Bird communities in Southeast Asia are shaped by human pressure more than habitat features

Bird communities in Southeast Asia are shaped by human pressure more than habitat features | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bird communities in Southeast Asia are shaped by human pressure more than habitat features Cezary Mitrus, Artur Goławski, Santi Xayyasith, Przemysław Obłoza, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8484369/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Southeast Asia, including Laos, is considered a biodiversity hotspot. However, significant environmental changes caused by human activity have a huge impact on fauna, including bird populations. In 2024, we observed and recorded birds at 43 routes and 125 points in various places in Laos. We identified 99 bird species, the most frequently recorded being widespread species of open areas and shrubs, such as the Brown Shrike ( Lanius cristatus ), Common Tailorbird (Orthotomus sutorius ), and Greater Coucal ( Centropus sinensis ). However, species associated with old, mature forests, such as the Ground Cuckoo ( Carpococcyx renauldi ), Great Slaty Woodpecker ( Mulleripicus pulverulentus ), and Fire-breasted Flowerpecker ( Dicaeum ignipectus ), were significantly less frequently recorded. The habitats of the observation points were dominated by forests, woodlands, and open areas. However, statistical analyses showed that the only factor influencing bird species diversity was distance to the nearest buildings. This indicates that indirect and direct human pressure, both related to habitat changes (clearing and burning of forests, conversion to farmland) and hunting, have a significant impact on the avifauna of the Southeast Asian region. bird species richness human impact biodiversity hotspot Laos Figures Figure 1 Figure 2 Intoduction Natural habitats in many regions of the world are suffering from biodiversity loss: species and population extirpation and critically, decline of local endemic species (Hanski 2011 , Dirzo et al. 2014 , Keck et al. 2025 ). There are many reasons for this phenomenon, most of which stem from various forms of human activity. The main factors affecting changes in biodiversity and the occurrence of wild flora and fauna are habitat loss caused by their conversion into human settlements, cropland, pastures and industrial areas (Liu 2016; van Vliet 2019 ; Li et al. 2022 ). This applies to many regions of the world, including those with high natural value, such as the tropical areas of Southeast Asia. This region is located in the zone of greatest biodiversity and has the highest average percentage of native endemic bird species among all tropical regions (Myers et al. 2000 ; Sodhi et al. 2011 ; Tordoff et al. 2012 ). Southeast Asia has a higher proportion of bird species categorized on the Red List as globally threatened (Sodhi et al. 2011 ). In addition to globally threatened species, this region also supports numerous widespread and migratory birds, for which Southeast Asia serves as an important breeding and non-breeding ground (Robson 2020 ). In this region, in addition to very rapid changes in the natural habitats mainly through deforestation and forest burning (Chen et al. 2023 ; Sims et al. 2025 ), there is also direct human pressure on birds populations (Xayyasith et al. 2020 ). Birds often respond behaviourally and spatially to direct human pressures such as hunting, trapping and disturbance. Experimental and observational studies show that in areas with frequent human disturbance, birds increase their flight initiation distances and shift activity patterns, leading to reduced use of otherwise suitable habitats (Sreekar et al. 2015 ). Hunting and related disturbance not only decrease local abundances but can also alter distribution by causing birds to avoid areas close to roads and settlements, even where habitat structure remains intact (Benítez‑López et al. 2017). Across tropical systems, intensive nonspecific hunting has been linked to reduced occupancy and apparent declines in bird species in hunted compared with less disturbed sites, suggesting that behavioural avoidance and local depletion operate together to shape community composition (Benítez‑López et al. 2017; Tilker et al. 2019 ). Such behavioural and distributional responses to human pressure highlight that effects extend beyond simple mortality, influencing how birds use space and resources in landscapes with high human presence. Southeast Asia is one of the areas fraught with large-scale wildlife hunting, capture and trade (Li et al. 2000; Zhang et al. 2008 ; Bhupathy et al. 2013 ; Roldán-Clarà et al. 2014 ; Li & Jiang 2014 ; Xayyasith et al. 2020 ). However, direct human pressure on birds in this region takes many forms beyond hunting alone. These include widespread trapping using mist nets, glue traps, and snares, intentional disturbance (e.g., slingshot shooting, nest destruction, scaring birds away from crops), and capture for ornamental or pet trade (Benítez-López et al. 2017; Dai and Hu 2017 ). Such pressures can lead to reduced bird abundance, altered species composition, and behavioral changes even in habitats that remain structurally intact. One country in this region experiencing both rapid environmental changes and direct strong human pressure is Laos. Many bird species there are deliberately persecuted or killed because they are considered crop pests or, more typically, for food. At the same time, it is one of the countries in Asia with the highest percentage of their GTBs (Globally Threatened Birds) impacted by infrastructure development (Sodhi et al. 2011 ). Although current Laos law restricts the killing of wildlife, birds in particular are still harvested, traded, and used for culinary purposes (Pruvot et al. 2019 ). Given the variety of direct human pressures on birds, including hunting, trapping, and frequent disturbance near settlements, we hypothesised that bird species richness would decrease with proximity to human settlements. We assumed that the intensity of human disturbance is greatest near inhabited areas, where persecution, trapping, and disturbance are most frequent. To isolate the effect of human proximity from habitat variation, we also accounted for local habitat composition, which is known to strongly influence avian diversity (Keck et al. 2025 ). Our aim was to test whether distance from settlements and others land-use characteristics (forests and woodlands, open areas with fields and pastures, water surface and roads) have an impact on the bird species richness. Methods Study area The study was conducted in Laos, largely mountainous country (northern mountainous areas above 1,000 m) covers an area of 236,800 km 2 within the Indochinese Peninsula, in Southeast Asia. The population in 2024 was about 7,7 ml of people with density of 33 persons/km 2 (Lao Statistics Bureau 2025 ). The climate of Laos is described as tropical under the influence of monsoons, which bring 70% of the annual rainfall (as 3,000 mm per year) and high humidity, with two distinct seasons: the rainy season (monsoon, from May to mid-October) and the dry season (from mid-October to April). Mean annual temperatures in the plains reach at 25–27°C and about 20°C in the northern and eastern mountainous areas and the plateaus (Phongoudome and Sirivong 2007 : Cosslett and Cosslett 2018 ). National-level land-use assessments indicate that the landscape is dominated by a mosaic of forested areas and agricultural systems, shaped largely by both swidden and permanent agriculture, each occupying approximately 29% of the national territory, while forests cover around 40–42% (Messerli et al. 2009 ). These lowland and foothill mosaics often include paddy rice fields, fallows, open farmland, and scattered trees. In recent decades, however, forested areas have been increasingly converted into agricultural land (Sims et al. 2025 ), particularly in the southern part of the country, where plains and plateaus support more permanent cultivation (Cosslett and Cosslett 2018 ). Bird data The studies were caried out between 9 April and 2 May, 2024, between 06:00 and 10:00, and only in favorable weather, i.e. without rainfall and/or stronger winds. Data were collected at 43 pre-selected sites located along unpaved roads or paths. The study sites were located in various regions of Laos, always outside residential areas (Fig. 1 ). Study sites were selected using map analysis (Google Maps) and were separated from each other by at least 1 km, typically by 5 km or more. Within each site, a single morning survey was conducted along a walk route followed by one observer. Along each route, points of bird observation and recording (125 in total) were typically arranged in sets of three, spaced 1 km apart and positioned at increasing distances from the nearest human settlement. In a few cases, only two points were used per route due to terrain limitations. At each observation point, bird species were surveyed over a 15-minute period. Species identification was based on two complementary methods: direct visual observations by the observer within a 100 m radius, and simultaneous acoustic recordings using a PUC device (Portable Unit for Recording-BW-001). The PUC was placed on a natural elevation at the same location to optimise sound detection. The recordings were subsequently analysed to species level using the Birdweather application ( https://app.birdweather.com/ ), which applies automated sound recognition. All recordings were processed using the application’s default confidence threshold settings. This dual approach allowed for more comprehensive species detection, integrating visual and acoustic data collected during the same survey sites. Only birds identified to species were included in the analysis. Habitat data For each of point of recordings and observations (n = 