Integrating Habitat-Quality and Entropy Coefficient Methods
to Derive Habitat Resistance Surfaces for Simulation of
Ecological Connectivity Network

Aicel Mae Alvarez1

1 Graduate of Master in Tropical Landscape Architecture, College of Architecture, University of the Philippines

都市計画で緑地を整備する場合、現地の生物多様性を守るには計画段階で生態学的な接続性を重視し、野生生物の移動回廊を確保することが重要だ。本稿ではケソン市を例に、都市計画が生態系に及ぼす影響をシミュレーションした。

Abstract

Closely coupled with the issue of broadscale loss of natural habitats is the challenge of maintaining and conserving
biodiversity in landscapes now dominated by human land use. In Quezon City, the LGU plans to create the Green
Lung Network featuring a chain of developments linking La Mesa Nature Reserve, the largest green space in Metro
Manila, to all the open spaces and parks in the city. However, it is focused solely on creating green corridors forhuman mobility. Hence, it is a challenge to connect La Mesa Nature Reserve to other green spaces of the city tocreate an ecological network which will disperse and enrich biodiversity all over the city.
To address this, the study aims to create an ecological connectivity network for Quezon City based on behaviour
and preference of focal species as parameters and indicators for movement along corridors. This study considers
not only the distance between habitat patches, but also the spatial, ecological, and other landscape factors to model
the integrated structural and functional connectivity of the landscape. It involves simulating the sensitivity of different land covers to impact factors. The set of procedures used in this study illustrated how the concept of ecologicalnetworks may be integrated into the urban planning process.
Keywords

Ecological Connectivity Network; Ecological Corridors; Focal Species; urban birds; corridor simulation

Introduction

Even before the Covid-19 pandemic which forced
people to limit outdoor activities and made people cravefor green and blue infrastructures, the economic growthin urban areas has been making humans more dependent on ecosystem services and biodiversity. Improving
human well-being might be a by-product of successfulconservation in urban areas, but this effect can, in turn,catalyse people to be more supportive of other efforts atbiodiversity conservation. Therefore, it is particularly
important to maximize ecosystem service values byconstructing networks that enhance the functionality ofurban ecosystem services.
Planners are faced with urban landscapes often inneed of policies directed to the conservation of

biodiversity. Planned green spaces and those that act asbuffers or fillers in urban developments are usually notdesigned as potential habitats for biodiversity, founddotted in many cities, and often cater only to the recreational needs of urban dwellers [1].
Ecological network studies provide a framework
which offers a design that regards the combination of asystem of nature reserves and their interconnections
which make a fragmented natural system coherent tosupport more biological diversity than in its non-connected form [2]. It ensures biodiversity conservation byprotecting core areas and connecting them through corridors that should enable species to move across unsuitable areas.
In the Philippines, Quezon City (QC) is one of the175Aicel Mae Alvarez

most developed cities, ranking first in economic dynamism and second in infrastructures [3]. As a result, thehabitat patches in this city are significantly affected byurbanization. Among the developments is the MRT 7line which already led to removal of 1,858 trees [4] andcan possibly lead to loss of natural habitats and further
landscape fragmentation which will go against the goalof the city government in creating a garden city.
Moreover, La Mesa Nature Reserve which acts as
mother node in the metapopulation zone of QC, an
important breeding and roosting area for a variety ofwildlife species including birds [5], and represents 36%of the green spaces in QC, is separated from other ecological cores by the MRT 7 line and wide highways traversing the city.
Along with the new transportation system, the city
government made a Comprehensive Land Use Plan for2030 which includes the Green Lung Network featuring
a chain of developments linking all the open spaces andparks in the city [6]. However, it does not address thefunctional and structural connectivity of green spaces
and is focused solely on creating green corridors forhuman mobility.

Previous research undertaken by this author [8],identified the parameters for movement along green
spaces using empirical habitat-use data on the preference of biological indicators/focal species in QC. The
study was able to identify the focal nodes which represent the key habitat patches of interest in QC between
which flows are modelled in circuit analysis for simulating an ecological connectivity network. The species
used were Eurasian tree sparrow (Passer montanus) andYellow-vented bulbul (Pycnonotus goiavier) which are
the top 2 urban bird species in Metro Manila [9]. These
species represent different functional guilds and habitat
preference that help to link the structure and functional
connectivity of green space [10]. Both are importantseed dispersal animals in urban areas which makes
them suitable model species to study the conservation
priorities closely linked to ecological and human environments [11]. Therefore, this study integrates theresulting focal nodes to the creation of resistance surfaces for modelling the connectivity of green spaces forthe two mentioned bird species found in QC.

