4 Relations between landscape structure and biodiversity – scientific state of the art

Biological diversity in all its dimensions and facets is always tied to habitats, which need a concrete areal section of the earth’s surface for their existence. Biological diversity is therefore always defined for a certain reference area, and landscape structure is a key element for the understanding of species diversity. Spatial heterogeneity, as an expression of landscape structure, indicates the variability of the system’s properties in spatial terms (Kolasa and Rollo, 1991; Li and Reynolds, 1995). Therefore it is regarded as essential for the explanation of the occurrence and distribution of species from the local to the global level (Ernoult et al., 2003, 240). Against this background, an increasing number of studies analyse the relationship between landscape structure and biodiversity. The goal is to find variables for modelling the spatio-temporal distribution patterns of species and communities (see Bissonette, 1997; Dufour et al., 2006; Schindler et al., 2008*, 503).

4.1 Landscape structure and diversity of species

It is often mentioned in very general terms that the spatial pattern of the landscape influences many ecologically relevant processes, e.g., the distribution of materials and nutrients or the persistence and movement of organisms (Turner, 1989*, 189). But what connections between the type and structure of land cover and biodiversity can be found in the literature? An initial overview is given in Uuemaa et al. (2009, 8–11). Numerous studies have shown such relationships to be determinants of species diversity (Ricotta et al., 2003*, 373). In the following sections, examples from the literature of linkages and variables are given. These are considered important to the relationship between landscape structure and species diversity / patterns of species distribution.


Important preconditions for high biological diversity are the abiotic site conditions and the geomorphology. Habitats with spatially heterogeneous abiotic conditions provide a greater variety of potentially suitable niches for plant species as habitats with homogenous characteristics. Variations in physical structure (e.g., slope direction, soil structure) have proven to be an appropriate factor for the prediction of the richness, diversity and dominance of plant species (e.g., Hobbs, 1988; Lapin and Barnes, 1995; Burnett et al., 1998*; Nichols et al., 1998*; Honnay et al., 2003*, 241). For example, in studies by Burnett et al. in deciduous forests, the sites with high geomorphological heterogeneity were those with the highest plant diversity (Burnett et al., 1998*, 367–368). There, the variances in plant abundance and diversity were explained best by slope direction and the water balance. Because of the strong correlation of the abiotic variables and biological diversity, these factors can be used to predict relative levels of biological diversity (Burnett et al., 1998*, 368).

By contrast, in a landscape like the Central European cultural landscape, the composition and diversity of plant species depend on the structure of use affected by people. With respect to area size, Bastian and Haase (1992*, 27) found that the relationship between the number of plant species and area size can be described with statistical assurance by means of a logarithmic function. With increasing surface area of shrubs, the proportion of typical forest species in the total number of species also increased (Bastian and Haase, 1992*, 27).

The shape of habitats can affect the number of species, too. For a greater number of environmental transitions between irregularly shaped habitats, areas can generally include more plant species (Honnay et al., 1999, 2003*, 241–242). Therefore, shape complexity can be used to analyse land cover data as an index for species richness (O’Neill et al., 1988; Miller et al., 1997), which improves the accuracy of the prediction of plant richness. Geometric landscape complexity proved to be a sensitive indicator of plant richness, especially in agricultural landscapes (Moser et al., 2002*, 666).

In fragmented landscapes, the distance to viable habitats (isolation) also determines the composition and abundance of plant species (Grashof-Bokdam, 1997*; Butaye et al., 2001*). Less isolated habitats are generally more species-rich because they can be easily settled. The constant influx of new individuals prevents local extinction due to demographic and environmental coincidences (Shaffer, 1981; Honnay et al., 2003*, 241).

An increase in the degree of urbanisation (increase in area proportions and sizes of settlements and green spaces, traffic density and shrubs structures) correlates in particular with an increase in the number of species of neophytes, but also with an increase in archaeophytes and indigenous species. The same is true for the increase in border and seam structures in the landscape, which create possibilities for settlement (Deutschewitz, 2001*, 88). Some species are closely related to elements of landscape structure, such as edges, roads and certain land use types (Brosofske et al., 1999*, 212).

