Forest fragmentation refers to the division of large, continuous expanses of forest into smaller discrete patches, which are separated by some other type of land cover (such as agricultural land) commonly referred to as the landscape matrix (Forman and Godron 1986). Fragmentation can be caused by natural forms of disturbance, and some forests are naturally fragmented because they are associated with particular edaphic conditions (such as soil type or climate) that are patchily distributed. However, it is the widespread forest fragmentation caused by human activities that is currently of such concern to conservationists, given that many
species appear to be negatively affected by fragmentation of their habitat. Forest fragmentation is widely considered to be one of the major causes of biodiversity loss, and consequently the development of methods to estimate forest fragmenta- tion has been the focus of much research interest. The issue is considered further in section 7.4.
Descriptions of the pattern of forest landscapes can be produced from maps of land cover derived from field survey, aerial photography, or satellite remote sensing imagery. Few attempts have been made to describe landscape pattern by using field-based approaches. Kleinn and Traub (2003) indicate that plot-based designs used in ecological surveys and forest inventories can be used to produce unbiased estimates of some spatial attributes, such as total forest area or total perimeter length. However, other attributes of interest, such as mean patch area or mean patch perimeter, cannot be directly derived. Typically, analyses are done in GIS, although other software tools such as statistical analysis packages can also be used for estimating some spatial patterns. A number of specialized software tools have been developed specifically for assessment of landscape pattern, and this has greatly encouraged use of these methods among the research community.
Turner et al. (2001) provide a valuable overview of how to describe landscape pattern, and highlight the following important caveats that should be borne in mind before using these methods:
● Objectives. A clear statement of objectives of the analysis, or an explicit hypothesis, can avoid misleading or confusing results. As many different met- rics are available, it is important to select those most appropriate for address- ing the objectives set.
● Classification scheme. The choice of the land-cover categories, or classes, used in the pattern analysis has a major influence on the results obtained. The classes selected, for example during the classification of remote sensing imagery, should be appropriate to the objectives set and must be rigorously applied across all the landscapes being compared.
● Scale. The spatial resolution and extent of the data will also have a major influence on the results obtained. Comparison of analyses done at different scales may be invalid because of scale-related artefacts. As the size of the study area declines, the risk of the map boundary truncating patches increases, producing additional artefacts. On the basis of an analysis of Landsat imagery, O’Neill et al. (1996) presented the following rule of thumb: the spatial resolution of the map should be 2–5 times smaller than the size of the features being analysed, and map extent should be 2–5 times larger than the size of the largest patches.
● Patch identification. Many of the available metrics are based on the concept of a forest patch. It is important to consider how this concept relates to reality, and how a patch should be defined. Methods used for analysis of raster imagery, for example, usually define a patch as a contiguous group of cells of the same mapped land cover category. But what does contiguous mean in this Describing landscape pattern | 77
context? Should only the four nearest cells be considered, as is often used in practice (the ‘four-neighbour rule’), or should diagonal neighbours be included also? Also, different species have different perceptions of what constitutes a habitat patch, which may differ markedly from those per- ceived by people. Careful consideration should therefore be given to how patches are mapped and defined, as this will have a major bearing on the results obtained.
2.7.1 Choosing appropriate metrics
Many metrics have been developed that can be used to describe the spatial pattern and characteristics of forest landscapes (Franklin 2001, Jorge and Garcia 1997, McGarigal 2002, Trani and Giles 1999) (Table 2.9). These may be divided into two general categories: those that assess composition, or the variety and abundance of different types of land-cover patches within a landscape, and those that quantify their spatial configuration (McGarigal 2002, Wolter and White 2002).
Importantly, composition metrics are only applicable at the landscape level, as they are integrated over all patch types within a landscape (McGarigal 2002).
