DOI: 10.5061/dryad.9j0ks22
title: Data from: Evidence for nonconsumptive effects from a large predator in an ungulate prey?

description: Pedators can indirectly affect prey survival and reproduction by evoking
 costly anti-predator responses. Such non-consumptive effects may be as
 strong or stronger than consumptive predator effects. However, evidence
 for this in large terrestrial vertebrate systems is equivocal and few
 studies quantify the actual fitness costs of non-consumptive effects. Here
 we investigated whether non-consumptive effects elicited by Eurasian lynx
 (Lynx lynx), a large terrestrial predator, reduced survival in an ungulate
 prey, the European roe deer (Capreolus capreolus). To reveal the
 behavioral processes underlying non-consumptive effects, we distinguished
 between proactive risk avoidance of areas with high lynx encounter
 probability, and reactive risk avoidance in response to actual lynx
 encounters and analyzed these responses using step selection functions. We
 also quantified the consequences of these behaviors for deer survival.
 Deer reacted differently at day and at night, but avoided high-risk areas
 proactively during the day and at night in the summer. During a predator
 encounter, deer increased avoidance of high-risk areas at night but not
 during the day. Thus, roe deer exhibited a behavioral response race that
 involved temporally and spatially varying tradeoffs with environmental
 constraints. We found evidence that non-consumptive effects of lynx
 predation risk reduced deer survival and that survival was more sensitive
 to variation in non-consumptive effects of lynx than to variation in human
 proximity. Our findings highlight that non-consumptive effects may depend
 on the spatiotemporal distribution of risks and the environmental context,
 and we discuss how human factors contribute to predator-prey dynamics in
 human dominated landscapes.
description: DeerDayDataDeerNightDataLynxDataDeerMortalityDataThis table contains the mortality data of 65 collared deer used to build the cox proportional hazard model in Gehr et al. 2018 (Behavioral Ecology). The column nr_days refers to the number of days a deer was monitored whereas entry_day and exit_day refer to the day the animal entered or left the study relative to the starting date of the study (30 November 2011). Fate refers to the fate of the animal (1 indicates died of non-lynx related causes, 0 indicates either alive at the end of monitoring or killed by lynx). Mort_cause refers to the cause of mortality. The code for mortalities: 2=disease/starvation, 3=road kill, 6=lynx kill, 7=dog kill, 10=hunting, 11=unknown. Human_risk refers to the mean human disturbance index value for all locations for each deer (composite of house density and distance to the closes road). Mean alti refers to the mean altitude of all locations for each deer. Mean_pr refers to the mean predation risk value (based on a lynx resource selection function) of all locations of each deer. Human_risk, mean_alti and mean_pr need to be scaled (mean=0 and SD=1) for the survival model.PredRisk_RandomLynxDataThis table contains the predations risk values (based on a lynx resource selection function) of the 41569 random locations drawn within lynx home ranges used in Gehr et al. 2018 (Behavioral Ecology). Predation risk was calculated for midnight. For information on the raw data or for R-code used to calculate the predation risk please contact the data owner.PredRisk_LynxDataThis table contains the predations risk values (based on a lynx resource selection function) of the 5487 lynx locations that occurred during the night (sun angle &lt;=0) used in Gehr et al. 2018 (Behavioral Ecology). For information on the raw data or for R-code used to calculate the predation risk please contact the data owner.PredRisk_KillDataThis table contains the predations risk values (based on a lynx resource selection function) of the 199 lynx killed roe deer used in Gehr et al. 2018 (Behavioral Ecology). As time of kill was unknown the predation risk values were calculated for midnight. For information on the raw data or for R-code used to calculate the predation risk please contact the data owner.