CHAPTER 13 Multi-Scale Resilience Estimates for Health Assessment of Real Habitats in a Landscape G. Zurlini, N. Zaccarelli, and I. Petrosillo Vegetation or habitat types are ecological phases that can assume multiple states. Transformations from one type of phase to another are called ecological phase transitions. If an ecological phase maintains its condition of normality in the linked processes and functions that constitute ecosystems then it is believed to be healthy. An adapti ve cycle, such as that given in Holling’s model, has been proposed as a fundamental unit for understanding complex systems. Such model alternates between long periods of aggregation and transformation of resources and shorter periods that create opportunities for innovation. The likelihood of shifts among different domains largely depends on domain resilience, measurable by the size of scale domains, but these do not provide any indication on resistance — the external pressure to displace a system by a given amount. We argue that the type, magnitude, length, and timing of external pressure, its predictability, the exposure of the habitat, and the habitat’s inherent resistance, have important interactive relationships which determine resilience, and in turn, ecosystem health. Different resilience levels are expected to be intertwined with different scale domains of real habitats in relation to the type and intensity of natural and human disturbances from Copyright © 2005 by Taylor & Francis management activities and land manipulation. In this paper, we provide an operational framework to derive operational indices of short-term retro- spective resilience of real grasslands in a northern Italy watershed, from multi- scale analysis of landscape patterns, to find scale domains for habitat edges where change is most likely — that is, where resilience is lowest and fragility highest. This is achieved through cross-scale algorithms such as fractal analysis coupled with change de tection of ecological response indices. The framework implements the integration of habitat-edge fractal geometry, the fitting of empirical power functions by piecewise regressions, and change- detection procedures as a method to find scale domains for grassland habitat edges where change is most likely and consequently resilience is lowest. Changes due to external pressure significantly related to habitat scale domains, according to their scaling properties resulting from the interaction between ecological, physical, and social controls shaping the systems. Grassland scale domains provided evidence and support for identifying and explaining scale- invariant ecological processes at various scales, from which much insight could be gained for characterizing grassland adaptive cycles and capabilities to resist disturbances to facilitate ecosystem health assessment. 13.1 INTRODUCTION The rapid progress made in the conceptual, technical, and organizational requirements for generating synoptic multi-scale views and explanations of the Earth’s surface, and for linking remote sensing at multi-resolution levels from satellite and airborne imageries, geographical information systems, spatial analysis of landscape patterns, and habitat classification methods, provides an outstanding potential support to: 1. Identify real landscape patches as habitats and land use types 2. Detect ecological processes by remotely sensed response variables 3. Relate response variables to habitats, by observing at different times ecological changes in habitat pattern as well as in the scales of habitat pattern (Simmons et al., 1992). Multi-scale studies are increasingly conducted (Wu and Qi, 2000), which give emphasis to the identification of scale domains (Li, 2000; Brown et al., 2002), that are self-similarity regions of the scale spectrum over which, for a particular phenomenon, patterns do not change or change monotonically with scale. Thresholds are, in general, difficult to delineate across scales, because it remains difficult to detect multiple scales of variability in ecological data and to relate these scales to the processes generating the patterns (Levin, 1992; Ward and Salz, 1994). The likelihood of sharp shifts is linked to an ecosystem’s resilience, which is the capacity of a system to undergo disturbance and maintain its functions and controls (Gunderson and Holling, 2002). The importance of a clear and measurable definition of resilience has become paramount (Carpenter et al., 2001) for evaluating the health of an ecosystem, Copyright © 2005 by Taylor & Francis defined as being stable an d sustainable, maintaining its organization and autonomy over time, and its resilience to stress (Costanza, 1992; Mageau et al., 1995). Scaling domains of habitats can be identified, for instance, by shifts in the fractal dimension of patch edges, and can indicate a substantial change in processes generating and maintaining