1 Fu, Liu, Degloria, Dong,& Beazley (2010)Characterizing the “fragmentation–barrier” effect of...

7/31/2019 1 Fu, Liu, Degloria, Dong,& Beazley (2010)Characterizing the fragmentationbarrier effect of road networks on l

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Landscape and Urban Planning 95 (2010) 122129

Contents lists available at ScienceDirect

Landscape and Urban Planning

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / l a n d u r b p l a n

Characterizing the fragmentationbarrier effect of road networks onlandscape connectivity: A case study in Xishuangbanna, Southwest China

Wei Fu a, Shiliang Liu a,, Stephen D. Degloria b, Shikui Dong a, Robert Beazley c

a School of Environment, State Key Laboratory of Water Environment Simulation, Beijing Normal University, No. 19, Xinjiekowai Street, Beijing 100875, Chinab Department of Crop and Soil Sciences, Cornell University, Ithaca, NY 14853, USAc Graduate Student Department of Natural Resources, College of Agriculture and Life Sciences, Fernow Hall 302, Cornell University, Ithaca, NY 14853, USA

a r t i c l e i n f o

Article history:Received 11 February 2009

Received in revised form

16 November 2009

Accepted 4 December 2009

Available online 12 January 2010

Keywords:

Landscape ecology

Effective distance

Fragmentation effect

Barrier effect

Connectivity index

a b s t r a c t

The fragmentationbarrier effect of road network expansion on landscape function draws attentionthroughout the world. Presently, there is a lack of quantitative analysis to integrate different ecological

effects of road networks at landscape scale to depict, compare and evaluate landscape changes. In this

study, the Probability of Connectivity (PC) index was used to evaluate the effects of road networks

on landscape connectivity for an area in Xishuangbanna, Southwest China. The results indicate that the

fragmentation effectof roadnetworks decreasedin PCvalueby 15.81%, thebarriereffectof road networks

decreased in PC value by 11.73% and the combination of the two effects decreased PC value by 32.78%.

In conclusion, the combined fragmentation and barrier effects of road networks considerably degraded

landscape connectivitymore than either individualeffect. In addition, the fragmentation effect influenced

connectivityto a greater degree for ecological processes havinglow movement abilities. The barrier effect

influencedconnectivity to a greater degreefor mediumto high movement abilities. Thecombinedeffects

influenced connectivity for those ecological processes of low movement ability within the study area.

2009 Elsevier B.V. All rights reserved.

1. Introduction

As early as 1970, wildlife biologists began publishing research

on the effect of roads on wildlife populations as barriers to move-

ment (Bhattacharya et al., 2003; Mech et al., 1988; Oxley et al.,

1974), sources of mortality (Bellis and Graves, 1971; Dodd et al.,

2004) and causes of behavior modification (Rost and Bailey, 1979;

Tigas et al., 2002) at less than ecosystem scale. Coincident with the

development of landscape ecology and landscape scale analysis,

attention to road ecology has turned to the broader scale effects

of landscape and habitat fragmentation and, specifically, the qual-

itative effect of roads on fragmentation and interactions with the

landscape to sustain ecological processes (Coffin, 2007). Quantita-

tive methods to assess the impacts of road networks on landscape

connectivity at broad spatial scales, however, are lacking and needto be developed.

The concept and measurement of landscape connectivity are

needed to overcome the limitations of empirical indices at small

scale and landscape pattern indices at large scale for characterizing

landscape change. Landscape connectivity has been defined as the

Corresponding author. Tel.: +86 13522671206; fax: +86 1058800397.

E-mail addresses: [email protected] (W. Fu), [email protected] (S. Liu),

[email protected](S.D. Degloria), [email protected](S.Dong), [email protected]

(R. Beazley).

