Nestedness has been a research focus in ﬁelds of island biogeography and community ecology in recent decades. Although nestedness of faunal assemblages has been investigated in natural wet- lands, it remains largely unknown whether and why waterbird communities in artiﬁcial wetlands follow nested patterns. We examined the existence of nestedness and underlying drivers in water- bird communities in subsidence wetlands that are recently created by large-scale underground coal mining in the North China Plain. Twelve point-count surveys for waterbirds were undertaken approximately every 2 weeks in 55 subsidence wetlands from September 2016 to April 2017. We used the metric WNODF to estimate nestedness of the assemblages. Partial Spearman rank corre- lations were performed to examine the association between the nestedness and habitat variables (wetland area, landscape connectivity, wetland age, and habitat diversity) as well as life-history traits (body size, clutch size, dispersal ratio, geographical range size, and migrant status) related to species extinction risk and colonization rate. Waterbird assemblages in the subsidence wetlands were signiﬁcantly nested. After controlling for other independent variables, the magnitude of nest- edness was signiﬁcantly and negatively correlated with wetland area and species trait linked to extinction risk (i.e., geographical range size). Our results indicate that selective extinction may be the main driver of the nestedness of waterbird assemblages in our study system. However, the nestedness was not due to passive sampling, selective colonization, or habitat diversity. From a conservation viewpoint, both large wetlands and waterbirds with a small geographic range should be protected to maximize the preserved species richness. Key words: geographical range size, nested pattern, selective extinction, waterbirds, WNODF. Biotic communities are heterogeneously distributed in space and found in a wide range of taxa from bacteria to mammals in various time, and the pattern of nestedness has been increasingly proposed systems (Wright et al. 1998; Schouten et al. 2007; Wang et al. 2010; as an essential metric to measure spatially hierarchical patterns of Soininen and Ko ¨ nga ¨ s 2012; Soininen et al. 2018). Quantifying regional biodiversity (Soininen et al. 2018). Nestedness occurs when nested metacommunity structures and understanding the causal species present at relatively depauperate locations constitute subsets drivers shaping the dynamic nature of biodiversity can provide of those present at more species-rich locations (Patterson and Atmar insights into how biodiversity is maintained and help conceive ef- 1986). Since its formalization, nested species distributions have been fective management plans (Socolar et al. 2016). V C The Author(s) (2018). Published by Oxford University Press. 1 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact firstname.lastname@example.org Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zoy034/4983039 by guest on 13 July 2018 2 Current Zoology, 2018, Vol. 0, No. 0 Four main mechanisms have been proposed to explain nested observation), and may provide an effective and interesting habitat is- patterns of biotic assemblages, including selective extinction, select- land system to test nestedness of biotic communities in fragmented ive colonization, habitat nestedness, and passive sampling (Cutler habitats. First, because of relatively small size and clear geographical 1994). The selective extinction hypothesis predicts that island area boundaries, waterbirds in these subsidence wetlands can be readily will be the main driver of nestedness in systems experiencing species and thoroughly surveyed. Second, the subsidence wetlands were cre- loss or “relaxation” (Wright et al. 1998). This is because species ated in different years, with asynchronous colonization of water- with large minimum area requirement may have greater extinction birds. These man-made wetlands also differ in a wide range of risk, especially in fragmented habitats, resulting