RESEARCH ARTICLE

The effects of landscape patterns on ecosystem services:
meta-analyses of landscape services

Gabriela Teixeira Duarte . Paloma Marques Santos . Tatiana Garabini Cornelissen .

Milton Cezar Ribeiro . Adriano Pereira Paglia

Received: 25 August 2017 / Accepted: 19 June 2018 / Published online: 25 June 2018

� Springer Nature B.V. 2018

Abstract

Purpose The recently introduced concept of ‘land-

scape services’—ecosystem services influenced by

landscape patterns—may be particularly useful in

landscape planning by potentially increasing stake-

holder participation and financial funding. However,

integrating this concept remains challenging. In order

to bypass this barrier, we must gain a greater

understanding of how landscape composition and

configuration influence the services provided.

Methods We conducted meta-analyses that consid-

ered published studies evaluating the effects of several

landscape metrics on the following services: pollina-

tion, pest control, water quality, disease control, and

aesthetic value. We report the cumulative mean effect

size (E??), where the signal of the values is related to

positive or negative influences.

Results Landscape complexity differentially influ-

enced the provision of services. Particularly, the

percentage of natural areas had an effect on natural

enemies (E?? = 0.35), pollination (E?? = 0.41), and

disease control (E?? = 0.20), while the percentage of

no-crop areas had an effect on water quality

(E?? = 0.42) and pest response (E?? = 0.33). Fur-

thermore, heterogeneity had an effect on aesthetic

value (E?? = 0.5) and water quality (E?? = - 0.40).

Moreover, landscape aggregation was important to

explaining pollination (E?? = 0.29) and water quality

(E?? = 0.35).

Conclusions The meta-analyses reinforce the impor-

tance of considering landscape structure in assessing

ecosystem services for management purposes and

decision-making. The magnitude of landscape effect

varies according to the service being studied. There-

fore, land managers must account for landscape

composition and configuration in order to ensure the

maintenance of services and adapt their approach to

suit the focal service.

Electronic supplementary material The online version of
this article (https://doi.org/10.1007/s10980-018-0673-5) con-
tains supplementary material, which is available to authorized
users.

G. T. Duarte (&) � P. M. Santos � A. P. Paglia

Laboratório de Ecologia e Conservação (LEC),

Departamento de Biologia Geral, Instituto de Ciências

Biológicas, Universidade Federal de Minas Gerais, Av.

Antônio Carlos, 6627, Belo Horizonte,

Minas Gerais 31270-901, Brazil

e-mail: gabitduarte@gmail.com

G. T. Duarte � P. M. Santos � M. C. Ribeiro

Laboratório de Ecologia Espacial e Conservação (LEEC),

Departamento de Ecologia, Instituto de Biociências,

Universidade Estadual Paulista - UNESP, Av. 24A, 1515,

Rio Claro, São Paulo 13506-900, Brazil

T. G. Cornelissen

Laboratório de Ecologia Vegetal e Interações (LEVIN),

Departamento de Ciências Naturais, Universidade Federal

de São João Del-Rei, Praça Dom Helvécio, 74 Fábricas,

São João Del-Rei, Minas Gerais 36301-160, Brazil

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Landscape Ecol (2018) 33:1247–1257

https://doi.org/10.1007/s10980-018-0673-5

http://orcid.org/0000-0002-8640-2347
https://doi.org/10.1007/s10980-018-0673-5
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https://doi.org/10.1007/s10980-018-0673-5


Keywords Landscape metrics � Spatial patterns �
Structure � Management � Complexity � Ecological

benefits

Introduction

Landscape patterns emerge from the composition and

configuration of its basic elements, and these patterns

influence both ecological processes and ecosystem

functions (Turner 2005). This also holds true for

ecosystem services, which heavily depend on the

health of these functions, and on the spatial interac-

tions and flow between ecosystems and anthropogenic

areas (Termorshuizen and Opdam 2009; Syrbe and

Walz 2012). To identify target areas for conservation,

restoration, or enhancement of ecosystem services,

decision-makers must consider spatial contexts and

landscape patterns. Undoubtedly, human population

growth has increased the demand for high-quality

multifunctional landscapes (DeClerck et al. 2016;

Garbach et al. 2016), and created an urgent need for

more practical and applicable information to guide

efficient decision-making toward this end.