Conceptual Framework

Assessing connectivity for development of an ecological network by using efficient models is essential to
improve these networks under rapid urban expansion.
With the Focal-Species Approach, this study employs
two target species with different uses of landscapestructure. This approach aims to optimize the continuity and conditions of green spaces within the study area
so that opportunities for individual passage may be
maximized for a wide range of species. Species that arepresent within the identified habitat patches may benefit
from the establishment of connective landscape features
between them if the composition of vegetation withinsuch patches is sufficiently similar. As similar species
may benefit to a greater extent from particular landscape attributes than others, the approach used hereeffectively aims to restore the condition of habitat andthus most likely to suit the individual requirements ofthe species present. [7]176Fig. 1: Conceptual Framework

For a corridor design to become defensible and successful, the design framework must be able to address
objections that may arise in the process specially whenusing focal-species approach [12]. Hence, the conceptual framework (Fig. 1) in this study addresses thecommon objections in the use of Ecological Network6th AFC Best Papers (2022)

Plan (ENP) in designing corridors for urban planning.
The most important step when creating corridor simulation using graph theory is the appropriate parameterization of the model which means construction of resistancesurfaces. It has a major impact on the simulation of corridors; hence, many studies construct resistance surfaces based on various methods.
Most studies have constructed landscape resistancesurfaces based on expertise and overall ratings for certain land-use types, resulting to landscape resistancesurfaces being heavily dependent on grading factors[13][14]. There are differences between the same landuse types owing to their different locations and surroundings. As a result, previous studies have weakened
the differences in resistance of the same land-use type.
In this research study, the author proposes a habitat
quality-based method used by Gao et al., [17]. It involves
simulating the sensitivity of different land-use types to
impact factors. With this method, the functional connectivity of the habitat patches is measured. For the
structural connectivity, creation of the resistance surface is based on the entropy coefficient method which
utilizes landscape indexes as the ecological attributes
for each land-use type in the weighted calculation.
To identify potential corridors for the focal species,
this study combines circuit models and least-cost models
by integrating structure and function of green space
patches for providing reliable ecological connectivity
network models in the cities. This approach (also calledgraph theory) allows for multiple least-cost pathways tobe evaluated for their contribution to the configurationof the overall network.
The ecological network developed by the integrated
models in this study simplifies and systematizes thecomplex landscape, helping to identify the significanceof each green space and guiding urban planning for biodiversity conservation by identifying the relative
high-quality habitats and choosing the best opportunities to maintain and restore connectivity.

Methodology

The research strategy used in this study was modelling or simulation using different tools and techniquesin Geographic Information Systems. A data-based
framework was applied, minimizing the use of subjective assumptions to assess habitat connectivity. Creating
an ENP starts with identification of focal nodes. The
focal node maps for each species as well as the final
focal node map for QC which were generated from theprevious study made by the author [8] were used as the
focal nodes for the circuit model and least-cost model
analysis. Hence, in this study the focus is on the succeeding steps which are construction of habitat resistance surfaces and modelling of different connectivity
maps using circuit model and least-cost model and integrating the two models to create the ENP.
Figure 2 shows the methodological framework whichreflects the conceptual framework and GIS tools. In creating resistance maps, two methods were used: theHabitat-Quality Method (HQM) and the Entropy
Coefficient Method (ECM), both methods were proposed by Gao et al [15]. HQM generates resistance map
using functional metrics whereas ECM uses structural
metrics. The landscape resistance and green spacestructure linked to the behaviour of species were used asparameters and indicators for movement along
corridors.

Fig. 2: Methodological Framework

Developing landscape connectivity models using circuit theory parameterised with green space structure
characteristics such as size and density allows the177Aicel Mae Alvarez

modelling of multiple paths between nodes [16]. The useof circuit theory to depict spatial patterns of landscaperesistance or conductance provides an easily interpretable method for calculating metric values and modelled
linkages [17]. The least-cost path analysis represents theroute of maximum efficiency between two locations as afunction of the distance travelled and the costs traversed. It is a valuable method for conservation planning by analysing and designing habitat corridors as itallows quantitative comparisons of potential movement
routes over large study area; incorporates simple orcomplex models of habitat effects on movement; andinfluences functional connectivity for species movement [18]. Corridor designs for multiple focal species
were combined into a preliminary linkage design whichbecame the final linkage design after it was modified toaccommodate ecological processes, incorporate otherpixels of conservation interest, buffer against edge
effects, or achieve other objectives.
To provide an idea of how the connectivity models
could be used to improve connectivity for future planning, the combined model was overlaid to Green Lung
Network Plan of QC.
1. Construction of habitat resistance surfaces:
a. Habitat-Quality Method (HQM)
To simulate the sensitivity of different land-use types
to impact factors, this study uses the HQM which
involves analysing how the intensity of human activity
inﬂuences habitat quality of QC’s landscape. Pixel-scale
habitat quality was used to characterize the optimal survival, reproduction, and energy ﬂow conditions that anassessment unit provided to organisms. This was
adopted from the habitat quality component of the biodiversity module InVEST Tool (Integrated Valuation of
Ecosystem Service and Tradeoffs) which uses a framework delineating “supply, service, and value” to linkproduction functions to the benefits provided to people[19]. Its models are “based on production functions thatdefine how changes in an ecosystem’s structure andfunction are likely to affect the flows and values of ecosystem services across a land- or a seascape” [20]. Oneof these models is the HQM which combines