landscape patches at different scales (Krummel et al., 1987; Sugihara and May, 1990; Milne, 1991), so that different processes dominate at different scales (Peterson, 2000). One way to appreciate the interaction between pattern and pro cesses is to look at temporal changes detected by remote sensing, and whether they are significantly associated with different scale domains. If such processes change in type and intensity across scales, the ability of ecosystems to resist lasting change caused by disturbances — their resilience (Gunderson et al., 1997; Gunderson and Holling, 2002) — will change accordingly, so that habitat resilience and scaling are expected to be intertwined (Peterson, 2000). This study was designed to address some specific questions: 1. Can we objectively and accurately identify scale breaks delimiting ecolog- ically equivalent scales in real habitat patches in a landscape? 2. Are temporal changes detected significantly related to habitat scale domains, providing evidence on the types of biophysical and social controls shaping the systems? 3. If so, can we derive an operational index of short-term retrospective resilience, through cross-scale algorithms like fractal analysis coupled with remotely sensed change detection, to find scale domains where change is most likely — that is, where resilience is lowest? To make this approach practical, we need: 1. A really effective classification procedure for habitat recognition from general and vague categories of habitats to more specific categories 2. A statistically objective procedure for identifying shifts in scale domains 3. Suitable ecological response variables for change detection. We describe an operational framework for the accurate identification of self-similar domains in few specific grassland habitats, and for estimating their displayed short-term resilience. First, we looked for scale domains in real grassland patches of a stream watershed in northern Italy (Zurlini et al., 2001), resulting from long-term natural and man-induced interactive disturbance regimes. We then quantified short-ter m intensity changes of habitat scale domains, based on remotely-sensed ecological response indices. This frame- work implements the integration of edge fractal analysis, the fitting of power laws by piecewise regressions and hypothesis testing of scale shifts (Grossi et al., 1999; 2001), together with procedures of change detection, as a method to find scale domains for grassland edges where change is most likely. Together they rep resent a framework for spatially defining critical landscape thresholds and scale domains, habitat adaptive cycles (Gunderson and Holling, 2002), and habitat resilience by which scale-dependent ecological models could be developed and applied. By introducing this approach, we address some basic concepts of ecological phases and multiple states, with a general discussion on Copyright © 2005 by Taylor & Francis self-similarity regions, fractal analysis, and on the statistical procedures for the objective identification of shifts among scale domains, as well as on resilience and its practi cal measure. The detailed and often complex composition of real landscape habitat mosaics in terms of habitat types and land use has been rarely considered in the understanding of the relationships between landscape pattern and process response variables. Much of the insight obtained is related to the coupling of change-detection procedures with the availability of detailed habitat type distribution in a stream watershed. The potential of such approach for ecosystem health assessment, planning, and management of habitats mosaics is also discussed. 13.2 RATIONALE 13.2.1 Ecological Phases, States, and Scale Domains From several long-term observations, experimentations, and comparative studies of many sites, it is now evident that alternate and alternative states arise in a wide variety of ecosystems, such as lakes, marine fisheries, benthic systems, wetlands, forests, savannas, and rangelands (Gunderson and Holling, 2002). A phase state of a system at a particular instant in time is the collection of values of the state variables at that time (Grimm et al., 1992; Walker et al., 2002), different from other states the system can visit over and over again (alternate), or from those typical of other systems (alternative). Vegetation or habitat types are considered ecological phases which can assume multiple states, and transformations from one type to another (alternative) correspond to ecolog- ical phase transitions, which change the integral structures of the systems (Li, 2002). Multiple states (alternate) can be assumed by ecological phases without losing their basic identity. For example, a forest stand or grassland may remain a forest patch or grassland over and over again, each with its own dynamic states of rapid growth, conservation, collapse, and reorganization