The recently introduced concept of ‘landscape

services’ is an essential part of the emerging field

sustainability science (Termorshuizen and Opdam

2009). Its primary difference from the ‘ecosystem

services’ concept is the dependence of rendered

services on spatial configuration and the influence of

elements external to the ecosystem (Bastian et al.

2014). The landscape services concept encompasses

the notion that a complete landscape can provide

services through its multi-functionality and the pro-

cesses that emerge from a set of unique ecosystems

(Frank et al. 2012; Hodder et al. 2014), in both natural

and human-modified habitats. This concept may prove

useful for landscape planning, as the integration of

ecological services could increase stakeholder partic-

ipation, financial funding, and encompass working

landscapes (Chan et al. 2006, 2011; Goldman et al.

2008; Carpenter et al. 2009; Duarte et al. 2016).

However, integrating this concept into the planning

process remains a global challenge (de Groot et al.

2010). To bypass this barrier, we must improve our

understanding of how particular landscape patterns

influence its services (Mitchell et al. 2015).

Landscape metrics are widely used in studies

describing landscape patterns and their relationship

to land use/land cover changes, biodiversity distribu-

tion, ecological processes, and ecosystem functions

(Uuemaa et al. 2013). However, such analysis requires

an awareness of the metrics’ interrelationships and

redundancy (Cushman et al. 2008) and their consis-

tency for landscape management. For this purpose,

aggregating landscape metrics into more general

groups can facilitate stakeholders’ understanding of

landscape management (Cushman et al. 2008).

Previous studies have investigated how specific

landscape patterns and features relate to the provision

of ecosystem services (e.g., Bastian et al. 2014;

Hodder et al. 2014; Chaplin-Kramer et al. 2015;

Mitchell et al. 2015). In addition, recent reviews and

quantitative analyses have begun synthesizing avail-

able knowledge in this area (Chaplin-Kramer et al.

2011; Garibaldi et al. 2011; Mitchell et al. 2013;

Shackelford et al. 2013); however, the focus remains

on relatively few services and landscape features (e.g.,

landscape complexity). This study aimed to thor-

oughly review and evaluate the relationship between

several aspects of landscape patterns and certain

ecosystem services. The primary target of this research

was to provide support for more practical decision-

making in landscape planning and management in

order to ensure the maintenance of key landscape

services.

Meta-analysis is the quantitative, scientific synthe-

sis of research results aiming to achieve broad

generalizations across a large number of study

outcomes (Gurevitch et al. 2018). Meta-analyses have

largely been used in ecology, evolution, and conser-

vation biology (Gerstner et al. 2017) to estimate the

overall magnitude of effects, and to identify factors

that modulate such effects. In this meta-analytical

review, we considered published studies that evalu-

ated indicators of ecosystem services and the land-

scape patterns to maintain or influence their provision.

We hypothesized that (1) landscape complexity would

have a significant effect on the provision of all services

evaluated due to its relationship with high multi-

functionality; (2) for services related directly to fauna

biodiversity and water quality, we expected a positive

relationship with both landscape aggregation and

habitat quantity; and (3) we expected landscape

heterogeneity to affect cultural services, as cultural

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1248 Landscape Ecol (2018) 33:1247–1257



landscapes involve both natural and anthropogenic

land types.

Methods

Study selection and inclusion criteria

For the present meta-analyses, we used three distinct

approaches to conduct a systematic literature search

aimed at collecting the most representative sample of

existing primary research studies. First, we used

articles already reviewed by Chaplin-Kramer et al.

(2011; a meta-analysis of the effect of landscape

complexity on pest control services), Garibaldi et al.

(2011; a synthesis regarding landscape effects on the

stability of pollination services), Shackelford et al.

(2013; a meta-analysis of landscape and local effects

on the abundance and richness of pollinators and

natural enemies) and Uuemaa et al. (2013; a review of

trends in the use of landscape metrics). We then

performed an extensive search in the Web of Science

database, using the keywords ‘‘landscape metrics,’’

‘‘landscape indexes,’’ and ‘‘landscape indices’’ to

complement the research by Uuemaa et al. (2013)—

which reviewed studies between 2000 and 2010 using

the same keywords—by adding studies published

between 2011 and 2016. Finally, we reviewed relevant

articles provided in the reference lists of all previously

selected studies.

