Abstract The objective of this paper is to

investigate the relationship between landscape

pattern metrics and agricultural biodiversity at

the Temperate European scale, exploring the role

of thematic resolution and a suite of biological

and functional groups. Factor analyses to select

landscape-level metrics were undertaken on 25

landscapes classified at four levels of thematic

resolution. The landscapes were located within

seven countries. The different resolutions were

considered appropriate to taxonomic and func-

tional group diversity. As class-level metrics are

often better correlated to ecological response, the

landscape-level metric subsets gained through

exploratory analysis were additionally used to

guide the selection of class-level metric subsets.

Linear mixed models were then used to detect

correlations between landscape- and class-level

metrics and species richness values. Taxonomic

groups with differing requirements (plants, birds,

different arthropod groups) and also functional

arthropod groups were examined. At the coarse

scale of thematic resolution grain metrics (patch

density, largest patch index) emerged as rough

indicators for the different biological groups

whilst at the fine scale a diversity metric (e.g.

Simpson’s diversity index) was appropriate. The

intermediate thematic resolution offered most

promise for biodiversity monitoring. Metrics in-

cluded largest patch index, edge density, nearest

neighbour, the proximity index, circle and Simp-

son’s diversity index. We suggest two possible

applications of these metrics in the context of

biodiversity monitoring and the identification of

biodiversity hot spots in European agricultural

landscapes.

Keywords Temperate Europe Æ Landscape

structure Æ Functional biodiversity Æ Vascular

plants Æ Arthropods Æ Birds Æ Biodiversity hot spots

Introduction

Biodiversity in agricultural landscapes is at risk

(e.g. Krebs et al. 1999; Stoate et al. 2001; Robin-

son and Sutherland 2002). As a result there is an

urgent need for monitoring the status and evolution

D. Bailey (&) Æ S. Aviron Æ F. HerzogAgroscope Reckenholz-Tanikon Research Station(ART), Reckenholzstrasse 191, Zurich CH-8046,Switzerlande-mail: debra.bailey@art.admin.ch

R. BilleterETH Swiss Federal Institute of Technology,Geobotanical Institute, Universitaetsstrasse 16,Zurich CH-8092, Switzerland

O. SchweigerDepartment of Community Ecology, UFZ - Centrefor Environmental Research, Theodor-Lieser-Strasse4, Halle D-06120, Germany

Landscape Ecol (2007) 22:461–473

DOI 10.1007/s10980-006-9035-9

123

RESEARCH ARTICLE

The influence of thematic resolution on metric selection forbiodiversity monitoring in agricultural landscapes

Debra Bailey Æ Regula Billeter ÆStephanie Aviron Æ Oliver Schweiger ÆFelix Herzog

Received: 2 September 2005 / Accepted: 18 August 2006 / Published online: 19 October 2006� Springer Science+Business Media B.V. 2006

of biodiversity (Tscharntke et al. 2005). Ideally,

monitoring methods should be relatively cheap

and rapidly identify biodiversity decline. Direct

monitoring methods are costly however, as they

require extensive field work as well as the use of

multiple species groups with contrasted ecological

requirements. Applications at the landscape, re-

gional or even continental scale are limited due to

the size of such studies and to time-lag effects,

which may mask change.

Hence, there is a need for inexpensive indica-

tors which can act as ‘early warning systems’. The

potential use of metrics which measure the spatial

pattern and composition of landscapes has al-

ready been proposed by O’Neill et al. (1988).

However, choosing appropriate metrics for land-

scape monitoring, let alone for biodiversity

monitoring, is a major challenge. The identifica-

tion of a generic set of metrics which can capture

landscape composition and configuration has

proven to be difficult because the metrics are

computed from interpreted data—i.e. satellite or

aerial photographs or digital maps. The metrics’

values thus not only depend on the landscapes’

characteristics but on a number of decisions made

in preparing the landscape data (see for example

Baldwin et al. 2004; Wu 2004). Metric selection

should also match the ecological processes under

examination and, ideally, requires a priori

understanding of the link between landscape

pattern and the ecological process (Gustafson

1998; Wu and Hobbs 2002).

Still, it is widely acknowledged that biodiver-

sity depends on landscape properties. Therefore,

metrics which are relatively robust with respect to

landscape data acquisition and which measure

independent components of landscape pattern

might predict the functional response of species

to spatial heterogeneity (Li and Reynolds 1994)

and act as general indicators of biodiversity

(Hunter 1990; Gustafson 1998).

The multivariate exploratory technique, factor

analysis, has been used to select metrics that de-

scribe the main independent components of pat-

tern in a landscape (Riitters et al. 1995). During

this process an initial large number of metrics are

reduced to a much smaller set. It is a relatively

unbiased methodology that will identify redun-

dancy and it has been used to explore landscapes

at different scales of thematic resolution, spatial

resolution and spatial extent (e.g. Cain et al. 1997;

Griffith et al. 2000). However, although explor-

atory analyses identify independent components

of landscape pattern and metrics that are relevant

to a landscape, there is still a need to establish

whether they reflect ecological functions.