as proposed by Holling’s adaptive cycle model (Holling et al., 1995). For grasslands, such model proposes that as young grasses grow wi thout grazing or cutting, they gradually become denser and accumulate fuel, and thus become increasingly susceptible to fire. After a fire, the system is reorganized as vegetation resprouts from roots or seeds, producing new grassland. All these states are deemed as multiple configuration states (attractors) of the same ecological phase. Another e xample is provided by a simple meta- model describing different common ecological states of coral reefs, and the factors that may cause or maintain these states (McClanahan et al., 2002). Individual coral reefs that are exposed to a combination of human and natural influences may be a mosaic of several states. Which state the ecosystem currently assumes is function of its history and of the driving forces operating. Multi-scale analysis corresponds to the detection of self-similar scale domains of alternate states, a central point for the development of a Copyright © 2005 by Taylor & Francis scalar theory in ecology (Levin, 199 2; Holling, 1992; Wiens, 1995). Such self-similarity or fractality implies a particular kind of structural composition or dynamic behavior — that is, the fundamental features of the system exhibit an invariant, hierarchical organization that holds over a wide range of spatial scales (Gell-Mann, 1994; Li, 2000). A spati al ecological phase transition, or ecotone, is a ‘‘zone of transition between adjacent ecological systems, having a set of characteristics uniquely defined by space and time scales and by the strength of the interacti ons between adjacent ecological systems,’’ (di Castri et al., 1988). Therefore the nature of a habitat’s edge is not just a property of a specific habitat, but is the outcome of interactions at the landscape level. Ecological phases like vegetation or habitat types are dynamic in space and time, each trying to expand and invade adjacent ones whenever environmental and management conditions are beneficial to one of the adjacent ecosystems (Risser, 1995). They can have different regions of the scale spectrum over which there are several possible ecological states, equivalent or self-similar for a particular phenomenon, and which do not change or change monotonically with changes in scale. This would allow drawing the same ecological conclusions statistically from any scale (Sugihara and May, 1990; Milne, 1991; Li, 2000). 13.2.2 Resilience and Resistance Abrupt shifts among several very different (alternative) stable domains are plausible in local and regional ecosystems more susceptible to changes; the likelihood of such shifts depends on resilience and resistance (see chapter 2), whereas the costs of such shifts depend on the degree of and duration for reversibility from one domain to another (Gunderson and Holling, 2002). Two systems, or two states of the same system, may have the same resilience but differ in their resistance. We can surmise that if the same external pressure is applied to two systems with different intrinsic resistances, they will show a different ability, or resilience, to resist lasting change caused by disturbances. Resilience estimates differ from ecological indicators in that they refer to socio- ecological systems and ecosystem services (Costanza et al., 1997) they provide (Carpenter et al., 2001). Most studies in the literature refer to theoretical approaches, using resilience as a metaphor or a theoretical construct (Carpenter et al., 2001). Where resilience has been defined operationally, this has occurred in a few cases within a mathematical model of a particular system (Carpen ter and Cottingham, 1997; Peterson et al., 1998; Janssen et al., 2000; Casagrandi and Rinaldi, 2002). In this context, bifurcation analysis of simple dynamic models has been often suggested or adopted, together with the size of stability domains, or the magnitude of disturbance the system can tolerate and still persist before the system changes its structure by changing the variables and processes that control behavior (Peterson et al., 1998; Gunderson and Holling, 2002). Copyright © 2005 by Taylor & Francis However, not all those definitions, even though measurable in models, are operationally measurable in the field. In an operational sense, resilience needs to be considered in a specific context. As discussed by Carpenter et al. (2001), it requires defining the resilience of what to what. One important distinction, along with those on space–time scales advanced by Carpenter et al. (2001), is whether resilience has to be measured prospectively — to predict the ability of ecosystems to resist lasting change caused by disturbances, or retrospec- tively — to evaluate such ability as observed by past exposure to extern al pressures. 