Also, in agricultural landscapes, ecotones, which are linear landscape structures between different habitat types, have significant benefits, mainly because they provide habitats after the harvest and for hibernation. Ecotones with high structural heterogeneity, such as forest fringes and hedgerows, provide an improvement, too, for regional biodiversity, as they do for the richness and diversity of beneficial organisms (Duelli, 1997*, 82).

Natural disturbances along streams, the structure and variety of land use in floodplains and natural distribution mechanisms are linked to high biodiversity of indigenous species, but also promote the establishment and spread of neophytes and archaeophytes (Deutschewitz, 2001*, 88).

These relationships can be made comprehensible by means of landscape metrics. Honnay et al. (2003*, 248) were able to show that regional plant variety can be predicted satisfyingly on the basis of relatively simple landscape metrics.


The linkages between wildlife and landscape structure are similar. However, there are differences, in particular due to the mobility of animals. Thus, species with good ability to spread depend mainly on landscape composition, i.e., the proportion of their preferred habitat type. Landscape structure is less important for these mobile species (Visser and Wiegand, 2004*, 59). By contrast, for species with poor dispersal ability, both landscape composition and landscape structure have an arbitrative influence on the frequency of the species. The effect of landscape structure can be reduced to a scale-dependent metric: the average frequency of suitable habitat in a species-specific distance (Visser and Wiegand, 2004*, 59). Edge effects and distances between patches can influence the permeability of a landscape. For example, results by Romero (2007) show the dependence of the migration behaviour of beetles on landscape structure.

The process of fragmentation of landscapes, in the sense of the piece-meal conversion of a formerly contiguous habitat, usually primarily affects animals with relatively large territories – e.g., birds or large mammals. On the other hand, animals with limited mobility are separated into isolated populations more rapidly by such elements as roads or urban structures (Swenson and Franklin, 2000*, 714). However, in science there is no consistent understanding of the term landscape fragmentation (Jaeger, 2002). Today, the term is increasingly used internationally as synonymous for all anthropogenic invasions of landscapes and habitats. By contrast, the German concept of Zerschneidung (lit.: cutting apart), which is usually translated as “fragmentation”, emphasises the network of linear and areal artificial land use elements, such as roads and settlements. This is understood to be an active process which “cuts” spatial connections and interrupts functions. Such land use changes caused by the demand for land for settlement and landscape fragmentation are currently seen as a major cause of the continuing loss of biological diversity worldwide (SRU – Sachverständigenrat für Umweltfragen, 2005, 52). Several landscape metrics are now used for the measurement of landscape fragmentation through infrastructure (Walz and Schauer, 2009). The most widespread fragmentation metrics are the number and size of unfragmented areas with low traffic (UVR) (Lassen, 1979*; Bundesamt für Naturschutz, 2008*) and of effective mesh size (meff) (Jaeger, 2000*).

In many cases, the direct loss of habitats or ecosystems is probably the superior predictor (Strand et al., 2007*, 147–148), even ahead of landscape fragmentation, because habitat size and diversity play an important part. Certain species prefer more diverse territories (greater number of patches, smaller size, more edges), as demonstrated by Fernández et al. (2007*, 437), e.g., for the Iberian lynx or the ocelot (Jackson et al., 2005, 733). For bats, relationships between patch size and patch density have been shown in forest areas. Thus, the species richness of bats was highest in partially deforested landscapes (Gorresen and Willig, 2004, 688). Bees also need specific habitat combinations that can be described using landscape metrics. This makes it possible to predict the potential diversity of bees (Bailey et al., 2007*, 470).

The shape of patches may play a role, too. It was shown for the ruffed grouse (Bonasa umbellus) that regularly shaped patches are preferred (Fearer and Stauffer, 2003, 109). Overall, it is clear that animals can react differently to habitat diversity. Different scales have to be taken into account. The identification of such scales remains a key objective in landscape ecology (Turner, 2005*, 329).

