Elsevier

Ecological Indicators

Volume 9, Issue 1, January 2009, Pages 64-71
Ecological Indicators

Mapping functional connectivity

https://doi.org/10.1016/j.ecolind.2008.01.011Get rights and content

Abstract

An objective and reliable assessment of wildlife movement is important in theoretical and applied ecology. The identification and mapping of landscape elements that may enhance functional connectivity is usually a subjective process based on visual interpretations of species movement patterns. New methods based on mathematical morphology provide a generic, flexible, and automated approach for the definition of indicators based on the classification and mapping of spatial patterns of connectivity from observed or simulated movement and dispersal events. The approach is illustrated with data derived from simulated movement on a map produced from satellite imagery of a structurally complex, multi-habitat landscape. The analysis reveals critical areas that facilitate the movement of dispersers among habitat patches. Mathematical morphology can be applied to any movement map providing new insights into pattern-process linkages in multi-habitat landscapes.

Introduction

Central to our efforts to preserve and restore threatened populations in fragmented ecosystems is the understanding of how movement of organisms is affected by landscape change. Maintaining connectivity, defined as the degree to which the landscape facilitates or impedes movement among resource patches (Taylor et al., 1993), is generally regarded as an essential goal of environmental conservation (Forman and Godron, 1986, Crooks and Sanjayan, 2006). Quantifying connectivity has been problematic.

The connections in a landscape are typically quantified by its structural elements such as stepping stone patches or habitat corridors. The importance of these elements has been widely advocated in ecological theory, although empirical evidence that corridors improve movement across the landscape remains equivocal (Harris, 1984, Noss, 1987, Simberloff and Cox, 1987, Harrison, 1992, Hobbs, 1992, Simberloff et al., 1992, Lindenmayer and Nix, 1993, Beier and Noss, 1998). The effectiveness of potential wildlife corridors depends, for example, on the species, the quality of habitat within the corridor, the matrix that surrounds the corridor, and the width, length and redundancy of the corridor network, among other factors (Collinge, 1998, Haddad et al., 2003, Malanson, 2003, Baum et al., 2004, Bender and Fahrig, 2005). Further complicating the evaluation of structural connectors is the variety of ways in which terms such as corridor have been defined, ranging from linear landscape elements to any space that enhances the spread of biota between regions (Puth and Wilson, 2001, Hilty et al., 2006, Calabrese and Fagan, 2004).

The assessment of functional connectivity (how species move through a landscape) would remove some of the ambiguity associated with relying solely on the physical arrangement of landscape elements (structural connectivity) to determine connectedness. Measures of functional connectivity recognize that connectivity is species-specific and explicitly consider the ability of a species to disperse between patches (Crooks and Sanjayan, 2006). Habitat patches need not be physically connected by contiguous habitat in order for organisms to move among them. A species may be capable of crossing habitat gaps, or the matrix separating patches, and thus functionally connect areas that are not structurally connected. The matrix between two patches may consist of complex ensembles of multiple land uses, some more amenable to organism movement than others. Preferred movement pathways through matrix elements are thus functional (but not necessarily structural) corridors.

In practice, the identification of functional connectors (i.e., pathways for dispersal and immigration) remains an open issue due to at least two challenges: (1) the absence of observational data required to make species-specific assessments of movement potential and (2) the lack of quantitative and objective methods for analyzing the movement data in a spatial context (Lambeck, 1997, Vos et al., 2001, Vos et al., 2002, Lindenmayer et al., 2002). Mathematical models are available for establishing potential connectivity among patches (as defined by Calabrese and Fagan, 2004), but these methods generally provide a list of patches that are connected rather than a description of the preferred pathways used to successfully move between patches. However, it is precisely this spatially explicit mapping of functional corridors that is necessary from a management perspective in order to preserve, and, in some cases, restore connectivity.

