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Ecological Engineering 46 (2012) 24– 33

Contents lists available at SciVerse ScienceDirect

Ecological Engineering

jo u r n al hom ep age: www.elsev ier .com/ locate /ec oleng

cological restoration planning based on connectivity in an urban area

eyong Yua,∗, Bin Xuna, Peijun Shia, Hongbo Shaob, Yupeng Liua

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, ChinaInstitutes of Life Sciences, Qingdao University of Science & Technology, 266042 Qingdao, China

r t i c l e i n f o

rticle history:eceived 1 March 2012eceived in revised form 17 April 2012ccepted 28 April 2012vailable online 24 May 2012

eywords:rbanizationcological sustainabilitycological engineeringcologically primary areaand use/land cover

a b s t r a c t

In urban areas, the competition between land development and ecological conservation is intense. Tomitigate the negative effects of urbanization, we developed a methodology to plan a spatially explicitconservation network based on widely available remotely sensed data and other auxiliary data. First, asan area of strategic significance for the conservation of regional flora and fauna and for maintaining highenvironmental quality to promote human well-being, the remaining natural and semi-natural areas wereidentified as ecologically primary areas. Second, integrating the graph-theoretic model, we evaluatedthe overall connectivity of core habitats and identified which core habitats were and what landscape-pattern-context was most important to the conservation network. Third, focusing on maximizing theability to utilize the existing conditions to reduce construction costs while meeting the ecological aims,an ecological corridor system was suggested to improve both the ecological connectivity and the livableenvironment. Finally, a comprehensive optimization scheme was suggested for the overall conservationplanning. We concluded that successful and pragmatic ecological restoration planning in an urban area

should consider the requirements of socially, economically and ecologically sustainable development andoptimize the structure and function of the urban ecosystem, rather than maximize certain ecological aims.Our planning has been adopted by the local government, and a legally binding system of regulations hasbeen established to guarantee the plan’s enforcement. Our findings may provide an actual reference for theworld, especially to manage the intertwined issues of economic development and ecological sustainabilityin rapidly urbanizing areas.

Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved.

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. Introduction

Globally, urbanization, agricultural intensification and defor-station are the main forces that cause biodiversity loss andcosystem degeneration. Of these forces, urbanization is consid-red to exert the greatest local pressure on biodiversity. In urbanreas, due to the stark contrast between the urban environmentnd the pre-existing natural condition, biological diversity is chieflyaintained in small fragments of indigenous vegetation that are set

side during development (Marzluff and Ewing, 2001; Rudd et al.,002). On a regional scale, urbanization is particularly importantnd should be considered to be a critical threat to the conversa-

ion linkages, especially in critical areas (Gurrutxaga et al., 2010).n urban areas, the requirements of regional sustainable devel-pment are usually intertwined with the problems of land use

∗ Corresponding author at: State Key Laboratory of Earth Surface Processes andesource Ecology, Beijing Normal University, Beijing 100875, China.ax: +86 1058800219.

E-mail address: dyyucas@163.com (D. Yu).

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925-8574/$ – see front matter. Crown Copyright © 2012 Published by Elsevier B.V. All rittp://dx.doi.org/10.1016/j.ecoleng.2012.04.033

ntransigence, fragmentation and deterioration of the quality ofatural systems.

Urbanization is irreversible across the world; therefore, it is nec-ssary to maintain urbanization at a rational scale and speed toitigate the negative effect on the ecosystem. The management of

onnectivity is of significance to urban sustainable development.onnectivity of habitat patches is thought to be important for theovement of genes, individuals, populations and species over mul-

iple time scales (Minor and Urban, 2007). Connectivity is especiallymportant when a habitat is rare, fragmented, or otherwise widelyistributed and can be a critical component of conversation plan-ing (Flather and Bevers, 2002).

Several researchers have argued that efforts to increase connec-ivity should be made only after attempting to increase the sizend quality and meanwhile suggested that the number of pro-ected areas and the effectiveness of connectivity enhancementor species persistent in a changing climate is less certain than

he effectiveness of increasing the size of protected areas (e.g.,odgson et al., 2009). However, the distances that many speciesre expected to move are too great to be accommodated by simplyxpanding reserve boundaries (Krosby et al., 2010). The primary

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ationale for increasing connectivity is that if the effects of land-over fragmentation can be mitigated, the ability of species to movento new regions as climate changes should be enhanced, therebyecreasing the probability of extirpation or extinction (Krosby et al.,010). Therefore, managing connectivity means the actions of tak-

ng measures to facilitate dispersal of organisms among natural,emi-natural and even human-dominated areas through the con-ection of ecological corridors or stepping stones with habitats orhrough actions that increase matrix traversability/permeability.

