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Influence of landscape matrix on urban bird abundance: evidence from Malaysian citizen science data
Journal of Asia-Pacific Biodiversity 12 (2019) 369e375
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Journal of Asia-Pacific Biodiversity journal homepage: http://www.elsevier.com/locate/japb
Original Article
Influence of landscape matrix on urban bird abundance: evidence from Malaysian citizen science data Chong Leong Puan a, b, c, f, *, Kok Loong Yeong d, e, Kang Woei Ong a, f, Muhd Izzat Ahmad Fauzi f, Muhammad Syafiq Yahya a, Swee Seng Khoo f a
Faculty of Forestry, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia Biodiversity Unit, Institute of Bioscience, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia d Leverhulme Centre for Climate Change Mitigation (LC3M), University of Sheffield, Department of Animal and Plant Sciences, Alfred Denny Building, Western Bank, Sheffield, S10 2TN, United Kingdom e South East Asia Rainforest Research Partnership, Danum Valley Field Centre, 91112, Lahad Datu, Sabah, Malaysia f Malaysian Nature Society, 50480, Kuala Lumpur, Malaysia b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 4 January 2019 Received in revised form 11 March 2019 Accepted 14 March 2019 Available online 21 March 2019
Large cities in the tropics often comprise a myriad of manmade and natural elements that influence wildlife composition. Based on a citizen scienceebased project conducted in 2015, this study examined the combined effects of landscape factors on the bird assemblages in Kuala Lumpur and its conurbation, Peninsular Malaysia. A total of 48 species including 2,599 individual birds were recorded; the majority of which (>80%) were species of open habitat. Generalized linear mixed model indicated that the area of green cover had the strongest influence on number of individuals. Specifically, the abundance of individuals was increased by the presence of river corridors and roadside reserves. Areas located further away from water bodies and with less green cover had fewer birds. Our findings highlight the importance of incorporating a varied landscape matrix into urban planning so as to maintain urban bird diversity and demonstrate the usefulness of citizen science in biodiversity monitoring. Ó 2019 National Science Museum of Korea (NSMK) and Korea National Arboretum (KNA), Publishing Services by Elsevier. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Generalized linear mixed model Green cover Kuala Lumpur Urbanization Urban bird habitat
Introduction Urbanization replaces natural areas and substantially alters native and nonnative species composition (Aronson et al 2014; Lim et al 2003; McKinney 2008; Pei et al 2018; Ramli 2001) as well as the associated ecological functions and services. Improper and rapid urban planning will degrade or destroy habitats for wildlife including birds (Lim et al 2003; Lim and Sodhi 2004; Ramli 2001). Since 1950, urban areas around the world have experienced a rapid population growth from 751 million to 4.2 billion in 2018 with 54% of the current urban population that came from Asia (United Nations 2018). By 2050, it is projected that there will be an
* Corresponding author. Faculty of Forestry, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia. Tel.: þ603 8946 7583; fax: þ603 8943 2514. E-mail address: [email protected] (C.L. Puan). Peer review under responsibility of National Science Museum of Korea (NSMK) and Korea National Arboretum (KNA).
increase of 2.5 billion people with almost 90% of such addition coming from Asia and Africa (United Nations 2018). Following the global urban population growth rate of 1.73%, the urban population in Malaysia is expanding at a rate of 1.87% annually (United Nations 2018), and it is 1.5% for Peninsular Malaysia alone (Schneider et al 2015). By 2020, it is expected that more than 75% of the country’s population will be living in urban areas (Ismail et al 2018; United Nations 2018), and this may result in further loss of wildlife habitats. Being the largest city of Malaysia, Kuala Lumpur has been experiencing a rapid development over the last three decades (McGee 2008). It is predicted that the present annual urban agglomeration rate for Kuala Lumpur is at 3.09% which is the highest in the country (United Nations 2018). This implies that the green cover in Kuala Lumpur is still under pressure for development (Kanniah 2017; Nor et al 2017), despite the increase in total green coverage due to tree-planting initiatives in the recent years (Hong and Nooi 2014).
https://doi.org/10.1016/j.japb.2019.03.008 pISSN2287-884X eISSN2287-9544/Ó 2019 National Science Museum of Korea (NSMK) and Korea National Arboretum (KNA), Publishing Services by Elsevier. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Urban green cover is an important bird habitat (Cannon 1999; Evans et al 2009; Ikin et al 2013; Jasmani et al 2017; Mohamad 2011) for providing food, nesting, and roosting places (Idilfitri and Mohamad 2012; Jasmani et al 2017; Ramli 2001). Past studies have shown that the increase in size and heterogeneity of urban green spaces had a positive influence on bird species richness (Aida et al 2016; Hudson and Bird 2009; Jasmani et al 2017; Nielsen et al 2014; Nooten et al 2018; Shanahan et al 2011; Threlfall et al 2017). Having a vision to becoming a tropical garden city by 2020, Kuala Lumpur aims to have an additional of 10% of the public parks and open space as well as create a network of green spaces comprising river corridors, roadside reserves, forest patches, and urban parks (Kuala Lumpur City Hall 2004; Nor Akmar et al 2011). Hence, if the city is to manage for the benefits of the citizens and birds (Foo 2016; Hong and Nooi 2014; Kuala Lumpur City Hall 2004), there is a need to assess the combined effects of these green connectors that may contribute toward bird composition in this city. In addition, with the continued expansion of cities, engaging people living in these areas in monitoring urban wildlife composition and abundance is relevant considering the benefits (e.g. ecological, psychological, and social values) and costs (e.g. urban nuisance issues) that are linked to wildlife (Foo 2016; Idilfitri and Mohamad 2012; McCaffrey 2005; Mohamad 2011). Hence citizen science will be an option for such monitoring work and data obtained in turn can be beneficial to local residents comprising mostly nonscientists. Citizen science involves collaborative and mostly voluntary efforts of the general public, to collect information that is useful for the generation of scientific knowledge. Despite its limitations and challenges (Conrad and Hilchey 2011; Dickinson et al 2010; Silvertown 2009), such approaches can not only save money, time, and manpower, producing enormous data over large spatial and temporal scales (McCaffrey 2005; Silvertown 2009), but also involve the public into the conservation process (Cooper et al 2007; Devictor et al 2010; Dickinson et al 2012). Over the last two decades, there has been an increasing use of citizen science in bird monitoring, although most was used in Europe and North America (Forman et al 2002; Lepczyk 2005; McCaffrey 2005; Morgan and Styche 2012; Spurr 2012) rather than in the tropics (Mundkur et al 2017; Puan et al 2015). Initiated in 2010, MY Garden Birdwatch (MYGB) is a nonprofit nationwide project conducted by the Bird Conservation Council of the Malaysian Nature Society which involves collecting data on urban birds through volunteers. The aim of the project is to assess the composition of urban birds in Malaysia and their potential changes over time. Based on the MYGB data obtained in 2015, this study examined the combined effects of urban landscape factors on the bird assemblages in Kuala Lumpur and its conurbation within the Selangor state. Kuala Lumpur is the center of urbanization in Malaysia, whereas Selangor comprises emerging suburbs, allowing the assessment of bird composition at different levels of urban landscape features to be made. Most past studies had examined the influence of landscape factors on urban birds based on the main effect of multiple factors (e.g. Aida et al 2016; Jasmani et al 2017), whereas this study emphasized on the interaction effects of these factors using generalized linear mixed model (GLMM; Stroup 2012). Here, we aimed to demonstrate that a network of green spaces, such as river corridors and roadside reserves, can improve the local bird composition even at the center of an urban development. Such findings can serve as an additional guideline for urban landscape planning and design in the tropics.
