Activity-based measures of landscape fragmentation

Abstract

Context: Landscape fragmentation, which has demonstrated links to habitat loss, increased isolation, a loss of connectivity, and decreased biodiversity, is difficult to quantify. Traditional pattern-based approaches to measuring fragmentation use landscape metrics to quantify aspects of the composition or configuration of landscapes.

Objective: The objective of this study was to examine the relative improvements of an alternative activity-based approach using the cost of traversing a landscape as a proxy for fragmentation and compare it with the traditional approach.

Methods: One thousand binary landscapes varying in composition and configuration were simulated, and least-cost path analysis provided the data to calculate the activity-based metrics, which were compared with computed traditional pattern-based metrics.

Results: Activity-based fragmentation assessments were sensitive to levels of landscape fragmentation, but offered improvements over exiting pattern-based methods in that some metrics varied monotonically across the spectrum of landscape configurations and thus makes their interpretation more holistically meaningful.

Conclusions: This study provides a modular conceptual framework for assessing fragmentation using activity-based metrics that offer functional improvements over existing pattern-based approaches. While we present a focused theoretical implementation, the process to be measured and the scale of observation can be altered to suit specific user requirements, ecosystems, or species of interest.

Keywords: Activity-based metrics; Composition; Configuration; Fragmentation; Least-cost-path analysis; Simulation.

Fig. 1

Subset of a binary cost surface raster derived from a simulated landscape with white cells representing 1 “cost unit” and black cells representing 100 “cost units”

Fig. 2

Overview of the research design and workflow, where c is the proportion of white landscape cells, ρ is the spatial autocorrelation parameter, LCP is the least-cost path, and CAR is the conditional autoregressive model used to simulate landscapes

Fig. 3

Distributions of landscape simulation parameters for a composition and b configuration

Fig. 4

Symmetric unimodal pattern-based metrics versus proportion boxplots: a edge density, b total edge, c coefficient of variance of the shape index, and d standard deviation of the shape index

Fig. 5

Four examples of strongly bimodal pattern-based metric distributions versus landscape composition: a coefficient of variation for patch area, b mean core area, c number of patches, and d patch density

Fig. 6

A summary of pair-wise correlation coefficients for our 25 pattern-based fragmentation metrics. The area of the circles shows the absolute correlation value, and the colour identifies the directionality of the relationship

Fig. 7

Number of steps (Steps) versus the proportion of low-cost cells (%)

Fig. 8

The log-scale of total cost (TotCost) to traverse the landscape versus the proportion of low-cost-cells (%)

Fig. 9

The cost-per-step (AvgCost) versus proportion of low-cost cells (%)