Whilst the effect of changing spatial resolution

and extent on landscape pattern analysis has been

investigated (e.g. Wu et al. 2002, Wu 2004) the

influence of thematic resolution is still poorly

understood. Landscape pattern and hence the

metrics selected through exploratory analysis are

likely to change depending on the scale of the-

matic resolution (Cain et al. 1997). Bailey et al.

(2006) recently found that grain and dominance

metrics better describe landscapes which were

defined using a low level of thematic resolution

(few land cover classes). Landscapes defined

using a fine scale of thematic resolution (many

land cover classes) were better described using

shape, configuration and diversity metrics. This

means that the affectivity of metrics to monitor

biodiversity will be highly influenced by the way

that the map has been defined as different aspects

of landscape pattern are likely to be relevant

depending on the thematic resolution. Biological

groups might also relate differently to pattern

metrics which have been calculated at different

thematic levels.

In this paper we investigate the relation be-

tween landscape pattern metrics and agricultural

biodiversity at the Temperate European scale,

exploring the role of thematic resolution and a

suite of biological and functional species groups.

We examine twenty five landscapes located

within seven countries and use exploratory anal-

yses to select landscape-level metrics (indices that

measure the aggregate properties of all the pat-

ches belonging to the landscape) which differen-

tiate well between landscapes at four scales of

thematic resolution. To investigate the relation-

ship of metrics with biodiversity, we consider

taxonomic groups with contrasted requirements

(i.e. groups having different ecological strategies

and traits) and also functional arthropod groups.

As class-level metrics (indices that measure the

aggregate properties of patches belonging to a

particular land cover class) are often better

462 Landscape Ecol (2007) 22:461–473

123

correlated to ecological response variables than

their landscape-level counterparts (Tischendorf

2001; Luck and Wu 2002), we complement the

initial landscape-level metric sets by using the

corresponding metrics at the class-level. By doing

so, we hope to derive recommendations for the

use of landscape pattern metrics to monitor bio-

diversity at different levels of thematic resolution.

Methods

Study sites

Twenty-five study sites of 16 km2 each were lo-

cated within seven temperate European countries

using a priori knowledge of local experts (Bel-

gium (4 study sites), Czech Republic (3), Estonia

(4), France (3), Germany (4), the Netherlands (4),

Switzerland (3); for location see Herzog et al.

2006). Selection criteria were that each site should

be predominantly agricultural, relatively flat and

be part of a larger structurally homogeneous

landscape that is farmed at a similar level of

intensity. The sites were selected to cover inde-

pendent gradients of land-use intensity and

landscape complexity. Thus, heterogeneous sites

were not necessarily associated with a low level of

farming intensity as might be expected. The

amount of arable land varied between 44 and

88% of the surface area.

The land cover of the study sites were digitised

in ArcGis 8.1 (ESRI 2003) from recent true col-

our orthophotos (with a spatial resolution < 1m),

and classified using an adaptation of the Euro-

pean EUNIS habitat classification system (Davies

and Moss 1999). Spatial resolution of the maps

was 1 m2, and patch (e.g. arable fields, grassland,

woodland; minimum size 25 m2), line (e.g.

hedgerows, tree rows, grassy margins; minimum

size 1 · 25 m) and point elements (solitary trees)

were digitised. The resulting vector maps (re-

ferred to as HABITAT_47) were classified using

a potential of forty seven EUNIS habitat types

(36 semi-natural habitats, 2 arable habitats, 9

‘non-habitats’ e.g. roads, buildings, surface water;

see Bailey et al. 2006 for more details). The

EUNIS habitats were verified through ground

truthing to ensure map reliability.

To study whether thematic resolution affects

the response of the biodiversity groups to land-

scape metrics, the maps were reclassified using

three coarser classification systems. Namely:

HABITAT_14 (14 classes of main habitat types,

e.g. grassland, woodland, scrubland, hedgerows,

arable land, ‘non-habitat’ (roads, buildings, sur-

face water)), HABITAT_3 (3 classes; herbaceous

landscape elements, woody landscape elements,

arable land and ‘non-habitat’) and HABITAT_2

(2 classes; semi-natural habitats, arable land and

‘non-habitat’).

Taxonomic and functional groups

The biodiversity of the sites was assessed by

measuring the species richness of taxonomic

groups that have different ecological require-

ments and operate at different spatial scales. The

arthropod groups were also divided into func-

tional groups representing body size which re-

flects the mobility of the species (Schweiger et al.

2005). We investigated the taxonomic groups of

herbaceous plants, birds, and five arthropod taxa:

wild bees (Apoidea), true bugs (Heteroptera),

carabid beetles (Carabidae), hoverflies (Syrphi-

dae) and spiders (Araneae).

Herbaceous plant species richness was esti-

mated for each study site. A stratified random

sampling scheme was used to locate 4 m2 plots

(between 200 and 250 per site) in all available

habitat types. The plots were located within the

arable fields and linear semi-natural elements and

along the edge of semi-natural patch elements

using a sampling ratio of 1:5:4. Species presence

and cover-abundance (Braun-Blanquet, Bonham

1989) was recorded.

