Landscape Metrics & Ecological Processes. Concepts and Applications

Transcription

1 Landscape Metrics & Ecological Processes Concepts and Applications Literature Review & Exercise Hernandez, Alexander. FRWS 7910 Directed Study Class Project

2 Abstract Understanding the relationship between landscape pattern and ecological processes constitutes the foundation of landscape ecology. Generally, a set of landscape metrics is derived from land cover maps, and subsequently related with a measured ecological response by statistical methods to assess their prediction power. Here, I reviewed several concepts regarding these pattern-process interactions, the main shortcomings of landscape metrics, and the techniques used to diminish or reduced the problems. In addition, I examined several real-world and simulation attempts to understand and describe such interactions. I also decided to conduct an exercise on this topic, and gain experience for my research needs. The general perception is that three basic problems are found with the utilization of landscape metrics. These are: a high degree of correlation among metrics, ambiguous responses for different conditions, and sensitivity to changes in spatial scale (grain and extent). However, several statistical techniques like factor analysis, and the utilization of interactions between landscape metrics have been proposed to reduce redundant information, and diminish ambiguity respectively. Promising results have been documented regarding the utilization of landscape metrics as explanatory factor for bird s abundance, and stream water quality. Both approaches have in common the statement that habitat amount is more important than habitat fragmentation when predicting a system s response. The results of assessing the power of landscape metrics to predict the behavior of invasive spread in the State of Utah for the period were not entirely successful. Yet, the overall results exhibited agreement with the importance of habitat amount mentioned before. A common context derived from this review and the exercise is that landscape metrics are a potential tool for management purposes, provided that they are generated accordingly to the research question, are well interpreted, and most importantly, are not used independently of the ecology dynamics on the landscape.

3 Table of Contents 1. INTRODUCTION STAGE SETTING OBJECTIVES CONCEPTUAL FRAMEWORK REVIEW OF CONCEPTS LANDSCAPE PATTERN, ECOLOGICAL PROCESSES AND THRESHOLDS CONSISTENCY IN THE USE OF LANDSCAPE METRICS Things to be careful about Techniques used for improvement PREDICTING ECOLOGICAL PROCESSES RESPONSES FROM LANDSCAPE METRICS Bird s abundance Riverine systems EXERCISE ON LANDSCAPE METRICS INVASIVE ANNUAL GRASSES OVER SAGEBRUSH ECOSYSTEMS Method Results Discussion CONCLUSIONS LITERATURE CITED List of figures Fig 1. Theoretical assertion of how the distribution of patches and environmental conditions influence the spread of disturbance. Source: Groffman et al., (2006)... 7 Fig. 2. The probability of invasive spread for species constrained to disperse through adjacent cells of disturbed habitat (A), and those capable of crossing cells of unsuitable habitat (B). Source: With (2004)... 8 Fig. 3. General types of problems in spatial pattern analysis with emphasis on landscape metrics. Source: Li & Wu (2004) Fig. 4. A relationship between a landscape metric and percentage of habitat indicates that the same value can be obtained with different habitat conditions. Source: Tischendorf (2001) Fig. 5. Relationship between benthic metabolism and riparian canopy cover. Source: Bunn et al., (1999). 20 Fig. 6. Spatial distribution of sagebrush (green) in 1995 and invasive annual grasses 2004 (red). The dark colored areas in red (inset) denote the invaded areas in the period Fig. 7. Distribution of the sampled quadrants to obtain the proportion of invaded area and landscape metrics in the sagebrush ecosystem. The table shown the structure of the database Fig. 8. Cohesion Index as a function of Number of Patches indicating that ambiguities are present i.e. same values are observed regardless of the change in fragmentation List of tables Table 1. Landscape class-level metrics derived for the 1995 sagebrush ecosystem Table 2. Adjusted r2 obtained for the individual landscape metrics interpreted as the prediction power of invasive spread

4 1. Introduction 1.1 Stage setting Landscape ecology spotlights the interactions that exist between spatial patterns and ecological processes and functions (Turner 2005, Gustafson 1998). In order to comprehend those interactions, landscape pattern and ecological processes need to be quantified. This process of understanding generally involves deriving landscape indices (i.e. dominance) and measuring response variables (i.e. species presence or absence), that are subsequently related by using statistical methods (Tischendorf 2001). Despite that many quantitative measures of landscape pattern have been suggested (Gustafson 1998), the statistical behavior of these metrics continue to be inadequately understood (Turner 2005). Further, the ecological understanding derived from landscape pattern study has not met the expectations, mainly attributable to conceptual flaws, limitations and improper use of landscape indices (Li and Wu 2004). Regardless of the conceptual limitations briefly mentioned before, the use of landscape indices to explore the relationships between landscape configuration and ecological processes constitutes an important tool for environmental management (O Neill et al., 1997). Given budget constraints in ecological studies to analyze the entirety of an area, use of these tools to enhance the understanding of pattern-processes relationships is justified. Furthermore, remote sensing imagery at different spatial scales is readily available nowadays, giving the opportunity to derive information for the composition and structure of entire landscapes in reasonable terms (Jensen 1996). 1

