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Ocean & Coastal Management 93 (2014) 60e66

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Ocean & Coastal Management

journal homepage: www.elsevier .com/locate/ocecoaman

Clear-cutting decreases nest occupancy of Brahminy Kite Haliasturindus in a managed mangrove forest of Southeast Asia

Abolghasem Khaleghizadeh a,*,1, Andrea Santangeli b, Shahrul Anuar a

a School of Biological Sciences, Universiti Sains, USM 11800 Pulau Pinang, MalaysiabDepartment of Biosciences, University of Helsinki, P.O. Box 65 (Viikinkaari 1), FI-00014 Finland

a r t i c l e i n f o

Article history:Available online 1 April 2014

* Corresponding author. Tel.: þ60 124690746.E-mail address: akhaleghizadeh@gmail.com (A. Kh

1 Permanent address: Ornithology Laboratory, AgDepartment, Iranian Research Institute of Plant Prot19395, Iran.

http://dx.doi.org/10.1016/j.ocecoaman.2014.03.0150964-5691/� 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

Mangroves are localised and fragile ecosystems that provide important services to society. The rate ofconversion and disappearance of mangroves globally is alarmingly high and unabated. In order toeffectively manage this ecosystem and the species it supports, it is crucial to understand its ecologicalproperties and relationships. Unfortunately this information is often scarce or lacking. Here we studyfactors affecting nest occupancy of the Brahminy Kite Haliastur indus in mangrove forests under differentmanagement practices in Malaysia. We modelled effects of different land-use variables as well as dis-tance to infrastructures at different spatial scales around a nest.

We show that, among all variables considered (including cover of agriculture, productive forest,restricted production forest, protected forest, unproductive and young forests, clear-cut, estuary, distanceto villages and main roads), the area of clear-cut was the only significant determinant of nest occupancy.As clear-cut area around a nest increased from zero to just few hectares, the probability of nest occu-pancy was steeply reduced. This effect was particularly evident when clear-cuts occurred in the im-mediate surroundings of a nest (i.e. within 150 m distance). These results clearly indicate that forestrypractices, such as clear-cutting, have a detrimental effect on Brahminy Kite and should thus be avoided intheir nest vicinity. Our findings reinforce the importance of understanding the basic ecology of a systemin order to take evidence-based conservation management decisions.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Mangroves are fragile but important ecosystems which providemany kinds of benefits (Bartolucci et al., 2007; Kathiresan andBingham, 2001). They provide a variety of ecosystem services,such as medicinal and forest products, and host a high diversity ofwildlife with great potential for ecotourism (Chong, 2006; Getznerand Islam, 2013). Mangroves also support local livelihoods viaaquaculture and fishery (Aburto-Oropeza et al., 2008; Alongi et al.,2003). In Bangladesh, mangroves support 41% of the total nationalrevenue and deliver 45% of total timber and fuel wood demand ofthe country (Hoque and Datta, 2005). Mangroves also supplybreeding and foraging grounds for a large variety of wildlife; over4000 species of flora and fauna are recorded in Indian mangroves(Sandilyan and Kathiresan, 2012).

aleghizadeh).ricultural Zoology Researchection, P.O. Box 1454, Tehran

Mangroves are among the ecosystems with highest rate ofconversion and disappearance. Approximately one-third of theworld’s mangrove forests have been lost over the past 50 years(Sandilyan and Kathiresan, 2012). They are being converted toagriculture, aquaculture, urban and industrial development(Chong, 2006), and the rate of conversion is quite alarming(Ibrahim et al., 2000). In Malaysia, 16% (or 111,045 ha) of mangroveshave been lost during 1973e2000 (Chong, 2006). The loss ofmangroves will result in immediate loss of biodiversity, medicinaland fishery resources, coral and sea grass beds, and wild seed foraquaculture (Sandilyan and Kathiresan, 2012).

Over all extant mangroves of the world, 42% are located in Asia(Giri et al., 2011). With an area size of 40,483 ha, the MatangMangrove Forest Reserve (hereafter MMFR) is the largest mangrovein Peninsular Malaysia and contributes to about 39% of 105,537 haof the total mangrove forests in the region (Latiff, 2005). The MMFRis a national heritage site and an economic asset because of itsnatural (renewable) resources and the opportunities for localcommunity employment that exist in forestry work (Ismail et al.,2005). The MMFR is recognized as the best managed, sustainable

Fig. 1. Location of the Matang Mangrove Forest Reserve in Peninsular Malaysia and distribution of Brahminy Kite nests in this wetland.