Birds were surveyed in five of the nine

1 km2 grid cells of each site (i.e. the 9 central cells

of the 4 · 4 1 km2 grid) using a checkerboard

design. Four observation points were selected

within each of these five cells. Bird sighting and

song were recorded at every point for 5 min in

April, May and June within a period starting half

an hour before dawn and ending 2 h after sunrise.

The data were sorted into the number of species.

Arthropods were sampled in the sixteen 1 km2

grid cells of each site. In each grid cell, two trap

units, each consisting of a pitfall trap and a

Landscape Ecol (2007) 22:461–473 463

123

combined flight trap (a combination of a glass

window and a yellow pan trap, see Duelli et al.

1999) were placed in an ecotone between semi-

natural habitat and arable land. The trap units

were from 25 to 50 m apart from each other and

were sampled according to Duelli (1997). The

arthropods were identified to species level and

divided into taxa. Subsequently, the arthropods

were classified into functional groups according to

body size (large, medium, small, very small). For

detailed information of the sampling and classifi-

cation of the arthropod species see Schweiger

et al. (2005).

Metric selection

The vector map of each study site was converted

into raster format. Forty-one common landscape-

level metrics and, where available, their equiva-

lents at the class-level (habitat) were then

calculated in FRAGSTATS 3.3 (McGarigal et al.

2002). Five main aspects of landscape structure

were represented by these indices, namely ‘grain’,

‘edge’, ‘shape’, ‘configuration’ and ‘diversity’.

To obtain a manageable subset of landscape-

level metrics, which described the most relevant

landscape pattern for each level of thematic res-

olution, an exploratory approach (Riitters et al.

1995) was adopted. The selection procedure for

the landscape-level metrics was the same for each

scale of thematic resolution. Firstly, the metrics,

whose values did not vary between study sites,

were removed from the analyses. This resulted in

several diversity metrics (patch richness, patch

richness density, relative patch richness) being

removed from the analyses at the HABITAT_2

and HABITAT_3 scales of resolution. The nor-

mal distributions of the remaining metric values

were then examined and as a result one German

site was identified as an outlier and rejected.

Spearman’s correlation was subsequently calcu-

lated for every possible pair of metrics at the

different levels of thematic resolution in order to

identify the strongly correlated, redundant met-

rics. Metric pairs with a correlation coefficient of

0.9 or above were identified and one metric was

dropped. The decision of which metric to retain

was based upon the ease by which the metrics

could be interpreted and whether an ecological

meaning had been reported in the literature.

Factor analyses for each level of thematic reso-

lution were then conducted using the varimax

rotation method with Kaiser normalisation. The

landscape-level metric with the highest loading on

the factors with an Eigenvalue ‡1 was selected as

a subset metric. This procedure resulted in four

landscape-level metric subsets (Table 1; see also

Bailey et al. (2006) for more details).

In a second step class-level metrics were se-

lected using the results of the factor analyses at

the landscape-level as a guide. Subsets were

formed by selecting the equivalent metrics at the

class-level that were present in the landscape-

level subset. Separate class-level subsets were

formed for the habitat types that were being

investigated at the different scales of thematic

resolution. Hence, at the HABITAT_2 level, one

class-level subset was selected for the ‘semi-

natural habitats’ and one for the ‘arable land and

non-habitat’ whilst at the HABITAT_3 scale,

subsets were selected for ‘grassland’, ‘woodland’

and ‘arable land and non-habitat’. At the HAB-

ITAT_14 scale of resolution, class metric subsets

were only selected for the three main habitat

types (arable land, grassland, woodland) as they

were the only habitats to be present in all study

sites. Subsets were not selected for HABITAT_47

as there was only one common habitat type

(arable land) found in each site. All of the other

more common habitat types (e.g. mesic grassland,

deciduous woodland, hedgerows) were not avail-

able in each site. This led to the problem of too

small sample sizes and that these habitat types

were neither representative of all study sites nor

of the biodiversity of a particular study site. Thus,

this scale of resolution was excluded from the

subsequent class-level analyses.

Statistical analysis

The species richness of each taxonomic and

functional group per study site was used in the

statistical analyses. Firstly, the landscape-level

subsets were analysed for the four different scales

of thematic resolution. Linear mixed models were

created in SPSS to detect effects between the

landscape-level subset metrics and the species

richness values of the different taxonomic and

464 Landscape Ecol (2007) 22:461–473

123

functional groups. Separate models were created

for every taxonomic and functional group at each

level of thematic resolution. To correct for the

geographical range of our study sites, country was

treated as a random factor. In the herbaceous

plant models, the numbers of sampling plots (log-

transformed) were included due to some variation

between study sites. The data for the wild bees,

hoverflies, true bugs and all arthropod functional

groups were log-transformed to reach normality.

The metric ‘proximity’ was also log-transformed

when present in a subset.

A stepwise backward model building proce-

dure was used starting with the full model

including all landscape-level subset metrics and

then excluding step-by-step the landscape vari-

ables which were not significant. The procedure

was stopped when all metrics remaining in the

model were statistically significant (P < 0.05) or

when all landscape variables had been removed

from the model. The Akaike’s Information Cri-

terion (AIC) was used to select the best model for

each taxonomic and functional group (Burnham

and Anderson 2000). The models with the best

AIC (i.e. the smaller the value the better the

model) did not necessarily contain metrics with

significant terms.

