acv552-sup-001.doc409K Figure S1. Southern portion of Primorski Krai area surveyed for Amur tigers in the Russian Far East showing sampling unit design used in resource selection function with sampling units (polygons) where tigers were present (in red) or absent (grey) were treated as a used-unused design to develop RSF models for extrapolation to the Chinese portion of the Changbaishan study area.
acv552-sup-001.doc409K Figure S2. (a) Potential tiger habitat predicted by the ENFA model in the Changbaishan landscape in Northeast China and the southern Russian Far East. All cells are shown (direct, interpolated and extrapolated). (b) Predicted habitat for the Amur Tiger from a resource selection function (RSF) model in the Changbaishan landscape in Northeast China and the southern Russian Far East. Major cities (> 50 000) and major roads are shown. (c) Potential tiger habitat, as predicted by the expert model excluding data on prey densities. The higher the value (towards color green) the better the habitat potential.
acv552-sup-001.doc409K Figure S3. Comparison of predictions of the environmental spatial covariates-only RSF model (GIS Habitat) and the same RSF model with covariates of relative density of the top three prey species for Amur tigers in the southern portion of their range in the Russian Far East.
acv552-sup-001.doc409K Table S1. Predictor variables included in the three complementary Amur tiger habitat modelling approaches, Environmental Niche Factor Analysis (ENFA), Resource Selection Function (RSF) modelling, and the expert-opinion based model.
acv552-sup-001.doc409K Table S2. Cost allocations of five predictor variables used in the Expert Model: the lower the cost, the higher the value in terms of habitat suitability for tigers
acv552-sup-001.doc409K Table S3. Friction values of the environmental variables for Amur tiger habitat connectivity modeling based on expert opinion. Values ranging from 1 (easy to cross) to 1000 (impossible to cross). RFE = Russian Far East.

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