' requirements. Human disturbance (HD) was defined and assessed according to distance from different disturbance sources. This allowed for the definition of overall habitat quality (oHQ). Landscape permeability to the animal movement was focused as the main concept to measure landscape fragmentation. Landscape graph theory was applied to perform a spatial comparison of the ENs robustness. Many binary and probabilistic indices and landscape morphological spatial pattern analysis (MSPA) were used in this perspective. We obtained a set of ecological networks, including nodes, patches (i.e., habitat patches), linkages, and corridors, all intertwined in one giant component. The multi-temporal analysis showed many indices' stationary values, while MSPA yielded an increase of habitat quality and habitat patches in core areas. This methodological approach allowed for assessing the regional EN's robustness in the time-span considered, thus providing a reliable tool for landscape planners and communities.

Graph4lg: A package for constructing and analysing graphs for landscape genetics in R

Abstract In landscape genetics, habitat connectivity and population genetic structure have been analysed using graph‐theoretic approaches to understand how landscape features influence demography (i.e. dispersal and population size). Despite substantial advances in enhancing both genetic and landscape graph use, a software tool bringing together a large range of construction and analysis parameters for these two types of graphs was lacking in the landscape genetic toolbox. Moreover, although these two types of graphs appear complementary for answering landscape genetic questions, methods for comparing them have not been forthcoming. We have developed an R package to improve and encourage the use of these graphs. It includes functions for converting and importing genetic data and for genetic distance computing. It also implements time‐efficient geodesic and cost‐distance calculations from spatial data. A large range of parameters can be used to create genetic and landscape graphs from these data, including several graph pruning methods. We made available to R users the command‐line facilitaties of Graphab software to easily model landscape graphs in R. The package functions perform preliminary analysis to adapt methodological choices to research questions. Landscape and genetic graphs created can be analysed with node‐level metrics as well as link‐level and modularity analyses. Users can compare and visualise these graphs and export them to shapefiles to facilitate interpretation and subsequent analyses. graph4lg contributes to expanding landscape and genetic graph potential for analysing ecological connectivity while encouraging further investigations on methodological implications related to these tools.

Comparison of landscape graph modelling methods for analysing pond network connectivity

Landscape fragmentation negatively impacts species populations by isolating them. Assessing landscape connectivity could help to improve biodiversity conservation. Among various methods available to model and analyse connectivity, graph-theoretic approaches are recognized as powerful tools, even if their ecological significance may be questionable in some cases. Indeed, there are many ways to construct a landscape graph and their impacts on the assessment of connectivity are rarely explored. Our aim was to compare three methods of constructing landscape graphs to identify differences and similarities in the resulting network connectivity. The methods can be distinguished according to the type of data used: expert opinions, field data or a combination of the two. The methodological framework was applied to seven pond-dwelling species (Alytes obstetricans, Bufo bufo, Epidalea calamita, Hyla arborea, Natrix natrix, Rana temporaria, Triturus cristatus) in the Ile-de-France region (France). Three common methods were applied to construct landscape graphs: (1) using a land cover map (LM) and expert opinions to define nodes and links; (2) using a habitat suitability model (HSM) and species occurrence data to define nodes and links; and (3) using a HSM to define nodes and a land cover map to define links (HSM_LM). To carry out our study, we produced a land cover map, collected and prepared input data for HSMs, generated HSMs to map the probability of species occurrence and constructed landscape graphs from the three methods. For each of them, several connectivity metrics were calculated and compared. The results revealed large differences in the statistical distribution of connectivity values, even though the spatial location of the main areas of low and high connectivity was roughly the same. In general, the LM method provided lower values of connectivity and smaller areas of high values than the other two, regardless of species. Conversely, the HSM method had the highest connectivity values, while the combined HSM_LM method appeared to be intermediate. Our study was not intended to conclude whether one method is better than another; only to point out that results vary greatly depending on the graph construction method. To evaluate the predictive performance of each model, a validation process should be conducted with another independent biological dataset, which was not available in our study. The high variability of results argues for taking care to ensure that the construction of models is carefully consistent with ecological assumptions and the objective pursued.