In a second step, the procedure was repeated

using the class-level subsets. At each scale of

thematic resolution, a separate model was created

for each taxonomic and functional group and only

the subset metrics for one habitat type were

included in any particular analysis.

Results

Landscape-level metrics

Very few landscape level metrics demonstrated a

significant correlation to the biodiversity data

(Table 2) at the coarser scales of thematic reso-

lution (HABITAT_2, HABITAT_3) with effects

only being observed in the perhaps more robust

groups (herbaceous plants, large-sized arthro-

pods). The grain metrics, patch density and larg-

est patch index, were the only metrics to correlate

significantly to the biodiversity data at these lev-

els. Higher levels of patch density (i.e. a more

patchy landscape) indicated greater species

Table 1 Landscape-level metric subsets identified through factor analysis for the different scales of thematic resolution

Metric type Acronym Thematic resolution (++ metric present insubset)

_2 _3 _14 _47

Grain metricsPatch Density: PD ++ ++Largest Patch Index: LPI ++ ++ ++Edge metricEdge Density ED ++ ++ ++Configuration metricsProximity Index Distribution: PROXMN1 ++

PROXCV2 ++Euclidean Nearest Neighbour Distribution: ENNMN ++

ENNCV ++Shape metricsPerimeter Area Ratio Distribution: PARACV ++Shape Index Distribution: SHAPEAM 3 ++Related Circumscribing Circle Distribution: CIRCMN ++ ++

CIRCCV ++ ++ ++Diversity metricsPatch Richness: PR ++Simpson’s Diversity Index: SIDI ++ ++

MN1, mean; CV2, Coefficient of Variation; AM3, Area-Weighted Mean

(see http://www.umass.edu/landeco/research/fragstats/documents/fragstats_documents.html for description of the metrics)

Landscape Ecol (2007) 22:461–473 465

123

richness for the herbaceous plants but a decrease

in species richness for the large-sized arthropod

group. An increase in the largest patch index (i.e.

an increase in the proportion of landscape domi-

nated by a particular patch) consistently resulted

in a decline in species richness.

At the medium scale of thematic resolution

(HABITAT_14) many more metrics were signif-

icantly correlated to the biodiversity data

(Table 2). With relatively few exceptions (birds,

spiders, carabid beetles, medium-sized arthro-

pods), effects were observed for most taxonomic

and functional groups. The most significant vari-

ables were Simpson’s diversity index (reflecting

landscape diversity), proximity and nearest

neighbour (landscape configuration) and the cir-

cle metric (patch shape). The grain metrics

(largest patch index, edge density) only showed

significant correlations to the wild bees (LPI, ED)

and large-sized arthropods (ED).

Increases in species richness were associated

with higher values of the Simpson diversity index

(i.e. more habitats), the coefficient of variation of

the nearest neighbour metric (i.e. a more heter-

ogeneous distribution of patches in the landscape)

and the mean of the shape metric, circle (i.e.

landscapes with more elongated patches). De-

creases in species richness were associated with

higher values for the largest patch index, edge

density (i.e. a higher amount of edges) and the

mean value of the proximity metric (i.e. a land-

scape increasingly occupied by one patch type,

which is more contiguous in distribution).

At the finest scale of thematic resolution

(HABITAT_47), very few landscape level metrics

showed a significant correlation to the biodiversity

Table 2 Correlation of landscape level metrics with the species richness of taxonomic plant and animal groups and withfunctional arthropod groups (body size) at different scales of thematic resolution

Taxonomic Group HABITAT_2 HABITAT_3 HABITAT_14 HABITAT_47

Metrics +/– F-ratio Metrics +/– F-ratio Metrics +/– F-ratio Metrics +/– F-ratio

Plants LPI – 5.95* LPI – 3.27 SIDI + 5.54*

PD + 6.54* PD + 11.27**

Birds ED + 3.15Wild bees LPI – 3.29 LPI – 12.76** PR + 15.20***

ED – 11.67**

ENNCV + 8.70*

CIRCMN + 12.08**

SIDI + 16.91***

SpidersHoverflies LPI – 3.34 LPI – 3.31 ENNCV + 10.39**

SIDI + 11.86**

Carabid beetles CIRCV – 3.52SIDI + 3.59

True bugs LPI – 3.72 LPI – 3.43 ENNCV + 16.31*

PROXMN – 16.21SIDI + 16.30**

Large LPI – 5.57* LPI – 9.13** ED – 17.55*** SIDI + 4.93*

PD – 15.33** PD – 15.88** ENNCV + 14.03***

CIRCMN + 23.06***

SIDI + 25.61***

Medium PD – 3.86 CIRCCV – 3.30 CIRCMN + 3.42 PROXMN + 3.44CIRCMN + 3.98

Small ENNCV + 5.08*

PROXMN – 4.15SIDI + 3.17

Very Small ENNCV + 8.48** ENNMN – 10.72**

PROXMN – 6.11*

SIDI + 4.67*

The significant metrics (P < 0.05, highlighted in bold) and also trends were taken from the best model (lowest AIC). F-ratiois directly proportional to explained variance and indicates which significant metrics explain the most variation in theindividual models. +/- indicates positive/negative correlation. Significant levels * £ 0.05, ** £ 0.01, *** £ 0.001

