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A quantile count model of water depth constraints on Cape Sable seaside sparrows
Article first published online: 1 NOV 2007
DOI: 10.1111/j.1365-2656.2007.01311.x
Additional Information
How to Cite
Cade, B. S. and Dong, Q. (2008), A quantile count model of water depth constraints on Cape Sable seaside sparrows. Journal of Animal Ecology, 77: 47–56. doi: 10.1111/j.1365-2656.2007.01311.x
Publication History
- Issue published online: 1 NOV 2007
- Article first published online: 1 NOV 2007
- Received 15 March 2007; accepted 13 August 2007; Handling Editor: Bryan Manly
Keywords:
- Ammodramus maritimus mirabilis;
- Everglades;
- habitat models;
- hydrology;
- quantile regression;
- zero-inflated Poisson
Summary
- 1A quantile regression model for counts of breeding Cape Sable seaside sparrows Ammodramus maritimus mirabilis (L.) as a function of water depth and previous year abundance was developed based on extensive surveys, 1992–2005, in the Florida Everglades. The quantile count model extends linear quantile regression methods to discrete response variables, providing a flexible alternative to discrete parametric distributional models, e.g. Poisson, negative binomial and their zero-inflated counterparts.
- 2Estimates from our multiplicative model demonstrated that negative effects of increasing water depth in breeding habitat on sparrow numbers were dependent on recent occupation history. Upper 10th percentiles of counts (one to three sparrows) decreased with increasing water depth from 0 to 30 cm when sites were not occupied in previous years. However, upper 40th percentiles of counts (one to six sparrows) decreased with increasing water depth for sites occupied in previous years.
- 3Greatest decreases (–50% to –83%) in upper quantiles of sparrow counts occurred as water depths increased from 0 to 15 cm when previous year counts were 1, but a small proportion of sites (5–10%) held at least one sparrow even as water depths increased to 20 or 30 cm.
- 4A zero-inflated Poisson regression model provided estimates of conditional means that also decreased with increasing water depth but rates of change were lower and decreased with increasing previous year counts compared to the quantile count model. Quantiles computed for the zero-inflated Poisson model enhanced interpretation of this model but had greater lack-of-fit for water depths > 0 cm and previous year counts 1, conditions where the negative effect of water depths were readily apparent and fitted better with the quantile count model.