An An, Yong Zhang, Lei Cao, Qiang Jia, Xin Wang. 2018: A potential distribution map of wintering Swan Goose (Anser cygnoides) in the middle and lower Yangtze River floodplain, China. Avian Research, 9(1): 43. DOI: 10.1186/s40657-018-0134-5
Citation: An An, Yong Zhang, Lei Cao, Qiang Jia, Xin Wang. 2018: A potential distribution map of wintering Swan Goose (Anser cygnoides) in the middle and lower Yangtze River floodplain, China. Avian Research, 9(1): 43. DOI: 10.1186/s40657-018-0134-5

A potential distribution map of wintering Swan Goose (Anser cygnoides) in the middle and lower Yangtze River floodplain, China

Funds: 

the National Natural Science Foundation of China 31370416

the National Natural Science Foundation of China 31500315

the State Key Laboratory of Urban and Regional Ecology, Chinese Academy of Sciences SKLURE2013-1-05

the China Biodiversity Observation Network (Sino BON) 

More Information
  • Corresponding author:

    Lei Cao, leicao@rcees.ac.cn

  • Received Date: 13 Mar 2018
  • Accepted Date: 02 Dec 2018
  • Available Online: 24 Apr 2022
  • Publish Date: 09 Dec 2018
  • Background 

    Reliable information on the distribution of target species and influencing environmental factors is essential for effective conservation management. However, ecologists have often derived data from costly field surveys. The Swan Goose (Anser cygnoides), a vulnerable Anatidae species, winters almost exclusively in China's Yangtze River floodplain, but wintering numbers have been steadily decreasing. To better safeguard this unique species, modern modeling approaches can be used to quantify and predict its suitable wintering habitat. Specifically, a potential wintering distribution map of this species is critically important.

    Methods 

    This study used the maximum entropy approach to model a distribution map of this species. In total, data from 97 up-to-date sites were extracted from 1263 survey sites (excluding duplicate data). After eliminating spatial autocorrelation, 11 environmental variables, including factors related to climate, land structure, vegetation, and anthropogenic activities, were used for model prediction.

    Results 

    The prediction distribution map shows that the population has concentrated mainly in the boundary area of Anhui, Hubei, and Jiangxi provinces, especially along the Yangtze River. Modeling results suggest that areas within the middle and lower Yangtze River floodplain, such as those in Hunan and Hubei provinces and the eastern coastal area of Zhejiang Province, demonstrate a potential level of "medium" suitability for this species to winter.

    Conclusions 

    Results from this study provide fundamental information for the restoration and management of the Swan Goose. Our "visualized" potential distribution map can assist in planning optimal conservation strategies, and consequently may help to increase the number of wintering populations in China.

  • Trait polymorphism in natural populations can evolve as a consequence of frequency-dependent selection (Majerus, 1998). This implies that parasites, predators or other selective agents impose variable intensities of selection on the phenotype depending on the frequency in the population. Brood parasites and their hosts provide one such possible case of frequencydependent selection resulting in the evolution of polymorphic eggs in both host and parasite (Kilner, 2006; Yang et al., 2010a). Obligate avian brood parasites lay their eggs in nests belonging to other species of birds, thereby transferring the costs of parental care to their victims. As a consequence, hosts evolve defenses to counter brood parasitism, which in turn selects for corresponding counter-adaptations for better trickery of parasites (Davies and Brooke, 1989). The well-known arms race between parasitic cuckoos and their hosts are regarded as a textbook example of co-evolutionary interactions. Theoretically, the cuckoo-host system, when acting in a frequency-dependent manner, should be able to produce polymorphisms in co-evolved traits in the interacting parties. This hypothetical scenario has been found in the Common Cuckoo (Cuculus canorus) and one of its hosts, the Ashy-throated Parrotbill (Paradoxornis alphonsianus), in which both species have evolved matching egg polymorphism manifested in discrete immaculate white, pale blue and blue egg phenotypes within a single population (Fig. 1; Yang et al., 2010a). However, egg mimicry assessment is not always straightforward. Inspection using spectrophotometric methods suggested that the eggs of the Great Spotted Cuckoo (Clamator glandarius) were not significantly related to the appearance of its Magpie (Pica pica) host eggs (Soler et al., 2003). In the Red-chested Cuckoo (Cuculus solitarius), cuckoo eggs actually match the eggs of their hosts most closely at wavelengths that cannot be perceived by the human eye (Cherry and Bennett, 2001). Starling et al. (2006) revealed by reflectance spectrophotometry that the color of Pallid Cuckoo (Cuculus pallidus) eggs differed between four host species of Melaphagid Honeyeaters (Lichenostomus penicillatus, L. chrysops, L. melanops, and Melithreptus affinis), and mimicked their hosts' eggs closely in both spectral shape and brightness. The Pallid Cuckoo eggs from the four different hosts' nests matched their respective hosts closely. However, host eggs exhibited a small peak in the ultraviolet that was not mimicked by the cuckoo eggs (Starling et al., 2006). Using digital image analysis and modelling of avian vision, Stoddard and Stevens (2010) recently showed that various features of host egg pattern are mimicked by the eggs of their respective cuckoo host-race. These studies revealed that cuckoos have host-specific egg types that have not been detected by human observation, and emphasize potential inadequacy of human comparisons applied to the coloration of bird eggs, and the importance of techniques such as spectrophotometry to measure color objectively (Starling et al., 2006).