466 Landscape Ecol (2007) 22:461–473

123

data (Table 2). Higher values of the diversity

metrics, Simpson’s diversity index and patch

richness, were associated with increases in species

richness for the wild bees and large-sized arthro-

pods. A decline in species richness for the very

small sized arthropods was associated with higher

values for the mean of the nearest neighbour

metric, i.e. greater mean distances between pat-

ches in the landscape

Class-level metrics

At the coarser scales of resolution (HABITAT_2

& HABITAT_3), the significant correlation ob-

served for taxonomic and functional groups to the

various landscape-level metrics were reflected

and further explained by their class-level equiva-

lents by indicating which habitat type was causing

the increase or decrease in species richness

(Table 3). For example, landscape-level metrics

were correlated to an increase (high patch

density) or decrease (high largest patch index) in

the species richness of the plant taxonomic group.

At the class-level the correlation was further

explained by the ‘arable land and non-habitat’

equivalents of these metrics (Table 3).

Effects observed at the HABITAT_3 scale of

resolution were not only a reflection of the land-

scape-level metrics. With the exception of the

functional arthropod groups where only the large-

sized arthropods correlated with the largest patch

index, many more taxonomic groups showed

correlations with class-level metrics compared to

the landscape-level equivalents (Tables 2 and 3).

Metrics describing edge and shape characteristics,

in addition to the grain metrics at the landscape-

level were also observed to correlate to the tax-

onomic data (Table 3). Also at the HABITAT_14

scale of resolution many more class-level metrics

were associated with a significant correlation to

the taxonomic (but not functional) groups (Ta-

ble 3). Metrics were associated with every taxo-

nomic group with the exception of spiders. The

class-level metrics allowed a better understanding

of the correlation of the taxonomic group ob-

served for the landscape-level metrics by identi-

fying particular habitat types. The increase or

decrease in species richness according to the in-

crease or decrease in the value of the metric was

normally consistent between landscape and class

level metrics. The circle metric was the main

exception. At the landscape-level, an increase in

the mean of this metric indicated an increase in

species richness. However, at the class-level,

higher values of this metric were related to a

decline in species richness regardless of habitat

type (i.e. arable land, grassland, woodland).

The actual habitat type(s) of the metric that

was found to correlate to a particular taxonomic

or functional group was observed to vary with the

biological group and the level of thematic reso-

lution (Table 3). At the coarser scales of resolu-

tion the habitat type most frequently observed to

correlate to the taxonomic or functional group

was the ‘arable land or non-habitat’. At the

HABITAT_14 level of resolution, woodland

metrics were associated with the small and large

functional groups and the taxonomic groups of

hoverflies and carabid beetles. The other taxo-

nomic groups were related to metrics represent-

ing all habitat types (true bugs and wild bees) or

arable land and grassland (herbaceous plants) or

arable land and woodland (birds). The correlation

observed between the metric and the biological

group was dependent upon the habitat type that

was being measured by the metric. For example,

at the HABITAT_14 level of resolution, high

densities of arable patch edges indicated a de-

crease in herbaceous plant species richness.

Contrarily, plant species richness was increased

by a higher density of grassland edge.

Discussion

The exploratory approach

Within the scope of this project, the exploratory

analysis allowed for the identification of land-

scape-level metrics and the general areas of

landscape pattern which correlated with some

taxonomic and functional groups at the different

levels of thematic resolution. It enabled the gui-

ded identification of class-level metrics which

correlated with more biological groups and al-

lowed the further identification of landscape

pattern components which are important for

biological groups. By way of comparison, Billeter

Landscape Ecol (2007) 22:461–473 467

123

et al. (submitted) used an expert methodology for

metric selection within the realms of this project

to examine the same taxonomic groups at the

HABITAT_2 and HABITAT_3 scales of resolu-

tion. Landscape- and class-level metrics were se-

lected using expert knowledge but only the share

of semi-natural habitats was found to be signifi-

cantly related to the plant and bird groups whilst

the wild bees were correlated with patch richness.

In our study, the ‘exploratory metrics’ provided

information about the structural characteristics of

the agricultural landscapes that have an impact on

biodiversity at different scales of thematic reso-

lution. For example, how larger patches, patch

density, edge density or patch shape affect species

richness, which groups particularly profit from a

higher landscape diversity or a more heteroge-

neous spatial configuration of the landscape.