Material and methods Study site Kuala Lumpur (3 8ʹ28ʺN, 10141ʹ11ʺE) covers an area of approximately 243 km2 with a population of 1.79 million (Malaysian Department of Statistics, 2017). It is a metropolis that is situated in the middle of Selangor state and comprises commercial, industrial, and residential areas with green spaces scattered within the high density of concrete infrastructures and buildings (Bunnell et al 2002; Kanniah 2017). Selangor (7,931 km2) has a lower population density of 793 per km2, that is, 6.29 million (Malaysian Department of Statistics, 2017) and has relatively less commercial activities and more green spaces within mostly suburb settings (Aida et al 2016). As of 2010, Kuala Lumpur and Selangor had been the most urbanized states having 100% and 91.4% of their residents living in urban areas, respectively (Hasan and Nair 2014). Survey protocols The survey protocols were adapted from the Royal Society for the Protection of Birds’ Big Garden Birdwatch (www.rspb.org.uk/ get-involved/activities/birdwatch/). For the MYGB, the survey was carried out by members of the general public from all over Malaysia on a volunteer basis and the primary focus was on species of open habitat. The announcement of the count dates was made as early as January each year through the official website (www. mygardenbirdwatch.com), social media, and e-mails sent to past volunteers or those signed up during the past outreach events organized by the MYGB committee. During a weekend, that is, 20 and 21 June 2015, volunteers were required to stand at one location in their residential garden or nearby park for 30 minutes. The coordinate of count locations was pinpointed and generated from the project official website when a count address or location was entered. Volunteers were asked to count the numbers of bird species and individuals sighted. The recording should only include perched birds, that is, with the highest number of individuals sighted at one time for each species, excluding those which flew overhead or were heard only. This was to reduce the chances of double counting. To ensure the validity of the data submitted particularly with respect to species identification, new volunteers were advised to use the printed materials provided by the committee or download the official count sheet from the website coupled with personal field guidebooks. Volunteers were allowed to send field notes to our data validators through e-mail or phone calls to get help on species identification. All observations were submitted online through the digital count sheet available in the webpage within a two-week period after the count ended. Two to three expert validators comprising experienced birdwatchers reviewed all the submitted observations, to ensure the accuracy and validity of the data, which can be judged by the location, time of the day, and overall local species distributions. In the case of doubtful observations, if any, volunteers were contacted for further clarification. Statistical analysis We performed GLMM to examine the effect of four landscape variables, namely distance to the nearest forested area (FOREST), distance to the nearest main road (ROAD), distance to the nearest water body (WATER), and percentage of green patches within 100 m radius (GREEN) on bird species and abundance. We did this by
CL Puan et al. / Journal of Asia-Pacific Biodiversity 12 (2019) 369e375
fitting FOREST, ROAD, WATER, and GREEN as fixed factors; bird species richness or abundance as a response variable; and survey period as a random factor into the model, after a test of multicollinearity was performed. The landscape variables were measured using the ruler tool (Liew et al 2016) in Google Earth Pro (Google Inc., USA) based on the coordinate of each survey point. It should be noted that forested area was identified based on the land area of at least 50 ha comprising primarily native tree stands (Foo 2016) as compared with smaller green patches planted with nonnative species of relatively lower density. The latter covered river corridors, roadside reserves, sport fields, urban parks, and gardens, excluding agricultural areas. For roads, we defined them as tarred roads having the minimum lane width of more than 3.25 m, and this includes expressways and arterial and collector roads. The GLMM used ‘glmer’ function with ‘poisson’ error distribution using the lme4 package (Bates et al 2015) for R3.3.1 (R Core Team 2016). The proportion of green patches area data were transformed using logit transformation to correct nonnormal residuals and nonhomogenous variances (Warton and Hui 2011). All fixed factors were standardized to have a mean of zero and standard deviation of one to allow direct comparison of the relative importance of the variables in predicting changes in bird species and abundance (Grueber et al 2011). Models were ranked using Akaike Information Criteria (AICc) and executed model averaging on models that were equally probable (i.e. DAICc < 2; Burnham and Anderson 2002). With respect to recommendations for urban landscape planning and design, we performed a separate logistic regression analysis on bird abundance against GREEN, ROAD, and WATER.