178

information on Land Use and Land Cover and threats tobiodiversity to generate two key sets of information thatare useful in making an initial assessment of conservation needs: the habitat quality index and habitat degradation index of a landscape. It characterizes thesensitivity of habitat types to various threats and allows
users to estimate the relative impact of one threat overanother so that threats that are more damaging to habitats of the focal species and to biodiversity persistenceon the landscape can be represented as such. [19] The
impact of threats on habitat in a grid cell is mediated byfour factors: 1) relative impact of each threat, 2) distance
between habitat and the threat source and the impact of
the threat across space, 3) the level of legal / institutional/ social / physical protection from disturbance ineach cell, and 4) relative sensitivity of each habitat typeto each threat on the landscape. These factors are incorporated in the modelling process through the rasterdatasets and CSV files.
The HQM considers threats to be human-modified
land cover types that cause habitat fragmentation, edge,
and degradation in neighbouring habitat. They arethreats that do not directly affect the focal species but
the habitat quality of the green spaces which then affect
the quality of possible nesting and breeding patches ofthe focal species. In this study, there are four types ofidentified factors that impact the habitats of the two bird
species: horizontal infrastructure represented by map ofroads and MRT7 line), vertical infrastructure (represented by building footprints/built-up area map), hydrological factor represented by waterbodies map), andanthropogenic factor (represented by human population
density map). Below are reasons why these factors werechosen for constructing the resistance surface of QC:• Horizontal Infrastructure
Linear disturbances such as roads and railways tend to
be completely connected, relatively straight and subject
to regular human disturbance; hence, they serve as barriers that subdivide populations of species into metapopulations [21]. Based on several studies [22][23][24][25],bird abundance and breeding success tends to decrease
with increasing noise associated with road development
disturbances and birds are more vulnerable to roadkill

6th AFC Best Papers (2022)

than mammals on divided highways with forested medians due to their willingness to cross narrow gaps. Birds
alter their flight patterns near the high-speed railways,
including showing some avoidance of it [26]. However,birds risk train collision because such avoidance is notcomplete. Moreover, road noise significantly affects the
habitat preference of birds. Effect distances ranged from
20-1500 meters at 10,000 vehicles per day and increased
to 70-2800 meters at 60,000 vehicles per day [24]. In thecase of QC, year 2016 reported 272, 255 vehicles per day
passing through Commonwealth Avenue, which is also
the route of MRT7 line [27]. This means that buffer widthof more 2,800 meters is needed in habitats along
Commonwealth Avenue.
• Vertical Infrastructure
Aside from scarcity of food supply and place tobreed, built-up areas pose a great threat to birds due tobuildings. In the study by Loss et al. [28] involving thereview and analysis of more than 92,000 records across
23 studies, it was found that between 365 and 988 million birds are likely killed in the United States each year
as a result of collisions with buildings. Towering skyscrapers might seem like the most obvious culprits, yet
Loss’s team found that 56 percent of the mortality occurs
at low-rises (4-11 stories tall); 44 percent at residences(1-3 stories tall), and less than one percent at high-rises(12 stories and up).
The number of trees and buildings are also the mostsignificant predictors of bird abundances. Trees andbuildings in Metro Manila affect the distribution andabundance of urban exploiting species Passer montanus
and Pycnonotus goiavier with the former favouring thebuilt spaces and the latter favouring trees [9].
• Hydrological Factor
Water bodies are considered physical barriers to movement of birds [29]. The focal species used in this study
both have a maximum dispersal distance of only 1,000
meters; hence, it will be difficult for them to cross La
Mesa Lake which is more than 1 kilometre in diameter.
Aside from effect on dispersal distance, water bodies inurban areas most often are not of good quality, negatively
affecting the vegetation in habitat patches. Based on the2018 report from the Department of Environment andNatural Resources, only 85 percent of the total garbage
volume from Metro Manila is being collected to be disposed at sanitary landfills and QC showed to be the highest waste producer in the metro generating 3,151,961 kg a
day [30]. The uncollected waste ends up mostly in rivers,
esteros, and other water bodies, thus, polluting majorwater bodies and clogging the drainage systems. The
Tullahan River alone, which stretches from QC to Navotas
City, has an average coliform level of 500 million mostprobable number (MPN), far from the safe level of 200
MPN [31]. Hence, urban water bodies not only negatively
affect dispersal distance, but also quality of possible nesting and breeding patches.
• Anthropogenic Factor
Decreased habitat availability, reduced patch size,
increased edge, increased non-native vegetation,
decreased vegetative complexity, and increased nest
predation were commonly associated with bird declines
in response to human settlement [32]. Based on several
studies [33][34] sheer population density is associatedwith lower numbers of bird species and individuals, so
regardless of income or poverty, bird diversity is lowest
where human populations are most dense.
• Threats data CSV file
Based on the habitat preference of the two bird species [8], a CSV (comma-separated value, .csv) table of
all threats to be modelled was created. The table contains information on each threat’s relative importance or
weight and its impact across space. Each row in Table 1
is a degradation source. The maximum distance, measured in kilometres, is the maximum distance overwhich each threat affects habitat quality.
Table 1. Attributes of Threats based on habitat preferenceof the focal species
ThreatHorizontal
Infrastructure
VerticalInfrastructure
Hydrological
Factors
Anthropological
Factors2

