The Bird Community in a Coastal Wetland in East China and Its Spatial Responses to a Wind Farm

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However, numerous wind turbines have been built in or near these wetlands in recent years, which might disturb the bird community in the area. Therefore, investigating the bird community and its responses to wind farms in coastal wetlands of East China is of great significance for bird conservation. In the spring and autumn of 2019 and 2020, we investigated the bird community in the Rudong coastal wetland in East China using point counts. We determined 4 geographical factors at each census point, i.e., distance to the wind farm boundary (DW), distance to the suburbs, distance to the sea, and vegetation area, and analysed the relationship between bird number and DW through partial correlation analysis. A total of 11 orders and 103 species of birds, including 4 endangered species, were observed during our survey. Charadriiformes was the dominant taxon in the wetland, and Calidris alpina was the most common species in both spring and autumn. Passeriformes exhibited high species richness but low numbers. The results of partial correlation analysis indicated that birds’ responses to the wind farm varied depending on their dominance and category: dominant and subdominant birds tended to avoid the wind farm, whereas rare birds tended to approach them; aquatic birds were alert to the wind farm, whereas terrestrial birds better adapted to them. We concluded that the dominant aquatic birds, including the endangered species Calidris tenuirostris , were most negatively impacted by the wind farm; the occasional birds and rare aquatic birds might be disturbed by wind farm but not significantly so; and the rare terrestrial birds were least disturbed by or even benefited from the wind farm. Biogeography Dominance Aquatic birds Endangered species Wind turbine Coastal wetland Figures Figure 1 Figure 2 Introduction The coastal wetlands in East China are essential stopover places for birds along the East Asian-Australian Flyway (EAAF) (Cao et al., 2009 ; Yong et al., 2018 ). Numerous aquatic birds stop in these wetlands to accumulate energy reserves before or after they fly over the Yellow Sea (Fig. 1 a) (Ma et al., 2006 ); moreover, some terrestrial birds also rest in these wetlands during their migration (Yong et al., 2015 ). For threatened species in the flyway, such as Calidris pygmaea and Tringa guttifer , these wetlands are crucial for future survival (Peng et al., 2017 ; Yang et al., 2020 ). In recent decades, bird surveys have been conducted in some coastal wetlands of East China (Ma et al., 2009 ; Bai et al., 2015 ; Peng et al., 2017 ). However, most of these surveys focused on shorebirds or endangered species, whereas investigations of entire communities are limited. Moreover, in recent years, birds in the area have been threatened by wetland loss caused by agricultural and industrial development (Lin and Yu, 2018 ; Jackson et al., 2021 ). Thus, further observations of the bird community are necessary for bird conservation in the area. Wind energy is increasingly used for electricity generation because it is clean and renewable (Herbert et al., 2014 ; Kumar et al., 2016 ). Recent reports have shown that the capacity of the world’s wind farms is growing at ~ 10% per year and reached 744 GW by the end of 2020, covering more than 7% of the global electricity demand (WWEA, 2021 ). Despite the benefits of wind power generation, considerable research has shown that wind turbines are disadvantageous to birds. First, birds can be killed by collisions with wind turbines (Newton and Little, 2009 ; Grodsky et al., 2013 ). Bird carcass collection in a coastal wind farm showed that collisions accounted for ~ 3% of bird deaths (Newton and Little, 2009 ). Second, birds tend to avoid wind farms behaviourally (Plonczkier and Simms, 2012 ; Villegas-Patraca et al., 2014 ). Specifically, they usually change their migration routes and stopover sites due to wind farms, and this expenditure of energy could induce an increase in fatal casualties during their migration (Hilgerloh et al., 2011 ). Third, constructions of wind turbines and attached facilities can induce fragmentation and functional loss of bird habitat (Pruett et al., 2009 ; Marques et al., 2020 ), which is essential for bird foraging and breeding. China is the largest energy consumer worldwide (Yang et al., 2017 ). In terms of sustainable development, China hosts over one-third of the world’s wind power capacity (Yang et al., 2017 ; WWEA, 2021 ). In recent years, numerous wind farms have been established in the coastal areas of East China (He et al., 2016 ; Wang et al., 2019 ), which might disturb wetland bird communities. In this context, we wish to know the bird community composition in wetlands with wind farms in the region; we also wonder if all the species in the community tend to avoid the wind farms, and if not, whether we can uncover some rules underlying their responses. These issues have great significance for protecting birds in the EAAF. In this paper, we investigated the bird community in the Rudong coastal wetland of East China, where wind turbines have been installed, and analysed the relation between bird number and distance to wind farms. The main objectives of this study were to (1) provide community data for bird studies on the EAAF; (2) explore the implications of wind farms for bird communities; and (3) provide recommendations for future bird conservation in the area. Materials And Methods The study area Our research was conducted in the Rudong coastal wetland (120°56′32″~121°12′35″E, 32°29′47″~32°39′12″N), East China (Fig. 1 a). The wetland has a northern subtropical monsoon climate, with an annual mean temperature of 14.8°C and an annual mean precipitation of 1029 mm (Li et al., 2018 ). The vegetated marshes are dominated by Spartina alterniflora . Tides in the region are semidiurnal. The wetland image shown in Fig. 1 b was extracted from Landsat 8 OLI images (02:30:14Z, May 3rd, 2020), which were taken at the middle-tide level. Many birds in the EAAF, including the endangered species Calidris pygmaea (critically endangered), Calidris tenuirostris , and Platalea minor , stop in the Rudong coastal wetland during their migration (Ma and Chen, 2018 ; Yang et al., 2020 ). Thus, the wetland is well-known to birdwatchers worldwide. However, hundreds of wind turbines (capacity of each turbine ~ 3 MW, hub height ~ 90 m, rotor diameter ~ 110 m, distances between neighbouring turbines 0.6 ~ 1.2 km) have been established within and near the wetland since 2013 (Fig. 1 b), which might disturb the bird community in the area. Bird survey We used point counts to investigate the bird community in the Rudong coastal wetland (Ralph et al., 1995 ; Bibby et al., 2000 ). A total of 40 census points were randomly established in the wetland (Fig. 1 b); the points were separated by a distance of at least 400 m. The counting radius and duration at each point were 100 m and 5 min, respectively (Ralph et al., 1995 ; Bibby et al., 2000 ). Our surveys were carried out in 2019 and 2020. Because the bird migration peak in East China’s coastal wetlands occurs in spring and autumn (Ma et al., 2009 ), the counts were performed from March to May (spring) and from September to November (autumn) in the two years. The counts were conducted in the first 3 hours after dawn and at middle or low tide under fair weather conditions. The point counts were repeated 10 times per season per year. Each time 4, teams of trained observers (2 observers per team) visited the points (10 points for each team). All birds seen or heard around the points were recorded. Binoculars (Nature DX ED 10×50, Celestron, USA) were used for observation. Bird flocks were recorded by cameras (EOS 700D, Canon, Japan) with spotting scopes (Ultima 80, Celestron, USA) and were counted after the surveys. For each season, the point count result was obtained by summing the 20 replicates over the two years. The total number of a species was the sum of its numbers at the 40 points. Data preparation and analysis In this study, we divided species dominance into 4 classes according to the proportion (F) of the total number of a species to that of all species, i.e., dominant species (F > 5%), subdominant species (2% < F ≤ 5%), occasional species (1% < F ≤ 2%), and rare species (F ≤ 1%) (Lenz, 1990 ). Moreover, we divided the recorded birds into two general categories. Birds of Charadriiformes, Ciconiiformes, Anseriformes, Gruiformes, and Podicipediformes were all regarded as aquatic birds, whereas those of Passeriformes, Falconiformes, Coraciiformes, Columbiformes, Cuculiformes, and Galliformes were regarded as terrestrial birds. To investigate the spatial responses of the bird community to the wind farm, we determined the distance between each census point and the wind farm boundary (DW) in Fig. 1 b. DW was given a negative value if the point was located within the wind farm. Moreover, in this study, we considered 3 other geographical factors at the census points that influenced bird distributions (Niemuth et al., 2006 ; Chapman and Reich, 2007 ; Ma and Chen, 2018 ), i.e., the distance between each census point and the suburbs (DR), the distance between each census point and the sea (DS), and the vegetation area surrounding (100 m radius) each census point (VA). DR, DS, and VA were also determined in Fig. 1 b and were treated as controlled factors in partial correlation analysis. Partial correlation analysis was used to examine the relationship between bird number and DW; DR, DS, and VA were all regarded as controlled factors. A positive correlation indicated that the number increased with DW, whereas a negative correlation indicated a decrease. Furthermore, redundancy analysis (RDA) was conducted to sketch the responses of the bird community to the 4 factors. The point count results were standardized by Hellinger transformation before RDA (Borcard et al., 2011 ). To show the RDA results more clearly, bird species were divided into Groups A and B in each season. Species in Group A had higher RDA scores, and their scores were magnified by 2.5 times; those in Group B had lower RDA scores, and the scores were magnified by 15 times. Moreover, the census points are not shown in the plots because the geographical factors at each point can be found in Fig. 1 b. Finally, stepwise regression was used to describe the variation of bird number with DW as well as other 3 factors. Factors were eliminated from the regression equations when their significance levels were higher than 0.10. The partial correlation analysis, RDA, and stepwise regression were performed in RStudio 1.3.959. Results A total of 52,571 birds of 11 orders and 103 species were counted during the survey. A total of 30,609 birds of 88 species were recorded in spring, while 21,962 birds of 78 species were recorded in autumn (Table 1 ). The total count and species richness were all higher in spring. Charadriiformes was the dominant order in the wetland, comprising 43 species and 95.8% of the total count over the two seasons. Passeriformes comprised 39 species, which was the second highest richness. However, the passerines in the community were mostly rare species, and they comprised only 2.7% of the total count. Other recorded bird orders included Ciconiiformes, Anseriformes, Gruiformes, Podicipediformes, Coraciiformes, Columbiformes, Cuculiformes, Falconiformes, and Galliformes. These 9 orders had low species richnesses and bird numbers; they comprised only 21 species and only 1.5% of the total count. Calidris alpina was the most common species in both spring and autumn. It comprised 18.5% and 24.0% of the total counts in spring and autumn, respectively. Other dominant species included Calidris ruficollis , Charadrius alexandrines , Calidris tenuirostris , and Tringa nebularia (spring). The subdominant species included Xenus cinereus , Numenius phaeopus , Calidris acuminata , Charadrius mongolus , Charadrius leschenaultii , Calidris canutus (spring), Arenaria interpres (spring), Pluvialis squatarola (spring), Tringa nebularia (autumn), and Chroicocephalus saundersi (autumn). According to the IUCN Red List of threatened species (IUCN, 2021 ), 10 of the 103 recorded species are near threatened, 3 of them are vulnerable, 3 are endangered, and 1 is critically endangered. The 3 endangered species were Calidris tenuirostris , Numenius madagascariensis , and Platalea minor . Calidris pygmaea was the only critically endangered species. Table 1 