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The Analysis of Crow Population Dynamics as a Surveillance Tool
Author(s) -
Ludwig A.,
BigrasPoulin M.,
Michel P.
Publication year - 2009
Publication title -
transboundary and emerging diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.392
H-Index - 63
eISSN - 1865-1682
pISSN - 1865-1674
DOI - 10.1111/j.1865-1682.2009.01090.x
Subject(s) - geography , population , west nile virus , demography , ecology , biology , zoology , virus , virology , sociology
Summary West Nile virus (WNV) infection, a zoonotic disease for which birds act as a reservoir, first appeared in North America in August 1999. It was first reported in Quebec in 2002. The Quebec surveillance system for WNV has several components, including the surveillance of mortality in corvid populations, which includes the American crow ( Corvus brachyrhynchos ). The main objectives of this study are to better understand the population dynamics of this species in Quebec and to evaluate the impact of WNV on these dynamics. We obtained observation data for living crows in this province for the period of 1990–2005 and then conducted a spectral analysis of these data. To study changes in crow population dynamics, the analysis was carried out before and after the appearance of WNV and space was divided in two different areas (urban and non‐urban). Our results show the importance of cycles with periods of less than 1 year in non‐urban areas and cycles with periods of greater than 1 year in urban areas in the normal population dynamics of the species. We obtained expected fluctuations in bird densities using an algorithm derived from spectral decomposition. When we compared these predictions with data observed after 2002, we found marked perturbations in population dynamics beginning in 2003 and lasting up to 2005. In the discussion, we present various hypotheses based on the behaviour of the American crow to explain the normal population dynamics observed in this species and the effect of type of area (urban versus non‐urban). We also discuss how the predictive algorithm could be used as a disease surveillance tool and as a measure of the impact of a disease on wild fauna.