The present study only considered studies that used

landscape metrics related to empirical data of func-

tions or ecological indicators that directly benefit

human well-being. We did not consider habitat

function for biodiversity, except when primary studies

explicitly cited biodiversity as being (or potentially

being) directly related to certain ecosystem services

(e.g., pollination and pest control). Furthermore, we

chose to exclude habitat function, as this is not the

focus of the present research and several previous

scientific works and reviews have already studied the

effect of landscape patterns on biodiversity and its

conservation (Fahrig 2003, 2017; Uuemaa et al. 2013).

In addition, we restricted our research to terrestrial

landscapes in rural, agricultural, mixed rural–urban or

natural habitats regions, thus excluding strictly urban

or marine landscapes. One study inclusion criterion

was the reporting of statistical parameters (e.g., r, F,

v2, Spearman-rho, t or R2, and sample size) on the

relationship between at least one landscape metric and

one landscape function, or the partial contribution of at

least one landscape metric. In some cases, we

extracted and carefully reanalyzed the original raw

data. When primary studies reported data in figures,

we digitized them and extracted raw data using the

Image J software version 1.46 (Schneider et al. 2012).

Landscape explanatory variables

The present study used landscape complexity vari-

ables, primarily those related to increasing the amount,

spatial heterogeneity, and landscape connectivity of

natural and semi-natural areas as predictors for

explaining ecosystem services. These explanatory

variables were grouped into four major groups

according to the patterns they measured: (a) percentage

of natural areas; (b) percentage of non-crop areas;

(c) landscape aggregation, which ‘‘refers to the

tendency of patch types to be spatially aggregated;

that is, to occur in large, aggregated or ‘contagious’

distributions’’ (MacGarigal et al. 2012); and (d) land-

scape heterogeneity, which refers to the degree of

heterogeneity of landscape elements, including met-

rics such as diversity, evenness and richness indexes

for natural and semi-natural land cover classes, and for

the entire landscape (see example and metric defini-

tions in MacGarigal et al. 2012; Fig. 1). Additionally,

landscape aggregation was also subdivided into: (c1)

landscape connectivity—metrics related to the prox-

imity/connectance of landscape natural elements; and

(c2) landscape fragmentation—metrics related to the

number of patches, edges, and the isolation of natural

and semi-natural areas. By using these groups, it is

possible to provide greater detail regarding manage-

able landscape characteristics that affect ecosystem

services. The analyses in the present study evaluated

the landscape metrics described in Fig. 1. However,

only the groups that had at least five independent

comparisons were included in the meta-analyses. In

relation to the landscape metrics of natural area

fragmentation and percentage of crops, we used

inverted values of the reported statistical data, as

these metrics are considered inversely related to the

positive ecological effects of landscape complexity.

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Landscape Ecol (2018) 33:1247–1257 1249



Ecosystem service response variables

Many ecosystem services related to water quality have

been described by Keeler et al. (2012). Additionally,

there is evidence to support landscape patterns influ-

encing the provision of these services (Allan 2004). As

response variables, we grouped several water func-

tions, which are collectively referred to as ‘water

quality-related services’ in the present study. Further-

more, we followed the study of Keeler et al. (2012) and

searched for articles reporting concentrations of

nitrogen, phosphorus, and sediments (including sus-

pended solids and turbidity) as indicators of water

quality. Based on these findings, we then inverted the

sign of results from the meta-analysis in order to

understand the effects of landscape complexity on the

water quality indicators more easily.

The same approach was utilized to assess the

service of disease control. This involved searching for

primary studies reporting indicators of loss of disease

control, such as disease prevalence, host and vector

abundances, and infection levels. After the meta-

analysis, the sign of its results was then inverted to

assess the effects of landscape metrics on disease

control indicators. To assess the service of pest

control, two contrasting approaches were used. The

first approach measured pest response through indi-

cators related to pest abundance, richness, and dam-

age. The second approach measured natural enemies’

response through indicators such as natural enemy

abundance, richness, diversity, and direct effects on

pest reduction. As a result, two types of indicators of

the effects of landscape complexity on pest control

were available: one related to service providers

(natural enemies) and other to disservice providers

(pests). We also evaluated the service of pollination of

agricultural areas using the abundance, richness,

diversity, and effects of pollinators as indicators, as

well as the service of aesthetic value using indicators

from landscape preference studies.

For studies that exhibited a multi-scale approach

(e.g., concentric radii to determine different land-

scapes) or multiple sampling seasons, we chose the

most predictive scale or period of the year (following

Chaplin-Kramer et al. 2011; Shackelford et al. 2013).