Maximum
Distance

Passer montanus
WeightDecay

1linear2

5

0.7

exponential

0.5

0.3

linear6

0.8

linearMaximum
Distance

Pycnonotus goiavier
WeightDecay

1linear5

0.8

exponential

0.5

0.1

linear6

0.8

linearTable 1: Attributes of Threats based on habitat preference of the focal species

The impact of each degradation source decline tozero at this maximum distance. In general, the impact of179

Aicel Mae Alvareza threat on habitat decreases as distance from the degradation source increases, so that grid cells that are more
proximate to threats will experience higher impacts.
Weight is the impact of each threat on habitat quality,
relative to other threats. Weights range from 1 at thehighest impact, to 0 at the lowest. Since horizontal
infrastructure has a threat weight of 1 and the threat
weight of vertical infrastructure is set equal to 0.7 thenthe horizontal infrastructure causes 30 percent higher
disturbance, all else equal, to all habitat types. The
threats and weights were specific to the modelled focal
species. The type of decay over space for the threat canhave the value of either “linear” or “exponential”.
• Accessibility to Threats shapefile
This data presents the degree to which the land islegally protected. The HQM of InVEST tool assumes
that the legal protection of land is effective and that all
threats to a landscape are additive. Is the grid cell in the
map located in a formal protected area? Or is it inaccessible to people due to high elevations? Or is the grid cell
open to harvest and other forms of disturbance? The
Habitat Quality Model assumes that the more legal /institutional / social / physical protection from degradation a cell has, the less it will be affected by nearby
threats, no matter the type of threat [19]. The polygon
shapefile containing data on the relative protection thatlegal / institutional / social / physical barriers provide
against threats was created in ArcGIS Desktop 10.3.
Polygons with minimum accessibility (e.g., strict nature
reserves, well protected private lands) are assigned anumber less than 1, while polygons with maximum
accessibility (e.g., extractive reserves) are assigned avalue 0. Any cells not covered by a polygon was assumed
to be fully accessible and assigned values of 1. Table 2
shows the attribute table of the shapefile.

180

Table 2: Accessibility Score of the areas in Quezon City
ID

Location

Accessibility Score

1

La Mesa Nature Reserve

0.1

2

Private Property

0.1

3

Arboretum Forest

0.2

4

Balara Filters Park

0.3

5

Capitol Hills Golf & Country Club

0.4

6

Bagbag Resevoir

0.4

7Camp Aguinaldo Golf Course

0.4

8

Quezon Institute

0.5

9ADMU, Miriam College, Pansol Area

0.6

10

Veterans Memorial Medical Center

0.6

11

UP Diliman

0.7

12

Ninoy Aquino Parks and Wildlife Center

0.7

Table 2: Accessibility Score of the areas in Quezon City

• Sensitivity of Land Cover Types to Each Threat
CSV file
The CSV file containing the values of relative sensitivity of each habitat type to each threat on the landscape is the final factor used when generating the total
degradation in a cell with habitat. The HQM assumes
that the more sensitive a habitat type is to a threat, themore degraded the habitat type will be by that threat[19]. Each land cover type (Fig. 3) was assigned a habitatscore from 0 to 1 (where 1 indicates the highest habitat
suitability and 0 as the non-habitat area) based on habitat preference of the two bird species (Table 3). A ranking of less than 1 indicates habitat where a species orfunctional group may have lower survivability. Applying
this second approach greatly expands the definition of
habitat from the simple and often artificial binary
approach (e.g., “natural” versus “unnatural”) to include
a broad spectrum of both managed and unmanaged land
cover types.
Since the continuum of habitat suitability is relevant,weights with a roster of land cover types on a landscape
was applied in reference to habitat preference of the
focal species. Since they prefer highly vegetated areas
above all other habitat types the forest covers were given
the score of 1. However, since they also make use grasslands and lawns if highly vegetated areas are not available, these land covers were given scores of 0.4 and 0.2
relatively based on vegetation density. The areas whichwere not vegetated were considered non-habitats and6th AFC Best Papers (2022)

thus assigned the score of 0. Table 3 shows the relative
sensitivity value of each habitat type to each threat.
Values range from 0 to 1, where 1 represents high sensitivity to a threat and 0 represents no sensitivity. The
raster files, shapefile, and CSV files were then loaded to
the HQM of INVEST tool to compute the Habitat
Quality index values with a raster map as final product.
The Habitat Quality map produced by InVEST is thenloaded to ArcGIS Desktop 10.3 to construct the landscape resistance surfaces. The quality values are converted to resistance values. Units of better habitat quality
have smaller landscape resistance, and vice versa.