Ecotones or edges, as transition zones, are often particularly rich in species. In studies of edge biotopes in the agricultural landscape of Saxony-Anhalt, species numbers were almost twice as high as those within the fields. The species composition and dominance of edge biotopes were very different from those in the fields. Introducing edge habitats to forests can also affect the fauna. In such cases, species richness may temporarily increase due to migration of specific edge species, but only at the expense of species of the forest interior. Therefore, birds can be useful as an ecological indicator (Noss, 1983*, 702).

The environment of the habitats, i.e., the context and surrounding landscape matrix, plays an important part. For the management of grassland birds, for example, it is important to include quantity and context of the embedded habitats, i.e., the surrounding matrix as well as food resources (Hamer et al., 2006, 581).

The degree of disturbance by landscape change and other factors of human influences in the surroundings have a significant impact on species richness. Thus, for the correlation of birds communities with road density and forest area, the distance to the nearest built-up area, the density of human settlement, and the degree of imperviousness were found to be significant factors (Sundell-Turner and Rodewald, 2008*, 223).

Geomorphological diversity also emerged as a significant impact for the fauna. Due to the mobility of animals, however, it appears to be less limiting than it is to plants. However, many animal species depend on certain plant species (Burnett et al., 1998*, 368).

Habitat modelling

Landscape metrics are also used for habitat modelling of individual species or species groups, e.g., by Dormann et al. (2004*); Fauth et al. (2000); Fernández et al. (2007); or Grillmayer (2000). For example, Steiner and Köhler (2002) were able to show the existence of a clear dependence of the species diversity on landscape structure in model experiments. With a decreasing degree of landscape heterogeneity in the model, both local and regional species diversity also decreased. The importance of considering space, habitat structure and landscape patterns is illustrated by Dormann et al. (2004, 70–71).

Results on linkages between landscape structure and species

In the literature analysed, the following properties of landscape patterns that have a positive effect on biodiversity were mentioned:

  • a high proportion of semi-natural biotope types;
  • large areas;
  • high biotope diversity;
  • high structural diversity;
  • high connectivity;
  • high geomorphological diversity.

However, some of these properties are mutually exclusive (for example, high structural diversity and large surface area of individual patches) (Zebisch, 2004*, 27). In addition, properties that are beneficial for a single species can be definitely disadvantageous for another. Depending on the specific characteristics of the organisms, and depending on the spatial scale, the effect of landscape structure on the viability of the organisms can vary greatly (Visser and Wiegand, 2004, 62). No clear assignment to a quality (e.g., “high structural diversity is desirable”) is possible (Zebisch, 2004, 27).

Furthermore, land use in and of itself may be not sufficient to predict species richness and distribution. In studies by Cardillo et al. (1999, 432–433), it explained less than half of the species richness and occurrence. Therefore, a set of variables should be used, which includes the land use in conjunction with such other landscape characteristics as habitat structure, composition of vegetation and soil characteristics. However, particularly in cultural landscapes, the influence of land use on patterns of species distribution could be greater than the influence of the original and natural landscape parameters. For investigations at the regional level, land use can be crucial for species composition and richness (Deutschewitz, 2001, 78).

All in all, Duelli (1997*, 88) and many other authors (e.g., Bailey et al., 2007*; Ortega et al., 2004) hold that the evaluation of patterns of the landscape mosaic can serve as a substitute for the recording of regional biological diversity, as a form of knowledge-based assessment. In general, broad environmental diversity leads to high species diversity (Ricotta et al., 2003, 373). Size, surface area and spatial relationships between patches thereby play an important role (Dale et al., 2000, 639). In accordance with the “mosaic” concept, regional biodiversity depends mainly on such structural parameters as habitat diversity or landscape heterogeneity, and the dynamics of meta-communities (Duelli, 1997*, 81). As relevant measures, he mentions the diversity and heterogeneity of habitats, and the portions of natural and semi-natural habitats (see Table 2). Thereby it is assumed that such areas rather have a great diversity of habitats due to their size and therefore also greater biological diversity (Dramstad et al., 1996*; Botequilha Leitão et al., 2006*, 11). Also Honnay et al. (2003*, 248) come to the conclusion that landscape metrics appear to be suitable to predict biotic processes. Therefore, according to Duelli (1997*), the assessment of biodiversity at a higher, integrated level can be based on landscape parameters.