For primary studies yielding results in different years,

Fig. 1 A flowchart representing the landscape-metric groups

and subgroups. Group names are in the dark gray boxes.

Descriptions of each group and subgroup, with their related

landscape metrics, are in the light gray boxes. NAT and SEMI

stand for natural and semi-natural areas, respectively. Descrip-

tions for each landscape metric can be found in McGarigal et al.

(2012). Asterisks at a landscape level, these metrics also account

for anthropogenic (non-natural) areas

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1250 Landscape Ecol (2018) 33:1247–1257



we considered every year for which authors reported a

change in land use/land cover. Furthermore, following

Shackelford et al. (2013), the mean of reported effect

sizes for multi-subgroups of taxa was used (for

example, spider families and bee genera).

Data analysis

Pearson product-moment correlation coefficients (r)

were used as a measure of effect size, weighted by

sample sizes for the meta-analyses. When studies did

not report r values, the statistical results provided by

the authors (F, v2, Spearman-rho, t or R2) were

converted to the correlation coefficient (r). All r values

were then converted into Fisher’s Z (Rosenthal and

DiMatteo 2001), as follows:

z ¼ 1

2
ln

1 þ r

1 � r

� �

and the asymptotic variance of z was calculated as:

vz ¼
1

n� 3

where n represents the sample size. Fisher’s z

transforms ranges from - ? to ? ?, where negative

values of z represent a negative effect, positive values

of z represent a positive effect, and z = 0 represents no

effect. We calculated the 95% confidence intervals

around a cumulative effect size for each variable of

interest. Moreover, we considered estimates of the true

effect size to be significant if confidence intervals did

not overlap with zero. We conducted all analyses

using MetaWin software version 2.0 (Rosenberg et al.

2000). In addition, we used mixed models to calculate

the cumulative effect sizes (E??) for each group of

landscape metrics, assuming that studies within a

group share a common mean effect and that both

random variation and sampling variation exists within

a group. Then, the average of z values was weighted by

the inverse of their variance.

Once the effect sizes for each landscape metric and

service were calculated, we examined total and group

heterogeneity among effects by partitioning variance

within groups and testing whether categorical land-

scape groups were homogeneous with respect to effect

sizes. We used the Q-statistic, and total heterogeneity

(QT) was partitioned into within-class heterogeneity

(QW) and between-class heterogeneity (QB). Total

heterogeneity (QT) was calculated as QT = R wi

(Ei - E??)2, where wi is the reciprocal of the

variance, Ei is the effect size for each study, and

E?? is the cumulative effect size for the set of studies

under evaluation. QT follows a Chi square distribution

with k - 1 degrees of freedom. Since we based our

analyses on published studies alone, we checked for

publication bias and the file-drawer problem by

calculating Rosenthal’s fail-safe number (Rosenthal

and DiMatteo 2001). This number determines the

hypothetical number of missing or unpublished studies

that, if added, would change the effects from signif-

icant to non-significant. If this number is sufficiently

high (larger than 5k ? 10, where k = number of

independent comparisons), the results can be consid-

ered robust despite publication bias.

Results

A total of 121 articles fit the inclusion criteria and were

used in the meta-analyses. Following a critical review

and evaluation of data available for analysis, the

services described in the Ecosystem Services Response

Variables section were those that data could be located

for, which included: (a) water quality, (b) disease

control, (c) loss of pest control by increase in pest

response, (d) pest control by increase of natural

enemies’ response, (e) pollination, and (f) aesthetic

value.

Studies addressing natural enemies’ response rep-

resented c.a. 30% of the articles included in the present

review (N = 36 articles), while 28% concerned water

quality (N = 34), and 21% evaluated pollination

services (N = 26), which was followed by pest

response (N = 11), disease control (N = 8), and

aesthetic value (N = 6). These selected studies gener-

ated 90, 327, 62, 40, 41, and 23 independent compar-

isons, respectively. Additional details regarding the

primary data used for the present analyses is located in

the supplementary material (Online Resource 1). Also,

landscape complexity significantly influenced all

services evaluated in this research, with the exception

of disease control. The main results of each evaluated

service are presented as follows:

• Water quality Our results indicate an increase of

nearly 30% [Cumulative mean effect size

(E??) = 0.29, Confidence interval (CI)

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Landscape Ecol (2018) 33:1247–1257 1251