Representation, analysis and application of landscape graph based on graph theory

Landscape structure and spatial pattern are the core issues in landscape ecology. The application of graph theory provides a research framework for landscape pattern analysis. Landscape graph based on graph theory is gradually applied to the connectivity modeling of biodiversity conservation and decision support of landscape planning. The representation, analysis and application of landscape graph have become a hot topic in conservation biology and landscape ecology. In this review, we first introduced the graph theory basis of landscape map. Based on the Scopus database, 257 published journal papers with the words "landscape graph", "connectivity" and "network" in titles, abstracts, and keywords from 1993 to 2019 were retrieved. We analyzed the research progress and development trend of this field from the aspects of annual published papers, journal sources, research areas, research institutions and landscape types involved. The results showed that before 2017, the number of journal papers published showed an overall increasing trend, and after 2017, the annual number of published papers decreased year by year. The main research forces were concentrated in United States, France, Canada, and China, contributing to 86.8% of the total published papers. Most of the research results were published in "Landscape Ecology", "Landscape and Urban Planning", and "Biological Conservation". In the research content, the representation of landscape graph mainly included the definition of nodes, the measurement of edges and the simulation of landscape. The analysis of landscape graph involved analysis index and landscape graph partitioning. This study mainly focused on the application of landscape graph in science and practice, including biodiversity conservation, landscape (ecological network) planning and management, the assessment of landscape impacts. Landscape graph based on graph theory influences conservation science and planning practitioners by helping understand landscape connectivity changes, animal behavior and habitat conservation. The impact of graph theory on conservation science and planning comes from the rich theoretical basis and mature research methods. Landscape graph based on graph theory provides a springboard for ecological understanding of landscape structure and pattern, and is an important tool for global researchers and practitioners.

Modelling patch mosaic connectivity and ecological corridors with GraphScape

Out of many approaches to landscape connectivity, we choose patch-mosaic, flow-neutral framework for modelling. Our new software system GraphScape transforms a vector mosaic map into landscape graph, finds shortest paths and combines them into minimum spanning tree. The result is an optimal network of connections for a given set of patches. To capture mobile agent properties, we use two types of resistance assigned to patches and transfer resistance assigned to combinations of patch classes. Another alternative is step-model, when transfers occur as if on unweighted graph. Resulting MST is rendered by linear skeleton (binary), Transit Density measure assignable to patches (continuous), and summary statistics. A novelty is compete workflow from vector-based map to complex graph structures. We describe concepts and operation of software. GraphScape can be used for ecological corridors planning, identification of key areas of corridors overlap, identification of the role of particular mobility factors, assessment of patch isolation.

Correction to: Coupling landscape graph modeling and biological data: a review

In the original publication of the article, the sixth author name has been misspelt. The correct name is given in this Correction. The original article has been corrected.

Coupling landscape graph modeling and biological data: a review

Landscape graphs are widely used to model networks of habitat patches. As they require little input data, they are particularly suitable for supporting conservation decisions (and decisions about other issues as e.g. disease spread) taken by land planners. However, it may be problematic to use these methods in operational contexts without validating them with empirical data on species or communities. Since little is known about methodological alternatives for coupling landscape graphs with biological data, we have made an exhaustive review of these methods to analyze links between the main purposes of the studies, the way landscape graphs are constructed and used, the type of field data, and the way these data are integrated into the analysis. We systematically describe a corpus of 71 scientific papers dealing with terrestrial species, with particular emphasis on methodological choices and contexts of the studies. Despite a great variability of types of biological data and coupling strategies, our analyses reveal a dichotomy according to the objective of the studies, between (i) approaches aimed at improving ecological knowledge, mainly based on land-cover maps and using biological data to test the influence of landscape connectivity on biological responses, and (ii) approaches with an operational aim, in which biological data are directly integrated into the graph construction and assuming a positive effect of connectivity. Beyond these main contrasts, the review shows that landscape graphs can benefit from field data of different types at varying scales. The great variability of approaches adopted reveals the flexible nature of these tools.

Multi-node selection of patches for protecting habitat connectivity: Fragmentation versus reachability

Landscape connectivity is of major importance in biodiversity conservation, and is one of the key aspects to be taken into consideration in the spatial design of networks of protected areas. Graph-theoretical approaches are useful in modelling habitat connectivity and defining priority areas for the protection of connectivity. This prioritization can be done based on rankings of the centrality (or importance) of individual habitat patches. Moreover, the centrality of a set of n habitat patches can also be calculated. Importantly, the most central single patch is not necessarily a member of the most central group of n patches (non-nested topology). Multi-node analyses identify groups of patches that maximally complement each other in order to increase the protection of connectivity for the whole network. We apply multi-node analyses to the prioritization of habitat patches for five vulnerable bird species in Catalonia, Spain, using two different approaches to connectivity, based on fragmentation and reachability. Groups of patches based on fragmentation are usually concentrated in core areas, while reachability groups are widely spread. Fragmentation sets have higher centrality value for low-mobility species, and reachability sets for long distance dispersers. The protection of the networks against fragmentation requires fewer patches, allows for more gradual implementation and is currently better accounted for by the Natura 2000 network of protected areas, while the protection of reachability is less costly and more efficient in terms of area requirements. Our work contributes to the development of landscape graph analysis for reserve design towards multi-node approaches.