5 In order to acquire an improved insight about landscape metrics, and the inherent set of conditions and precautions that have to be observed before using them, I conducted a literature review on the topic. Here, I present the summary in the following order: Conceptual framework for the use of landscape indices, consistency in the prediction power of ecological processes by landscape indices. Review of actual landscape metrics applications with emphasis as habitat predictors and as human impact indicators to river systems. Results of conducting an exercise to analyze the effectiveness of landscape metrics for prediction of invasive spread. 1.2 Objectives o Obtain a better understanding of the utilization of landscape indices as explanatory factors of ecological processes emphasizing in their applications and shortcomings reported in peer-reviewed literature. o Obtain experience in deriving landscape indices from real GIS databases to further appreciate the problems and possible solutions mentioned in the literature. 2

6 2. Conceptual framework 2.1 Review of concepts Gustafson (1998) points out that there is uncertainty over what should be measured, and above this, what these measurements might mean in terms of landscape pattern. This insecurity is caused in part due to the lack of knowledge about the expected response of ecological systems to the patterns produced by management actions. In this context, some background information and core concepts presented by Gustafson (1998), and O Neill et al., (1997) and that are considered relevant throughout this report are presented now. Spatial heterogeneity: refers to the complexity and variability of a system property in time and space, spatial heterogeneity is considered synonymous of spatial pattern. A system property is any measurable entity, for instance the configuration of the landscape mosaic, plant biomass, soil nutrient concentration. Spatial structure is a major subset of the concept of spatial heterogeneity, usually referring to the spatial configuration of the system property. Two factors affect measurements of spatial heterogeneity: Grain which is the resolution of the data or minimum mapping unit, and Extent that refers to the size of the area being mapped or studied (Turner et al., 1989). A patch is a homogenous area with respect to the system property at a particular scale and that shows abrupt transition to adjacent areas (patches) that have different intensity or quality of the system property. The criteria for defining a patch may be arbitrary depending on how much variation will be allowed within a patch, on the minimum size of patches to be mapped, and the components of the system that are ecologically relevant to the process of interest (i.e. species presence-absence). Landscape indices are either patch- 3

7 oriented or neighborhood-oriented. Patch-oriented indices are calculated considering only a single patch and its edges, whereas Neighborhood-oriented indices are calculated using spatial neighborhoods and may consider complete patches within the neighborhood or only neighboring pixels. Composition of a landscape is described by the number of classes, the proportion of each class relative to the entire map, and diversity. Diversity measures combine richness, which refers to the number of classes present, and evenness, which is the distribution of area among the classes. Dominance indicates the extent to which the landscape is dominated by one or a few classes. Patch-based measures usually incorporate edge information derived from perimeter and frequency of patch adjacencies. Shape information is usually derived from perimeter-area relationships. Some extensively used indices relate patch size and shape, which is defined as Core area, and that is area apparently not influenced by edge effects. Isolation is a measure of the distance to the nearest neighboring patch of the same class. There is another category of indices which are considered pixel-based. The most common of these indices is referred as Contagion designed to quantify composition and configuration, and measures the extent to which cells of similar class are aggregated. Many studies are not based on the analysis of the patch; instead they focus on point-data analysis. Point-data analysis assumes that the system property varies continuously in space, and applies modeling techniques to represent gradual spatial change or continuums. Fractal dimension of patches is a measure of complexity of shapes on the landscape (O Neill et al., 1988), and indicates the extent to which human are reshaping (disturbing and simplifying) the landscape structure regardless of the land uses present (O Neill et al., 1997). 4

8 2.2 Landscape pattern, ecological processes and thresholds It has been long recognized that landscape changes have direct impact on ecological processes. O Neill et al., (1997) mention examples of disturbances such as clearcutting for lumber, loss of wetlands, and conversion of forest into crop and grazing systems that occur at the spatial scale of landscapes and produce an impact on ecological processes. Examples of these impacts are the decline of biological diversity and the loss of resilience of the ecosystem to recover from disturbances. Turner (2005) provides additional examples of these landscape pattern-ecological processes interactions. In her review, she provides references of pollination and seed dispersal being facilitated by the presence of corridors that connect habitat patches. Goodwin and Fahrig (2002) investigated the impact of landscape structure on landscape connectivity using simulated and empirical experiments. They found that increasing interpatch distance significantly decreased connectivity opportunities for T. borealis habitats. Also, they concluded that the influence of matrix elements on landscape connectivity was small in comparison to the influence of habitat elements. However, in their analysis they found some limitations on the use of the indices, which will be retaken later in this document. It becomes clear that disturbances have a clear effect on the landscape pattern, and this pattern subsequently affects the ecological processes that are associated with the habitats being disturbed. However, influences of the landscape pattern over the behavior of disturbances have also been reported. Turner (2005) mentions the work of Jules et al., (2002) as a clear example of these influences. They investigated how landscape configuration affected the spread of an invasive disease over conifers. Their results suggested that forest patches located along creeks and crossed by roads were more 5