Fig. 3: Land Cover of Quezon City
Table 3: Habitat Score of Land Cover and Sensitivity of
Habitats to Each Threat based on habitat
preference of the focal species
Habitat Score
Land CoverPM

PG

Sensitivity of Habitats to Each Threat
Horizontal
Infrastructure

VerticalInfrastructure

HydrologicalfactorAnthropogenic
Factor

PM

PG

PM

PG

PM

PG

PM

PG

Closed forest

1

1

1

1

0.9

0.9

0

0

1

1

Medium closed forest

1

1

0.9

0.9

0.9

0.9

0

0

0.9

0.9

Open forest

1

1

0.8

0.8

0.8

0.8

0

0

0.8

0.8

Agricultural land

1

1

0.2

0.2

0.3

0.3

0

0

0.7

0.7

Grassland

0.4

0.5

0.4

0.4

0.6

0.6

0

0

0.4

0.4

Lawn

0.2

0.5

0.3

0.3

0.5

0.5

0

0

0.4

0.4

Waterbodies

0

0

0.7

0.7

0.8

0.8

0

0

0.8

0.8

Secondary roads

00Primary roads and roads with MRT 700

Built up spaces

00Table 3: Habitat Score of Land Cover and Sensitivity of Habitats to Each Threat based on habitat preference of the focal species

b. Entropy Coefficient Method (ECM)
ECM, as utilized by Gao et al [15] in their study, is anobjective weighting method in which weight is determined by entropy. Entropy is the amount of additional
information needed to specify the exact physical state ofa system which in this study, is the spatial structure of
QC. The greater the entropy, the more information it
provides. It involves three steps: 1) normalizing the initial information matrix, 2) calculating entropy weight,
and 3) calculating resistance. In this study, the landscapeindexes were used as the ecological attributes for eachland-use type in the weighted calculation.
To obtain the initial information matrix of landscapeindex values, Fragstats 4.3 tool was used, a spatial pattern analysis program for quantifying the structure/spatial heterogeneity (i.e., composition and configuration)of landscapes as represented in either a categorical map(i.e., landscape mosaic) or continuous surface (i.e., landscape gradient) [35]. Fragstats computes several statistics for each patch and class in the landscape and for thelandscape as a whole. For the purpose of this study, theclass level metrics was used. It measures the aggregate
properties of the patches belonging to a single class orpatch type. To quantify the configuration of patches atthe class level is to summarize the aggregate distribution of the patch metrics for all patches of the corresponding patch type. Since the class represents anaggregation of patches of the same type, the class ischaracterized by summarizing the patch metrics for thepatches that comprise each class [36]. The landscapeindexes measured in this study are the following:
Largest Patch Index, Patch Area, Shape Index, Fractal
Dimension Index, Interspersion- Juxtaposition Index,
and Connectance Index.
The raster file of QC’s land cover was loaded to
Fragstats 4.2 tool to produce an ASC text file containing
the class-level values of the landscape indexes. This creates the initial information matrix to which the formulas
proposed by Gao et al [15] are applied. In ArcGIS
Desktop 10.3, the resistance values were used to reclassify the values of the Land Cover raster map to generatea resistance map based on spatial structure. The ECM
was applied to the resistance values from HQM and181

Aicel Mae Alvarez

resistance values from Entropy Coefficient calculationof spatial structure. The output values were then considered the overall resistance values which incorporated
both functional and structural metrics. In ArcGIS
Desktop 10.3, the overall resistance values were used toreclassify the values of the Land Cover raster map togenerate the overall resistance map.
2. Modelling of Corridor Map
Least-Cost Model has been proven by different studies to be an effective way to calculate distances and to
identify the most optimal routes between source sites[7][36]. However, it does not consider all possible routes
that could contribute to connectivity, and it provides
connectivity assessments that are only related to asingle, most cost-efficient route identified in a given
landscape [38]. In simulating the corridors, the use of
Circuit Theoretic Model to overcome the limitation of
the Least Cost Model is proven effective by several
studies [7][16][37]. The circuit model was also able tospot critical connections that contribute the most to network connectivity and to identify corridors with optimal connectivity [7]. Hence, in the corridor simulationof QC, the circuit model is integrated to the least-costmodel. To achieve this, Circuitscape and Linkage
Mapper Toolkits are utilized to generate corridors basedon least-cost model and circuit model. Both tools are
installed and ran inside ArcGIS Desktop, although
Circuitscape also has its standalone version.
In Circuitscape tool, landscapes are represented as
conductive surfaces, with low resistances assigned tolandscape feature types that are most permeable tomovement or best promote gene flow, and high resistances assigned to movement barriers. Effective resistances, current flow, and voltages calculated across thelandscapes can then be related to ecological processes,
such as individual movement and gene flow [38].
Linkage Mapper automatically ran this when Pinchpoint
Mapper tool is used.
Linkage Mapper, a suite of several ArcGIS tools,
defines corridors through the landscape that minimize
the cumulative resistance between pairs of nodes. In thisstudy, three Linkage Mapper tools are used: Linkage