Graph theory provides a simple solution for unifying and eval-ating multiple aspects of habitat connectivity and can be appliedt the patch and landscape levels to quantify either structural orunctional connectivity (Minor and Urban, 2007). Empirical evi-ence indicates that the population dynamics for a species living

n fragmented landscapes, particularly for small mammals, inver-ebrates and birds, follow a pattern of stochastic local extinctionnd recolonization, thereby occurring as metapopulations (Hanski,994). Graphs provide a simple but effective means of depictinghe overall structure of a habitat mosaic in terms of a metapopula-ion structure (Urban et al., 2009). The application of graph theoryas undergone explosive growth in many disciplines, including

andscape ecology and conservation biology (Urban et al., 2009).he guide to construction, analysis and application for conserva-ion using patch-based graphs of landscape connectivity has beenopular among scientists and decision-makers (see Galpern et al.,011).

Ecological restoration is the process of assisting the recovery ofn ecosystem that has been degraded, damaged, or destroyed andhe goal of this process is to emulate the structure, functioning,iversity and dynamics of the specified ecosystem using referencecosystems as models (Lewis, 2005). There are multiple ecologicalestoration schemes for sustainable land management reported byhe published literatures (e.g., Schuller et al., 2000; Huang et al.,009; Zhang et al., 2012), however, few are based on the relativelyew graph theory from the view point of connectivity. Our objec-ives in this work were to (1) make use of remotely sensed andther spatial data in a rapid urbanization area to identify ecolog-cally primary areas and core habitats and assess their landscapeonfiguration and context; (2) analyze the areas’ contribution tocological connectivity using a graph-theoretical approach; and3) develop a methodological prototype of pragmatic conservationlanning that considers the requirements of socially, economicallynd ecologically sustainable development to be applied in otherreas undergoing rapid urbanization.

. Study area

Shenzhen is located in the middle of Guangdong provincen China between 22◦26′59′′ to 22◦51′49′′N and 113◦45′44′′ to14◦37′21′′E (Fig. 1) and has a total area of 1991.64 km2 (SSE, 2011).henzhen is adjacent to Dongguan City and Huizhou City in theorth and Hong Kong in the south and is bounded by the sea in theoutheast.

Shenzhen is located in the subtropical marine climate zone, theean annual temperature is 22.4 ◦C and the annual temperature

ange is 14.1 ◦C. The mean annual total sunshine hours are 2020 h,he mean annual precipitation is 1933 mm, and the rainfall duringhe rainy season (April to September) accounts for 85% of the totalrecipitation in a year.

Shenzhen region is rich in flora and fauna with 530 known

pecies of terrestrial wild animals, including 389 types of birds,7 types of mammals, 31 types of amphibians and 73 types of rep-iles, of which five are nationally protected class I wild animalsPython molurus, Tragopan caboti, Neofelis nebulosa, Paa spinosa,

tnl

ering 46 (2012) 24– 33 25

latysternon megalorcephalum) and 43 are nationally protectedlass II wild animals (e.g., Lutra lutra, Accipiter soloensis, Falco tin-unculus Linnaeus, Malayan pangolin, Macaca mulattca). In total,889 types of wild vascular plants are present, which belong to05 subjects and 857 genuses.

Shenzhen was municipalized in 1979 and has changed fromn impoverished fishery village to a modern megalopolis. By thend of 2010, the permanent population totaled 10.37 million (SSE,011). Shenzhen is famous for its great socioeconomic achievementut is facing environmental problems. For instance, accompany-

ng the rapid urbanization process, urban sprawl has encroachedn many flourished forests and vegetation coverage has decreasedreatly. In addition, the built-up of the city pushed into ecologicalands in a disorganized manner with little or no attention paid toegional biological conservation, meaning that the biodiversity isnly maintained in isolated island-shaped mountains. All of theseactors have posed a great threat to the regional biodiversity andndermined the utility of the ecosystem for human well-being.

. Materials and methods

.1. Database development

.1.1. Land cover mappingThe selection of specific image resolutions and land cover

chemes is one of the critical steps in our study efforts. For mostpplications, coarser-scale data (e.g., 30-m Landsat) is adequate forharacterizing the landscape context, although data from multipleensors may ultimately be appropriate or necessary based on dif-erent objectives of landscape monitoring (Townsend et al., 2009).yperspatial data sources provide fine resolution but are not suit-ble for monitoring large areas and may, in fact, not be able torovide coverage of the full spatial extent necessary to characterizehe external context of a region (Townsend et al., 2009). In addition,0-m Landsat TM imagery offers significant benefits, most notably

long record of data availability (since 1982) and low cost.We used Landsat remotely sensed data as the primary data

ource for derivation of the land-coverage information. Two scenesf cloud-free Landsat TM (Bands: 1–7, Path: 121, Row: 44 and Path:22, Row: 44) images for vegetation growth season on August

n 2005 were selected to extract land cover. We also collectedther auxiliary data, including 1:10,000 topographic maps and highesolution air photos to support the geometric rectification andlassification on the Landsat TM images.