9 susceptible to become infected than those patches without road crossings. In general, the position of the landscape exerts an influence over the disturbance spread behavior, provided that the disturbance has a different locational specificity (Turner 2005). Groffman et al., (2006) also provide theoretical background to relate landscape pattern with ecological processes. In their review, they focus on ecological thresholds as the points at which there are abrupt changes in a system property provoked by disturbances. An example of relating the degree of impervious surface in watersheds and indices of aquatic biological health is provided in their paper. Since increases in the impervious surface (considered a landscape metric) of a watershed will increases the rates of rainfall runoff and reduce sediment supply to the streams, the energy dissipation will be altered. This situation will invariably have a negative impact on stream health, which could be ranked according to the percentage of impervious area. Another example of how a landscape metric would provide useful information for fire management is also included in their review. The presence, distribution and connectivity of flammable fuels across the landscape will invariably affect the spread behavior of fire, more importantly; fire cannot spread without adequate spatial connectivity of fuel (see figure 1 for graphical depiction purposes). Another insight on this topic is provided by Fahrig (2002) using the relationship of habitat fragmentation with extinction thresholds. In his review of several prediction models he concluded that models predict that, as habitat is lost, the relative rate of landscape-scale mortality and landscape-scale reproduction shift in favor of mortality. 6

10 Therefore, fragmentation can increase the extinction threshold such that more habitats are needed for population persistence in more fragmented landscapes. He points out the importance of deriving landscape indicators of fragmentation. This significance is based on the fact that if fragmentation does have a large effect on the extinction threshold, then alterations of the habitat pattern from management practices will become an effective instrument for species conservation. Example of a management practice would be to conduct forestry operations to achieve a singular spatial pattern, in such a way that actually compensates for the loss of forest cover. Nevertheless, he emphasizes that there are several needs of further research to support these observations. Fig 1. Theoretical assertion of how the distribution of patches and environmental conditions influence the spread of disturbance. Source: Groffman et al., (2006). With (2004) explored how alterations in the landscape structure (habitat loss and fragmentation) contribute to the risk of invasive spread by conducting simulations using neutral landscape models NLMs. Her approach to defining critical threshold in invasive spread relies on identifying landscape pattern of the disturbance and the dispersal abilities of the species. She simulated the interaction effects of landscape structure (Random process or Fractal distribution) with increasing levels of landscape disturbance 7

11 (% increases) and with two types of species dispersal abilities (constrained to disperse and capable of performing gap-crossing) on the potential for invasive spread. According to her postulations, landscape pattern becomes important for predicting rates of invasive spread only when some threshold of disturbance has been crossed. The following figure depicts some of her findings through simulation based on percolation theory the study of flows through spatially heterogeneous media -, to assess the probability of invasive spread. Fig. 2. The probability of invasive spread for species constrained to disperse through adjacent cells of disturbed habitat (A), and those capable of crossing cells of unsuitable habitat (B). Source: With (2004). In general (figure 2), invasive spread occurs at lower levels of disturbance when disturbances are large or clumped in distribution over the landscape for those species with less favorable dispersal abilities. On the other hand, when the species has better dispersal abilities (i.e. gap-crossing skills), the probability of invasive spread will increase on landscapes with small and localized disturbances. Although the statements are based on simulations without any field testing for accuracy, the implications for management recommendations are important. This suggestion is based on the finding that 8

12 even small disturbances can propagate rapidly throughout the landscape depending on the spatial pattern found and the species crossing abilities. In her final remarks, she points out the need of future research on this topic, since the alteration of landscape structure by human-use activities may well be facilitating the spread of invasive species, and we still lack appropriate knowledge. 2.3 Consistency in the use of landscape metrics In spite of the prediction power of landscape indices (Section 2.4), several shortcomings have also been associated with the generalization of relationships between landscape patterns and ecological processes (Turner 2005, Tischendorf 2001, Gustafson 1998) Things to be careful about Li and Wu (2004) highlight the problem that many landscape analyses treat quantitative descriptions of spatial pattern as an end itself and fail to investigate relationships between pattern and ecological processes. They are also very critic about the ecological relevance of landscape indices. For instance, if a set of metrics do not exhibit important attributes of spatial pattern that can be linked to the dynamics of ecological processes; they become mathematical constructs that have no inherent ecological meaning. Figure 3 was obtained from this paper, and graphically summarizes the problems with landscape metrics exposed by Li and Wu (2004). Generally, the problems with the use of landscape metrics can be summarized in three points: (a) conceptual flaws, (b) improper use or misuse of landscape indices, and (c) intrinsic limitations of landscape indices. Each point has several manifestations that usually extend beyond to the other points. 9

