Green spaces are critical for connecting urban habitat in the tropics

doi:10.21203/rs.3.rs-1546889/v1

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Green spaces are critical for connecting urban habitat in the tropics

https://doi.org/10.21203/rs.3.rs-1546889/v1

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Tropical Southeast Asia is a hotspot for global biodiversity, and also a hotspot for rapidly expanding urbanisation. There is a need to identify, protect, restore, and connect remaining green spaces in the urban matrix before this opportunity is lost to urban development. The objective of this study is to characterise ecological connectivity for mammals and identify important patches and linkages for connecting urban green spaces for Greater Kuala Lumpur (KL), Malaysia. We first map land cover across the region using linear mixture model with preprocessed multi-date cloud free mosaics derived from Sentinel 2 remote sensing data in Google Earth Engine. We then model connectivity using the land cover maps, expert-based parameterisation of Euclidian distance, and graph-based connectivity models for a range of dispersal guilds representing small and medium terrestrial and arboreal mammals. Our analysis showed large differences in the effects of fragmentation within Greater KL on the different dispersal groups, with some groups perceiving the landscape as disconnected. However, our analysis identified a network of green patches and pathways which potentially could support connectivity in the urban landscape. Our results demonstrate the potential for Southeast Asian mega cities to support biodiversity in the urban context, and the potential for a different kind of urban development, which supports biodiversity in its urban fabric.

Urban green space

connectivity

remote sensing

graph metrics

tropical biodiversity

Southeast Asia

The fragmentation and loss of habitat reduce wildlife connectivity, leading to a constriction of species movement and an increase in the risk of extinction (Richards et al., 2017; Scolozzi & Geneletti, 2012; Syphard et al., 2011; Vergnes et al., 2012). In urban environments, green spaces are isolated by a matrix of buildings and roads, which restricts movement between green spaces such as urban forests and parklands (Goddard et al., 2010; Nor, Corstanje, Harris, & Brewer, 2017). Urbanisation is a significant cause of habitat fragmentation, particularly in low- and middle-income countries, where cities are rapidly urbanising due to high rates of population growth, rural-urban migration, and economic modernisation (Lechner et al., 2020; Richards et al., 2017; Richards & Belcher, 2019; Shathy, 2016).

Southeast Asia is one of the most rapidly urbanising regions in the world, with poorly planned cities resulting in habitat degradation, loss, and fragmentation, negatively impacting on biodiversity and urban ecosystem services (Lourdes et al., 2021; Richards et al., 2017; Seto et al., 2012). Urbanisation in Southeast Asia is of particular concern for conservation globally, as the region is home to four out of 34 of the planet’s biodiversity hotspots (Myers et al., 2000), and many of the countries in the region still have some relatively intact landscapes with high forest cover (Lechner et al., 2021; Hughes, 2017; Wilcove et al., 2013). Southeast Asia is also undergoing rapid social and environmental changes due to infrastructure development spurned on by increasing investment such as in transport networks (Ng et al., 2020; Teo et al., 2019) and massive urban development schemes such as Indonesia’s new planned capital Borneo (Teo et al., 2020). However, in Southeast Asia - unlike many cities in the highly urbanised Global North - urban green spaces such as in the Greater Kuala Lumpur, an urban conurbation with a population of around 7.5 million people, have the capacity to support biodiversity, even though they are found with a high-density urban matrix (Aida et al., 2016; Samantha et al., 2020; Tee et al., 2019a; Teo et al., 2021). Even within Southeast Asia, Greater Kuala Lumpur is remarkable for its rich wildlife, including threatened mammals like tapirs (Tapirus indicus), serows (Capricornis sumatrensis), gibbons (Hylobates bar), pig-tailed macaques (Macaca nemestrina), and pangolins (Manis javanica.

The continuing loss of biodiversity due to anthropogenic land use changes from urbanisation is especially concerning in countries like Malaysia, one of the most urbanised countries in Southeast Asia (United Nations, 2018). Malaysia's urbanisation percentage was 42% in 1980 and 76% in 2018 (United Nations, 2018). Urbanisation in Greater Kuala Lumpur has led to the loss of green space, including primary and secondary forest and former plantations (Mc Donald et al., 2008; Ramakreshnan et al., 2018; Seto et al., 2012), resulting in a decline of the total green space per capita from 13 m² in 2010 to 8.5 m² in 2014 (Kanniah & Siong, 2017). Clearly, given the rapid pace and scale of infrastructure development in Malaysia and other cities in the region, there is a need to protect and conserve existing green space before it is lost to development (Lechner et al., 2021). Conservation of green spaces for biodiversity and ecosystem services need to be embedded into planning, in particular ensuring that these cities incorporate urban greening efforts to ensure resiliency and support ecological processes such as connectivity (Lourdes et al., 2021; Sanusi & Bidin, 2020).