Table 3 Correlation of class level metrics with the species richness of taxonomic plant and animal groups and withfunctional arthropod groups (body size) at different scales of thematic resolution

Taxonomic Group HABITAT_2 HABITAT_3 HABITAT_14

Metrics +/– F-ratio Metrics +/– F-ratio Metrics +/– F-ratio

Plants PD1 + 8.13* PD1 + 8.13** LPI4 + 7.97*

LPI1 – 6.07* LPI1 – 6.34* ED4 – 12.00**

LPI2 + 3.05 ENNCV4 + 4.05PROXMN4 – 11.58**

LPI5 + 4.33ED5 + 4.92*

Birds ENNCV4 + 3.53LPI6 + 5.87*

ENNCV6 + 4.98PROXMN6 – 3.40

Wild bees LPI1 – 5.12* LPI1 – 4.80* ED4 – 6.07*

CIRCCV1 – 4.83* CIRCCV1 – 5.98* ENNCV4 + 8.42*

CIRCMN4 – 14.24**

CIRCMN5 – 9.69**

PROXMN6 + 6.26*

Spiders PD3 – 8.44* ENNCV4 + 4.10ED3 + 8.41* CIRCMN6 – 4.04CIRCCV3 + 4.49

Hoverflies LPI1 – 3.10 LPI1 – 3.31 ENNCV + 11.72**

CIRCMN6 – 5.78*

Carabid beetles CIRCCV2 + 8.81 ENNCV6 + 11.70**

ED3 + 3.66 CIRCMN6 – 7.22*

True bugs LPI1 – 3.72 LPI1 – 3.43 LPI4 + 11.25**

LPI2 + 3.36 LPI2 + 9.14** PROXMN4 – 6.33*

CIRCMN4 – 12.58**

ED5 + 5.28*

ED6 + 7.24*

Large LPI1 – 5.66* LPI1 – 5.08* PROXMN6 + 15.81***

PD2 – 5.13*

Medium LPI1 – 3.15 CIRCMN4 – 3.52CIRCMN5 – 3.52

Small LPI3 – 3.67 LPI6 – 6.13*

CIRCMN6 – 3.93Very Small LPI2 + 3.75 LPI6 – 3.57

LPI3 – 3.52

The significant metrics (P < 0.05, highlighted in bold) and also trends were taken from the best model (lowest AIC). Thenumber after metric acronym relates to the habitat type; 1 = arable land and non-habitat, 2 = all herbaceous landscapeelements, 3 = all woody landscape elements, 4 = arable land only, 5 = grassland only, 6 = woodland only. F-ratio is directlyproportional to explained variance and indicates which significant metrics explain the most variation in the best models forthe different habitat types. +/- indicates positive/negative correlation. Significant levels * £ 0.05, ** £ 0.01, *** £ 0.001

468 Landscape Ecol (2007) 22:461–473

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However, the importance of the species-area

relationship as observed by others (e.g. Gustafson

and Parker 1992, Billeter et al. (submitted))

partly prevailed at the coarse scales of resolution

and is reflected by correlations being observed

only for grain metrics (in particular largest patch

index). At an intermediate scale of resolution the

biological groups correlated with other areas of

landscape pattern and it would be interesting to

test whether this effect remains if metrics mea-

suring the share of habitat were included in the

metric subset.

Equally, it would be useful to investigate the

effect of the level of land-use intensity on land-

scape structure. Although the test sites were se-

lected to represent independent gradients of land-

use intensity and landscape structure, we still

anticipate that land-use intensity was a con-

founding factor (see for e.g. Billeter et al. sub-

mitted, Schweiger et al. 2005). Land-use intensity

variables might correlate more with biological

groups at the coarse and fine scales of thematic

resolution where relatively few correlations were

identified using landscape metrics. We did not

include land-use variables into the linear mixed

models as this would have increased the possible

sources of variation. Our sample size with 24

landscapes is already small, and further inclusion

of variables or a division of the data into two

subsets, one for analysis and one for validation,

would have reduced the explanatory power con-

siderably. For the same reason it was not possible

to group the test sites according to land-use

intensity.

Landscape-level metrics as biological

indicators?

The correlations of the landscape-level metrics

with the biodiversity data were sensitive to the-

matic resolution and an intermediate scale was

the most informative for both the taxonomic and

functional groups. Landscape-level metrics pro-

vide general information about increases or de-

creases in species richness due to overall effects of

landscape structure. Higher overall biodiversity

can be expected in European agricultural land-

scapes that have a high habitat diversity (SIDI),

large variations in patch distribution (ENNCV)

and more edge and patch density (ED, PD).

Biodiversity will tend to be less in agricultural

landscapes with very large, dominating and spa-

tially aggregated patches (PROXMN, LPI).

Landscape heterogeneity and heterogeneity in

the distribution of habitat patches are therefore

constructive for biodiversity while homogeneity is

not. Whilst this is no surprise, the results dem-

onstrate how very coarse parameters of landscape

structure indicate what is important for certain

biological groups and also whether landscapes are

likely to support higher or lower numbers of

species.

Our results are supportive of other authors

(e.g. Benton et al. 2003). Species richness of mo-

bile groups such as birds and butterflies, for

example, has been linked to landscape heteroge-

neity (Pino et al. 2000; Weibull et al. 2000; Atauri

and de Lucio 2001) and in this study, the land-

scape-level metrics which reflect both spatial and

landscape heterogeneity were often linked to

groups which can be expected to have a higher

dispersal capacity (e.g. wild bees, hoverflies, true

bugs, large-sized arthropods). However, habitat

diversity, grain and variation in average distance

between habitats were also positively affecting

some of the potentially less mobile groups such as

the plants, small- and very small-sized arthropods.