13 Fig. 3. General types of problems in spatial pattern analysis with emphasis on landscape metrics. Source: Li & Wu (2004). One of the major troubles that are continuously mentioned in the literature is the predicament that many commonly used landscape metrics provide redundant information or collinearity problems (Tischendorf 2001, O Neill et al., 1988). Hargis et al., (1998) conducted several simulations to assess six commonly used metrics (edge density, contagion, mean nearest neighbor distance, proximity index, fractal dimension, and mass fractal dimension) to describe habitat fragmentation. In their simulations they used two types of patches (rectangular and irregular), three modes of disturbance growth (enlarging, abutting and buffered), and various proportions of disturbance with 10% increases. Their results showed that the type of disturbance growth affected the strength of the pairwise relationships among metrics, being greatest when the proportion of disturbances exceeded 40%. The strongest correlations were found among 10

14 edge density, contagion, and mass fractal dimension. According to their conclusions, this degree of collinear relationships is to be expected because of the landscape metrics dependency on the same measures of patch area, edge length, and inter-patch distance. Despite the problems found, the authors stated that an individual analysis of each metric proved to give unique information not contained in other metrics. The fact that some landscape indices provide ambiguous information about spatial pattern has also been reported as a major problem. In general, this problem refers to the situation where one landscape metric may have the same numerical value for different spatial patterns (Turner 2005). As an example, in a study conducted by Tischendorf (2001), strong correlations were found among a set of landscape metrics and three response variables for a simulated dispersal process. However, these relationships were considered inconsistent due to the ambiguity of the results (i.e. only 25 out of the 78 analyzed statistical relationships were unambiguous). A graphical example of the type of ambiguity that one index can exhibit is shown in figure 4. Fig. 4. A relationship between a landscape metric and percentage of habitat indicates that the same value can be obtained with different habitat conditions. Source: Tischendorf (2001) 11

15 In this work, the author estimated different levels of metrics, namely habitat classlevel and landscape-level. When these metrics were evaluated to explain the simulated dispersal process, the habitat class-level indices outperformed the landscape class ones. The author also stated that in order to be successful in relating landscape pattern and ecological processes, one must be consistent in the quantification of the latter so that comparisons between different studies can be performed more appropriately. Another problem indicated in the references is that landscape indices are sensitive to changes in the spatial scale. Turner et al., 1989 provide a thorough analysis of the repercussions of changing the grain and scale of landscape data on observed spatial patterns. The basis for their work relies on the fact that parameters and processes that can be important at a given scale may not be significant or lose their predictive power at another scale. As a generality, the results showed that indices of dominance and contagion decreased when the grain size increased. Further, rare land cover classes were lost when the analysis was coarser (larger grain). Nevertheless, the loss behavior responded to the spatial pattern. For instance, patches that were aggregated disappeared slowly, while isolated patches were lost quickly. When increases in the spatial extent were evaluated, the number of land cover classes increased as expected. The metrics dominance and contagion appear to diminish with increasing extent until other land cover class was included, when this occurred, larger increases in the metrics were observed. Although the study by Hargis et al., (1998) did not include a component of changes in grain or extent, I consider valuable to mention one of their findings. Their 12

16 results indicated that none of the landscape metrics under scrutiny showed sensitivity to variations in the spatial arrangement of patches in the landscape. This illustration could prove to be useful when comparing results from different landscapes with different spatial patterns, provided they have the same spatial scale Techniques used for improvement Until this point it might look as if the utilization of landscape metrics is hopeless because of the problems mentioned before. However, some studies and their results show that according to the faced problem, a technique can be used to eliminate it. For instance, Riitters et al., (1995) utilized a multivariate factor analysis to eliminate redundancy. An original set of 55 metrics were calculated from 85 land cover maps and was analyzed to identify common axes of pattern and structure. An initial approach which consisted of reviewing the pairwise correlation coefficients produced a reduced set of 26 metrics. The reduced set (26) was analyzed using principal components analysis. The axis (6) containing the majority of variation (87%) were selected. The final results obtained suggested that the factors obtained could be represented in a much simpler way by just six metrics which span the important dimensions of pattern and structure, but which are not redundant. This methodological approach is a simplification, which avoids the difficulty of interpreting linear combinations of many metrics, and the need to estimate them all for each map. It is clear that in an analysis which includes several tens of landscapes it becomes useful to estimate and analyze just a small subset of metrics. The methodological approach presented by Giles and Trani (1999) is more straightforward. They hypothesized that six variables (total area, total classes, proportion 13