A. Khaleghizadeh et al. / Ocean & Coastal Management 93 (2014) 60e66 61

mangrove ecosystem in the world (Muda et al., 2005). The Brah-miny Kite Haliastur indus (Boddaert, 1783) is the most commonraptor species in the Sundarbans (the world’s largest mangrove)and MMFR, where this species requires large mangrove forests forbreeding (Indrayanto, 2011; Sarkar, 1986).

Given the rapid and unabated pace at which mangrove eco-systems are being lost or degraded, it is of crucial importance tounderstand how wildlife responds to anthropogenic changes(mostly owing to resource extraction, such as timber or fish har-vest) in this fragile ecosystem (Sandilyan and Kathiresan, 2012).Brahminy Kites, as well as many other species inhabiting mangroveforests, may be affected by forest clear-cutting and other anthro-pogenic activities occurring in the landscape where they breed.However, the impact of such activities on breeding Brahminy Kitesis still unknown in mangrove ecosystems. We attempt to fill thisgap by studying the association between a breeding raptor andmangrove forest management practices in Malaysia.

The overall aim of the present study is to quantify the effect ofdifferent landscape and anthropogenic features on nest occupancyof Brahminy Kites in mangrove forests of Malaysia. Specifically, weevaluate how contrasting land-uses (e.g. cover of clear-cut, estuary,protected forest, among others) affect nest occupancy at threedifferent spatial scales (150, 500 and 1000 m) around a nest. We

also considered effects of distance to the closest village and mainroad as proxies for anthropogenic disturbance. We believe the re-sults will be relevant to aid sustainable forest management prac-tices in the region and will stimulate further such studies in otherregions and with other species where similar forestry practices areapplied.

2. Materials and methods

2.1. Study area

The MMFR is located at approximately 4�250 to 5�010 N and100�250 to 100�450 E, c.10 kmwest of the city of Taiping, in theState ofPerak, Peninsular Malaysia. Matang Mangrove Forest Reserve inwestern Peninsular Malaysia is the largest managedmangrove forestin the world. The MMFR has four divisions: Kuala Sepetang North,Kuala Sepetang South, Kuala Trong and Sungai Kerang (Fig. 1). TheMMFR is known as a sustainable mangrove forest (Chong, 2006) andhas been consistently managed on a 30-year forestry rotation since1950, under a series of 10-year forestry plans (Muda andNikMustafa,2003). It is managed especially for the production of fuel wood,charcoal and poles. Typically, valuable trees are harvested first, andthen the area is clear-felled and manually restocked (Wong, 2004).

Table 1Description of the seven land cover variables used as predictors of Brahminy Kitenest occupancy.

Term Description

Productiveforest

Timber production forest where highly productiveRhizophora and patches of Bruguiera are grown for timberproduction. This forest type covers 29,794 ha or 74% ofMatang mangrove forest (Wong, 2004).

Restrictedproductionforest

An area of mangrove forest located between productive andprotective mangrove forest and showing characteristics ofboth but with only limited value for the production oftimber. This forest type covers about 2892 ha or 7% ofMatang mangrove forest (Wong, 2004).

Protectiveforest

Mangrove forest which includes tree species that do notattain great heights (Sonneratia spp. and Avicennia spp). Thisforest type covers 7360 ha or 18.2% of theMatangmangroveforest (Wong, 2004)).

Unproductive/Young forest

Tiny forest patches where mangrove trees are damaged orhave recently been replanted and cannot be considered aseither protective or productive (Wong, 2004).

Clear-cut area An area where nearly all of the mangrove trees haverecently been harvested.

Estuary Including shallow muddy areas and open water areas closeto a river mouth

Agriculture Land converted to oil palm grooves, orchards and pasturesthat were not under intensive human built environments.

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The Forestry Department Peninsular Malaysia is managing the vastforests with different practices: productive (or harvested), restrictedproduction (or transitional), protective (or reserve) and unproductive(or non-harvested) forest (Muda et al., 2005). The area around theMMFR supports a wide variety of human activities such as wild andfarmed fishing industry, forestry and reforestation programmes, amangrove nursery, traditional charcoal processing sites, and tourism(including bird-watching; Awang-Noor, 2005; Wong, 2004). Thismangrove forest supports a richdiversityofwildlife: 156bird,116fish,48 crab and 20 prawn and shrimp species have been recorded here(Wong, 2004), including several threatened species.