Composition of the bird community in the Rudong coastal wetland. Species number a Spring Autumn Bird order b Scientific name c Total number d Partial correlation coefficient e Bird order b Scientific name c Total number d Partial correlation coefficient e 1 Cha. Calidris alpina (NT) 5652 +++ 0.581 Cha. Calidris alpina 5272 +++ 0.735 2 Calidris ruficollis 4808 +++ 0.319 Charadrius alexandrinus 3626 +++ 0.434 3 Charadrius alexandrinus 3780 +++ 0.398 Calidris ruficollis (NT) 3135 +++ 0.524 4 Calidris tenuirostris (EN) 2226 +++ 0.530 Calidris tenuirostris (EN) 1404 +++ 0.467 5 Tringa nebularia 1803 +++ 0.459 Calidris acuminata 1026 ++ 0.365 6 Xenus cinereus 1468 ++ 0.425 Xenus cinereus 912 ++ 0.425 7 Numenius phaeopus 1073 ++ −0.094 Tringa nebularia 745 ++ 0.262 8 Calidris acuminata 987 ++ 0.527 Numenius phaeopus 664 ++ −0.112 9 Charadrius mongolus 978 ++ 0.227 Charadrius leschenaultii 602 ++ 0.201 10 Calidris canutus (NT) 822 ++ −0.063 Charadrius mongolus 541 ++ 0.534 11 Charadrius leschenaultii 816 ++ 0.308 Chroicocephalus saundersi (VU) 469 ++ 0.303 12 Arenaria interpres 789 ++ −0.196 Calidris canutus (NT) 422 + 0.190 13 Pluvialis squatarola 657 ++ 0.393 Limosa lapponica (NT) 396 + 0.207 14 Numenius arquata (NT) 617 + −0.308 Pas. Passer montanus 327 + −0.399 15 Limosa lapponica (NT) 556 + −0.229 Cha. Numenius arquata (NT) 236 + −0.067 16 Chroicocephalus saundersi (VU) 467 + 0.046 Arenaria interpres 180 0.211 17 Tringa brevipes (NT) 382 + 0.254 Calidris alba 4 −0.398 18 Calidris alba 34 −0.320 Calidris ferruginea (NT) 27 −0.233 19 Calidris ferruginea (NT) 34 −0.284 Calidris pygmaea (CR) 8 −0.137 20 Calidris melanotos 26 −0.005 Calidris subminuta 13 −0.551 21 Calidris subminuta 12 −0.256 Chroicocephalus ridibundus 179 −0.200 22 Chroicocephalus ridibundus 197 0.115 Gelochelidon nilotica 48 0.091 23 Gallinago gallinago 7 −0.091 Haematopus ostralegus (NT) 3 0.036 24 Gelochelidon nilotica 80 −0.157 Himantopus himantopus 2 −0.070 25 Haematopus ostralegus (NT) 9 0.032 Larus canus 62 0.061 26 Himantopus himantopus 9 −0.078 Larus crassirostris 14 −0.360 27 Larus canus 58 0.127 Larus mongolicus 133 0.019 28 Larus crassirostris 17 −0.432 Limosa limosa (NT) 40 −0.213 29 Larus mongolicus 97 0.422 Numenius madagascariensis (EN) 162 −0.040 30 Limicola falcinellus 18 −0.116 Pluvialis squatarola 153 −0.187 31 Limosa limosa (NT) 268 −0.099 Recurvirostra avosetta 24 0.051 32 Numenius madagascariensis (NT) 15 0.139 Sterna hirundo 21 −0.089 33 Pluvialis fulva 216 −0.172 Sternula albifrons 9 0.012 34 Recurvirostra avosetta 18 −0.278 Tringa brevipes (NT) 100 −0.211 35 Sterna hirundo 14 0.056 Tringa erythropus 7 0.102 36 Sternula albifrons 45 −0.088 Tringa glareola 210 −0.206 37 Tringa erythropus 21 −0.082 Tringa hypoleucos 20 −0.202 38 Tringa glareola 42 −0.326 Tringa stagnatilis 18 0.080 39 Tringa hypoleucos 16 −0.080 Tringa totanus 19 −0.059 40 Tringa totanus 299 0.086 Cic. Ardea alba 10 0.117 41 Vanellus cinereus 2 0.025 Ardea cinerea 7 −0.131 42 Cic. Ardea alba 28 −0.236 Bubulcus ibis 8 0.010 43 Ardea cinerea 26 0.101 Egretta garzetta 195 0.036 44 Bubulcus ibis 8 0.028 Egretta intermedia 18 0.081 45 Egretta garzetta 168 −0.170 Ans. Anas falcata (NT) 8 −0.083 46 Egretta intermedia 108 −0.185 Anas penelope 16 0.091 47 Platalea minor (EN) 8 −0.375 Anas platyrhynchos 20 0.141 48 Ans. Anas falcata (NT) 23 0.174 Anas zonorhyncha 15 0.117 49 Anas platyrhynchos 37 −0.151 Gru. Porzana pusilla 6 −0.338 50 Anas querquedula 9 0.171 Rallus aquaticus 1 0.115 51 Anas zonorhyncha 31 −0.230 Pas. Tachybaptus ruficollis 6 −0.107 52 Gru. Fulica atra 6 −0.137 Acrocephalus bistrigiceps 4 −0.170 53 Porzana pusilla 4 0.035 Acrocephalus tangorum (VU) 1 0.042 54 Rallus aquaticus 3 −0.143 Anthus richardi 7 −0.082 55 Pod. Tachybaptus ruficollis 7 −0.227 Cecropis daurica 22 −0.165 56 Pas. Acrocephalus orientalis 4 −0.222 Cisticola juncidis 3 0.040 57 Alauda gulgula 4 0.258 Cyanopica cyanus 42 −0.220 58 Cecropis daurica 28 −0.314 Emberiza pallasi 6 −0.099 59 Cisticola juncidis 6 −0.151 Emberiza pusilla 14 0.042 60 Cyanopica cyanus 25 −0.127 Ficedula mugimaki 4 −0.176 61 Emberiza cioides 17 0.172 Fringilla montifringilla 14 −0.208 62 Emberiza rutila 15 −0.084 Hirundo rustica 21 −0.049 63 Ficedula mugimaki 6 −0.251 Lanius schach 18 −0.226 64 Ficedula zanthopygia 6 0.073 Luscinia calliope 7 0.186 65 Fringilla montifringilla 40 −0.274 Luscinia cyane 1 −0.177 66 Hirundo rustica 61 −0.332 Motacilla tschutschensis 9 −0.176 67 Lanius schach 22 −0.194 Muscicapa griseisticta 20 −0.381 68 Locustella lanceolata 4 −0.260 Muscicapa latirostris 5 −0.125 69 Locustella pleskei (VU) 8 0.231 Paradoxornis heudei (NT) 69 −0.236 70 Luscinia calliope 4 −0.178 Phylloscopus borealis 8 −0.014 71 Motacilla tschutschensis 10 −0.155 Phylloscopus coronatus 3 −0.122 72 Muscicapa griseisticta 14 −0.192 Phylloscopus tenellipes 8 0.200 73 Paradoxornis heudei (NT) 36 −0.151 Pycnonotus sinensis 10 0.088 74 Paradoxornis webbianus 8 −0.292 Remiz consobrinus 98 −0.350 75 Parus major 10 −0.173 Turdus hortulorum 6 0.028 76 Passer montanus 225 −0.216 Turdus obscurus 8 −0.078 77 Phoenicurus auroreus 8 −0.244 Cor. Halcyon pileata 1 0.299 78 Phylloscopus coronatus 6 −0.239 Gal. Phasianus colchicus 10 −0.153 79 Phylloscopus inornatus 20 −0.302 80 Phylloscopus tenellipes 20 0.033 81 Pycnonotus sinensis 32 −0.346 82 Remiz consobrinus 48 −0.149 83 Spodiopsar sericeus 7 −0.075 84 Turdus cardis 9 −0.195 85 Cor. Upupa epops 2 −0.272 86 Col. Spilopelia chinensis 1 −0.118 87 Cuc. Cuculus poliocephalus 1 −0.054 88 Fal. Falco tinnunculus 1 0.299 Total 30609 21962 a The dominant, subdominant, and occasional species are arranged according to total bird number. The rare species are arranged according to taxonomy. b Cha.: Charadriiformes; Pas.: Passeriformes; Gal.: Galliformes; Gru.: Gruiformes; Ans.: Anseriformes; Pod.: Podicipediformes; Cic.: Ciconiiformes; Cor.: Coraciiformes; Col.: Columbiformes; Cuc.: Cuculiformes; Fal.: Falconiformes. c The superscripts with brackets represent the endangered categories according to IUCN ( 2021 ). NT: near threatened; VU: vulnerable; EN: endangered; CR: critically endangered. The species of least concern have no superscript. d The superscripts +++, ++, and + indicate dominant species, subdominant species, and occasional species, respectively. Rare species have no superscript. e The partial correlation coefficient between bird number and DW. A positive value indicates that the bird number increased with DW, whereas a negative value indicates a decrease. Values in bold indicate significant correlations at the p < 0.05 level. The partial correlation coefficients showed that the birds’ responses to the wind farm varied among species. Generally, however, the numbers of most dominant and subdominant birds were positively correlated with DW. In spring, 76.9% (10 of 13) of the dominant and subdominant bird species exhibited positive correlations, and 7 of them had significant positive correlations ( p < 0.05). The percentage was 90.9% (10 of 11) in autumn, and 7 of the dominant and subdominant bird species had significant positive correlations ( p < 0.05). Moreover, the total numbers of dominant birds and subdominant birds were all significantly and positively correlated with DW ( p < 0.05) (Table 2 ), indicating that dominant and subdominant birds tended to avoid the wind farm. In contrast, the numbers of most rare birds were negatively correlated with DW. A total of 73.2% (52 of 71) and 60.3% (38 of 63) of the rare bird species exhibited negative correlations in spring and autumn, respectively. However, most of these correlations were nonsignificant because the rare birds were low in number and were recorded at only a few points. Nevertheless, the total number of rare birds exhibited a significant negative correlation ( p < 0.05) with DW (Table 2 ), indicating that rare birds tended to approach the wind farm. The partial correlation coefficients of occasional birds, however, showed no obvious tendencies (Tables 1 and 2 ). Their responses to the wind farm were unclear and might be a transitional type between those of dominant/subdominant birds and rare birds. The rare birds in the wetland comprised terrestrial birds and aquatic birds. These two groups had different responses to the wind farm. As shown in Table 1 , 81.8% (27 of 33) and 70.4% (19 of 27) of the terrestrial bird species exhibited negative correlations with DW in spring and autumn, respectively. Moreover, the total number of terrestrial birds had a significant negative correlation with DW ( p < 0.05) (Table 2 ). By comparison, only 65.8% (25 of 38) and 52.8% (19 of 36) of the aquatic birds were negatively correlated with DW in spring and autumn, respectively, and the correlation between the total number of terrestrial birds and DW was nonsignificant ( p > 0.05). The above comparisons indicate that the aquatic birds were more alert to the wind farms, whereas the terrestrial birds better adapted to them. Table 2 Partial correlations between the total numbers of birds of different dominances and categories and the DW. Dominance and category Partial correlation coefficient a Spring Autumn Dominant 0.543 0.700 Subdominant 0.337 0.403 Occasional −0.129 0.030 Rare −0.428 −0.343 Terrestrial b −0.208 −0.212 Aquatic b −0.391 −0.329 a Values in bold indicate significant correlations at the p < 0.05 level. b Terrestrial and aquatic birds of rare species in the community. Discussion Bird community composition and the endangered species The dominant and subdominant bird species in the Rudong coastal wetland were all members of Charadriiformes. Most of the dominant and subdominant birds, such Calidris alpina , Calidris ruficollis , and Charadrius alexandrines , are common species in the West Pacific (IUCN, 2021 ). The dominant species recorded in this study were generally consistent with those detected in previous shorebird surveys of nearby wetlands (20 ~ 50 km from our study area, without wind farms) in spring and autumn (Peng et al., 2017 ). However, previous surveys indicated that the dominant species accounted for 72.3 ~ 94.6% of the total number of aquatic birds (Peng et al., 2017 ), whereas the proportions were ~ 62% in this study. The proportions were much lower in our study area, probably because many dominant birds had been driven away by the wind farm. Despite low bird number, the rare species comprised ~ 80% species in the entire community in the Rudong coastal wetland. In addition to aquatic birds, terrestrial birds (mostly Passeriformes) were also common among the rare species, accounting for ~ 40% of them. Thus, we conclude that the wetland is rich in both aquatic and terrestrial bird species. Moreover, the proportion of rare birds (~ 8%) in our study (for comparison, only aquatic birds were considered) was much higher than that (~ 3%) in nearby wetlands (20 ~ 180 km from our study area, without wind farms) (Ma et al., 2006 ; Ge et al., 2009 ; Peng et al., 2017 ), which indicates that rare birds were less affected by the wind farm than dominant birds. Four endangered species, i.e., Calidris tenuirostris , Numenius madagascariensis , Platalea minor , and Calidris pygmaea were recorded during our survey. These 4 species breed in Eastern Siberia or Northeast China and winter in Southeast Asia to Australia. Recent reports showed that the estimated population sizes of Numenius madagascariensis , Platalea minor , and Calidris pygmaea in the EAAF were ~ 32 thousand, ~ 3500, and ~ 450, respectively (Ma and Chen, 2018 ; IUCN, 2021 ). Calidris tenuirostris has a larger population consisting of ~ 290 thousand individuals (Ma and Chen, 2018 ). Despite the differences, the population sizes of all the species are decreasing, and habitat loss is the main reason for the decreases (IUCN, 2021 ). Thus, wind farm construction at their stopover sites might influence their future survival. In this study, Numenius madagascariensis , Platalea minor , and Calidris pygmaea were rarely observed in the Rudong coastal wetland, but Calidris tenuirostris was still a dominant species in the area. Correspondingly, their responses to the wind farm were different. Spatial responses of the bird community to the wind farm Our results showed that birds’ responses to wind farms might vary depending on their dominance and category. Two tendencies were concluded from our results. First, the dominant and subdominant species (‘dominant and subdominant’ is written as ‘dominant’ below) tended to avoid the wind farm, whereas the rare species tended to approach them. Second, terrestrial birds were more adaptable than aquatic birds to the wind farm. The variation in