125) habitat characteristics were analyzed within 100-metre radius buffers with a total area of ​​3.14 ha (Warren et al. 2005 ; Pickens and King 2014). Within each buffer, the area of the following land-use categories (variable name in parentheses) was calculated: roads including unpaved and paved (Road), water surface including ponds and rivers (Water), open habitat including fields, pastures and other marginal farmland (Open), forest and woodland (Forest). These factors were determined based on Google satellite images available through the web map services (WMS). Additionally, the distance from the observation point to the nearest village was measure. All geospatial analyses were conducted in QGIS 3.40.1 (QGIS Development Team 2024). Statistical analysis Statistical analyses were carried out in R version 4.4.2 (R Core Team 2024). Multicollinearity among the six habitat variables was examined prior to modelling. Very high variance inflation factors (VIF > 177) were detected for forest area, bush area and open area. Bush area was therefore removed, which reduced VIF values for the remaining predictors to below 2 (Quinn and Keough 2002 ). Mixed-effects generalised linear models with a Poisson error structure were fitted using the ‘glmmTMB’ package (Brooks et al. 2017 ). The number of bird species detected was used as the response variable, the habitat variables as fixed effects, and Site as a random effect. The global model was: Species ~ Road + Forest + Water + Open + DistBuild + (1|Site). Model assumptions were evaluated with the ‘DHARMa’ package (Hartig 2024 ). Simulated residuals were inspected using Q–Q plots and residuals versus fitted values, and models were tested for under- and overdispersion as well as zero inflation. No signs of dispersion problems or zero inflation were found, and the Poisson distribution was retained. Model selection followed an information-theoretic approach using AICc (Burnham and Anderson 2002 ). All possible combinations of predictors were generated using the MuMIn package (Bartoń 2024 ). To identify the most influential predictors, model averaging and relative variable importance (RVI) were calculated as the sum of Akaike weights (Burnham and Anderson 2002 , Arnold 2010 ). Averaging was performed for models with ΔAICc < 2 (Grueber et al. 2011 ). Predictors were considered supported when their model-averaged 95% confidence intervals did not include zero (Arnold 2010 ). Model fit was assessed using marginal and conditional R², representing the variance explained by fixed effects alone and by both fixed and random effects, respectively (Nakagawa et al. 2017 ). Results During study period we recorded 99 species on all study sites (Appendix, Table 1 ). On average, 3.0 species were detected per site, ranging from 0 to 10. The most often recorded species was Brown Shrike ( Lanius cristatus ) found at about 30% of the all points and another 4 species (Common Tailorbird Orthotomus sutorius , Greater Coucal Centropus sinensis , Black Drongo Dicrurus macrocercus , Grey-breasted Prinia Prinia hodgsonii ) were observed at more than 10% of the points. The vast majority of species (57 species − 57.6%) were recorded only at one or two observation points (0.8–1.6%). Most detected birds were relatively numerous and not endangered species (LC), while one species each were found as Endangered (EN) according to the IUCN scale (BirdLife International 2024 ) - Coral-billed Ground Cuckoo ( Carpococcyx renauldi ), Great Slaty Woodpecker ( Mulleripicus pulverulentus ) as Vulnerable (VU) and Red-breasted Parakeet ( Psittacula alexandri ) as Near Threatened (NT) (Appendix, Table 1 ). Table 1 Characteristics of variables describing habitat of studding sites Variable Abbreviation Mean ± SD, Range (in brackets) Forest area (ha) Forest 0.9 ± 10.93 (0.00-3.3) Bush area (ha) Bush 0.79 ± 0.80 (0.00-3.10) Roads (ha) Road 0.13 ± 0.05 (0.00-0.33 Water surface (ha) Water 0.04 ± 0.16 (0.00-1.39) Open habitat area (ha): Open 1.5 ± 10.95 (0.00-3.36) Distance to nearest built-up area (km) DistBuild 1.06 ± 0.73 (0.01–3.87) Table 1 List of species recorded at all points with a specific frequency (%) and threatened status based on BirdLife International ( 2024 ): LC –, EN – Endangered, VU – Vulnerable, NT - Near Threatened English name Scientific name Frequency IUCN Status Brown Shrike Lanius cristatus 32.8 LC Common Tailorbird Orthotomus sutorius 16.0 LC Greater Coucal Centropus sinensis 12.0 LC Black Drongo Dicrurus macrocercus 11.2 LC Grey-breasted Prinia Prinia hodgsonii 10.4 LC Black-crested Bulbul Rubigula flaviventris 9.6 LC Siberian Stonechat Saxicola maurus 9.6 LC Puff-throated Babbler Pellorneum ruficeps 8.0 LC House Swift Apus nipalensis 7.2 LC Olive-backed Sunbird Cinnyris jugularis 7.2 LC Paddyfield Pipit Anthus rufulus 7.2 LC Pied Bushchat Saxicola caprata 7.2 LC Barn swallow Hirundo rustica 6.4 LC Crimson Sunbird Aethopyga siparaja 6.4 LC Dark-neked Tailorbird Orthotomus atrgularis 6.4 LC Scaly-breasted Munia Lonchura punctulata 6.4 LC Lesser Coucal Centropus bengalensis 5.6 LC Little Spiderhunter Arachnothera longirostra 5.6 LC Taiga Flycatcher Ficedula albicilla 4.8 LC Yellow-browed Warbler Phylloscopus inornatus 4.8 LC Asian Barred Owlet Glaucidium cuculoides 4.0 LC Western Koel Eudynamys scolopaceus 4.0 LC Asian Green Bee-eater Merops orientalis 4.0 LC Stripe-throated Bulbul Pycnnonotus finlaysoni 4.0 LC White-throated Kingfisher Halcyon smyrnensis 4.0 LC Black-collared starling Gracupica nigricollis 3.2 LC Common Iora Aegitina tiphia 3.2 LC Indian Roller Coracias affinis 3.2 LC Large-billed Crow Corvus macrorhynchos 3.2 LC Pin-striped Tit-Babbler Mixornis gularis 3.2 LC Purple Sundbird Cinnyris asiaticus 3.2 LC Asian Palm Swift Cypsiurus balasiensis 2.4 LC Black-naped Oriole Oriolus chinensis 2.4 LC Coppersmith Barbet Psilopogon haemacephalus 2.4 LC Olive-backed Pipit Anthus hodgsoni 2.4 LC Oriental Magpie-Robin Copsychus saularis 2.4 LC Plaintive Cuckoo Cacomantis merulinus 2.4 LC Red-wattled Lapwing Vanellus indicus 2.4 LC White-rumped Shama Copsychus malabaricus 2.4 LC Yellow-bellied Warbler Abroscopus superciliaris 2.4 LC Zebra Dove Geopelia striata 2.4 LC Abbott's Babbler Malacocincla abbotti 1.6 LC Arctic Warbler Phylloscopus borealis 1.6 LC Ashy Minivet Pericrocotus divaricatus 1.6 LC Ashy-throated Warbler Phylloscopus maculipennis 1.6 LC Black-naped Monarch Hypothymis azurea 1.6 LC Blue-throated Barbet Psilopogon asiaticus 1.6 LC Chinese Pond Heron Ardeola bacchus 1.6 LC Eastern Yellow Wagtail Motacilla tschutschensis 1.6 LC Indian White-eye Zosterops palpebrosus 1.6 LC Red-breasted Parakeet Psittacula alexandri 1.6 NT Red-throated Pipit Anthus cervinus 1.6 LC Rufescent Prinia Prinia rufescens 1.6 LC Rufous-capped Babbler Cyanoderma ruficeps 1.6 LC Scarlet-backed Flowerpecker Dicaeum cruetatum 1.6 LC Small Minivet Pericrocotus cinnamomeus 1.6 LC Two-barred Warbler Phylloscopus plumbeitarsus 1.6 LC Yellow-bellied Prinia Prinia flaviventris 1.6 LC Yellow eyed Babbler Chrysomma sinense 1.6 LC Amur Falcon Falco amurensis 0.8 LC Ashy Drongo Dicrurus leucophaes 0.8 LC Asian Paradise Flaycatcher Terpsiphone paradisi 0.8 LC Black baza Aviceda leuphotes 0.8 LC Black-winged kite Elanus caeruleus 0.8 LC Blue-tailed Bee-eater Merops philippinus 0.8 LC Brown-cheeked Fulvetta Alcippe poioicephala 0.8 LC Buff-breasted Babbler Pellorneum tickell 0.8 LC Catle egret Bubulcus ibis 0.8 LC Cinereous Tit Parus cinereus 0.8 LC Common Myna Acridotheres tristis 0.8 LC Coral-billed Ground Cuckoo Carpococcyx renauldi 0.8 EN Crested Myna Acridotheres cristatellus 0.8 LC Dusky Warbler Phylloscopus fuscatus 0.8 LC Eurasian Hoopoe Upupa epopos 0.8 LC Fire-breasted Flowerpecker Dicaeum ignipectus 0.8 LC Great Eared nightjar Lyncornis macrotis 0.8 LC Great Slaty Woodpecker Mulleripicus pulverulentus 0.8 VU Grey-capped Woodpecker Yungipicus canicapillus 0.8 LC Grey wagtail Motacilla cinerea 0.8 LC House Sparrow Passer domesticus 0.8 LC Indian Cuckoo Cuculus micropterus 0.8 LC Indian Thick-knee Burhinus indicus 0.8 LC Indochinese Bushlark Plocealauda erythrocephala 0.8 LC Large-tailed Nightjar Caprimulgus macrurus 0.8 LC Lineated Barbler Psilopogon lineatus 0.8 LC Little egret Egretta garzetta 0.8 LC Little Ringed Plover Charadrius dubius 0.8 LC Oriental scops owl Otus sunia 0.8 LC Oriental Skylark Alauda gulgula 0.8 LC Pale-leged Leaf Warbler Phylloscopus tenellipes 0.8 LC Plain Prinia Prinia inornata 0.8 LC Red-rumped Swallow Cecropis daurica 0.8 LC Richard’s pipit Anthus richardi 0.8 LC Ruby-cheeked Sunbird Chalcoparia singalensis 0.8 LC Striated Swallow Cecropis striolata 0.8 LC Square-tailed Drongo-Cuckoo Surniculus lugubris 0.8 LC Tree sparrow Passer montanus 0.8 LC Van Hasselt's Sunbird Leptocoma brasiliana 0.8 LC Ashy Woodswallow Artamus fuscus 0.8 LC Within the analyzed buffers around the observation points, the largest area was occupied by open habitat - mainly cultivated fields and pastures, secondary forests, woodland and shrubs, and the least were roads and water surfaces (Table 1 ). Six models fell within ΔAICc < 2 (Appendix, Table 2 ). Model averaging indicated that distance to built-up areas was the only supported predictor. It had a relative variable importance of 0.83, and the 95% confidence interval of its model-averaged estimate did not include zero. Distance to built-up areas was positively related to the number of species detected (β = 0.184; Fig. 2 , Table 2 ). The marginal R 2 of the averaged models ranged from 0.037 to 0.066, and the conditional R 2 ranged from 0.042 to 0.074 (Appendix, Table 1 ). Table 2 Model-averaged coefficients (conditional averages) are presented for all models with ΔAICc < 2. For each predictor, the table reports the model estimate, unconditional standard error (SE), 95% confidence interval, and relative variable importance (RVI). Predictors with 95% confidence intervals not overlapping zero are shown