2.2. Study species

The Brahminy Kite is a conspicuous raptor distributed across theIndian subcontinent, Southeast Asia and Australia (BirdLifeInternational, 2012). It inhabits coastal areas (Robson, 2002),large estuaries and low-relief coasts. The primary habitat of thespecies is broad, muddy, mangrove-backed intertidal flats (Wells,1999). The Brahminy Kite is categorized as least concern becauseof its large range and large population size (BirdLife International,2012). The few studies that exist on this species are mostly fromIndia and Australia and focus on breeding biology and behaviour(Balachandran and Sakthivel, 1992; Indrayanto et al., 2011;Jayabalan, 1995; Lutter et al., 2006; Sivakumar and Jayabalan,2004). However, despite its vast range and large global popula-tion, its ecology is still largely unknown.

2.3. Field observations

This study was conducted during MarcheMay 2012 (over-lapping with nestling period of the species; Indrayanto et al., 2011).This species showed some levels of site-fidelity to nesting sites andmay use a nest year after year (Balachandran and Sakthivel, 1992;Wells, 1999). This is a common characteristic of large- andmedium-sized raptors (Newton, 1979). Surveys were conductedfrom 10:00 to 15:00. The Brahminy Kite nests are located alongrivers (Indrayanto, 2011). Therefore we searched the MMFR andadjacent areas for kite nests from boats with a speed of about 1e3m per second. Surveys were conducted by teams of two observersalong all navigablewater channels within theMMFR; the size of the

channels varied greatly. We searched for nests using10 � 42 bin-oculars, and upon detection, we determined the occupying species.Nests were identified as belonging to Brahminy Kites based on thepresence of adult or juvenile kites, and also by nest size and loca-tion in the tree. Nests were categorized as occupied (proven by thepresence of adults or juveniles at nest) or unoccupied (no sign ofpresence of Brahminy Kites). We recorded the location of all nestsusing a hand-held Garmin GPS 60. The location of nests wasmapped using ArcGIS 9.3 (ESRI, Redland CA, USA; Fig. 1).

2.4. Environmental data

Within the study area, we classified the land using sevendifferent land cover classes: Agriculture, productive forest,restricted production forest, protected forest, unproductive andyoung forests (lumped together; hereafter called “unproductive/young forest”), muddy areas and estuaries (hereafter called “estu-ary”), and clear-cut areas (see Table 1). Cover of productive forest,restricted production, protected and unproductive forest zones wasobtained from the Perak State Forestry Department maps (Wong,2004) which were not changed by the time of this study. Allother land covers, as well as river width (see below) and road andvillage data were extracted from SPOT 4 imagery.

We built three circular buffers around each nest using a radius of150 m, 500 m and 1000 m. These distances have been found to bebiologically relevant, as Brahminy Kites are found to be most sen-sitive to disturbance at close proximity to the nest (sensitivenesting distance of 150 m, limited activity buffer zone of 500 m asfound in Snail Kite Rostrhamus sociabilis and Red Kite Milvus milvus(Dean, 2006; Ruddock and Whitfield, 2007) and nest-site scale of1000 m (Lopez-Lopez et al., 2006)). We then calculated the area (inhectares) of each of the seven land cover classes within each of thethree buffers around each nest (Table 2). We also classified thewidth of the main river nearby the nest into three categories:narrow (<50 m), intermediate (50e250 m), and wide (>250 m).Finally we calculated the distance of each nest to the closest mainroad and village (in kilometres; Table 2).

2.5. Data analysis

We built three separate generalised logistic regression models(GLMs, with binomial distribution and logit link function) for an-alyses. In each GLM, we used each nest as a sample unit and nestoccupancy as the response (whether a nest was found occupied orempty). In each model, we included distance to closest road andvillage, and riverwidth, as predictors, and the threemodels differedonly in the spatial scale of the set of seven land cover variables. Inthe first model we used, in addition to previous variables (distancesand river width), land covers calculated within 150 m buffers. In asecond model we used land covers within 500 m buffers, while athird model included the set of land covers within 1000 m, again inaddition to the other three previously mentioned variables. Wedecided to run the three models separately with separate spatialscales of the land cover classes because habitat within larger buffersalso includes that already captured in the small buffers. This causescollinearity between variables across the three spatial scales,impeding their inclusion into the same model. At the start of themodel building, we also evaluated the multicollinearity amongcandidate variables within each model. We used variance inflationfactor (VIF; Neter et al., 1996) on the full set of variables separatelyfor each model and in turn removed those variables with VIF valuehigher than two until all those remaining had VIF lower than two.This screening lead to the elimination of only the productive forestvariable from the models at the 500 and 1000 m scale, while allother variables were retained.

