A Spatial Model to Estimate Habitat Fragmentation and Its Consequences on Long-Term Persistence of Animal Populations

Abstract

The increasing use of the landscape by humans has led to important diminutions of natural surfaces. The remaining patches of wild habitat are small and isolated from each other among a matrix of inhospitable land-uses. This habitat fragmentation, by disabling population movements and stopping their spread to new habitats, is a major threat to the survival of numerous plant and animal species. We developed a general model, adaptable for specific species, capable of identifying suitable habitat patches within fragmented landscapes and investigating the capacity of populations to move between these patches. This approach combines GIS analysis of a landscape, with spatial dynamic modeling. Suitable habitat is identified using a threshold area to perimeter ratio. Potential movement pathways of species between habitat patches are modeled using a cellular automaton. Habitat connectivity is estimated by overlaying habitat patches with movement pathways. The maximum potential population is calculated within and between connected habitat patches and potential risk of inbreeding within meta-populations is considered. The model was tested on a sample map and applied to scenario maps of predicted land-use change in the Peoria Tri-county region (IL). It (1) showed area of natural area alone was insufficient to estimate the consequences on animal populations; (2) underscored the necessity to use approaches investigating the effect of land-use change spatially through the landscape and the importance of considering species-specific life history characteristics; and (3) highlighted the model's potential utility as an indicator of species likelihood to be affected negatively by land-use scenarios and therefore requiring detailed investigation.

Gap‐crossing decisions of forest birds in a fragmented landscape

“…Individual‐based modelling approaches (Tracey 2006), such as least‐cost pathway analysis (Adriaensen et al. 2003) or cellular automation (Aurambout et al. 2005), indirectly examine movement pathways by integrating spatial information with population modelling.…”

Connectivity in urbanizing landscapes: The importance of habitat configuration, urban area size, and dispersal

“…Urban and developed areas are expanding across many landscapes, and nearly half the world's population and 75% of developed nations' populations live in cities (United Nations 2002). Urban growth is typically associated with habitat loss, fragmentation, habitat isolation, changes in species composition, and altered hydrological and nutrient cycles (Crooks and Soulé 1999;D'Antonio and Meyerson 2002;Alberti et al 2003;Aurambout et al 2005;Ellis et al 2006;McKinney 2006;Pauchard et al 2006).…”

“…Species differ in their sensitivity to these changes. For example, habitat specialists are typically more sensitive to changes in landscape matrix than generalist species (e.g., Ricketts 2001;Jules and Shahani 2003;Aurambout et al 2005). In some cases, changes in the matrix inhibit movement and decrease landscape connectivity (e.g., the movement of salamanders through agricultural and developing lands (Trenham et al 2001)).…”

Cellular Automata Modeling of Stem‐Cell‐Driven Development of Tissue in the Nervous System

“…On the other hand, as a survey of these publications also demonstrates, the diversity of topics is remarkable, thereby giving testimony to the versatility of CA modeling as a powerful tool to better understand the theory of biological problems. Biological phenomena that have been modeled as CA include brain oscillations and neural network activities in neuroscience (Traub et al , ; ; Lewis and Rinzel, ; Kozma and Puljic, ; Matsubara and Torikai, ); heart rhythms in cardiac physiology (Bardou et al , ; Barbosa, ; Sabzpoushan and Pourhasanzade, ; Makowiec et al , ); host–pathogen interactions in microbiology and virology (Agur, ; Bru and Cardona, ; Wcisło et al , ; Sinha et al , ); epidemics caused by viruses (Zorzenon dos Santos and Coutinho, ; Beauchemin et al , ; Xiao et al , ; White et al , ); interspecific competition, habitat invasion, and land use in ecology and environmental sciences (Aurambout et al , ; Grimm et al , ; Johnston and Purkis, ; ; Kalmykov and Kalmykov, ; Qiang and Lam, ); and behavior of tumor cells in cancer biology (Deroulers et al , ; DuBois et al , ; Chen et al , ; Monteagudo and Santos, ; Tzedakis et al , ; Dufour et al , ).…”