DOI: 10.5061/dryad.6djh9w1bs
title: Data from: Ecological and intrinsic drivers of foraging parameters of Eurasian lynx across Europe

description: The estimation of foraging parameters is fundamental for understanding
 predator ecology. Predation and feeding can vary with multiple factors,
 such as prey availability, presence of kleptoparasites, and human
 disturbance. However, our knowledge is mostly limited to local scales,
 which prevents studying effects of environmental factors across larger
 ecological gradients. Here, we compared inter-kill intervals and handling
 times of Eurasian lynx (Lynx lynx) across a large latitudinal gradient
 from subarctic to the Mediterranean ecosystems using a standardised
 dataset of predicted adult ungulate kills from 107 GPS-collared lynx from
 nine distinct populations in Europe. We analysed variations in these two
 foraging parameters in relation to proxies reflecting prey availability,
 scavengers’ presence, and human disturbance, to improve our understanding
 of lynx predation at a continental scale. We found that inter-kill
 intervals and handling times varied between populations, social status and
 in different seasons within the year. We observed marked differences in
 inter-kill intervals between populations, which do not appear to be driven
 by variation in handling time. Increases in habitat productivity
 (expressed by NDVI, used as a proxy for prey availability) resulted in
 reduced inter-kill intervals (i.e. higher kill rates). We observed less
 variation in handling (i.e. feeding) times, although presence of dominant
 scavengers (wild boars and brown bears) and higher human impact led to
 significantly shorter handling times. This suggests that kleptoparasitism
 and human disturbance may limit the energetic input that lynx can obtain
 from their prey. We also observed that the human impact on foraging
 parameters can be consistent between some populations but
 context-dependent for others, suggesting local adaptations by lynx. Our
 study highlights the value of large-scale studies based on standardised
 datasets, which can aid the implementation of effective management
 measures, as patterns observed in one area might not be necessarily
 transferable to other regions. Our results also indicate the high degree
 of adaptability of these solitary felids, which enables them to meet their
 energy requirements and persist across a wide range of environmental
 conditions despite the constraints imposed by humans, dominant scavengers
 and variable prey availability.
description: We used GPS and kill-sites data from nine populations across
 Europe. Data were collected through the EUROLYNX network, a collaborative
 bottom-up platform of lynx researchers across Europe for sharing data and
 expertise (Heurich et al, 2021). We first developed a predictive model to
 classify GPS location clusters into adult ungulate kills or other
 (non-kill and/or small prey kill), following Oliveira et al. (2022) - see
 Appendix S2 for more details. The dataset used for this part of the
 analyses is entitled "dataset_GLC_classification". 
 Secondly, we used these predicted GPS location clusters to
 calculate inter-kill intervals and handling times. For each parameter, we
 extracted environmental covariates trelated to prey availability, human
 disturbance, and scavengers presence. We provide two datasets, one for
 inter-kill intervals ("inter_kill"), and another for handling
 times ("feeding_t").  For more details,
 please see the README document
 ("README_dataset_randomforest_classification.txt") and the
 accompanying published article. Oliveira et al., 2022. Predicting kill
 sites from an apex predator from GPS data in different multi-prey systems.