0.24–0.32, degrees of freedom (df) = 327], with an

increase in landscape complexity (Fig. 2a). Fur-

thermore, water quality varied significantly when

various aspects of landscape metrics were evalu-

ated (QB = 86.89, df = 3, P\ 0.001; Table 1),

and the strongest (and most positive) effect was

observed for the percentage of non-crop areas

(E?? = 0.42, CI 0.35–0.49, df = 120). Landscape

connectivity also positively influenced water qual-

ity (E?? = 0.35, CI 0.20–0.50, df = 32), though

landscape fragmentation did not (E?? = 0.06, CI

- 0.08 to 0.20, df = 35). Landscape heterogeneity

exhibited a significant and negative effect on water

quality (E?? = - 0.40, CI - 0.58 to - 0.23,

df = 20), indicating that heterogeneous matrices

of non-natural areas, such as agricultural, urban,

commercial and industrial areas, may decrease the

effects of this service. Amongst the articles

analyzed, approximately 90% evaluated water

quality in landscapes that included a very hetero-

geneous matrix of non-natural areas within cited

land use classes. In summary, these results suggest

that an increase in landscape characteristics such

as non-crop areas, the percentage of natural

habitat, and landscape aggregation enhances the

provision of services related to water quality.

• Disease control The effect of landscape complex-

ity on this service was slightly positive, though not

Fig. 2 Effects of the selected landscape-metric groups (see

Fig. 1 for more information regarding the groups) on the

following ecosystem services: a water quality, b disease control,

c pest response, d natural enemies’ response, e pollination, and

f aesthetic value. Values in parentheses denote the total number

of independent comparisons/total number of primary studies,

respectively. Lines indicate the 95% confidence interval around

the effect size for each group

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1252 Landscape Ecol (2018) 33:1247–1257



significant (E?? = 0.04, CI - 0.01 to 0.1, df =

40). Amongst the landscape metrics evaluated,

the percentage of natural areas in the landscape

had a significant and positive effect on this service

(Fig. 2b), enhancing disease control by approxi-

mately 20% (E?? = 0.20, CI 0.07–0.33, df = 13)

in areas with higher percentages of natural habi-

tats. However, fail-safe values indicated that

results were not robust (Table 1) and should be

interpreted with caution. Furthermore, among the

independent comparisons evaluated for disease

control, heterogeneity analyses remained signifi-

cant (QT = 154.60, P\ 0.0001), even after parti-

tioning the mean effect into different landscape

metrics.

• Pest response An evaluation of pest response data

(Fig. 2c) indicated that the percentage of non-crop

areas increased pest response by nearly 35%

(E?? = 0.33, CI 0.09 to 0.57, df = 17), though

the percentage of natural areas surrounding agri-

cultural areas did not influence the loss of pest

control (E?? = 0.08, CI - 0.23 to 0.40, df = 10).

• Natural enemies’ response With regard to pest

control services by natural enemies, we deter-

mined that an increase in landscape complexity

enhanced natural enemies’ response by nearly

25% (E?? = 0.23, CI 0.14–0.32, df = 89;

Fig. 2d). However, although this service was

influenced homogeneously within different land-

scape-metric groups (QB = 5.85, P = 0.11, df =

3; Table 1), an increase in the percentage of

natural habitats (E?? = 0.35, CI 0.15–0.54, df =

22) and non-crop areas (E?? = 0.30, CI

0.14–0.44, df = 32) had the strongest effects on

natural enemies’ responses.

• Pollination This service was 31% higher in

complex landscapes (E?? = 0.31, CI 0.21–0.42,

df = 61; Fig. 2e). The percentage of natural habi-

tats increased this service by 41% (E?? = 0.41, CI

0.22–0.58, df = 24), whereas landscape aggrega-

tion increased pollination by 29% (E?? = 0.29, CI

0.11–0.45, df = 24). However, the effects of

landscape-metric groups similarly influenced pol-

lination (QB = 5.61, P = 0.13, df = 3; Table 1).

• Aesthetic value Relatively few studies evaluated

aesthetic value (i.e., the perception of landscape as

a cultural service) from a landscape perspective.

Although this service increased due to landscape

aspects such as heterogeneity (E?? = 0.50, CI

0.10–0.90, df = 9; Fig. 2f), it did not differ

amongst landscape-metric groups (QB = 1.42,

P = 0.49).