in a predictable se- environmental attributes, allowing us to explore effects of habitat quence of extinction in relation to island size. Selective colonization heterogeneity on the waterbird metacommunity structures. Finally, can also produce nested patterns, where species with greater disper- these wetlands support both resident and migratory species that dif- sal ability are more likely to colonize a larger number of sites fer greatly in habitat requirements, patch occupancy, and other (Patterson 1987). The habitat nestedness hypothesis ascribes the behaviors. Phenology of migratory birds results in highly vagile nestedness of species assemblages to the associated habitat nested- communities in these wetlands. Investigating hierarchical metacom- ness (Honnay et al. 1999). Nestedness can also be due to passive munity structure of waterbirds in the subsidence wetland network sampling process because common species are more likely to be may provide new insights into nestedness theory beyond traditional observed than rare species in a given habitat (Higgins et al. 2006). study systems. In practice, the results may help conceive effective As passive sampling does not imply ecological significance, it is sug- management plans in the less-studied human-dominated landscape. gested that this should be tested prior to other hypotheses (Wright In this study, we examined the spatially hierarchical distribution et al. 1998). pattern of waterbird communities in the subsidence wetlands in the Species life-history traits may also provide useful information for North China Plain. We first tested the hypothesis that waterbird assessing the importance of different processes in generating nested- assemblages in these fragmented, man-made, wetlands would follow ness (Wang et al. 2010, 2012). For example, if dispersal ability is a a nested pattern. In addition, we determined the processes and influ- main driver of nestedness, then functional traits reflecting the rela- encing factors, particularly habitat variables and life-history traits, tive mobility of species may shape the structure of communities underlying nestedness of the waterbird assemblages. (Frick et al. 2009). In contrast, if selective extinction is the strong de- terminant of nestedness, life-history traits linked to higher extinction vulnerability might play a major role in structuring species assemb- Materials and Methods lages (Wang et al. 2010, 2012). Despite the links between these spe- Study area cies traits and environmental variables (Ulrich et al. 2009), few The study was carried out in the Huainan–Huaibei coal mining area studies have combined them simultaneously to examine their roles (3.74 10 ha) in Anhui Province, located at the southern part of in generating nestedness. the North China Plain that encompasses an area of 3 10 ha Nestedness of biotic assemblages can be found in almost all (Figure 1; 32.73 –33.73 N, 116.03 –117.52 E). The region is domi- habitat patches including forest remnants and wetland systems nated by flat landscape with a mean elevation of approximately (Paracuellos and Tellerı´a 2004; Martı ´nez-Morales 2005). Like other 30 m above sea level. Some low knolls occasionally up to 300 m are habitat islands, wetland networks patchily immersed in surrounding located in the northeastern part of the plain. Influenced by typical terrestrial landscape matrix perform biologically as real islands and warm temperate semi-humid monsoon climate, the average annual provide an interesting system to study nestedness in a variety of temperature is 14.7 C, and the average annual rainfall is 970 mm. wetland-dependent taxa (De Meester et al. 2005; Soininen et al. Most of the precipitation is concentrated in warm seasons between 2007; Soininen and Ko ¨ nga ¨ s 2012; Hill et al. 2017). Studies on nat- April and August. ural wetlands have found nested metacommunity structures in The Huainan–Huaibei Plain is one of the 14 largest coal bases in waterbird assemblages that are highly sensitive to habitat changes, China, which produces 