responses with dominance might be related to the following reasons. The dominant species, which are characterized by very high numbers of individuals, often fly in large groups, whereas the rare species fly singly or in small groups. Previous studies reported that larger bird groups have higher collision risks with obstacles because they have more social interactions (Croft et al., 2013 ; Croft et al., 2015 ), which can filter the information of obstacle cues and then disturb individuals’ avoidance (Croft et al., 2013 ; Croft et al., 2015 ). Thus, the dominant species tended to avoid high collision risks, whereas the rare species could better avoid collisions when flying in the wind farm. Another reason for this phenomenon might be interspecific competition. Many studies have shown that dominant species usually govern the optimal resource, and subordinate species are often driven to seek novel resources to reduce competition (Pimm and Pimm, 1982 ; McKinney et al., 2011 ; Freshwater et al., 2014 ). In this study, the dominant species were more concentrated in the undisturbed portion of the wetland; thus, many rare species chose to forage in or near the wind farm, which were less utilized by the dominant species. In this respect, we think that wind farms might act as refuges for rare species. The difference in responses between aquatic and terrestrial birds might be related to their morphologies. Aquatic birds, which mainly inhabit open seashores, usually have high wing aspect ratios, i.e., long and narrow wings (Norberg, 2004 ; Sheard et al., 2020 ). This wing form has a high lift-to-drag ratio and smaller wing − tip vortices, making it more suitable for gliding, soaring, and continuous flight (Norberg, 2004 ). However, long and narrow wings have low aerodynamic roll torque and a high moment of inertia, which reduces flight manoeuvrability and result in a higher turn radius and longer take-off distance (Norberg, 2004 ; McFarlane, 2014 ). Moreover, aquatic birds usually have short tails (Thomas and Balmford, 1995 ; Thomas, 1997 ), which is disadvantageous for maintaining stability and balance in flight and turning (Thomas and Balmford, 1995 ). In conclusion, aquatic birds have low flight manoeuvrability, which may hinder them from avoiding wind turbines and incline them to stay away from wind farms. In contrast, terrestrial birds inhabit cluttered environments such as forest and spend much of their foraging time climbing, clinging and hanging. They usually have low wing aspect ratios (i.e., broad and rounded wings) and long tails, which are more suitable for manoeuvrable short flights (Norberg, 2004 ). Correspondingly, terrestrial birds can better avoid obstacles such as wind turbines; thus, they are more adaptable to wind farms. According to the above analysis, we conclude that the dominant aquatic birds were most negatively impacted by the wind farm. These birds include the endangered species Calidris tenuirostris and some vulnerable and near threatened species. In contrast, the rare terrestrial birds were least disturbed by even benefited from the wind farm in some respect. The situation was more complex for rare aquatic birds. Their group sizes result in low collision risk, and they have fewer dominant competitors in the wind farm. On the other hand, their morphology is disadvantageous for flying in wind farms. Thus, similar to those of the occasional species, their responses exhibited more uncertainty. Responses of the bird community to the 4 geographical factors The RDA results showed the approximate responses of the bird community to DW, DR, DS, and VA. Figure 2 indicates that the responses to DR, DS, and VA differed considerably between aquatic and terrestrial birds, but they did not vary significantly depending on dominance. For most aquatic birds, the numbers tended to increase with a decrease in DS and with increases in DR and VA, indicating that aquatic birds tended to occur in low tidal flats and bare lands and tended to avoid suburbs. The terrestrial birds, however, exhibited the reverse response; they were more likely to occur in the high marsh, vegetated areas, and areas near the suburbs. The above differences occurred because aquatic birds mainly feed on the macrobenthos, fishes, and aquatic plants (Collis et al., 2002 ; Wade and Hickey, 2008 ; Ma and Chen, 2018 ), which are mainly distributed in low bare flats and shallow water, whereas terrestrial birds mainly feed on Arthropoda and seeds, which are concentrated in the high marsh and vegetated areas (Muñoz et al., 2017 ). Moreover, the terrestrial birds in the wetland mainly came from the suburbs, whereas the aquatic birds were more unfamiliar with them, which induced their different responses to DR. Fig.2 also shows that the biplot scores of DW are lower than those of DR, DS, and VA (i.e., the arrow length of DW is shorter than those of DR, DS, and VA), indicating that the contribution of DW to the spatial variation in the community is lower than those of the other 3 factors. Consequently, the bird responses to the wind farm inferred from partial correlation analysis are not obvious in Fig. 2 because the responses are covered by the effects of DR, DS, and VA. Based on the results of partial correlation analysis and RDA, we conclude that the bird community exhibits notable responses to the wind farm, but the responses are still inferior to those to suburbs, the sea and vegetation. The stepwise regression equations described the variations of total bird numbers of different dominances and categories with the geographical factors (Table 3 ). The results indicate that the coefficient of DW was positive for dominant and subdominant birds and negative for rare birds; moreover, DW was eliminated from the equations of occasional and rare aquatic birds because the responses of these birds to wind farm were nonsignificant. The above results are consistent with those of partial correlation analysis. In addition, the coefficients of the other 3 factors are generally consistent with the RDA results. The occasional birds in autumn have no regression result, because their distribution exhibited great uncertainties and none of the factors could explain the variation of their number. Table 3 Stepwise regression equations between the total numbers of birds of different dominances and categories and the geographical factors. Dominance and category Spring Autumn Equation a p R 2 Equation a p R 2 Dominant N = 0.070DW + 0.209DR − 0.017VA + 297 < 0.001 0.646 N = 0.054DW + 0.098DR − 0.043DS − 0.011VA + 308 < 0.001 0.757 Subdominant N = 0.020DW + 0.099DR − 0.009VA + 133 < 0.001 0.609 N = 0.019DW + 0.059DR − 0.006VA + 93 < 0.001 0.536 Occasional N = 0.028DR − 0.010DS − 0.002VA + 39 < 0.001 0.546 − − − Rare N = − 0.008DW + 0.013DR − 0.001VA + 67 0.001 0.311 N = − 0.005DW − 0.002VA + 71 0.001 0.299 Terrestrial b N = − 0.004DW − 0.013DR + 0.011DS + 26 < 0.001 0.505 N = − 0.002DW − 0.007DR + 0.006DS + 15 < 0.001 0.522 Aquatic b N = 0.025DR − 0.014DS − 0.001VA + 44 < 0.001 0.447 N = 0.013DR − 0.011DS − 0.002VA + 52 0.001 0.291 a N : total bird number; DW: distance to the wind farm boundary; DR: distance to the suburbs; DS: distance to the sea; VA: vegetation area surrounding each census point. b Terrestrial and aquatic birds of rare species in the community. Conclusion As an important stopover site of birds in the EAAF, the Rudong coastal wetland comprises rich bird species. Charadriiformes accounts for an extremely high proportion of the total bird number, and Charadriiformes and Passeriformes comprise the majority of the species in the community. Numerous studies have demonstrated negative effects of wind farms on birds, which indicates a conflict between bird conservation and wind power development in coastal areas of East China. Nevertheless, our study suggested that birds’ responses to wind farms might vary according to their dominance and category. The most negatively impacted birds were the dominant aquatic birds in the wetlands. These birds included the endangered species Calidris tenuirostris , the vulnerable species Chroicocephalus saundersi , and some near threatened species. Wind farms might compress the habitats of these species and then contribute to future population declines. Thus, the protection degrees of these species in the area should be upgraded. We think that retaining sufficient undisturbed intertidal mudflats, which are major foraging places for these birds, will be crucial for maintaining their population sizes in the future. The occasional birds and rare aquatic birds in the area, including the critically endangered species Calidris pygmaea , the endangered species Numenius madagascariensis and Platalea minor , and various vulnerable and near threatened species, exhibit great uncertainties in their responses to wind farms. They might be disturbed by wind farms but not significantly so. We think that monitoring their population dynamics in the area is a major task for the short term. Finally, the rare terrestrial birds in the area might accept the wind farms as refuges under interspecific competition, and they can better adapt to the wind farm environment. Thus, we conclude that these species, including the vulnerable species Acrocephalus tangorum and Locustella pleskei , would be least disturbed by or would even benefit somewhat from future wind farm construction. Wind power development has altered the wetland environment and bird habitat selection in the Rudong coastal wetland. Our two-year observation of the area will provide a guide for future bird conservation in wetlands with wind farms. Declarations Availability of Data and Material The Landsat 8 OLI image used here are available at http://eds.ceode.ac.cn/nuds/freedataquery . Acknowledgements We sincerely thank the editors and the anonymous reviewers for their valuable comments and suggestions for this manuscript. We also thank Shanshan Chang, Lijuan Chen, Chuangqi Hu, and Yuqiao Hou for their help in our bird survey. We greatly appreciate Nanjing Normal University to support this work. Author Contributions YC contributed to the study conception and design. Data acquisition was performed by YC, YZ, GW, DD and data analysis were performed by WZ and CT. The original draft was written by YC and YZ reviewed and edited subsequent versions of the manuscript. Funding This work was supported by the National Natural Science Foundation of China (No. 41671428) and Nanjing Normal University. Data Availability All data are presented in the paper Code Availability The R-code generated and analyzed is available upon request from the corresponding author. Ethics approval This paper contains findings of our original research. The Law of the People’s Republic of China on the Protection of Wildlife and IUCN Policy Statement on Research Involving Species at Risk of Extinction were followed during the study. No animal was harmed or captured. No animal or plant samples were taken from the study area. We adhered to the ethical standards in this study and in production of this manuscript. 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Bird Conservation International 25(1):1–37 Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2021 Read the published version in Community Ecology → Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-573013","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":31293188,"identity":"5c3b5e99-b83e-4cb7-8b42-fd2a2600db2c","order_by":0,"name":"Yinrui Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYPACCTl+9uYDBz78IF6LhbFkz7HEgzN7iNdSkbjhho/xYQ42ItTyz8h9+OFHjQRjww2eD4cZeBjk+cUO4NcicSMd5CYJZsbZvRsOF1gwGM6cnYBfi4FEGhszY4MEG7PM2Q2HZ/AwJBjcJlILD5tEzoPDPGwkaJHgkchhIE6LxJlnzCC/GEjwHDMABrIEYb/wt6cxAkOsrn7/8ebHHz78sJHnlyaghUEAVYEEAeVgaw4QoWgUjIJRMApGNgAAheNBleM3A4sAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8754-3304","institution":"Nanjing Normal University School of Geography","correspondingAuthor":true,"prefix":"","firstName":"Yinrui","middleName":"","lastName":"Cheng","suffix":""},{"id":31293189,"identity":"90561b88-3b76-4c25-9c3e-18772cc69e4f","order_by":1,"name":"Yong Zha","email":"","orcid":"","institution":"Nanjing Normal University School of Geography","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Zha","suffix":""},{"id":31293190,"identity":"e2636837-27cd-4dc6-ab16-86ec2d458f9b","order_by":2,"name":"Wenmin Zhang","email":"","orcid":"","institution":"Nanjing Normal University School of Geography","correspondingAuthor":false,"prefix":"","firstName":"Wenmin","middleName":"","lastName":"Zhang","suffix":""},{"id":31293191,"identity":"c9a70bb1-357f-4a53-9189-affc08349671","order_by":3,"name":"Geng Wei","email":"","orcid":"","institution":"Nanjing Normal University School of Geography","correspondingAuthor":false,"prefix":"","firstName":"Geng","middleName":"","lastName":"Wei","suffix":""},{"id":31293192,"identity":"40c68b72-81e9-4f16-8e17-1c10cf56db18","order_by":4,"name":"Chuan Tong","email":"","orcid":"","institution":"Fujian Normal University","correspondingAuthor":false,"prefix":"","firstName":"Chuan","middleName":"","lastName":"Tong","suffix":""},{"id":31293193,"identity":"a0a050f3-c8cd-44ac-988d-521d4cf5de92","order_by":5,"name":"Dandan Du","email":"","orcid":"","institution":"Anhui Normal University","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2021-05-29 18:12:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-573013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-573013/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42974-021-00065-4","type":"published","date":"2021-11-15T21:49:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":10296357,"identity":"99b860f8-35d1-464d-b9ce-c917f2458f1e","added_by":"auto","created_at":"2021-06-12 16:30:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70829,"visible":true,"origin":"","legend":"(a) Location of the study area and (b) distribution of census points and wind turbines in the Rudong coastal wetland.