4.2 Landscape metrics for monitoring biodiversity

Since the complexity of biological diversity is difficult to describe, most ecologists have taken the practical way to research and to identify the biological diversity at the species level (Feest et al., 2010*, 1078). Therefore, the selection of structural indicators was undertaken specific to the habitat type or tested species studied. Local data on species diversity can provide information as a proxy for regional biodiversity. An investigation of flora and fauna is, however, typically not comprehensive, but rather generally covers only a small proportion of all species. The clear determination of the diversity of various taxonomic groups requires very high efforts, knowledge and money. Hence a good substitute is needed. By combination of indicator species and groups with spatial environmental data (Heino, 2010, 112) and landscape structure, the power and deputy information can be increased and expanded geographically (Faith et al., 2003, 317).

Which parameters are suitable for the characterisation and description of landscape diversity, and can therefore be used as an indicator for biodiversity? In principle, a few indicators are sufficient to ascertain landscape patterns (Riitters et al., 1995; Cain et al., 1997; Lausch and Herzog, 2002, 13). However, biodiversity cannot be described only by a simple number, as there are various qualities of spatial patterns (Tischendorf, 2001; McAlpine and Eyre, 2002; Neel et al., 2004). A selection of indices representing various aspects of biodiversity is much more informative and capable of interpretation (Feest et al., 2010, 1080). However, the use of many highly correlated indices provides no new information, and leads to problems in interpreting the results (Jones et al., 2001; Li and Wu, 2004). For this reason, mutually independent indices should be selected (Schindler et al., 2008, 503).

By means of indicators in monitoring, dramatic changes in values can be detected and serve as an early warning, and as an indication of the necessity for deeper investigation, even if no specific limit values can be defined (Bock et al., 2005*, 336). Landscape metrics may also be used to identify hot spots of biodiversity in rural Europe. Although they do not replace direct measurement of species biodiversity, these surveys can help make them more effective and less costly (Bailey et al., 2007*, 472). Often mentioned as possible parameters are distribution, abundance and area proportions of land use types (e.g., Schüpbach et al., 1999, 212). Other aspects are the richness (number of land use types) and the uniformity of the landscape (Nagendra, 2002, 178).

Indicator systems

Due to the importance of landscape structure for biodiversity, there are currently a number of activities to develop indicators for monitoring biodiversity at the level of ecosystems or landscapes (EEA, 2007, 2005; BMU, 2007*). Ideally, the same biodiversity indicators should be used at the global, national, regional and local level. However, this is not possible for practical reasons. The specific requirements for monitoring and for financial resources vary from country to country. Many monitoring systems have their own historical developments and even the methods for the same indicator differ from place to place (Strand et al., 2007*, 17).

In Germany there are a number of indicator systems for monitoring land use change and biodiversity (see also Table 1):

Table 1: Selected indicators reliable to biodiversity and land use change in Germany and their use in the different indicator systems

Indicator set / Institution

Sustainable development in Germany

Core environmental indicators

Indicators of the German states

National strategy on biodiversity

Dissection of the landscape (Landscape fragmentation)




Urban sprawl



Natura 2000 area designations




Size of strictly protected areas




Land use: Increase in land used for housing and transport





Recreation areas


Species diversity and landscape quality





However, there is still no complete and interoperable, nationwide monitoring system for biodiversity at the federal level. An earlier approach that could have served that purpose was so-called “ecological area sampling” (ökologische Flächenstichprobe) (Dröschmeister, 2001), whereby indicators of cultural influence and intensity of use, rarity or threat of habitats, and structural diversity were to be surveyed at the landscape and habitat levels in defined test areas (Hoffmann-Kroll et al., 1995, 595, see also Table 2). Complementarily, the Shannon Diversity and Evenness and the Fractal Dimension were proposed (Back et al., 1996, 21–33). Unfortunately, this concept was never fully implemented nationwide.