4.17% of the national coal output (Hu et al. and are often identified as focus of conservation (Paracuellos and 2014). Coal mining in this region began more than 100 years ago Tellerı ´a 2004; Sebastia ´ n-Gonza ´ lez et al. 2010). Due to global loss and the modern industrialization in recent 3 decades has vastly and degradation of natural wetlands, waterbirds increasingly use increased the coal production. Since most of the coals are extracted artificial wetlands in human-dominated landscapes, which has be- from underground, land subsidence and submergence have occurred come a widely debated topic in conservation (Navedo et al. 2012; in the coal mining areas. It is estimated that 0.2–0.5 ha of land sub- Rajpar and Zakaria 2013). Quantifying nestedness of waterbird sidence will be created by 10,000 tons of raw coal production (Bian communities in artificial wetlands, and exploring the causal underly- et al. 2010). Up to 2010, the massive and continuing coal mining in ing drivers may have important implications both in theory and in this region had resulted in more than 3 10 ha of subsidence area practice. with an annual expansion of more than 2,000 ha (Xie et al. 2013). Despite increasing interest in waterbird use of various man-made Due to the high groundwater level and abundant rainfall in this re- wetlands, little is known about avian assemblages in subsidence wet- gion, two-thirds of the subsidence land has been flooded, creating lands which are mainly created by underground mining (Zhang hundreds of isolated wetlands scattered on the agricultural matrix. et al. 2017). During the last 3 decades, massive and continuing These subsidence wetlands have attracted a large number of resident underground coal mining in China has created large-scale land sub- and migratory waterbirds to rest, forage, and breed (C. Li, personal sidence with an annual increase of 7 10 ha (Hu et al. 2014). Due observation, but also see Supplementary Table S1). to high groundwater levels and abundant rainfall, hundreds of sub- sidence wetlands, ranging from several hectares to several square Waterbird surveys kilometers, have been created in the North China Plain (Xie et al. 2013). These man-made wetlands have attracted a wide array Point counts of waterbirds were carried out in 55 subsidence wet- of waterbird species to rest, forage, or nest (C. Li, personal lands with an area of 6,226 ha, accounting for approximately 40% Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zoy034/4983039 by guest on 13 July 2018 Li et al. Nestedness of waterbird assemblages 3 Figure 1. Land-cover and location of the 55 surveyed subsidence wetlands in Huainan–Huaibei coal mining area, China. of the man-made wetlands in the Huainan–Huaibei coal mining to the Ramsar Convention (Gardner and Davidson 2011). They area. These wetlands were selected randomly to represent a wide were identified to species level according to the taxonomy by range of environmental conditions. Depending on the wetland area BirdLife International (2016). We classified all the watebird species and accessibility (Cam et al. 2000), we placed 1–6 counting points into 3 groups according to their migration status, namely residents, along its boundary to get an unobstructed view of each sampling winter migrants, and summer migrants (Zheng 2011). wetland. We defined areas within a radius of 1 km at counting points as observation areas that were not overlapped to avoid dou- Habitat variables ble counting. For each subsidence wetland, we selected 4 habitat variables that are From September 2016 to April 2017, we carried out 12 field sur- commonly considered to influence nestedness, that is, wetland area, veys approximately every 2 weeks, each covering all the 55 wetlands landscape connectivity, wetland age, and habitat diversity (Wright within 3 clear and calm days. During the field surveys, the “look- et al. 1998; Table 1). The age of a wetland was defined as the time see” total counting method (Delany 2005) was employed by the since it was created. This was determined by comparing