\n\nNote: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-573013/v1/87cbaee6b18c490f5f0c4c3a.jpg"},{"id":10295715,"identity":"7e3baddd-d43e-4105-98d9-3bc1fde6197e","added_by":"auto","created_at":"2021-06-12 16:27:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73526,"visible":true,"origin":"","legend":"Results of redundancy analysis of bird numbers and geographical factors. (a) Spring: Group A; (b) Spring: Group B; (c) Autumn: Group A; (d) Autumn: Group B. The species scores in Group A have been magnified by 2.5 times, and those in Group B have been magnified by 15 times. The colours of the circles indicate the dominance of the species: red, orange, green, and purple represent the dominant, subdominant, occasional, and rare species, respectively. The solid circles indicate aquatic birds, whereas the open circles indicate terrestrial birds. Numbers beside the circles are the species numbers in Table 1. DW: distance to the wind farm boundary; DR: distance to the suburb; DS: distance to the sea; VA: vegetation area surrounding each census point.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-573013/v1/1aede2a2dc8b813517994a5b.jpg"},{"id":15568518,"identity":"359e546b-8290-4c00-8f44-1edf5f98ca92","added_by":"auto","created_at":"2021-11-15 21:49:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":816500,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-573013/v1/d8fde606-4fcd-4c75-98ef-06ae0b0bf914.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eThe Bird Community in a Coastal Wetland in East China and Its Spatial Responses to a Wind Farm\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eThe coastal wetlands in East China are essential stopover places for birds along the East Asian-Australian Flyway (EAAF) (Cao et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yong et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Numerous aquatic birds stop in these wetlands to accumulate energy reserves before or after they fly over the Yellow Sea (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) (Ma et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); moreover, some terrestrial birds also rest in these wetlands during their migration (Yong et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For threatened species in the flyway, such as \u003cem\u003eCalidris pygmaea\u003c/em\u003e and \u003cem\u003eTringa guttifer\u003c/em\u003e, these wetlands are crucial for future survival (Peng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In recent decades, bird surveys have been conducted in some coastal wetlands of East China (Ma et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, most of these surveys focused on shorebirds or endangered species, whereas investigations of entire communities are limited. Moreover, in recent years, birds in the area have been threatened by wetland loss caused by agricultural and industrial development (Lin and Yu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jackson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, further observations of the bird community are necessary for bird conservation in the area.\u003c/p\u003e \u003cp\u003eWind energy is increasingly used for electricity generation because it is clean and renewable (Herbert et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Recent reports have shown that the capacity of the world\u0026rsquo;s wind farms is growing at ~\u0026thinsp;10% per year and reached 744 GW by the end of 2020, covering more than 7% of the global electricity demand (WWEA, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite the benefits of wind power generation, considerable research has shown that wind turbines are disadvantageous to birds. First, birds can be killed by collisions with wind turbines (Newton and Little, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Grodsky et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Bird carcass collection in a coastal wind farm showed that collisions accounted for ~\u0026thinsp;3% of bird deaths (Newton and Little, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Second, birds tend to avoid wind farms behaviourally (Plonczkier and Simms, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Villegas-Patraca et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Specifically, they usually change their migration routes and stopover sites due to wind farms, and this expenditure of energy could induce an increase in fatal casualties during their migration (Hilgerloh et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Third, constructions of wind turbines and attached facilities can induce fragmentation and functional loss of bird habitat (Pruett et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Marques et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is essential for bird foraging and breeding.\u003c/p\u003e \u003cp\u003eChina is the largest energy consumer worldwide (Yang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In terms of sustainable development, China hosts over one-third of the world\u0026rsquo;s wind power capacity (Yang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; WWEA, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In recent years, numerous wind farms have been established in the coastal areas of East China (He et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which might disturb wetland bird communities. In this context, we wish to know the bird community composition in wetlands with wind farms in the region; we also wonder if all the species in the community tend to avoid the wind farms, and if not, whether we can uncover some rules underlying their responses. These issues have great significance for protecting birds in the EAAF. In this paper, we investigated the bird community in the Rudong coastal wetland of East China, where wind turbines have been installed, and analysed the relation between bird number and distance to wind farms. The main objectives of this study were to (1) provide community data for bird studies on the EAAF; (2) explore the implications of wind farms for bird communities; and (3) provide recommendations for future bird conservation in the area.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe study area\u003c/h2\u003e \u003cp\u003eOur research was conducted in the Rudong coastal wetland (120\u0026deg;56\u0026prime;32\u0026Prime;~121\u0026deg;12\u0026prime;35\u0026Prime;E, 32\u0026deg;29\u0026prime;47\u0026Prime;~32\u0026deg;39\u0026prime;12\u0026Prime;N), East China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The wetland has a northern subtropical monsoon climate, with an annual mean temperature of 14.8\u0026deg;C and an annual mean precipitation of 1029 mm (Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The vegetated marshes are dominated by \u003cem\u003eSpartina alterniflora\u003c/em\u003e. Tides in the region are semidiurnal. The wetland image shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb was extracted from Landsat 8 OLI images (02:30:14Z, May 3rd, 2020), which were taken at the middle-tide level.\u003c/p\u003e \u003cp\u003eMany birds in the EAAF, including the endangered species \u003cem\u003eCalidris pygmaea\u003c/em\u003e (critically endangered), \u003cem\u003eCalidris tenuirostris\u003c/em\u003e, and \u003cem\u003ePlatalea minor\u003c/em\u003e, stop in the Rudong coastal wetland during their migration (Ma and Chen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, the wetland is well-known to birdwatchers worldwide. However, hundreds of wind turbines (capacity of each turbine\u0026thinsp;~\u0026thinsp;3 MW, hub height\u0026thinsp;~\u0026thinsp;90 m, rotor diameter\u0026thinsp;~\u0026thinsp;110 m, distances between neighbouring turbines 0.6\u0026thinsp;~\u0026thinsp;1.2 km) have been established within and near the wetland since 2013 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), which might disturb the bird community in the area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBird survey\u003c/h2\u003e \u003cp\u003eWe used point counts to investigate the bird community in the Rudong coastal wetland (Ralph et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Bibby et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). A total of 40 census points were randomly established in the wetland (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb); the points were separated by a distance of at least 400 m. The counting radius and duration at each point were 100 m and 5 min, respectively (Ralph et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Bibby et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Our surveys were carried out in 2019 and 2020. Because the bird migration peak in East China\u0026rsquo;s coastal wetlands occurs in spring and autumn (Ma et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the counts were performed from March to May (spring) and from September to November (autumn) in the two years. The counts were conducted in the first 3 hours after dawn and at middle or low tide under fair weather conditions. The point counts were repeated 10 times per season per year. Each time 4, teams of trained observers (2 observers per team) visited the points (10 points for each team). All birds seen or heard around the points were recorded. Binoculars (Nature DX ED 10\u0026times;50, Celestron, USA) were used for observation. Bird flocks were recorded by cameras (EOS 700D, Canon, Japan) with spotting scopes (Ultima 80, Celestron, USA) and were counted after the surveys. For each season, the point count result was obtained by summing the 20 replicates over the two years. The total number of a species was the sum of its numbers at the 40 points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData preparation and analysis\u003c/h2\u003e \u003cp\u003eIn this study, we divided species dominance into 4 classes according to the proportion (F) of the total number of a species to that of all species, i.e., dominant species (F\u0026thinsp;\u0026gt;\u0026thinsp;5%), subdominant species (2% \u0026lt; F\u0026thinsp;\u0026le;\u0026thinsp;5%), occasional species (1% \u0026lt; F\u0026thinsp;\u0026le;\u0026thinsp;2%), and rare species (F\u0026thinsp;\u0026le;\u0026thinsp;1%) (Lenz, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Moreover, we divided the recorded birds into two general categories. Birds of Charadriiformes, Ciconiiformes, Anseriformes, Gruiformes, and Podicipediformes were all regarded as aquatic birds, whereas those of Passeriformes, Falconiformes, Coraciiformes, Columbiformes, Cuculiformes, and Galliformes were regarded as terrestrial birds.\u003c/p\u003e \u003cp\u003eTo investigate the spatial responses of the bird community to the wind farm, we determined the distance between each census point and the wind farm boundary (DW) in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. DW was given a negative value if the point was located within the wind farm. Moreover, in this study, we considered 3 other geographical factors at the census points that influenced bird distributions (Niemuth et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Chapman and Reich, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ma and Chen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), i.e., the distance between each census point and the suburbs (DR), the distance between each census point and the sea (DS), and the vegetation area surrounding (100 m radius) each census point (VA). DR, DS, and VA were also determined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and were treated as controlled factors in partial correlation analysis.