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A two-species occupancy model accommodating simultaneous spatial and interspecific dependence.

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    This study introduces a new occupancy model for estimating species presence, accounting for both species interactions and spatial patterns. The model accurately predicted red fox and coyote occupancy by considering these combined factors.

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    Area of Science:

    • Ecology
    • Wildlife Biology
    • Statistical Modeling

    Background:

    • Occupancy models estimate species presence with imperfect detection.
    • Existing models struggle to simultaneously address interspecific and spatial dependencies.
    • This limitation hinders accurate ecological assessments in complex environments.

    Purpose of the Study:

    • To develop and validate a novel two-species occupancy model.
    • To simultaneously incorporate interspecific and spatial dependence.
    • To improve ecological predictions for interacting species.

    Main Methods:

    • Utilized a point-referenced multivariate hierarchical spatial model.
    • Employed predictive process models for spatial random effects.
    • Applied probit regression for efficient posterior sampling.
    • Modeled occupancy of red fox (Vulpes vulpes) and coyote (Canis latrans) using camera trap data.

    Main Results:

    • Red fox and coyotes showed spatial dependence at >0.8 km and >0.4 km, respectively.
    • Interspecific dependence between species varied geographically.
    • The model incorporating both spatial and interspecific dependence provided the best predictions.
    • This highlights the importance of simultaneous consideration of these factors.

    Conclusions:

    • The proposed model effectively integrates interspecific and spatial dependencies.
    • Simultaneous modeling significantly improves occupancy prediction accuracy.
    • This approach offers a more robust tool for wildlife and ecological studies.
    • Findings underscore the ecological significance of species interactions and spatial structuring.