Characterising landscape connectivity using spatially explicit GIS modelling approaches is critical to realistically describe networks of urban green spaces and their potential to support urban planning approaches that considers and maintains biodiversity values (Honeck et al., 2020). While there are extensive examples of the application of connectivity modelling to urban environments in the Global North such as across Europe and North America (Balbi et al., 2021; Han & Keeffe, 2021; Serret et al., 2014; Tannier et al., 2016), there are very few examples in the tropics, in particular Southeast Asia with the majority of connectivity studies confined to agricultural or forested landscapes (e. g.: Brodie et al., 2015; Hearn et al., 2018; de la Torre et al., 2019). This study represents one of the first urban connectivity studies conducted in a Southeast Asian city.

Common approaches to modelling the impact of fragmentation on connectivity utilise graph theoretic approaches to characterise a landscape as a network of patches and linkages, and their importance for connecting landscapes (Minor & Urban, 2008; Rayfield et al., 2011). Potential for dispersal between patches are based on ecological characteristics such as minimum viable patch size and interpatch dispersal distance (Lechner, Doerr, et al., 2015; Tiang et al., 2021). In addition to quantifying the importance of patches and linkages using graph metrics, graph components can identify which groups of patches are interconnected but isolated from other groups of patches (Minor & Urban, 2008; Rayfield et al., 2011). The patterns in size and shape of these components can be used to characterise fragmentation and isolation (Lechner and Doerr, et al., 2015).

Underlying any connectivity modelling approach is spatial data describing land cover for characterising habitat and how the matrix may impede or support dispersal. In countries where urbanisation is rapid, major changes in land use can occur within a space of years and therefore it is essential to use spatial data that represent recent land cover (Ngo et al., 2020; Gao and O’Neill, 2020; Richards & Belcher, 2019). In Malaysia and many other developing countries, available spatial data are either not publicly available or have unsuitable spatial or temporal resolution. Remote sensing approaches utilising multi-date mosaics can be used to address cloud cover in Google Earth Engine (GEE), which is particularly problematic in the tropics, hold great potential for providing high quality inputs for connectivity modelling (Ang et al. 2021).

The objective of this paper is to characterise ecological connectivity for mammals within Greater Kuala Lumpur and identify important patches and linkages for connecting urban green spaces. We first map land cover across Greater Kuala Lumpur with GEE to identify potential habitat. We then model connectivity using the remote sensing data, expert-based parameterisation, and graph metrics for a range of dispersal guilds representing small and medium terrestrial and arboreal mammals. We conclude by discussing how such methods could be rapidly applied to other urban areas and the implication of the analysis for connecting urban habitat in the tropics such as in Greater Kuala Lumpur, and other highly urbanising and fast developing Southeast Asian cities.

2.1 Study Approach

This study employs two main approaches: 1) Land cover mapping of remotely sensed imagery using Google Earth Engine (GEE), and 2) Characterising connectivity of urban green spaces using a graph theoretic approach (Fig. 1). The connectivity analysis included an assessment of how fragmentation in urban Greater Kuala Lumpur varies with the ecological characteristics of movement and patch size.

2.2 Study area: Greater Kuala Lumpur

The conurbation of Greater Kuala Lumpur (~ 3° 8' N, 101° 41' E) includes the federal capital of Malaysia Kuala Lumpur (KL) and the ten surrounding municipalities forming a contiguous urbanisation region. Kuala Lumpur evolved in the 1870s from an unknown small state to a booming mining city and finally became the capital of Malaysia, which subsequently developed into a megacity and has taken on all the challenges of rapid, often unplanned, urban growth (Nor Akmar et al., 2011). Kuala Lumpur is the fastest growing metropolitan region and has become the economic, financial and cultural capital of the country and one of the three Malaysian Federal Territories (Department of Statistics Malaysia, 2018).

