Supplementary Information for “Humans and climate change drove the Holocene decline of the brown bear”
Jörg Albrecht1,*,§, Kamil A. Bartoń1, Nuria Selva1, Robert S. Sommer2, Jon E. Swenson3,4, Richard Bischof3
1
Institute of Nature Conservation, Polish Academy of Sciences, Mickiewicza 33, PL-31-120
Kraków, Poland. 2
Department of Zoology, Institute of Bioscience, University of Rostock, Universitätsplatz 2,
D-18055 Rostock, Germany. 3
Faculty of Environmental Sciences and Natural Resource Management, Norwegian
University of Life Sciences, PO Box 5003, NO-1432 Ås, Norway 4
Norwegian Institute for Nature Research, NO-7485 Trondheim, Norway.
*Correspondence and requests for materials should be addressed to J.A. (email:
[email protected]) §
Present address: Senckenberg Biodiversity and Climate Research Centre (BiK-F),
Senckenberganlage 25, 60325 Frankfurt am Main, Germany.
1
Supplementary Methods Evaluation of potential collinearity problems in the meta-analysis. To evaluate potential collinearity problems that may arise from linear relationships between model covariates, we calculated variance inflation factors (VIFs)1. VIFs for all covariates were lower than 8.16, less than the threshold of 10 above which collinearity may adversely affect regression results1 (Supplementary Table 1). To further assess how robust our analysis was with respect to collinearity among predictors, we calculated two alternative variables to characterize the growing season. First, we used growing degree-days (GDD; base 5 °C; data available at http://www.sage.wisc.edu/atlas/)2, as a proxy for the length of the growing season, because the length of the growing season is indirectly related to net primary productivity via plant growth3. Second, we used the mean monthly temperature of the growing season (Tgs), because, similar to the length of the growing season, the mean temperature of the growing season is indirectly related to net primary productivity via plant growth3. We calculated Tgs as the mean temperature across the months, in which the minimum monthly temperature was above 5 °C. We used a threshold of 5 °C for the growing season to be consistent with the base temperature for growing degree-days. Using these alternative variables in the analysis reduced VIFs from 8 to 3 but led to identical conclusions, suggesting that collinearity does not affect the conclusions from this analysis (Supplementary Table 1). Uncertainty in assumptions about Holocene changes in land-use intensification and colonization of Europe by the brown bear from Asia. To account for uncertainties due to different assumptions about historical changes in land-use intensification (i.e., per capita landuse intensity)4,5, we considered two different scenarios in our analysis (Supplementary Fig. 4). The first scenario (HYDE 3.1 baseline) effectively omitted land-use intensification by assuming that per capita land-use remained approximately constant over time and that the proportion of land used for agriculture, pastures and urban areas increased linearly with
2
increasing population density. In this scenario, land-use intensity closely resembled the pattern in 1961 CE, the first year for which global Food and Agriculture Organization of the United Nations statistics are available4–6. The second scenario (HYDE 3.1 concave) assumed a non-linear, concave relationship between population density and per capita land use through time. That is, low-density populations with high per capita land use first expanded to fill all usable land and then intensified land use (i.e., use less land per capita) as population densities increased over time4,5. This assumption is similar to the assumption of the KK10 model7,8. The global estimates of land use for the early Holocene from the HYDE 3.1 concave scenario and the KK10 model4,7 closely resembled each other and provided an upper-bound estimate of land use in the early Holocene, whereas the HYDE 3.1 baseline scenario provided a lowerbound estimate of land use during the same period. To account for uncertainty regarding colonization of the European continent by the brown bear from Asia9–11 we considered two scenarios. The first scenario assumed that there was no permanent source population at the eastern border of the study area, while the second scenario allowed for colonization from Asia from a permanent source population at the eastern border. To implement these two scenarios, we fixed the state of the eastern most cells in the occupancy matrix Zs,t in the second scenario to permanently occupied, i.e., we set zs,t = 1, whereas these cells were not informed a priori in the first scenario, so that the occupancy state of these cells was estimated by the model (Supplementary Fig. 5). In the main text we report the parameter estimates after pooling the posterior samples from the two-factorial design with two scenarios for changes in per capita land-use intensity during the Holocene (constant versus decreasing) and two scenarios and two scenarios for colonization of the European continent by the brown bear from Asia (yes versus no), respectively.