in bold. Site was included as a random effect in averaged GLMMs. Parameter Estimate (β) SE CI lower CI upper RVI (Intercept) 1.033 0.193 0.654 1.412 1.00 DistBuild 0.184 0.080 0.028 0.340 0.83 Road -1.728 1.108 -3.899 0.443 0.52 Forest -0.085 0.066 -0.213 0.043 0.44 Water 0.300 0.312 -0.312 0.911 0.34 Appendix Table 2 Ranking of the best candidate models describing the influence of habitat parameters on detected numbers of bird species. Degrees of freedom (df), model log-likelihood (LL), corrected AIC (AICc), difference between the model and the best model in the data set (ΔAIC), and weight for the model (AICw), R²m – variance explained by fixed effects; R²c – variance explained by the full model (fixed and random effects) are shown. Fixed effects df LL AICc ΔAICc AICw R 2 m R 2 c Intercept + DistBuild + Road 4 -256.181 520.7 0.00 0.227 0.052 0.058 Intercept + DistBuild 3 -257.407 521.0 0.32 0.194 0.037 0.042 Intercept + DistBuild + Road + Forest 5 -255.267 521.0 0.34 0.191 0.066 0.074 Intercept + DistBuild + Forest 4 -256.571 521.5 0.78 0.154 0.052 0.058 Intercept + DistBuild + Road + Water 5 -255.743 522.0 1.29 0.119 0.057 0.064 Intercept + DistBuild + Water 4 -256.856 522.0 1.35 0.116 0.045 0.050 Discusion The list of species recorded during the study (N = 99) is not extensive, considering that Laos is considered a hotspot, and together with resident, migratory, and wintering birds, 600 species can be found here (Duckworth et al. 1999; BirdLife International 2025 ). The identified species list could have been richer, as the methods used were unable to detect all species, particularly those that are secretive, quiet, or nocturnal. Furthermore, we generally recorded birds at distances of up to 100 meters, but we are aware that some loudly vocalizing species could have been recorded at the PUC from a greater distance. Nevertheless, the species detected reflected the habitat types surveyed and the influence of human pressure. Most recorded birds were common, widely distributed species associated with open landscapes, farmland, and forest edges: Common Tailorbird ( Orthotomus sutorius ), Black Drongo ( Dicrurus macrocercus ), Grey-breasted Prinia ( Prinia hodgsonii ), and Black-crested Bulbul ( Rubigula flaviventris ). Several regularly observed species, including Brown Shrike ( Lanius cristatus ), Siberian Stonechat ( Saxicola maurus ), Taiga Flycatcher ( Ficedula albicilla ), and Yellow-browed Warbler ( Phylloscopus inornatus ), were migratory species wintering in Southeast Asia. Only a few records involved forest-dependent or uncommon species, such as Coral-billed Ground Cuckoo ( Carpococcyx renauldi ), Great Slaty Woodpecker ( Mulleripicus pulverulentus ), Grey-capped Woodpecker ( Yungipicus canicapillus ), and Fire-breasted Flowerpecker ( Dicaeum ignipectus ), all of which are typically associated with mature, undisturbed tropical forests (BirdLife International 2025 ). The detection of the Red-breasted Parakeet ( Psittacula alexandri ), a species declining in several parts of its range due to trade and persecution, further reflects the mixed character of the avifauna observed (BirdLife International 2025 ). Our results showed that the closer to buildings, the fewer bird species were found, which is related to the greater human pressure. The close distance to human settlements is associated with greater transformations of the natural environment and direct human impact. Local people often hunt birds in vicinity their homes, they shoot them with slingshots and catch them in mist nets right next to buildings, limiting the number and diversity of birds near human settlements (Xayyasith et al. 2020 ). During our observations we both met hunters with guns hunting birds, as well as we encountered nets set up. These practices likely reduce both the number of individuals and the diversity of species willing to use habitats near settlements. We made also a number of incidental observations that further illustrate the diverse forms of human pressure on avifauna. At several Buddhist temples, we recorded small birds, mainly House Sparrows ( Passer domesticus ) and Scaly-breasted Munias ( Lonchura punctulata ), kept in cages, apparently for ritual release as part of Buddhist merit-making practices. Additionally, individual songbirds such as Ashy Bulbul ( Hypsipetes thompsoni ), Hill Myna ( Gracula religiosa ), Red-whiskered Bulbul ( Pycnonotus jocosus ), and Spotted Dove ( Spilopelia chinensis ) were observed being kept in cages as ornamental or vocal pets. In three cases, we also noted dead chicks, likely taken directly from nests, being sold openly in groups of around 20 individuals at local markets. We further encountered three rice fields near settlements where mist nets were actively deployed for bird trapping. In two cases, bird carcasses or wings were mounted on sticks above vegetable plots, likely serving as scare devices to deter birds from feeding on crops. These records provide further evidence of direct and opportunistic pressure on both adult and juvenile birds, supporting the broader pattern of human disturbance observed in the landscape. Hunting and related disturbance not only decrease local bird abundances but also alter spatial distribution by causing birds to avoid areas close to settlements and access routes, even where habitat structure remains relatively intact (Benítez-López et al. 2017). Across tropical systems, intensive and largely non-selective hunting has been linked to reduced occupancy and apparent declines in bird species in hunted compared with less disturbed sites, suggesting that behavioural avoidance and local depletion operate together to shape community composition (Benítez-López et al. 2017; Tilker et al. 2019 ). These behavioural and distributional responses indicate that human pressure affects bird communities not only through direct mortality but also by modifying how birds use space in human-dominated landscapes. In addition, animals such as cats and dogs, which are found in quite large numbers in rural settlements and often roam freely, are closely related to people and their homes. It is known that cats, in particular, are dangerous predators, especially affecting bird populations (Loss et al. 2013 ; Lepczyk 2023). In contrast, roads did not show a significant direct effect on species richness. However, their role in facilitating human access and disturbance should not be overlooked. Roads can facilitate human movement and allow hunting in more remote locations and allow for more rapid land-use changes, indirectly impacting bird communities by increasing human penetration in areas that were previously less disturbed (Laurance et al. 2009 ; Hughes 2017 ). It was found that other habitats (such as open areas and forests) did not influence the number of bird species. Open areas were an essential element of most of the sites, and species associated with them dominated among those identified. The lack of influence of forests and woodlands on species richness is puzzling. The character of the forests seems to be the main reason for this type of relationship. The vast majority of them were secondary forests, young stands after previous logging or burning. In Laos (especially in northern part) still traditional nomadic agriculture and burning mountain forests is still common and these activities are the leading causes of forest, woods and shrubland degradation (Ma et al. 2024 ). Moreover, forests are fragmented, heavily penetrated and surrounded by farmland and human settlements. Furthermore, some of them were used as rubber plantations or banana cultivation, and such forests are usually poorer, degraded (Baird 2014 ), contain few old and dead trees, which are important for many bird species. Our study shows that bird species richness in rural Laos declines significantly near human settlements. This pattern likely results from direct persecution, such as hunting and disturbance, as well as indirect pressures like habitat degradation and the presence of domestic predators. Habitat characteristics, including forest type and structure, had no clear effect on species richness, probably due to the dominance of secondary and degraded forest patches. Overall, human presence appears to be a stronger driver of avian diversity patterns than habitat features alone. Declarations Funding The research was carried out during an internship in Laos (National University of Laos) and was supported by the University of Siedlce (ZK, AG, PO), and the Wroclaw University of Environmental and Life Sciences (CM). Ethical standards The authors declare that this publication complies with the law in force in Laos. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author contributions. This study was conceived by ZK, AG and SX. Data were collected by ZK, AG and CM. Analysis were made by ZK, CM and PO, write up: CM and all authors contributed critically to the manuscript and gave final approval for publication. 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Environ Res Lett 20:074027. 