Results A total of 2,599 individuals of 48 species were recorded in Kuala Lumpur and Selangor by 46 and 93 volunteers, respectively. The majority of the species (>80%) are those of open habitat. The proportion of valid surveys was 97.89%, and there were only three rejections. The most abundant species were the Eurasian tree sparrow (Passer montanus; 546 individuals or 21.01% of the total bird numbers), Javan myna (Acridotheres javanicus; 302 or 11.62%),
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house crow (Corvus splendens; 252 or 9.70%), and Asian glossy starling (Aplonis panayensis; 224 or 8.62%). Bird species and abundance Our findings showed that the number of bird species was not related to distance to nearest forested area (FOREST), distance to the nearest main road (ROAD), distance to the nearest water body (WATER), or percentage of green patches within 100 m radius (GREEN; Table 1, Figures 1 and 2). There was a negative but negligible interaction effect between WATER and GREEN on bird species (mean effect size ¼ -0.08, confidence intervals ¼ 0.19 to 0.01; Table 1, Figures 1 and 2). Bird abundance was positively related with GREEN and FOREST and negatively related with ROAD (Table 1, Figures 1 and 2). The GLMM showed four negative interaction effects, that is, between WATER GREEN, FOREST ROAD, FOREST WATER, and ROAD GREEN on the number of individuals. Green patch appeared to have a stronger influence for determining bird abundance in urban areas, which was shown in many of the good models derived from the GLMM (Table 1). Discussion With the global urbanization trends (McGee 2008; United Nations 2018; Zhang 2016), many cities in the tropics are expected to follow the pace of the developed countries having increasing human populations living in highly urbanized areas. Kuala Lumpur is among the cities in Southeast Asia that are experiencing rapid development over the past three decades (McGee 2008). With the aim of becoming one of the top 20 most livable cities in the world by 2020 (Hong and Nooi 2014; Kanniah 2017), urban greening is certainly playing a major role in fulfilling such goal. Our study found there were combined effects of landscape level variables, namely green patches, water bodies, roads, and/or forest areas in influencing the abundance of birds in Kuala Lumpur and the surrounding conurbation. This implies urban landscape planners should incorporate different landscape matrix into township or park design if the fundamental goal is to
Table 1. Generalized linear mixed model (GLMM, based on AICc) for determining urban bird species numbers and abundance at the study sites. Model Number of species Null ROADþWATERþGREENþWATER*GREEN FORESTþROADþWATERþGREENþFOREST*ROADþFOREST*WATERþROAD*GREENþWATER*GREEN ROAD FORESTþROADþWATERþGREENþFOREST*ROADþFOREST*WATERþWATER*GREEN WATERþGREENþWATER*GREEN ROADþGREEN FORESTþROADþFOREST*ROAD GREEN FORESTþROADþWATERþGREENþFOREST*ROADþWATER*GREEN FORESTþROADþGREENþFOREST*ROADþROAD*GREEN FORESTþROADþWATERþGREENþFOREST*ROADþROAD*GREENþWATER*GREEN Number of individuals Null FORESTþROADþWATERþGREENþFOREST*ROADþFOREST*WATERþROAD*WATERþ ROAD*GREENþWATER*GREEN FORESTþROADþWATERþGREENþFOREST*ROADþFOREST*WATERþROAD*GREENþWATER*GREEN
Log-lik
AICc
Di
e(-Di/2)
wi
R2GLMM(m)
R2GLMM(c)
357.9 353.0 348.9 356.7 350.1 354.6 355.8 354.8 354.8 351.6 352.8 350.6
719.7 718.6 719.5 719.5 719.7 719.7 719.9 720.0 720.3 720.4 720.4 720.6
e 0.00 0.82 0.90 1.02 1.04 1.29 1.37 1.67 1.75 1.80 1.98
e 1.00 0.66 0.63 0.60 0.59 0.52 0.50 0.43 0.42 0.41 0.37
e 0.073 0.049 0.047 0.044 0.044 0.041 0.038 0.037 0.032 0.030 0.030
0.000 0.029 0.057 0.007 0.047 0.020 0.012 0.019 0.005 0.040 0.032 0.047
0.229 0.029 0.260 0.241 0.279 0.278 0.267 0.225 0.253 0.259 0.222 0.239
882.5 813.4
1765.0 1650.9
e 0.00
e 1.00
e 0.702
0.000 0.136
0.067 0.136
815.4
1652.6
1.61
0.45
0.298
0.056
0.260
FOREST ¼ distance to nearest forested area; ROAD ¼ distance to the nearest main road; WATER ¼ distance to the nearest water body; GREEN ¼ percentage of green patches or areas within 100 m radius; Log-lik ¼ natural logarithm of likelihood function; AICc ¼ measure of the relative quality of a model with small sample sizes; Di ¼ difference to a model with the lowest AIC model; e(-Di/2) ¼ relative likelihood; wi ¼ AIC weight; R2GLMM (m) ¼ variance explained by fixed factors; R2GLMM (c) ¼ variance explained by fixed and random factors. GLMM was built based on four variables (FOREST, ROAD, WATER, and GREEN) with main two-way interactions. Data for models with Di < 2 are presented.
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Figure 1. Relationships between total number of (A) species and (B) individuals of urban birds in relation to FOREST, ROAD, WATER, and GREEN. Solid lines were plotted for significant relationships. FOREST ¼ distance to nearest forested area; ROAD ¼ distance to the nearest main road; WATER ¼ distance to the nearest water body; GREEN ¼ percentage of green patches or areas within 100 m radius.
Figure 2. Effect sizes of predictor variables based on model averaging of best-fitting models from GLMM analyzing the effects of FOREST, ROAD, WATER, and GREEN on total number of (A) species and (B) individuals of urban birds. Error bars show modelaveraged 95% confidence intervals (CIs). The main interaction effects between factors are shaded in grey. FOREST ¼ distance to nearest forested area; ROAD ¼ distance to the nearest main road; WATER ¼ distance to the nearest water body; GREEN ¼ percentage of green patches or areas within 100 m radius; GLMM ¼ generalized linear mixed model.