\u003c/p\u003e \u003cp\u003ePartial correlation analysis was used to examine the relationship between bird number and DW; DR, DS, and VA were all regarded as controlled factors. A positive correlation indicated that the number increased with DW, whereas a negative correlation indicated a decrease. Furthermore, redundancy analysis (RDA) was conducted to sketch the responses of the bird community to the 4 factors. The point count results were standardized by Hellinger transformation before RDA (Borcard et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). To show the RDA results more clearly, bird species were divided into Groups A and B in each season. Species in Group A had higher RDA scores, and their scores were magnified by 2.5 times; those in Group B had lower RDA scores, and the scores were magnified by 15 times. Moreover, the census points are not shown in the plots because the geographical factors at each point can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. Finally, stepwise regression was used to describe the variation of bird number with DW as well as other 3 factors. Factors were eliminated from the regression equations when their significance levels were higher than 0.10. The partial correlation analysis, RDA, and stepwise regression were performed in RStudio 1.3.959.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cp\u003eA total of 52,571 birds of 11 orders and 103 species were counted during the survey. A total of 30,609 birds of 88 species were recorded in spring, while 21,962 birds of 78 species were recorded in autumn (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The total count and species richness were all higher in spring.\u003c/p\u003e \u003cp\u003eCharadriiformes was the dominant order in the wetland, comprising 43 species and 95.8% of the total count over the two seasons. Passeriformes comprised 39 species, which was the second highest richness. However, the passerines in the community were mostly rare species, and they comprised only 2.7% of the total count. Other recorded bird orders included Ciconiiformes, Anseriformes, Gruiformes, Podicipediformes, Coraciiformes, Columbiformes, Cuculiformes, Falconiformes, and Galliformes. These 9 orders had low species richnesses and bird numbers; they comprised only 21 species and only 1.5% of the total count.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCalidris alpina\u003c/em\u003e was the most common species in both spring and autumn. It comprised 18.5% and 24.0% of the total counts in spring and autumn, respectively. Other dominant species included \u003cem\u003eCalidris ruficollis\u003c/em\u003e, \u003cem\u003eCharadrius alexandrines\u003c/em\u003e, \u003cem\u003eCalidris tenuirostris\u003c/em\u003e, and \u003cem\u003eTringa nebularia\u003c/em\u003e (spring). The subdominant species included \u003cem\u003eXenus cinereus\u003c/em\u003e, \u003cem\u003eNumenius phaeopus\u003c/em\u003e, \u003cem\u003eCalidris acuminata\u003c/em\u003e, \u003cem\u003eCharadrius mongolus\u003c/em\u003e, \u003cem\u003eCharadrius leschenaultii\u003c/em\u003e, \u003cem\u003eCalidris canutus\u003c/em\u003e (spring), \u003cem\u003eArenaria interpres\u003c/em\u003e (spring), \u003cem\u003ePluvialis squatarola\u003c/em\u003e (spring), \u003cem\u003eTringa nebularia\u003c/em\u003e (autumn), and \u003cem\u003eChroicocephalus saundersi\u003c/em\u003e (autumn).\u003c/p\u003e \u003cp\u003eAccording to the IUCN Red List of threatened species (IUCN, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), 10 of the 103 recorded species are near threatened, 3 of them are vulnerable, 3 are endangered, and 1 is critically endangered. The 3 endangered species were \u003cem\u003eCalidris tenuirostris\u003c/em\u003e, \u003cem\u003eNumenius madagascariensis\u003c/em\u003e, and \u003cem\u003ePlatalea minor\u003c/em\u003e. \u003cem\u003eCalidris pygmaea\u003c/em\u003e was the only critically endangered species.\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\u003eComposition of the bird community in the Rudong coastal wetland.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies number \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBird order \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScientific name \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal number \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePartial correlation coefficient \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBird order \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScientific name \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal number \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePartial correlation coefficient \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"40\" rowspan=\"41\"\u003e \u003cp\u003eCha.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCalidris alpina\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5652 \u003csup\u003e+++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.581\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"12\" rowspan=\"13\"\u003e \u003cp\u003eCha.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCalidris alpina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5272 \u003csup\u003e+++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.735\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCalidris ruficollis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4808 \u003csup\u003e+++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCharadrius alexandrinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3626 \u003csup\u003e+++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.434\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCharadrius alexandrinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3780 \u003csup\u003e+++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.398\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCalidris ruficollis\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3135 \u003csup\u003e+++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.524\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCalidris tenuirostris\u003c/em\u003e \u003csup\u003e(EN)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2226 \u003csup\u003e+++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.530\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCalidris tenuirostris\u003c/em\u003e \u003csup\u003e(EN)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1404 \u003csup\u003e+++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.467\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTringa nebularia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1803 \u003csup\u003e+++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.459\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCalidris acuminata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1026 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.365\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eXenus cinereus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1468 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.425\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eXenus cinereus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e912 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.425\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNumenius phaeopus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1073 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTringa nebularia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e745 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCalidris acuminata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e987 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.527\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eNumenius phaeopus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e664 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCharadrius mongolus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e978 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCharadrius leschenaultii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e602 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCalidris canutus\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e822 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCharadrius mongolus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e541 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.534\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCharadrius leschenaultii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e816 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eChroicocephalus saundersi\u003c/em\u003e \u003csup\u003e(VU)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e469 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eArenaria interpres\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e789 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCalidris canutus\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e422 \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePluvialis squatarola\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e657 \u003csup\u003e++\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.393\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eLimosa lapponica\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e396 \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNumenius arquata\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e617 \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePasser montanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e327 \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.399\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLimosa lapponica\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e556 \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"24\" rowspan=\"25\"\u003e \u003cp\u003eCha.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eNumenius arquata\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e236 \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChroicocephalus saundersi\u003c/em\u003e \u003csup\u003e(VU)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467 \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eArenaria interpres\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTringa brevipes\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382 \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCalidris alba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.398\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCalidris alba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCalidris ferruginea\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCalidris ferruginea\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCalidris pygmaea\u003c/em\u003e \u003csup\u003e(CR)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCalidris melanotos\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCalidris subminuta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.551\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCalidris subminuta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eChroicocephalus ridibundus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChroicocephalus ridibundus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eGelochelidon nilotica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGallinago gallinago\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eHaematopus ostralegus\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGelochelidon nilotica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eHimantopus himantopus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHaematopus ostralegus\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eLarus canus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHimantopus himantopus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eLarus crassirostris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.360\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLarus canus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eLarus mongolicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLarus crassirostris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.432\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eLimosa limosa\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLarus mongolicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.422\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eNumenius madagascariensis\u003c/em\u003e \u003csup\u003e(EN)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLimicola falcinellus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePluvialis squatarola\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLimosa limosa\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eRecurvirostra avosetta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNumenius madagascariensis\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSterna hirundo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePluvialis fulva\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSternula albifrons\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRecurvirostra avosetta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTringa brevipes\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSterna hirundo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTringa erythropus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSternula albifrons\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTringa glareola\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTringa erythropus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTringa hypoleucos\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTringa glareola\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.326\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTringa stagnatilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTringa hypoleucos\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTringa totanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTringa totanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCic.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eArdea alba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVanellus cinereus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eArdea cinerea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eCic.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eArdea alba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eBubulcus ibis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eArdea cinerea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eEgretta garzetta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBubulcus ibis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eEgretta intermedia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEgretta garzetta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAns.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAnas falcata\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEgretta intermedia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAnas penelope\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePlatalea minor\u003c/em\u003e \u003csup\u003e(EN)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.375\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAnas platyrhynchos\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAns.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAnas falcata\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAnas zonorhyncha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAnas platyrhynchos\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGru.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePorzana pusilla\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.338\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAnas querquedula\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eRallus aquaticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAnas zonorhyncha\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"25\" rowspan=\"26\"\u003e \u003cp\u003ePas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTachybaptus ruficollis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGru.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFulica atra\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAcrocephalus bistrigiceps\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePorzana pusilla\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAcrocephalus tangorum\u003c/em\u003e \u003csup\u003e(VU)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRallus aquaticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAnthus richardi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePod.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTachybaptus ruficollis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCecropis daurica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"28\" rowspan=\"29\"\u003e \u003cp\u003ePas.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAcrocephalus orientalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCisticola juncidis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAlauda gulgula\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCyanopica cyanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCecropis daurica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eEmberiza pallasi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCisticola juncidis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eEmberiza pusilla\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCyanopica cyanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eFicedula mugimaki\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEmberiza cioides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eFringilla montifringilla\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEmberiza rutila\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eHirundo rustica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFicedula mugimaki\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eLanius schach\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFicedula zanthopygia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eLuscinia calliope\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFringilla montifringilla\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eLuscinia cyane\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHirundo rustica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.332\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eMotacilla tschutschensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLanius schach\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eMuscicapa griseisticta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.381\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLocustella lanceolata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eMuscicapa latirostris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLocustella pleskei\u003c/em\u003e \u003csup\u003e(VU)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eParadoxornis heudei\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLuscinia calliope\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus borealis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMotacilla tschutschensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus coronatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMuscicapa griseisticta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus tenellipes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eParadoxornis heudei\u003c/em\u003e \u003csup\u003e(NT)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePycnonotus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eParadoxornis webbianus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eRemiz consobrinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.350\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eParus major\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTurdus hortulorum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePasser montanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTurdus obscurus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePhoenicurus auroreus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCor.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eHalcyon pileata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus coronatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGal.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePhasianus colchicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus inornatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePhylloscopus tenellipes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePycnonotus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.346\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRemiz consobrinus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSpodiopsar sericeus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTurdus cardis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCor.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eUpupa epops\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCol.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSpilopelia chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCuc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCuculus poliocephalus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFal.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFalco tinnunculus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e The dominant, subdominant, and occasional species are arranged according to total bird number. The rare species are arranged according to taxonomy.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003e Cha.: Charadriiformes; Pas.: Passeriformes; Gal.: Galliformes; Gru.: Gruiformes; Ans.: Anseriformes; Pod.: Podicipediformes; Cic.: Ciconiiformes; Cor.: Coraciiformes; Col.: Columbiformes; Cuc.: Cuculiformes; Fal.: Falconiformes.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ec\u003c/sup\u003e The superscripts with brackets represent the endangered categories according to IUCN (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). NT: near threatened; VU: vulnerable; EN: endangered; CR: critically endangered. The species of least concern have no superscript.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ed\u003c/sup\u003e The superscripts +++, ++, and +\u0026thinsp;indicate dominant species, subdominant species, and occasional species, respectively. Rare species have no superscript.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ee\u003c/sup\u003e The partial correlation coefficient between bird number and DW. A positive value indicates that the bird number increased with DW, whereas a negative value indicates a decrease. Values in bold indicate significant correlations at the \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe partial correlation coefficients showed that the birds\u0026rsquo; responses to the wind farm varied among species. Generally, however, the numbers of most dominant and subdominant birds were positively correlated with DW. In spring, 76.9% (10 of 13) of the dominant and subdominant bird species exhibited positive correlations, and 7 of them had significant positive correlations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The percentage was 90.9% (10 of 11) in autumn, and 7 of the dominant and subdominant bird species had significant positive correlations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, the total numbers of dominant birds and subdominant birds were all significantly and positively correlated with DW (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that dominant and subdominant birds tended to avoid the wind farm.\u003c/p\u003e \u003cp\u003eIn contrast, the numbers of most rare birds were negatively correlated with DW. A total of 73.2% (52 of 71) and 60.3% (38 of 63) of the rare bird species exhibited negative correlations in spring and autumn, respectively. However, most of these correlations were nonsignificant because the rare birds were low in number and were recorded at only a few points. Nevertheless, the total number of rare birds exhibited a significant negative correlation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with DW (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that rare birds tended to approach the wind farm. The partial correlation coefficients of occasional birds, however, showed no obvious tendencies (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Their responses to the wind farm were unclear and might be a transitional type between those of dominant/subdominant birds and rare birds.\u003c/p\u003e \u003cp\u003eThe rare birds in the wetland comprised terrestrial birds and aquatic birds. These two groups had different responses to the wind farm. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 81.8% (27 of 33) and 70.4% (19 of 27) of the terrestrial bird species exhibited negative correlations with DW in spring and autumn, respectively. Moreover, the total number of terrestrial birds had a significant negative correlation with DW (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By comparison, only 65.8% (25 of 38) and 52.8% (19 of 36) of the aquatic birds were negatively correlated with DW in spring and autumn, respectively, and the correlation between the total number of terrestrial birds and DW was nonsignificant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The above comparisons indicate that the aquatic birds were more alert to the wind farms, whereas the terrestrial birds better adapted to them.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePartial correlations between the total numbers of birds of different dominances and categories and the DW.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominance and category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePartial correlation coefficient \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.543\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.700\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubdominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.337\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.403\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.428\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.343\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerrestrial \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAquatic \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.391\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.329\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003e Values in bold indicate significant correlations at the \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003eb\u003c/sup\u003e Terrestrial and aquatic birds of rare species in the community.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBird community composition and the endangered species\u003c/h2\u003e \u003cp\u003eThe dominant and subdominant bird species in the Rudong coastal wetland were all members of Charadriiformes. Most of the dominant and subdominant birds, such \u003cem\u003eCalidris alpina\u003c/em\u003e, \u003cem\u003eCalidris ruficollis\u003c/em\u003e, and \u003cem\u003eCharadrius alexandrines\u003c/em\u003e, are common species in the West Pacific (IUCN, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The dominant species recorded in this study were generally consistent with those detected in previous shorebird surveys of nearby wetlands (20\u0026thinsp;~\u0026thinsp;50 km from our study area, without wind farms) in spring and autumn (Peng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, previous surveys indicated that the dominant species accounted for 72.3\u0026thinsp;~\u0026thinsp;94.6% of the total number of aquatic birds (Peng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), whereas the proportions were ~\u0026thinsp;62% in this study. The proportions were much lower in our study area, probably because many dominant birds had been driven away by the wind farm.\u003c/p\u003e \u003cp\u003eDespite low bird number, the rare species comprised\u0026thinsp;~\u0026thinsp;80% species in the entire community in the Rudong coastal wetland. In addition to aquatic birds, terrestrial birds (mostly Passeriformes) were also common among the rare species, accounting for ~\u0026thinsp;40% of them. Thus, we conclude that the wetland is rich in both aquatic and terrestrial bird species. Moreover, the proportion of rare birds (~\u0026thinsp;8%) in our study (for comparison, only aquatic birds were considered) was much higher than that (~\u0026thinsp;3%) in nearby wetlands (20\u0026thinsp;~\u0026thinsp;180 km from our study area, without wind farms) (Ma et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ge et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which indicates that rare birds were less affected by the wind farm than dominant birds.\u003c/p\u003e \u003cp\u003eFour endangered species, i.e., \u003cem\u003eCalidris tenuirostris\u003c/em\u003e, \u003cem\u003eNumenius madagascariensis\u003c/em\u003e, \u003cem\u003ePlatalea minor\u003c/em\u003e, and \u003cem\u003eCalidris pygmaea\u003c/em\u003e were recorded during our survey. These 4 species breed in Eastern Siberia or Northeast China and winter in Southeast Asia to Australia. Recent reports showed that the estimated population sizes of \u003cem\u003eNumenius madagascariensis\u003c/em\u003e, \u003cem\u003ePlatalea minor\u003c/em\u003e, and \u003cem\u003eCalidris pygmaea\u003c/em\u003e in the EAAF were ~\u0026thinsp;32 thousand, ~\u0026thinsp;3500, and ~\u0026thinsp;450, respectively (Ma and Chen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; IUCN, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eCalidris tenuirostris\u003c/em\u003e has a larger population consisting of ~\u0026thinsp;290 thousand individuals (Ma and Chen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite the differences, the population sizes of all the species are decreasing, and habitat loss is the main reason for the decreases (IUCN, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, wind farm construction at their stopover sites might influence their future survival. In this study, \u003cem\u003eNumenius madagascariensis\u003c/em\u003e, \u003cem\u003ePlatalea minor\u003c/em\u003e, and \u003cem\u003eCalidris pygmaea\u003c/em\u003e were rarely observed in the Rudong coastal wetland, but \u003cem\u003eCalidris tenuirostris\u003c/em\u003e was still a dominant species in the area. Correspondingly, their responses to the wind farm were different.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSpatial responses of the bird community to the wind farm\u003c/h2\u003e \u003cp\u003eOur results showed that birds\u0026rsquo; responses to wind farms might vary depending on their dominance and category. Two tendencies were concluded from our results. First, the dominant and subdominant species (\u0026lsquo;dominant and subdominant\u0026rsquo; is written as \u0026lsquo;dominant\u0026rsquo; below) tended to avoid the wind farm, whereas the rare species tended to approach them. Second, terrestrial birds were more adaptable than aquatic birds to the wind farm.\u003c/p\u003e \u003cp\u003eThe variation in responses with dominance might be related to the following reasons. The dominant species, which are characterized by very high numbers of individuals, often fly in large groups, whereas the rare species fly singly or in small groups. Previous studies reported that larger bird groups have higher collision risks with obstacles because they have more social interactions (Croft et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Croft et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which can filter the information of obstacle cues and then disturb individuals\u0026rsquo; avoidance (Croft et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Croft et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Thus, the dominant species tended to avoid high collision risks, whereas the rare species could better avoid collisions when flying in the wind farm. Another reason for this phenomenon might be interspecific competition. Many studies have shown that dominant species usually govern the optimal resource, and subordinate species are often driven to seek novel resources to reduce competition (Pimm and Pimm, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; McKinney et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Freshwater et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this study, the dominant species were more concentrated in the undisturbed portion of the wetland; thus, many rare species chose to forage in or near the wind farm, which were less utilized by the dominant species. In this respect, we think that wind farms might act as refuges for rare species.\u003c/p\u003e \u003cp\u003eThe difference in responses between aquatic and terrestrial birds might be related to their morphologies. Aquatic birds, which mainly inhabit open seashores, usually have high wing aspect ratios, i.e., long and narrow wings (Norberg, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sheard et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This wing form has a high lift-to-drag ratio and smaller wing\u0026thinsp;\u0026minus;\u0026thinsp;tip vortices, making it more suitable for gliding, soaring, and continuous flight (Norberg, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, long and narrow wings have low aerodynamic roll torque and a high moment of inertia, which reduces flight manoeuvrability and result in a higher turn radius and longer take-off distance (Norberg, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; McFarlane, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moreover, aquatic birds usually have short tails (Thomas and Balmford, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Thomas, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), which is disadvantageous for maintaining stability and balance in flight and turning (Thomas and Balmford, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In conclusion, aquatic birds have low flight manoeuvrability, which may hinder them from avoiding wind turbines and incline them to stay away from wind farms. In contrast, terrestrial birds inhabit cluttered environments such as forest and spend much of their foraging time climbing, clinging and hanging. They usually have low wing aspect ratios (i.e., broad and rounded wings) and long tails, which are more suitable for manoeuvrable short flights (Norberg, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Correspondingly, terrestrial birds can better avoid obstacles such as wind turbines; thus, they are more adaptable to wind farms.\u003c/p\u003e \u003cp\u003eAccording to the above analysis, we conclude that the dominant aquatic birds were most negatively impacted by the wind farm. These birds include the endangered species \u003cem\u003eCalidris tenuirostris\u003c/em\u003e and some vulnerable and near threatened species. In contrast, the rare terrestrial birds were least disturbed by even benefited from the wind farm in some respect. The situation was more complex for rare aquatic birds. Their group sizes result in low collision risk, and they have fewer dominant competitors in the wind farm. On the other hand, their morphology is disadvantageous for flying in wind farms. Thus, similar to those of the occasional species, their responses exhibited more uncertainty.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eResponses of the bird community to the 4 geographical factors\u003c/h2\u003e \u003cp\u003eThe RDA results showed the approximate responses of the bird community to DW, DR, DS, and VA. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates that the responses to DR, DS, and VA differed considerably between aquatic and terrestrial birds, but they did not vary significantly depending on dominance. For most aquatic birds, the numbers tended to increase with a decrease in DS and with increases in DR and VA, indicating that aquatic birds tended to occur in low tidal flats and bare lands and tended to avoid suburbs. The terrestrial birds, however, exhibited the reverse response; they were more likely to occur in the high marsh, vegetated areas, and areas near the suburbs. The above differences occurred because aquatic birds mainly feed on the macrobenthos, fishes, and aquatic plants (Collis et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wade and Hickey, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ma and Chen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which are mainly distributed in low bare flats and shallow water, whereas terrestrial birds mainly feed on Arthropoda and seeds, which are concentrated in the high marsh and vegetated areas (Mu\u0026ntilde;oz et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, the terrestrial birds in the wetland mainly came from the suburbs, whereas the aquatic birds were more unfamiliar with them, which induced their different responses to DR.