    Figure  1.  Egg polymorphism of Common Cuckoo (Cuculus canorus) and Ashy-throated Parrotbill (Paradoxornis alphonsianus). (a), (b) and (c) refer to blue, pale blue and white clutches of parrotbill, respectively, with a cuckoo egg (larger egg) being present in each clutch (Photos by C. Yang).

    The objective of this study was to quantify egg color by spectrophotometry and assess the extent of egg mimicry of Common Cuckoo to the eggs of its Ashythroated Parrotbill host for blue, pale blue and white clutches, respectively.

    The study was performed in the Kuankuoshui Nature Reserve, Guizhou, south-western China (28°10′N, 107° 10′E) during April–July 2008–2009. The study site is situated in a subtropical moist broadleaf and mixed forest, interspersed with abandoned tea plantations, shrubby areas, and open fields used as cattle pastures (see also Yang et al., 2010a, b).

    Nests were found by systematically searching all typical and potential nest sites and by monitoring the activities of adult hosts throughout the breeding season. We recorded date of the first egg laid, egg color morph, clutch size and occurrence of brood parasitism for each nest. When a nest was found during the incubation period, eggs were floated in water to estimate approximate laying date (Hays and Lecroy, 1971). We used three spectrophotometers for quantification of egg coloration: the USB4000-VIS-NIR, GZ03P and Avantes-2048 to measure the visible (VIS) range (400–700 nm) of blue and white clutches, ultraviolet (UV) range (300–400 nm) of blue and white clutches (Fig. 2) and VIS-UV range (300–700 nm) of pale blue clutches (Fig. 3), respectively (Yang et al., 2009, 2011). Due to equipment limit, we did in such way, which was surely a suboptimal way of doing it. In earlier years, our spectrophotometer can only measure the spectrum range from 400 to 700 nm (VIS). And an additional UV spectrophotometer was used to supplement the UV data. But these data are from quite different machines and cannot be merged together. Finally, the pale blue eggs were measured by the Avantes spectrophotometer which covers the spectrum range from 300–700 nm. However, cuckoo eggs were few and phenotypes we found were very variable in different years.

    Figure  2.  Ultraviolet and visible reflectance spectrum of the egg phenotypes in Common Cuckoo (Cuculus canorus) and Ashy-throated Parrotbill (Paradoxornis alphonsianus). The curves represent the spectrum for one cuckoo egg and average spectra for all host eggs in the observed parasitized nest. B1 and B2 refer to the UV and VIS spectrum of blue clutches; W1 and W2 refer to the UV and VIS spectrum of white clutches.
    Figure  3.  Ultraviolet and visible reflectance spectra of the pale blue egg phenotype in Common Cuckoo (Cuculus canorus) and Ashythroated Parrotbill (Paradoxornis alphonsianus). The curves represent the spectrum for one cuckoo egg and average spectra for all host eggs in the observed parasitized nest.

    Both the Ashy-throated Parrotbill (hereafter parrotbill) and the Common Cuckoo (hereafter cuckoo) laid immaculate eggs (Fig. 1), and we obtained six measurements of spectral reflectance for each egg, with two at the blunt end, two at the middle and two at the sharp end of the egg. To represent the egg coloration of the cuckoo, the mean of each egg was summarized from these six measurements. For the parrotbill, egg coloration was represented as the mean of all host eggs in each clutch. Each measurement covered ca. 1 mm2 and was taken at a 45° angle to the egg surface, with the spectrometer and light source connected with a coaxial reflectance probe (Yang et al., 2009, 2010b). We also classified the degree of cuckoo eggs mimicry on a 5-degree scale based on human vision relying on 30 volunteers who scored the degree of mimicry (contrast) from 1 (non-mimetic) to 5 (perfect mimicry) following the approach first developed by Moksnes and Røskaft (1995).