have more birds reside in urban areas. However, the extent to which the level of connectivity of green areas (Shanahan et al 2011) to be implemented, whether having fruiting trees or native vegetation matters (Hudson and Bird 2009; Parsons et al 2006; Threlfall et al 2017) as well as the response of different bird species or feeding guilds (Hodgkison et al 2007; Shanahan et al 2011), require further investigation. Results from the GLMM implied four situations: (a) urban bird abundance was reduced at areas located further from water body and with less green cover; (b) nearer from forested area and further from roads; (c) nearer from forested area and further from water body, and (d) further from main road but with little green cover. Green cover particularly influences urban birds in terms of abundance, and such an effect will be more prominent when larger green cover is located close to water bodies or roads. Our results corroborated with other studies conducted in highly populated cities in the temperate region (Cicero 1989; Hudson and Bird 2009; Melles et al 2003). Green areas that are located closer to roads will also lead to higher bird abundance (but see Forman et al 2002) but not species. This implies the disturbance of roads may be species specific. Through observations, mynas and starlings seem to adapt well to roads by building their nests in the crevices of concrete walls built for expressways and flyovers. Furthermore, it should be noted that the majority of the species recorded in this study (>80%) belong to open habitat, and this includes feral, human commensal or cosmopolitan species which may not be associated with forest habitat. This may explain why the number of birds increased when in distance to forested area increased. Despite that the MYGB survey protocols may favor more conspicuous nonforest species that frequent open areas, our findings also implies that forest species may not be able to thrive in urban small green patches. The latter still rely on forests for their survival (Kuala Lumpur City Hall 2004; Ramli 2001), unlike temperate species that may respond otherwise even in urban environment (Melles et al 2003; Mörtberg and Wallentinus
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2000). Nevertheless, our findings stressed on the importance of urban green spaces, particularly those larger in size (Aida et al 2016) in maintaining urban biodiversity, at least for nonforest birds. More importantly, connecting these green spaces through river corridors and roadside reserves is expected to increase the abundance of birds in the city. In the case of Kuala Lumpur, although some green areas may be privately owned and inaccessible to public, they still serve the same environmental and ecological functions (Kanniah 2017), for example, bird habitats, as demonstrated in our study. Based on our logistic model (see Appendix 2), we suggest that the size of green cover to be retained should be greater than 10% within an area that is located not less than 1 km from water bodies or 50 m from roads. This implies that the target of Kuala Lumpur to have an addition of 10% public parks and open space by 2020 is only sensible, in terms of increasing bird numbers, when distance to water bodies and roads are taken into account. Implications for conservation Considering the country’s aspiration of becoming one of the most livable cities (Hong and Nooi 2014; Ismail et al 2018; Kanniah 2017) and its vision of being a top 20 nation that emphasizes on citizen well-being besides economic development as a long-term development plan for the next three decades, our study supports two components that may help achieve these goals, that is, (a) promoting green spaces and connectors in urban areas for both human and wildlife and (b) engaging public to do science for the society (Dickinson et al 2012). In the case of the MYGB, sustaining the volunteers, maintaining their interests and competency in bird identification following the specified survey protocols, as well as obtaining funding are the main challenges to keep the project going. Nonetheless, citizen science is expected to be an emerging field in the tropics. We feel the need of promoting citizen science in biodiversity monitoring work in this part of the world, and continuous monitoring will allow assessment on temporal changes in biodiversity in relation to urbanization or other forms of ecological disturbance in the long run. Conflict of interest The authors declare that there is no conflict of interest. Author contribution C.L.P. conceived the presented idea and wrote the manuscript with inputs from S.S.K., K.L.Y., K.W.O., and M.I.A.F. K.L.Y. performed data analysis based on data sorted out by M.S.Y. S.S.K. initiated the MYGB project and validated as well as provided the original data. S.S.K., K.W.O., M.I.A.F., and C.L.P. are also the committee members of the MYGB. Acknowledgments The authors are grateful to all MYGB project coordinators and co-coordinators over the years, that is, Mark Ng (2010-2011), Chin Pik Wun (2012), Tan Beng Hui (2012-2014), Tashia Peterson (20132014), and Lim Bing Yee (2015-2019). The authors also thank Yvonne Ang, Eileen Chiang, Nina Cheung, Caroline Ho, Andy Lee,
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Lim Aun Tiah, and Jasmine Steed as part of the committee members of the project as well as all volunteers who had contributed one way or another in the project. Chin Pik Wun was instrumental in the design and production of the pocket guides, count sheets, and other publicity and outreach materials as well as website development with the help from Shravan Rao. The seed fund to start the MYGB project was sponsored by Wild Asia. The subsequent funding was provided by Schmidt Marketing (M) Sdn Bhd and the Malaysian Nature Society (Perak Branch)’s Tan Kean Cheong Bird Conservation Memorial Fund to run the project and set up the MYGB website.
Appendix 1. List of bird species recorded
Common name
Scientific name
Eurasian tree sparrow Oriental magpie robin Common myna Javan myna Rock pigeon Spotted dove Zebra dove House crow Yellow-vented bulbul Black-naped oriole Asian koel Little heron Asian glossy starling Common iora Olive-backed sunbird Brown-throated sunbird Paddyfield pipit White-throated kingfisher White-breasted waterhen Long-tailed parakeet Pink-necked green pigeon Painted stork Grey heron Intermediate egret Scaly-breasted munia Common tailorbird Pied fantail Lineated barbet Pacific swallow Oriental white-eye Coppersmith barbet Olive-winged bulbul* Red-eyed bulbul* Black-bellied malkoha* Blue-tailed bee-eater Blue-throated bee-eater Ashy tailorbird Baya weaver Pied triller Red-whiskered bulbul Grey-breasted spiderhunter* Brown-capped woodpecker* Scarlet-backed flowerpecker* Cattle egret Little egret Little green pigeon* Golden-bellied gerygone Greater racket-tailed drongo*
Passer montanus Copsychus saularis Acridotheres tristis Acridotheres javanicus Columba livia Streptopelia chinensis Geopelia striata Corvus splendens Pycnonotus goiavier Oriolus chinensis Eudynamys scolopaceus Butorides striata Aplonis panayensis Aegithina tiphia Cinnyris jugularis Anthreptes malacensis Anthus rufulus Halcyon smyrnensis Amaurornis phoenicurus Psittacula longicauda Treron vernans Mycteria leucocephala Ardea cinerea Mesophoyx intermedia Lonchura punctulata Orthotomus sutorius Rhipidura javanica Psilopogon lineatus Hirundo tahitica Zosterops palpebrosus Psilopogon haemacephalus Pycnonotus plumosus Pycnonotus brunneus Phaenicophaeus diardi Merops philippinus Merops viridis Orthotomus ruficeps Ploceus philippinus Lalage nigra Pycnonotus jocosus Arachnothera modesta Dendrocopos moluccensis Dicaeum cruentatum Bubulcus ibis Egretta garzetta Treron olax Gerygone sulphurea Dicrurus paradiseus
*
Species of forest or wooded habitats.
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Appendix 2. Summary statistics pertaining to the relationships between urban birds and landscape matrix
Variables
R2GLMM(m)
Number of species FOREST 0.003 ROAD 0.009 WATER 0.003 GREEN 0.006 Number of individuals FOREST 0.019 ROAD 0.020 WATER 0.001 GREEN 0.019
R2GLMM(c)
Intercept
Slope
CIs L
U
0.358 0.348 0.341 0.360
1.55 1.57 1.57 1.56
0.031 0.051 0.009 0.043
0.098 0.114 0.057 0.024
0.383 0.014 0.080 0.109
0.600 0.611 0.608 0.633
2.628 2.626 2.623 2.608
0.011 0.059 0.086 0.060
0.010 0.096 0.030 0.021
0.050 0.022 0.049 0.099
FOREST ¼ distance to nearest forested area; ROAD ¼ distance to the nearest main road; WATER ¼ distance to the nearest water body; GREEN ¼ percentage of green patches or areas within 100 m radius; L ¼ lower confidence bound, U ¼ upper confidence bound, R2GLMM (m) ¼ variance explained by fixed effects (numbers of bird species or individuals); R2GLMM (c) ¼ variance explained by fixed and random effects (time); CI, confidence interval. Values in bold indicate significant relationships with numbers of bird species or individuals (i.e. 95% CIs of slope values exclude zero).