\u003c/p\u003e \u003cp\u003eFig.2 also shows that the biplot scores of DW are lower than those of DR, DS, and VA (i.e., the arrow length of DW is shorter than those of DR, DS, and VA), indicating that the contribution of DW to the spatial variation in the community is lower than those of the other 3 factors. Consequently, the bird responses to the wind farm inferred from partial correlation analysis are not obvious in Fig. 2 because the responses are covered by the effects of DR, DS, and VA. Based on the results of partial correlation analysis and RDA, we conclude that the bird community exhibits notable responses to the wind farm, but the responses are still inferior to those to suburbs, the sea and vegetation.\u003c/p\u003e \u003cp\u003eThe stepwise regression equations described the variations of total bird numbers of different dominances and categories with the geographical factors (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results indicate that the coefficient of DW was positive for dominant and subdominant birds and negative for rare birds; moreover, DW was eliminated from the equations of occasional and rare aquatic birds because the responses of these birds to wind farm were nonsignificant. The above results are consistent with those of partial correlation analysis. In addition, the coefficients of the other 3 factors are generally consistent with the RDA results. The occasional birds in autumn have no regression result, because their distribution exhibited great uncertainties and none of the factors could explain the variation of their number.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStepwise regression equations between the total numbers of birds of different dominances and categories and the geographical factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominance and category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquation \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEquation \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.070DW\u0026thinsp;+\u0026thinsp;0.209DR\u0026thinsp;\u0026minus;\u0026thinsp;0.017VA\u0026thinsp;+\u0026thinsp;297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054DW\u0026thinsp;+\u0026thinsp;0.098DR\u0026thinsp;\u0026minus;\u0026thinsp;0.043DS\u0026thinsp;\u0026minus;\u0026thinsp;0.011VA\u0026thinsp;+\u0026thinsp;308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubdominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020DW\u0026thinsp;+\u0026thinsp;0.099DR\u0026thinsp;\u0026minus;\u0026thinsp;0.009VA\u0026thinsp;+\u0026thinsp;133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019DW\u0026thinsp;+\u0026thinsp;0.059DR\u0026thinsp;\u0026minus;\u0026thinsp;0.006VA\u0026thinsp;+\u0026thinsp;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028DR\u0026thinsp;\u0026minus;\u0026thinsp;0.010DS\u0026thinsp;\u0026minus;\u0026thinsp;0.002VA\u0026thinsp;+\u0026thinsp;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.008DW\u0026thinsp;+\u0026thinsp;0.013DR\u0026thinsp;\u0026minus;\u0026thinsp;0.001VA\u0026thinsp;+\u0026thinsp;67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.005DW\u0026thinsp;\u0026minus;\u0026thinsp;0.002VA\u0026thinsp;+\u0026thinsp;71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerrestrial \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.004DW\u0026thinsp;\u0026minus;\u0026thinsp;0.013DR\u0026thinsp;+\u0026thinsp;0.011DS\u0026thinsp;+\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.002DW\u0026thinsp;\u0026minus;\u0026thinsp;0.007DR\u0026thinsp;+\u0026thinsp;0.006DS\u0026thinsp;+\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAquatic \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025DR\u0026thinsp;\u0026minus;\u0026thinsp;0.014DS\u0026thinsp;\u0026minus;\u0026thinsp;0.001VA\u0026thinsp;+\u0026thinsp;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013DR\u0026thinsp;\u0026minus;\u0026thinsp;0.011DS\u0026thinsp;\u0026minus;\u0026thinsp;0.002VA\u0026thinsp;+\u0026thinsp;52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e \u003cem\u003eN\u003c/em\u003e: total bird number; DW: distance to the wind farm boundary; DR: distance to the suburbs; DS: distance to the sea; VA: vegetation area surrounding each census point.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003e Terrestrial and aquatic birds of rare species in the community.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion","content":" \u003cp\u003eAs an important stopover site of birds in the EAAF, the Rudong coastal wetland comprises rich bird species. Charadriiformes accounts for an extremely high proportion of the total bird number, and Charadriiformes and Passeriformes comprise the majority of the species in the community.\u003c/p\u003e \u003cp\u003eNumerous studies have demonstrated negative effects of wind farms on birds, which indicates a conflict between bird conservation and wind power development in coastal areas of East China. Nevertheless, our study suggested that birds\u0026rsquo; responses to wind farms might vary according to their dominance and category. The most negatively impacted birds were the dominant aquatic birds in the wetlands. These birds included the endangered species \u003cem\u003eCalidris tenuirostris\u003c/em\u003e, the vulnerable species \u003cem\u003eChroicocephalus saundersi\u003c/em\u003e, and some near threatened species. Wind farms might compress the habitats of these species and then contribute to future population declines. Thus, the protection degrees of these species in the area should be upgraded. We think that retaining sufficient undisturbed intertidal mudflats, which are major foraging places for these birds, will be crucial for maintaining their population sizes in the future. The occasional birds and rare aquatic birds in the area, including the critically endangered species \u003cem\u003eCalidris pygmaea\u003c/em\u003e, the endangered species \u003cem\u003eNumenius madagascariensis\u003c/em\u003e and \u003cem\u003ePlatalea minor\u003c/em\u003e, and various vulnerable and near threatened species, exhibit great uncertainties in their responses to wind farms. They might be disturbed by wind farms but not significantly so. We think that monitoring their population dynamics in the area is a major task for the short term. Finally, the rare terrestrial birds in the area might accept the wind farms as refuges under interspecific competition, and they can better adapt to the wind farm environment. Thus, we conclude that these species, including the vulnerable species \u003cem\u003eAcrocephalus tangorum\u003c/em\u003e and \u003cem\u003eLocustella pleskei\u003c/em\u003e, would be least disturbed by or would even benefit somewhat from future wind farm construction.\u003c/p\u003e \u003cp\u003eWind power development has altered the wetland environment and bird habitat selection in the Rudong coastal wetland. Our two-year observation of the area will provide a guide for future bird conservation in wetlands with wind farms.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material\u0026nbsp;\u003c/strong\u003eThe Landsat 8 OLI image used here are available at\u0026nbsp;\u003ca href=\"http://eds.ceode.ac.cn/nuds/freedataquery\"\u003ehttp://eds.ceode.ac.cn/nuds/freedataquery\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e We sincerely thank the editors and the anonymous reviewers for their valuable comments and suggestions for this manuscript. We also thank Shanshan Chang, Lijuan Chen, Chuangqi Hu, and Yuqiao Hou for their help in our bird survey. We greatly appreciate Nanjing Normal University to support this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003eYC contributed to the study conception and design. Data acquisition was performed by YC, YZ, GW, DD and data analysis were performed by WZ and CT. The original draft was written by YC and YZ reviewed and edited subsequent versions of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the National Natural Science Foundation of China (No. 41671428) and Nanjing Normal University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e All data are presented in the paper\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e The R-code generated and analyzed is available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval \u0026nbsp;\u0026nbsp;\u003c/strong\u003eThis paper contains findings of our original research. The Law of the People\u0026rsquo;s Republic of China on the Protection of Wildlife and IUCN Policy Statement on Research Involving Species at Risk of Extinction were followed during the study. No animal was harmed or captured. No animal or plant samples were taken from the study area. We adhered to the ethical standards in this study and in production of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u0026nbsp; \u0026nbsp;The article submitted herewith contains the findings of our original research, is not under consideration for publication elsewhere, and is approved by all authors of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing Interests\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Authors declare no conflict of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBai QQ, Chen JZ, Chen ZH, Dong GT, Dong JT, Dong WX, Fu VWK, Han YX, Lu G, Li J, Liu Y, Lin Z, Meng DR, Martinez J, Ni GH, Shan K, Sun RJ, Tian SX, Wang FQ, Xu ZW, Yu Y, Yang J, Yang ZD, Zhang L, Zhang M, Zeng XW (2015) Identification of coastal wetlands of international importance for waterbirds: a review of China Coastal Waterbird Surveys 2005\u0026ndash;2013. 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Bird Conservation International 25(1):1\u0026ndash;37\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dominance, Aquatic birds, Endangered species, Wind turbine, Coastal wetland","lastPublishedDoi":"10.21203/rs.3.rs-573013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-573013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoastal wetlands in East China are essential stopover places for birds along the East Asian-Australian Flyway. However, numerous wind turbines have been built in or near these wetlands in recent years, which might disturb the bird community in the area. Therefore, investigating the bird community and its responses to wind farms in coastal wetlands of East China is of great significance for bird conservation. In the spring and autumn of 2019 and 2020, we investigated the bird community in the Rudong coastal wetland in East China using point counts. We determined 4 geographical factors at each census point, i.e., distance to the wind farm boundary (DW), distance to the suburbs, distance to the sea, and vegetation area, and analysed the relationship between bird number and DW through partial correlation analysis. A total of 11 orders and 103 species of birds, including 4 endangered species, were observed during our survey. Charadriiformes was the dominant taxon in the wetland, and \u003cem\u003eCalidris alpina\u003c/em\u003e was the most common species in both spring and autumn. Passeriformes exhibited high species richness but low numbers. The results of partial correlation analysis indicated that birds’ responses to the wind farm varied depending on their dominance and category: dominant and subdominant birds tended to avoid the wind farm, whereas rare birds tended to approach them; aquatic birds were alert to the wind farm, whereas terrestrial birds better adapted to them. We concluded that the dominant aquatic birds, including the endangered species\u003cem\u003e Calidris tenuirostris\u003c/em\u003e, were most negatively impacted by the wind farm; the occasional birds and rare aquatic birds might be disturbed by wind farm but not significantly so; and the rare terrestrial birds were least disturbed by or even benefited from the wind farm.\u003c/p\u003e","manuscriptTitle":"The Bird Community in a Coastal Wetland in East China and Its Spatial Responses to a Wind Farm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-06-12 16:27:06","doi":"10.21203/rs.3.rs-573013/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b209fc82-0ff9-4eec-a438-bf76ccb3f65a","owner":[],"postedDate":"June 12th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4971197,"name":"Biogeography"}],"tags":[],"updatedAt":"2021-11-15T21:49:38+00:00","versionOfRecord":{"articleIdentity":"rs-573013","link":"https://doi.org/10.1007/s42974-021-00065-4","journal":{"identity":"community-ecology","isVorOnly":false,"title":"Community Ecology"},"publishedOn":"2021-11-15 21:49:38","publishedOnDateReadable":"November 15th, 2021"},"versionCreatedAt":"2021-06-12 16:27:06","video":"","vorDoi":"10.1007/s42974-021-00065-4","vorDoiUrl":"https://doi.org/10.1007/s42974-021-00065-4","workflowStages":[]},"version":"v1","identity":"rs-573013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-573013","identity":"rs-573013","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}