Table 2: Indicators of quality of landscape structure in the framework of spatial ecological sampling (Dierßen and Hoffmann-Kroll, 2004, 291–293).

Superordinate issue

Special issue


Use intensity

Naturalness / hemeroby

Surface areas of natural and semi-natural habitat types [in %]

Degree of sealing

Proportion of sealed surface [in %]

Erosion risk caused by water, depletion of arable soil

Proportional area of arable land, viticulture and intensive woody plants with slope > 9%

Fragmentation and isolation of habitats

Total length of all roads (5 m wide) outside of settlements [in m/sq. km]

Structural diversity

Habitat diversity / diversity of living conditions

Number of non-technical habitat types per sq. km

Monotony of living conditions

Average size of parcel of arable land and vineyards [in ha]

Density of linear refuges and wildlife dispersion axis

Length of linear elements/ edge structures (hedges, forest belts, tree rows, avenues, seams) per sq. km

Density of small habitats as refuges and dispersal centres for wild species

Number of small habitats (< 400 sq. m) per sq. km

(tarns, ponds, springs, rocks, trees, individual trees, small trees, etc.)

Density of small-scale stepping stones and network structures for species with low range of action

Mean number of quadrants per sq. km, in which structural elements occur

Diversity of selected species groups

Average number of bird or butterfly species per sq. km

Rarity / threat

Occurrence of rare and endangered habitats of wildlife species

Percentage of endangered habitat types (according to Red List or Habitat Directive) [in %]

Stachow (1995*) has proposed a system of indicators for monitoring agricultural landscape change. As a complex of factors important for the formation of communities, he mentions the natural conditions of the site (terrain, climate and soil type) and the type and intensity of human impact. The indicator system, therefore, is composed of three landscape indicators: the physical or natural diversity of landscapes, the diversity of land use, and the naturalness of land use. He starts from the assumption that increasing naturally the animation of the terrain is associated with an increase in various site conditions. Based on the criteria “length of contour lines in m/ha per community”, “height difference between the highest and lowest contour lines in the community”, and “river length and area of surface waters”, he arrives at statements regarding natural spatial diversity (Bork et al., 1995*, 290). The variety of land use is identified based on the diversity of major land use types, length of forest fringes, field sizes and variety of crops within agricultural areas. The degree of naturalness is derived from natural conditions of sites, and the situation regarding crops (Bork et al., 1995, 292–293).

A number of authors have emphasized the importance of landscape diversity as an indicator of species diversity in monitoring agricultural landscapes. In addition to land use practices, especially habitat heterogeneity plays an important part. It has often been noted that even using a few landscape and land use parameters, inferences can be made regarding large-scale patterns of species diversity (Benton et al., 2003; Tews et al., 2004; Billeter et al., 2008, 141–142).

In Germany, the avifauna is used as a nation-wide indicator of biological diversity at the species level (BMU, 2007; Sukopp, 2007). This indicator is contained both in the national set of sustainability indicators (Federal Government, 2002) and in the set of indicators for the national biodiversity strategy. For the calculation of the indicator, trends in the stocks of 59 selected bird species are recorded, representing the most important landscape and habitat types and land uses in Germany (agricultural land, forests, settlements, inland waters, coasts and seas, and the Alps). The size of the stocks should directly reflect the suitability of the landscape as a habitat for selected bird species. However, the condition of the landscape (structure and intensity of uses) is not registered.

In the case of landscape fragmentation by infrastructure, nationwide regular monitoring takes place. The German Federal Agency for Nature Conservation (BfN) regularly determines unfragmented open spaces equal to or larger than 100 sq. km. Also, “effective mesh size” meff (see above) has in recent years been applied in several German states, including Baden-Wurttemberg and Hesse, and is now established as a core indicator in the environmental system of indicators (LIKI, 2011).

At the European level, the European Environment Agency already in 2000 submitted a report on Landscape Diversity in the EU (EEA, 2000*), in which landscape indicators for fragmentation, diversity or heterogeneity, and spatial arrangement and organisation of landscapes were used. The landscape metrics applied were: Patch Density (PD), Edge Density (ED), Perimeter/Area Ratio (PAR), Number of Classes (NC), Shannon’s Diversity Index (SHDI), the Interspersion and Juxtaposition Index (IJI), and the Land Cover Diversity Index (LCDI).