The preferred approach for gathering data to map functional corridors is direct observation of movement, ideally in a designed experiment (e.g., Collinge, 1998, Haddad, 1999, Haddad et al., 2003, Baum et al., 2004). But direct observation of movement is impractical over broad extents or for a large number of species. Functional connectivity analysis through movement simulations (e.g., Gustafson and Gardner, 1996, Gardner and Gustafson, 2004, Hargrove et al., 2005) provides an alternative, objective evaluation of connectivity for many species in real or artificial landscapes.

Attempts to analyze movement data (whether from direct observation or simulation) include calculating the fraction of dispersers arriving at a “destination patch” from a “source patch” (e.g., Kramer-Schadt et al., 2004). The approach is equivalent to creating an adjacency matrix to define connectivity for graph analysis (e.g., Minor and Urban, 2007). Graph representations are highly useful for sensitivity analyses at the scale of network connectivity (e.g., Urban and Keitt, 2001); however, species-specific assessments of functional connectivity often require direct knowledge of inter-patch dispersal pathways. By simplifying the landscape into an adjacency matrix format, graphs do not retain the information necessary to identify the specific spatial pathways that facilitate movement within the matrix environment between patches. This information may be required for conservation management.

The objective of this paper is to demonstrate the use of mathematical morphology as a conceptual idea for identifying functional corridors and other interesting features of simulated or observed movement data. The method is robust and objective, allowing connecting elements to be identified by an unsupervised process (Vogt et al., 2007a) and can be applied to any kind of binary input map derived from, e.g., least cost surfaces (Singleton et al., 2002); dispersal or movement maps from individual-based simulation models, such as J-walk (Gardner and Gustafson, 2004) or PATH (Hargrove et al., 2005); synthesized complex movement patterns using minimum convex hull or k-means clustering (Graves et al., 2007); or spatially explicit data of observed species movements (Revilla et al., 2004, Kramer-Schadt et al., 2004). We provide a short summary of the method; adapt the naming scheme of the resulting geometric classes to the functional nature of the input data; and suggest ideas for their interpretation, which may be beneficial for the analysis of movement data and landscape planning in general.

Section snippets

Maps of movement

To illustrate the approach, we first needed to generate movement data. Because the objective of the analysis was to provide a realistic yet clear illustration of the proposed methods, we choose to simulate the movement of a hypothetical organism. The movement of an animal can be divided into day-to-day movement within the animal's home range and infrequent, long-range dispersal events that result in the relocation of the home range (Forman, 1995). We investigated the simulated dispersal

Results

Successful movements were recorded from each patch to at least one other patch in the landscape; therefore all nine habitat patches were connected as part of one large cluster. Fig. 2 provides the output of the morphological analysis for the two input maps. Considering first the D-map of dispersal movement (Fig. 2, left), the majority of the grid cells are coded as core areas, which are connected by bridges and loops. Here, core represents broad pathways for potential dispersal among patches.

Discussion

The conceptual basis of the morphological analysis is of a generic nature because it is a geometric analysis process. Consequently, any type of input data can be analyzed and the interpretation of the results is directly related to the interpretation of the input data. In this paper, the input data were derived from a dispersal simulator calibrated for a broad class of forest mammals. The application of the very same classification scheme would be equally valid for input data derived for

Conclusion

Our ultimate interest centers on regional to continental scale impacts of landscape change on pattern and connectivity for which the illustrated method provides two important types of information. First, in addition to tabular summaries of structural and or functional pattern indicators, a map of patterns is a powerful communication device to increase the awareness of spatial pattern in policy formulation, implementation, and monitoring. Second, because patterns are mapped at the pixel level,

Acknowledgements

The research described in this article was performed as a part of the Collaboration Agreement (No. 22832-2005-06 S0SC ISP) between the Joint Research Centre of the European Commission, Institute for Environment and Sustainability and the United States Department of Agriculture, Forest Service. Additional support was provided by a cooperative agreement (No. FRRE40 03-JV-11242328-001) between the University of Maryland Center for Environmental Science and the United States Department of

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