Based on the context and objectivity of this study, we created scheme for mapping land cover for 2005 Landsat TM imageshat were classified into five categories: (1) built-up, (2) cropland,3) forests, (4) orchard and (5) water. The land cover map wassed to estimate the net primary production (NPP) of differentcosystems and was also categorized into two classes: habitat (for-st and orchard) and nonhabitat (other types). In the study area,ragmentation of the habitat for vegetation-dwelling species thatre dependent on contiguous vegetation is of primary concern toesource managers. In 2005, the Shenzhen government launched aaw to adjust the role of the orchard, mainly consisting of Litchihinensis Sonn and Dimocarpus longan Lour from the traditionalgricultural fruit harvest, to ecological usage and stipulated thatesticides, fertilizers and fruit harvest were prohibited such thathe orchard could serve as a potential habitat-like forest for certainpecies.

We used global positioning system (GPS) instruments to obtainhe exact location of ground control points (GCPs), while simulta-eously investigating the features of different land cover at these

ocations. In total, 389 evenly distributed GCPs were used to make

26 D. Yu et al. / Ecological Engineering 46 (2012) 24– 33

Fig. 1. The location in China of Shenzhen city (a) and Guangdong province (b) and the land cover for Shenzhen in 2005 derived from Landsat TM satellite images (colorp r is ref

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anel). (For interpretation of the references to color in this figure legend, the reade

eometric corrections of each TM image, with a root mean squarerror (RMSE) less than 0.5 pixels being observed. Over 50 fieldnvestigation sites were used for each land coverage type. We pro-ected all images into Universal Transverse Mercator (UTM) mapoordinates and conducted geometric rectification with 1:10,000opographic maps as the base. A supervised classification approachas chosen for mapping the land cover using the maximum like-

ihood method. The classification result is verified and modifiedccording to field survey and high-resolution air photos. The accu-acy of the images was assessed using the ground truth dataollected during the field survey. We used error matrices to reporthe accuracy between classification results and field reference sam-les. With an overall accuracy of 89.5%, the land coverage mapeets the need of this study. The land coverage map for 2005 is

llustrated in Fig. 1.

.1.2. MODIS-based NDVIModerate Resolution Imaging Spectroradiometer (MODIS) Nor-

alized Difference Vegetation Index (NDVI) product (MODIS/Terraegetation indices 16-day L3 global 250 m, freely downloadedrom: https://wist.echo.nasa.gov/api/) for 2005 was selected toerve as one of the key inputs for estimating the fractional veg-tation coverage and NPP. These NDVI data are 16-day compositesf atmospherically corrected maximal value at 250 m spatial reso-

ution. We produced a time series of a 32-day composite product ofhe maximal value. Given that each period covers 32 days, one yearherefore includes approximately 11 time series of the compositeroduct of maximal NDVI.

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erred to the web version of the article.)

We designed a linear NDVI-decomposed model to obtain theDVI for the three vegetation types (cropland, forest and orchard)y multiplying the MODIS-NDVI by their area percentages in aODIS pixel. The NDVI values of other land cover types were

et to zero. Thus, we obtained a MODIS-based NDVI productn a Landsat TM scale. If a MODIS pixel consisted of a landover type on a Landsat TM scale, usually thought of as a pureODIS pixel, the NDVI-decomposed model actually takes no

ffect; however, if a MODIS NDVI pixel is a mosaic of differentand cover on a Landsat TM scale, the results are more objec-ive than allocating the same NDVI value to different land coverypes.

.1.3. Meteorological data interpolationThe meteorological data required as input for the Carnegie Ames

tanford Application (CASA) NPP model are the monthly mean airemperatures, monthly total precipitation and monthly total solaradiation in 2005. To obtain spatially extrapolated meteorologicalata, we selected the monthly mean temperature, total precipita-ion and solar radiation from seven weather stations around andithin the Shenzhen area and bilinearly interpolated these data

nto raster-format images with a spatial resolution of 30 m, whichorresponds to a Landsat TM pixel.

.1.4. Vegetation coverageIn vegetated landscapes, satellite NDVI values are more closely

elated to fractional vegetation cover (fc) than other vegetation

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ndices (Carlson and Ripley, 1997). The annual mean fractional veg-tation coverage (fc) was calculated using

c = 111

11∑i=1

n∑j=1

NDVIij − NDVIsNDVIv − NDVIs

, (1)

here n is the number of pixels across the study area, NDVIij is theODIS-based NDVI value of the ith time serial and j pixel. NDVIv is

he NDVI value when the pixel consists of pure vegetation, NDVIss the NDVI value when the pixel is constituted by bare soil. Basedn the results of 96 samples from field investigations, NDVIv andDVIs were averagely set to 0.676 and 0.023, respectively.

