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Analyzing and modelling spatial distribution of summering lesser kestrel: The role of spatial autocorrelation

Author

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  • de Frutos, Ángel
  • Olea, Pedro P.
  • Vera, Rubén

Abstract

In modelling spatial distribution of species, ignoring spatial autocorrelation (SA) and multicollinearity may lead to false ecological conclusions. Here we take into account both issues for examining and modelling the spatial pattern of abundance of the globally threatened lesser kestrel (Falco naumanni) during summer in a 38,400ha area of northwestern Spain where large premigratory aggregations of the species occur. Spatial pattern was examined using Moran's correlogram, and models were built including geographical coordinates and autocovariate terms (which account for SA) in generalized linear models (GLM) and hierarchical partitioning (HP) models. HP models allow to alleviate multicollinearity. A grid-based approach was used by dividing the study area in 24 contiguous 4km×4km squares where birds were counted in 2–3 visits per square (response variable). Environmental coarse-grained variables were extracted from a geographic information system (GIS) at three spatial extents. Moran's correlogram showed that lesser kestrel mean abundance per square was spatially autocorrelated up to 4–8km. The results from both GLM and HP analyses were roughly compatible. The GLM models explained 80.0% of the variation in kestrel abundance and were the same at the three spatial extents. Lesser Kestrel abundance was not significantly explained by landscape variables, but was negatively related to both the distance to the nearest communal roost and distance to the nearest breeding colony with more of 10 breeding pairs of lesser kestrel. An autocovariate term added later in the GLM models improved both their explanatory power (from 74.5 to 80.0%) and model residuals, which were not longer spatially autocorrelated, fulfilling thus the statistical assumption of independent errors. Findings suggest that the spatial distribution of abundance of summering lesser kestrel is, at least, partially driven by endogenous causes, such as conspecific attraction. Exogenous causes such as finer-scale variables (e.g. type of crops and food available) are yet likely needed for lesser kestrel-environment relationships.

Suggested Citation

  • de Frutos, Ángel & Olea, Pedro P. & Vera, Rubén, 2007. "Analyzing and modelling spatial distribution of summering lesser kestrel: The role of spatial autocorrelation," Ecological Modelling, Elsevier, vol. 200(1), pages 33-44.
  • Handle: RePEc:eee:ecomod:v:200:y:2007:i:1:p:33-44
    DOI: 10.1016/j.ecolmodel.2006.07.007
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    Citations

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    Cited by:

    1. Chao Xu & Didit O Pribadi & Dagmar Haase & Stephan Pauleit, 2020. "Incorporating spatial autocorrelation and settlement type segregation to improve the performance of an urban growth model," Environment and Planning B, , vol. 47(7), pages 1184-1200, September.
    2. Walsh, Andrew S. & Louis, Thomas A. & Glass, Gregory E., 2007. "Detecting multiple levels of effect during survey sampling using a Bayesian approach: Point prevalence estimates of a hantavirus in hispid cotton rats (Sigmodon hispidus)," Ecological Modelling, Elsevier, vol. 205(1), pages 29-38.
    3. Schaefer, James A. & Mayor, Stephen J., 2007. "Geostatistics reveal the scale of habitat selection," Ecological Modelling, Elsevier, vol. 209(2), pages 401-406.
    4. Jingyu Liu & Walter W. Piegorsch & A. Grant Schissler & Susan L. Cutter, 2018. "Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 803-823, June.
    5. Saeed Khorram & Mustafa Ergil, 2018. "Spatiotemporal patterns of sediment transport rate and beach–ocean profile for multi-hazard risk management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(1), pages 421-444, January.
    6. Marmion, Mathieu & Luoto, Miska & Heikkinen, Risto K. & Thuiller, Wilfried, 2009. "The performance of state-of-the-art modelling techniques depends on geographical distribution of species," Ecological Modelling, Elsevier, vol. 220(24), pages 3512-3520.
    7. Wu, Daqian & Liu, Jian & Zhang, Gaosheng & Ding, Wenjuan & Wang, Wei & Wang, Renqing, 2009. "Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk (Tamarix chinensis Lour.)," Ecological Modelling, Elsevier, vol. 220(24), pages 3490-3498.

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