    The experiments comply with the current laws of China in which they were performed. Experimental procedures were in agreement with the Animal Research Ethics Committee of Hainan Provincial Education Centre for Ecology and Environment, Hainan Normal University.

    Data analyses were performed in SPSS 13.0 for Windows (SPSS Inc, Chicago, Illinois). One-way ANOVA and Kruskal-Wallis ANOVA were used for comparison of normally and non-normally distributed data, respectively. Values were presented as mean ± SD.

    Mimicry score based on human vision showed that the contrasts between cuckoo and parrotbill eggs of the matched-phenotype (blue versus blue, pale blue versus pale blue, and white versus white) differed significantly among the three egg phenotypes (χ2 = 4.41, df = 2, p = 0.015). The mimicry of blue cuckoo eggs to blue host eggs was the highest and significantly higher than that of the white matched pair (blue: 1.03 ± 0.18 vs. white: 1.30 ± 0.47, n = 30 for each category, p = 0.015, post hoc test). The mimicry of pale blue cuckoo egg was intermediate between the blue and the white egg (1.13 ± 0.35, n = 30), with no statistical significant difference from blue (p = 0.273) or white eggs (p = 0.070).

    Egg reflectance spectra revealed that the wave shape, wave peak and wave trough of cuckoo and parrotbill egg spectrum for the blue phenotype were perfectly matching in both visible (VIS) and ultraviolet (UV) ranges (Figs. 12), which indicated that they were very similar in egg color hue and chroma. However, the wave shape of the white cuckoo egg was more variable, with a wave peak in the blue region (Fig. 2), which were lacking in the parrotbill egg. The reflectance spectra for UV between white cuckoo and parrotbill eggs were discrete in 300–340 nm. A similar pattern was found for pale blue cuckoo and parrotbill eggs for which the reflectance curves matched well in wavelengths 340–700 nm.

    Our results show that egg reflectance spectra agree well with the assessment based on human vision that cuckoo eggs mimic those of the parrotbill host. Our previous studies have also indicated that the classification of parrotbill egg morphs based on human vision is consistent with avian visual modelling (Yang et al., 2010a). The sensitivities of UVS-receptor of many birds are concentrated around 340–400 nm with a peak at 370 nm (Chen et al., 1984; Bennett et al., 1994). Recent work by Aidala et al. (2012) also showed that both the Shining Cuckoo (Chalcites [Chrysococcyx] lucidus) and the Long-tailed Cuckoo (Urodynamis [Eudynamis] taitensis) in New Zealand are predicted to possess the short wavelength-sensitive type 1 (SWS1) opsins with maximal sensitivity in the human-visible violet portion of the short-wavelength light spectrum, and not in the UV. Therefore, the UV curves for the three egg phenotypes in cuckoo and its parrotbill host should be regarded as well matching.

    The likelihood of nest predation was not significantly different between nests with white and blue egg in the parrotbill (Yang et al., 2010a). Furthermore, other Paradoxornis species that have no known history of interaction with the cuckoo lay monomorphic eggs in blue color (Jiang et al., 2009; Yang et al., 2011). Given that the cuckoo ancestrally had egg colors that were neither white nor blue (Davies, 2000), it was reasonable to conclude that nest predation is not responsible for the evolution of egg polymorphism in the parrotbill, and selection on the cuckoo for countering the evolution of multiple parrotbill egg types was evidenced by hosts generally having evolved good abilities to reject even partly mimetic eggs (Yang et al., 2010a).

    However, we found that mimicry of blue cuckoo eggs is better than that of white cuckoo eggs in their corresponding host clutches, implying that the white morph may potentially be a secondary egg morph that has not yet evolved fine mimetic features.

    In conclusion, we have shown evidence from photospectrometry that different egg color morphs in the Cuckoo have evolved in response to selection against poor mimics imposed by parrotbill hosts. This evidence supports the hypothesis that the white egg morph in the cuckoo-parrotbill system might be a secondary phenotype that has evolved under the strong selection pressure of brood parasitism.