Table 2Descriptive statistics (mean and SE within brackets) of the continuous variables used to predict Brahminy Kite nest occupancy at each of the three spatial scales for occupiedand empty nests. All values for land use cover are in hectares, while distances are in km.

Variables 150 m 500 m 1000 m

Occupied Empty Occupied Empty Occupied Empty

Agriculture 0.09 (0.09) 0.02 (0.02) 2.02 (1.47) 1.48 (0.42) 11.03 (7.22) 9.72 (2.37)Productive forest 3.94 (0.59) 3.84 (0.21) 44.88 (4.96) 37.11 (1.64) 176.17 (16.68) 154.99 (5.36)Restricted production forest 0.11 (0.08) 0.05 (0.03) 1.64 (0.88) 0.98 (0.28) 9.06 (3.87) 5.76 (1.16)Protective forest 0.08 (0.06) 0.30 (0.08) 2.70 (1.09) 4.05 (0.64) 16.00 (4.82) 18.16 (1.96)Clear-cut 0.04 (0.03) 0.43 (0.08) 2.36 (1.17) 8.13 (0.97) 13.80 (4.75) 33.32 (3.68)Unproductive/young forest 0.33 (0.19) 0.31 (0.07) 5.62 (2.41) 5.70 (0.84) 19.91 (8.51) 24.55 (3.11)Estuary 0.34 (0.19) 0.30 (0.08) 5.83 (2.62) 3.48 (0.79) 26.42 (10.35) 15.26 (2.96)Distance to roads 3.42 (0.39) 3.53 (0.16)Distance to village 4.16 (0.50) 4.17 (0.16)

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We thus run the three GLMs, using each the set of predictorsdescribed above. We started from the full model and tested therelative importance of each predictor by removing each of them atone time and testing the change in the likelihood between the fullmodel and the reducedmodelwithout that predictor (LRT; likelihoodRatio Test based on Chi-square distribution). Finally, we checked forspatial autocorrelation in the residuals of eachof the three fullmodelsby visual investigation of spline correlograms based on the extent ofautocorrelation by distance. No sign of spatial autocorrelation wasfound in any of the three models. All analyses were performed in Rstatistical software (R Development Core Team, 2013).

3. Results

A total of 210 nests of Brahminy Kite were found in theMMFR, ofwhich 27 were found occupied. Productive forest was found as themost common land cover class around the nests, and this wasparticularly striking within 150 m from each nest, where other landuses were far rarer (Table 2).

Among all variables considered as potential predictors forBrahminy Kite nest occupancy, only the area of clear-cut was foundto significantly affect the probability of a nest to be occupied(Table 3). We found a strong decline in nest occupancy as the area ofclear-cut increased around a nest. The extent of this decline wasparticularly strong within the 150 m area surrounding a nest(Fig. 2). A still significant but less steep decline in the probability ofnest occupancy was observed with increases in clear-cut areas inthe larger areas includedwithin 500 and 1000 m of the nest (Fig. 2).

All other variables describing habitat composition (e.g. cover ofagricultural area, area of productive forest and so on) did not affectnest occupancy (Table 3). Occupancy was also similar at nestsnearby rivers of different width, and was not affected by variablesthat represent disturbance features, such as the distance to villagesor roads (Table 3).

4. Discussion

We found a strong negative impact of mangrove forest clear-cutting on nest occupancy of the Brahminy Kite. Nests which hadeven small amounts of clear-cut areas were largely deserted by thespecies, and this was particularly evident when cuts occurred in thenear vicinity (i.e. within 150 m) of a nest. On the other hand, noneof the other land-uses, as well as distances to roads and villages, didhave any apparent influence on nest occupancy.