.1.5. NPPNPP is the amount of solar energy converted into chemical

nergy through the process of photosynthesis and represents therimary source of food for Earth’s heterotrophic organisms. NPP

oss may affect the composition of the atmosphere, freshwatervailability, biodiversity and the ecological adjustment mechanismf energy supply and distribution (Field, 2001). Primary produc-ion, inferred from remotely sensed data obtained using NDVI, canrovide a reliable basis for stratifying surveys of biodiversity byighlighting areas of potentially high biodiversity across large areasBailey et al., 2004). In this study, NPP serves as one of the keyndicators representing the quality of the habitat. NPP was calcu-ated by our previously improved CASA-NPP model (Yu et al., 2009,011). The improved CASA-NPP model used in this study requireshree key inputs: (1) remote sensing inputs (land cover, MODIS-ased NDVI); (2) monthly surface meteorological inputs (monthlyolar radiation that was used to estimate absorbed photosynthet-cally active radiation (APAR)), monthly average temperature and

onthly total precipitation, which were used to estimate the tem-erature stress coefficients (Tε1, Tε2) and moisture stress coefficientWε); (3) biome-specific coefficients (observed NPP, ε and εmax).ased on the land cover, observed NPP, temperature stress coef-cients and moisture stress coefficient, the maximal light usefficacy (εmax) of the vegetation type was estimated to producehe light use efficiency (ε) of the vegetation type, which was sub-equently used with the APAR to produce the monthly NPP. Finalstimation of the annual NPP was obtained by adding the 11 timeeries of NPP in a year.

.1.6. Road networkThe 30-m resolution of Landsat TM does not guarantee an

ccurate presentation of linear road routes in the region; there-ore, special attention was paid to the incorporation of completeoad routes such that their fragmentation-barrier effects could beisually expressed during the step of conservation planning. The:10,000 road networks in vector format were provided by the localovernment, and we also validated and corrected the networkssing the multiple-spectrum SPOT images with a spatial resolu-ion of 2.5 m for 2005. Roads in this area can be divided into fivelasses: railway, highway, expressway, main artery and secondaryrtery. We obtained the road density by calculating the length ofll kinds of roads within each grid (30 m × 30 m) across the study

rea. In the road networks, railway and highway have more aver-ge daily traffic intensity and thus exert more intensive effects onhe ecosystem. The other three classes were mainly located withinrban blocks, and few measures were needed to avoid their ecolog-

cal effect such that during conservation planning, we can pay morettention to optimizing the areas where the railway and highwayross through.

rtsecvi

ering 46 (2012) 24– 33 27

.2. Identifying ecologically primary areas and core habitats

During the past three decades of rapid development, many flategions have been encroached upon by urban sprawl, and the resid-ally natural and semi-natural areas are configured as areas oftrategic significance for the conservation of regional flora andauna and the maintenance of high environmental quality for pro-

oting human well-being. As a result, core areas have a role of vitalmportance in the regulation of regional biodiversity and restrictionf disorderly urban extension beyond their intrinsic internal rele-ance. The land cover for maintaining ecosystem integrity shoulde legally protected from future urban sprawl, and we thereforeefined these regions as ecologically primary areas (EPA). Thetandards for delimiting the EPA in Shenzhen should be in accor-ance with (1) water source protection areas, scenic spots, natureeserves, stretches of primary farmland protection areas, forestsnd country parks; (2) preventing soil and water loss, mountainsnd woodlands with slope over 25◦ and uplands with elevationver 50 m are included; (3) main trunk rivers, reservoirs and wet-ands; (4) green space for the maintenance of ecosystem integrity;5) peninsulas and other ecologically important coastal areas. EPAsere spatially delineated by the support of DEM, land cover map

nd the Shenzhen governmental Master Plan for land use.Core habitats were identified using a combination of area

nd land cover criteria that were considered for the vegetation-welling species in the study area. Our criteria were that the coreabitat should (1) be forests or orchards that can provide resources

or the species and are less frequently visited by humans and (2)ave a minimum size greater than 0.2 km2.

We selected metrics from Fragstats (McGarigal et al., 2002). Theumber of core habitats (NC) and the mean area (MA) were selectedo provide a general summary of the amount and size of the habi-ats. The perimeter–area ratio (PAR) reflects the shape, i.e., the edgeffect, of a single core habitat. These metrics were used to describehe fragmented status of the core habitats.

.3. Construction of conservation network

We used graph theory, which is a well-developed mathematicalethod, to evaluate and manage habitats’ ecological connectivity.

graph is a set of nodes (points) connected by links (lines); a linketween two points indicates a functional connection between thewo nodes (Urban et al., 2009). A network is well-connected if everyatch is reachable from every other via a link of connected patches.

In this study, the nodes represent core habitats, and the linksndicate the dispersal ability of the species between patches (e.g.,cological flow, movement of organisms). The links were repre-ented by least-cost paths. The least-cost paths linking pairs of coreabitats were defined by two layers of data: the source layer (allhe core habitats) and the cost surface, with the latter indicating theispersal cost of an organism from the one core habitat to another.