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Original Article
Influence of landscape matrix on urban bird abundance: evidence from Malaysian citizen science data Chong Leong Puan a, b, c, f, *, Kok Loong Yeong d, e, Kang Woei Ong a, f, Muhd Izzat Ahmad Fauzi f, Muhammad Syafiq Yahya a, Swee Seng Khoo f a
Faculty of Forestry, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia Biodiversity Unit, Institute of Bioscience, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia d Leverhulme Centre for Climate Change Mitigation (LC3M), University of Sheffield, Department of Animal and Plant Sciences, Alfred Denny Building, Western Bank, Sheffield, S10 2TN, United Kingdom e South East Asia Rainforest Research Partnership, Danum Valley Field Centre, 91112, Lahad Datu, Sabah, Malaysia f Malaysian Nature Society, 50480, Kuala Lumpur, Malaysia b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 4 January 2019 Received in revised form 11 March 2019 Accepted 14 March 2019 Available online 21 March 2019
Large cities in the tropics often comprise a myriad of manmade and natural elements that influence wildlife composition. Based on a citizen scienceebased project conducted in 2015, this study examined the combined effects of landscape factors on the bird assemblages in Kuala Lumpur and its conurbation, Peninsular Malaysia. A total of 48 species including 2,599 individual birds were recorded; the majority of which (>80%) were species of open habitat. Generalized linear mixed model indicated that the area of green cover had the strongest influence on number of individuals. Specifically, the abundance of individuals was increased by the presence of river corridors and roadside reserves. Areas located further away from water bodies and with less green cover had fewer birds. Our findings highlight the importance of incorporating a varied landscape matrix into urban planning so as to maintain urban bird diversity and demonstrate the usefulness of citizen science in biodiversity monitoring. Ó 2019 National Science Museum of Korea (NSMK) and Korea National Arboretum (KNA), Publishing Services by Elsevier. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Generalized linear mixed model Green cover Kuala Lumpur Urbanization Urban bird habitat
Introduction Urbanization replaces natural areas and substantially alters native and nonnative species composition (Aronson et al 2014; Lim et al 2003; McKinney 2008; Pei et al 2018; Ramli 2001) as well as the associated ecological functions and services. Improper and rapid urban planning will degrade or destroy habitats for wildlife including birds (Lim et al 2003; Lim and Sodhi 2004; Ramli 2001). Since 1950, urban areas around the world have experienced a rapid population growth from 751 million to 4.2 billion in 2018 with 54% of the current urban population that came from Asia (United Nations 2018). By 2050, it is projected that there will be an
* Corresponding author. Faculty of Forestry, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia. Tel.: þ603 8946 7583; fax: þ603 8943 2514. E-mail address: [email protected] (C.L. Puan). Peer review under responsibility of National Science Museum of Korea (NSMK) and Korea National Arboretum (KNA).
increase of 2.5 billion people with almost 90% of such addition coming from Asia and Africa (United Nations 2018). Following the global urban population growth rate of 1.73%, the urban population in Malaysia is expanding at a rate of 1.87% annually (United Nations 2018), and it is 1.5% for Peninsular Malaysia alone (Schneider et al 2015). By 2020, it is expected that more than 75% of the country’s population will be living in urban areas (Ismail et al 2018; United Nations 2018), and this may result in further loss of wildlife habitats. Being the largest city of Malaysia, Kuala Lumpur has been experiencing a rapid development over the last three decades (McGee 2008). It is predicted that the present annual urban agglomeration rate for Kuala Lumpur is at 3.09% which is the highest in the country (United Nations 2018). This implies that the green cover in Kuala Lumpur is still under pressure for development (Kanniah 2017; Nor et al 2017), despite the increase in total green coverage due to tree-planting initiatives in the recent years (Hong and Nooi 2014).
https://doi.org/10.1016/j.japb.2019.03.008 pISSN2287-884X eISSN2287-9544/Ó 2019 National Science Museum of Korea (NSMK) and Korea National Arboretum (KNA), Publishing Services by Elsevier. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Urban green cover is an important bird habitat (Cannon 1999; Evans et al 2009; Ikin et al 2013; Jasmani et al 2017; Mohamad 2011) for providing food, nesting, and roosting places (Idilfitri and Mohamad 2012; Jasmani et al 2017; Ramli 2001). Past studies have shown that the increase in size and heterogeneity of urban green spaces had a positive influence on bird species richness (Aida et al 2016; Hudson and Bird 2009; Jasmani et al 2017; Nielsen et al 2014; Nooten et al 2018; Shanahan et al 2011; Threlfall et al 2017). Having a vision to becoming a tropical garden city by 2020, Kuala Lumpur aims to have an additional of 10% of the public parks and open space as well as create a network of green spaces comprising river corridors, roadside reserves, forest patches, and urban parks (Kuala Lumpur City Hall 2004; Nor Akmar et al 2011). Hence, if the city is to manage for the benefits of the citizens and birds (Foo 2016; Hong and Nooi 2014; Kuala Lumpur City Hall 2004), there is a need to assess the combined effects of these green connectors that may contribute toward bird composition in this city. In addition, with the continued expansion of cities, engaging people living in these areas in monitoring urban wildlife composition and abundance is relevant considering the benefits (e.g. ecological, psychological, and social values) and costs (e.g. urban nuisance issues) that are linked to wildlife (Foo 2016; Idilfitri and Mohamad 2012; McCaffrey 2005; Mohamad 2011). Hence citizen science will be an option for such monitoring work and data obtained in turn can be beneficial to local residents comprising mostly nonscientists. Citizen science involves collaborative and mostly voluntary efforts of the general public, to collect information that is useful for the generation of scientific knowledge. Despite its limitations and challenges (Conrad and Hilchey 2011; Dickinson et al 2010; Silvertown 2009), such approaches can not only save money, time, and manpower, producing enormous data over large spatial and temporal scales (McCaffrey 2005; Silvertown 2009), but also involve the public into the conservation process (Cooper et al 2007; Devictor et al 2010; Dickinson et al 2012). Over the last two decades, there has been an increasing use of citizen science in bird monitoring, although most was used in Europe and North America (Forman et al 2002; Lepczyk 2005; McCaffrey 2005; Morgan and Styche 2012; Spurr 2012) rather than in the tropics (Mundkur et al 2017; Puan et al 2015). Initiated in 2010, MY Garden Birdwatch (MYGB) is a nonprofit nationwide project conducted by the Bird Conservation Council of the Malaysian Nature Society which involves collecting data on urban birds through volunteers. The aim of the project is to assess the composition of urban birds in Malaysia and their potential changes over time. Based on the MYGB data obtained in 2015, this study examined the combined effects of urban landscape factors on the bird assemblages in Kuala Lumpur and its conurbation within the Selangor state. Kuala Lumpur is the center of urbanization in Malaysia, whereas Selangor comprises emerging suburbs, allowing the assessment of bird composition at different levels of urban landscape features to be made. Most past studies had examined the influence of landscape factors on urban birds based on the main effect of multiple factors (e.g. Aida et al 2016; Jasmani et al 2017), whereas this study emphasized on the interaction effects of these factors using generalized linear mixed model (GLMM; Stroup 2012). Here, we aimed to demonstrate that a network of green spaces, such as river corridors and roadside reserves, can improve the local bird composition even at the center of an urban development. Such findings can serve as an additional guideline for urban landscape planning and design in the tropics.