Measures of landscape composition include (Turner et al. 2001):
● the proportion of the landscape that is occupied by a given land-cover type
● the number of land-cover types present, as a percentage of the total possible number of cover types
● diversity, or relative evenness, which refers to how evenly the proportions of cover types are distributed
● dominance, which is the deviation from maximum possible diversity.
Note that either dominance or diversity could be reported, but not both, as they are correlated. However, as similar values can be reported for these metrics for landscapes that are very different in character, the usefulness of these measures is limited.
Spatial configuration refers to the spatial characteristics and arrangement or orientation of land-cover patches within a landscape (McGarigal 2002).
Configuration metrics are spatially explicit at the scale of individual patches, rather than the landscape, although measures of the spatial relationships between patches and patch types can also be derived.
The metrics that have been developed can be divided into the following principal groups (Echeverría 2005, Franklin 2001, McGarigal 2002):
● Area metrics. Metrics describing the area of patches, such as mean patch size, can be summarized for different patch types and for entire landscapes.
● Edge metrics. These measures of patch geometry represent the length of edge between land-cover types, and are useful for assessing the extent of edge habitats.
● Shape metrics. These refer to the shape of land-cover patches and are most commonly represented as the relative amount of patch perimeter per unit
area, or as a fractal dimension. The edge-to-area ratio indicates whether patches are compact and simple or elongated and complex in shape.
● Core metrics. These refer to the interior of patches, after a user-specified degree of edge buffer has been subtracted. This represents the part of the patch that is unaffected by edge effects. Core area metrics effectively integrate patch size, shape and distance from the edge into a single measure, which has been used to assess the extent of large forest patches in a landscape (Wolter and White 2002).
● Isolation/dispersion/proximity metrics. These describe whether patches are reg- ularly distributed or are clumped, and also describe how isolated patches are from each other. Calculation of these metrics is based on nearest-neighbour distance, which is defined as the distance from a patch to a neighbouring patch.
● Contagion and interspersion. Contagion refers to the tendency of patches to be spatially aggregated, and measures the extent to which cells (in a raster grid) of a similar type are aggregated. Interspersion refers to the extent to which dif- ferent types of patch are spatially intermixed, and is calculated on patch adja- cencies.
● Connectivity. This generally refers to the connections between patches, which can be based on strict adjacency (i.e. patches that are touching), a threshold distance, a decreasing function of distance that reflects the probability of connection at a given distance, or a resistance-weighted distance function.
As no single metric captures all aspects of fragmentation, a suite of selected met- rics tends to be used to characterize landscapes (Baskent and Jordan 1995, Hansen et al. 2001, Staus et al. 2002, Wolter and White 2002). The metrics should be selected carefully, attempting to avoid redundancy (Armenteras et al. 2003) and considering the characteristics of both the patches and the matrix (Lambin et al.
2001). Area metrics, particularly mean patch size, patch size distribution, and number of patches, have been used most widely in forest fragmentation studies.
For example, the size of largest patch was used to analyse forest fragmentation in relation to representativeness of protected areas in Colombian montane forest (Armenteraset al. 2003). Although this metric is simple, it is restricted to assessing forest fragmentation in areas where large patches occur. In landscapes dominated by patches of a wide range of sizes, metrics such as mean patch size or number of patches may be more suitable (Echeverría 2005). Largest patch size, edge density, and mean core area have been shown to be useful descriptors in the context of forest interior-dependent species (Riitters et al. 1995). In contrast, some studies have found patch density, number of patches, and perimeter-to-ratio metrics to behave erratically over time (Hargis et al. 1998, Millington et al. 2003, Trani and Giles 1999).
Edge density has been used to analyse patch edges in several landscapes (Hansen et al. 2001, Staus et al. 2002), and fractal dimension and perimeter-to-area ratio metrics to examine patch shape have been widely used (Imbernon and Describing landscape pattern | 79
|Forest extent and condition Table 2.9 Selected metrics used for assessing the spatial characteristics of forest landscapes (adapted from Baskent 1999, Echeverría
2005, Franklin 2001, McGarigal 2002).