land-cover same 2 experienced bird observers to record waterbirds in the changes interpreted by a time series of Landsat images (TM/ETM/ selected wetlands. Birds flying over the wetlands were not recorded, OLI) which were acquired every 16 days from 1987 to 2016. To de- except those being flushed out from within the observation areas. termine the other 3 variables of each subsidence wetland, we first Waterbird counting at each point lasted approximately 15 min interpreted a remotely sensed image to get a land-cover map of the with the help of binoculars (10 42 WB Swarovski) and a telescope study area. The image was acquired on 2 September 2016 (Level 1T (20–60 zoom Swarovski: ATM 80). We defined waterbirds as bird species that are “ecologically dependent upon wetlands” according of Landsat 8 OLI on path 122/row 37) with no cloud cover, and Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zoy034/4983039 by guest on 13 July 2018 4 Current Zoology, 2018, Vol. 0, No. 0 Table 1. Characteristics of the 55 study subsidence wetlands in the Huainan–Huaibei coal mining area, China Wetland identity Area (ha) Landscape connectivity Habitat diversity Wetland age (year) Species richness Nestedness rank N1 7.83 734.66 1.80 4.2 16 31 N2 16.65 756.72 1.54 13.4 15 33 N3 15.75 200.27 1.72 2.1 21 18 N4 51.30 667.83 1.99 13.4 23 13 N5 24.12 696.11 1.42 4.0 13 42 N6 230.94 820.49 1.34 28.0 31 3 N7 105.75 83.52 2.00 2.7 36 2 N8 45.45 214.00 2.00 2.2 23 12 N9 45.72 222.14 1.85 7.1 11 46 N10 70.38 219.37 1.74 6.7 26 7 N11 80.46 267.78 1.94 6.8 18 24 N12 85.32 224.68 1.90 3.6 30 4 N13 245.79 502.91 1.69 17.8 10 47 N14 166.32 520.42 1.39 16.6 22 15 N15 11.79 572.51 2.00 5.8 14 39 N16 20.34 658.55 1.94 6.3 8 50 N17 17.55 664.66 1.14 1.8 25 10 N18 34.29 664.66 1.85 3.5 14 40 N19 103.77 572.93 1.89 7.7 15 32 N20 218.34 663.92 1.69 10.7 8 51 N21 57.24 763.59 1.66 13.4 8 49 S1 106.74 2,187.34 1.43 24.2 24 11 S2 10.26 1,909.49 1.39 6.7 15 38 S3 15.12 1,943.48 1.70 18.8 9 48 S4 15.21 2,470.84 1.84 3.6 16 30 S5 54.09 2,479.19 1.42 14.8 21 19 S6 144.63 2,962.49 1.33 10.4 18 27 S7 84.51 2,942.58 1.56 25.5 26 6 S8 32.40 2,935.28 1.60 8.1 13 45 S9 37.80 2,930.15 1.87 8.5 7 52 S10 88.02 2,950.97 1.58 7.8 19 23 S11 65.88 1,972.41 1.80 6.2 18 26 S12 68.58 1,374.03 1.76 19.7 13 44 S13 31.86 1,287.44 1.41 19.4 18 25 S14 73.62 866.29 1.55 16.1 26 8 S15 66.33 848.49 1.39 14.8 23 14 S16 27.72 846.78 1.43 7.2 20 21 S17 95.40 1,082.90 1.46 3.4 38 1 S18 145.62 816.89 1.97 6.4 19 22 S19 970.38 4,479.91 1.12 8.8 25 9 S20 285.66 4,904.47 1.33 22.6 23 12 S21 136.17 4,506.72 1.32 10.5 16 29 S22 249.93 4,722.83 1.62 5.7 22 16 S23 233.46 2,363.18 1.79 13.7 29 5 S24 113.85 2,728.49 1.38 13.7 20 20 S25 82.80 2,716.35 1.45 14.9 15 37 S26 179.19 2,764.20 1.34 19.3 15 34 S27 296.10 1,922.23 1.13 13.6 22 17 S28 378.27 1,552.49 1.10 9.0 14 41 S29 57.69 1,177.92 1.21 2.5 13 43 S30 24.66 1,175.58 1.24 2.0 6 53 S31 32.94 1,172.60 1.42 2.0 15 36 S32 8.37 1,171.63 1.00 2.0 3 54 S33 139.23 1,226.71 1.61 4.2 17 28 S34 218.16 2,515.21 1.16 6.6 15 35 was downloaded from the USGS website (http://glovis.usgs.gov/). and aquatic vegetation within each wetland. To quantify habitat di- Maximum-likelihood classifier was used in ENVI 5.1 (Exelis VIS versity, we used the inverse of Simpson’s index: HD ¼ 1= p , i¼1 Inc.) to identify 5 land-cover categories: cropland, developed land, where p is the proportion of the total area occupied by the ith of n open water, aquatic vegetation, and woodlands. The overall classifi- habitat types (Simpson 1949). We defined landscape connectivity as cation accuracy was 94.4% and the kappa coefficient was 0.91. the total area of wetlands (>1 ha) within a 5-km buffer zone sur- Wetland area was measured by combining the area of open water rounding each wetland. We chose this radius because it may Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zoy034/4983039 by guest on 13 July 2018 Li et al. Nestedness of waterbird assemblages 5 encompass mean home range size for most waterbird species in this performed Spearman rank correlations between the