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SCALETOOL IntroductionDriversBiodiversityPolicies and managementConnectivity and protected areas
Differences between structural and functional connectivity How to assess connectivity - methods and tools From movement to dispersal to connectivity Connectivity and Natura 2000 Key messages for the connectivity of protected areas
 

How to assess connectivity - methods and tools

A broad range of methods assessing connectivity using different approaches has been developed and is described on this page. However, a major challenge for connectivity research is to assess the array of available methods, and identify the appropriate methods and metrics for a given objective or application.
In the following, we briefly present a non-comprehensive selection of the many measures and tools used to assess structural and functional connectivity. For extensive reviews, we recommend Kindleman & Burel (2008) and Uuemaa et al. (2009), and for scale-specific aspects, Simova and Gdulova (2012) and Schindler et al. (2013).

Assessing structural connectivity

'Structural' connectivity indices are often used for large-scale assessments of connectivity, such as an evaluation of fragmentation across Europe by the European Environmental Agency (EEA). Examples of such indices are the number of patches in a given landscape, or the distribution of patch sizes. Other commonly used metrics are Landscape Shape Index and patch cohesion (Schumaker 1996), both of which are based on the ratio between patch perimeter and area. Hundreds of indices can be calculated using the freeware Fragstats, GIS, and other tools. Since many of these indices correlate strongly with each other, a small subset may suffice to cover the specific needs of the user (Calabrese & Fagan 2004). For analysing spatial patterns, the process-based landscape simulator G-RaFFe was developed in the SCALES project (Pe'er et al. 2013).

Graph theory to assess landscape connectivity

Moving beyond a simple description of landscape structures, graph theoretical approaches enable consideration of the movement capacities of species and the landscape's resistance to such movements. The basic principle is to treat the landscape as a network of habitat patches ('nodes'), with certain movement probabilities between them ('links'). These probabilities can then be derived as a function of Euclidian distance, cost distance or least-cost paths, or alternatively, by simulating movements as an electric current in a resistance network, building on Electric Circuit theory (McRae et al. 2008). Connectivity measures derived by graph-theory approaches enable, for instance, ranking the contribution of different patches to connectivity by testing the effect of their removal. Notable applications of this approach are in projects aiming to identify optimal locations for corridor protection or restoration. For useful programs, see Conefor Sensinode, Circuitscape or GIS-based tools and www.corridordesign.org.

Assessing functional connectivity

To consider the complexity of 'functional' connectivity as the outcome of individual-landscape interactions, one can use individual-based simulation models (IBMs). By simulating the response of individuals to landscape structures, one can consider their internal state, perceptual range, or social interactions leading to density-dependent emigration and immigration. One can simulate movements as random or a correlated-random walks, but more sophisticated algorithms also consider habitat suitability and perception. As an outcome, one can calculate immigration and emigration rates within and among patches, or assess connectivity across entire landscapes. An example of such an IBM, FunCon concentrates on assessing functional connectivity as an outcome of either home-range (everyday) movements or dispersal (namely, movements resulting in depositing offspring elsewhere) (Pe'er et al. 2011). As another individual-based and spatially-explicit model, RangeShifter integrates population dynamics and dispersal (emigration, transfer, and settlement) within real or artificial landscapes (Bocedi et al. 2014). In contrast, MetaConnect uses land-cover maps and species life-history traits on individual-based level to simulate life and behaviour of individuals and assess functional connectivity (www.terroiko.fr/metaconnect.html). Other interesting applications of IBMs to evaluate connectivity involve efforts for species protection or reintroduction, e.g. for carnivores like bears or lynx in Europe. As a combination of species distribution models and graph theory, Network SDM was developed to assess the functionality of current conservation networks such as Natura 2000 (Mazaris et al. 2013).

Guidelines for users


Finding the appropriate method to assess connectivity strongly depends on the research or management question. For that purpose, a range of methods was developed to assess structural, landscape or functional connectivity (explanations and differences can be found here).

However, while many approaches and tools have been developed and applied to assess connectivity, it is less clear which level of detail is appropriate for which question, landscape, or species, and of the constraints resulting from data availability. Thus, a major challenge for connectivity research is to assess the array of available methods, and identify the appropriate methods and metrics for a given objective or application.



References

Bocedi, G., S. C. F. Palmer, G. Pe'er, R. K. Heikinen, Y. G. Matsinos, K. Watts, and J. M. J. Travis. 2014. RangeShifter: a platform for modelling spatial eco-evolutionary dynamics and species' responses to environmental changes. Methods in Ecology and Evolution 5:388-396.

Calabrese, J. M., and W. F. Fagan. 2004. A comparison-shopper's guide to connectivity metrics. Frontiers in Ecology and the Environment 2:529-536.

Kindlmann, P., and F. Burel. 2008. Connectivity measures: a review. Landscape Ecology 23:879-890.

Mazaris, A. D., A. D. Papanikolaou, M. Barbet-Massin, A.S. Kallimanis, F. Jiguet, D. S. Schmeller, and J. D. Pantis. Evaluating the connectivity of a protected areas' network under the prism of global change: the efficiency of the European Natura 2000 network for four birds of prey. PLoS ONE 8(3):e5964.

McRae, B. H., B. G. Dickson, T. H. Keitt, and V. B. Shah. 2008. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89:2712-2724.

Pe'er, G., K. Henle, C. Dislich, and K. Frank. 2011. Breaking functional connectivity into components: a novel approach using an individual-based model, and first outcomes. PLoS ONE 6:e22355.

Pe'er, G., G. A. Zurita, L. Schober, M. I. Bellocq, M. Strer, M. Müller, and S. Pütz. 2013. Simple process-based simulators for generating spatial patterns of habitat loss and fragmentation: A review and introduction to the G-RaFFe model. PLoS ONE 8:e64968.

Schindler, S., H. von Wehrden, K. Poirazidis, T. Wrbka, and V. Kati. 2013. Multiscale performance of landscape metrics as indicators of species richness of plants, insects and vertebrates. Ecological Indicators 31:41-48.

Schumaker, N. H. 1996. Using landscape indices to predict habitat connectivity. Ecology 77:1210-1225.

Simova, P., and K. Gdulova. 2012. Landscape indices behavior: A review of scale effects. Applied Geography 34:385-394.

Uuemaa, E., M. Antrop, J. Roosaare, R. Marja, and Ü. Mander. 2009. Landscape metrics and indices: An overview of their use in landscape research. Living Reviews in Landscape Research 3:[online].

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