McGarigal, Kevin; Marks, Barbara J. 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. Gen. Tech. Rep. PNW-GTR-351. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 122 p. This report describes a program, FRAGSTATS, developed to quantify landscape structure. FRAGSTATS offers a comprehensive choice of landscape metrics and was designed to be as versatile as possible. The program is almost completely automated and thus requires little technical training. Two separate versions of FRAGSTATS exist: one for vector images and one for raster images. The vector version is an Arc/Info AML that accepts Arc/Info polygon coverages. The raster version is a C program that accepts ASCII image files, 8or 16-bit binary image files, Arc/Info SVF files, Erdas image files, and IDRISI image files. Both versions of FRAGSTATS generate the same array of metrics, including a variety of area metrics, patch density, size and variability metrics, edge metrics, shape metrics, core area metrics, diversity metrics, and contagion and interspersion metrics. The raster version also computes several nearest neighbor metrics. In this report, each metric calculated by FRAGSTATS is described in terms of its ecological application and limitations. Example landscapes are included, and a discussion is provided of each metric as it relates to the sample landscapes. Several important concepts and definitions critical to the assessment of landscape structure are discussed. The appendices include a complete list of algorithms, the units and ranges of each metric, examples of the FRAGSTATS output files, and a users guide describing how to install and run FRAGSTATS.

https://www.fs.fed.us/pnw/pubs/pnw_gtr351.pdf

FRAGSTATS: spatial pattern analysis program for quantifying landscape structure

We describe a general framework for understanding the ecological processes that operate at landscape scales. The composition of habitat types in a landscape and the physiognomic or spatial arrangement of those habitats are the two essential features that are required to describe any landscape. As such, these two features affect four basic ecological processes that can influence population dynamics or community structure. The first two of these processes, landscape complementation and landscape supplementation, occur when individuals move between patches in the landscape to make use of nonsubstitutable and substitutable resources, respectively

http://www.jstor.org/stable/pdfplus/3544901.pdf

Ecological Processes That Affect Populations in Complex Landscapes

We develop a hierarchical model of heterogeneity that provides a framework for classifying patch structure across a range of scales. Patches at lower levels in the hierarchy are more simplistic and correspond to the traditional view of patches. At levels approaching the upper bounds of the hierarchy the internal structure becomes more heterogeneous and boundaries more ambiguous. At each level in the hierarchy, patch structure will be influenced by both contrast among patches as well as the degree of aggregation of patches at lower levels in the hierarchy. We apply this model to foraging theory, but it has wider applications as in the study of habitat selection, population dynamics, and habitat fragmentation. It may also be useful in expanding

Multiple scales of patchiness and patch structure: a hierarchical framework for the study of heterogeneity

The conventional approach to maintaining biological diversity generally has been to proceed species by species and threat by threat. We suggest that piecemeal approaches are not adequate by themselves to address the accelerating extinction crisis and, furthermore, they contribute to an unpredictable ecological and economic environment. Here, we describe a methodology called Gap Analysis, which identifies the gaps in representation of biological diversity (biodiversity) in areas managed exclusively or primarily for the longterm maintenance of populations of native species and natural ecosystems (hereinafter referred to as biodiversity management areas). Once identified, gaps are filled through new reserve acquisitions or designations, or through changes in management practices. The goal is to ensure that all ecosystems and areas rich in species diversity are represented adequately in biodiversity management areas. We believe this proactive strategy will eliminate the need to list many species as threatened or endangered in the future. Gap Analysis uses vegetation types and vertebrate and butterfly species (and/or other taxa, such as vascular plants, if adequate distributional data are available) as indicators of biodiversity. Maps of existing vegetation are prepared from satellite imagery (LANDSAT) and other sources and entered into a geographic information system (GIS). Because entire states or regions are mapped, the smallest area identified on vegetation maps is 100 ha. Vegetation maps are verified through field checks and examination of aerial photographs. Predicted species distributions are based on existing range maps and other distributional data, combined with information on the habitat affinities of each species. Distribution maps for individual species are overlaid in the GIS to produce maps of species richness, which can be created for any group of species of biological or political interest. An additional GIS layer of land ownership and management status allows identification of gaps in the representation of vegetation types and centers of species richness in biodiversity management areas through a comparison of the vegetation and species richness maps with ownership and management status maps. Underrepresented plant communities (e.g., present on only 1 or 2 biodiversity management areas or with a small total acreage primarily managed for biodiversity) also can be identified in this manner. Realization of the full potential of Gap Analysis requires regionalization of state data bases and use of the data in resource management and planning. Gap Analysis is a powerful and efficient first step toward setting land management priorities. It provides focus, direction, and accountability for conservation efforts. Areas identified as important through Gap Analysis can then be examined more closely for their biological qualities and management needs. As a coarse-filter approach to conservation evaluation, Gap Analysis is not a panacea. Limitations related to minimum mapping unit size (where small habitat patches are missed), failure to distinguish among most seral stages, failure to indicate gradual ecotones, and other factors must be recognized so that Gap Analysis can be supplemented by more intensive inventories. WILDL. MONOGR. 123, 1-41 GAP ANALYSIS: A GEOGRAPHIC APPROACH TO PROTECTION OF BIOLOGICAL DIVERSITY WILDLIFE MONOGRAPHS 6

https://www.webpages.uidaho.edu/wlf448/2011/Readings/scott%20et%20al%201993%20GAP%20Analysis.pdf

Gap analysis: a geographic approach to protection of biological diversity

Disperser effectiveness is the contribution a disperser makes to the future reproduction of a plant. Although it is a key notion in studies of seed dispersal by animals, we know little about what determines the effectiveness of a disperser. The role of the present paper is to review the available information and construct a hierarchical framework for viewing the components of disperser effectiveness.

/pdf/quantity-quality-and-the-effectiveness-of-seed-dispersal-by-542owyet3n.pdf

P. R. Evans

Papers

Habitat Selection in Birds.