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Spatial and temporal predictions of moose winter distribution.
J Månsson1, N Bunnefeld, H Andrén
1Grimsö Wildlife Research Station, Department of Ecology, Swedish University of Agricultural Sciences, Riddarhyttan, Sweden. johan.mansson@slu.se
Oecologia
|March 23, 2012
Summary
Understanding moose (Alces alces) distribution requires considering spatial data and forage availability. Models incorporating georeferencing improved predictions of winter moose distribution, highlighting the importance of landscape factors.
Area of Science:
- Ecology
- Wildlife Biology
- Spatial Analysis
Background:
- Herbivore distribution is often uneven due to varying resource availability across landscapes.
- Predicting large herbivore distribution is crucial for effective wildlife management and conservation strategies.
Purpose of the Study:
- To predict the winter distribution of moose (Alces alces) using spatial information and forage availability.
- To assess the effectiveness of zero-inflated generalized additive models (GLMMs) incorporating georeferencing for predicting herbivore distribution.
Main Methods:
- Utilized zero-inflated generalized additive models (GLMMs) to analyze moose pellet count data.
- Incorporated georeferencing (longitude and latitude) alongside forage availability and proximity to neighboring sites.
- Compared model performance with and without spatial information to evaluate predictive power.
Main Results:
- Models including spatial information (georeferencing) significantly improved the prediction of moose distribution compared to models using only forage availability.
- Moose distribution was predicted reasonably well in 2 out of 4 years when considering forage and spatial data.
- Moose distribution patterns were influenced by weather conditions, with increased clumping observed during snowy years.
Conclusions:
- A non-linear approach incorporating georeferencing is valuable for understanding herbivore distribution at fine spatial scales.
- Moose patch selection is influenced by the characteristics of the chosen patch, neighboring patches, and broader-scale factors like management and weather.
- Further research is needed to account for unexplained variation, including topography, predation, competition, and specific management strategies.
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