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Estimation of spatial sampling effort based on presence-only data and accessibility

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  • Fernández, Daniel
  • Nakamura, Miguel

Abstract

Sampling bias contained in data of biological surveys is very common. Bias is clearly a function of roads, cities, rivers, or other physical features that determines accessibility of collectors, and many data sets of species are presence-only. We set out to estimate spatial sampling bias in a region, based on presence-only data, by explicitly incorporating information on these accessibility factors, and by considering a target group of species that may share a common search pattern. In order to indirectly estimate the number of individuals, we also resort to the concept of species richness. A probabilistic (multinomial) model is proposed, enabling standard likelihood inference procedures to be implemented. Simulation scenarios for exploration of the model and experimentation with the estimation procedure are included. Illustrative examples over a region of Mexico with mammals and butterflies are also reported with insightful results. Our model is able to estimate the sampling bias in a region and enhance the inferences regarding presence-only data.

Suggested Citation

  • Fernández, Daniel & Nakamura, Miguel, 2015. "Estimation of spatial sampling effort based on presence-only data and accessibility," Ecological Modelling, Elsevier, vol. 299(C), pages 147-155.
    • Handle: RePEc:eee:ecomod:v:299:y:2015:i:c:p:147-155
    DOI: 10.1016/j.ecolmodel.2014.12.017
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    References listed on IDEAS

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    1. David I Warton & Ian W Renner & Daniel Ramp, 2013. "Model-Based Control of Observer Bias for the Analysis of Presence-Only Data in Ecology," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-9, November.
    2. Robert M. Dorazio, 2012. "Predicting the Geographic Distribution of a Species from Presence-Only Data Subject to Detection Errors," Biometrics, The International Biometric Society, vol. 68(4), pages 1303-1312, December.
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    Cited by:

    1. Adrien Guetté & Sébastien Caillault & Joséphine Pithon & Guillaume Pain & Hervé Daniel & Benoit Marchadour & Véronique Beaujouan, 2022. "Who and Where Are the Observers behind Biodiversity Citizen Science Data? Effect of Landscape Naturalness on the Spatial Distribution of French Birdwatching Records," Land, MDPI, vol. 11(11), pages 1-25, November.
    2. Aubry, Philippe & Francesiaz, Charlotte & Guillemain, Matthieu, 2024. "On the impact of preferential sampling on ecological status and trend assessment," Ecological Modelling, Elsevier, vol. 492(C).
    3. Liu, Fang & McShea, William J. & Li, Diqiang, 2017. "Correlating habitat suitability with landscape connectivity: A case study of Sichuan golden monkey in China," Ecological Modelling, Elsevier, vol. 353(C), pages 37-46.

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