degree to which the landscape facilitates or impedes movement

among resource patches (Taylor et al., 1993). Such connectivity is

considered important for ecological processes suchas movementof

genes, individuals, species, and populations over multiple spatio-

temporal scales, especially in fragmented landscapes (Minor and

Urban, 2008). Landscape connectivity is currently viewed either

structurally, where connectivity is entirely based on landscape

structure(usuallyhabitat contiguity),or functionally, where behav-

ioral responses to landscape elements (patches and edges) are

considered with the spatial structure of thelandscape (Tischendorf

and Fahrig, 2000). Being an essential issue of landscape analysis,

approaches andindicesusing differentkinds of measurements have

been suggested (Marulli and Mallarach, 2005; Pascual-Hortal and

Saura, 2006; Minor and Urban, 2008). Among many indices devel-

oped, a newly proposed index Probability of Connectivity (PC)which based on graph theory performs well in practical landscape

analysis for many good properties in expressing landscape dynam-

ics (Saura and Pascual-Hortal, 2007). Recent applications of this

index, however, have not used effective (minimum-cost) distances

to combine features of the landscapematrix with features of source

patches for connectivity analysis yet. These relevant evaluations of

connectivity broadly applied in biological conservation and open

area planning at large scale need to consider the impacts of road

networks.

Currently, road construction, especially high-level road con-

structionis occurringveryrapidly in China. In2007, thetotal length

0169-2046/$ see front matter 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.landurbplan.2009.12.009
http://www.sciencedirect.com/science/journal/01692046http://www.elsevier.com/locate/landurbplanmailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://localhost/var/www/apps/conversion/current/tmp/scratch597/dx.doi.org/10.1016/j.landurbplan.2009.12.009http://localhost/var/www/apps/conversion/current/tmp/scratch597/dx.doi.org/10.1016/j.landurbplan.2009.12.009mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://www.elsevier.com/locate/landurbplanhttp://www.sciencedirect.com/science/journal/01692046


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W. Fu et al. / Landscape and Urban Planning 95 (2010) 122129 123

Fig. 1. Location of studyarea in Xishuangbanna. Legend: China,Yunnan Province,Xishuangbanna,studyarea,expressway, first level road, secondlevelroad,thirdlevelroad,

fourth level road.

of roads in China reached 357 104 km including 5.36104 km

of expressways. To realize the modernization of its transporta-

tion network, Chinas national road construction plan calls for

200104 km of highways, including 6.5 104 km of expressways

by 2010; by 2050, the plan calls for 400 104 km of highways.

The Great Development in West China project has resulted in

many high-level roads being constructed in Yunnan Province to

connect China with Southeast Asia (Liu et al., 2008). As highways

account formorethan93% of the total transportationroutes inYun-

nan, Xishuangbanna, as a border administrative region under the

development of tourism, inevitably experiences the drastic pres-

sure of road network expansion on local ecosystems. In addition,

Xishuangbanna is a key biogeographic area and a hotspot for bio-

diversity (Myers, 1998). Therefore, research on the effects of road

networkson landscape connectivityin thisregionof China willhave

significant practical importance for biodiversity conservation and

road network planning.

The objectives of this study are to: (1) assess landscape changes

due to road networks in a typical landscape in southwest China by

applying a landscape connectivity index combined with effectivedistances; and (2) characterize the barrier-fragmentation effect

of road networks on specified landscapes under different ecolog-

ical process scenarios. The focused ecological processes are those

that occur in suitable patches and could benefit from increasing

connectivity from a landscape-level perspective. The different dis-

persal/movement distances are considered broadly representative

of different ecological processes in the study.

2. Materials and methods

2.1. Study area

Xishuangbanna is situated in a transitional zone from tropical

Southeast Asia to subtropical East Asia, and is at the junction of the

Indian and Burmese plates of Gondwana and the Eurasian plate of

Laurasia which has great biological diversity and typical ecosys-

tems (Zhu et al., 2006). The elevation varies from 475m to 2430 m

above sea level. Annual mean temperature is approximately 21 C

and the annual precipitation is over l500 mm. Primary vegetation

can be organized into four main types: tropical rain forest, tropi-cal seasonal moistforest, tropical montane evergreen broad-leaved

forest and tropical monsoon forest (Zhu, 2006). Human population

increased from 220,000 in 1953 to 990,000 in 2000 in Xishuang-

banna, most of which is now distributed in the city of Jinghong.

Due to increasing population pressures, large areas of tropical rain

forest and shifting cultivation lands at lower elevations have been

converted to rubber plantations during the past 50 years, thereby

inducing the harvest of forests at high elevations or steep slopes to

meet the requirement for new arable land (Li et al., 2007). Roads

motivated by human encroachment in the study area promote

landscape modification significantly (Li et al., 2009).

The location of the study area (21.721.8N, 100.4100.8E) is

situated near the boundary of Jinghong city and Mengla county

for our landscape connectivity analysis independent of impacts ofthe Lancang River which was excluded from our study ( Fig. 1).

