All landscape metrics represent some aspect of landscape pattern. However, the user must first define the landscape, including its thematic content and resolution, spatial grain and extent, and boundary before any of these metrics can be computed. In addition, for the functional metrics, the user must specify additional input parameters such as edge effect distances, edge contrast weights, resistance coefficients, and search distance. Hence, the computed value of any metric is merely a function of how the investigator chose to define and scale the landscape and parameterize the metric, if appropriate. If the measured pattern of the landscape does not correspond to a pattern that is functionally meaningful for the organism or process under consideration, then the results will be meaningless. For example, the criteria for defining a patch may vary depending on how much variation will be allowed within a patch, on the minimum size of patches that will be mapped, and on the components of the system that are deemed ecologically relevant to the phenomenon of interest (Gustafson 1998). Ultimately, patches occur on a variety of scales, and a patch at any given scale has an internal structure that is a reflection of patchiness at finer scales, and the mosaic containing that patch has a structure that is determined by patchiness at broader scales (Kotliar and Wiens 1990). Thus, regardless of the basis for defining patches, a landscape does not contain a single patch mosaic, but contains a hierarchy of patch mosaics across a range of scales. Indeed, patch boundaries are artificially imposed and are in fact meaningful only when referenced to a particular scale (i.e., grain size and extent). It is incumbent upon the investigator to establish the basis for delineating among patches and at a scale appropriate to the phenomenon under consideration. Extreme caution must be exercised in comparing the values of metrics computed for landscapes that have been defined and scaled differently.

Given the subjectivity in defining patches, scaling techniques can provide an objective means to help determine the scale of patchiness (Gustafson 1998). In many studies, the identification of patches reflects a minimum mapping unit that is chosen for practical or technical reasons and not for ecological reasons. Scaling techniques such as those described previously can provide insight into the scale of patchiness and whether there are hierarchies of scale. This information can then provide the empirical basis for choosing the scale for mapping patches, rather than relying on subjective and somewhat arbitrary criteria. Better yet, given the myriad ways to the define the landscape for the phenomenon under investigation, it may be may be desirable to evaluate alternative landscape definitions against ecological data and empirically determine the best definition. Few studies have adopted this approach, but see Thompson and McGarigal (2002) for an example.

The format (raster versus vector) and scale (grain and extent) of the data can have a profound influence on the value of many metrics. Because vector and raster formats represent lines differently, metrics involving edge or perimeter will be affected by the choice of formats. Edge lengths will be biased upward in raster data because of the stair-step outline, and the magnitude of this bias will vary in relation to the grain of the image. In addition, the grain-size of raster data can have a profound influence on the value of certain metrics. Metrics involving edge or perimeter will be affected; edge lengths will be biased upwards in proportion to the grain size - larger grains result in greater bias. Metrics based on cell adjacency information such as most of the aggregation metrics will be affected as well, because grain size effects the proportional distribution of adjacencies. For example, as resolution is increased (grain size reduced), the proportional abundance of like adjacencies (cells of the same class) increases, and the measured contagion increases. Finally, the boundary of the landscape can have a profound influence on the value of certain metrics. Landscape metrics are computed solely from patches contained within the landscape boundary. If the landscape extent is small relative to the scale of the organism or ecological process under consideration and the landscape is an "open" system relative to that organism or process, then any metric will have questionable meaning. Metrics based on nearest neighbor distance or employing a search radius can be particularly misleading. Consider, for example, a local population of a bird species occupying a patch near the boundary of a somewhat arbitrarily defined landscape. The nearest neighbor within the landscape boundary might be quite far away; yet, in reality, the closest patch might be very close but just outside the designated landscape boundary. In addition, those metrics that employ a search radius (e.g., proximity index) will be biased for patches near the landscape boundary because the searchable area will be much less than a patch in the interior of the landscape. In general, boundary effects will increase as the landscape extent decreases relative to the patchiness or heterogeneity of the landscape.