Material and methods Study site Kuala Lumpur (3 8ʹ28ʺN, 10141ʹ11ʺE) covers an area of approximately 243 km2 with a population of 1.79 million (Malaysian Department of Statistics, 2017). It is a metropolis that is situated in the middle of Selangor state and comprises commercial, industrial, and residential areas with green spaces scattered within the high density of concrete infrastructures and buildings (Bunnell et al 2002; Kanniah 2017). Selangor (7,931 km2) has a lower population density of 793 per km2, that is, 6.29 million (Malaysian Department of Statistics, 2017) and has relatively less commercial activities and more green spaces within mostly suburb settings (Aida et al 2016). As of 2010, Kuala Lumpur and Selangor had been the most urbanized states having 100% and 91.4% of their residents living in urban areas, respectively (Hasan and Nair 2014). Survey protocols The survey protocols were adapted from the Royal Society for the Protection of Birds’ Big Garden Birdwatch (www.rspb.org.uk/ get-involved/activities/birdwatch/). For the MYGB, the survey was carried out by members of the general public from all over Malaysia on a volunteer basis and the primary focus was on species of open habitat. The announcement of the count dates was made as early as January each year through the official website (www. mygardenbirdwatch.com), social media, and e-mails sent to past volunteers or those signed up during the past outreach events organized by the MYGB committee. During a weekend, that is, 20 and 21 June 2015, volunteers were required to stand at one location in their residential garden or nearby park for 30 minutes. The coordinate of count locations was pinpointed and generated from the project official website when a count address or location was entered. Volunteers were asked to count the numbers of bird species and individuals sighted. The recording should only include perched birds, that is, with the highest number of individuals sighted at one time for each species, excluding those which flew overhead or were heard only. This was to reduce the chances of double counting. To ensure the validity of the data submitted particularly with respect to species identification, new volunteers were advised to use the printed materials provided by the committee or download the official count sheet from the website coupled with personal field guidebooks. Volunteers were allowed to send field notes to our data validators through e-mail or phone calls to get help on species identification. All observations were submitted online through the digital count sheet available in the webpage within a two-week period after the count ended. Two to three expert validators comprising experienced birdwatchers reviewed all the submitted observations, to ensure the accuracy and validity of the data, which can be judged by the location, time of the day, and overall local species distributions. In the case of doubtful observations, if any, volunteers were contacted for further clarification. Statistical analysis We performed GLMM to examine the effect of four landscape variables, namely distance to the nearest forested area (FOREST), distance to the nearest main road (ROAD), distance to the nearest water body (WATER), and percentage of green patches within 100 m radius (GREEN) on bird species and abundance. We did this by
CL Puan et al. / Journal of Asia-Pacific Biodiversity 12 (2019) 369e375
fitting FOREST, ROAD, WATER, and GREEN as fixed factors; bird species richness or abundance as a response variable; and survey period as a random factor into the model, after a test of multicollinearity was performed. The landscape variables were measured using the ruler tool (Liew et al 2016) in Google Earth Pro (Google Inc., USA) based on the coordinate of each survey point. It should be noted that forested area was identified based on the land area of at least 50 ha comprising primarily native tree stands (Foo 2016) as compared with smaller green patches planted with nonnative species of relatively lower density. The latter covered river corridors, roadside reserves, sport fields, urban parks, and gardens, excluding agricultural areas. For roads, we defined them as tarred roads having the minimum lane width of more than 3.25 m, and this includes expressways and arterial and collector roads. The GLMM used ‘glmer’ function with ‘poisson’ error distribution using the lme4 package (Bates et al 2015) for R3.3.1 (R Core Team 2016). The proportion of green patches area data were transformed using logit transformation to correct nonnormal residuals and nonhomogenous variances (Warton and Hui 2011). All fixed factors were standardized to have a mean of zero and standard deviation of one to allow direct comparison of the relative importance of the variables in predicting changes in bird species and abundance (Grueber et al 2011). Models were ranked using Akaike Information Criteria (AICc) and executed model averaging on models that were equally probable (i.e. DAICc < 2; Burnham and Anderson 2002). With respect to recommendations for urban landscape planning and design, we performed a separate logistic regression analysis on bird abundance against GREEN, ROAD, and WATER.