wetland ranks study and thus explain most variation in species richness and abun- in the maximally packed matrix and ranked physical attributes of dance (Moilanen and Nieminen 2002; Roach and Griffith 2015). the wetlands (Table 1). Similarly, to determine the role of species The landscape measure of connectivity is an inverse measure of wet- life-history traits in generating nestedness, we calculated Spearman land isolation, that is, wetlands surrounded by a larger percentage rank correlations between the species ranks in the maximally packed of wetlands are less isolated (Diver 2008). matrix and ranked species traits (body size, clutch size, dispersal ratio, range size, and migrant status; Table 2). Because collinearities occurred among these variables, we computed partial Spearman Species life-history traits rank correlations to separate out their independent effects on nested- We selected 5 commonly cited species traits (body size, clutch size, ness (Frick et al. 2009; Wang et al. 2010). Partial Spearman rank dispersal ratio, geographical range size, and migrant status) that are correlation analyses were conducted with SAS* 9.2 (SAS Institute, linked to species extinction risk and colonization rate in waterbirds. Cary, NC, USA). Statistical significance was set at P< 0.05 and data Body size, clutch size, geographical range size, and migrant status were shown as means6 SD. are key traits associated with extinction risk (McKinney 1997; Purvis et al. 2000). Dispersal ratio was used as an index of a species’ mobility (Wang et al., 2015). We calculated a dispersal ratio (dp) Results for each species by dividing its mean wing length (mm) by the cube root of its mean mass (g) (Woinarski 1989; Wang et al. 2018). We Nestedness of waterbird assemblages used body length (mm) to represent body size (Wang et al. 2015). The waterbird assemblages in the 55 subsidence wetlands were sig- Clutch size was defined as the median number of eggs per nest nificantly nested (Table 3). The general nestedness estimator for the (Morrow and Pitcher 2003). Following Jones et al. (2003), the geo- whole waterbird-by-wetland abundance matrix (WNODF) exhib- ited a significantly stronger degree of nestedness than expected graphic range size (km ) was obtained from published species range maps by digitizing the area into a geographic information system (Table 3). Moreover, species composition (WNODF ) and species (ArcView 10.2). Migrant status was classified as resident (0), winter incidence (WNODF ) were also significantly nested (Table 3). migrant (1), and summer migrant (2) (Van Turnhout et al. 2010). All the above data were obtained from Zhao (2001) and Zheng Determinants of nestedness (2011). For each of the species traits, if a range instead of the mean The nestedness of waterbird assemblages was in accord with the se- was given, we used the arithmetic mean of the limits (Wang et al. lective extinction hypothesis (Table 4). After controlling for other 2018). independent variables, the nestedness was significantly and negative- ly correlated with wetland area and species trait linked to extinction Data analyses risk (i.e., geographical range size) (Table 4). We used the metric WNODF to quantify nestedness of the waterbird Nestedness of waterbird assemblages was not consistent with the communities (Almeida-Neto and Ulrich 2011). With this metric, selective colonization hypothesis (Table 4). Nestedness was not cor- related with either landscape connectivity or the dispersal ratio of nestedness can be calculated not only for the whole incidence matrix waterbird species (Table 4). (WNODF), but also for species (WNODF ) and sites (WNODF ). r c The nestedness of waterbird assemblages did not appear to result We analyzed the abundance metric of waterbird assemblages using the rc null model that maintained the original matrix size and the from habitat diversity. After controlling for other independent varia- original abundance in both rows and columns (Almeida-Neto and bles, the nestedness was not correlated with habitat diversity (Table 4). Ulrich 2011). We then sorted the abundance matrix according The nestedness of waterbird assemblages was also not due to to species richness and weights. We used the program NODF* 2.0 passive sampling (Figure 2). None of the observed data points fell (Almeida-Neto and Ulrich 2011) to calculate the above indices and compared them with the results of 1,000 randomly generated within 6 1 SD of the expected species–area curve (Figure 2), which rejected the random placement model. communities. The random placement model (Coleman 1981) was commonly used to test the passive sampling hypothesis (Bolger et al. 1991; Discussion Calme ´ and Desrochers 1999; Wang et al. 2012). We used this model to determine whether the nestedness of the waterbird assemblages We found that waterbird communities in the subsidence wetlands in could be explained simply by the passive sampling from species the North China Plain were significantly nested. The nestedness of waterbird assemblages in our study system was in accord with the abundance distributions (Supplementary Table S1). Under the ran- selective extinction hypothesis because species nestedness was sig- dom placement model, the number of species S to be found in a (a) given region depends on the region’s relative area, a ¼ ak=R a , nificantly correlated with wetland area and species trait linked to ex- k¼1 k and the overall abundances n , n , ... , n of the S species tinction risk such as geographical range size. Selective extinction is 1 2 s S ni 2 represented in C: SðaÞ¼ S R ð1 aÞ . The variance r of S is widely considered as a key driver of nestedness, particularly in frag- (a) i¼1 S n S 2n 2 i i mented habitats or land-bridge archipelagos that are experiencing determined as r ðaÞ¼ R ð1 aÞ R ð1 aÞ . If more than i¼1 i¼1 one-third of the points lie outside one standard deviation (SD) of the species loss or faunal relaxation (Wright et al. 1998; Hill et al. expected species–area curve, the random distribution hypothesis 2011). Species with large minimum area requirement or small geo- should be rejected (Coleman et al. 1982). graphical distribution range may go extinct first, resulting in a pre- The order in which sites and species are sorted by WNODF can dictable sequence of extinction accordingly (Purvis et al. 2000; be compared with numerous independent variables to evaluate their Jones et al. 2003). As wetland area was negatively correlated with possible roles in generating nestedness (Patterson and Atmar 2000). nestedness, large wetlands deserve more attention at a local scale To test the effects of wetland characteristics on nestedness, we when conservation investment is limited. In contrast, small wetlands Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zoy034/4983039 by guest on 13 July 2018 6 Current Zoology, 2018, Vol. 0, No. 0 Table 2. Life-history traits of waterbird species in 55 subsidence wetlands in the Huainan–Huaibei coal mining area, China. Nomenclature follows BirdLife International (2016) Species Migrant status Body size (mm) Clutch size (n) Dispersal ratio Geographical range size (km ) Nestedness rank Anser albifrons 3 700.00 4.5 27.96 633.50 53 Fulica atra 3 392.00 9.0 24.86 962.58 8 Egretta garzetta 1 596.50 4.5 35.20 495.09 4 Spatula querquedula 3 368.75 10.0 26.51 962.58 38 Platalea leucorodia 2 818.00 3.5 29.96 962.58 42 Mergellus albellus 3 413.25 8.0 22.59 959.04 30 Aythya nyroca 3 385.25 9.0 21.17 831.97 34 Tringa ochropus 3 234.00 3.5 32.24 962.58 24 Anas poecilorhyncha 1 570.50 9.5 25.92 962.58 11 Ardea cinerea 3 888.00 5.0 38.34 962.58 3 Ardeola bacchus 4 464.25 3.0 34.02 908.64 20 Mareca penelope 3 458.25 8.5 19.36 962.58 44 Tadorna ferruginea 3 594.00 9.0 32.57 959.04 37 Mareca strepera 3 499.50 10.0 27.28 962.58 15 Ardea alba 3 888.25 4.0 36.47 829.40 7 Botaurus stellaris 2 676.75 5.0 32.60 772.69 48 Anser fabalis 3 751.75 5.5 30.04 681.85 26 Podiceps cristatus 3 524.00 4.5 20.09 959.04 5 Vanellus vanellus 2 315.75 4.0 36.34 962.58 36 Aythya fuligula 2 409.75 9.0 22.64 962.58 32 Tringa erythropus 2 293.00 4.0 30.35 962.58 