182

Pathways, Pinchpoint Mapper, and Centrality Mapper.
The Linkage Pathways tool uses the vector map of core
habitat areas (shapefile of focal nodes) and raster mapsof resistance to movement to identify and map the leastcost linkages between focal nodes. Each cell in a resistance map is attributed with a value reflecting theenergetic cost, difficulty, or mortality risk of moving
across that cell. In this study, the resistance values are
determined by cell characteristics of land cover combined with species-specific landscape resistance models.
The Linkage Pathways tool identifies adjacent focal
nodes and calculate cost-weighted distances and leastcost paths between the focal nodes. It then creates mapsof least-cost corridors between them and mosaics theindividual corridors to create a single composite corridor map. The result shows the relative value of each gridcell in providing connectivity between core areas,
allowing users to identify which routes encounter moreor fewer features that facilitate or impede movement
between core areas [38].
Once corridors have been mapped using Linkage
Pathways, Pinchpoint Mapper utilizes circuit theory to
run Circuitscape within the resulting corridors. This
produces current maps that identify and map pinch
points (i.e., constrictions, a.k.a. bottlenecks or chokepoints) in least-cost corridors. This approach hybridizes
least-cost corridor and circuit theory approaches, showing both the most efficient movement pathways and critical pinch points within them. These pinchpoints could
be prioritized over areas that contribute little to connectivity. [39]Centrality Mapper analyses the resulting linkage
networks, calculating current flow centrality across the
networks. Current flow centrality is a measure of how
important a link or node is for keeping the overall network connected [39]. Centrality analysis simultaneously
considers the relations between all areas on a landscapeproviding a means to quantitatively incorporate connectivity within the planning process by ranking the contribution of those areas to facilitating ecological flows [40].
It utilizes Circuitscape to implement this circuit theory
approach. It treats each core as a “node”, each linkage asa single resistor, and assigns a resistance equal to the6th AFC Best Papers (2022)

cost-weighted distance of the corresponding least-cost
corridor [41]. It then iterates through all core area pairs,
injecting 1 amp of current into one core area and setting
the other to ground. It then adds up current flow for eachcore and linkage to generate a map of cumulative current flow, indicating the importance of each linkage inmaintaining connectivity across the entire network ofcores, and can be considered as a measure of linkage
and core centrality.

Results

a. Effects of Landscape Resistance Surfaces
Each cell in a resistance map is attributed with avalue reflecting the energetic cost, difficulty, or mortality risk of moving across that cell. In HQM, two types ofraster maps (Fig. 4 & 5) were created in analysing thecurrent state of the landscape.

Fig. 5: Relative level of habitat quality on the current
landscape

In Figure 5, as a grid cell’s degradation scoreincreases its habitat quality decreases. Higher numbers
indicate better habitat quality vis-a-vis the distributionof habitat quality across the rest of the landscape. Areason the landscape that are not habitat get a quality scoreof 0. This quality score is unitless and does not refer toany particular biodiversity measure. Hence, values areapplied only on the green spaces. It is noticeable that the
low-quality areas (orange, yellow, and yellow green) arelawns and grasslands which have low habitat score.

Fig. 4: Relative level of habitat degradation on the current
landscape.

A high score in a grid cell of the raster map (Fig. 4)means habitat degradation in the cell is high relative toother cells. Grid cells with non-habitat land cover get a
degradation score of 0; hence only the degradation levelof green spaces is shown. The two maps show almost thesame degradation values for both focal species. La Mesa
Nature Reserve appears to have the lowest degradation
value while the areas in the centre and south-easternareas have the highest degradation values. This can bebecause these are the areas where building footprint ishighly concentrated and where major roads pass through.

Fig. 6: Resistance Map based on Habitat Quality Method (Thehigher the values, the higher is the energetic cost,difficulty, or mortality risk of moving across thatarea.) Good habitat quality promotes the dispersal ofanimals, and thus corresponds to low resistance [15].
So, when the quality maps were converted toresistance map using ArcGIS Desktop 10.3, thebuilt-up areas had the highest resistance values.

183

Aicel Mae Alvarez

Table 4: Landscape indexes of different land-use types(first six columns from the left) and landscaperesistance estimates (last column) used in theentropy coefficient method (the axis at thebottom of the figure is the value corresponding
to the indexes shown at the top).

Fig. 7: Resistance Map based on Entropy Coefficient
Method (The higher the values, the higher is theenergetic cost, difficulty, or mortality risk ofmoving across that area.)In the ECM which started from generating the landscape index values of the land cover of QC (see Table 4),
the closed forest had the highest resistance value followed
by waterbodies and agricultural land (see Figure 7). These
habitat patches in QC are isolated due to properties of theedges themselves, the distance between patches, andproperties of the intervening matrix, resulting to fewer
individual movements among habitat patches.
Compared to the result of the HQM, the ECM put
higher resistance values to habitats than on built-up
areas and roads. In this method, connectivity wasassessed by the extent to which movement is facilitatedor impeded through different habitat types across thelandscape. The attributes of habitats in QC facilitate
movement through certain elements of the landscapeand impeding it in others. The attribute values of thehabitats in terms of functional connectivity facilitates
movement while attribute values in terms of structuralconnectivity impedes movement.