13.3 STUDY AREA AND METHODS 13.3.1 The Baganza Stream Watershed The Baganza watershed was selected as pilot study area of the Map of Italian Nature (MIN) program (Zurlini et al., 1999; 2001), since it is a good representative of the typical series of watersheds located along the same side of the northern Apennines ridge. The watershed is approximately 174.63 km 2 , and is located on the Emilian side of the northern Apennines (Figure 13.1), with a main stream 57 km long and a progressive elevation gradient in the southwest direction which varies from 57 m in the flat to the piedmont, up to 1943 m at the highest peak in the Apennines mountains. Mean monthly temperature varies from À0.6 to À17.1 C in the mountain range, and from þ1.5 to þ24.7 C in the lowland. Mean rainfall varies from 40 to 95 mm per year with most of the rain occurring during the fall and the spring seasons, with no deficit of evapo-transpiration during summer. Snow is usually present for four months above 1,400 m. In the past few centuries, due to human influence on Mediterranean ecosystems and the slow abandonment of agricultural and pastoral practices, plant communities have been shaped into a mosaic-like pattern composed of different man-induced degradation and regeneration stages (Naveh and Liebermann, 1994). In the past, this watershed was almost fully covered by ancient forests, still present during the ducat of Parma at the end of the eighteenth century. Around the end of the nineteenth century, much of the forests in the piedmont and hills were cleared for building the many miles of the national railway network. Many cleared areas were maintained as grass- lands with pastoral practices with sheep and cattle breeding on natural or cultivated pastures. In the last century, cattle breeding on pastures prevailed due to the increasing market success of diary products. Intensive agricultural land use is currently prevalent in the lowlands and the nearby Baganza stream, whereas abandonment of agricultural and pastoral practices in the hills and mountains is still in pr ogress. Conservation and endangered species legislation at the national and regional level reduce the possibility of clearing the land, whereas they are allowed to maintain pastures in the high-hill and mountain range. Copyright © 2005 by Taylor & Francis 13.3.2 Corine Habitats Using synoptic multi-scale views and classifications of the Earth’s surface now available, researchers, land managers, and land-use planners can quantita- tively place landscape units, from general and vague categories such as ‘‘forests’’ to more specific categories such as ‘‘Illyrian Holm-oak woodland, Orno-Quercetum Hilicis dominated formations,’’ in their large-area contexts. Remote sensing technologies represent the primary data source for habita t Figure 13.1 Location of the Val Baganza watershed and distribution of large habitat classes (modified from Zurlini et al., 2001). F is the flat, with urban/agricultural matrix; P is the piedmont, with agricultural/grassland/woods matrix; and A is the Apennines mountain range, with grassland/forest matrix. The list of main habitats corine habitat is given in Table 13.1. Copyright © 2005 by Taylor & Francis identification and landscape analysis, but often suffer from the Modifiable Areal Unit Problem (MAUP, Openshaw, 1984), that is a potential source of error that can affect spatial studies which utilise aggregate data sources. It states that a number of different and often arbitrary ways exist by which an area can be divided or aggregated into nonoverlapping areal units. We used the CORINE habitat classification (EU/DG XI, 1991) to identify ecosystems as patches (Tansley, 1935) for generating digital thematic maps as Geographic Information System GIS coverages of mosaics of contiguous patches. To avoid MAUP effects, the final delineation of habitat mosaics was performed by an iterative process based on integrated evidence from processed satellite imagery, aerial photos, hyperspectral imagery, existing vegetation and geological soil maps, digital elevation models (DEM), and field reconnaissance (Zurlini et al., 1999). The detailed CORINE habitat distribution for the Baganza watershed was available at a scale of 1:25,000 (Zurlini et al., 2001), in a revised and more detailed form with respect to the original habitat classification used in Grossi et al. (1999; 2001), with 2,327 irregular patches belonging to 69 different CORINE habitat and habitat mosaic types (Table 13.1). The flat and piedmon t sections are dominated by agricultural fields, with few relatively natural habitats, represented by typical wet woodlands (Figure 13.1). Hop-horn beam (CORINE code 41.812) mixed to Quercus pubescens (41.7314) woods, are dominant in the hills, while neutrophile beech forests (41.1744) are most frequent in the mountain range above 900 to 1000 m. Three of the most frequent grassland habitats in the watershed were considered for subsequent analyses (EU/DG