Category of metric Metric Description
Area Total landscape area Percentage of area accounted for by the largest patch.
Largest patch Number of patches per unit area.
Number of patches Patch density
Number of patch types Mean patch size
Edge Total edge Total length of all patch edges.
Edge density Length of patch edge per unit area.
Total edge contrast index The degree of contrast between a patch and its immediate neighbourhood.
Mean edge contrast index The average contrast for patches of a particular type.
Shape Mean shape Mean patch perimeter/area ratio for a patch type.
Fractal dimension The complexity of patch shape in a landscape.
Mean patch fractal dimension Length of diagonal of smallest enclosing box divided by the mean width.
Elongation Measures of landscape compared to a standard.
Landscape shape
Core Core area Area of interior habitat of patches defined by specified edge buffer width.
Number of core areas Number of core areas per unit area.
Core area density Mean core per patch
Describing landscape pattern|81 Isolation/dispersion/proximity Similarity The size and proximity distance of all patches, regardless of type, whose
edges are within a specified search radius of the focal patch.
Proximity The size and proximity distance over all patches of the corresponding patch type, whose edges are within a specified radius of the focal patch.
Mean proximity For a class or for the landscape as a whole.
Mean nearest-neighbour distance For a class or for the landscape as a whole.
Spatial autocorrelation Patch type spatial correlation; patch type distribution.
Dispersion Degree of fragmentation/complexity of patch boundaries.
Contagion and interspersion Clumpiness The frequency with which different pairs of patch types (including pairs of the same patch type) appear side-by-side on the map.
Aggregation The number of like adjacencies in a landscape, in which each class is weighted by its proportional area in the landscape.
Splitting The number of patches obtained by dividing the total landscape into patches of equal size in such a way that this new configuration leads to the same degree of landscape division as obtained for the observed cumulative area distribution.
Contagion The tendency of land cover types to clump within a landscape.
Interspersion The number of pixels in a square that are of a land cover type different from that of the central pixel.
Connectivity Patch cohesion The physical connectedness of the patch type.
Connectance The number of functional joinings between patches of the same type, where each pair of patches is either connected or not, based on a user-specified distance criterion.
Traversability Degree of resistance to movement of organisms.
Branthomme 2001, Jorge and Garcia 1997), as have isolation metrics (Cumming and Vernier 2002, Ranta et al. 1998). In contrast, metrics of connectivity are rela- tively uncommon in forest fragmentation analyses despite the emphasis given to habitat connectivity in the conservation literature. Connectivity is difficult to quantify (McGarigal 2002), and some confusion exists regarding the most appro- priate way to measure it (Tischendorf and Fahrig 2000b). Different conclusions about the connectivity of forest patches may be obtained when using different metrics (Tischendorf and Fahrig 2000a).
2.7.2 Estimating landscape metrics
Ideally, the choice of metrics should reflect some hypothesis about the observed landscape pattern and what processes might be responsible for generating it. Also, metric selection should include consideration of the initial pattern of a landscape.
If a landscape contains forest patches, or if the potential for patch formation exists, then the use of patch-level metrics is warranted (McGarigal 2002). Conversely, if the landscape comprises extensive areas of contiguous forest cover, then other met- rics (forest interior, percentage forest cover, or contiguity, for example) may be pre- ferred (Trani and Giles 1999). It is also important that the choice of metrics should be informed by an understanding of the ecological processes relevant to the inves- tigation. The size and degree of isolation of forest patches, for example, might most usefully be analysed in relation to the specific habitat needs of selected species of conservation concern. Many landscapes have been described by using the metrics available with little reference to the needs of individual species, and this has limited the value of such studies.
Although some analyses can be done using appropriate GIS software, several software tools have been developed by researchers explicitly for generating land- scape spatial metrics (Table 2.10). The most widely used of these is FRAGSTATS (McGarigal and Marks 1995). Note that some of these tools require specific GIS software in order to run, and some are limited to analysis of specific types of data.