2.3 Land cover mapping

The land cover map was derived from a combination of Sentinel 2, 10 m satellite data processed in GEE and ancillary GIS data including OpenStreetMap data (OSM) and agricultural plantation data derived from a secondary data source were used. The sentinel 2 data was used to accurately classify the spatial extent of major land cover classes, while the coarser scale ancillary data was used to reclassify vegetation into higher thematic resolution agriculture and non-agriculture land covers which are difficult to differentiate accurately with spectral data alone, however, their spatial resolution is commonly coarse. The final land cover maps were assessed for accuracy using high resolution freely available World View 2 imagery in Google Earth Pro.

2.3.1 Google earth engine land cover classification

We created a land cover map of Greater Kuala Lumpur within Google Earth Engine (GEE), which is a cloud-based platform for planetary geodata analysis. GEE hosts the entire Sentinel 2 archive, provides tools and applications for accessing, processing and mosaicking these images, and performs all analyses in parallel on Google's cloud-based processing platform (Gorelick et al., 2017). We used java scripting in GEE application programming interface (API) to acquire, pre-process and classify the data.

Firstly, a multi-temporal cloud free mosaic was produced using the best available imagery for 2018. Images included in the mosaic had less than 40% cloud cover and were selected from favourable seasonal conditions where rainfall and cloud cover were consistently low, between February and May. A total 20 images were included in the mosaic. Cloudy pixels within image date were removed using a cloud filtering algorithm based on bands 2 (blue – 490 nm) and 10 (Short wave infrared - high atmospheric absorption band). The multi-temporal image mosaic addresses difficulties associated with high cloud cover over the study area that otherwise makes it impossible to identify a single cloud free image. From the mosaic a single image was created based on the median value for each pixel.

We then applied spectral unmixing/endmember analysis to the mosaic, to identify percentage cover of nine land cover classes: impervious, non-agricultural vegetation, water, bare soil, minor roads, major roads, oil palm, rubber tree and other agriculture. For each of these landcover classes, 40 samples will be used to train the algorithm using a combination of the Sentinel 2 imagery and freely available high-resolution imagery provided by ArcGIS and Google Earth Pro.

Spectral unmixing provides a method by which the measurement spectrum of a mixed pixel is decomposed into a collection of constituent spectra or endmembers and a series of corresponding fractions or abundances are identified describing the proportion of each endmember present in a pixel. Common approaches to represent the synthesis of mixed pixels from different endmembers utilise the linear mixture model (LMM), where any packet of incident radiation interacts with only one component, i. e. there is no multiple scattering between the components. The LMM algorithm estimates the amount of different spectra (endmembers) that form the mixed pixels in the scene and then the final land cover class was determined for each individual pixel based on the land cover in the majority method (Keshava, 2003). The endmembers classified in this step were vegetation (which included agricultural and non-agricultural vegetation), impervious surfaces, water and bare soil (Appendix 1).

2.3.2 Ancillary data for mapping agriculture and roads

In the second step we combined the spectral unmixing outputs from GEE with ancillary data to identify different kinds of agriculture from vegetation which represents habitat, and major and minor roads. Firstly, we used land use data from Malaysia’s Iplan, integrated planning and information system (http://iplan.townplan.gov.my) to reclassify pixels classified as vegetation using spectral unmixing to non-agricultural vegetation, oil palm, rubber and other agriculture. While we used a road layer from Open Street Maps (OSM) (www.openstreetmap.org), an open access crowd sourced topographical spatial database to classify major and minor roads. From this database, road polylines were identified (Appendix 2) then buffered by 7.5 m on both sides for an average width of 15 m and overlayed to replace other land cover pixels.

2.3.3 Manual correction

After applying the supervised classification and update with ancillary data, several manual edits were made to the landcover layer using ArcMap 10.7.1 software. Specific landcovers were systematically misclassified, i.e., roads were classified as water. In addition, the Normalized Difference Water Index (NDWI) (threshold value 0.1) was applied in GEE and specific regions were reclassified to water using this data.

2.3.4 Accuracy Assessment

A randomly spatial distributed set of 40 validation points per land cover type (Appendix 1) were produced using the same method as the training data, to construct a confusion matrix and quantitatively assess how effectively land cover was mapped.

2.4 Connectivity modelling

A graph theoretic approach along with modelling Euclidian distance between patches based on ecological parameters was used to assess green space connectivity within the Greater Kuala Lumpur. This approach allowed us to characterise the landscape as a network of patches connected by links (Dale & Fortin, 2010; Minor & Urban, 2008). Modelling was conducted using Graphab 2.4 software (Foltête et al., 2012). The outputs were interpreted through visualising fragmentation by identifying linkages between patches and quantifying the importance of patches and linkages through graph metrics.

