Table 2 Candidate models and akaike weights

Model M*

~ TmKnown*NDVI + Ruggedness + Herd*(DistRd), rand(ElkID)

Model L*

~ TmKnown + Ruggedness + Herd*(DistRd) + NDVI, rand(ElkID)

Model K*

~ TmKnown + Ruggedness + Herd*(DistRd), rand(ElkID)

Model G*

~ TmKnown + NDVI + Herd*(DistRd + Traffic), rand(ElkID)

Model J

~ TmKnown + Ruggedness, rand(ElkID)

Model H*

~ TmKnown*NDVI, rand(ElkID)

Model I*

~ TmKnown*Aspect, rand(ElkID)

Model E

~ TmKnown + NDVI, rand(ElkID)

Model F

~ TmKnown + NDVI + Aspect + Canopy, rand(ElkID)

Model D*

~ NDVI + Herd*(DistRD + Traffic), rand(ElkID)

Model C

~ NDVI + Aspect + Canopy, rand(ElkID)

Model A

~ NDVI, rand(ElkID)

Model B

~ NDVI + Aspect, rand(ElkID)