10.1088/1748-9326/add60610.31223/x5hq6k 1031223/X5HQ6K Sreekar R, Goodale E, Harrison RD (2015) Flight initiation distance as behavioral indicator of hunting pressure: a case study of the sooty-headed bulbul ( Pycnonotus aurigaste r) in Xishuangbanna, SW China. Trop Conserv Sci 8:505–512. https://doi.org/10.1177/194008291500800214 Sodhi NS, Şekercioğlu ÇH, Barlow J, Robinson SK (2011) The State of Tropical Bird Biodiversity. In: Sodhi NS, Şekercioğlu ÇH, Barlow J, Robinson SK (eds) Conservation of Tropical Birds. Wiley-Blackwell, Oxford, pp 1–26. https://doi.org/10.1002/9781444342611.ch1 Tilker A, Abrams JF, Mohamed A, Nguyen A, Wong ST, Sollmann R, Wilting A (2019) Habitat degradation and indiscriminate hunting differentially impact faunal communities in the Southeast Asian tropical biodiversity hotspot. Commun Biol 2:396. https://doi.org/10.1038/s42003-019-0640-y Tordoff AW, Baltzer MC, Fellowes JR, Pilgrim JD, Langhammer PF (2012) Key biodiversity areas in the Indo Burma hotspot: process, progress and future directions. J Threat Taxa 4:2779–2787. https://doi.org/10.11609/JoTT.o3000.2779-87 van Vliet J (2019) Direct and indirect loss of natural area from urban expansion. Nat Sustain 2:755–763. https://doi.org/10.1038/s41893-019-0340-0 Warren TL, Betts MG, Diamond AW, Forbes GJ (2005) The influence of local habitat and landscape composition on cavity-nesting birds in a forested mosaic. For Ecol Manag 214:331–343. https://doiorg/101016/jforeco200504017 Xayyasith S, Douangboubpha B, Chaiseha Y (2020) Recent surveys of the bird trade in local markets in central Laos. Forktail 36:47–55 Zhang L, Hua N, Sun S (2008) Wildlife trade, consumption and conservation awareness in southwest China. Biodiver Conserv 17:1493–1516. https://doi.org/10.1007/s10531-008-9358-8 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviews received at journal 26 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor assigned by journal 20 Jan, 2026 Submission checks completed at journal 31 Dec, 2025 First submitted to journal 30 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":1668564,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Laos within Southeast Asia (left) and a detailed map of Laos (right). The map of Laos shows: white circles represent 20-km clusters of study sites, with numbers indicating the number of sites in each cluster, a star marking the capital city, Vientiane, and black squares representing major cities labelled by name. The basemap consists of a Digital Elevation Model, colour-rendered within the boundaries of Laos and displayed in greyscale outside the country.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8484369/v1/54785eac7b50322041691fef.png"},{"id":100915879,"identity":"9aecc0dc-06a5-48ea-9df2-183999c97ef7","added_by":"auto","created_at":"2026-01-22 18:31:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98243,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of detected bird species in relation to distance from built-up areas, based on the top GLMMs (ΔAICc \u0026lt; 2). The solid line shows model predictions, with 95% confidence intervals shaded in grey.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8484369/v1/aa43a38f74692cae35d43f2e.png"},{"id":101207648,"identity":"fcb539a3-eecc-4f76-aa4e-6db616b56c15","added_by":"auto","created_at":"2026-01-27 10:06:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2774828,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8484369/v1/1565fd29-2195-4e72-b018-1d67d6ed28af.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bird communities in Southeast Asia are shaped by human pressure more than habitat features","fulltext":[{"header":"Intoduction","content":"\u003cp\u003eNatural habitats in many regions of the world are suffering from biodiversity loss: species and population extirpation and critically, decline of local endemic species (Hanski \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Dirzo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Keck et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). There are many reasons for this phenomenon, most of which stem from various forms of human activity. The main factors affecting changes in biodiversity and the occurrence of wild flora and fauna are habitat loss caused by their conversion into human settlements, cropland, pastures and industrial areas (Liu 2016; van Vliet \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This applies to many regions of the world, including those with high natural value, such as the tropical areas of Southeast Asia. This region is located in the zone of greatest biodiversity and has the highest average percentage of native endemic bird species among all tropical regions (Myers et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sodhi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tordoff et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Southeast Asia has a higher proportion of bird species categorized on the Red List as globally threatened (Sodhi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition to globally threatened species, this region also supports numerous widespread and migratory birds, for which Southeast Asia serves as an important breeding and non-breeding ground (Robson \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this region, in addition to very rapid changes in the natural habitats mainly through deforestation and forest burning (Chen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sims et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), there is also direct human pressure on birds populations (Xayyasith et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBirds often respond behaviourally and spatially to direct human pressures such as hunting, trapping and disturbance. Experimental and observational studies show that in areas with frequent human disturbance, birds increase their flight initiation distances and shift activity patterns, leading to reduced use of otherwise suitable habitats (Sreekar et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Hunting and related disturbance not only decrease local abundances but can also alter distribution by causing birds to avoid areas close to roads and settlements, even where habitat structure remains intact (Benítez‑López et al. 2017). Across tropical systems, intensive nonspecific hunting has been linked to reduced occupancy and apparent declines in bird species in hunted compared with less disturbed sites, suggesting that behavioural avoidance and local depletion operate together to shape community composition (Benítez‑López et al. 2017; Tilker et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such behavioural and distributional responses to human pressure highlight that effects extend beyond simple mortality, influencing how birds use space and resources in landscapes with high human presence.\u003c/p\u003e \u003cp\u003eSoutheast Asia is one of the areas fraught with large-scale wildlife hunting, capture and trade (Li et al. 2000; Zhang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bhupathy et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Roldán-Clarà et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li \u0026amp; Jiang \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Xayyasith et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, direct human pressure on birds in this region takes many forms beyond hunting alone. These include widespread trapping using mist nets, glue traps, and snares, intentional disturbance (e.g., slingshot shooting, nest destruction, scaring birds away from crops), and capture for ornamental or pet trade (Benítez-López et al. 2017; Dai and Hu \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Such pressures can lead to reduced bird abundance, altered species composition, and behavioral changes even in habitats that remain structurally intact.\u003c/p\u003e \u003cp\u003eOne country in this region experiencing both rapid environmental changes and direct strong human pressure is Laos. Many bird species there are deliberately persecuted or killed because they are considered crop pests or, more typically, for food. At the same time, it is one of the countries in Asia with the highest percentage of their GTBs (Globally Threatened Birds) impacted by infrastructure development (Sodhi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Although current Laos law restricts the killing of wildlife, birds in particular are still harvested, traded, and used for culinary purposes (Pruvot et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the variety of direct human pressures on birds, including hunting, trapping, and frequent disturbance near settlements, we hypothesised that bird species richness would decrease with proximity to human settlements. We assumed that the intensity of human disturbance is greatest near inhabited areas, where persecution, trapping, and disturbance are most frequent. To isolate the effect of human proximity from habitat variation, we also accounted for local habitat composition, which is known to strongly influence avian diversity (Keck et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Our aim was to test whether distance from settlements and others land-use characteristics (forests and woodlands, open areas with fields and pastures, water surface and roads) have an impact on the bird species richness.