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Development of a Sightability Model for Low‐Density Elk Populations in Ontario, Canada
Author(s) -
MCINTOSH TERESE E.,
ROSATTE RICHARD C.,
HAMR JOSEF,
MURRAY DENNIS L.
Publication year - 2009
Publication title -
the journal of wildlife management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 111
eISSN - 1937-2817
pISSN - 0022-541X
DOI - 10.2193/2007-550
Subject(s) - canopy , deciduous , cervus elaphus , geography , akaike information criterion , forestry , diameter at breast height , ecology , population , cover (algebra) , aerial survey , habitat , tree canopy , physical geography , demography , biology , cartography , statistics , mathematics , sociology , mechanical engineering , engineering
ABSTRACT The status of recolonizing elk ( Cervus elaphus ) populations in Ontario, Canada, is unclear and there is a need for effective population survey methods that can be applied locally. We sought to develop a sightability model that could account for both low densities of elk and dense forest cover in elk‐release areas in Ontario. We corrected winter aerial survey counts for sightability based on radiocollared animals known to be within observable distance of the aircraft. The multivariate model with the highest Akaike's Information Criterion corrected for sample size weight ( w i = 0.427) revealed that elk group size, elk activity, dominant tree type, percent canopy cover, and percent conifer cover were significant predictors of elk sightability. The group‐size effect indicated that odds of sighting an elk increased by 1.353 (95% CI = 0.874‐3.689) for every additional elk. Standing elk were 5.033 (95% CI = 0.936‐15.541) times more likely to be observed than were resting elk, and those located in conifer cover were 0.013 (95% CI = 0.001‐0.278) times less likely to be sighted than elk in deciduous cover. Furthermore, elk located in >50% canopy cover and >50% conifer cover were 0.041 (95% CI = 0.003‐0.619) times and 0.484 (95% CI = 0.024‐9.721) times less likely to be sighted than elk in more open habitat, respectively. During model validation, observers detected 79% (113/143) of known elk in any given area, and population and sightability model predictions (±90% CI) overlapped with the population estimate, implying that our predictive model was robust. Unsurprisingly, large groups of elk in open habitat increased model precision, which highlights difficulties of counting Ontario elk in their northern range. We conclude that our model provided increased reliability for estimating elk numbers in Ontario compared to existing methods, and that the estimator may be useful in other areas where elk density is low and sightability is poor due to dense forest cover.