    We are grateful to Anders P. Møller for valuable comments that significantly improved the quality of the manuscript. We thank Eivin Røskaft, Bård G. Stokke and one anonymous reviewer for helpful comments on our manuscript. This work was supported by the National Natural Science Foundation of China (Nos. 31071938 and 31272328 to WL, 31101646 and 31260514 to CY), Program for New Century Excellent Talents in University (NCET-10-0111 to WL), and Key Project of Chinese Ministry of Education (No. 212136 to CY). We thank the Forestry Department of Guizhou Province and Kuankuoshui National Nature Reserves for support and permission to carry out this study, and J. Wu, X. Guo, X. Xu, N. Wang and L. Wang for assistance with field work.

  • An S, Li H, Guan B, Zhou C, Wang Z, Deng Z, Zhi Y, Liu Y, Xu C, Fang S. China's natural wetlands: past problems, current status, and future challenges. Ambio. 2007;36:335-42.
    Augustin NH, Mugglestone MA, Buckland ST. An autologistic model for the spatial distribution of wildlife. J Appl Ecol. 1996;33:339-47.
    Barter M, Chen L, Cao L, Lei G. Waterbird survey of the middle and lower Yangtze River floodplain in late January and early February 2004. Beijing: China Forestry Publishing House; 2004.
    Barter M, Chen L, Cao L, Lei G. Waterbird survey of the middle and lower Yangtze River floodplain (February 2005). Beijing: China Forestry Publishing House; 2006.
    Cadahía L, Labra A, Knudsen E, Nilsson A, Lampe HM, Slagsvold T, Stenseth NC. Advancement of spring arrival in a long-term study of a passerine bird: sex, age and environmental effects. Oecologia. 2017;184:917-29.
    Cao L, Barter M, Lei G. New Anatidae population estimates for eastern China: implications for current flyway estimates. Biol Conserv. 2008a;141:2301-9.
    Cao L, Barter M, Lei G, Yang Q. Anatidae in the Yangtze floodplain in winter 2004 and 2005. Casarca. 2008b;11(2):146-60.
    Cao L, Fox AD. Birds and people both depend on China's wetlands. Nature. 2009;460:173.
    Clausen KK, Stjernholm M, Clausen P. Grazing management can counteract the impacts of climate change-induced sea level rise on salt marsh-dependent waterbirds. J Appl Ecol. 2013;50:528-37.
    Cleasby IR, Bodey TW, Vigfusdottir F, McDonald JL, McElwaine G, Mackie K, Colhoun K, Bearhop S. Climatic conditions produce contrasting influences on demographic traits in a long-distance Arctic migrant. J Anim Ecol. 2017;86:285-95.
    Corsi F, de Leeuw J, Skidmore A. Modeling species distribution with GIS. In: Boitani L, Fuller T, editors. Research techniques in animal ecology. New York: Columbia University Press; 2000. p. 389-434.
    Cromsigt JPGM, Prins HHT, Olff H. Habitat heterogeneity as a driver of ungulate diversity and distribution patterns: interaction of body mass and digestive strategy. Divers Distrib. 2009;15:513-22.
    Cyranoski D. Putting China's wetlands on the map. Nature. 2009;458:134.
    de Boer WF, Cao L, Barter M, Wang X, Sun MM, van Oeveren H, de Leeuw J, Barzen J, Prins HHT. Comparing the community composition of European and Eastern Chinese waterbirds and the influence of human factors on the China waterbird community. Ambio. 2011;40:68-77.
    Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36:27-46.
    Elith J. Quantitative methods for modeling species habitat: comparative performance and an application to Australian plants. In: Ferson S, Burgman M, editors. Quantitative methods for conservation biology. New York: Springer; 2002. p. 39-58.
    Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Philips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberon J, Williams S, Wisz MS, Zimmermann NE. Novel methods improve prediction of species' distributions from occurrence data. Ecography. 2006;29:129-51.
    Fielding AH, Bell JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv. 1997;24:38-49.
    Fox AD, Hearn R, Cao L, Cong PH, Wang X, Zhang Y, Dou ST, Shao XF, Barter M, Rees EC. Preliminary observations of diurnal feeding patterns of Swan Geese Anser cygnoides using two different habitats at Shengjin Lake, Anhui Province, China. Wildfowl. 2008;58:20-30.