Within the entire region of Xishuangbannan, the study area pos-

sesses high biodiversity. Forest land occupies 40.08% of the land

area (1600km2) including nearly all four types of vegetation in the

study area.Therefore, there are sufficientand diverse forest patches

to accommodate ecological processesin the study region. However,

human exploitative activities that are supported by the high den-

sity of road networks (0.006km/km2) lead to severe disturbance of

the regional landscape.

2.2. Data acquisition

The road data in vector format for this study were digitized

using the present transportation map of Yunnan province and also


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124 W. Fu et al. / Landscape and Urban Planning 95 (2010) 122129

confirmed using the 1:250,000 scale roaddatabaseproducedby the

National Fundamental Geographical Information Centre in 2005.

Roads in this area can be divided into five levels: expressway, first

level (national road), second level (mainly county citycounty city

road), third level (mainly county citytown road) and fourth level

(mainly townvillage and villagevillage road). Landsat TM images

in 2007 were classified as forest, grassland, farmland, saline land

and marshland, urbanland, rural residential land, and construction

land for land cover mapping. Then the map was validated using

ground truth data which indicated the overall accuracy of at least

90% and Kappa coefficient of 0.88.

2.3. Connectivity analyses

The methodology in this study is to analyze the fragmentation

and barrier effects of road networks usingthe landscape connectiv-

ity index, PC. The fragmentation effect is related to the dissection

processes of increasing the number of landscape patches, decreas-

ing interior habitat area, or increasing the extent of opening edges

(Li and Reynolds, 1993). The barrier effect is with regard to patch

isolationeffects whichinhibitthe movement of organisms forfocal

ecological processes occurring between patches (Richardson et al.,

1997). The parameters of resource patch size reduction and the

resistance value of landscape elements in PC calculations repre-sent the two effects of road networks. The calculation of the index

proceeds under different scenarios of ecological processes such as

plants or animals dispersal movement defined by movement abil-

ities. In each scenario, the landscape without road network was set

as the control test ofthe landscapeto distinguishthe effects ofroads

from other landscape elements on ecological processes. Thus, the

analysis consists of several steps as described below.

2.3.1. Resource patch selection and fragmentation effect

quantification

According to Liu and Li (2008), the first processing step of

resource patch selectionshouldbe carried outfollowing these three

principles: (i) the patch should have high landscape suitability, (ii)

the patch area should be large enough to sustain ecological pro-cesses, and (iii) accessibility from any resource patch to others

should be considered.

Landscape suitability here means the fitness of the land-

scape type to support a particular ecological process such as

decomposition, nitrogen cycling, pollination, seed dispersal, ani-

mal mitigation, bioturbation and so forth (Forman and Deblinger,

2000; Nichols et al., 2008). A series of dispersal movements of

plants or animals that could benefit from improved connectivity

in specified patches was taken into account in this study. However,

available seed dispersal distance information seems more thanany

other ecological process in Xishuangbanna. Most of the seeds are

reported to travel a short distance to other places within approx-

imately 1km (Tang et al., 2008; Zhou et al., 2007), while others

have long dispersal distances to as much as 27km, though thisrarely occurs (Xiao and Gong, 2006). Without additional research

related to seed dispersal by large mammals, the long-distance seed

dispersals in Xishuangbanna may exist but are rarely studied as

compared with the discovery of 512km seed dispersal distances

by elephants in other parts of world (Theuerkauf et al., 2000). High

landscape suitability is indicated by the forest land cover type as

this type accommodates more ecological processes of interest in

this study (Liu et al., 2002). The size of patches is both process-

and region-specific. The lack of references regarding patch size for

selected ecological processes in Xishuangbanna made the selection

of patch size difficult. For identifying and comparing the degree of

impact, a set of minimum-sized patches needs to include as many

ecological processes as possible based on the methods of Pascual-

Hortal and Saura (2007) and Platt and Lowe (2002). Accessibility

is considered in determining size of the study area and in model-

ing dispersal movementdistance for our patch ecological processes

within the study area. Finally, we defined resource patches in the

study area as forest land cover with canopy density higher than

30% and size larger than 25ha based on the characteristics of the

environment and related researches mentioned previously (Liu et

al., 2002; Platt and Lowe, 2002; Pascual-Hortal and Saura, 2007).