4.1. Clear-cut impacts on Brahminy Kite nest occupancy

The Brahminy Kite showed a negative response to clear-cutareas in our study. Similar effects were also reported for othercoastal breeding raptors, such as Osprey Pandion haliaetus (Saurola,

1997) and Bald Eagle Haliaeetus leucocephalus (Gende et al., 1998).However, some other raptors, such asWhite-tailed Eagle Haliaeetusalbicilla and other forest nesting hawks in Boreal forests of North-ern Europe (Lõhmus, 2005; Santangeli et al., 2013, 2012), and theGoshawk Accipiter gentilis in Italy and France (Penteriani and Faivre,2001) were found to show some levels of tolerance to timber har-vesting even at short distance to the nest.

Clear-felling activities may cause direct high disturbance due toincreased boat traffic and humanpresence, as well as chainsaw noisein the area. Although the results indicate that river width did notsignificantly affect nest occupancy, we also see that nests near in-termediate river width were slightly more occupied (Fig. 2). In ourstudy area, the narrow rivers (less than 50 m wide) are generallyshallow andmuddy as a result of daily tides. Along the narrow rivers,the mangrove trees may be so dense that they may constitute anobstacle to the flight of a large raptor, like the Brahminy Kite.Conversely, wide rivers may be affected by high boat traffic. Thesepossible impacts should however be further tested with appropriatedirect empirical data before drawing solid conclusions. Additionally,clear-cutting may render the landscape surrounding the nest moreopen, and thus further expose a nest to disturbance and predation(Frid and Dill, 2002; Rudnicky and Hunter, 1993).

4.2. Effects of anthropogenic features

In the present study, there was not any significant relationshipbetween nest occupancy and distance to main roads and villages.Some raptors are negatively affected by roads (Livingston et al.,1990)and levels of traffic (Bautista et al., 2004). In our landscape, the mainroadswereoutside andnear theborderof the reserve, and thevillageslocated near estuaries. These infrastructureswere often at a relativelyhigh distance from each nest, which may explain the lack of theireffect on nest occupancy reported here. Effects of main roads andvillages at smaller distances to anest should be further investigated inorder to draw final conclusions on their effects on the species.

In the present study, nest occupancy was the lowest along thebig rivers. These kinds of rivers were main route of fishermen boatsto transfer sea products to the villages around MMFR. Therefore,boat traffic seems to be the most important factor in reducing nestoccupancy of the Brahminy Kite. Motorboat traffic had previouslynegative effect on number and behaviour of Bald Eagles (Stalmasterand Kaiser, 1998; US Fish Wildlife Service, 2007). More generally,the extent of impact of traffic of motorboat on raptors is still largelyunknown, even for those species breeding in riparian habitatsalong watercourses.

4.3. Implications for mangrove forest management

The structure of mangrove forests is a key factor for nesting ofmangrove-dependent birds (Etezadifar and Barati, 2013).

Table 3Effects of land cover and other variables as resulted from each of the three GLMs on Brahminy Kite nest occupancy at three different spatial scales. Coefficients and SE arederived from the full model including all predictors. LRT is the value of the likelihood ratio test (with associated p-value) used to assess the significance of each independentvariable in each model. Significant p-values are in bold. We do not report values for productive forest at 500 and 1000 m scales as this variable was not included due tocollinearity (see Section 2). For clarity, we only report values of distances and river width for the first model (at 150 m scale). Values were very similar at the other two scales, asthe variables included in these models were the same.

Variables 150 m 500 m 1000 m

Coeff (SE) LRT p-Value Coeff (SE) LRT p-Value Coeff (SE) LRT p-Value

Agriculture 0.30 (0.58) 0.26 0.61 �0.01 (0.04) 0.06 0.81 �0.00 (0.01) 0.26 0.61Productive forest �0.09 (0.09) 0.98 0.32Restricted production forest �0.05 (0.42) 0.01 0.91 0.01 (0.05) 0.09 0.77 0.01 (0.01) 0.27 0.60Protective forest �0.40 (0.38) 1.80 0.18 �0.03 (0.03) 0.62 0.43 �0.00 (0.01) 0.07 0.79Clear-cut �1.73 (0.99) 8.41 <0.01 �0.07 (0.04) 6.48 0.01 �0.01 (0.01) 4.08 0.04Unproductive/young forest �0.13 (0.23) 0.36 0.55 �0.02 (0.02) 1.03 0.31 �0.01 (0.01) 1.48 0.22Estuary �0.06 (0.21) 0.09 0.77 0.01 (0.02) 0.33 0.57 0.00 (0.01) 0.49 0.48Distance to village �0.07 (0.14) 0.30 0.59Distance to roads 0.03 (0.12) 0.08 0.78River width 3.21 0.20

