Santiago Saura

Comparison and development of new graph-based landscape connectivity indices: towards the priorization of habitat patches and corridors for conservation

The loss of connectivity of natural areas is a major threat for wildlife dispersal and survival and for the conservation of biodiversity in general. Thus, there is an increasing interest in considering connectivity in landscape planning and habitat conservation. In this context, graph structures have been shown to be a powerful and effective way of both representing the landscape pattern as a network and performing complex analysis regarding landscape connectivity. Many indices have been used for connectivity analyses so far but comparatively very little efforts have been made to understand their behaviour and sensitivity to spatial changes, which seriously undermines their adequate interpretation and usefulness. We systematically compare a set of ten graph-based connectivity indices, evaluating their reaction to different types of change that can occur in the landscape (habitat patches loss, corridors loss, etc.) and their effectiveness for identifying which landscape elements are more critical for habitat conservation. Many of the available indices were found to present serious limitations that make them inadequate as a basis for conservation planning. We present a new index (IIC) that achieves all the properties of an ideal index according to our analysis. We suggest that the connectivity problem should be considered within the wider concept of habitat availability, which considers a habitat patch itself as a space where connectivity exists, integrating habitat amount and connectivity between habitat patches in a single measure.

Short communication: Conefor Sensinode 2.2: A software package for quantifying the importance of habitat patches for landscape connectivity

Maintaining and restoring landscape connectivity is currently a central concern in ecology and biodiversity conservation, and there is an increasing demand of user-driven tools for integrating connectivity in landscape planning. Here we describe the new Conefor Sensinode 2.2 (CS22) software, which quantifies the importance of habitat patches for maintaining or improving functional landscape connectivity and is conceived as a tool for decision-making support in landscape planning and habitat conservation. CS22 is based on graph structures, which have been suggested to possess the greatest benefit to effort ratio for conservation problems regarding landscape connectivity. CS22 includes new connectivity metrics based on the habitat availability concept, which considers a patch itself as a space where connectivity occurs, integrating in a single measure the connected habitat area existing within the patches with the area made available by the connections between different habitat patches. These new metrics have been shown to present improved properties compared to other existing metrics and are particularly suited to the identification of critical landscape elements for connectivity. CS22 is distributed together with GIS extensions that allow for directly generating the required input files from a GIS layer. CS22 and related documentation can be freely downloaded from the World Wide Web.

EDITOR'S CHOICE: Stepping stones are crucial for species' long‐distance dispersal and range expansion through habitat networks

Summary 1. Climate and land-use changes will require species to move large distances following shifts in their suitable habitats, which will frequently involve traversing intensively human-modified landscapes. Practitioners will therefore need to evaluate and act to enhance the degree to which habitat patches scattered throughout the landscape may function as stepping stones facilitating dispersal among otherwise isolated habitat areas. 2. We formulate a new generalized network model of habitat connectivity that accounts for the number of dispersing individuals and for long-distance dispersal processes across generations. By doing so, we bridge the gap between complex dynamic population models, which are generally too data demanding and hence difficult to apply in practical wide-scale decisionmaking, and simpler static connectivity models that only consider the amount of habitat that can be reached by a single average disperser during its life span. 3. We find that the loss of intermediate and sufficiently large stepping-stone habitat patches can cause a sharp decline in the distance that can be traversed by species (critical spatial thresholds) that cannot be effectively compensated by other factors previously regarded as crucial for long-distance dispersal (fat-tailed dispersal kernels, source population size). 4. We corroborate our findings by showing that our model largely outperforms previous connectivity models in explaining the large-scale range expansion of a forest bird species, the Black Woodpecker Dryocopus martius, over a 20-year period. 5. The capacity of species to exploit the opportunities created by networks of stepping-stone patches largely depends on species-specific life-history traits, suggesting that species assemblages traversing fragmented landscapes may be exposed to a spatial filtering process driving long-term changes in community composition. 6. Synthesis and applications. Previous static connectivity models seriously underestimate the importance of stepping-stone patches in sustaining rare but crucial dispersal events. We provide a conceptually broader model that shows that stepping stones (i) must be of sufficient size to be of conservation value, (ii) are particularly crucial for the spread of species (either native or invasive) or genotypes over long distances and (iii) can effectively reduce the isolation of the largest habitat blocks in reserves, therefore largely contributing to species persistence across wide spatial and temporal scales.