The fragmentationeffect was quantified by the reductionin area

of resource patches due to the occupancy and incision by road net-

works. Patches were intersected by road networks with the true

width as follows: expressway, 30 m; first level road, 15m; second

level road, 10m; third level road, 7m; fourth level road, 3.5 m.

2.3.2. Scenario analysis

According to Kahn and Wiener (1967), scenarios were defined

as hypothetical sequences of events constructed with the pur-

pose of focusingattention on causal processes and decision points.

The quantitative (modeling) and qualitative (narrative) traditions

of scenario analysis have been applied in different fields of study in

addition to addressing environmental issues of concern here.

To test road network effects on landscape connectivity, this

study modeled a series of ecological processes (dispersal move-

ments of species) that could benefit from improved connectivity insuitable patches. Ecological processes are represented as different

dispersal/movement distances (scenarios) broadly taking place in

the study landscape. According to studies on dispersal distances

for different assemblages of species (Sutherland et al., 2000; Van

Vuren, 1998), four scenariosare definedin the model of PC with the

different distances and the same direct dispersal probability (pij)

set at 0.5. For each scenario, the same value of 0.5 will indicate the

probabilityof theoccurrenceof a randomevent. So,the directprob-

ability of the occurrence of dispersal movement between any two

patches equals 0.5 based on probability theory at cost-distances of

12,000 m, 8000m, 4000 m and 2000m, respectively.

2.3.3. Effective distance calculation

Connectivity analysis may consider effective distances and notjust Euclidean distances. Two layers, a source layer and a fric-

tion/resistance layer, form the input of the effective distance

computation. The source layer indicates the resource patches from

which effectivedistance is calculated. The resistancelayer indicates

both the resistance value and the geographical position and orien-

tation of all relevant landscape elements (Adriaensen et al., 2003).

Herein, the road data are treated as an individual layer other than

the mixture with other landscape elements in the land cover map

(Marulli and Mallarach, 2005). Theroad networks buffer zone layer

generated by the specific ecological zone constitutes the resistance

layer together with the land coverand slope gradientlayer(Liet al.,

2004). The land cover types are in accordance with the classifica-

tion of the TM image. The slope gradient layer is classified into five

types with equal interval of 19%. The resistance values for classify-

ing each layer are defined with reference to the literature (Marulli

and Mallarach, 2005) and consultation with local experts (Table 1).

Then, theresistance layerwas convertedto a raster withgrid size

of 25m25 m for distance calculations following the calculating

equation of effective distances (Yu, 1999).

2.3.4. Landscape connectivity calculation

The probability of connectivity index (PC) is defined as the

probability that two individuals in dispersal movements randomly

placed within the landscape fall into habitat areas that are accessi-

ble fromeach other (interconnected)givena set ofn habitatpatches

and the connections (pij) among them. In this study, the PC index

represents the probability of occurrence of focal ecological pro-

cesses defined before. The PC index is calculated by the following


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Table 1

Resistance values of different layers.

Layer Classification Buffer Daily traffic volume Resistance

Road Expressway 1000 m 25,000 8

First level 500 m 10,00025,000 8

Second level 250 m 200010,000 8

Third level 100 m 2002000 8

Fourth level 25 m 100200 8

Land cover Forest (except the source patches) (28.24%) 2

Grassland (22.34%) 2Farmland (15.54%) 3

Saline land and marshland (0.05%) 4

Urban land (11.23%) 6

Rural residential land (8.25%) 6

Construction land (2.51%) 6

Slope 019% 1

2039% 2

4059% 3

6079% 4

expression (Saura and Pascual-Hortal, 2007):

PC =

ni=1

nj=1

aiajp

ij

A

2

L

(1)

where ai and aj are the areas of the resource patches i and j, and ALis the total landscape area (area of the study region); p

ijis defined

as the maximum product probability of all possible paths between

patches i and j (including single-step paths); and pij

is determined

by the pij which is following a negative exponential as a function

of effective distances set in the study. It is given by the following

expression:

pij = edij (2)

where pij is the direct dispersal probability, dij is the effective

distances between each two patches,is a constantwhich is deter-

mined by a pair of pij and dij values. Conefor Sensinode 2.2 (Saura

and Torn, 2009) was used for all index calculations.Landscape connectivityof the study area is calculated underdif-

ferent movementability scenarioswithpij set at0.5when dij equals

2000 m,4000m, 8000 m,and 12,000 m,respectively.Also, thepaths

between any two patches withpij

over 0.5are selected to depictthe

degree of connection.

