Graph Theory as a Proxy for Spatially Explicit Population Models in Conservation Planning

Abstract

Spatially explicit population models (SEPMs) are often considered the best way to predict and manage species distributions in spatially heterogeneous landscapes. However, they are computationally intensive and require extensive knowledge of species' biology and behavior, limiting their application in many cases. An alternative to SEPMs is graph theory, which has minimal data requirements and efficient algorithms. Although only recently introduced to landscape ecology, graph theory is well suited to ecological applications concerned with connectivity or movement. This paper compares the performance of graph theory to a SEPM in selecting important habitat patches for Wood Thrush (Hylocichla mustelina) conservation. We use both models to identify habitat patches that act as population sources and persistent patches and also use graph theory to identify patches that act as stepping stones for dispersal. Correlations of patch rankings were very high between the two models. In addition, graph theory offers the ability to identify patches that are very important to habitat connectivity and thus long-term population persistence across the landscape. We show that graph theory makes very similar predictions in most cases and in other cases offers insight not available from the SEPM, and we conclude that graph theory is a suitable and possibly preferable alternative to SEPMs for species conservation in heterogeneous landscapes.

A Graph‐Theory Framework for Evaluating Landscape Connectivity and Conservation Planning

“…Graph theory may be well suited for selecting habitat reserves (Opdam et al 2006;Pascual-Hortal & Saura 2006;Minor & Urban 2007). Our results, however, do not suggest a formula for reserve design because each landscape and conservation problem is different.…”

“…1). Edges may be binary (connected or not) or contain additional information about the level of connectivity (i.e., flux of individuals moving between nodes; Minor & Urban 2007). Networks surround us in both the natural and anthropogenic world.…”

“…Clustering is common in small-world networks but not in scale-free networks. Clustering may also confer stability to populations (Minor & Urban 2007)-yet another desirable attribute in a habitat network.…”

“…Graph theory is well suited to patch-level analyses of fragmented landscapes (Minor & Urban 2007), but here we applied it to larger-scale ecological questions about a network of forest patches in the Triangle region of North Carolina (U.S.A.). With a focus on forest songbirds, we described landscape topology and made inferences about rates and paths of movement, vulnerability to disturbance, and conservation strategies.…”

Detecting black bear source–sink dynamics using individual-based genetic graphs

“…Graphs are simplified portrayals of natural systems that we have shown can be powerful tools for assessing and detecting source-sink dynamics [42,43]. Our method does not estimate migration rates.…”