XI 1991; Sburlino et al., 1993): 1. Lowland hay meadows (CORINE code 38.2) present with 378 patches 2. Northern Apennine Mesobromion (CORINE code 34.3266) with 77 patches 3. Brachypodium grassland (CORINE code 36.334) with 131 patches, corresponding roughly to increasing elevation gradients and to decreasing human influence and control (Figure 13.2). So-called lowland hay meadows are rich mesophile grasslands in the lowland, hills and submountain ranges, regularly manured, and when necessary irrigated, well-drained under direct human control , with species such as Arrhenaterum elatius, Trisetum flavescens,andAnthriscus sylvestris. They often begin from seeding of leguminous grasses or mixed fodder, and after are regularly cut in time for cattle breeding in farms. Northern Apennine Mesobromion are poor closed mesophile grass lands, sparse and rich in Bromus erectus and orchids, in local semiarid environments naturally exposed to drought and limited by the amount of organic matter in soil; they are not under direct human disturbances, apart from infrequent cutting, and grazing and manuring by cattle (which is an important source of organic matter). When lowland hay meadows are abandoned, they become Mesobromion grasslands. Brachypodium grasslands are subalpine thermophile siliceous habitats, often found on skeleton soils, and are not under direct human influence apart from Copyright © 2005 by Taylor & Francis sporadic grazing by cows and sheep at lower altitudes, with carpet communities hardly browsed by cattle, and almost pure in Brachypodium genuense, typical of higher elevations and of the summits. Fire is not currently used as a practice for controlling scrub formation and seldom occurs in the watershed. 13.3.3 Empirical Patterns of Self-Similarity Domains are delimited by relatively sharp transitions or critical points along the spatial scale continuum where a shift in the relative importance of variables influencing a process occurs (Meentemeyer, 1989; Wiens, 1989). To identify scales or hierarchical levels of landscape structures, some general statistical and spatial analysis methods, inherently multi-scaled, are available such as semi-variance analysis (Burrough, 1995; Meisel and Turner, 1998; Table 13.1 List of the main CORINE habitat type identified in the Baganza watershed (modified from Zurlini et al., 2001) CORINE code CORINE habitat type 42.1B1 Abies alba reforestations 41.812 Supra-mediterranean hop-hornbeam woods 41.813 Montane hop-hornbeam woods 41.74 Quercus cerris woods 41.1744 Beech forests 42.67 Black pine reforestation 44.614 Italian poplar galleries 83.324 Locust tree plantations 41.731 Semi-xerophile Quercus pubescens woods 41.7312 Xerophile Quercus pubescens woods 44.122 Mediterranean purple willow scrub 31.431 Juniperion nanae scrub 31.81 Medio-European rich-soils thickets 31.811 Blackthorn-bramble scrub 31.88 Common Juniper scrub 32.A Spanish-broom fields 34.3266 Northern Apennine Mesobromion 34.3267 Sub-Mediterranean Mesobromion 36.334 Sub-alpine thermophile siliceous grasslands with Brachipodium genuense 38.1 Mesophile pastures 38.13 Overgrown pastures 38.2 Lowland high meadows 61.311 Rough-grass screes 61.3124 Submontane calcareous screes with Calamagrostis varia 61.3125 Sedo-Scleranthetea Submontane calcareous screes 61.3126 Brometalia erecti submontane calcareous screes 62.213 Hercynian serpentine cliffs 87.24 Ruderal communities with Tussilago farfara 87.23 Ruderal communities with Melilotus albus 87.29 Ruderal communities with Agropyron repens 82.11 Field crops 62.4 Bare inland cliffs 82.11 Plough field crops 86.2 Villages 86.3 Active industrial sites 86.41 Quarries Copyright © 2005 by Taylor & Francis Bellehumeur and Legendre, 1998), multi-variate analysis of spatial autocorre- lations (Burrough, 1983; Ver Hoef and Glen-Lewis, 1989), spectral analysis (Platt and Denman, 1975), wavelet analysis (Bradshaw and Spies, 1992), lacunarity analysis (Plotnick et al., 1993), scale variance (Wu et al., 2000), fractal analysis (Krummel et al., 1987), and fractal dimension combined with variograms (He et al., 1994). Fractal analysis is a very useful tool for identifying hierarchical size scales of patches in nature, such as how to define bounda ries between hierarchical levels and how to determine scaling rules for extrapolating within each level domain (Sugihara and May, 1990; Milne, 1991; Li, 2000). When natural ‘‘objects’’ like vegetation are not constrained by human activities and land manipulation, or by natural obstacles, they result in highly irregular shapes determined by iterative and diffusive growth, which ca n reproduce at different scales indepen dently of size. In theory, a perfect fractal is self-similar at all scales, and it could be scaled up and down to infinity. Because of these limits to self-similarity, it is preferable to refer to these systems as fractal-like (Brown et al., 2002). Shifts in