Evaluation of publication bias

For the majority of analyzed effects, fail-safe values

were greater than 5k ? 10 (where k is the number of

independent comparisons) (Table 1). For water qual-

ity, natural enemies’ response, and pollination ser-

vices, these values were relatively large, indicating the

robustness of results on the mean effect, which

suggests the absence of publication bias. Scatter plots

of effect sizes against sample sizes for all services

separately exhibited a typical funnel shape (Online

Resource 2), indicating that studies with small sample

sizes had a large dispersion of effect sizes around the

Table 1 Results from heterogeneity analysis and Rosenthal’s fail-safe number following a meta-analysis regarding the effect of

landscape patterns on ecosystem services

Ecosystem services

Water quality Disease control Pest response Natural enemies Pollination Aesthetic value

QB (P value) 86.89 (� 0.001) 12.49 (0.006) 2.45 (0.48) 5.85 (0.12) 5.61 (0.13) 1.43 (0.49)

QW (P value) 509.68 (� 0.001) 142.11 (� 0.001) 35.12 (0.51) 95.26 (0.23) 89.82 (0.004) 19.83 (0.47)

QT (P value) 596.56 (� 0.001) 154.60 (� 0.001) 37.57 (0.53) 101.11 (0.18) 95.43 (0.003) 21.25 (0.50)

Fail-safe number 21,094 56 65 850 825 56

Using Q statistics, total heterogeneity (QT) was partitioned into within-class heterogeneity (QW) and between-class heterogeneity

(QB) following a Chi square distribution with k - 1 degrees of freedom (df). The fail-safe number was calculated as NR = ([R z

(pi)]2/za
2) - n, where z is the score of the normal distribution, za is the z score associated with the chosen alpha (0.05), and n is the

number of studies

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Landscape Ecol (2018) 33:1247–1257 1253



true effect value, whereas studies with larger sample

sizes tended to possess effect sizes around the true

mean value.

Discussion

We determined that specific groups of landscape

patterns influenced the provision of ecosystem ser-

vices differently. Composition landscape metrics, as

the percentage of natural or no-crop areas and

landscape heterogeneity, influenced all services eval-

uated in the present research, while configuration

metrics such as landscape aggregation influenced two

services: pollination and water quality. Our hypothesis

regarding landscape complexity was confirmed for

water quality, pollination, both pest control indicators,

and aesthetic value. Therefore, the role of landscape

complexity on increasing the provision of different

services suggests that the restoration of natural areas

using a land-sharing perspective could be important

for the provision of multi-ecosystem functions, which

corroborates with results from Barral et al. (2015).

We highlight that water quality can correspond to

several different services (Keeler et al. 2012); for

example, safe drinking water, commercial fishing, and

recreational benefits, among others. A reduction in

agricultural areas generally increases water quality

through the reduction of fertilizers and pesticides in

water bodies. Moreover, increased areas of natural

vegetation results in increased soil nutrients, as well as

decreased erosion. In the context of riverscapes, the

importance of increased connectivity, shown in this

study, is due to increased natural riparian vegetation

(Allan 2004). Therefore, restoration programs should

prioritize riparian areas in order to retain this set of

ecosystem services.

Notably, both water quality and pest control by

natural enemies’ indicators exhibited a trade-off with

food production services, as measured by the percent-

age of crop area. However, landowners and society

could benefit from an increase in natural areas in rural

regions, which consequently carries improved ecosys-

tem services. For example, landowners could benefit

from restoration strategies that increase habitat for

natural enemies on their lands (Chaplin-Kramer et al.

2011), or water for irrigation of their crops. Therefore,

land managers should consider the creation of mech-

anisms that lead to greater landowner cooperation on

actions that improve landscape conservation and the

provision of desired ecosystem services (Goldman and

Tallis 2009).

This study also revealed that the reduction of crop

areas (or increase in the non-crop percentage) could

increase the loss of pest control due to greater pest

abundance, richness, and/or damage. This study

follows Tscharntke et al.’s (2016) hypotheses to this

apparent contradiction, which stated that ‘‘the relative

importance of natural habitats for biocontrol can vary

dramatically depending on type of crop, pest, predator,

land management, and landscape structure’’. For

example, many primary studies included in our review

did not differentiate between organic or conventional

agricultural strategies, or did not report if natural

enemies were present in the study region. Chaplin-

Kramer et al. (2011) determined that neither the

percentage of natural non-crop vegetation nor the

percentage of crops has significant effects on pest

responses.