3
Compilation of the archaeofaunal database. Archaeofaunal records were obtained from the Holocene vertebrate database9,12–14. In most cases, subfossil bones were context dated from their assignment to an archaeological layer. These dates were often radiocarbon supported and obtained from other materials (e.g., bones or charcoal) from the layer in which subfossil bones were recovered9,12,13,15. In a minority of cases (10* -2.46 -0.22 >10* 0.70 >10* >10* >10* >10* Post. mean 0.12 0.71 -0.028 0.047 0.39 -0.053 0.79 -0.48 1.3 -0.31 2.5% CI 0.012 0.68 -0.087 -0.010 0.34 -0.10 0.60 -0.72 1.1 -0.42 97.5% CI 0.19 0.73 0.006 0.12 0.46 -0.008 0.99 -0.25 1.5 -0.20 The model tested for the direct and indirect effects of elevation (ELE), winter temperature (WT), net primary productivity (NPP) and land use (LU) on the extinction rate (EXT) of the European brown bear during the Holocene. List of the path models selected during the Markov Chain Monte Carlo search, along with the marginal selection probabilities of the path models [P(Sel)], and posterior means and 95 % credible intervals of the hypothesized paths, as well as the selection probabilities and Bayes Factors 21 [2loge(BF)] as a measure of support for each path. Paths with decisive support (2loge(BF) > 10) are highlighted in boldface type. Note that positive effects increase extinction rate, whereas negative effects decrease extinction rate. Uncertainty in model assumptions was incorporated by a two-factorial design with two scenarios for changes in per capita land-use intensity during the Holocene (constant versus decreasing) and two scenarios for colonization of the European continent by the brown bear from Asia (yes versus no), respectively (see Supplementary Methods and Supplementary Figs. 4 and 5).
16
Supplementary Table 3. Data on life histories of female brown bears from 43 populations. continent Europe Europe Europe Europe Europe Europe Asia Asia Asia Asia North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika North Amerika
country Sweden Sweden Croatia Spain Finland and Russia Russia Pakistan Japan Japan Russia Canada Canada Canada Canada Canada Canada Canada Canada Canada Canada Canada Canada USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA
locality Noppikoski and Älvdalen, Kopparberg county Kvikkjokk, Norrbotten county Dinara Mountains Western Cantabrian Mountains Finish/Russian Karelia Leningrad Oblast Deosai National Park Oshima Peninsula, Southern Hokkaido Teshio District, Northern Hokkaido Sakha (Yakutia) Rocky Mountain Foothills, Westcentral Alberta Parsnip River, British Columbia Glacier National Park, British Columbia Richardson River, Kugluktuk Anderson Horton Rivers, Inuvialuit Region Tuktoyaktuk Peninsula/Richards Island Vuntut National Park, Northern Yukon Kluane National Park, Yukon Mackenzie Mountains, Northwest Territories Banff National Park and Kananaskis Country Jasper National Park Selkirk Mountains, British Columbia Cabinet-Yaak, Northwest Montana Flathead, Montana Western Brooks Range, Alaska Eastern Brooks Range, Alaska Noatak River, Northwest Alaska McNeil River State Game Sanctuary Katmai National Park, Shelikof Strait coast Kodiak Island, Alaska Admiralty Island Nelchina Basin Black Lake, Alaska Alaska Range, Alaska Kuskokwim Mountains, Southwestern Alaska Swan Mountains East Front, Montana Yellowstone National Park 1959-1970 Yellowstone National Park 1975-1989 Mission Mountains Alaska Peninsula, Alaska Denali National Park, Alaska Susitna River, Talkeetna, Southcentral Alaska
lon 18.0 18.0 16.4 -5.0 33.6 31.3 75.5 140.3 141.9 129.2 -123.0 -122.6 -113.7 -115.5 -133.7 -133.0 -140.0 -138.3 -128.0 -115.0 -118.1 -117.0 -116.0 -114.0 -160.8 -149.5 -162.5 -154.3 -156.4 -153.4 -134.3 -146.8 -159.0 -151.0 -159.0 -113.6 -110.0 -110.5 -110.5 -113.9 -158.8 -151.2 -150.1
lat 61.0 67.0 44.1 43.0 63.7 59.9 35.0 41.9 45.0 66.4 56.5 54.7 48.7 67.9 68.4 69.4 68.5 60.6 64.0 51.0 52.8 49.0 48.0 48.3 68.9 68.2 67.0 59.1 58.7 57.5 57.7 62.0 56.0 63.1 60.0 47.7 47.0 44.6 44.6 47.4 56.5 63.3 62.3