2.3.5. Percentage of importance calculation

The relative ranking of landscape elements by theircontribution

to overall landscape connectivityaccordingto a certainindex (I)can

be obtainedby calculatingthe percentage of importance (dI) ofeach

resource patch (Pascual-Hortal and Saura, 2006). It is given by:

dI (%) =I I

I 100 (3)

where I is the PC index value when the resource patch is present

in the landscape and I is the PC index value after removal of that

patch (e.g. after loss of a certain habitat patch). Theranking of value

of dPC over the landscape indicates the importance of patches in

sustaining landscape connectivity under the influence of road net-

works. The importance of the patches is classified into three levels

based on PC as high importance, medium importance, and low

importance.

3. Results and discussions

After the selection of resource patches, there were 70 patches

occupying 10.91% of the 40km40 km extent of the study area.

3.1. Fragmentation effect on landscape connectivity

As the result of the intersection of the resource patches by road

networks,the resource patches in the study area increased in num-

ber to 124, but decreased in area to 10.61% of the study area. Using

Eq. (1), road networksdecreasedPC in differentscenarios by 15.81%

on average. The effect of fragmentation on resource patches and

paths of landscape connectivity are represented through the pij

selection above 0.5 and the classification of dPCof the patches for

each scenario (Fig. 2).

According to Fig. 2, compared with the landscape without road

networks, the paths increased mainly among the fragments of the

former resource patches (without road network). However, the

length andlocationof theincreasedpaths varied withdifferent eco-

logical process scenarios. In the entire landscape of the study area,

the total PC value decreased by 3.94%, 6.52%, 16.32% and 36.47% in

different scenarios with pij of 0.5 at distance of 12,000 m, 8000m,

4000m, and 2000m, respectively. At the patch scale, the impor-

tance of each patch changed only slightly as the movement abilitydecreased (from top to bottom in Fig. 2). In each scenario, the large

patches of high importance (dPC) reduced in area as the surround-

ing patches increased to the average importance.

The fragmentationinducedby roadnetworks generallyhad neg-

ative influence on the landscape in sustaining connectivity in the

study area, especially on those patches directly crossed by road

networks. The fragmentation affected to a greater degree land-

scapes that sustain ecological processes of low movement ability

(dispersal distance at approximate 2000 m). In other words, the

movements of those ecological processes which were often con-

strained in a limited number of patches within small extent were

more vulnerable to fragmentation of the patches.

3.2. Barrier effect on landscape connectivity

The quantification of the barrier effect using the resistancevalue

increased the effective distances between resource patches among

2415 paths associated with the scenarios. Based on Eq. (2), the

decreased rate of the PC index value was 11.73% on average in the

study area.

The barrier effect of road networks generally decreased the

connection paths (pij 0.5) between the patches (Fig. 3). In addi-

tion, the number of paths removed by the overlay of the road

networks was 56, 20, 7 and 3, respectively with movement abil-

ity decreased. Due to the removal of paths, PC value of the entire

study area decreased by 15.30%, 15.57%, 11.33% and 4.72%, in dif-

ferent scenarios with pij of 0.5 at distance of 12,000 m, 8000m,


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Fig. 2. Fragmentation effect on landscape connectivity under different ecological

processes. Legend: patch, path, study area, low importance, medium importance,

high importance. (AD) Four scenarios with pij set at 0.5 at dij of 12,000 m, 8000m,

4000 m and 2000 m, respectively. (i) With road network overlay; (ii) without road

network overlay. Path: cost-distance path withpij

over 0.5. Importance: classifiedas

high, medium, and low based on dPCwith equal interval.

4000 m, 2000 m, respectively. The importance of each patch, how-

ever, changed under different ecological process scenarios except

for a patch of which the importance changed from medium to low

with increase in movement ability.

Road networks had a negative effect on landscape connectivity

as a typical barrier and changed the importance of certain patches

in accommodating the ecological processes in general by increasing

the effective distance and then reducing the maximum probability

of the movementbetween any two patches. For different ecological

processes (defined by the pij = 0.5 at different distances), the road

Fig.3. Barrier effect on landscapeconnectivity underdifferent ecologicalprocesses.