17 of the area in the dominant class, number of polygons, estimated total edge length, and elevation) are to be considered major correlates of measures of landscape pattern, and the others are just derivatives. Their fundamental basis relied on the examination of major central components of many well-known expressions of pattern. Just to provide a few explanations, the justification to include area is that pattern is a function of the size of an area the larger the area, the greater will be the probability of occurrence of resources for a plant or animal. Proportion of dominant class is included to avoid ambiguity, since many different conditions can produce the same proportion (i.e. contradictory patterns may produce indistinguishable statistics) as illustrated before (i.e. Tischendorf 2001). Their justification is that if this dominant proportion is small, the other classes are probably abundant and small if large; there are probably few other classes or many very small ones. The authors seemed to be highly confident in their hypothesis. Yet, they did not include any relationship to an ecological process, or a successful modeling approach that uses these metrics to explain the behavior of any ecological process or function whatsoever. In this same context of trying to avoid ambiguity, Tischendorf (2001) proposes the utilization of the interaction of landscape metrics to further enhance the understanding of ecological processes in their relationship with landscape structure. As an example they mention that in their results less habitat edge combined with longer inter-patch distances corresponds to less habitat fragmentation. I assume this approach could become too a redundancy-reduction technique, provided that the interaction of two or more metrics is statistically significant and the individual factors are not. 14

18 I was not able to find references regarding how to reduce the effect of sensitiveness to the changes in grain and extent in the reviewed literature. I would assume that since the sensitiveness is inherent to each landscape metric, then it could not be solved, unless a combination of different metrics is used. This approach would not deal with the individual metric however. 2.4 Predicting ecological processes responses from landscape metrics Numerous attempts to explain dynamics in ecological processes from changes in the configuration of the landscape have been extensively documented in the literature. These endeavors generally involve deriving landscape indices (i.e. dominance) from categorical maps, and measuring a response variable(s) (i.e. species presence or absence, dispersal success) associated with the index. The significance of the relationships and the predictability are subsequently evaluated by using statistical methods (Tischendorf 2001). From this point forward, I will mention and discuss a few documented efforts that have dealt with the issue of associating landscape indices with an ecological process response. These examples have also evaluated the explanatory power of the metrics as independent variables to predict the variability in ecological responses. I concentrated on two broad topics: birds presence-absence and riverine systems Bird s abundance McGarigal and McComb (1995) evaluated the autonomous effects of forest availability (habitat area) and spatial pattern (habitat configuration) on the abundance of 15 bird species in 30 landscapes in the northwestern USA. Using a 20 m grain size vegetation map they derived 25 landscape indices. However, it was clear that most of the 15

19 metrics were providing redundant information and simply represented alternative formulations for the same spatial configuration information. A reduced set of metrics (4) that did not show collinearity problems was obtained by analyzing simple and multiple correlation coefficients and principal component analysis PCA. They independently conducted simple linear regressions having a bird abundance index for each species as the dependent variable. Their results indicated that the regression having habitat area as independent variable outperformed the one having the configuration metrics as explanatory factors. In other words, habitat area had a much greater effect that habitat fragmentation in explaining the variability of the abundance index. Furthermore, the fragmentation effects seemed to be positive; since species abundance augmented with more fragmented landscapes. Trzcinski et al., (1999) obtained results that can be considered similar to those obtained by McGarigal and McComb (1995). They also evaluated the effects of forest cover and a fragmentation index (derived from three landscape metrics using PCA) on the distribution of forest breeding birds by using multiple linear regressions. Their results suggested that most bird species were strongly related to forest cover, and no general relationship between the presence-absence of birds and forest fragmentation was evident. The effect of the interaction between forest cover and forest fragmentation was also analyzed. The statistical analysis reflected that the effect of landscape fragmentation on bird s distribution is not affected by changes (increasing or decreasing) in forest cover, hence no proof for interaction. These authors generally concluded that habitat loss is more important than habitat fragmentation for the population endurance of birds. 16

20 A different approach for the selection of landscape metrics was used by Lawler and Edwards (2002) in their study of landscape patterns as habitat predictors for cavitynesting birds. These authors chose a set of 14 metrics that would estimate both the composition and the structure of the landscape-level vegetation patterns from their own experience and based on biologically meaningful criteria. No technique for redundancy reduction was applied before executing the modeling, and the best explanatory metrics were chosen according to the prediction power exhibited using Classification and Regression Trees CART s. The trees obtained as results varied in the number of explanatory variables inserted in each tree for every bird species. It was evident in their results that the models varied in their ability to correctly predict nests (i.e. 84%, 80%, 75% and 50% were the correctly predicted rates). In general only three models proved to be accurate when validated in the field, but the authors considered that their methods and predictions provide the foundation to conduct studies at local scales, and thereby answer the general question what is where?. The general perception from the study cases cited in this section is that habitat amount has a greater effect on the dynamics of species abundance than habitat fragmentation. This perception can also be derived from the review of several studies by Andrén (1994). In addition to that observation, he draws attention to the fact that when the proportion of suitable habitat falls below 30%, the other components of habitat fragmentation (i.e. reduced patch size and isolation) will complement the effect of habitat loss alone. A striking effect on the abundance of species will be observed in landscapes with highly fragmented habitat. 17