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eStudy area\u003c/p\u003e\u003cp\u003eThe study was conducted in Laos, largely mountainous country (northern mountainous\u003c/p\u003e\u003cp\u003eareas above 1,000 m) covers an area of 236,800 km\u003csup\u003e2\u003c/sup\u003e within the Indochinese Peninsula, in Southeast Asia. The population in 2024 was about 7,7 ml of people with density of 33 persons/km\u003csup\u003e2\u003c/sup\u003e (Lao Statistics Bureau \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The climate of Laos is described as tropical under the influence of monsoons, which bring 70% of the annual rainfall (as 3,000 mm per year) and high humidity, with two distinct seasons: the rainy season (monsoon, from May to mid-October) and the dry season (from mid-October to April). Mean annual temperatures in the plains reach at 25–27°C and about 20°C in the northern and eastern mountainous areas and the plateaus (Phongoudome and Sirivong \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e: Cosslett and Cosslett \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNational-level land-use assessments indicate that the landscape is dominated by a mosaic of forested areas and agricultural systems, shaped largely by both swidden and permanent agriculture, each occupying approximately 29% of the national territory, while forests cover around 40–42% (Messerli et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These lowland and foothill mosaics often include paddy rice fields, fallows, open farmland, and scattered trees. In recent decades, however, forested areas have been increasingly converted into agricultural land (Sims et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), particularly in the southern part of the country, where plains and plateaus support more permanent cultivation (Cosslett and Cosslett \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBird data\u003c/p\u003e\u003cp\u003eThe studies were caried out between 9 April and 2 May, 2024, between 06:00 and 10:00, and only in favorable weather, i.e. without rainfall and/or stronger winds. Data were collected at 43 pre-selected sites located along unpaved roads or paths. The study sites were located in various regions of Laos, always outside residential areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Study sites were selected using map analysis (Google Maps) and were separated from each other by at least 1 km, typically by 5 km or more. Within each site, a single morning survey was conducted along a walk route followed by one observer. Along each route, points of bird observation and recording (125 in total) were typically arranged in sets of three, spaced 1 km apart and positioned at increasing distances from the nearest human settlement. In a few cases, only two points were used per route due to terrain limitations.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eAt each observation point, bird species were surveyed over a 15-minute period. Species identification was based on two complementary methods: direct visual observations by the observer within a 100 m radius, and simultaneous acoustic recordings using a PUC device (Portable Unit for Recording-BW-001). The PUC was placed on a natural elevation at the same location to optimise sound detection. The recordings were subsequently analysed to species level using the Birdweather application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://app.birdweather.com/\u003c/span\u003e\u003cspan address=\"https://app.birdweather.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which applies automated sound recognition. All recordings were processed using the application’s default confidence threshold settings. This dual approach allowed for more comprehensive species detection, integrating visual and acoustic data collected during the same survey sites. Only birds identified to species were included in the analysis.\u003c/p\u003e\u003cp\u003eHabitat data\u003c/p\u003e\u003cp\u003eFor each of point of recordings and observations (n = 125) habitat characteristics were analyzed within 100-metre radius buffers with a total area of ​​3.14 ha (Warren et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pickens and King 2014). Within each buffer, the area of the following land-use categories (variable name in parentheses) was calculated: roads including unpaved and paved (Road), water surface including ponds and rivers (Water), open habitat including fields, pastures and other marginal farmland (Open), forest and woodland (Forest). These factors were determined based on Google satellite images available through the web map services (WMS). Additionally, the distance from the observation point to the nearest village was measure. All geospatial analyses were conducted in QGIS 3.40.1 (QGIS Development Team 2024).\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were carried out in R version 4.4.2 (R Core Team 2024). Multicollinearity among the six habitat variables was examined prior to modelling. Very high variance inflation factors (VIF \u0026gt; 177) were detected for forest area, bush area and open area. Bush area was therefore removed, which reduced VIF values for the remaining predictors to below 2 (Quinn and Keough \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Mixed-effects generalised linear models with a Poisson error structure were fitted using the ‘glmmTMB’ package (Brooks et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The number of bird species detected was used as the response variable, the habitat variables as fixed effects, and Site as a random effect. The global model was: Species ~ Road + Forest + Water + Open + DistBuild + (1|Site).\u003c/p\u003e\u003cp\u003eModel assumptions were evaluated with the ‘DHARMa’ package (Hartig \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Simulated residuals were inspected using Q–Q plots and residuals versus fitted values, and models were tested for under- and overdispersion as well as zero inflation. No signs of dispersion problems or zero inflation were found, and the Poisson distribution was retained. Model selection followed an information-theoretic approach using AICc (Burnham and Anderson \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). All possible combinations of predictors were generated using the MuMIn package (Bartoń \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To identify the most influential predictors, model averaging and relative variable importance (RVI) were calculated as the sum of Akaike weights (Burnham and Anderson \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Arnold \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Averaging was performed for models with ΔAICc \u0026lt; 2 (Grueber et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Predictors were considered supported when their model-averaged 95% confidence intervals did not include zero (Arnold \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Model fit was assessed using marginal and conditional R², representing the variance explained by fixed effects alone and by both fixed and random effects, respectively (Nakagawa et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDuring study period we recorded 99 species on all study sites (Appendix, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On average, 3.0 species were detected per site, ranging from 0 to 10. The most often recorded species was Brown Shrike (\u003cem\u003eLanius cristatus\u003c/em\u003e) found at about 30% of the all points and another 4 species (Common Tailorbird \u003cem\u003eOrthotomus sutorius\u003c/em\u003e, Greater Coucal \u003cem\u003eCentropus sinensis\u003c/em\u003e, Black Drongo \u003cem\u003eDicrurus macrocercus\u003c/em\u003e, Grey-breasted Prinia \u003cem\u003ePrinia hodgsonii\u003c/em\u003e) were observed at more than 10% of the points. The vast majority of species (57 species \u0026minus;\u0026thinsp;57.6%) were recorded only at one or two observation points (0.8\u0026ndash;1.6%). Most detected birds were relatively numerous and not endangered species (LC), while one species each were found as Endangered (EN) according to the IUCN scale (BirdLife International \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) - Coral-billed Ground Cuckoo (\u003cem\u003eCarpococcyx renauldi\u003c/em\u003e), Great Slaty Woodpecker (\u003cem\u003eMulleripicus pulverulentus\u003c/em\u003e) as Vulnerable (VU) and Red-breasted Parakeet (\u003cem\u003ePsittacula alexandri\u003c/em\u003e) as Near Threatened (NT) (Appendix, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of variables describing habitat of studding sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, Range (in brackets)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest area (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93 (0.00-3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBush area (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBush\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80 (0.00-3.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoads (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 (0.00-0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater surface (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 (0.00-1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen habitat area (ha):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.95 (0.00-3.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to nearest built-up area (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistBuild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73 (0.01\u0026ndash;3.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of species recorded at all points with a specific frequency (%) and threatened status based on BirdLife International (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e): LC \u0026ndash;, EN \u0026ndash; Endangered, VU \u0026ndash; Vulnerable, NT - Near Threatened\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnglish name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScientific name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIUCN Status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown Shrike\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLanius cristatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon Tailorbird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOrthotomus sutorius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreater Coucal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCentropus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack Drongo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDicrurus macrocercus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrey-breasted Prinia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePrinia hodgsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack-crested Bulbul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRubigula flaviventris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSiberian Stonechat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSaxicola maurus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePuff-throated Babbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePellorneum ruficeps\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouse Swift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eApus nipalensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlive-backed Sunbird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCinnyris jugularis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaddyfield Pipit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAnthus rufulus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePied Bushchat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSaxicola caprata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarn swallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHirundo rustica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrimson Sunbird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAethopyga siparaja\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDark-neked Tailorbird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOrthotomus atrgularis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScaly-breasted Munia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLonchura punctulata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesser Coucal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCentropus bengalensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLittle Spiderhunter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eArachnothera longirostra\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaiga Flycatcher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFicedula albicilla\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow-browed Warbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus inornatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian Barred Owlet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGlaucidium cuculoides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Koel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEudynamys scolopaceus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian Green Bee-eater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMerops orientalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStripe-throated Bulbul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePycnnonotus finlaysoni\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite-throated Kingfisher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHalcyon smyrnensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack-collared starling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGracupica nigricollis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon Iora\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAegitina tiphia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndian Roller\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCoracias affinis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge-billed Crow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorvus macrorhynchos\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePin-striped Tit-Babbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMixornis gularis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurple Sundbird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCinnyris asiaticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian Palm Swift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCypsiurus balasiensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack-naped Oriole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOriolus chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoppersmith Barbet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePsilopogon haemacephalus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlive-backed Pipit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAnthus hodgsoni\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOriental Magpie-Robin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCopsychus saularis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaintive Cuckoo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCacomantis merulinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed-wattled Lapwing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVanellus indicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite-rumped Shama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCopsychus malabaricus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow-bellied Warbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAbroscopus superciliaris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZebra Dove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGeopelia striata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbbott's Babbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMalacocincla abbotti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArctic Warbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus borealis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAshy Minivet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePericrocotus divaricatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAshy-throated Warbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus maculipennis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack-naped Monarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHypothymis azurea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue-throated Barbet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePsilopogon asiaticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese Pond Heron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eArdeola bacchus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Yellow Wagtail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMotacilla tschutschensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndian White-eye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZosterops palpebrosus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed-breasted Parakeet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePsittacula alexandri\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed-throated Pipit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAnthus cervinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRufescent Prinia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePrinia rufescens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRufous-capped Babbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCyanoderma ruficeps\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScarlet-backed Flowerpecker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDicaeum cruetatum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall Minivet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePericrocotus cinnamomeus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo-barred Warbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus plumbeitarsus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow-bellied Prinia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePrinia flaviventris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow eyed Babbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChrysomma sinense\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmur Falcon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFalco amurensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAshy Drongo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDicrurus leucophaes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian Paradise Flaycatcher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTerpsiphone paradisi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack baza\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAviceda leuphotes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack-winged kite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eElanus caeruleus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue-tailed Bee-eater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMerops philippinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown-cheeked Fulvetta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAlcippe poioicephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuff-breasted Babbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePellorneum tickell\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatle egret\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBubulcus ibis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCinereous Tit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eParus cinereus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon Myna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAcridotheres tristis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoral-billed Ground Cuckoo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCarpococcyx renauldi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrested Myna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAcridotheres cristatellus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDusky Warbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus fuscatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurasian Hoopoe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eUpupa epopos\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFire-breasted Flowerpecker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDicaeum ignipectus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreat Eared nightjar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLyncornis macrotis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreat Slaty Woodpecker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMulleripicus pulverulentus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrey-capped Woodpecker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYungipicus canicapillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrey wagtail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMotacilla