Appendix A. Additional information explaining the statistical models with figures and tables, in specific, the area dependencies of landscape indices, the home range size of red and roe deer across spatio-temporal scales, table of random effects for mixed models on all spatio-temporal scales for red and roe deer, and tables of the mixed models with different correlation structure for all spatio-temporal scales for red and roe deer.

Additional information explaining the statistical models with figures and tables, in specific, the area dependencies of landscape indices, the home range size of red and roe deer across spatio-temporal scales, table of random effects for mixed models on all spatio-temporal scales for red and roe deer, and tables of the mixed models with different correlation structure for all spatio-temporal scales for red and roe deer

Multiscale bark beetle disturbance data

Multiscale bark beetle disturbance dat

Country, Cover or Protection: What Shapes the Distribution of Red Deer and Roe Deer in the Bohemian Forest Ecosystem?

<div><p>The Bohemian Forest Ecosystem encompasses various wildlife management systems. Two large, contiguous national parks (one in Germany and one in the Czech Republic) form the centre of the area, are surrounded by private hunting grounds, and hunting regulations in each country differ. Here we aimed at unravelling the influence of management-related and environmental factors on the distribution of red deer (<i>Cervus elaphus</i>) and roe deer (<i>Capreolus capreolus</i>) in this ecosystem. We used the standing crop method based on counts of pellet groups, with point counts every 100 m along 218 randomly distributed transects. Our analysis, which accounted for overdispersion as well as zero inflation and spatial autocorrelation, corroborated the view that both human management and the physical and biological environment drive ungulate distribution in mountainous areas in Central Europe. In contrast to our expectations, protection by national parks was the least important variable for red deer and the third important out of four variables for roe deer; protection negatively influenced roe deer distribution in both parks and positively influenced red deer distribution in Germany. Country was the most influential variable for both red and roe deer, with higher counts of pellet groups in the Czech Republic than in Germany. Elevation, which indicates increasing environmental harshness, was the second most important variable for both species. Forest cover was the least important variable for roe deer and the third important variable for red deer; the relationship for roe deer was positive and linear, and optimal forest cover for red deer was about 70% within a 500 m radius. Our results have direct implications for the future conservation management of deer in protected areas in Central Europe and show in particular that large non-intervention zones may not cause agglomerations of deer that could lead to conflicts along the border of protected, mountainous areas.</p></div

Red deer (1-h and 15-min fix rates) and roe deer (1-h and 4-h fix rates) relocation data along with environmental predictors

Abbreviations: "id": deer identification code (name). "loc_id": relocation stratum."used": binomial response variable (1: used; 0: available). "sun_elev": elevation of the sun with respect to the horizon, in degrees. "solrad_fac": categorical proxy for microclimate conditions (see Table S1 for additional details). "rugged_10": terrain ruggedness derived from the digital elevation model, in meters. "dist_trails": distance to the closest hiking trail, in meters. "ndvi_preds": NDVI."map_reclass": photo-interpreted habitat map of the BFNP from year 2012 "corine_reclass": corine land cover classes. "res5_PC1": 5-m resolution LiDAR, PC1. "res5_PC2": 5-m resolution LiDAR, PC2. "res5_PC3": 5-m resolution LiDAR, PC3. “res5_PC4": 5-m resolution LiDAR, PC4. "res5_PC5": 5-m resolution LiDAR, PC5. "res5_PC6": 5-m resolution LiDAR, PC6. "res5_PC7": 5-m resolution LiDAR, PC7. “res5_PC8": 5-m resolution LiDAR, PC8. "res5_PC9": 5-m resolution LiDAR, PC9. "res5_PC10": 5-m resolution LiDAR, PC10. "res5_PC11": 5-m resolution LiDAR, PC10. "res10_PC1" : 10-m resolution LiDAR, PC1. "res10_PC2" : 10-m resolution LiDAR, PC2. "res10_PC3": 10-m resolution LiDAR, PC3. "res10_PC4": 10-m resolution LiDAR, PC4. "res10_PC5": 10-m resolution LiDAR, PC5. "res10_PC6": 10-m resolution LiDAR, PC6. "res10_PC7": 10-m resolution LiDAR, PC7. "res10_PC8": 10-m resolution LiDAR, PC8. "res10_PC9": 10-m resolution LiDAR, PC9. "st_deci": number of deciduous trees. "st_conif": number of conifer trees. "st_dead_all": number of standing or lying dead trees. "UStory": understory vegetation. "MStory": midstory vegetation. "OStory": overstory vegetation. "MeanHeight": mean height of vegetation

Proportion of juveniles in the standing population and among kills.

<p>Data on the standing population were from camera traps; kills were those found in the field. As the proportion of juvenile red deer in the standing population varied significantly between years, red deer data are presented separately for each year. 95% confidence intervals were calculated using the Clopper-Pearson exact method (Clopper and Pearson, 1934).</p

Effect of elevation on the number of deer pellet groups.

<p>Shaded areas indicate bootstrapped point-wise 95% confidence intervals; confidence intervals are only shown for areas outside national parks to improve readability. A) Red deer pellet groups; model parameters are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120960#pone.0120960.t002" target="_blank">Table 2</a>. B) Roe deer pellet groups; model parameters are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120960#pone.0120960.t003" target="_blank">Table 3</a>.</p

Percentage of variable importance in the final selected zero-inflated negative binomial model of red deer and generalized linear mixed-effects model of roe deer.

<p>The variable forest is the percentage of forest within a 500 m radius around the centre of the triangular transects. Elevation is in m a.s.l.</p><p>Percentage of variable importance in the final selected zero-inflated negative binomial model of red deer and generalized linear mixed-effects model of roe deer.</p

Effect of forest cover on the number of deer pellet groups.

<p>Shaded areas indicate bootstrapped point-wise 95% confidence intervals; confidence intervals are only shown for areas outside national parks to improve readability. A) Red deer pellet groups; model parameters are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120960#pone.0120960.t002" target="_blank">Table 2</a>. B) Roe deer pellet groups; model parameters are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120960#pone.0120960.t003" target="_blank">Table 3</a>.</p

Output of the Cox proportional hazards model with fixed and time-dependent variables.

<p>Output of the Cox proportional hazards model with fixed and time-dependent variables.</p

microsatellite data by locus and population

This file contains microsatellite data (12 loci) for 152 individuals from nine Eurasian lynx populations in Europe (and Russia). The data were used to examine the genetic variability in autochthonous and reintroduced lynx populations. Data is provided by locus and population