IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i23p6814-d292814.html
   My bibliography  Save this article

Response of Anatidae Abundance to Environmental Factors in the Middle and Lower Yangtze River Floodplain, China

Author

Listed:
  • Qiang Jia

    (School of Life Sciences, University of Science and Technology of China, Hefei 230026, China)

  • Yong Zhang

    (Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China)

  • Lei Cao

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Understanding and predicting animal distribution is one of the most elementary objectives in ecology and conservation biology. Various environmental factors, such as habitat area, habitat quality, and climatic factors, play important roles in shaping animal distribution. However, the mechanism underlying animal distribution remains unclear. Using generalized additive mixed models, we analyzed the effects of environmental factors and years on the population of five Anatidae species: Tundra swan, swan goose, bean goose, greater and lesser white-fronted goose, across their wintering grounds along the Middle and Lower Yangtze River floodplain (MLYRF) during 2001–2016. We found that: (1) All populations decreased except for that of the bean goose. (2) The patch area was not included in any of the best models. (3) NDVI was the most important factor in determining the abundance of grazing geese. (4) Climatic factors had no significant effect on the species in question. Our results suggest that, when compared to habitat area, habitat quality is better in predicting Anatidae distribution on the basin scale. Thus, to better conserve wintering Anatidae, we should keep a sufficiently large area at the single lake, as well as high quality habitat over the whole basin. This might be achieved by developing a more strategic water plan for the MLYRF.

Suggested Citation

  • Qiang Jia & Yong Zhang & Lei Cao, 2019. "Response of Anatidae Abundance to Environmental Factors in the Middle and Lower Yangtze River Floodplain, China," Sustainability, MDPI, vol. 11(23), pages 1-10, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6814-:d:292814
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/23/6814/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/23/6814/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qun Huang & Zhandong Sun & Christian Opp & Tom Lotz & Jiahu Jiang & Xijun Lai, 2014. "Hydrological Drought at Dongting Lake: Its Detection, Characterization, and Challenges Associated With Three Gorges Dam in Central Yangtze, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5377-5388, December.
    2. Wen, Li & Rogers, Kerrylee & Saintilan, Neil & Ling, Joanne, 2011. "The influences of climate and hydrology on population dynamics of waterbirds in the lower Murrumbidgee River floodplains in Southeast Australia: Implications for environmental water management," Ecological Modelling, Elsevier, vol. 222(1), pages 154-163.
    3. Rigby, R.A. & Stasinopoulos, D.M. & Akantziliotou, C., 2008. "A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 381-393, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Willmot, Gordon E. & Woo, Jae-Kyung, 2022. "Remarks on a generalized inverse Gaussian type integral with applications," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    2. Moritz Berger & Gerhard Tutz, 2021. "Transition models for count data: a flexible alternative to fixed distribution models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1259-1283, October.
    3. Baíllo, A. & Berrendero, J.R. & Cárcamo, J., 2009. "Tests for zero-inflation and overdispersion: A new approach based on the stochastic convex order," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2628-2639, May.
    4. A. Baccini & L. Barabesi & M. Cioni & C. Pisani, 2014. "Crossing the hurdle: the determinants of individual scientific performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 2035-2062, December.
    5. Marcelo Bourguignon & Diego I. Gallardo & Rodrigo M. R. Medeiros, 2022. "A simple and useful regression model for underdispersed count data based on Bernoulli–Poisson convolution," Statistical Papers, Springer, vol. 63(3), pages 821-848, June.
    6. Tzougas, George & Jeong, Himchan, 2021. "An expectation-maximization algorithm for the exponential-generalized inverse Gaussian regression model with varying dispersion and shape for modelling the aggregate claim amount," LSE Research Online Documents on Economics 108210, London School of Economics and Political Science, LSE Library.
    7. George Tzougas & Himchan Jeong, 2021. "An Expectation-Maximization Algorithm for the Exponential-Generalized Inverse Gaussian Regression Model with Varying Dispersion and Shape for Modelling the Aggregate Claim Amount," Risks, MDPI, vol. 9(1), pages 1-17, January.
    8. Vilhelm Verendel, 2023. "Tracking artificial intelligence in climate inventions with patent data," Nature Climate Change, Nature, vol. 13(1), pages 40-47, January.
    9. José Rodríguez-Avi & María José Olmo-Jiménez, 2017. "A regression model for overdispersed data without too many zeros," Statistical Papers, Springer, vol. 58(3), pages 749-773, September.
    10. Fatemeh Hassanzadeh & Iraj Kazemi, 2017. "Regression modeling of one-inflated positive count data," Statistical Papers, Springer, vol. 58(3), pages 791-809, September.
    11. Liu, Yafen & He, Zhen & Shu, Lianjie & Wu, Zhang, 2009. "Statistical computation and analyses for attribute events," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3412-3425, July.
    12. Cordeiro, Gauss M. & Andrade, Marinho G. & de Castro, Mário, 2009. "Power series generalized nonlinear models," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1155-1166, February.
    13. Rahma Abid & Célestin C. Kokonendji & Afif Masmoudi, 2021. "On Poisson-exponential-Tweedie models for ultra-overdispersed count data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 1-23, March.
    14. Wen, Li & Rogers, Kerrylee & Saintilan, Neil & Ling, Joanne, 2011. "The influences of climate and hydrology on population dynamics of waterbirds in the lower Murrumbidgee River floodplains in Southeast Australia: Implications for environmental water management," Ecological Modelling, Elsevier, vol. 222(1), pages 154-163.
    15. Ben Omrane, Walid & Heinen, Andréas, 2010. "Public news announcements and quoting activity in the Euro/Dollar foreign exchange market," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2419-2431, November.
    16. Tzougas, George, 2020. "EM estimation for the Poisson-Inverse Gamma regression model with varying dispersion: an application to insurance ratemaking," LSE Research Online Documents on Economics 106539, London School of Economics and Political Science, LSE Library.
    17. Tzougas, George & Pignatelli di Cerchiara, Alice, 2021. "The multivariate mixed Negative Binomial regression model with an application to insurance a posteriori ratemaking," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 602-625.
    18. Rodríguez-Avi, J. & Conde-Sánchez, A. & Sáez-Castillo, A.J. & Olmo-Jiménez, M.J. & Martínez-Rodríguez, A.M., 2009. "A generalized Waring regression model for count data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3717-3725, August.
    19. Farouk Mselmi, 2022. "Generalized linear model for subordinated Lévy processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 772-801, June.
    20. Tzougas, George & Vrontos, Spyridon D. & Frangos, Nickolaos E., 2015. "Risk classification for claim counts and losses using regression models for location, scale and shape," LSE Research Online Documents on Economics 70921, London School of Economics and Political Science, LSE Library.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6814-:d:292814. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.