Class-level metrics—attention to detail

The class-level metrics aided the interpretation of

the landscape-level metrics and identified both

important habitat types and other relevant areas

of landscape pattern. Generally, the class-level

metrics correlate with more biological groups and

provide more scope for pattern to process inter-

pretation than the landscape-level metrics. For

example, the configuration and shape metrics of

woodland correlated with the carabid beetles at

an intermediate scale of resolution. Previously,

cultivation intensity rather than landscape struc-

ture has been emphasized as an indicator for

carabid beetle diversity (Brose 2003) although

forest species are known to be limited by the

spatial distribution of woodlands and hedgerows

(Petit 1994; Petit and Burel 1998; Petit and Usher

1998). Here, the increase of the circle index of

woodland habitat was related to lower numbers of

Landscape Ecol (2007) 22:461–473 469

123

carabid beetle species whilst a high variation be-

tween nearest neighbouring woodlands was re-

lated to higher species richness. Both measures

provide us with information about the structure of

the landscape. One possible interpretation could

be that landscapes with narrow, elongated (rem-

nant?) woodland patches will have lower carabid

species numbers whilst a high variation in wood-

land spatial distribution benefits carabid diversity.

The nearest-neighbour metric has been criticized

for being too simplistic as a connectivity measure

(Moilanen and Nieminen 2002) and as an immi-

gration measure it is known to be sensitive to

changes in patch size and shape (Bender et al.

2003). The coefficient of variation of this param-

eter, however, gives an indication of the range of

variation around the average distance between

habitat patches and acts as a simple indicator of

landscape complexity.

Biological groups

Generally, the plant group was the least sensitive

to thematic resolution and interacted with metrics

describing grain at most levels (Tables 2 and 3).

Previously, the patch number metric (a measure

describing landscape grain) has been identified as

an important landscape parameter for quantifying

plant species diversity in agricultural landscapes

(Duelli 1997; Ortega et al. 2004). At the coarser

scales of resolution we found that a high number

of arable patches indicated increased plant spe-

cies richness (i.e. more but smaller patches).

Landscapes with many and smaller arable patches

are structurally more heterogeneous and will have

a higher number of field margins. An increase in

the proximity metric (i.e. larger more contiguous

patches) for the class ‘‘arable’’ led to a decrease in

species richness and supports the hypothesis that

plants require a heterogeneous landscape. Large

patches of arable land and non-habitat were also

associated with decreased species richness at

coarse levels of thematic resolution but an in-

crease in plant species number was observed with

larger patches of arable land at the HABI-

TAT_14 scale. This is difficult to explain in terms

of an ecological process and contradicts the po-

sitive effect observed through higher levels of

arable patch density and the negative effect

observed due to high arable proximity. The re-

sults demonstrate how metrics calculated at dif-

ferent scales of resolution can sometimes provide

seemingly conflicting information.

Patch shape complexity has shown promise for

the prediction of the species richness of vascular

plants and bryophytes (Moser et al. 2002). The

circle index, which measures the degree of overall

elongation of the patch, emerged as the repre-

sentative shape parameter through the explor-

atory analyses. The arable, grassland and

woodland circle metrics did not relate to plant

species richness but were correlated with the de-

creases in species richness of several faunistic

groups at an intermediate scale of resolution.

Narrow and elongated patches of these habitats

resulted in lower species richness of bees, hover-

flies, carabid beetles and true bugs.

Wild bees and the functional arthropod size

groups were the most sensitive to landscape-level

metrics calculated for different landscape pattern

aspects at the intermediate scale of thematic

resolution. Class-level metrics at this scale inter-

acted mainly with the wild bees, plants and true

bugs. Other authors have shown the important

role of landscape pattern and context for bee

species (e.g. Steffan-Dewenter 2003). The ability

of landscape metrics to predict the diversity po-

tential of bees as a group could be important for

biological conservation as the group includes

ecologically demanding species, which require

specific habitat combinations (Westrich 1996).

Schweiger et al. (2005) accounted for the var-

iability of arthropod body size to agricultural

landscape characteristics and in particular to the

connectivity of semi-natural habitats. Smaller

sized arthropods were observed to be negatively

affected by decreasing connectivity and Schwei-

ger et al. (2005) emphasize that arthropod com-

munities within agricultural landscapes are

predominantly affected by dispersal limitations.

Although no class-level metrics indicated this

type of interaction, at the landscape-level, high

variations in the nearest neighbour distance were

positively associated with the species richness of

three of the four functional arthropod groups. At

the class-level high variations in the nearest

neighbour distance also correlated with the bees,

hoverflies and carabid beetles. Our results

470 Landscape Ecol (2007) 22:461–473

123

together with those of others (e.g. Miller et al.

1997; Schweiger et al. 2005) support the hypoth-

esis that metrics, which adequately quantify the

structure of the agricultural landscape, can act as

coarse indicators of arthropod diversity.