LI 1.74 2.44 2.06 3.30 NA NA 5.70 2.30 NA NA 4.00 3.50 NA 3.30 4.90 3.30 3.50 3.10 3.80 4.40 3.50 3.00 3.00 3.10 4.10 4.24 3.30 3.94 5.60 4.60 3.90 3.80 3.00 4.00 4.53 3.00 2.60 3.20 2.60 3.30 3.00 2.10 2.10
LS 2.29 2.43 2.39 2.26 2.50 2.35 1.33 1.62 1.59 1.89 1.90 1.95 1.70 2.26 2.27 2.30 2.00 1.70 1.83 1.84 2.00 2.18 2.07 2.20 2.03 1.78 2.17 2.15 2.06 2.50 1.80 2.10 2.57 2.20 1.94 1.64 2.20 2.20 1.90 2.12 2.30 2.10 2.09
RR 1.32 1.00 1.16 0.68 NA NA 0.23 0.70 NA NA 0.48 0.56 NA 0.68 0.46 0.70 0.57 0.55 0.48 0.42 0.57 0.73 0.69 0.71 0.50 0.42 0.66 0.55 0.37 0.54 0.46 0.55 0.86 0.55 0.43 0.55 0.85 0.69 0.73 0.64 0.77 1.00 1.00
FM 117 120 128 94 132 127 73 102 103 142 146 113 NA 126 105 124 116 121 110 120 129 123 123 114 117 108 132 160 213 202 169 144 256 154 170 112.64 125 152 134 127 226 125 170
nLI 126 59 17 NA NA NA 24 30 NA NA 1 2 NA 6 24 8 4 NA 11 15 NA 8 7 17 16 NA 10 35 19 41 7 44 NA 51 34 6 11 68 20 NA 81 NA NA
nLS 136 75 56 23 31 31 33 13 32 119 5 20 35 19 NA 28 6 11 6 38 3 17 14 26 23 13 35 137 51 29 32 64 46 36 33 17 41 173 232 NA 200 42 91
nFM 59 46 67 12 81 15 4 17 31 NA 8 29 NA 60 NA 36 35 35 28 17 7 21 21 16 35 31 NA NA 59 16 18 21 34 52 23 6 6 72 63 3 63 65 50
Tgs 14.100 10.940 15.940 15.280 13.660 13.840 13.040 16.610 14.240 10.620 12.100 13.150 15.180 9.720 10.550 10.550 7.450 8.520 9.930 12.160 10.700 14.190 15.770 14.300 7.610 9.950 10.280 11.380 11.620 10.090 10.700 10.260 9.680 11.950 11.060 15.060 18.400 14.330 14.330 15.060 9.860 11.950 11.950
Tws -3.860 -11.110 -0.910 3.600 -8.610 -7.500 -9.540 -3.090 -5.300 -34.220 -12.540 -9.250 -7.340 -19.640 -21.540 -21.540 -20.930 -13.910 -18.580 -8.550 -8.650 -6.200 -5.420 -7.690 -17.530 -22.740 -15.410 -7.030 -8.200 -2.430 -1.980 -9.740 -3.350 -13.590 -9.070 -6.900 -6.940 -8.640 -8.640 -6.900 -4.440 -13.590 -13.590
NPP 0.910 0.540 1.380 1.130 0.710 0.920 0.700 1.310 1.080 0.090 0.560 0.710 0.790 0.200 0.220 0.220 0.170 0.340 0.260 0.660 0.620 0.910 0.950 0.830 0.220 0.190 0.330 0.670 0.670 0.870 0.970 0.560 0.850 0.430 0.620 0.820 0.680 0.750 0.750 0.820 0.760 0.430 0.430
GDD 1169 446 1975 2050 997 1386 935 1945 1545 232 696 808 1224 246 433 433 75 179 294 608 440 989 1425 977 91 312 358 552 690 621 678 551 528 468 608 1299 1793 1181 1181 1299 583 468 468
MAT 5.1 -1.7 9.7 10.7 1.2 4.2 1.6 8.9 6.2 -18.4 -1.4 1.2 4.2 -11.0 -10.2 -10.2 -12.7 -6.2 -8.7 0.4 -0.3 4.1 4.6 3.0 -10.2 -11.5 -6.5 0.6 0.6 3.5 4.8 -1.4 3.2 -3.9 -0.2 3.4 6.4 1.8 1.8 3.4 2.0 -3.9 -3.9
MAP 545.0 528.7 963.6 714.0 572.2 620.3 400.1 1330.9 1228.2 371.0 528.9 654.3 461.3 249.0 190.3 190.3 165.9 576.8 492.8 578.9 713.4 758.1 651.0 692.5 189.7 205.0 244.5 780.5 550.2 1412.2 2465.9 946.3 673.7 529.1 601.7 483.3 386.3 575.3 575.3 483.3 572.4 529.1 529.1
Ref LS 22 22 24 25 23 23 26 27 28 23 29 31 17 32 32 33 33 17 33 34 33 36 36 17 33 33 38 39 40 17 17 17 40 17 41 42 17 17 17 33 17 40 40
Ref LI 22 22 24 16 26 27 30 31 30 30 30 33 30 33 34 35 36 36 17 30 33 38 39 30 17 17 17 33 30 41 42 17 17 17 30 17 44 44
lon and lat, longitude and latitude (decimal degree); LI, inter-birth interval (years); LS, litter size (cubs female−1); RR, reproductive rate (cubs female−1 year−1); FM, female mass (kg); nLI, nLS, nFM, sample sizes for the three life-history variables; Tgs, temperature of the growing season; Tws, temperature of the winter season; NPP, net primary productivity (kg m−2a−1); GDD, growing degree days (base 5 °C); MAT, mean annual temperature (°C); MAP, total annual precipitation (mm); Ref LS, Ref LI, Ref FM, codes of the references for the three life-history variables.
17
Ref FM 23 23 23 23 23 23 26 23 23 23 30 31 30 30 30 30 30 30 30 30 37 37 17 30 30 30 30 30 17 17 17 23 30 30 43 17,23 17 17 30 17 23 23
Supplementary Computer Code Supplementary Computer Code 1. JAGS code of the metapopulation model. Black text indicates the model code that is compiled by JAGS, # comments are highlighted in blue. var wM[ncell, ncell], swM[ncell, ncell], cM[ncell, ncell, ntime]; model { ##### priors for SSVS variable selection sd.beta ~ dunif(0, 100) tau.beta