25 Himantopus himantopus 3 353.75 4.0 41.04 962.58 29 Calidris alpina 2 195.50 4.0 29.40 631.37 43 Gavia arctica 2 686.25 1.5 21.23 254.14 59 Gallinula chloropus 1 290.00 8.0 24.28 962.58 2 Zapornia akool 1 265.00 5.0 23.14 217.77 60 Tringa totanus 2 270.00 4.0 30.82 860.71 22 Aythya ferina 2 459.25 8.0 21.02 959.04 31 Larus ridibundus 3 386.75 3.0 45.68 962.58 45 Anser cygnoid 3 850.25 6.0 28.94 800.62 47 Charadrius alexandrinus 3 162.50 4.0 31.08 873.54 28 Ixobrychus sinensis 4 332.50 7.0 28.92 606.69 35 Vanellus cinereus 2 342.00 4.0 35.57 676.36 27 Anser anser 3 807.50 4.5 29.15 962.58 52 Actitis hypoleucos 2 189.25 4.5 29.54 962.58 19 Charadrius dubius 4 168.00 3.5 34.53 962.58 17 Mareca falcata 3 461.25 8.0 27.14 751.21 21 Anas crecca 3 388.50 9.5 26.18 962.58 9 Anas platyrhynchos 3 543.75 9.0 26.78 962.58 10 Bubulcus ibis 4 509.75 6.0 33.99 955.94 23 Spatula clypeata 2 466.25 10.0 28.11 962.58 40 Phalacrocorax carbo 3 798.00 4.0 27.36 962.58 14 Mergus merganser 3 627.50 10.5 24.88 958.93 51 Sterna hirundo 2 341.50 3.0 55.99 881.76 54 Tadorna tadorna 3 570.75 9.0 30.40 959.04 55 Calidris temminckii 2 147.00 4.0 32.91 962.58 49 Tringa nebularia 2 318.75 4.0 31.43 962.58 12 Aythya baeri 2 438.50 7.5 23.03 793.04 33 Gallinago gallinago 2 272.50 4.0 25.08 962.58 18 Hydrophasianus chirurgus 4 445.00 4.0 37.20 292.11 50 Tachybaptus ruﬁcollis 1 258.25 5.5 18.94 962.58 1 Cygnus columbianus 3 1,165.50 3.5 28.30 659.93 39 Zapornia pusilla 2 174.25 7.5 24.64 838.82 56 Chlidonias hybrida 4 251.50 3.0 49.62 824.74 13 Nycticorax nycticorax 4 525.00 4.0 32.94 842.36 16 Larus argentatus 3 614.50 2.5 43.55 438.37 46 Aix galericulata 3 429.75 9.5 26.75 606.69 57 Charadrius placidus 2 210.75 3.5 34.05 796.58 58 Anas acuta 2 567.50 8.5 28.25 962.58 41 Ardea intermedia 4 666.50 4.0 39.68 492.24 6 Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zoy034/4983039 by guest on 13 July 2018 Li et al. Nestedness of waterbird assemblages 7 will have less conservation value because they have a large degree of riparian grassland. Further studies may consider identifying water overlap in species composition with large wetlands (Supplementary areas at different water depths which may provide habitats for dif- ferent species. Table S1). In addition, by assessing the risk of local extinction in The nestedness of waterbird assemblages in the subsidence wet- waterbird species with different life histories, management strategies lands was also not resulted from passive sampling. Nestedness is designed to prevent their future extinction can be implemented more hypothesized to arise from random samples of species differing in effectively (Wang et al. 2010, 2012; Soga and Koike 2013). As spe- their relative abundances (Andre ´ n 1994; Cutler 1994; Higgins et al. cies with small geographical distribution range are more vulnerable 2006). However, passive sampling played little role in the develop- to extinction (Purvis et al. 2000; Jones et al. 2003), these waterbird ment of waterbird nestedness in our study system because the ran- species need prior conservation. dom placement model was rejected. Although some ecologists The selective colonization hypothesis could not explain the nest- emphasize that the passive sampling hypothesis should be tested edness in our study system because species nestedness was not corre- prior to other hypotheses (Andre ´ n 1994; Cutler 1994), the sampling lated with landscape connectivity or species dispersal ratio. Three effect has rarely been examined probably because of the difficulty main factors may explain why this correlation is weak. First, the iso- involved in collecting abundance data (Wright et al. 1998). Our lation of subsidence wetlands may not effectively prevent the disper- study provides further test for the passive sampling hypothesis sal of waterbirds with high mobility among wetlands in our study (Wang et al. 2010, 2012; Xu et al. 2017). system (Figure 1). In addition, the stepping stone effect of some Two potential caveats may exist in our study. First, our study small wetlands may