There are two important issues regarding the interpretation of metrics. Firstly, as noted earlier, they are sensitive to scale. Consequently, landscape metrics calculated by using different types of satellite imagery might not be comparable because of differences in pixel size, which can influence the computation of individual metrics (McGarigal 2002). However, it has been shown that patch size, some edge metrics, and a patch diversity metric appear to be relatively insensitive to variation in spatial resolution between 30 and 1100 m (Millington et al. 2003).
To minimize the risk of erroneous interpretation, it is recommended that imagery with the same scale and spatial resolution be used for analysis (Franklin 2001).
This may be difficult to achieve when comparing images obtained from different dates. To address this problem, some researchers have converted the pixel size reso- lution in all images to a common standard (Imbernon and Branthomme 2001).
The second constraint is that many landscape metrics are highly correlated. This reflects the fact that only a few primary measurements can be made on land-cover patches; most metrics are then derived from these primary measures. Several
Describing landscape pattern|83 Table 2.10 Software tools for generating spatial metrics for forest landscapes (adapted from Echeverría 2003 and Turner et al. 2001).
Product Comments URL
Spatial Analyst An ArcView extension that provides tools to create, query, analyse, and www.esri.com/software/arcview/extensions/
map cell-based raster data and to perform integrated vector–raster analysis. spatialanalyst/
Patch analyst v3.1 An ArcView extension software that facilitates the spatial analysis of http://flash.lakehead.ca/~rrempel/patch/
landscape patches and modelling of attributes associated with patches.
Patch Analyst (Grid) extends these capabilities to gridded data. Both require the ESRI Spatial Analyst extension to ArcView, and neither works with ArcGIS software. Available as a free download.
FRAGSTATS Has been widely used to assess landscape structure, offering a comprehensive www.umass.edu/landeco/research/fragstats/
choice of landscape metrics including area metrics, patch density, edge, core fragstats.html metrics, etc. (McGarigal and Marks 1995). Version 3 includes a graphical
user interface and the addition of several new landscape metrics, and analysis capabilities (McGarigal et al. 2002). Works with raster data. Available as a free download.
LEAPII (Landscape Designed to explore, monitor, and assess a landscape for its ecological www.ai-geostats.org/software/
ecological analysis status. Both raster and vector data can be imported. Freeware. Geostats_software/leap.htm v 2.0)
ATTILA v2.0 An ArcView extension designed to generate common landscape indicators. http://epamap1.epa.gov/emap/ca/pages/
Does not calculate landscape metrics per se, but allows the user to combine nca_at_frame.htm classes and obtain class areas and neighbours relatively easily. Requires the
Spatial Analyst extension to ArcView.
APACK A stand-alone analysis package for rapid calculation of landscape metrics http://landscape.forest.wisc.edu/projects/
on large-scale data sets, developed at the University of Wisconsin-Madison. apack Works with raster data. Freeware.
r.le A suite of programs that interface with GRASS GIS software. http://grass.itc.it/gdp/terrain/r_le_22.html
investigators have attempted to identify a parsimonious suite of independent metrics (Li and Reynolds 1993, Riitters et al. 1995, Trani and Giles 1999) by using correlation matrices along with factor or variance analysis, which should be considered for any forest area under investigation. Some studies have evaluated large numbers of metrics: 55 and 61 metrics were used to explain landscape pattern change by Riitters et al. (1995) and Imbernon and Branthomme (2001), respectively. By means of statistical analysis, it was demonstrated that only 5 and 6 metrics, respectively, were needed to capture most of the information. An alter- native is to use metrics that display similar behaviour through time and across different sites (Millington et al. 2003). Some studies have selected those metrics most related to the ecological questions being addressed (Armenteras et al. 2003, Hansen et al. 2001, Wolter and White 2002), and this is surely the best approach (Tischendorf 2001).