Evaluating ecological connectivity requires the inclusion ofontinuous variables that are relevant surrogates of landscaperaversability (permeability), such as road density and vegetationoverage (e.g., Urban, 2005), as well as NPP, which is an impor-ant indicator of environmental quality and resource availabilityor biological survival. Our basis was that higher NPP and vegeta-ion coverage signifies less human disturbance and more availableesources and therefore is more easily traversable for the specieshat are sensitive or vulnerable to human disturbances. A costurface integrating NPP, vegetation cover, impervious surface cov-

rage (urban area), road density and water body was created toalculate the functional distance between core habitats. NPP andegetation cover layers were rescaled to a range of 0–100 accord-ng to the reciprocal value of each cell. Cells with higher reciprocal

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alues of NPP and vegetation coverage were thus more costly toisperse and vice versa. The road density data were rescaled to aange of 0–100 to match the range of values in the rescaled NPP andegetation coverage maps, with higher values being more costlyo traverse. Each data layer (NPP, vegetation cover and road den-ity) was considered to have equal weight in the development ofhe cost surface, thereby resulting in a final cost surface map withalues between 0 and 300. Water bodies and built-up areas wereirectly assigned the highest cost (lowest suitability) value of 300.

Cells with higher cost values were thus more costly to traversend vice versa. The least-cost paths between pairs of the core habi-ats were delineated by the support of ArcGIS 9.2 (ESRI), and theetwork was subsequently assembled. The distance between twodjacent core habitats was calculated by adjusting the centroid-to-entroid distance by subtracting the radius of each patch from thepproximate distance between the patch boundaries.

.4. Connectivity analysis

We used graph-theoretic metrics, degree, betweenness andloseness to analyze the connectivity of the conservation network.he degree of a core habitat is the number of least-cost pathsirectly connecting to it, i.e., the number of its neighbors. Patchesith low degree may be isolated and vulnerable to extinction when

he neighbors are removed. The betweenness of a core habitat ishe fraction of all the least-cost paths between pairs of other coreabitats that pass through it. Patches with higher betweenness andentrality can serve as stepping stones. The closeness of a core habi-at was calculated based on the total least-cost distance betweenhis core habitat and all of the others, with reduced relative dis-ance to all others from each core habitat yielding higher closenessentrality scores. Each metric plays a similar role in measuring theonnectivity of the landscape, and thus core habitats with a higheregree, betweenness and closeness are helpful in maintaining thetructural cohesion of the network and are conducive to the spreadr acquirement of more resources of species across the landscape.

Considering the quality of a patch, we developed another met-ic, namely, potential ecological flow (PEF), to measure the relativeignificance of a core habitat patch in maintaining ecological con-ectivity within the conservation network.

PEF from path i to path j was defined as

EF = 12

⎛⎝Qi + 1

m

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QiP∗ij

⎞⎠ (2)

PEF includes two parts, with the former depicting the basic con-itions (Qi) of patch i and the latter characterizing the mean effectf patch i in absorbing organisms from any other patch or the abil-ty of organisms produced by patch i to disperse to any other patch

ithin the conservation network. In formula (2), m is the total num-er of core habitats in the network; Qi is the area-weighted quality,epresented in this formula by the area-weighted NPP of patch i toerve as the surrogate of habitat availability for the key species;nd P∗

ijis the probability of ecological flow successfully dispersing

etween patch i and patch j. P∗ij

is negatively related with the short-st least-cost distance (d∗

ij) between each pair of core habitats. The

ispersal probability can be approximated as negative-exponentialecay (e.g., Urban and Keitt, 2001):

∗ = exp(� × d∗ ), (3)

ij ij

here � is the distance-decay constant coefficient (� < 0). If a regiononsists of an intact habitat patch across the area, in this case,∗ij

= 0, P∗ij

= 1, PEF acquires the maximal value Qmax. If a patch is

tas

ering 46 (2012) 24– 33

solated from others, d∗ij

→∝, P∗ij

= 0, PEF acquires the minimal value/2Qi. Generally, the higher the PEF score of a core habit is, the more

mportant it is to maintain ecological coherence in the conservationetwork. We set P = 0.5 and d∗

ij= 2 km, covering the most territory

iameters of the protected species in the study area (SECCP, 2006),o obtain �.

. Results and discussion

.1. Ecologically primary areas

According to our criteria, EPAs were delineated as shown inig. 2. Covering an area of 9.53 km2, which is approximately 47.94%f the study area, EPAs include the most ecological lands in thetudy area, and farmland, orchard, forest and water body compose1%, 62%, 86% and 36% of the corresponding land coverage, respec-ively. EPAs will play an important role in restricting disorderlyrban extensions and maintaining regional ecological process. EPAsre the control boundaries of urban development and should beegally protected from development in the future.