Effects of remote sensor spatial resolution and data aggregation on selected fragmentation indices

Analyzing the effect of scale on landscape pattern indices has been a key research topic in landscape ecology. The lack of comparability of fragmentation indices across spatial resolutions seriously limits their usefulness while multi-scale remotely sensed data are becoming increasingly available. In this paper, we examine the effect of spatial resolution on six common fragmentation indices that are being used within the Third Spanish National Forest Inventory. We analyse categorical data derived from simultaneously gathered Landsat-TM and IRS-WiFS satellite images, as well as TM patterns aggregated to coarser resolutions through majority rules. In general, majority rules tend to produce more fragmented patterns than actual sensor ones. It is suggested that sensor point spread function should be specifically considered to improve comparability among satellite images of varying pixel sizes. Power scaling-laws were found between spatial resolution and several fragmentation indices, with mean prediction errors under 10% for number of patches and mean patch size and under 5% for edge length. All metrics but patch cohesion indicate lower fragmentation at coarser spatial resolutions. In fact, an arbitrarily large value of patch cohesion can be obtained by resampling the pattern to smaller pixel sizes. An explanation and simple solution for correcting this undesired behaviour is provided. Landscape division and largest patch index were found to be the least sensitive indices to spatial resolution effects.

Effects of minimum mapping unit on land cover data spatial configuration and composition

A key issue when generating a land cover map from remotely sensed data is the selection of the minimum mapping unit (MMU) to be employed, which determines the extent of detail contained in the map. This study analyses the effects of MMU in land cover spatial configuration and composition, by using simulated landscape thematic patterns generated by the Modified Random Clusters method. This approach allows a detailed control of the different factors influencing landscape metrics behaviour, as well as taking into account a wide range of land cover pattern possibilities. Land cover classes that are sparse and fragmented can be considerably misrepresented in the final map when increasing MMU, while the classes that occupy a large percentage of map area tend to become more dominant. Mean Patch Size and Number of Patches are very poor indicators of pattern fragmentation in this context. In contrast, Landscape Division (LD) and related indices (Splitting Index and Effective Mesh Size) are clearly suitable for comparing the fragmentation of landscape data with different MMUs. We suggest that the Mean Shape Index, the most sensitive to MMU of those considered in this study, should not be used in further landscape studies if land cover data with different MMU or patch size frequency distribution are to be compared. In contrast, the Area Weighted Mean Shape Index presents a very robust behaviour, which advocates the use of this index for the quantification of the overall irregularity of patch shapes in landscape spatial patterns. The results presented allow quantifying the biases resulting from selecting a certain MMU when generating a land cover dataset. In general, a bigger MMU implies underestimating landscape diversity and fragmentation, as well as over-estimating animal population dispersal success. Guidelines are provided for the proper use and comparison of spatial pattern indices measured in maps with different MMUs.

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.

Landscape connectivity analysis for conservation: insights from combining new methods with ecological and genetic data

Landscape connectivity is a multi-scalar concept allowing the investigation of how the interaction between species movement abilities and landscape structure affects species survival, gene flow and other key ecological processes in fragmented landscapes. This requires the determination of functional connectivity which is the end result of a complex combination of multiple factors such as habitat amount and arrangement, matrix quality and permeability, species perceptions and dispersal behaviour, population density, etc. Functional connectivity quantification necessitates also the consideration of the impacts and constraints imposed by the increasing rates of landscape and environmental change, which are ultimately driven by socioeconomic factors and are likely to continue putting more pressures on both managed and natural landscapes. Connectivity is nowadays an important concern in almost any modern conservation plan around the globe. The challenge of these conservation plans is to identify the spatial scale(s) and key landscape elements needed to maintain or restore connectivity and the ecological processes that are promoted by it. To meet the final objective of favouring species viability and ecosystem diversity, landscape ecologists should be able to deliver conservation guidelines and indicators at the spatial scale at which the impacts of landscape change are most prominently affecting the abundance and persistence of the focal species. They also need to convincingly demonstrate the effectiveness and benefits of connectivity investments compared to other competing conservation alternatives. In this context, integrated approaches offering synergies and new capabilities for connectivity conservation planning should allow us to interpret the effects of landscape spatial heterogeneity and to define critical threshold levels at which landscapes are connected allowing the persistence of species (Opdam et al. 2003). The series of papers in this special issue constitute a valuable addition towards this end, by showing how a diversity of analytical approaches and data types can be applied, and in many case combined in an integrated fashion, in order to address various aspects that are at play while quantifying landscape connectivity and to support related management decisions. The contributions from this special issue include (i) empirical assessments of the role of different connectivity-related S. Luque (&) Cemagref, Institute for Agricultural and Environmental Engineering Research, Mountain Ecosystems Research Unit, 2 Rue de la Papeterie, 38402 Saint-Martin d’Heres, France e-mail: sandra.luque@irstea.fr; sandra.luque@cemagref.fr