Results A total of 2,599 individuals of 48 species were recorded in Kuala Lumpur and Selangor by 46 and 93 volunteers, respectively. The majority of the species (>80%) are those of open habitat. The proportion of valid surveys was 97.89%, and there were only three rejections. The most abundant species were the Eurasian tree sparrow (Passer montanus; 546 individuals or 21.01% of the total bird numbers), Javan myna (Acridotheres javanicus; 302 or 11.62%),
371
house crow (Corvus splendens; 252 or 9.70%), and Asian glossy starling (Aplonis panayensis; 224 or 8.62%). Bird species and abundance Our findings showed that the number of bird species was not related to distance to nearest forested area (FOREST), distance to the nearest main road (ROAD), distance to the nearest water body (WATER), or percentage of green patches within 100 m radius (GREEN; Table 1, Figures 1 and 2). There was a negative but negligible interaction effect between WATER and GREEN on bird species (mean effect size ¼ -0.08, confidence intervals ¼ 0.19 to 0.01; Table 1, Figures 1 and 2). Bird abundance was positively related with GREEN and FOREST and negatively related with ROAD (Table 1, Figures 1 and 2). The GLMM showed four negative interaction effects, that is, between WATER GREEN, FOREST ROAD, FOREST WATER, and ROAD GREEN on the number of individuals. Green patch appeared to have a stronger influence for determining bird abundance in urban areas, which was shown in many of the good models derived from the GLMM (Table 1). Discussion With the global urbanization trends (McGee 2008; United Nations 2018; Zhang 2016), many cities in the tropics are expected to follow the pace of the developed countries having increasing human populations living in highly urbanized areas. Kuala Lumpur is among the cities in Southeast Asia that are experiencing rapid development over the past three decades (McGee 2008). With the aim of becoming one of the top 20 most livable cities in the world by 2020 (Hong and Nooi 2014; Kanniah 2017), urban greening is certainly playing a major role in fulfilling such goal. Our study found there were combined effects of landscape level variables, namely green patches, water bodies, roads, and/or forest areas in influencing the abundance of birds in Kuala Lumpur and the surrounding conurbation. This implies urban landscape planners should incorporate different landscape matrix into township or park design if the fundamental goal is to
Table 1. Generalized linear mixed model (GLMM, based on AICc) for determining urban bird species numbers and abundance at the study sites. Model Number of species Null ROADþWATERþGREENþWATER*GREEN FORESTþROADþWATERþGREENþFOREST*ROADþFOREST*WATERþROAD*GREENþWATER*GREEN ROAD FORESTþROADþWATERþGREENþFOREST*ROADþFOREST*WATERþWATER*GREEN WATERþGREENþWATER*GREEN ROADþGREEN FORESTþROADþFOREST*ROAD GREEN FORESTþROADþWATERþGREENþFOREST*ROADþWATER*GREEN FORESTþROADþGREENþFOREST*ROADþROAD*GREEN FORESTþROADþWATERþGREENþFOREST*ROADþROAD*GREENþWATER*GREEN Number of individuals Null FORESTþROADþWATERþGREENþFOREST*ROADþFOREST*WATERþROAD*WATERþ ROAD*GREENþWATER*GREEN FORESTþROADþWATERþGREENþFOREST*ROADþFOREST*WATERþROAD*GREENþWATER*GREEN
Log-lik
AICc
Di
e(-Di/2)
wi
R2GLMM(m)
R2GLMM(c)
357.9 353.0 348.9 356.7 350.1 354.6 355.8 354.8 354.8 351.6 352.8 350.6
719.7 718.6 719.5 719.5 719.7 719.7 719.9 720.0 720.3 720.4 720.4 720.6
e 0.00 0.82 0.90 1.02 1.04 1.29 1.37 1.67 1.75 1.80 1.98
e 1.00 0.66 0.63 0.60 0.59 0.52 0.50 0.43 0.42 0.41 0.37
e 0.073 0.049 0.047 0.044 0.044 0.041 0.038 0.037 0.032 0.030 0.030
0.000 0.029 0.057 0.007 0.047 0.020 0.012 0.019 0.005 0.040 0.032 0.047
0.229 0.029 0.260 0.241 0.279 0.278 0.267 0.225 0.253 0.259 0.222 0.239
882.5 813.4
1765.0 1650.9
e 0.00
e 1.00
e 0.702
0.000 0.136
0.067 0.136
815.4
1652.6
1.61
0.45
0.298
0.056
0.260
FOREST ¼ distance to nearest forested area; ROAD ¼ distance to the nearest main road; WATER ¼ distance to the nearest water body; GREEN ¼ percentage of green patches or areas within 100 m radius; Log-lik ¼ natural logarithm of likelihood function; AICc ¼ measure of the relative quality of a model with small sample sizes; Di ¼ difference to a model with the lowest AIC model; e(-Di/2) ¼ relative likelihood; wi ¼ AIC weight; R2GLMM (m) ¼ variance explained by fixed factors; R2GLMM (c) ¼ variance explained by fixed and random factors. GLMM was built based on four variables (FOREST, ROAD, WATER, and GREEN) with main two-way interactions. Data for models with Di < 2 are presented.
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Figure 1. Relationships between total number of (A) species and (B) individuals of urban birds in relation to FOREST, ROAD, WATER, and GREEN. Solid lines were plotted for significant relationships. FOREST ¼ distance to nearest forested area; ROAD ¼ distance to the nearest main road; WATER ¼ distance to the nearest water body; GREEN ¼ percentage of green patches or areas within 100 m radius.
Figure 2. Effect sizes of predictor variables based on model averaging of best-fitting models from GLMM analyzing the effects of FOREST, ROAD, WATER, and GREEN on total number of (A) species and (B) individuals of urban birds. Error bars show modelaveraged 95% confidence intervals (CIs). The main interaction effects between factors are shaded in grey. FOREST ¼ distance to nearest forested area; ROAD ¼ distance to the nearest main road; WATER ¼ distance to the nearest water body; GREEN ¼ percentage of green patches or areas within 100 m radius; GLMM ¼ generalized linear mixed model.