2.4.1 Parameterisation and model scenarios

This study characterises green spaces connectivity based on movement patterns and patch sizes described by two key parameters: 1) a minimum patch size required to support viable populations and 2) an interpatch crossing distance that limits the maximum distance an animal is able to move between patches. Due to the lack of empirical data these parameters were obtained through consultation with 8 experts (Table 1) which include mammal ecologists from local NGOs and universities who have extensive experience in conservation work with local species, including the co-authors. Experts were consulted both individually and in group meetings using an iterative approach where the final parameter values were decided by the co-author group through synthesising the various expert opinions (Lechner et al., 2017). The parameter values were derived by considering allometric relationships between species body mass (Sutherland et al., 2000) similar to previous studies which have used expert opinion to derive dispersal distances (Sahraoui et al., 2017).

Four dispersal guilds were characterised which represent functional groups ranging from small to medium, arboreal and terrestrial mammals. Due to the highly urbanised nature of Greater Kuala Lumpur, we modelled connectivity only for small and medium sized mammals only. We did not model large mammals as large patches that support larger mammals are concentrated at the eastern border of the study area, specifically at the Selangor-Pahang border. These mammals have no incentive to move westward within Greater Kuala Lumpur as there are no patches that fit their habitat profile (5000 ha – 10,000 ha).

Table 1

Functional groups and parameters used for connectivity modelling. Species names by order of appearance in the table: Callosciurus caniceps, Tupaia flis, Prionailurus bengalensis, Macaca fascicularis, Ratufa bicolor, Manis javanica, Hystrix brachyura, Veverra tangalunga.
Group	Sub-group	Weight (kg)	Minimum habitat patch size (ha)	Interpatch crossing distance (m)	Example species of conservation concern
Small	Arboreal	< 2	5	250	Common Grey-Bellied Squirrel, Common Tree shrew
Small	Terrestrial	< 2	5	1000	Leopard cat
Medium	Arboreal	2–20	100	500	Long-tailed macaque, Black giant squirrel
Medium	Terrestrial	2–20	100	5000	Sunda Pangolin, Malayan porcupine, Malay civet

We ran the connectivity model using Euclidean distance as there is insufficient knowledge of how species move through urban landscapes in Malaysia. In addition, we included a scenario for medium and small terrestrial subgroups, whereby movement across main roads were considered to be a barrier. This was used to determine the impact of main roads on connectivity for species that rely on terrestrial dispersal.

2.4.2 Characterising fragmentation and quantifying the importance of patches and links

Landscape connectivity was quantified using graph metrics and visualised with component boundaries. Firstly, we visualised patch isolation using component boundaries. The boundaries were identified at the midpoint between two isolated patches or interconnected groups of patches. Large components describe multiple patches that are linked and characterising regions that are connected. Numerous small components represented by a single or several small patches describe regions where dispersal is highly constrained. Spatial patterns of these components are useful for characterising fragmentation and barriers to connectivity at the regional scale (Lechner et al., 2015).

After, connectivity was quantified using graph metrics. We calculated two patch-scale graph metrics: (1) delta integral index of connectivity (dIIC) (Pascual-Hortal & Saura, 2006; Saura & Pascual-Hortal, 2007) and (2) clustering coefficient (CC) (Minor & Urban, 2008; Ricotta et al., 2000). The dIIC describes the impact of the loss of habitat availability caused by the removal of the focal patch or linkage between patches relative to the connectivity network. The higher the value, the greater the importance for landscape connectivity. While the CC measures path redundancy between the patch and its neighbouring patches. A higher value means alternative pathways exist for linking neighbouring patches. We also calculated the landscape-scale graph metric, number of components (NC). As the number of components increases as connectivity across the landscape is poor and decreases as connectivity improves, the number of components acts as an index of overall connectivity (Urban & Keitt, 2001).

At the landscape scale, to compare between the different species and the two major roads as barrier scenarios we calculated a range of descriptive statistics and landscape scale graph metrics. The descriptive statistics were: average patch size (ha), standard deviation of patch size (ha), size of largest patch (ha), total patch area (ha), number of linkages, average interpatch crossing distance (m) and standard deviation of interpatch crossing distance (m). For the graph metrics we calculated largest component size (ha), number of components, mean size of components (ha) and integral index of connectivity (IIC) (A. M. Lechner, Harris, et al., 2015). The IIC represents the probability that two dispersers randomly located in the landscape can access each other, where higher values describe more connected landscapes and vice versa) (Pascual-Hortal & Saura, 2006; Saura & Pascual-Hortal, 2007).