21 2.4.2 Riverine systems Gergel et al., (2002) emphasize that although the detection of human impacts on riverine systems is difficult because of the diverse components to be assessed i.e. biological, hydrological, chemical, and geophysical the utilization of landscape indicators to assess the status of rivers is reasonable. The foundation for their assessment relies on the fact that many studies have demonstrated that upland land use can influence riverine ecosystems which would point towards a watershed-based approach. Nonetheless, they also draw attention to a riparian-based attitude. Therefore these two scales of analysis become the starting point to deriving spatial pattern metrics. A watershed-based approach example can be found in the published works of Paul et al., (2002). This study analyzed the relationships between spatial pattern and estuarine sediment contamination by using multiple linear regression. A total of 14 landscape metrics were calculated originally, but were considerably redundant. After a redundancy reduction process (i.e. correlation coefficients and PCA), a reduced set of six metrics was obtained. The supporting statistical evidence showed that sediment contaminant levels increased with an increasing urban land proportion in the watershed. Conversely, the sediment levels decrease with escalating fractions of the watershed in non-forested wetlands. Comparable results were found by Snyder et al., (2005), who utilized watershedlevel and riparian-level metrics to analyze how well stream health rankings were predicted. In this study the primary influence on stream health was the amount of impervious surface area ISA, thereby becoming a strong predictor. The statistical analysis 18

22 did not show differences between the watershed-based and the riparian-based metrics. Moreover, the metrics derived at the watershed level proved to be more powerful predictors than those extracted at the riparian level, which were obtained from higher resolution imagery. The overall analysis also identified that a threshold could be derived from the relationships evaluated. Whenever the ISA reached 20% the watersheds were invariably ranked in a fair or poor condition. The results have important applications in the way that simply knowing the amount of ISA in a watershed is more useful than knowing the patterns of dispersion or aggregation. Further, it was concluded that stream health relationships can be derived confidently from coarse spatial resolution imagery (i.e. Landsat), since no evidence of statistical difference was observed when compared to those relationships derived from high spatial resolution (i.e. IKONOS). The statements made by Snyder et al., (2005) comply with the findings of Richards et al., (1996) that used both, watershed and riparian metrics to analyze streams habitats and biota. Their results showed that whole catchments may be as important as 100-m buffers around streams for determining several components of stream habitat. In other words, the influence of landscape cover characteristics all over the watershed is as important as the influence of riparian vegetation for understanding streams ecosystems. Riparian-based derived metrics also appear to be promising in explaining the variability of certain biological functions in streams. The efficiency of riparian tree canopy to elucidate changes in benthic metabolism was evaluated by Bunn et al., (1999). Two measures of benthic metabolism were evaluated: gross primary production GPP and 19

23 respiration R 24 which are surrogates for the amounts of organic carbon produced and consumed within the system. The theoretical foundation for this study was that the amount of carbon produced within a stream system is undoubtedly sensitive to changes in catchment s land use (e.g. increase nutrient runoff) and, particularly, to changes in riparian condition. The results indicated that riparian canopy percent cover explained 44% of the variability of GPP and 32% of R 24. Although the authors did not conduct a prediction modeling effort, they stated that the results obtained laid the groundwork to accomplish it. Figure 5 shows the graphic relationship between this spatial pattern metric and both measures of benthic metabolism, notice the how strong the associations are. Most importantly, the graphical association depicted in figure 5 might be used as a stream health threshold indicator. According to the authors, striking declines in the health of forest streams can be observed when GPP substantially exceeds R24. In this case, whenever the canopy cover falls below 40 50%, negative changes will be observed in the stream health. Fig. 5. Relationship between benthic metabolism and riparian canopy cover. Source: Bunn et al., (1999). 20

24 3. Exercise on landscape metrics I conducted an exercise to gain experience with the generation of landscape metrics, and at the same time observe if the problems mentioned before (i.e. section 2.3) were also evident in this simple model. In view of the fact that problems were present, I tried some of the techniques to eradicate or diminish the problems (i.e. section 2.3.2). Finally, I investigated the potential of the landscape indices to explain the variability of an ecological response. 3.1 Invasive annual grasses over sagebrush ecosystems According to With (2004), there has been little theoretical or empirical research that has addressed how the alteration of landscape structure might promote invasive spread. Departing from this statement, I chose to explore if a significant relationship could be found between landscape pattern of sagebrush ecosystems and invasive spread of annual grasses in the state of Utah. The GIS and statistical procedure utilized to accomplish this objective is explained below Method First, I subjectively chose to divide the whole state in quadrants of 100 km 2 (squares of 10 x 10 km). These quadrants were used as landscapes or sampling units to estimate a set of landscape metrics (habitat amount and fragmentation) and the dynamics of spread. Subsequently, I used the 1995 Utah GAP and 2004 Southwest Region GAP land cover digital information (USU RS/GIS Laboratory, 2006) to extract the following data: Sagebrush ecosystem SBE spatial distribution in 1995 Invasive annual grasses IAG spatial distribution in