cinerea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouse Sparrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePasser domesticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndian Cuckoo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCuculus micropterus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndian Thick-knee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBurhinus indicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndochinese Bushlark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlocealauda erythrocephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge-tailed Nightjar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCaprimulgus macrurus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLineated Barbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePsilopogon lineatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLittle egret\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEgretta garzetta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLittle Ringed Plover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCharadrius dubius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOriental scops owl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOtus sunia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOriental Skylark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAlauda gulgula\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePale-leged Leaf Warbler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus tenellipes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlain Prinia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePrinia inornata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed-rumped Swallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCecropis daurica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichard\u0026rsquo;s pipit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAnthus richardi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRuby-cheeked Sunbird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChalcoparia singalensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStriated Swallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCecropis striolata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquare-tailed Drongo-Cuckoo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSurniculus lugubris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree sparrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePasser montanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVan Hasselt's Sunbird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLeptocoma brasiliana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAshy Woodswallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eArtamus fuscus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWithin the analyzed buffers around the observation points, the largest area was occupied by open habitat - mainly cultivated fields and pastures, secondary forests, woodland and shrubs, and the least were roads and water surfaces (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSix models fell within ΔAICc\u0026thinsp;\u0026lt;\u0026thinsp;2 (Appendix, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Model averaging indicated that distance to built-up areas was the only supported predictor. It had a relative variable importance of 0.83, and the 95% confidence interval of its model-averaged estimate did not include zero. Distance to built-up areas was positively related to the number of species detected (β\u0026thinsp;=\u0026thinsp;0.184; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The marginal R\u003csup\u003e2\u003c/sup\u003e of the averaged models ranged from 0.037 to 0.066, and the conditional R\u003csup\u003e2\u003c/sup\u003e ranged from 0.042 to 0.074 (Appendix, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel-averaged coefficients (conditional averages) are presented for all models with ΔAICc\u0026thinsp;\u0026lt;\u0026thinsp;2. For each predictor, the table reports the model estimate, unconditional standard error (SE), 95% confidence interval, and relative variable importance (RVI). Predictors with 95% confidence intervals not overlapping zero are shown in bold. Site was included as a random effect in averaged GLMMs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCI upper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRVI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistBuild\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.184\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.080\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.340\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAppendix\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRanking of the best candidate models describing the influence of habitat parameters on detected numbers of bird species. Degrees of freedom (df), model log-likelihood (LL), corrected AIC (AICc), difference between the model and the best model in the data set (ΔAIC), and weight for the model (AICw), R\u0026sup2;m \u0026ndash; variance explained by fixed effects; R\u0026sup2;c \u0026ndash; variance explained by the full model (fixed and random effects) are shown.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAICc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔAICc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAICw\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003em\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003ec\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u0026thinsp;+\u0026thinsp;DistBuild\u0026thinsp;+\u0026thinsp;Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-256.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e520.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u0026thinsp;+\u0026thinsp;DistBuild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-257.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e521.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u0026thinsp;+\u0026thinsp;DistBuild\u0026thinsp;+\u0026thinsp;Road\u0026thinsp;+\u0026thinsp;Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-255.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e521.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u0026thinsp;+\u0026thinsp;DistBuild\u0026thinsp;+\u0026thinsp;Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-256.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e521.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u0026thinsp;+\u0026thinsp;DistBuild\u0026thinsp;+\u0026thinsp;Road\u0026thinsp;+\u0026thinsp;Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-255.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e522.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u0026thinsp;+\u0026thinsp;DistBuild\u0026thinsp;+\u0026thinsp;Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-256.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e522.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discusion","content":"\u003cp\u003eThe list of species recorded during the study (N\u0026thinsp;=\u0026thinsp;99) is not extensive, considering that Laos is considered a hotspot, and together with resident, migratory, and wintering birds, 600 species can be found here (Duckworth et al. 1999; BirdLife International \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The identified species list could have been richer, as the methods used were unable to detect all species, particularly those that are secretive, quiet, or nocturnal. Furthermore, we generally recorded birds at distances of up to 100 meters, but we are aware that some loudly vocalizing species could have been recorded at the PUC from a greater distance.\u003c/p\u003e \u003cp\u003eNevertheless, the species detected reflected the habitat types surveyed and the influence of human pressure. Most recorded birds were common, widely distributed species associated with open landscapes, farmland, and forest edges: Common Tailorbird (\u003cem\u003eOrthotomus sutorius\u003c/em\u003e), Black Drongo (\u003cem\u003eDicrurus macrocercus\u003c/em\u003e), Grey-breasted Prinia (\u003cem\u003ePrinia hodgsonii\u003c/em\u003e), and Black-crested Bulbul (\u003cem\u003eRubigula flaviventris\u003c/em\u003e). Several regularly observed species, including Brown Shrike (\u003cem\u003eLanius cristatus\u003c/em\u003e), Siberian Stonechat (\u003cem\u003eSaxicola maurus\u003c/em\u003e), Taiga Flycatcher (\u003cem\u003eFicedula albicilla\u003c/em\u003e), and Yellow-browed Warbler (\u003cem\u003ePhylloscopus inornatus\u003c/em\u003e), were migratory species wintering in Southeast Asia. Only a few records involved forest-dependent or uncommon species, such as Coral-billed Ground Cuckoo (\u003cem\u003eCarpococcyx renauldi\u003c/em\u003e), Great Slaty Woodpecker (\u003cem\u003eMulleripicus pulverulentus\u003c/em\u003e), Grey-capped Woodpecker (\u003cem\u003eYungipicus canicapillus\u003c/em\u003e), and Fire-breasted Flowerpecker (\u003cem\u003eDicaeum ignipectus\u003c/em\u003e), all of which are typically associated with mature, undisturbed tropical forests (BirdLife International \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The detection of the Red-breasted Parakeet (\u003cem\u003ePsittacula alexandri\u003c/em\u003e), a species declining in several parts of its range due to trade and persecution, further reflects the mixed character of the avifauna observed (BirdLife International \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results showed that the closer to buildings, the fewer bird species were found, which is related to the greater human pressure. The close distance to human settlements is associated with greater transformations of the natural environment and direct human impact. Local people often hunt birds in vicinity their homes, they shoot them with slingshots and catch