3.1 Land cover mapping

The combined remote sensing analysis with ancillary mapping of Greater Kuala Lumpur (Fig. 2) was made up of the following land covers: impervious (19.4%), non-agricultural vegetation (47.5%), water (1.9%), bare soil (2.2%), minor roads (10.9%), major roads(2.0%), oil palm (7.9%), rubber tree (3,04%) and other agriculture (5,23%). Non-agricultural vegetation was the dominant type of classified land cover, accounting for about 147,959 ha of the total study area, followed by impervious (60,359 ha), while the least classified land cover was water and accounted for 584,718 ha of the total study area (311,522 ha).

The overall accuracy of our land cover classification was 89.4% and the individual accuracy for the classes varied between 78.6% for non-agricultural vegetation and 100% for minor and major roads (See Appendix 3 for confusion matrix). The impervious area includes settlements, groups of buildings and other artificial surfaces (industrial, residential). Producer accuracy was between 78.62% for non-agricultural vegetation and 100% for major and minor roads and the accuracy of the user was between 65% for other agriculture and 100% for minor and major roads and 100% for water.

3.2 Wildlife connectivity modelling

The dIIC describes important linkages and patches which were represented on a map by thick lines and large circles, respectively with higher values representing greater connectivity (Fig. 3). While the CC measures path redundancy between the patch and its neighbouring patches (Minor & Urban, 2008; Ricotta et al., 2000). Large circles denote patches that have low redundancy and represent crucial patches for landscape connectivity at the neighbourhood scale. A low CC value means that connections through the patch is unique. Because the number of components increases as connectivity across the landscape is poor and decreases as connectivity improves, the number of components acts as an index of overall connectivity (Urban & Keitt, 2001). This allowed us to compare connectivity across the different interpatch dispersal distances.

3.2.1 Assessment of connectivity pathways and graph analysis

From the perspective of small arboreal mammal subgroup with a minimum patch size of 5 ha and an interpatch crossing distance of 250 m (Fig. 3a) the landscape is very fragmented especially in the highly urbanised areas away from the larger contiguous green areas. At this interpatch dispersal distance, connectivity was low, with total of 274 components represented isolating the patches. A total of 2218 paths connect patches within Greater Kuala Lumpur. However, there was also a belt of connected patches around the outskirts of the highly urbanised areas, from the northwest down to the southeast, consisting of larger patches. In contrast the small terrestrial mammal subgroup (Fig. 3b) with a minimum patch size of five ha and an interpatch crossing distance of 1000 m perceived the landscape as generally well connected, with 6722 connectivity paths connecting patches and 28 component boundaries. These 28 components were found to the edge of the study areas and the majority of the study area was well-connected.

The medium-size arboreal mammal subgroup (Fig. 3c) with a minimum patch size of 100 ha and an interpatch crossing distance of 500 m had poor connectivity. In total there were 34 components, 41 linkages and 67 patches. As large patches were far apart from each, mainly of the patches were isolated. Connectivity existed mainly within small patch clusters. In contrast the medium terrestrial mammal subgroup (Fig. 3d) with a minimum patch size of 100 ha and an interpatch crossing distance of 5000 m perceived the landscape as well connected, with only 2 components i.e., the majority of patches were connected.

The analysis of dIIC (Fig. 3e, f, g, h) for all subgroups show that the eastern ranges provide the largest and most connected areas of habitat. Important linkages extend from the east into the urban matrix, especially for terrestrial small and medium mammals (Fig. 3e, g). In addition, several large patches such as Ayer Hitam are key focal points for connectivity deep within the urban matrix. The CC analysis showed similar values for most patches and subgroups, indicating there were very small differences in terms of neighbourhood connectivity and levels of redundancy (Appendix 5). Only for large terrestrial mammals were specific patches identified as important stepping-stones with little redundancy.

3.2.2 Major roads as barriers

The assessment of main roads acting as a barrier for movement for terrestrial mammals (Fig. 4) showed large reduction in connectivity compared to when major roads were not considered as barriers (Fig. 3). The small terrestrial mammal subgroup (Fig. 4a) had 394 components, 2240 connectivity paths and 1612 patches. The only part of the landscape connected was located in the east. The medium terrestrial mammal subgroup (Fig. 4b) had 41 components, 41 connectivity paths and 67 patches. The largest components were located in the east, where continuous forest can be found.

