Online Resource 2
Journal: Landscape Ecology
Title: Cumulative effects of infrastructure and human disturbance: a case study with reindeer Sindre Eftestøl1,2, Diress Tsegaye1,2*, Kjetil Flydal1, and Jonathan E.Colman1,2
1 Department of Biosciences, University of Oslo, Blindern, P.O. Box 1066, 0316 Oslo, Norway
2 Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway
*Corresponding author: Diress Tsegaye
Address for correspondence (Tel: +47 22854576; Fax: +47 22 85 47 26; E-mail: d.t.alemu@ibv.uio.no)
Fig S1 Relative probability (±95% CI) of reindeer resource selection for the disturbance distance intervals 0.25-2 km and 0.25-3 for snow free and winter seasons (because neither the 0.25-1 km or 0.25-2 km interval did show negative effects for these two seasons) and 1-3 km for spring (because 1-2 did not show negative effects) in relation to cumulative disturbance (i.e. sum of disturbance intensity levels) from the top predictive model for reindeer resource selection (RSF) from 2011-2018 in the Ildgruben reindeer district in Nordland, Norway. The relative probability of selection for the cumulative disturbance predictor variable were calculated while keeping the other continuous predictor variables constant (at their mean values).
Fig S2 Relative probability (±95% CI) of reindeer resource selection in relation to cumulative disturbance (i.e.
sum of disturbance intensity levels) at different scales (i.e. multi-grain analysis) in relation to cumulative disturbance (i.e. sum of disturbance intensity levels) from the top predictive model for reindeer resource selection (RSF) in the snow-free seasons (i.e. calving, postcalving, summer and autumn) from 2011-2018 in the Ildgruben reindeer district in Nordland, Norway. The relative probability of selection for the cumulative
disturbance predictor variable were calculated while keeping the other continuous predictor variables constant (at their mean values).According to Laforge et al. (2015), multi-grain analysis is based on generating buffers of increasing radius around a telemetry relocation on the landscape. Larger buffers incorporate greater landscape context.