    Franklin J, Miller JA. Mapping species distributions: spatial inference and prediction. Cambridge: Cambridge University Press; 2010.
    Guisan A, Thuiller W. Predicting species distribution: offering more than simple habitat models. Ecol Lett. 2005;8:993-1009.
    Guisan A, Zimmerman NE. Predictive habitat distribution models in ecology. Ecol Model. 2000;135:147-86.
    Gong P, Niu ZG, Cheng XA, Zhao KY, Zhou DM, Guo JH, Liang L, Wang XF, Li DD, Huang HB, Wang Y, Wang K, Li WN, Wang XW, Ying Q, Yang ZZ, Ye YF, Li Z, Zhuang DF, Chi YB, Zhou HZ, Yan J. China's wetland change (1999‒2000) determined by remote sensing. Sci China Earth Sci. 2010;53:1036-42.
    Hernandez PA, Graham CH, Master LL, Albert DL. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography. 2006;29:773-85.
    Hestbeck JB, Nichols JD, Malecki RA. Estimates of movement and site fidelity using mark resight data of wintering Canada Geese. Ecology. 1991;72:523-33.
    Heuermann N, van Langevelde F, van Wieren SE, Prins HHT. Increased searching and handling effort in tall swards lead to a type IV functional response in small grazing herbivores. Oecologia. 2011;166:659-69.
    Jia Q, Kazuo K, Chang-Yong C, Hwa-Jung K, Cao L, Gao D, Liu G, Fox AD. Population estimates and geographical distributions of swans and geese in East Asia based on counts during the non-breeding season. Bird Conserv Int. 2016;26:397-417.
    Kear J. Bird families of the world: ducks, geese and swans. Oxford: Oxford University Press; 2005.
    Klaassen M, Bauer S, Madsen J, Possingham H. Optimal management of a goose flyway: migrant management at minimum cost. J Appl Ecol. 2008;45:1446-52.
    Liao B, Liu G, Jin J, Liu F. Number and distribution of large waterbirds in winter of 2013‒2014 at Poyang Lake. 2013‒2014 Reports of Wetland Research and Monitoring at Poyang Lake. 2014. p. 27‒41 (in Chinese).
    Lu JJ. The status and conservation needs of Anatidae and their habitat in China. China Ornithological Research. Beijing: China Forestry Publishing House; 1996a. p. 129‒42.
    Lu J. Distribution and bioenergetics of wintering swan geese (Anser cygnoides) in the Yangtze River valley, China. Gibier Faune Sauvage. 1996;13:327-35.
    Martin TG, Chadès I, Arcese P, Marra PP, Possingham HP, Norris DR. Optimal conservation of migratory species. PLoS ONE. 2007;2:e571.
    Moriguchi S, Amano T, Ushiyama K. Creating a potential distribution map for Greater White-fronted Geese wintering in Japan. Ornithol Sci. 2013;12:117-25.
    Nolet BA, Fuld VN, van Rijswijk MEC. Foraging costs and accessibility as determinants of giving-up densities in a swan-pondweed system. Oikos. 2006;112:353-62.
    Pearce J, Ferrier S. An evaluation of alternative algorithms for fitting species distribution models using logistic regression. Ecol Model. 2000;128:127-47.
    Phillips SJ, Dudik M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. 2008;31:161-75.
    Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Model. 2006;190:231-59.
    R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna; 2011. .
    Scott DA. A directory of Asian Wetlands, IUCN, Gland, Switzerland and Cambridge, UK. 1989;1‒14: 1‒1181.
    Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB. Predicting species occurences: Issues of accuracy and scale. Washington, DC: Island Press; 2002.
    Shimada T. Current status and distribution of Greater White-fronted Geese in Japan. Ornithol Sci. 2009;8:163-7.
    Sutherland WJ, Allport GA. A spatial depletion model of the interaction between Bean Geese and Wigeon with the consequences for habitat management. J Anim Ecol. 1994;63:51-9.
    Suárez-Seoane S, Álvarez-Martínez JM, Wintle BA, Palacín C, Alonso JC. Modelling the spatial variation of vital rates: an evaluation of the strengths and weaknesses of correlative species distribution models. Divers Distrib. 2017;23:841-53.
    Tavares DC, Guadagnin JF, de Moura S, Siciliano DL, Merico A. Environmental and anthropogenic factors structuring waterbird habitats of tropical coastal lagoons: implications for management. Biol Conserv. 2015;186:12-21.