Similar to findings by Shackelford et al. (2013),

landscape complexity increased pollination service in

the present study. This increase was primarily due to

the percentage of natural areas, but also due to the

positive relationship with landscape aggregation.

Therefore, landscape managers should consider both

restoration approaches in landscape planning pro-

cesses for regions with naturally pollinated crops.

Ricketts et al. (2008) and Garibaldi et al. (2011) also

determined that the distance to habitat influences

pollination services. Combined, these findings lend

support to the theoretical design proposed by Brosi

et al. (2008), who suggested that, in order to increase

pollination services in agricultural landscapes, there

should be areas large enough to sustain pollinator

populations, with other smaller natural areas within

the crop matrix with distances not much greater than

pollinators’ foraging distances. Although we observed

a significant effect of aggregation metrics on one

fauna-related service (pollination), this did not fully

confirm our primary hypotheses. However, a recent

review by Fahrig (2017) suggests that ecological

responses to fragmentation typically have other influ-

ences aside from landscape structure.

With regard to disease control, although landscape

configuration has an important and significant impact,

the determined fail-safe value was insufficiently

robust. Additionally, articles related to disease control

services were difficult to locate using our research

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1254 Landscape Ecol (2018) 33:1247–1257



method. Relatively few studies evaluated the impacts

of landscape structure on epidemiological processes,

though other reviews indicate that the integration of

landscape ecological and epidemiological knowledge

can be fruitful (e.g., Elliott and Wartenberg 2004;

Graham et al. 2005; Ostfeld et al. 2005; Killilea et al.

2008). As disease risk and incidence are related to the

communities and dynamics of their pathogens, vec-

tors, reservoirs or hosts, the configuration and com-

position of landscapes has the potential to influence

them, making this type of information useful for

landscape managers in regions with high disease risk

(Prist et al. 2017).

Notably, landscape heterogeneity had an important

effect on the perception of aesthetic value reported by

the interviewees in the selected studies, which con-

firms our hypothesis. However, due to the small

sample size available (only six studies), this result was

insufficiently robust. Although many other articles

have studied this landscape service, their data was not

adequately reported for inclusion in our meta-analysis.

However, the conclusions of these excluded articles

are similar in that landscape heterogeneity is important

to peoples’ perception of aesthetic value (e.g., Dram-

stad et al. 2001; De Groot and Van Den Born 2003;

Franco et al. 2003; Sang et al. 2008; Herbst et al. 2009;

Ode and Miller 2011; Frank et al. 2013; Surová et al.

2014). Furthermore, increased aesthetic value has the

potential to increase satisfaction through regional

tourism, and offers other spiritual and cultural benefits

for society.

Overall, these meta-analyses reinforce the impor-

tance of considering landscape structure on assessing

ecosystem services for management purposes and

decision-making. The magnitude of landscape effect

varies according to the landscape metrics and services.

Therefore, the results presented in the present study

advance our understanding of landscape patterns, and

offer guidance for land management activities regard-

ing the provision of landscape services. Land man-

agers must account for landscape composition and

configuration in ensuring that services are maintained,

and adapt their approach depending upon the relevant

focal services.

Acknowledgements We would like to thank all LEC-UFMG

and LEEC-UNESP members for their various forms of

contribution, especially Rafaela Silva, Julia Assis and Arleu

Viana for their support during this work. We thank Coordination

for the Improvement of Higher Education Personnel (CAPES)

and National Council for Scientific and Technological

Development (CNPq) for the G.T. Duarte and P.M. Santos

scholarships. M.C. Ribeiro is funded by CNPq (Grant Nos.

312,045/2013-1 and 312292/2016-3), PROCAD/CAPES

(Project # 88881.068425/2014-01) and The São Paulo

Research Foundation (FAPESP - Grant 2013/50421-2).

T. Cornelissen is funded by CNPq (Grant 307210-2016-2). A.

Paglia is funded by CAPES, CNPq and FAPEMIG. We also

thank two anonymous reviewers for their valuable comments

and suggestions.

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	The effects of landscape patterns on ecosystem services: meta-analyses of landscape services
	Abstract
	Purpose
	Methods
	Results
	Conclusions

	Introduction
	Methods
	Study selection and inclusion criteria
	Landscape explanatory variables
	Ecosystem service response variables
	Data analysis

	Results
	Evaluation of publication bias

	Discussion
	Acknowledgements
	References