184

Fig. 8: Overall Resistance Map for the two focal species(The higher the values, the higher is the energetic
cost, difficulty, or mortality risk of moving acrossthat area.)

When the resistance surfaces from HQM and ECM
were combined (see Figure 8) the waterbodies had thehighest resistance values followed by built-up areas.
Due to combination of resistance based on functionaland structural connectivity the resulting resistancevalues referred not just to degree of barrier to flightmovement but also on the energetic cost, difficulty, ormortality risk of moving across that area.

6th AFC Best Papers (2022)

b. Connectivity
Figure 9 shows the cost-weighted distance (cwd) of
the grid cells in the map to the nearest focal node. If apathway from one node to another must pass throughthe allocation zone of a third, then the two nodes are
considered nonadjacent. The nodes 4 and 8 below are
not adjacent because it is impossible to move from one
to the other without at least passing through the allocation zones of nodes of 2 or 9. The same is the case fornodes 13 and 10, which are blocked by allocation zones
of nodes 11 and 12. This simply shows that even if there
is no apparent physical barrier, connectivity of two
nodes is blocked because of the attribute values of theresistance surface.
Based on the node adjacency and distances between
nodes, Linkage Pathways tool created a “stick map”
connecting focal node pairs that are candidates for corridor mapping. This map shows potential links between
adjacent focal nodes. The links correspond to Euclidean(straight-line) distances between polygon edges. Using
the focal nodes, resistance surface, and stick map,
Linkage Pathways tool performed cwd calculations
from each focal node area.

Fig. 9: Adjacency of Focal nodes (Cost-weighted distanceallocation zones).

Fig. 10: Left: As each cost-weighted distance surface iscreated, the minimum cost-weighted distances
between source and target focal node pairs werealso extracted. It shows the total movement
resistance accumulated as animals move awayfrom specific focal nodes. Right: The resulting
cost-weighted distance raster and a direction
raster for each node created the least-cost paths(the route along which the least resistance isaccumulated) from node to node (right).

ArcGIS Mosaic function created a composite linkage
map in which each cell represents the minimum value ofall individual normalized corridor layers. Figure 11(left) shows these normalized and mosaicked least cost
corridors. Blue grid cells are closer to corridor centres,with red cells showing routes that accumulate up to 97.5
kilometres cwd more than the optimal (least-cost) route.
The blue areas, when clipped from the map resulted tothe delineated corridors forming the Ecological Network
Plan (Fig. 11 right).
By using Circuit theory to complement least-cost
analyses important areas for connectivity conservation
are identified. Pinchpoint Mapper tool hybridizes theoutputs of least-cost corridor and circuit theory
approaches to get the best of both approaches, showing
both the most efficient movement pathways and criticalpinch points within them.
Aside from allotting higher priority to pinch points,
nodes and corridors with high centrality values should
also be given higher attention for conservation. Centrality
Mapper tool was able to delineate contribution of eachnode and corridor to facilitating ecological flows acrossthe ecological network. Figure 14 shows the current flow

185

Aicel Mae Alvarez

centrality of nodes. Those in blue are considered the hubor the most important nodes for keeping the overall network connected. It is interesting to note that La Mesa
Reserve came in as second priority. When it comes toimportance of the corridors, links between the priority
nodes also got the highest centrality values. However, onepriority corridor linking UP Diliman to residential area

Fig. 11: Least Cost Corridors (left) and Ecological Network
Plan (right)

Fig. 12: The blue and green pinch points represent areas
where movement would be funnelled and thus
particularly important to keeping intact. Most ofthese pinch points are residential and institutional
areas (schools, hospitals, areas around barangay
halls) with open to medium closed forests andprivate lands with closed forests. Even a small lossof area in these pinch points would
disproportionately compromise connectivity. These
areas could be prioritized over areas that contributelesser to connectivity, such as the orange areas.

186

of New Era crosses Commonwealth Ave and the MRTline which have high resistance surfaces. This goes toshow that linking the two nodes is important to the overall connectivity of the network and a corridor between
them should be given the utmost importance.

Fig. 13: Centrality Values of the Corridors. The higher thevalue, the more important it is for keeping the
network connected. (Red line is the MRT 7 line)

Fig 14: Centrality Values of the Focal Nodes (The higher
the value, the more important it is for keeping the
network connected.)6th AFC Best Papers (2022)

c. Ecological Network Plan vs Green Lung NetworkPlan

to achieve the goal of connecting La Mesa Nature
Reserve to the centre of QC.