    The State Forestry Administration. China wetlands resource master volume. Beijing: China Forestry Publishing House; 2015 (In Chinese).
    Tu Y, Yu C, Huang X, Shan J, Sun Z, Wang Z. Distribution and population of the over wintering Anatidae waterfowl in the Poyang Lake. Acta Agric Univ Jiangxi. 2009;4:760-71 (In Chinese).
    Wang WJ, Fraser JD, Chen JK. Wintering waterbirds in the middle and lower Yangtze River floodplain: changes in abundance and distribution. Bird Conserv Int. 2017;27:167-86.
    Wang X, Fox AD, Cong PH, Barter M, Cao L. Changes in the distribution and abundance of wintering Lesser White-fronted Geese Anser erythropus in eastern China. Bird Conserv Int. 2012;22:128-34.
    Wang X, Kuang F, Tan K, Ma Z. Population trends, threats, and conservation recommendations for waterbirds in China. Avian Res. 2018;9:14.
    Wetlands International. Waterbird population estimates. 5th ed. UK: Wetlands International Press; 2012.
    Wisz M, Dendoncker N, Madsen J, Rounsevell M, Jespersen M, Kuijken E, Courtens W, Verscheure C, Cottaar F. Modelling pink-footed goose (Anser brachyrhynchus) wintering distributions for the year 2050: potential effects of land-use change in Europe. Divers Distrib. 2008;14:721-31.
    Wu X, Lv M, Jin Z, Michishita R, Chen J, Tian H, Tu X, Zhao H, Niu Z, Chen X, Yue T, Xu B. Normalized difference vegetation index dynamic and spatiotemporal distribution of migratory birds in the Poyang Lake wetland. China. Ecol Indic. 2014;47:219-30.
    Xu QJ, Jin XC, Yan CZ. Macrophyte degradation status and countermeasures in China. Ecol Environ. 2006;15:1126-30 (In Chinese).
    Yang HY, Chen B, Barter M, Piersma T, Zhou CF, Li FS, Zhang ZW. Impacts of tidal land reclamation in Bohai Bay, China: ongoing losses of critical Yellow Sea waterbird staging and wintering sites. Bird Conserv Int. 2011;21:41-59.
    Ydenberg RC, Prins HHT. Spring grazing and the manipulation of food quality by Barnacle Geese. J Appl Ecol. 1981;18:443-53.
    Zeng Q, Wei Q, Lei G. Contribution of citizen science towards cryptic specie census: "many eyes" define wintering range of the Scaly-sided Merganser in mainland China. Avian Res. 2018;9:6.
    Zhang J, Kissling WD, He F. Local forest structure, climate and human disturbance determine regional distribution of boreal bird species richness in Alberta, Canada. J Biogeogr. 2012;40:1131-42.
    Zhang Y, Cao L, Barter M, Fox AD, Zhao MJ, Meng FJ, Shi HQ, Jiang Y, Zhu WZ. Changing distribution and abundance of Swan Goose Anser cygnoides in the Yangtze River floodplain: the likely loss of a very important wintering site. Bird Conserv Int. 2011;21:36-48.
    Zhang Y, Jia Q, Prins HHT, Cao L, de Boer WF. Effect of conservation efforts and ecological variables on waterbird population sizes in wetlands of the Yangtze River. Sci Rep. 2015a;5:17136.
    Zhang Y, Jia Q, Prins HHT, Cao L, de Boer WF. Individual-area relationship best explains goose species density in wetlands. PLoS ONE. 2015b;10:e0124972.
    Zhang Y, Prins HHT, Cao L, Zhao MJ, de Boer WF. Variation in elevation and sward height facilitate coexistence of goose species through allometric responses in wetlands. Waterbirds. 2016;39:34-44.
    Zhao H, Liu S, Dong S, Su X, Wang X, Wu X, Wu L, Zhang X. Analysis of vegetation change associated with human disturbance using MODIS data on the rangelands of the Qinghai-Tibet Plateau. Rangeland J. 2015;37:77.
    Zhao MJ, Cong PH, Barter M, Fox AD, Cao L. The changing abundance and distribution of Greater White-fronted Geese Anser albifrons in the Yangtze River floodplain: impacts of recent hydrological changes. Bird Conserv Int. 2012;22:135-43.
    Zhu Q, Zhan Y, Liu G, Wu J, Zhan H, Huang Y, Huang J, Zhang B, Hu B, Li Y. Investigation of number and distribution of the waterfowl of Poyang Lake in the winter of 2011. 2012-2012 Report of Wetland Research and Monitoring at Poyang Lake; 2012. p. 57‒73 (In Chinese).
    Zöckler C, Miles L, Fish L, Wolf A, Rees G, Danks F. Potential impact of climate change and reindeer density on tundra indicator species in the Barents Sea region. Clim Change. 2008;87:119-30.
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