3.2.3 Landscape-scale graph metrics

A number of landscape-level graph metric statistics were calculated to allow for comparison between different groups and with the major road scenario (Table 2). For the default scenarios, both small terrestrial and arboreal groups had a larger total patch area than their medium sized counterparts due to the smaller minimum patch size. However, all terrestrial groups had higher IIC values and fewer components than arboreal groups, indicating a more connected landscape. Species in the small mammal groups had more patches and larger total patch area compared to species in the medium-size groups. Higher IIC values were observed for the terrestrial groups, with small terrestrial species having the highest IIC value as many of the patches were connected with a large number of linkages and there was a greater overall area of habitat in the landscape. The opposite is true for the medium and small terrestrial groups in the scenario where major roads acted as barriers to movement. These scenarios resulted in lower IIC values, increased number of components and reduced number of linkages indicating an overall poorer degree of connectivity compared to the default scenarios.

Table 2

Descriptive statistics and landscape-scale graph metrics for each sub-group and major roads as barriers scenario.
			Patches				Linkages		Components
Group	Subgroup	Model	N	Mean ± SD size (ha)	Largest (ha)	Total area (ha)	N	Mean ± SD distance (m)	N	Mean size (ha)	Largest (ha)	IIC
Small	Arboreal	default	1612	68 ± 1109	42334	109554	2218	75.6 ± 76.5	274	1137.1	170321.6	0.01059
	Terrestrial	default					6722	458 ± 325	28	11127	276476	0.01211
	Terrestrial	roads					2240	271.7 ± 281.0	394	790.8	77781	0.00765
Medium	Arboreal	default	67	1298 ± 5329	42334	86966	41	81.9 ± 131.4	34	8902.1	116094	0.00981
	Terrestrial	default					152	2179.4 ± 1650	2	155786.1	277177	0.01114
	Terrestrial	roads					41	912.6 ± 1228.5	41	7599.3	85943	0.00745

4.1 Overall connectivity

Our connectivity analysis of the four dispersal guilds showed large differences in the effects of fragmentation within Greater KL (Fig. 3). The landscape ranges from one dominated by isolated patches to one where species exist in large metapopulations of connected patches, depending on the dispersal capacity of the dispersal guild. In general, the landscape was less connected for smaller-bodied mammals, while terrestrial mammals perceived the landscape as more connected, due to their capacity to disperse farther than arboreal mammals. The concentration of small components representing clusters of linked but isolated patches for arboreal mammals is spatially consistent with highly urbanised areas (townships like Kuala Lumpur, Petaling Jaya, Shah Alam, Klang, Gombak, and Sepang), where forested areas are few and far between. Patches available to medium-sized arboreal mammals were also isolated in clusters within highly urbanised areas, while terrestrial mammals were able to disperse across the landscape, even in urbanised areas.

Our analysis also showed that, if roads were treated as total barriers for small and medium terrestrial mammals, the landscape became considerably more fragmented (Fig. 4). The number of components increased by 1307% and 1950% for small and medium terrestrial mammals, respectively. These components were distributed evenly throughout the study area for both guilds and were only absent where large forest patches exist, in the east, indicating that habitat patches within urban areas are most likely isolated from each other. Thus, how species respond to infrastructure barriers is likely to have a large influence on connectivity.

4.2 Implications for connectivity and habitat availability within Greater KL

As urban expansion continues to occur, forested patches and dispersal corridors are increasingly fragmented, leading to decreased connectivity and increased habitat isolation (Kong et al., 2010). However, the effects of urbanisation can have different impacts on different functional groups, as shown by our results. Following a dispersal guild approach in (Lechner et al., 2017), we were able to identify which groups were most vulnerable to urbanisation. Greater KL may be perceived as well connected or highly fragmented, depending on species dispersal abilities and minimum patch size requirements. The discrepancy in connectivity between small and medium arboreal versus small and medium terrestrial mammals has important implications for species persistence within the study area.