dilute the effect of isolation by distance (Soga cannot completely distinguish selective extinction mediated through and Koike 2013; Pe ´ rez-Herna ´ ndez et al. 2014). Finally, the biologic- area effects from the target effect. The target effect indicates that ally meaningful quantification of isolation is notoriously difficult colonization rates may also increase with habitat area because larger (Lomolion 1996; Bergerot et al. 2012), which may preclude strong islands are easier to be found (Russell et al. 2006). To test the target inference about selective colonization on nestedness. effect, multi-year survey data are required to calculate the coloniza- The nestedness of waterbird assemblages was not attributable to tion rate and extinction rate (Russell et al. 2006). As waterbirds in habitat diversity. Habitat nestedness is considered as the most parsi- the studied wetlands are surveyed only in 1 year, the target effect monious process to explain species nestedness because it points dir- cannot be tested in our study. Long-term monitoring is thus needed ectly to associations between species and their habitats (Calme ´ and to confirm that target effects are not muddling our results. In add- Desrochers 1999). Up to now, few studies have explicitly examined ition, the difference in detection probabilities among waterbird spe- the relationship between habitat nestedness and species nestedness. cies (McKinney 1997; Cam et al. 2000) may confound our estimates Our results are inconsistent with several previous studies (e.g., of abundance, which in turn may bias our test of the passive sam- Calme ´ and Desrochers 1999; Schouten et al. 2007; Wang et al. pling hypothesis. In our case, the abundance of some rare species 2012). The weak correlation between waterbird nestedness and was low (Supplementary Table S1), suggesting that our estimate of habitat diversity is probably due to the little variation in habitat di- waterbird abundance may be biased. Investigating to what extent versity (Table 1). Due to intense human activities, the subsidence wetlands were dominated by open water and some aquatic vegeta- tion. We could not identify other habitat types, such as mudflats and Table 3. Results of nestedness analyses using the program NODF conducted on the species-by-sites abundance matrix of waterbird assemblages in the 55 subsidence wetlands in Huainan–Huaibei coal mining area, China Nestedness metric WNODF WNODF P-values obs exp WNODF 41.12 73.9361.32 <0.001 WNODF 45.49 75.3861.00 <0.001 WNODF 37.45 72.7561.97 <0.001 Notes: Given are observed WNODF (WNODF ), expected WNODF obs (WNODF ), and Monte Carlo-derived probabilities that the matrix was exp Figure 2. Comparison of observed data to expected values under the random randomly generated 1,000 permutations. WNODF, general nestedness esti- placement model for waterbirds in subsidence wetlands in the Huainan– mator for the whole abundance matrix; WNODF , column nestedness estima- c Huaibei coal mining area, China. Expected values (solid line) and associated tor among sites (species composition); WNODF , row nestedness estimator standard deviations (61 SD; dashed line) are shown. Filled triangles repre- among species (species incidence). sent observed species richness. Table 4. Relationships between rank orders of sites and species in the maximally nested matrix and orders of sites and species after rearranging the matrix according to each explanatory variable Habitat variables Species life-history traits Wetland area (ha) Landscape Habitat Wetland Migrant Body Clutch Dispersal Geographical connectivity diversity age status size (mm) size (n) ratio range size (km ) 0.423** 0.093 0.132 0.341 0.134 0.020 0.010 0.018 0.355** Notes: Values are partial Spearman rank correlations. *P < 0.05, **P< 0.01, ***P < 0.001. Downloaded from https://academic.oup.com/cz/advance-article-abstract/doi/10.1093/cz/zoy034/4983039 by guest on 13 July 2018 8 Current Zoology, 2018, Vol. 0, No. 0 Frick WF, Hayes JP, Heady PAI, 2009. 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