them in mist nets right next to buildings, limiting the number and diversity of birds near human settlements (Xayyasith et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). During our observations we both met hunters with guns hunting birds, as well as we encountered nets set up. These practices likely reduce both the number of individuals and the diversity of species willing to use habitats near settlements. We made also a number of incidental observations that further illustrate the diverse forms of human pressure on avifauna. At several Buddhist temples, we recorded small birds, mainly House Sparrows (\u003cem\u003ePasser domesticus\u003c/em\u003e) and Scaly-breasted Munias (\u003cem\u003eLonchura punctulata\u003c/em\u003e), kept in cages, apparently for ritual release as part of Buddhist merit-making practices. Additionally, individual songbirds such as Ashy Bulbul (\u003cem\u003eHypsipetes thompsoni\u003c/em\u003e), Hill Myna (\u003cem\u003eGracula religiosa\u003c/em\u003e), Red-whiskered Bulbul (\u003cem\u003ePycnonotus jocosus\u003c/em\u003e), and Spotted Dove (\u003cem\u003eSpilopelia chinensis\u003c/em\u003e) were observed being kept in cages as ornamental or vocal pets. In three cases, we also noted dead chicks, likely taken directly from nests, being sold openly in groups of around 20 individuals at local markets. We further encountered three rice fields near settlements where mist nets were actively deployed for bird trapping. In two cases, bird carcasses or wings were mounted on sticks above vegetable plots, likely serving as scare devices to deter birds from feeding on crops. These records provide further evidence of direct and opportunistic pressure on both adult and juvenile birds, supporting the broader pattern of human disturbance observed in the landscape. Hunting and related disturbance not only decrease local bird abundances but also alter spatial distribution by causing birds to avoid areas close to settlements and access routes, even where habitat structure remains relatively intact (Ben\u0026iacute;tez-L\u0026oacute;pez et al. 2017). Across tropical systems, intensive and largely non-selective hunting has been linked to reduced occupancy and apparent declines in bird species in hunted compared with less disturbed sites, suggesting that behavioural avoidance and local depletion operate together to shape community composition (Ben\u0026iacute;tez-L\u0026oacute;pez et al. 2017; Tilker et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These behavioural and distributional responses indicate that human pressure affects bird communities not only through direct mortality but also by modifying how birds use space in human-dominated landscapes. In addition, animals such as cats and dogs, which are found in quite large numbers in rural settlements and often roam freely, are closely related to people and their homes. It is known that cats, in particular, are dangerous predators, especially affecting bird populations (Loss et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lepczyk 2023).\u003c/p\u003e \u003cp\u003eIn contrast, roads did not show a significant direct effect on species richness. However, their role in facilitating human access and disturbance should not be overlooked. Roads can facilitate human movement and allow hunting in more remote locations and allow for more rapid land-use changes, indirectly impacting bird communities by increasing human penetration in areas that were previously less disturbed (Laurance et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hughes \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt was found that other habitats (such as open areas and forests) did not influence the number of bird species. Open areas were an essential element of most of the sites, and species associated with them dominated among those identified. The lack of influence of forests and woodlands on species richness is puzzling. The character of the forests seems to be the main reason for this type of relationship. The vast majority of them were secondary forests, young stands after previous logging or burning. In Laos (especially in northern part) still traditional nomadic agriculture and burning mountain forests is still common and these activities are the leading causes of forest, woods and shrubland degradation (Ma et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, forests are fragmented, heavily penetrated and surrounded by farmland and human settlements. Furthermore, some of them were used as rubber plantations or banana cultivation, and such forests are usually poorer, degraded (Baird \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), contain few old and dead trees, which are important for many bird species.\u003c/p\u003e \u003cp\u003eOur study shows that bird species richness in rural Laos declines significantly near human settlements. This pattern likely results from direct persecution, such as hunting and disturbance, as well as indirect pressures like habitat degradation and the presence of domestic predators. Habitat characteristics, including forest type and structure, had no clear effect on species richness, probably due to the dominance of secondary and degraded forest patches. Overall, human presence appears to be a stronger driver of avian diversity patterns than habitat features alone.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe research was carried out during an internship in Laos (National University of Laos) and was supported by the University of Siedlce (ZK, AG, PO), and the Wroclaw University of Environmental and Life Sciences (CM).\u003c/p\u003e\n\u003cp\u003eEthical standards\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that this publication complies with the law in force in Laos.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eAuthor contributions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was conceived by ZK, AG and SX. Data were collected by ZK, AG and CM. Analysis were made by ZK, CM and PO, write up: CM and all authors contributed critically to the manuscript and gave final approval for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArnold TW (2010) Uninformative parameters and model selection using Akaike's information criterion. J Wildl Manag 74: 1175\u0026ndash;1178. https://doiorg/101111/j1937-28172010tb01236x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaird I (2014) Degraded forest, degraded land and the development of industrial tree plantations in Laos. 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Biodiver Conserv 17:1493\u0026ndash;1516. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10531-008-9358-8\u003c/span\u003e\u003cspan address=\"10.1007/s10531-008-9358-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"bird species richness, human impact, biodiversity hotspot, Laos","lastPublishedDoi":"10.21203/rs.3.rs-8484369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8484369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoutheast Asia, including Laos, is considered a biodiversity hotspot. However, significant environmental changes caused by human activity have a huge impact on fauna, including bird populations. In 2024, we observed and recorded birds at 43 routes and 125 points in various places in Laos. We identified 99 bird species, the most frequently recorded being widespread species of open areas and shrubs, such as the Brown Shrike (\u003cem\u003eLanius cristatus\u003c/em\u003e), Common Tailorbird \u003cem\u003e(Orthotomus sutorius\u003c/em\u003e), and Greater Coucal (\u003cem\u003eCentropus sinensis\u003c/em\u003e). However, species associated with old, mature forests, such as the Ground Cuckoo (\u003cem\u003eCarpococcyx renauldi\u003c/em\u003e), Great Slaty Woodpecker (\u003cem\u003eMulleripicus pulverulentus\u003c/em\u003e), and Fire-breasted Flowerpecker (\u003cem\u003eDicaeum ignipectus\u003c/em\u003e), were significantly less frequently recorded. The habitats of the observation points were dominated by forests, woodlands, and open areas. However, statistical analyses showed that the only factor influencing bird species diversity was distance to the nearest buildings. This indicates that indirect and direct human pressure, both related to habitat changes (clearing and burning of forests, conversion to farmland) and hunting, have a significant impact on the avifauna of the Southeast Asian region.\u003c/p\u003e","manuscriptTitle":"Bird communities in Southeast Asia are shaped by human pressure more than habitat features","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 18:31:20","doi":"10.21203/rs.3.rs-8484369/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-07T03:46:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T17:46:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T13:47:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T14:29:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14532748571514698126303125781098754457","date":"2026-01-21T08:43:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305284706910442919522437960910927403879","date":"2026-01-21T08:31:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325459820812418128280127614358429340694","date":"2026-01-21T07:01:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T02:57:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T01:58:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-31T08:26:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2025-12-30T21:23:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3242900d-21d5-42d6-84cc-d816406b5d33","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-07T03:53:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 18:31:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8484369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8484369","identity":"rs-8484369","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}