Fig. 15: Green Spaces in the Green Lung Network (CLUP,
2030) (right) that matched the nodes in the
Ecological Network Plan (left)

Fig. 16: Greenways of the Green Lung Network that matchthe corridors in the Ecological Network Plan

Seven out of 10 green spaces in the Green Lung
Network Plan matched the focal nodes in the Ecological
Network Plan (Fig. 15). These green spaces comprise 58
percent of the total area of the focal nodes; however, 89
percent of these seven green spaces is just the La Mesa
Nature Reserve. The 11 percent are composed of Holy
Cross Memorial Park, Himlayang Filipino, Balara
Filters Park, UP Oval, Ninoy Aquino Park and Wildlife
Center, and Camp Aguinaldo. The three green spaces in
the Green Lung Network Plan which were not includedin the Ecological Network Plan are Quezon Memorial
Circle, Arboretum Forest, and Ateneo de Manila
University. These areas got high ICC values but not highenough to reach the value of 1 to make them focal nodes
in the connectivity of QC’s landscape for the use of the
focal species. When it comes to corridor match, theGreen Lung Network matches only seven percent of theroutes of corridors in the ENP (Figure 16).
According to the CLUP 2030 [6] of QC, the Green
Lung Network Plan, will link the La Mesa Nature
Reserve to Quezon Memorial Circle (QMC) at the heartof the city by the green-lined Commonwealth Avenue,
aqueducts, and rivers. In the ENP, QMC is not includedin the focal nodes; however, its adjacent green spaces,
NAPWC and Veterans Memorial Medical Center, were
included in the focal nodes. Hence, the ENP is still able

Conclusion

In restoring previously altered ecosystems or protecting existing fragments of natural systems, landscapeplanners must recognize that the most effective way tore-establish or maintain the viability of these systems is
to ensure they exist as a part of a larger functioning
system. The development and articulation of a planning
method based on ecological concepts, in particular theconcept of ecological networks, can be integrated into theurban planning process. This study provided a framework of integrated models to test this proposition.
The impacts of Quezon City’s land cover were considered within the larger, ecological context of landscape. To optimise corridor effectiveness for the focal
species, this study integrated circuit and least-cost modelling which were parameterised with green spacestructures This study emphasizes the importance ofconsidering the behaviour and preferences of existing
species in the landscape when developing priority corridors of ecological connectivity networks.
Conserving the existing green spaces of QC and creating greenways to link them should not be just based onaesthetic reasons that affect only the populace. Using
the Focal-Species Approach can help us ensure that weare managing the linkage as a semblance of a fully functioning ecosystem, rather than a narrow gauntlet that187

Aicel Mae Alvarez

lets focal species pass between areas that threatens mortality rate. In such a rapidly evolving, heterogeneous,and highly fragmented landscapes, the identification ofcorridors which should be prioritized is important tobetter design, preserve, and improve ecological networks. Certain critical links or patches are required inorder to support a viable existing patch so that isolation
effect does not cause deterioration over time. In the
resulting ENP, aside from it does not connect only theecological cores of QC but also other tiny patches ofgreen spaces, some corridors crossed the route of MRT
7 line instead of avoiding it. Hence, landscape plannersof QC will have a more challenging task of designing
the greenways to incorporate a design that will cater toan ecological corridor crossing the wide CommonwealthAvenue with MRT 7 line. For now, the MRT 7 line may
not yet have a major impact on the fragmentation of QC,
but if the LGU is not able to create corridors across it,
the line may create a major divide in the city, causing
greater loss of interior habitats along CommonwealthAvenue and worsening habitat fragmentation.
The ENP created in this study can be used to preserveor restore the ecological integrity of critical natural systems while allowing for compatible human activities
within the network and continued productive (economic)
use of adjacent lands. Some modification to adjacentland would enhance the viability of the network.
Understanding the consequences of habitat change anddeveloping effective strategies to maintain biodiversity
in developed and disturbed landscapes are major challenges to both scientists and landscape planners. To beeffective, it must integrate scientific skills with appliedmanagement and policy to achieve practical outcomesthat have long-term benefits for species and biologicalcommunities. For landscape planners, the challenge is todesign and implement land-use strategies that will
ensure the conservation of natural resources in the faceof competing demands for land use. This is especially
important for government agencies responsible for theadministration and management of large areas of land,
but also relevant to community groups and individuals
managing small parcels of land in fragmented landscapes subject to a wide range of land uses. For urban

188

areas, it is much more of a challenge to create ecologicalcorridors due to land use and ownership of land.
A possible solution to mitigate biodiversity loss inurban area is to ensure that the green spaces can provide
refuge and resource areas for the focal species. Small
patches or satellite nodes along the ecological network
can be effective in providing habitats in which focal species can pause and/or breed, resulting in a higher survival
rate in dispersing focal species, and hence more dispersing individuals in the network. In the corridors which are
currently reserved for building development, pocket gardens, rooftop gardens and parks can be integral to thelandscape design to prevent gaps in the connectivity network. Planting plans should also encourage addition offruit trees and grains that the focal species eat.
Urban planners, landscape designers, and policy
makers can profoundly affect how and where cities
grow. If the Local Government Unit decides to add aroad, a park, or housing tract, the use of the ENP will
help accomplish the goal by maximizing the ecologicalintegrity and minimizing land degradation. As such, theGreen Lung Network Plan can be further improved
using the ENP as this provides a data-driven basis forpotentially effective green infrastructure for QC. Spatial
pattern matters. It is no longer appropriate to plan based
on totals or averages of prices, jobs, wages, parkland,
bicycle paths, and so forth. Rather, the arrangement ofland uses and habitats is crucial to planning, conservation, design management, and policy.

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