Species inhabiting urban patches are highly susceptible to anthropogenic pressures (Baker & Harris, 2007; Burger et al., 2004; Mooney & Hobbs, 2000), and the lack of immigration to these isolated patches can lead to a shrinking population (Bouchy et al., 2005). Both these effects correlate to deleterious impacts on overall health (Romero, 2004) and lack of genetic diversity (Lacy, 1987; Morandin et al., 2014), and can ultimately lead to local extinction (Fahrig, 2002). By contrast, terrestrial mammals capable of dispersing to new patches are able to exploit new resources, find new mates, and avoid conspecific threats and competition (Ramakrishnan, 2008); the long-term survival of a metapopulation depends on colonisation through local dispersal (van Nouhuys, 2016). The overwhelming number of isolated patches for arboreal mammals can be interpreted as a low likelihood for species persistence within Greater KL, while terrestrial mammals are more likely to persist within the landscape due to their greater dispersal capabilities.

Linear infrastructure such as roads and highways pose an overwhelmingly high mortality risk for many species and are sometimes linked to a strong population decline in surrounding areas (Jamhuri et al., 2020; Zimmermann Teixeira et al., 2017). Connectivity for terrestrial mammals decreased drastically when roads were treated as total barriers, with their NC and dIIC values falling within the same range as for arboreal mammals of the same size (Fig. 4). Mortality and road avoidance are the two outcomes that contribute to decreased dispersal and genetic diversity in areas fragmented by roads (Forman et al., 2003), and this is especially true in highly urbanised areas, where roads are in abundance. Populations separated by roads have reduced gene flow and increased divergence between populations (Jackson & Fahrig, 2011). If this scenario closely represents the current state of connectivity within Greater KL, forest-dependant terrestrial mammal populations will undergo biotic homogenisation (McKinney, 2006; Tee et al., 2018), and species more adapted to urban conditions may prevail (Francis & Chadwick, 2012).

4.3 Priorities for connectivity in Greater KL

The effects of urbanisation on the broader patterns of fragmentation and connectivity are practically irreversible (though theoretically possible) for most cities across the world. Proper land-use management is therefore necessary to mitigate such effects. Restoring ecological connectivity is essential for urban sustainable development (Nor, Corstanje, Harris, Grafius, et al., 2017), and this is done by ensuring the movement of individuals and genes over multiple scales (Minor & Urban, 2007). Our analysis identified a potential network of green patches which could be used to connect all the key forest reserves and remnant vegetation patches in Greater KL (Fig. 5). Revegetation efforts should be carried out to support existing connectivity, creating steppingstones between those patches (Lechner et al., 2017). Urban wildlife corridors, if properly designed and established, have been shown to improve ecological connectivity within urban areas (Jain et al., 2014; McRae et al., 2012). It is important for urban planners to realise the great potential for creating a functional network of urban green spaces supporting biodiversity in Greater KL and the high risk of habitat and connectivity collapse due to urban development if no action is taken (Jamhuri et al., 2018; Kanniah & Siong, 2017).

Figure 5 illustrates the potential for connections between protected areas and forest reserves (FR) being severed due to urban development. Kota Damansara Community FR, Ayer Hitam FR, Rantau Panjang FR, and Bukit Cherakah FR few areas that could be isolated from surrounding areas for species with low dispersal capacity (Fig. 3a, Fig. 3b and Fig. 3c). While the 545-ha forested site of the Forest Research Institute of Malaysia (FRIM) and adjacent Bukit Lagong FR are connected to the large forest patches at east of the study area, many of the patches at their south and west are unreachable to arboreal mammals (Fig. 3a and Fig. 3c). Efforts should be made to connect these isolated patches with FRIM, as isolated patches are immune to the “rescue effect”; where small patches receive emigrants from larger patches (Van Schmidt & Beissinger, 2020). Overtime isolation may lead to a gradual population decline in these smaller and isolated patches.

A wildlife connectivity scheme in Greater KL would also require the mitigation of the negative impacts of roads on connectivity. Wildlife crossings or eco-viaducts over major roads and other linear infrastructure can be a viable solution to reduce wildlife collisions (Clavenger, 2005) and improve connectivity across such anthropogenic features (Mimet et al., 2016). Several eco-viaducts have been constructed in Peninsular Malaysia for the purpose of enabling free crossings for wildlife across roads such as the Sungai Yu eco-viaduct (MYCAT, 2019) and several along the Kuala Berang Highway (Samantha et al., 2020; Tan, 2014a). However, questions have been raised concerning their effectiveness (Samantha et al., 2020; Tan, 2014b), as many have been observed to be used by very few species, and examples from Sabah, Malaysian Borneo, have been observed to be ineffective (Kan, 2017). Furthermore, existing studies in Southeast Asia are generally from rural settings, and the efficacy within urban environments needs to be tested. Several factors such as sound from passing cars and the amount of light at wildlife crossing, which is much more common in urban environments, might discourage fauna from utilising wildlife crossing measures effectively (van der Ree et al., 2015).