Some biological groups such as the birds and

spiders were relatively insensitive to the metrics

regardless of thematic resolution. Common

assumptions such as the importance of landscape

heterogeneity to birds could not be supported by

our results. A degree of sensitivity of birds to

habitat amount was observed through an increase

in bird species richness in landscapes with larger

areas dominated by woodland. Our results are

consistent with previous studies which have not

demonstrated any effect of landscape structure on

spiders (e.g. Burel and Baudry 1995). Generally,

whether metrics correlate with biological data,

will partly depend on whether landscapes are

defined using the appropriate spatial resolution,

extent and thematic resolution for the particular

biological group under investigation. Here, we

demonstrate that particular groups (wild bees,

plants and true bugs) correlate consistently with

landscape metrics even at a European scale.

Conclusions

Using different levels of thematic resolution, the

appropriate scale for the study of particular bio-

diversity groups and landscape pattern metrics

could be identified. At the coarse scale of the-

matic resolution we would suggest the use of

grain metrics (PD, LPI) as rough indicators for

the different biological groups whilst at the fine

scale a diversity metric (SIDI, PR) might be

appropriate when using relatively heteroge-

neously defined biological groups. Metrics for use

at the intermediate scales could include LPI, ED

ENNCV, PROXMN, CIRCMN and SIDI.

The thematic scale issue should not be under-

estimated. If only simple landscape classification

data are available (two or three classes in our

example) it is perhaps easier to choose metrics for

particular habitat types using an expert approach

as the main effect of landscape pattern is likely to

be the species-area relationship. The landscape

classification system is too coarse to highlight the

other main landscape pattern requirements of the

taxonomic group. Billeter et al. (submitted) have

shown that at a coarse level of spatial data reso-

lution, landscape composition explains (plant and

bird) biodiversity best, therefore the share of the

main habitats should be investigated in conjunc-

tion with the two landscape pattern metrics PD

and LPI.

If high thematic resolution landscape data is

available (47 classes in our study), landscape

pattern metrics might not correlate reliably with

biodiversity and cannot be recommended as

indicators. High thematic resolution maps are

required to investigate specific research questions

related to biodiversity, analysing e.g. individual

species or functional groups in relation to their

habitat requirements and the spatial organisation

of these habitats in the landscape. The biodiver-

sity data (sampling design and effort) then has to

match the spatial and thematic resolution of the

landscape data.

With respect to more general biodiversity

monitoring, our findings suggest that data of an

intermediate level of thematic resolution (14

classes in our study) are sufficient. At this level

several of landscape-level metrics seem to corre-

late well with the diversity of most species groups.

These correlations can be further explored and

understood by means of class-level metrics. As at

the coarse thematic level, the share of the habitats

should also be considered. The intermediate level

of thematic resolution, however, informs better

about the actual interactions between landscape

properties and biodiversity than the coarse level

and as a consequence, eventual changes in bio-

diversity can be interpreted.

Almost 20 years ago, O’Neill et al. (1988)

asked the basic question: ‘‘Knowing only the

values of the indices and how they change

through time, how well can one specify the cor-

responding ecological changes?’’. Our answer

with respect to biodiversity is that we cannot infer

species richness values from landscape pattern

metrics in quantitative terms particularly as no

landscape or situation is alike. However, relative

values could be used and we suggest two possible

applications in the context of research and mon-

itoring of biodiversity in European agricultural

landscapes:

Landscape Ecol (2007) 22:461–473 471

123

– The landscape metrics which were identified in

this study could be integrated in a monitoring

system for rural landscapes. Areas where

repeated measurements of landscape metrics

indicate negative changes in landscape struc-

ture and, thus, a potential loss of biodiversity

over time should be considered at risk and

there, actual biodiversity measurements should

be carried out.

– The metrics could be used to identify regions of

potentially high biodiversity related to agricul-

ture and could, for example, refine the pre-

liminary estimations made by Hogeveen et al.

(2004). On consistent land cover maps for Eur-

ope (e.g. Corine; EEA 1995) areas where the

metrics indicate high levels of biodiversity

according to our findings could be considered as

regions with potentially high nature value

farming systems. Again, this potential would

require verification by biodiversity measure-

ments.

Landscape metrics could thus be used as early

warning indicators for regions where changes are

happening as well as for the identification of

biodiversity hotspots in rural Europe. Although

they cannot replace actual biodiversity measure-

ments they can help to guide these surveys in an

effective and cost efficient way.

Acknowledgements The European Union (EU-Refer-ence EVK2-CT-2000-00082) and the Swiss State Secre-tariat for Education and Research (SER Nr. 00.0080-1)funded part of this research. We thank Isabel Augenstein,Riccardo De Filippi, Nicolas Schermann and Erich Szer-encsits for their advice and GIS support, the Greenveinsconsortium for the collection of the plant, insect and birddata and also the taxonomic specialists: Tim Adriaens,Frank Burger, Rafael De Cock and Jaan Luig (bees),Roland Bartels, Jean-Yves Baugnee and Ralph Heckman(bugs), Konjev Desender, Ringo Dietze, Rein Karulaas,Keaty Maes and Viki Vandomme (carabid beetles), Mar-tin Musche and Dieter Doczkal (hover flies), Herman DeKoninck, Mart Meriste, Johan Van Keer and ValerieVanloo (spiders). Thanks also go to Rob Bugter for hisproject guidance.

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