In addition to these technical issues, current use of landscape metrics is constrained by the lack of a proper theoretical understanding of metric behavior. The interpretation of a landscape metric is contingent upon having an adequate understanding of how it responds to variation in landscape patterns (e.g., Gustafson and Parker 1992, Hargis et al. 1998, Jaeger 2000). Failure to understand the theoretical behavior of the metric can lead to erroneous interpretations (e.g., Jaeger 2000). Neutral models (Gardner et al. 1987, Gardner and O'Neill 1991, With 1997) provide an excellent way to examine metric behavior under controlled conditions because they control the process generating the pattern, allowing unconfounded links between variation in pattern and the behavior of the index (Gustafson 1998, Neel et al. 2004). Unfortunately, to my knowledge, software for generating neutral landscapes is currently limited to controlling the composition and the aggregation of the landscape (Gardner 1999), but not other aspects of landscape configuration.

In practice, the interpretation of landscape metrics is plagued by the lack of a proper reference framework. Landscape metrics quantify the pattern of a single landscape at a snapshot in time. Yet it is often difficult, if not impossible, to determine the ecological significance of the computed value without understanding the range of natural variation in landscape pattern in space and time. For example, in disturbance-dominated landscapes, patterns may fluctuate widely over time in response to the interplay between disturbance and succession processes (e.g., Wallin et al. 1996, He and Mladenoff 1999, Haydon et al. 2000, Wimberly et a. 2000). It is logical, therefore, that landscape metrics should exhibit statistical distributions that reflect the natural temporal dynamics of the landscape. By comparison to this distribution, a more meaningful interpretation can be assigned to any computed value. Unfortunately, despite widespread recognition that landscapes are dynamic, there is a dearth of empirical work quantifying the range of natural variation in landscape metrics. In part, this stems from the difficulty of defining a meaningful temporal reference, but more often it stems from the lack of historical spatial data. In the absence of historical data, however, a spatial reference framework may be a viable option in some cases, whereby the focal landscape is compared to other landscapes within the broader regional context (e.g., Cardille et al. 2005). Establishing a reference framework to aid in the interpretation of landscape metrics should be a priority in future landscape pattern analyses.

Although the literature is replete with metrics now available to describe landscape pattern, there are still only two major components--composition and configuration, and only a few aspects of each of these. Metrics often measure multiple aspects of this pattern. Thus, there is seldom a one-to-one relationship between metric values and pattern. Most of the metrics are in fact correlated among themselves (i.e., they measure a similar or identical aspect of landscape pattern) because there are only a few primary measurements that can be made from patches (patch type, area, edge, and neighbor type), and most metrics are then derived from these primary measures. Some metrics are inherently redundant because they are alternate ways of representing the same basic information (e.g., mean patch size and patch density). In other cases, metrics may be empirically redundant, not because they measure the same aspect of landscape pattern, but because for the particular landscapes under investigation, different aspects of landscape pattern are statistically correlated. Several investigators have attempted to identify the major components of landscape pattern for the purpose of identifying a parsimonious suite of independent metrics (e.g., Li and Reynolds 1995, McGarigal and McComb 1995, Ritters et al. 1995, Cushman et al. 2008). Although these studies suggest that patterns can be characterized by only a handful of components, consensus does not exist on the choice of individual metrics. These studies were constrained by the pool of metrics existing at the time of each investigation. Given the expanding development of functional metrics, particularly those based on a landscape mosaic perspective, it seems unlikely that a single parsimonious set exists. Ultimately, the choice of metrics should explicitly reflect some hypothesis about the observed landscape pattern and what processes or constraints might be responsible for that pattern.

In summary, the importance of fully understanding each landscape metric before it is selected for interpretation cannot be stressed enough. Specifically, these questions should be asked of each metric before it is selected for interpretation:

  • Does it represent landscape composition or configuration, or both?
  • What aspect of composition or configuration does it represent?
  • Is it spatially explicit, and, if so, at the patch-, class-, or landscape-level?
  • How is it effected by the designation of a matrix element?
  • Does it reflect an island biogeographic or landscape mosaic perspective of landscape pattern
  • How does it behave or respond to variation in landscape pattern?
  • What is the range of variation in the metric under an appropriate spatio-temporal reference framework?

Based on the answers to these questions, does the metric represent landscape pattern in a manner and at a scale ecologically meaningful to the phenomenon under consideration? Only after answering these questions should one attempt to draw conclusions about the pattern of the landscape.