Single‐node vs. multi‐node centrality in landscape graph analysis: key habitat patches and their protection for 20 bird species in NE Spain

Summary Habitat connectivity is a major concern in biodiversity conservation. Network analysis provides efficient tools for assessing landscape connectivity and identifying priority areas for protection. Widespread approaches consist of ranking individual habitat patches by their importance for connectivity. However, depending on the spatial arrangement of patches, and on the complementarity or redundancies between different patches in the network, the group of patches that together best contribute to connectivity may differ from the top individual patches. Here we apply individual ranking (single‐node) and group prioritization (multi‐node) methods to identify key patches in the habitat network of 20 bird species in Catalonia, Spain. We compare single‐node evaluation using the landscape index probability of connectivity (PC) and two multi‐node evaluations that focus on two different aspects of connectivity: reachability and fragmentation. We quantify how well the species’ habitats and key patches are covered by the Natura 2000 network of protected areas, as given by all three methods. We discuss some species‐specific differences between results, and general, multi‐species solutions. The key patches for reachability were widely scattered, while patches that best prevented fragmentation were concentrated in core areas. Key patches according to single‐node PC were in intermediate position, although more similar to the fragmentation patches. The patches that minimized fragmentation were highlighted as more crucial for low‐mobility species, while reachability patches scored higher for long‐distance dispersers. For most species, the key patches were not better protected than total habitat. We identify two main areas that concentrate priority patches for all the studied species (including the endangered Neophron percnopterus) and offer suggestions for the potential improvement of the Natura 2000 network. Our work provides a significant step towards the inclusion of multi‐node prioritization in landscape network analysis. The concept of multi‐node reachability, especially, provides an additional viewpoint to the assessment of connectivity. The multi‐patch algorithm we applied optimizes efficiency, overcoming computational limits associated with the high number of combinations that potentially arise in multi‐node analysis. We believe that a combination of multi‐node evaluations and PC has the potential to increase the realism and applicability of landscape network analysis for biodiversity conservation.

Social and landscape effects on food webs: a multi-level network simulation model

One source of complexity in ecological systems is the hierarchical organization of parallel biological processes. Our ‘horizontal’ knowledge describing different levels is quite massive, but the understanding of their vertical interactions is very poor. We present a toy model linking social networks, food webs and a landscape graph. Horizontal processes refer to population, community and metacommunity dynamics, while vertical processes connect the three organizational levels. The model is stochastic and individual-based. We parametrized it by using reasonable empirical values found in the literature. Sensitivity analysis shows how the parameters describing the dynamics of a particular species (e.g., probability of social tie formation with conspecific individuals, or migration rate) can affect metapopulation size and spatial heterogeneity of all food web species. Changing the values of various parameters at any of the three levels have commensurable effects on the population size of all species. In contrast to the general intuition, community dynamics do not dominate population biology; social and landscape processes can trigger greater effects than food web interactions. More rapidly changing social relationships lead to a decrease in social network cohesion, thus impairing the feeding efficiency of consumers. In food webs, trophic specialization provides an advantage when it contributes to avoid competition, being detrimental otherwise. Highest migration rates result in a more heterogeneous metapopulation distribution of the generalist consumer, indirectly supporting its specialist competitor. We discuss conceptual and methodological aspects of the model, demonstrating the importance of an integrative view. We also emphasize the relevance of vertical connections, suggesting how such a modelling framework could support conservation biology. Further studies should focus on methods to approximate external pressures with changes in model parameters, thus allowing to characterize possible impacts on ecological systems.

Identifying functionally connected habitat compartments with a novel regionalization technique

Landscape ecologists have increasingly turned to the use of landscape graphs in which a landscape is represented as a set of nodes (habitat patches) connected by links representing inter-patch-dispersal. This study explores the use of a graph-based regionalization method, Graph-based REgionalization with Clustering And Partitioning (GraphRECAP), to detect structural groups of habitat patches (compartments) in a landscape graph such that the connections (i.e. the movement of individual organisms) within the groups are greater than those across groups. Specifically, we mapped compartments using habitat and dispersal data for ring-tailed lemurs (Lemur catta) in an agricultural landscape in southern Madagascar using both GraphRECAP and the widely-used Girvan and Newman method. Model performance was evaluated by comparing compartment characteristics and three measures of network connectivity and traversability: the connection strength of habitat patches in the compartments (modularity), the potential ease of individual organism movements (Harary index), and the degree of alternative route presence (Alpha index). Compartments identified by GraphRECAP had stronger within-compartment connections, greater traversability, more alternative routes, and a larger minimum number of habitat patches within compartments, all of which are more desirable traits for ecological networks. Our method could thus facilitate the study of ecosystem resilience and the design of nature reserves and landscape networks to promote the landscape-scale dispersal of species in the fragmented habitats.