Legend: patch, path, study area, low importance, medium importance, high impor-

tance. (AD) Four scenarios with pij set at 0.5 at dij of 12,000m, 8000m, 4000m

and 2000 m, respectively. (i) With road network overlay; (ii) without road network

overlay. Path: cost-distance path with pij

over 0.5. Importance: classified as high,

medium, and low based on dPCwith equal interval.

network exhibited different degrees of barrier effect on the land-

scape, generally,withthe movementabilitydecreased,the negative

effect degree increasedand thendecreased, reachingthe maximum

at 8000m (pij = 0.5). Results indicated that certain ecological pro-

cesses of higher movement ability (dispersal movement distance

at approximate 8000m) within the large extent of the landscape

were more influenced than those within the small extent by the

networks of roads as a type of barrier. However, the barrier effect

merely changed the importance of patches for landscape connec-

tivity under different ecological process scenarios


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3.3. Combined effects on landscape connectivity

Up to this point, we have evaluated the fragmentation and

barrier effects individually on landscapes to sustain the connectiv-

ity for different ecological processes. The combined effect of the

two will be analyzed here and compared with the two individ-

ual effects. Both the reduction in source patches and the increases

in resistance value were calculated. The combined effects of frag-

mentation and barriers produced an average decreased rate of PC

value in comparison to the control test of 32.78% under differ-

ent ecological process scenarios. This result was higher than the

Fig. 4. Combined effects on landscape connectivity under different ecological pro-

cesses. Legend: patch, path, study area, low importance, medium importance, high

importance. (AD)Four scenarioswithpij setat0.5at dij of12,000m,8000m,4000m

and 2000 m, respectively. (i) With road network overlay; (ii) without road network

overlay. Path: cost-distance path with pij

over 0.5. Importance: classified as high,

medium, and low based on dPCwith equal interval.

Fig. 5. Comparisonof differenteffects underdifferent ecological processes. Legend:

AD. Label: PC num (1015 m2 ), effects. Control: without road network overlay; f:

fragmentation effects; b: barrier effects; c: combined effects. (AD) Four scenarios

with pij set at 0.5 at dij of 12,000m, 8000 m, 4000m and 2000m, respectively.

fragmentation effect of 15.81% and barrier effect of 11.73%, indi-

vidually.

The combined effects made the importance changes of patchesfor landscape connectivity a little different from those under indi-

vidual effects (Fig.4). Althoughit is the same as the individual effect

that most of the patches importance decreased except that few

patches decreased, the changes of importance took place in differ-

entpatches at differentdegree. The total decreased PC value rate to

control test was 26.34%, 27.25%, 31.62% and 45.92%, with pij of 0.5

at distance of 12,000 m, 8000m, 4000m, and 2000m, respectively

which are higher than the individual effects.

Comparing the different effects, the combined effects of road

networks in the study area resulted in a significantly higher

decrease in PC value thanthe two effects considered independently

as shown in Fig. 5. Although each effect decreased landscape con-

nectivity, the degree of decrease differed with movement abilities

of ecological process defined before. The fragmentationaffectedtheecological processes of low movement abilities (dispersal move-

ment distance at approximate 2000 m) to a greater extent. The

barrier effect affected the medium high movement abilities (dis-

persal movement distance at approximate 8000m) to a greater

extent. The combined effects affected the ecological processes of

low movement abilities (dispersal movement distance at approx-

imate 2000m) to a greater extent. To summarize, the combined

effects, rather than the individual effects, of road networks may

considerably decrease landscape connectivity and further degrade

the landscape function for facilitating movement of organisms.

4. Summary and conclusions

This case study utilized landscape connectivity as a new thrustfor the study of ecological effects of road networks. The study

took different dispersal/movement distances broadly representa-

tive of selected ecological processes to analyze landscape pattern

and associated function changes related to road networks and the

surrounding landscape. Our results indicate that (i) the PC index is

a good measure of landscape dynamics, (ii) landscape connectivity

is a simple approach for scenario analysis, and (iii) the analysis of

landscape connectivity is useful for advancing our understanding

of the ecological effect of road networks.