EPAs play an important role in mitigating the effect of anthro-ogenic activities on the elements within it; however, this doesot mean that elements within EPAs are isolated from the exterioregions but only that as organisms move across landscapes at aegional scale, ecological processes result in the flow of energy andaterials along a series of pathways. These flows may be impor-

ant to ecological function in influencing local ecological processes,uch as primary productivity or habitat suitability, and the extenthat land use conversion and intensification alters ecological flowscross the landscape may impact the ecological functioning andiodiversity within protected areas (Hansen and Defries, 2007).herefore, EPAs should be interpreted as the minimal guaranteeor biological conservation during the urbanization process. In fact,n EPA does not include all of the ecological elements; certainlements may still be important to maintain ecological processesetween the EPA and external areas and thus impact the qual-

ty and service of the urban ecosystem. It is more appropriate todentify EPAs when a city is first built rather than after much eco-ogical destruction, such as in the Shenzhen case. Taking remedial

easures to mitigate or eliminate human effects on an EPA is dif-cult. The area of developed land within the EPA was 153.8 km2

r approximately 7.7% of the total area, in which the urban com-on infrastructure (e.g., road networks) made up 27.33 km2 and

he other developed areas should be removed and returned to eco-ogical usage.

.2. Core habitats

A total of 329 discrete core habitats covering 687.93 km2 weredentified, accounting for approximate 34.61% of the study areas.orest core habitats primarily consist of natural and semi-naturalorest patches. Most forest core habitats were identified in theoutheast, including the Wutong Mountains, Maluan Mountains,aiya Mountains, Qiniang Mountains and the Xichong Mountains,hich are located farthest to the southeast (Fig. 2). The northeast

orest core habitats include the Huangzhukeng and Pingdi forestarks; the middle and west forest core habitats are mainly com-osed of the Bijia Mountains, Yangtai Mountains and Fenghuangountains. Comparably, human induced orchard core habitats areainly distributed in the west and northeast EPAs (Fig. 2).

The mean and median areas of forest core habitats are larger

han that of orchards; in contrast, the mean and median perimeter-rea ratios are higher for orchard core habitats, reflecting theirmaller size and narrow linear shape (Table 1). The mean and total

D. Yu et al. / Ecological Engineering 46 (2012) 24– 33 29

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ig. 2. A cumulative cost surface (described in Section 3.3) is shown in the grayscaepicted as color patches. (For interpretation of the references to color in this figur

roductivity of the orchard core patches is also lower than thatf the forest core habitats (Table 1). Generally, orchard core habi-ats consist of a large number of fragmented smaller patches (Fig. 3)ith a higher perimeter-area ratio, indicating stronger edge effects,

uffering more easily from human disturbances and thus beingore vulnerable or less insulated from exotic, invasive and par-

sitic species. Reconstructing the composition of the orchard landsithin EPAs using more indigenous species to gradually inosculateith forest core habitats is necessary.

Generally, patches are considered core habitats when the min-mum suitable area of 0.02 km2 of open vegetation is met, andhis should be the minimum size needed to sustain a local pop-lation (Warren, 1992). In our planning, the minimal area criterionf the core habitat was set to 0.2 km2, given that a smaller vege-

ation patch may be easily removed during the rapid urbanizationrocess and is not suitable to serve as a core habitat because ofhe strong edge effect and suffering from frequent human distur-ances. In fact, the minimal area standard for a core habitat may be

rsat

able 1tatistics on the features of core habitats.

Core habitat type Number of corehabitat (NC)

Mean area (km2) Median area (km2) Meaperimratio

Forest 130 4.0 0.56 148.Orchard 199 0.85 0.38 213.

a Multiplied by 10,000.b 1 Pg = 109 g.

kground (the same as for Figs. 4 and 5). The identified EPAs and core habitats ared, the reader is referred to the web version of the article.)

pecies-specific or context-specific such that there is no universalriterion to identify them (e.g., Castellón and Sieving, 2007; Goetzt al., 2009).

.3. Conservation network and connectivity

Considering feasibility, construction cost and the needs of bio-ogical conservation, we planned an approximate planar habitatetwork (Fig. 4), with corridors (least-east paths) connecting theore habitats within 10 km and uncrossing with each other, exceptor in specific vulnerable regions. The core habitats resemble a net-ork, with several large hubs being connected to multiple smalleratches to create a landscape with heterogeneous node degree and

esilience to patch removal. There are four components in the con-ervation network, with the largest one consisting of 320 patchesnd the other three components including five patches in the south,hree patches in the north and one patch in the west, respectively.

neter–area

(m/m2)a

Medianperimeter–arearatio (m/m2)a

Mean NPP (gC/m2) Total NPP (PgC)b

64 141.19 926.98 560.4270 210.56 750.57 133.41

30 D. Yu et al. / Ecological Engine

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ig. 3. Patch size and frequency for orchard habitat (a) and forest habitat (b). The-axis and secondary y-axis indicate frequency and area, respectively. The x-axisorresponds to the log of different area ranges.