have more birds reside in urban areas. However, the extent to which the level of connectivity of green areas (Shanahan et al 2011) to be implemented, whether having fruiting trees or native vegetation matters (Hudson and Bird 2009; Parsons et al 2006; Threlfall et al 2017) as well as the response of different bird species or feeding guilds (Hodgkison et al 2007; Shanahan et al 2011), require further investigation. Results from the GLMM implied four situations: (a) urban bird abundance was reduced at areas located further from water body and with less green cover; (b) nearer from forested area and further from roads; (c) nearer from forested area and further from water body, and (d) further from main road but with little green cover. Green cover particularly influences urban birds in terms of abundance, and such an effect will be more prominent when larger green cover is located close to water bodies or roads. Our results corroborated with other studies conducted in highly populated cities in the temperate region (Cicero 1989; Hudson and Bird 2009; Melles et al 2003). Green areas that are located closer to roads will also lead to higher bird abundance (but see Forman et al 2002) but not species. This implies the disturbance of roads may be species specific. Through observations, mynas and starlings seem to adapt well to roads by building their nests in the crevices of concrete walls built for expressways and flyovers. Furthermore, it should be noted that the majority of the species recorded in this study (>80%) belong to open habitat, and this includes feral, human commensal or cosmopolitan species which may not be associated with forest habitat. This may explain why the number of birds increased when in distance to forested area increased. Despite that the MYGB survey protocols may favor more conspicuous nonforest species that frequent open areas, our findings also implies that forest species may not be able to thrive in urban small green patches. The latter still rely on forests for their survival (Kuala Lumpur City Hall 2004; Ramli 2001), unlike temperate species that may respond otherwise even in urban environment (Melles et al 2003; Mörtberg and Wallentinus
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2000). Nevertheless, our findings stressed on the importance of urban green spaces, particularly those larger in size (Aida et al 2016) in maintaining urban biodiversity, at least for nonforest birds. More importantly, connecting these green spaces through river corridors and roadside reserves is expected to increase the abundance of birds in the city. In the case of Kuala Lumpur, although some green areas may be privately owned and inaccessible to public, they still serve the same environmental and ecological functions (Kanniah 2017), for example, bird habitats, as demonstrated in our study. Based on our logistic model (see Appendix 2), we suggest that the size of green cover to be retained should be greater than 10% within an area that is located not less than 1 km from water bodies or 50 m from roads. This implies that the target of Kuala Lumpur to have an addition of 10% public parks and open space by 2020 is only sensible, in terms of increasing bird numbers, when distance to water bodies and roads are taken into account. Implications for conservation Considering the country’s aspiration of becoming one of the most livable cities (Hong and Nooi 2014; Ismail et al 2018; Kanniah 2017) and its vision of being a top 20 nation that emphasizes on citizen well-being besides economic development as a long-term development plan for the next three decades, our study supports two components that may help achieve these goals, that is, (a) promoting green spaces and connectors in urban areas for both human and wildlife and (b) engaging public to do science for the society (Dickinson et al 2012). In the case of the MYGB, sustaining the volunteers, maintaining their interests and competency in bird identification following the specified survey protocols, as well as obtaining funding are the main challenges to keep the project going. Nonetheless, citizen science is expected to be an emerging field in the tropics. We feel the need of promoting citizen science in biodiversity monitoring work in this part of the world, and continuous monitoring will allow assessment on temporal changes in biodiversity in relation to urbanization or other forms of ecological disturbance in the long run. Conflict of interest The authors declare that there is no conflict of interest. Author contribution C.L.P. conceived the presented idea and wrote the manuscript with inputs from S.S.K., K.L.Y., K.W.O., and M.I.A.F. K.L.Y. performed data analysis based on data sorted out by M.S.Y. S.S.K. initiated the MYGB project and validated as well as provided the original data. S.S.K., K.W.O., M.I.A.F., and C.L.P. are also the committee members of the MYGB. Acknowledgments The authors are grateful to all MYGB project coordinators and co-coordinators over the years, that is, Mark Ng (2010-2011), Chin Pik Wun (2012), Tan Beng Hui (2012-2014), Tashia Peterson (20132014), and Lim Bing Yee (2015-2019). The authors also thank Yvonne Ang, Eileen Chiang, Nina Cheung, Caroline Ho, Andy Lee,
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Lim Aun Tiah, and Jasmine Steed as part of the committee members of the project as well as all volunteers who had contributed one way or another in the project. Chin Pik Wun was instrumental in the design and production of the pocket guides, count sheets, and other publicity and outreach materials as well as website development with the help from Shravan Rao. The seed fund to start the MYGB project was sponsored by Wild Asia. The subsequent funding was provided by Schmidt Marketing (M) Sdn Bhd and the Malaysian Nature Society (Perak Branch)’s Tan Kean Cheong Bird Conservation Memorial Fund to run the project and set up the MYGB website.
Appendix 1. List of bird species recorded
Common name
Scientific name
Eurasian tree sparrow Oriental magpie robin Common myna Javan myna Rock pigeon Spotted dove Zebra dove House crow Yellow-vented bulbul Black-naped oriole Asian koel Little heron Asian glossy starling Common iora Olive-backed sunbird Brown-throated sunbird Paddyfield pipit White-throated kingfisher White-breasted waterhen Long-tailed parakeet Pink-necked green pigeon Painted stork Grey heron Intermediate egret Scaly-breasted munia Common tailorbird Pied fantail Lineated barbet Pacific swallow Oriental white-eye Coppersmith barbet Olive-winged bulbul* Red-eyed bulbul* Black-bellied malkoha* Blue-tailed bee-eater Blue-throated bee-eater Ashy tailorbird Baya weaver Pied triller Red-whiskered bulbul Grey-breasted spiderhunter* Brown-capped woodpecker* Scarlet-backed flowerpecker* Cattle egret Little egret Little green pigeon* Golden-bellied gerygone Greater racket-tailed drongo*
Passer montanus Copsychus saularis Acridotheres tristis Acridotheres javanicus Columba livia Streptopelia chinensis Geopelia striata Corvus splendens Pycnonotus goiavier Oriolus chinensis Eudynamys scolopaceus Butorides striata Aplonis panayensis Aegithina tiphia Cinnyris jugularis Anthreptes malacensis Anthus rufulus Halcyon smyrnensis Amaurornis phoenicurus Psittacula longicauda Treron vernans Mycteria leucocephala Ardea cinerea Mesophoyx intermedia Lonchura punctulata Orthotomus sutorius Rhipidura javanica Psilopogon lineatus Hirundo tahitica Zosterops palpebrosus Psilopogon haemacephalus Pycnonotus plumosus Pycnonotus brunneus Phaenicophaeus diardi Merops philippinus Merops viridis Orthotomus ruficeps Ploceus philippinus Lalage nigra Pycnonotus jocosus Arachnothera modesta Dendrocopos moluccensis Dicaeum cruentatum Bubulcus ibis Egretta garzetta Treron olax Gerygone sulphurea Dicrurus paradiseus
*
Species of forest or wooded habitats.
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Appendix 2. Summary statistics pertaining to the relationships between urban birds and landscape matrix
Variables
R2GLMM(m)
Number of species FOREST 0.003 ROAD 0.009 WATER 0.003 GREEN 0.006 Number of individuals FOREST 0.019 ROAD 0.020 WATER 0.001 GREEN 0.019
R2GLMM(c)
Intercept
Slope
CIs L
U
0.358 0.348 0.341 0.360
1.55 1.57 1.57 1.56
0.031 0.051 0.009 0.043
0.098 0.114 0.057 0.024
0.383 0.014 0.080 0.109
0.600 0.611 0.608 0.633
2.628 2.626 2.623 2.608
0.011 0.059 0.086 0.060
0.010 0.096 0.030 0.021
0.050 0.022 0.049 0.099
FOREST ¼ distance to nearest forested area; ROAD ¼ distance to the nearest main road; WATER ¼ distance to the nearest water body; GREEN ¼ percentage of green patches or areas within 100 m radius; L ¼ lower confidence bound, U ¼ upper confidence bound, R2GLMM (m) ¼ variance explained by fixed effects (numbers of bird species or individuals); R2GLMM (c) ¼ variance explained by fixed and random effects (time); CI, confidence interval. Values in bold indicate significant relationships with numbers of bird species or individuals (i.e. 95% CIs of slope values exclude zero).
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