IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0055097.html
   My bibliography  Save this article

Explaining Local-Scale Species Distributions: Relative Contributions of Spatial Autocorrelation and Landscape Heterogeneity for an Avian Assemblage

Author

Listed:
  • Brady J Mattsson
  • Elise F Zipkin
  • Beth Gardner
  • Peter J Blank
  • John R Sauer
  • J Andrew Royle

Abstract

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition.

Suggested Citation

  • Brady J Mattsson & Elise F Zipkin & Beth Gardner & Peter J Blank & John R Sauer & J Andrew Royle, 2013. "Explaining Local-Scale Species Distributions: Relative Contributions of Spatial Autocorrelation and Landscape Heterogeneity for an Avian Assemblage," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-12, February.
  • Handle: RePEc:plo:pone00:0055097
    DOI: 10.1371/journal.pone.0055097
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0055097
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0055097&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0055097?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Tomohiro Ando, 2007. "Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models," Biometrika, Biometrika Trust, vol. 94(2), pages 443-458.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yanguang Chen, 2015. "A New Methodology of Spatial Cross-Correlation Analysis," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-20, May.
    2. Eve Bohnett & Jessica Schulz & Robert Dobbs & Thomas Hoctor & Dave Hulse & Bilal Ahmad & Wajid Rashid & Hardin Waddle, 2023. "Shorebird Monitoring Using Spatially Explicit Occupancy and Abundance," Land, MDPI, vol. 12(4), pages 1-15, April.
    3. Yan-Yan Chen & Xi-Bao Huang & Ying Xiao & Yong Jiang & Xiao-wei Shan & Juan Zhang & Shun-Xiang Cai & Jian-Bing Liu, 2015. "Spatial Analysis of Schistosomiasis in Hubei Province, China: A GIS-Based Analysis of Schistosomiasis from 2009 to 2013," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-14, April.

    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. Tomohiro Ando & Ruey S. Tsay, 2009. "Model selection for generalized linear models with factor‐augmented predictors," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 207-235, May.
    2. Bai, Jushan & Ando, Tomohiro, 2013. "Multifactor asset pricing with a large number of observable risk factors and unobservable common and group-specific factors," MPRA Paper 52785, University Library of Munich, Germany, revised Dec 2013.
    3. Tomohiro Ando, 2012. "Bayesian portfolio selection under a multifactor asset return model with predictive model selection," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 14(1/2), pages 77-101.
    4. Ando, Tomohiro, 2009. "Bayesian inference for the hazard term structure with functional predictors using Bayesian predictive information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1925-1939, April.
    5. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.
    6. Yuki Kawakubo & Tatsuya Kubokawa & Muni S. Srivastava, 2015. "A Variant of AIC Using Bayesian Marginal Likelihood," CIRJE F-Series CIRJE-F-971, CIRJE, Faculty of Economics, University of Tokyo.
    7. Stefano Grassi & Francesco Ravazzolo & Joaquin Vespignani & Giorgio Vocalelli, 2023. "Global Money Supply and Energy and Non-Energy Commodity Prices: A MS-TV-VAR Approach," CAMA Working Papers 2023-13, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Ando, Tomohiro, 2009. "Bayesian factor analysis with fat-tailed factors and its exact marginal likelihood," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1717-1726, September.
    9. Nandram Balgobin, 2016. "Bayesian Predictive Inference of a Proportion Under a Twofold Small-Area Model," Journal of Official Statistics, Sciendo, vol. 32(1), pages 187-208, March.
    10. Tsay, Ruey S. & Ando, Tomohiro, 2012. "Bayesian panel data analysis for exploring the impact of subprime financial crisis on the US stock market," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3345-3365.
    11. Wang, Yixin & So, Mike K.P., 2016. "A Bayesian hierarchical model for spatial extremes with multiple durations," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 39-56.
    12. Filidor Vilca & Caio L. N. Azevedo & N. Balakrishnan, 2017. "Bayesian inference for sinh-normal/independent nonlinear regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 2052-2074, August.
    13. Yuki Kawakubo & Tatsuya Kubokawa & Muni S. Srivastava, 2018. "A Variant of AIC Based on the Bayesian Marginal Likelihood," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 60-84, May.
    14. Jincheol Park & Shili Lin, 2017. "A random effect model for reconstruction of spatial chromatin structure," Biometrics, The International Biometric Society, vol. 73(1), pages 52-62, March.
    15. Ando, Tomohiro, 2009. "Bayesian portfolio selection using a multifactor model," International Journal of Forecasting, Elsevier, vol. 25(3), pages 550-566, July.
    16. Michael T. Owyang & Hannah Shell & Daniel Soques, 2022. "The Evolution of Regional Beveridge Curves," Working Papers 2022-037, Federal Reserve Bank of St. Louis.
    17. Liang Yulan & Kelemen Arpad, 2016. "Bayesian state space models for dynamic genetic network construction across multiple tissues," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 273-290, August.
    18. Cristina Mollica & Luca Tardella, 2017. "Bayesian Plackett–Luce Mixture Models for Partially Ranked Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 442-458, June.
    19. Toda, Motomu & Doi, Kazuki & Ishihara, Masae I. & Azuma, Wakana A. & Yokozawa, Masayuki, 2020. "A Bayesian inversion framework to evaluate parameter and predictive inference of a simple soil respiration model in a cool-temperate forest in western Japan," Ecological Modelling, Elsevier, vol. 418(C).
    20. Zellner, Arnold & Ando, Tomohiro, 2010. "Bayesian and non-Bayesian analysis of the seemingly unrelated regression model with Student-t errors, and its application for forecasting," International Journal of Forecasting, Elsevier, vol. 26(2), pages 413-434, April.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0055097. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.