Degree values varied across the study area; however, severalarge, centrally located mountain forest hubs are present withigher degree values, indicating their connectivity with many otherore habitats (Fig. 4a). In certain heavy urbanization areas, core

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Fig. 4. Analysis of connectivity in the study with metrics of degree (panel a), betwe

ering 46 (2012) 24– 33

abitats (e.g., the northwest orchard patches in Fig. 4a) have theighest degree such that there are more path choices for the speciesf other patches to cross them and finally have access to the hubsnd, meanwhile, corridors across the urban areas are helpful toitigate urban problems, such as the heat island and turbidity

sland effect. In our planning, betweenness values are higher forhe central and east hubs surrounded by a high proportion ofrban impervious coverage, indicating high conductive to disper-al within these areas (Fig. 4b). Closeness values are highest for theore habitats in the central part of the study area, thereby signifying

closer average distance between them (Fig. 4c). Fig. 4a–c indicateshat the central core habitats were assigned with higher connec-ivity to mitigate the effect of human activities on the ecosystems.ertain other core habitats in the study area also had a high degree,etweenness, and closeness values, indicating their importance foraintaining regional ecological connectivity. The PEF metric indi-

ated that the larger forest core habitats had higher connectivitynd can serve as hotspots for biodiversity maintenance in the studyrea, whereas in the current conditions, the role of orchard coreabitats in this aspect is relatively secondary (Fig. 4d).

The least-cost distance is a more appealing approach than theuclidean distance when defining the link between core habitatsecause it is thought of as a realistic choice for the organism. Theifficult and key point is how to produce an appropriate resistanceurface for the organism. In the most published studies, the least-ost path is acquired by a resistance surface in which one type ofand coverage was set to the same resistance value according toxpert’s experiences (e.g., Adriaensen et al., 2003; Gurrutxaga et al.,010; Joan and Josep, 2005; Rabinowitz and Zeller, 2010). We firsteasured the quality of the core habitats and the heterogeneity

f the urban matrix and subsequently parameterized the cost sur-aces to calculate the least-cost distance between the core habitats.ased on the resistance value of the cell but not the same land cover,

ur method is an improvement and to some extent can avoid theubjectivity inherent in expert judgment. The least-cost path doesot mean that all the organisms commonly use the same dispersaloute. The least-cost paths should be interpreted as potential paths

enness (panel b), closeness (panel c) and potential ecological flow (panel d).

D. Yu et al. / Ecological Engineering 46 (2012) 24– 33 31

Fig. 5. The final conservation network planned in this study, which includes the set of EPAs, core habitats, corridors, buffer zones, other vegetation patches measuring under2 are cow sent a

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0 ha and defragmentation–barrier areas. Obstructive points and obstructive lines

here the road crosses through the least-cost path, whereas obstructive lines repre

hat minimize the cost of mobility, rather than functional expres-ions of the dispersal process (Theobald, 2006), which dependsn the dynamics of the populations, such as the individuals withufficient mobility capabilities (Carroll, 2006). Ecological corridorsear the least-cost paths were suggested in this study, and thispproach may underestimate the connectivity, as in actual cases,ultiple corridors may be used by an organism between core habi-

ats with an accumulative resistance that is relatively low. Habitatso not necessarily need to be structurally connected to be func-ionally connected; nevertheless, managing structural connectivity

ay, for certain taxa in certain situations, also improve functionalonnectivity (Taylor et al., 2006). Arranging corridors around theeast-cost paths indicates the largest ability to utilize the existingonditions to save construction costs and meanwhile achieve thecological aims regarding which decision-makers are usually mostoncerned in urban areas. Many other vegetation patches less than.2 km2 can be brought into the ecological corridor system (Fig. 5).

An appealing feature of graph-theoretic approaches is the easeith which they can be used to refine our knowledge of species

iology, develop better parameter estimates and provide feedbacko improve themselves in an iterative, targeted sampling approachUrban et al., 2009). The flexible data requirements and well-eveloped algorithms make these models accessible and usefulo a wide variety of applications in landscape ecology and con-ervation planning. Graph-based connectivity metrics provide anique perspective on identifying potential habitats with contenthat is more useful for management action or acquisition targetinghan statistics on the extent, length or shape of specific corridors

Goetz et al., 2009). We used degree, betweenness and closeness

etrics to evaluate and manage the ecological connectivity of aonservation network, especially in areas surrounded by a high pro-ortion of imperious coverage. In the cited publications, although

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llectively labeled as fragmentation–barrier areas. Obstructive points indicate sitesreas that are bisected by the road.

he researchers have mentioned that the quality of habitat is impor-ant, most of these studies still used the area of habitat as theurrogate for landscape traversability (e.g., Laita et al., 2010; Saurand Pascual-Hortal, 2007). Compared to an area-based metric, thePP-based PEF metric indicated a different connectivity pattern;

herefore, it is necessary to develop appropriate connectivity met-ics to provide appropriate information for conservation planninghen relating to different landscape contexts.

.4. Optimization for the conservation network

Once the core habitats and least-cost paths were identified, andhe connectivity of the conservation network was evaluated, itas easy to identify the interaction between them with main road

outes and urban areas built prior to this planning, thereby laying foundation for suggesting preventive and optimized measures.