25 As it is depicted in figure 6, in each quadrant that exhibited SBE 1995 and IAG 2004 cover, there was the possibility of finding areas that shared both covers. Although temporarily separated, I decided to designate such areas as invaded by annual grasses in the period. I found 748 quadrants that contained invaded areas, but for practical purposes I decided to use only those that included more than 1% (1 km 2 ) of such invaded areas within each quadrant. There were 168 quadrants that complied with this condition. 10 km Sagebrush 1995 Invasive Annual Grasses 2004 Fig. 6. Spatial distribution of sagebrush (green) in 1995 and invasive annual grasses 2004 (red). The dark colored areas in red (inset) denote the invaded areas in the period The distribution of the selected quadrants was concentrated in a band that runs from north to south in the eastern section of the state. I systematically chose half (68) of the quadrants to estimate class-level metrics for the 1995 SBE cover (figure 7). 22

26 Invaded Areas occupancy i.e. Quadrant 1306 = 2.07% 1995 SBE class-level metrics i.e. Quadrant 1306 = PLAND proportion of the landscape occupied FRAC_MN Fractal Index Distribution Fig. 7. Distribution of the sampled quadrants to obtain the proportion of invaded area and landscape metrics in the sagebrush ecosystem. The table shown the structure of the database. The class level metrics were estimated by using the software FRAGSTATS (McGarigal and Marks 1995). A list of these 26 metrics classified by type that were obtained can be seen on the following table. 23

27 Table 1. Landscape class-level metrics derived for the 1995 sagebrush ecosystem Group Type AREA/DENSITY/EDGE SHAPE ISOLATION/PROXIMITY CONTAGION/INTERPERSION CONNECTIVITY Landscape Class-Level Metric Total (Class) Area (CA), Percentage of Landscape (PLAND), Number of Patches (NP), Patch Density (PD), Total Edge (TE), Edge Density (ED), Landscape Shape Index (LSI), Normalized Landscape Shape Index (nlsi), Largest Patch Index (LPI), Patch Area Distribution (AREA_MN), Radius of Gyration Distribution (GYRATE_MN) Perimeter-Area Fractal Dimension (PAFRAC), Perimeter-Area Ratio Distribution (PARA_MN), Shape Index Distribution (SHAPE_MN), Fractal Index Distribution (FRAC_MN), Linearity Index Distribution (LINEAR_MN), Related Circumscribing Circle Distribution (CIRCLE_MN), Contiguity Index Distribution (CONTIG_MN) Proximity Index Distribution (PROX_MN), Euclidean Nearest Neighbor Distance Distribution (ENN_MN) Percentage of Like Adjacencies (PLADJ), Clumpiness Index (CLUMPY), Aggregation Index (AI), Mass Fractal Dimension (MFRAC), Landscape Division Index (DIVISION), Splitting Index (SPLIT), Effective Mesh Size (MESH) Patch Cohesion Index (COHESION), Connectance Index (CONNECT) Given these sources of information, my overall goal was to investigate if the 1995 SBE class-level metrics could be used as explanatory factors of the invaded areas occupation (%) found in the landscape (quadrants). In other words, does the spatial configuration of SBE found in 1995 have any measurable and significant relationship with the proportion of the landscape occupied by invasive grasses in 2004? I checked for redundant information among the explanatory variables by analyzing cross-correlations in a two-step fashion. First, I reviewed the intra-type correspondences (covariance among metrics from the same group i.e. habitat amount, contagion), to select at least one representative metric from each group. Secondly, I contrasted the representatives to see if they still were producing redundant information. If 24

28 they were not, I would therefore be able to consolidate a reduced set of metrics that explained well enough the overall spatial characteristics of habitat amount and habitat fragmentation. Finally, I tried to fit linear regression models having the selected metrics as independent variables, and the percent occupation of invaded areas as dependent factor. I conducted the regression approach in order to see if there was any explanatory power, and if there was; how strong it was by looking at the adjusted r Results The multitemporal overlay of the GAP land cover maps revealed that of the km 2 of original sagebrush, approximately 8.9% was successfully invaded by annual grasses. These figures have to be observed with skepticism, given the differences in the methodological approaches to derive each land cover map, and the level of categorization (Lowry 2006 personal communication). Nevertheless, for the purposes of this exercise, and the level of generalization, I considered it was well to undertake the exercise. As indicated in the previous section, 26 metrics were derived for the 68 landscapes or quadrants. The analysis of Pearson Correlation Coefficients showed that several of the metrics were highly correlated with others, therefore producing redundant information. Just to mention some cases, in the AREA/DENSITY/EDGE group PLAND (Percentage of Landscape) was completely correlate (r = 1) with CA (Total Class Area), and virtually explained (r = ) by LPI (Landscape Shape Index). Similar results were found for the SHAPE group. Here PARA_MN (Perimeter-Area Ratio Distribution) showed an almost perfect negative relationship (r = ) with CONTIG_MN (Contiguity Index Distribution). This situation is also observed for FRAC_MN (Fractal 25