4.4 Planning urban green spaces in Southeast Asia

Our study represents the first connectivity modelling study in a Global South Southeast Asian city to the best of our knowledge. Field-based studies in Greater KL have shown that the larger urban forests have the capacity to support a surprising amount of biodiversity, including species of conservation concern (such as IUCN threatened species), even though they are surrounded by urbanisation (Tee et al., 2018) (Fig. 6). Unlike many high-income nations, urbanisation is relatively recent in Southeast Asia, and the remnant green patches are surprisingly biodiverse (Fig. 6). Thus, there is an urgent need to prioritise these last remaining green spaces to provide a unique opportunity to develop cities where both highly diversity nature and human residents co-exist. City’s such as Indonesia’s new planned capital in Borneo (Teo et al., 2020) and Johor’s Forest City (in Malaysia; Rahman, 2017) claim to promote green and sustainable urbanisation models. Commonly, urban design approaches in Southeast Asia focus on developing urban green spaces which are aesthetically pleasing but may not be support ecological processes and functions, such as connectivity, required to maintain self-sustaining populations (Lechner et al., 2021). There is a need to embed quantitative approaches to assessing connectivity in urban design and planning in the region. Supporting biological diverse and functioning green spaces will provide benefits to residents in terms of ecosystem services. Coexisting with nature maybe also bring ecosystem disservices in the form of human-wildlife conflicts, although urban citizens are relatively tolerant towards these (e.g., Tan et al. 2020). Coexisting with nature maybe also bring ecosystem disservices in the form of human-wildlife conflicts, although urban citizens are relatively tolerant towards these (Tan et al., 2020). Residents in Southeast Asian cities have the potential and unique opportunity of living side-by-side with their unique and biodiverse fauna.

Tropical Southeast Asia is a hotspot for global biodiversity as well as a region for growing urbanisation. Before the last green spaces and the functional connectivity between them are lost to urban growth, it is necessary to identity, conserve, and link them. Our research identified a network of green spaces and linkages that could support connecting the major urban forests in Greater KL. Our findings show that Southeast Asia's Global South megacities have the capacity to support biodiversity in urban settings, as well as the possibility for new models of urban development that promote biodiversity through the protection, restoration, and creation of networks of high-biodiversity green spaces.

Acknowledgements

Thanks to the help of LEC lab members including Karen Lourdes and Gangul Nelaka for their assistance with the mapping

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Conflicts of interest/Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Availability of data and material

Data will be made available on the following website (https://leclab.wixsite.com/spatial) or please contact the corresponding author.

Code availability

N/A

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jennifer Danneck, Darrel Tiang , and Khanh Ngo Duc. The first draft of the manuscript was written by Jennifer Danneck, Darrel Tiang and Alex Lechner, and all authors commented on forthcoming versions of the manuscript. All authors read and approved the final manuscript.

Additional declarations for articles in life science journals that report the results of studies involving humans and/or animals

N/A

Ethics approval

N/A

Consent to participate

N/A

Consent for publication

All authors have provided consent for the publication.

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You are reading this latest preprint version

Green spaces are critical for connecting urban habitat in the tropics

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Version 1

Abstract

Figures

1 Introduction

2 Methods

2.1 Study Approach

2.2 Study area: Greater Kuala Lumpur

2.3 Land cover mapping

2.3.1 Google earth engine land cover classification

2.3.2 Ancillary data for mapping agriculture and roads

2.3.3 Manual correction

2.3.4 Accuracy Assessment

2.4 Connectivity modelling

2.4.1 Parameterisation and model scenarios

2.4.2 Characterising fragmentation and quantifying the importance of patches and links

3 Results

3.1 Land cover mapping

3.2 Wildlife connectivity modelling

3.2.1 Assessment of connectivity pathways and graph analysis

3.2.2 Major roads as barriers

3.2.3 Landscape-scale graph metrics

4 Discussion

4.1 Overall connectivity

4.2 Implications for connectivity and habitat availability within Greater KL

4.3 Priorities for connectivity in Greater KL

4.4 Planning urban green spaces in Southeast Asia

5 Conclusions

Declarations

References

Supplementary Files

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