fractal dimension of irregular patch edges have been used to find substantial changes of spatial patterns at different scales (Krummel et al., 1987; Grossi et al., 1999; 2001). Krummel et al. (1987) were the first to develop a method for detecting different scaling regions in a landscape for a population of forest patches, based on perimeter-area relationships. Grossi et al. (1999) conceived a general statistical procedure to detect objectively the change points between different scaling domains in real patch populations, based on the selection of the best piecewise regression model using a set of statistical tests. Given its significance within the framework of this paper, it seems worth providing a few details. Two distinct basic models were hypothesi zed to fit the data: continuous piecewise linear models and discontinuous piecewise linear models. To estimate the fractal dimension D of each scale domain, we Figure 13.2 Distributions of: (A) Mesobromiom grasslands (CORINE code 34.3266); (B) Brachypodium grasslands (CORINE code 36.334); and (C) lowland hay meadows (CORINE code 38.2) in the Baganza watershed. Copyright © 2005 by Taylor & Francis [...]... SSE ^ where SSE! and SSE are the residual sum of squares of ! and , respectively ^ ^ So, the rejection region can be expressed equivalently as: l > c1 13: 7Þ or F¼ n À p À q SSE! ^ À 1 > c2 SSE q ^ 13: 8Þ We wanted to test different null models H0:! vs alternative models HA:, without knowing the exact distribution of the LR l (in 13. 7) or of the F statistic Copyright © 2005 by Taylor & Francis... fractal dimensions of the first, second, and third domain, respectively; 0 is an intercept and " the error term The simple continuous model given by 13. 1 is called C0, whereas the discontinuous model (13. 2), called D0, is a piecewise regression (Draper and Smith, 1998) with two parallel discontinuous segments and no change of slope, thereby with one single fractal domain Models 13. 3 and 13. 5, called C1... C2, Copyright © 2005 by Taylor & Francis Figure 13. 3 Nested collection of continuous and discontinuous piecewise linear models for hypothesis testing The number of regression parameters to be estimate is between brackets (modified from Grossi et al., 1999) have two and three continuous segments, and one and two breakpoints, respectively Models 13. 4 and 13. 6, called D1 and D2, are models with two and... perimeter-area relationships as suggested by Lovejoy (1982) Given areas and perimeters of n patches, we can write the relationship as follows: Pi ¼ cAD=2 i where Pi and Ai are the perimeter and the area of the ith patch, respectively, and c is a constant Taking the logarithm transform we get: yi ¼ c þ D xi , i ¼ 1, 2, , n 2 13: 1Þ where yi ¼ ln(Pi), and xi ¼ ln(Ai ), so that D is twice the slope of a... Ix> þ 1 x þ " y ¼ 0 þ 01 ðx Ix þ Ix> Þ þ 00 ð Ix 1 13: 2Þ þ x Ix> Þ þ " y ¼ ð 00 þ 01 xÞ Ix þ ð 00 þ 00 xÞ Ix> þ " 0 1 y ¼ 0 þ 01 ðx Ix 11 þ 1 Ix>1 Þ þ 00 ð1 Ix 1 13: 3Þ þ 000 ð2 Ix 2 1 y ¼ ð 00 þ 01 xÞ Ix 13: 4Þ 1 þ x I1 2 Þ þ x Ix>2 Þ þ " 1 þ ð 00 þ 00 xÞ I1 2 þ " 0 1 13: 6Þ where I is an indicator variable equal to one when... number of parameters to be estimated is 2(r þ 1) with one intercept, r breakpoints, and (r þ 1) slopes Let Dr, with r ¼ 0, 1, 2, , be the discontinuous piecewise linear model with r breakpoints, in Dr the number of parameters to be estimated is three (two intercepts and one slope) when r ¼ 0, and 3r þ 2 — one slope and one intercept for each of r þ 1 domains and r breakpoints — when r ! 1 Therefore... each of r þ 1 domains and r breakpoints — when r ! 1 Therefore we can depict a nested collection of models (Figure 13. 3) Which of the nested models is the best is a typical problem of variable selection that, in multiple linear regressions, is usually based on the F test to measure the statistical significance of adding variables If ! and are two nested regression models having the same 2, with p and... regression model by assuming self-affinity (Milne, 1991) — that is, all patches are similarly shaped independently of scale Different hierarchical size scales of patches in nature can be identified by breakpoints, where parameters in Equation 13. 1 change, which can be detected comparing this model to more complex models We considered five alternative models If is the breakpoint of models with one breakpoint, . stream watershed. The potential of such approach for ecosystem health assessment, planning, and management of habitats mosaics is also discussed. 13. 2 RATIONALE 13. 2.1 Ecological Phases, States, and. 0.68, 0.65, 0.61 0.57, 0.56, 0.54 Brachypodium 1.05, 1.11, - 0.87, 0.95, - 0.79, 0.83, - Mesobromiom 1.063, -, - 0.87, -, - 0.79, -, - Copyright © 2005 by Taylor & Francis . CHAPTER 13 Multi-Scale Resilience Estimates for Health Assessment of Real Habitats in a Landscape G. Zurlini, N. Zaccarelli, and I. Petrosillo Vegetation or habitat types are ecological