As part of the EU project SPIN (Spatial Indicators for European Nature Conservation) (Bock et al., 2005*), the potential of landscape metrics for pan-European nature conservation was explored, especially for the Natura 2000 network. Thereby, landscape metrics were applied, e.g., for the determination of the size of the ecologically effective protected areas. For this purpose, indices such as TCA/TCCA (total core area and total class core area), NCA (number of core areas) and CAI use (core area index) were used.

In a joint project for Cultural Landscape Research in Austria, landscape metrics were calculated nationwide (Wrbka, 2003). They have been used, for example, in the fields of landscape composition, habitat area, landscape configuration, ecological functions, habitat fragmentation, diversity and anthropogenic influence. For biodiversity monitoring in South Tyrol, five indicators were selected by experts to measure both heterogeneity of landscape structure and human impact (Tasser et al., 2008*, 208). These include an index of landscape diversity (EEA, 2000), effective mesh size (Moser et al., 2007), hemeroby (Steinhardt et al., 1999), naturalness of near-river areas (adapted from Xiang, 1996) and agricultural intensity (UNEP, 2001). Tasser et al. (2008*, 208) expanded this set by two indicators of species diversity: the area-weighted richness of vascular plants, and the frequency-weighted absolute species richness of vascular plants.

In the United States, the Heinz Center developed landscape indicators, of which eight refer to the landscape structure (Heinz Center, 2008). They are to be used to identify large-scale landscape patterns and human-induced landscape changes at the national level.

Metrics for monitoring

The monitoring of biodiversity is carried out almost solely at the level of species diversity, primarily on the basis of species richness, mostly using surrogate species or groups (especially birds and vascular plants). In the last few years, however, doubts as to the suitability of species or species groups for the estimation of biodiversity have increased. The criticism has concerned, in particular, conclusions drawn from the recording of species regarding the diversity of organisms of other taxa, or at other scales (spatial requirements etc.).

As a result, the focus has been directed towards the importance of landscape diversity for the expression of biological diversity. Increasingly, approaches and indicators for this level of biodiversity are being developed, especially for landscape diversity in agricultural and rural landscapes. It should be noted, however, that despite the presence of previous approaches, indicators of landscape and environmental diversity are not included in the indicator system of the United Nations, or in the German National Biodiversity Strategy. Here, there is a clear need to catch up.

Reference is often made to the potentials of remote sensing for cost-effective collection and presentation of landscape diversity. In particular, due to sophisticated sensor technology and resolution, as well as better availability of data, remote sensing, in combination with climate and environmental data, could lead to a more precise characterisation of landscape diversity, and thus a better assessment of species diversity.

Several examples from the great variety of landscape metrics found in the literature analysed have been compiled; these are repeatedly mentioned, or stand out as particularly significant (Table 3).

It is obvious that landscape metrics must always be selected for different tasks or problems, and in accordance with the available resources. A single index, or always the same set of indices, is not automatically appropriate for all study objects. Similarly, because of their complexity, a combination of indices should generally be preferred to individual indices for the estimation of biodiversity.

Table 3: Important landscape metrics in the field of biodiversity




Prediction and assessment of biodiversity in landscape mosaics of the agricultural landscape

(1) habitat diversity (number of habitat types per unit area)

(2) habitat heterogeneity (number of habitat patches, lengths of ecotones per landscape unit)

(3) portions of natural, semi-natural and intensive land used

(Duelli, 1997, 88)

Prediction of biodiversity

Surface area of semi-natural ecosystems

Patch distribution, edge and patch density

(Dramstad et al., 1996; Botequilha Leitão et al., 2006*, 11)

(Bailey et al., 2007*, 466–467)

Prediction of species diversity

Patch Density PD, Largest Patch Index LPI, Simpson’s Diversity Index SIDI, Proximity PROXMN, Patch Richness PR, Edge density ED, Euclidean Nearest Neighbour ENNCV, Circumscribing Circle: CIRCMN