Finding the functional grain: comparing methods for scaling resistance surfaces

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

Coupling inter‐patch movement models and landscape graph to assess functional connectivity

AbstractLandscape connectivity is a key process for the functioning and persistence of spatially‐structured populations in fragmented landscapes. Butterflies are particularly sensitive to landscape change and are excellent model organisms to study landscape connectivity. Here, we infer functional connectivity from the assessment of the selection of different landscape elements in a highly fragmented landscape in the Île‐de‐France region (France). Firstly we measured the butterfly preferences of the Large White butterfly (Pieris brassicae) in different landscape elements using individual release experiments. Secondly, we used an inter‐patch movement model based on butterfly choices to build the selection map of the landscape elements to moving butterflies. From this map, functional connectivity network ofP. brassicaewas modelled using landscape graph‐based approach. In our study area, we identified nine components/groups of connected habitat patches, eight of them located in urbanized areas, whereas the last one covered the more rural areas. Eventually, we provided elements to validate the predictions of our model with independent experiments of mass release‐recapture of butterflies. Our study shows (1) the efficiency of our inter‐patch movement model based on species preferences in predicting complex ecological processes such as dispersal and (2) how inter‐patch movement model results coupled to landscape graph can assess landscape functional connectivity at large spatial scales.

Assessing habitat connectivity for land-use planning: a method integrating landscape graphs and Delphi survey

Increasing fragmentation is occurring in most valley floors in Alpine regions, where urbanization and infrastructure development are reducing connectivity among remnant natural areas. This undermines the conservation of alpine biodiversity. Using a landscape graph-based approach, we visualized and assessed the dispersal opportunities for three target species (Rana synk. esculenta, Erinaceus europaeus, Moscardinus avellanarius) on a municipal scale. We considered the barrier effect caused by landscape objects, such as linear infrastructures and artificial land covers. Species-specific barrier effects were estimated through a Delphi survey involving 25 experts. The information collected was then used to draw a landscape graph of local connections and to estimate the functioning of the networks of habitat patches in terms of their capability of sustaining local populations of target species. The methodology was applied in an alpine valley floor in Trentino, northern Italy, and resulted in the mapping of all possible and remnant linkages between habitat patches in its current state. The approach may provide a better qualitative understanding of the impacts of proposed land-use changes, and was found to be particularly helpful in contexts where availability of data is limited.

Least-cost modelling on irregular landscape graphs

Least-cost modelling is becoming widely used in landscape ecology to examine functional connectivity. Traditionally the least-cost modelling algorithm creates a regularly structured landscape graph for connectivity analysis by converting all the cells from a cost-surface into vertices in a landscape graph. However, use of a regular landscape graph is problematic as it: contains a great deal of redundant information that in turn increases processing times, is constructed in a deterministic manner that precludes examination of the effects of graph structure on connectivity measures, and is known to produce results with directional bias. I present, and provide Python code for, an algorithm to produce an irregular landscape graph from a cost-surface. Tests demonstrate that comparable results to those of the traditional regular landscape graph approach can be achieved, while at the same time reducing computational expense, enabling variations in graph structure to be incorporated into an analysis, and avoiding directional bias. Therefore, this approach may allow for more robust ecological decision-making when examining matters of functional connectivity using least-cost modelling.

Integrating graph-based connectivity metrics into species distribution models

Species distribution models (SDMs) are commonly used in ecology to map the probability of species occurrence on the basis of predictive factors describing the physical environment. We propose an improvement on SDMs by using graph methods to quantify landscape connectivity. After (1) mapping the habitat suitable for a given species, this approach consists in (2) building a landscape graph, (3) computing patch-based connectivity metrics, (4) extrapolating the values of those metrics to any point of space, and (5) integrating those connectivity metrics into a predictive model of presence. For a given species, this method can be used to interpret the significance of the metrics in the models in terms of population structure. The method is illustrated here by the construction of an SDM for the European tree frog in the region of Franche-Comte (France). The results show that the connectivity metrics improve the explanatory power of the SDM and emphasize the important role of the habitat network.

Study on Fusion of Aerial and TM Image and its Application

The aerial remote sensing image has the advantage of high resolution, while the TM image has the predominance of multi spectral information. This paper introduces the theory and method of the fusion of aerial and TM images, and discusses the application of fusion image to make three dimensional landscape graph, inundate analysis and loss evaluation, monitoring the conditions of reforesting and regrassing , boundary survey and virtual reality simulation system.