Chapter3 The Occupancy

Obtaining data for studies of animal populations is costly and wasteful, and it is not always possible to measure population density or demographic parameters such as birth or mortality rates. That is why the estimation of habitat occupation (\(\psi\)) is a good study tool since it is a reflection of other important population parameters such as abundance and density, which require a high number of records, with the economic and logistical costs involved. Additionally, and because detectability (p) in wild animals is not complete, the use of raw data generates underestimates of habitat occupation. Using repeated sampling, it is possible to generate estimates of detectability and, with this estimate, obtain unbiased values of habitat occupancy. Occupancy analysis methods were initially developed by (MacKenzie et al., 2002) and later expanded by other authors (Kéry & Royle, 2008; MacKenzie et al., 2006; MacKenzie & Royle, 2005; Royle et al., 2005; Royle, 2006; Royle & Kéry, 2007). These types of models allow inferences to be made about the effects of continuous and categorical variables on habitat occupancy. Furthermore, if sampling is done over long periods, it is also possible to estimate extinction and recolonization rates, which are useful in metapopulation studies (MacKenzie et al., 2003). This is a field of great development in biostatistics that has produced a great explosion of studies that use occupation taking detectability into account (Guillera-Arroita et al., 2010, 2015, 2014; Guillera-Arroita, 2011; Guillera-Arroita & Lahoz-Monfort, 2012; Kéry et al., 2013).

References

Guillera-Arroita, G. (2011). Impact of sampling with replacement in occupancy studies with spatial replication. Methods in Ecology and Evolution, 2(4), 401–406. https://doi.org/10.1111/j.2041-210X.2011.00089.x

Guillera-Arroita, G., & Lahoz-Monfort, J. J. (2012). Designing studies to detect differences in species occupancy: power analysis under imperfect detection. Methods in Ecology and Evolution, 3(5), 860–869. https://doi.org/10.1111/j.2041-210X.2012.00225.x

Guillera-Arroita, G., Lahoz-Monfort, J. J., Elith, J., Gordon, A., Kujala, H., Lentini, P. E., McCarthy, M. A., Tingley, R., & Wintle, B. A. (2015). Is my species distribution model fit for purpose? Matching data and models to applications. Global Ecology and Biogeography, 24(3), 276–292. https://doi.org/10.1111/geb.12268

Guillera-Arroita, G., Ridout, M. S., & Morgan, B. J. T. (2010). Design of occupancy studies with imperfect detection. Methods in Ecology and Evolution, 1(2), 131–139. https://doi.org/10.1111/j.2041-210X.2010.00017.x

Guillera-Arroita, G., Ridout, MartinS., & Morgan, ByronJ. T. (2014). Two-Stage Bayesian Study Design for Species Occupancy Estimation. Journal of Agricultural, Biological, and Environmental Statistics, 1–14. https://doi.org/10.1007/s13253-014-0171-4

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Kéry, M., & Royle, J. A. (2008). Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys. Journal of Applied Ecology, 45(2), 589–598. https://doi.org/10.1111/j.1365-2664.2007.01441.x

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MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Andrew Royle, J., & Langtimm, C. A. (2002). Estimating site occupancy rates when detection probabilities are less than one. Ecology, 83(8), 2248–2255. https://doi.org/10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2

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