Number of species, population sizes, number of viable populations and habitat area

Landscape diversity, intensity of agricultural use, frequency weighted absolute species richness of vascular plants

(Bailey et al., 2007, 466–467)

(Strand et al., 2007, 121)

(Tasser et al., 2008*, 219)

Planning of biotope networks

Proximity Index (allows assessment of individual patches depending on functional connection with surrounding habitats)

Density of landscape elements, indices of connectivity/ isolation

(Kiel and Albrecht, 2004*, 331)

(Baguette and Van Dyck, 2007*, 1125–1126)

Assessment of protected areas, habitat requirements of species of the core areas and edges

Total Core Area TCA,

Total Class Core Area TCCA,

Number of Core Areas NCA,

Core Area Index CAI,


(Bock et al., 2005)

Landscape fragmentation

Effective mesh size

Area of unfragmented open spaces

(Jaeger, 2000)

(Lassen, 1979; Bundesamt für Naturschutz, 2008)

Quantification of the floristic diversity (habitat function)

Shannon Diversity SHDI,

Number of different classes and their distribution

(Herbst et al., 2007*)

Smallness, shape richness as well as structuredness of a landscape (natural spatial diversity)

Edge density ED,

Density of patch boundaries or linear elements in a landscape

Length of contour lines per area, elevation difference between highest and lowest point, river length and area of surface waters

(Herbst et al., 2007*)

(Stachow, 1995*)

Diversity of land use

Diversity of main land use types, length of forest edges, field sizes

(Stachow, 1995)

Floristic species richness


Distance (isolation) to usable habitat, largest patch index LPI, patch size coefficient of variation PSCV

(Grashof-Bokdam, 1997; Butaye et al., 2001)

(Banko et al., 2000, 28)

Floristic species richness

(in natural ecosystems)

Topographic and edaphic variables, in particular slope direction and water balance

Shape complexity of the habitats

(Burnett et al., 1998*, 368)

(Honnay et al., 2003, 241–242)

Floristic species richness

(in landscapes)

Surface area of land use,

Geometric landscape complexity, Number of Shape Characterizing Points NSCP

Length of edges

(Bastian and Haase, 1992*, 27)

(Moser et al., 2002, 666)

(Bastian and Haase, 1992, 27)

Faunal species richness

Road density, forested area, distance to nearest built-up area, density of human settlements, degree of soil imperviousness

(Sundell-Turner and Rodewald, 2008*, 223)


Büchs et al. (2003) summarise limitations which occur in relation to the use of indicators for biodiversity monitoring. First, it must be noted that there is no indicator for biodiversity as a whole. Every aspect of biodiversity also requires its own indicator with very specific and well-defined characteristics, with agreed-upon definitions for their use (Tasser et al., 2008, 205). Furthermore, the classification of land use and habitat type mapping must be considered precisely. Default typifications may not necessarily be suitable for a specific question. Often, visually delimitable units are equated with functional structures and habitats (Filip et al., 2008*, 534–535).

The indices used must also be questioned. In particular, the Shannon Index is used almost as a “standard” for large-scale landscape analysis (Filip et al., 2008*, 536). However, it does not reflect the spatial distribution of classes, although this is crucial for the diversity of a landscape. “Thus, it is irrelevant to the result value of the index whether the landscape elements present a large area or a mosaic, even though this factor should be crucial for the diversity of a landscape.” (Filip et al., 2008, 536). Moreover, even at the species level, the Shannon or the Simpson index is generally considered as not useful for large-scale monitoring of the integrity of biological diversity (Lamb et al., 2009, 439). Another problem, not only in the field of biodiversity, is that the selection of indicators is often driven by the availability of information. However, with respect to biodiversity, this can lead to delusive or adverse results (Failing and Gregory, 2003*, 129).

A review of the literature makes it clear that a wide variety of indicators and systems is now available which are usually hardly comparable to one another. Especially in the field of sectoral indicators, a large number of other systems can be expected, which – and this seems to be an underlying trend – have been developed and used relatively independently of one another (Müller and Wiggering, 2004, 122).

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