Previously, many studies have concentrated on the effects of

road networks at large spatial scales related to land cover changes

(Liu et al., 2006), habitat fragmentation (Theobald et al., 1997),

barrier effects (Forman and Deblinger, 2000), and function change

(Wen et al., 2007). Roads are considered as sources of fragmen-


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Fig.6. Theimpactdegreeof thedifferentroads in quantificationof barrier effect. Legend: cross-roadmovement.Accumulated resistance = 840=320.dij= 8(40d0 + 39 25).

Expressway accumulated resistance = 81= 8. dij= 8d0 . Fourth level road. d

ij: effective distance of the expressway; d

ij: effective distance of the fourth level road; d0: the

distance of the first grid the movement arrived (equal set for the comparison of the two levels of roads).

tation, barriers for most of the species considered, and causes oflandscape function degradation. Road networks appear to have a

disadvantage over other anthropogenic causes as elicitors of land-

scape fragmentation and barrier to the ecological processes within

the landscape (Forman et al., 2003). With the extension and con-

nection of roadnetworks, the landscape becomes more fragmented

and less well connected.

Few studies, however, have incorporated a series of ecologi-

cal processes in the quantification of road effects due to the large

requirements of data to characterize the behavior of target species

and logistical difficulties of investigations at such scales. Estima-

tion of the road effect zone and the selection of target species may

be very difficult at landscape scale for the reason that the extent

of road effect zones may be different for different regions (Forman

andDeblinger, 2000; Li et al., 2004). In addition, target species can-not represent all of the important species found in a given study

area. Therefore, the road effect zone and the dispersal distances

of species as reported in the literatures are utilized to quantify

the road effect and to integrate ecological processes in the study.

The fragmentation effect was quantified by the direct dissection of

resource patches whilethe barrier effectwas quantified bythe allo-

cation of the cost in effective distance calculations. Using the same

cost value of theroad networks mayseem counterintuitive, butthe

large extentof the ecological effectzones forhigh-levelroadscould

reveal thehigher barrier effecton theecologicalprocesses than the

lower level roads. The large extent of the ecological effect zone

has already been considered as having a higher degree of impact

by higher level roads (Li et al., 2004). This impact, through cross-

road movement of ecological processes, is the focus movement for

quantifying the barrier effect (Dyer et al., 2002). The higher levelroads (such has expressway) represent higher accumulated resis-

tance values andeffective distances thanthe lower levelroads (such

as fourth level roads) (see Fig. 6).

This study evaluated landscape changes by combining quan-

titative methods with the PC index to characterize the effect of

road networks on landscape connectivity. Herein, the separate

and combined fragmentationbarrier effects were calculated and

compared on the landscape by PC in scenarios of different eco-

logical processes. In conclusion, the fragmentationbarrier effect

had a negative effect on landscape connectivity. The combined

influence of the two effects had much higher degree of impact

than the individual effects. The different ecological processes may

endure different degrees of threats from different types of road

network effects: the ecological processes of low movement abil-ities (dispersal movement distance at approximate 2000m) are

impacted more from the fragmentation effect the combined effects

of fragmentation and barriers; those of medium high movement

abilities (dispersal movement distance at approximate 8000m) are

impacted more by the barrier effect.

While this study provided an example of the usefulness of land

connectivity analysis in road network assessment, additional stud-

ies are still required. For example, more detailed investigations

related to dispersalmovement behavior of targetspeciesneed to be

conductedat the same spatial scalefor improving the application of

the PC index for biodiversity conservation. Furthermore, the study

only considered two effects of road networks forthe specified land-

scape in one typical study area. The applicability of our results is

only constrained for road network planningand assessment in this


8/8


area, and should not be used to develop policies to prevent land-

scape connectivity from other negative effects of road networks.

Also,additional analysis should be expanded to other landscapes of

high heterogeneity with integration of detailed dispersal informa-

tion of species to identify the ecological effects of road networks at

suitable spatial scalesusingthePC index as themetricfor reference.

Acknowledgments

The paper was financially supported by the National Natural

Science Foundation of China (40871237), National Basic Research

Program of China (No. 2003CB415104) and was inspired by San-

tiago Saura and Luca Pascual-Hortals research on landscape

connectivity measurements (PC) and software (Conefor Sensinode

2.2) development.

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Wei Fu, Ph.D., School ofEnvironment,BeijingNormalUniversity,Major inLandscapeEcology. Her research interests include: GIS application in landscape ecology andecological processes, road ecology, environmental and ecological planning.

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