First, buffer zones were defined around the EPAs and eco-ogical corridors with an aim of mitigating edge effects comingrom anthropogenic activities that occur in the surrounding urban

atrix. Buffer zones play a transitional role between the con-ersation network and the urban matrix. Considering biologicalequirements and actual conditions, the width of ecological cor-idor variably ranged from 12 m in urban areas to 1200 m in lessestricted areas around the least-cost paths, which was proven toe suitable to maintaining biodiversity (Bueno et al., 1995; Carlsont al., 1989; Forman and Gordon, 1986).

Second, interaction areas called ‘fragmentation–barrier areas’ inhis study and located between ecological areas with high-volume

raffic lines were identified. Road networks appear to have a disad-antage over other anthropogenic causes as elicitors of landscaperagmentation and barriers to the ecological processes within theandscape (Forman and Deblinger, 2000). Fragmentation–barrier

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reas were selected by overlaying the main traffic lines (railway,ighway) with core habitats and ecological corridors. Appropri-te defragmentation–barrier measures (e.g., ecoducts, oversizedrainages, perimeter fencing, sound barriers, and viaducts) shoulde employed to guarantee the permeability of traffic lines for fauna.

Finally, ecological corridors longer than 10 km were suggestedo connect the north and the south components with the largestomponent in the conservation network. Being of less biologicalonservation value, the west component consisting of one patchas not considered when we finished the final conservation net-ork.

The set of EPAs, core habitats, corridors, buffer zones, otheregetation patches less than 0.2 km2 and defragmentation–barrierreas commonly comprises the conservation network in the studyrea (Fig. 5).

Successful and pragmatic conservation planning in an urbanrea should consider the requirements of socially, economicallynd ecologically sustainable development. In this context, con-ervation planning with a special emphasis on creating spatiallyonnected and ecologically functioning elements is a useful tooloward providing mutual benefits for both human society and theatural ecosystem.

Conservation planning in this study provides a spatially explicitethodology that is useful for the policy-makers in decision-aking process. In fact, the planning has been adopted by the local

overnment as an important reference and basis for making theuture Urban Master Plan (2010–2020), and more importantly, aegally binding system of regulations concerning EPAs was estab-ished. A legal guarantee to protect the EPA from development inhe future is the first step to acquire the aim of the conservationlanning.

Our findings only suggest a starting point for the construc-ion of a more comprehensive conservation network for the studyrea. More concrete management strategies are required regardinghe holdings within the conservation network. Empirical data andnvestigations on understanding how organisms interact with theandscape pattern and context are still needed. The combination ofraph theory with empirical data will provide a more objective andeliable basis for conservation planning.

. Conclusions

In the rapid urbanization areas, the competition between landevelopment and ecological conservation is intense. It is necessaryo plan a conservation network that reflects the requirements ofmproving the cohesion of an urban ecosystem and reducing theegative effect of habitat fragmentation on the biological popu-

ation. For the particular needs of an urban ecosystem, namely,erving human well-being, comprehensive urban conservationlanning should reflect history and real development trends. Fur-hermore, the planning should also consider the requirements ofocially, economically and ecologically sustainable developmentnd optimize the structure and function of the urban ecosystem,ather than maximizing a specific ecological aim.

In this study, we provided a methodology to plan a spatiallyxplicit conservation network in a rapid urbanization area, suchs Shenzhen. First, as an area of strategic significance for theonservation of regional flora and fauna and maintaining high envi-onmental quality for promoting human well-being, the remainingatural and semi-natural areas were identified as ecologically

rimary areas that should be legally protected from urban develop-ent. Second, integrating a graph-theoretic model, we evaluated

he overall connectivity of core habitats and identified which coreabitats were and what landscape-pattern-context was the most

G

ering 46 (2012) 24– 33

mportant to the conservation network. An ecological corridor sys-em was suggested to improve both the ecological connectivity andhe livable environment of human society. Finally, a comprehensiveptimization scheme was proposed for conservation planning.

The methodology had clear theoretical basis, and the datasetssed in this study are widely available; most of these datasets cane obtained from remotely sensed data and other observed data.ll of these features make it possible to meet the urgent need ofecision-makers to understand the effect of anthropogenic impactn the nature and to provide a spatially explicit tool for resourceanagers to create a better plan for future development and assess

he effect of conservation planning.Globally, most of the initiatives to develop ecological networks

re at the beginning stage; therefore, practically none has been fullymplemented, and few networks can rely on legal support to pro-ide complete protection (Bennett, 2004). With a legally bindingystem of regulations, we believe that our planning will providen actual reference for the world, especially for rapidly urbanizingreas, to deal with the intertwined issues of economic developmentnd ecological sustainability. Additionally, further decisions madey stakeholders through negotiations will help to reinforce thisubject and may lead to improved methods in facilitating multiple-bjective ecological sustainability.

cknowledgements

This research was funded by the Program of National Nat-ral Science Foundation of China (41171404), the Fundamentalesearch Fund for the Central Universities and the Project of Stateey Laboratory of Earth Surface Processes and Resources Ecology.pecial thanks are given to the referees and the editors for theirnstructive comments, suggestions and editing for the manuscript.

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