29 Index Distribution) that is highly associated with CIRCLE_MN (Circumscribing Circle Distribution) i.e. r = Some of the metrics from the CONTAGION group also exhibited this kind of problem. I also checked for ambiguous responses in this exercise, and found some worth mentioning. For instance, in the following figure (8) the Patch Cohesion Index COHESION is depicted as a function of Number of Patches. COHESION should increase as the patch type becomes more clumped or aggregated in its distribution. Therefore, higher values should be found if the landscape is more physically connected (McGarigal and Marks 1995). One would assume that if more patches are in the landscape, then the more fragmented the land cover class is. Nevertheless, this is not the generality depicted here, because it is clear that practically the same values can be observed whether we have less than 100 patches or more than Fig. 8. Cohesion Index as a function of Number of Patches indicating that ambiguities are present i.e. same values are observed regardless of the change in fragmentation. 26

30 It is evident that serious problems are noticeable here. Still, I continued looking for an appropriate reduced set of metrics, based on coefficients comparison. I finally selected five metrics that although seemed problematic, exhibited low levels of correlation among them. The metrics selected were: Percentage of Landscape (PLAND) Number of Patches (NP) Total Edge (TE) Proximity Index Distribution (PROX_MN) Landscape Division Index (DIVISION) Patch Cohesion Index (COHESION) I used this reduced set of landscape metrics to evaluate their potential in predicting the response variable (percent occupation of the landscape by invaded areas). Here, the overall model to test was: Y i = β i X i Where: Y i = % of the landscape invaded by annual grasses β i = Set of coefficients estimated by regression X i = Reduced set of landscape class-level metrics for the sagebrush ecosystem in 1995 This activity was not successful at all. I tried all the individual options (i.e. one independent variable at a time), and different combinations of independent variables. 27

31 Only the individual models were significant (α = 0.05), but none of the combinations turned out significant. Furthermore, the adjusted r 2 exhibited that the prediction power of the individual metric was very low. As it can be seen (table 1), in the best cases only 25% of the variability was explained by the model, the rest can be considered noise. Table 2. Adjusted r2 obtained for the individual landscape metrics interpreted as the prediction power of invasive spread. Landscape Class-Level Metric Adjusted r 2 Percentage of Landscape PLAND Number of Patches NP Total Edge TE Landscape Division Index DIVISION Patch Cohesion Index COHESION It is definitely clear that none of this metrics was able to provide sufficient variability explanation of the dependent variable. I also followed the suggestions made by Tischendorf (2001) regarding the utilization of interactions. However, none of the interactions turned out to be significant, much less provided an improvement in the adjusted r 2 values Discussion As it was clearly observed in the exercise, several problems arise from the indiscriminate generation of landscape metrics. One can easily find oneself with plenty of redundant and ambiguous information. However, this cornucopia might be deceitful, or worse; might lead to erroneous conclusions if careless evaluations are performed. Here, I think that the remarks given by Lawler and Edwards (2002) are without a doubt useful. 28

32 Their approach to selecting metrics by biologically meaningful criteria seems more convenient than conducting time-consuming processes of analyzing associations for individual metrics. With respect to the power of the landscape metrics to predict the behavior of invasive spread, I think that absolute conclusions cannot be drawn. I lacked to include other factors that undoubtedly drive the invasive spread i.e. types of disturbances that originate or encourage the invasive process. A large shortcoming is that I considered the quadrant as representation of a landscape, without taking into account the variability due to elevation, precipitation, aspect, and other driving factors. Since I conducted this exercise for practicing purposes, I did not consider including additional information. This situation would have proven to be useless, given the level of generalization (100 km 2 ). I must point out that although several weaknesses were present in this exercise, the results tended to exhibit accordance with other references. For instance, it was found that the amount of habitat did prove to be a better descriptive feature than habitat fragmentation. This shows complete agreement with the findings of McGarigal and McComb (1995), Trzcinski et al., (1999), and Andrén (1994). 29

33 4. Conclusions After conducting this research on the conceptual framework of landscape metrics, I have come to the following conclusions: A suite of landscape indices should be generated accordingly to the research question. If one is to generate dozens of metrics without a previous evaluation of what needs to be measured and for what purpose, the whole effort might turn unsuccessful. The estimation of landscape metrics must never be seen as a final objective. The foundation to derive such metrics must always be in harmony with the purpose of relating those metrics with an ecological processes response. If the behavior of such metrics is not related with the dynamics of an ecological function, then no further understanding for resources management will be achieved. It was clear from the conceptual review and from the exercise results that several limitations and flaws are inherent to landscape indices (i.e. collinearity, ambiguity, sensitiveness). Nonetheless, this should not be seen as a hopeless situation because several techniques can be used to diminish or even eliminate the problems. In the event that these approaches turn to be useless, then the possibility of generating new metrics is at hand. Such was the case of Schuft et al., (1999), that developed their own metrics for riparian networks. The observations and results that were evaluated from the literature strongly suggest that landscape pattern studies can be used for management planning. For instance, the estimation of the maximum amount (threshold point) of impervious area that can be sustained without degrading the quality of streams or the inference of how wide and 30