Discrimination of native and exotic forest patterns through shape irregularity indices: An analysis in the landscapes of Galicia, Spain

Landscapes resulting from human activity may be expected to present simpler shapes than more natural landscapes. In the case of forest landscapes, the boundaries of native forest patches may be more irregular than those of exotic forest plantations. There is however a lack of quantitative results to this respect, and it is not clear which shape indices are more adequate for such discrimination. In this study, we analysed the shape of a large number of forest classes in the region of Galicia (Spain) using the Spanish Forest Map at a scale 1:50 000 as the spatial information source. We considered a set of fifteen shape irregularity indices including those that have been commonly used in landscape ecology studies. We found systematic differences in the shape of the analysed forest classes, with native forests presenting both more complex and elongated boundaries than exotic forests. We suggest that these differences are due to the combined effects of human action and other topographical and hydrological factors. The only index that perfectly discriminated both types of forest was the mean circumscribing circle index. Other six indices provided also a significantly good discrimination: density of shape characteristic points, area-weighted mean perimeter-area ratio, area-weighted mean contiguity index, mean shape index, perimeter-area fractal dimension and mean largest axis index. Comparisons of pure and mixed forests with the same dominant species indicated that an increase in tree species richness is in general associated with more irregular boundaries in the forest. Discarding indices on the basis of a high statistical correlation may not be an adequate procedure to retain the best-performing indices. Finally, we discussed several limitations of some frequently used indices that may be relevant to prevent an improper characterization of landscape shape.

On how much biodiversity is covered in Europe by national protected areas and by the Natura 2000 network: insights from terrestrial vertebrates

The European Union has made extensive biodiversity conservation efforts with the Habitats and Birds Directives and with the establishment of the Natura 2000 network of protected areas, one of the largest networks of conservation areas worldwide. We performed a gap analysis of the entire Natura 2000 system plus national protected areas and all terrestrial vertebrates (freshwater fish excluded). We also evaluated the level of connectivity of both systems, providing therefore a first estimate of the functionality of the Natura 2000 system as an effective network of protected areas. Together national protected areas and the Natura 2000 network covered more than one-third of the European Union. National protected areas did not offer protection to 13 total gap species (i.e., species not covered by any protected area) or to almost 300 partial gap species (i.e., species whose representation target is not met). Together the Natura 2000 network and national protected areas left 1 total gap species and 121 partial gap species unprotected. The terrestrial vertebrates listed in the Habitats and Birds Directives were relatively well covered (especially birds), and overall connectivity was improved considerably by Natura 2000 sites that act as stepping stones between national protected areas. Overall, we found that the Natura 2000 network represents at continental level an important network of protected areas that acts as a good complement to existing national protected areas. However, a number of problems remain that are mainly linked to the criteria used to list the species in the Habitats and Birds Directives. The European Commission initiated in 2014 a process aimed at assessing the importance of the Birds and Habitats Directives for biodiversity conservation. Our results contribute to this assessment and suggest the system is largely effective for terrestrial vertebrates but would benefit from further updating of the species lists and field management.

Scale dependence in habitat selection: the case of the endangered brown bear Ursus arctos in the Cantabrian Range NW Spain

Animals select habitat resources at multiple spatial scales. Thus, explicit attention to scale dependency in species–habitat relationships is critical to understand the habitat suitability patterns as perceived by organisms in complex landscapes. Identification of the scales at which particular environmental variables influence habitat selection may be as important as the selection of variables themselves. In this study, we combined bivariate scaling and Maximum entropy (Maxent) modeling to investigate multiscale habitat selection of endangered brown bear (Ursus arctos) populations in northwest Spain. Bivariate scaling showed that the strength of apparent habitat relationships was highly sensitive to the scale at which predictor variables are evaluated. Maxent models on the optimal scale for each variable suggested that landscape composition together with human disturbances was dominant drivers of bear habitat selection, while habitat configuration and edge effects were substantially less influential. We found that explicitly optimizing the scale of habitat suitability models considerably improved single-scale modeling in terms of model performance and spatial prediction. We found that patterns of brown bear habitat suitability represent the cumulative influence of habitat selection across a broad range of scales, from local resources within habitat patches to the landscape composition at broader spatial scales.