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Silvicultural practicesmay affect the primary structure of mangroveforests and associated wildlife. These changes often occur throughtree harvesting and regenerating practices (Thompson et al., 1999).In these systems, different methods are being performed such assingle-tree selection, group selection, and strip cutting. Thesemethods have different impacts on breeding birds (Doyon et al.,2005; King and DeGraaf, 2004). Silvicultural practices should bemodified towards more extensive management regimes whichwould more closely mimic natural processes and ultimately allowthe conservation of diurnal raptors (Bartolucci et al., 2007). Forexample, it is well known that a mixture of different silviculturalmethods could sustain higher levels of habitat diversity and theirvaluable wildlife compared to application of a single harvestmethod across a region (Doyon et al., 2005).

Retaining actual and potential nest trees in riparian forest stripsis an important management intervention for maintaining certainwildlife species in areas where clear-cutting is being carried out(Potvin and Bertrand, 2004). This can be one of the most importantactions during logging (Arnett et al., 2001) of mangrove forests inorder to conserve Brahminy Kite nests. Current management plansconsider retaining potential nest trees mostly in lines or narrowstrips. It is suggested that instead of a 3e10 m buffer of all trees(Muda et al., 2005), the width of strips along stretches of openwater be at least 25 m (Hågvar and Bækken, 2005).

Our results show that the impact of clear-cuts on the breeding ofthe species can be remarkable. It seems that clear-cutting should atleast be avoided in the areas where Brahminy Kites are breeding(Saurola, 1997). This calls for greater attention and caution in the

Fig. 2. Decrease in the probability of nest occupancy with increasing area of clear-cut within(Malaysia). Predicted occupancy values are derived from the three full models, holding all this presented separately by each of the three classes of river width. Dashed lines refer to nintermediate width river (see Section 2).

application of clear-cutting as a mangrove forest managementpractice.

5. Conclusions

Studies that evaluate the impact of anthropogenic activities onwildlife, such as raptor species, are increasing, but mostly in thedeveloped world (Santangeli et al., 2013, 2012). The paucity of suchstudies in mangrove ecosystems is pervasive, and this preventsevidence-based conservation to be implemented (Sutherland et al.,2004). This study contributes to fill such knowledge gap. Our re-sults show that clear-cutting of even small areas of mangrove forestin the immediate surroundings of a Brahminy Kite nest significantlyreduces nest occupancy at post-harvest stages. This clearly suggeststhat habitat changes in the area close to a nest should be avoided.This could be achieved, for example, by establishing a protectionbuffer (Richardson and Miller, 1997) around the nest that wouldprevent clear-cutting within the most sensitive zone of 150 msurrounding the nest, while some harvestings may be allowed inthe surrounding landscape.

We urge that studies like this one could be repeated on otherspecies that may be affected by mangrove timber harvest in theregion. For example, three red-listed bird species (IUCN, 2013) wererecorded in MMFR. Two threatened storks have been recorded inthis mangrove, Milky Stork Mycteria cinerea (Vulnerable) andLesser Adjutant Leptoptilos javanicus (Vulnerable), in addition to theChestnut-bellied Malkoha Phaenicophaeus sumatranus (NearThreatened; Noramly, 2005).

(a) 150 m, (b) 500 m, and (c) 1000 m of a Brahminy Kite nest in the study area of Perake other continuous predictors to their mean values. In each panel, predicted occupancyests close to a wide river, continuous lines to a narrow river, and dotted lines to an

A. Khaleghizadeh et al. / Ocean & Coastal Management 93 (2014) 60e66 65

More research is needed on this fragile ecosystem (Sukardjo andYamada, 1992), in particular on mangrove-dependent fauna, in or-der to ultimately take appropriate management decisions andallow for the persistence and service provision of mangroves in thefuture.

Acknowledgements

We are thankful to the School of Biological Sciences and RU-Research Grant 815075, Universiti Sains Malaysia, for financial andlogistic support. We especially appreciate the help of Mr KalimuthuSubramanian during field surveys. The local knowledge of Brah-miny Kite nesting sites contributed by our boatmen Encik Yus andhis colleagues Mohamad Sani and Encik Farhan was extremelyhelpful. Last but not the least, we thank DrMichael McGrady and DrDerek Scott and two anonymous referees for their invaluablecomments on the manuscript.

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