 Ecological Applications. Accepted
description: # Data for the Eurasian lynx GLCs' characteristics for classification
 with random forest algorithm This README file describes the provided
 dataset for the random forest classification analysis, and variation of
 foraging parameters (handling time and inter-kill interval). **Description
 of the data and file structure** * According to our paper, this dataset
 contains the information required to run the random forest models. It
 includes the type of cluster (class), animal social status
 (social_status), and cluster characteristics (latitude, cluster duration,
 number of cluster locations, fidelity, maximum foray, average cluster
 distance, maximum cluster radius, and night proportion fixed and
 automatic). All these parameters were extracted from GPS location
 clusters, using the GPSeqClus R package
 ([https://doi.org/10.1111/2041-210X.13572](https://doi.org/10.1111/2041-210X.13572)). * Format(s): .csv * Size(s): 138 KB * Dimensions: 3435 rows x 11 columns * Variables: *class - Type of cluster; 0 - non-kills and small prey kills, 1 - adult ungulate kills *latitude - Latitude of the cluster *clus_dur_hr - Cluster duration (hours), from the first cluster point to the last *n_clus_locs - Number of locations within a cluster *fid - Fidelity to cluster, calculated as the total number of cluster locations minus the locations away from the cluster. If equal to 0, it has the same number of locations inside and outside of the cluster. If it is more than 0, more locations fall outside the cluster, while if it is less than 0, more locations are inside the cluster. *max_foray - Maximum foray from cluster centroid (m), as the maximum distance from any location to the centroid *max_clus_radius - Maximum cluster radius (m) based on the fixes within the cluster *avg_clus_dist - Average cluster radius (m) based on the fixes within the cluster *night_proportion_fixed - Proportion of night locations, as the number of night points divided by the number of cluster location. The night period is fixed between 5pm and 7 am. *night_proportion_auto - Proportion of night locations, as the number of night points divided by the number of cluster location. Each GPS location is classified as night (or not) automatically from the suncalc package ([https://cran.r-project.org/web/packages/suncalc/index.html](https://cran.r-project.org/web/packages/suncalc/index.html)). *social_status - Social status of the lynx; 0 - adult males; 1 - adult females; 2- family groups (females with offspring) ; 3- juvenile males ; 4- juvenile females * Missing values - there are no missing values in this dataset * This file is the direct input to perform random forest classification, with binary classification. Methods of data collection/generation: see publication for details. **Dataset Title : Data for modelling the variation of handling time of Eurasian lynx GLCs reflecting adult ungulates across Europe** **Description of the data and file structure** * This dataset contains the information required to run the GAM models related to feeding time, accordingly to our paper. It includes cluster id (animal_cluster_id), lynx id (animals_id), population (population_id_cat), social status (social_status), handling time (feeding_t), month, and environmental characteristics (distance to settlements, proportion of wild boar, bear, and wolverine, and scavenger count). These characteristics had different origins and methods of extraction; please check the publication for details. * Format(s): .csv * Size(s): 348 KB * Dimensions: 4425 rows x 12 columns * Variables: *animal_cluster_id - Unique ID for each cluster *animals_id - Individual lynx id *population_id_cat - Population of each lynx *social_status - Social status of the lynx; 'a_m' - adult male, 'a_f' - adult female, and 'fam' - family groups *feeding_t - Handling time, estimated as the time, in days, between the start of kn and its abandonment (first and last GPS fixes of the given cluster, respectively) *month - Month of the first fix of the GLC *h_mod_index_17_kill - Human modification index extracted for each GLC (more details: Appendix S3- Table S3) *dist_stlm_kill - Distance to settlements for each kill (more details: Appendix S3- Table S3) *scavengers_count - Number of scavenger species potentially present at the GLC according to occurrence maps *wildboar_period_prop - Proportion of the area of each tracking sequence (95% MCP) overlapping with wild boar occurrence map *bear_period_prop - Proportion of the area of each tracking sequence (95% MCP) overlapping with bear occurrence map *wolverine_period_prop - Proportion of the area of each tracking sequence (95% MCP) overlapping with wolverine occurrence map * Missing values - there are no missing values in this dataset * This file is the direct input to run the GAMs with the model structure provided in Appendix S4: Table S4. Methods of data collection/generation: see publication for details. **Dataset Title : Data for modelling the variation of inter-kill intervals of Eurasian lynx GLCs reflecting adult ungulates across Europe** **Description of the data and file structure** * This dataset contains the information required to run the GAM models related to handling, accordingly to our paper. It includes cluster id (animal_cluster_id), lynx id (animals_id), population (population_id_cat), social status (social_status), inter-kill interval (inter_kill), month, number of daily fixes used per tracking sequence (n_locs_original), and environmental characteristics (distance to settlements, proportion of wild boar, brown bear, and wolverine, scavenger count, forest edge density, NDVI, and MCP 85%). These characteristics had different origins and methods of extraction; please check the publication for details. * Format(s): .csv * Size(s): 348 KB * Dimensions: 4166 rows x 16 columns * Variables: *animal_cluster_id - Unique ID for each cluster *animals_id - Individual lynx id *population_id_cat - Population of each lynx *social_status - Social status of the lynx; 'a_m' - adult male, 'a_f' - adult female, and 'fam' - family groups *inter_kill - Inter-kill interval, estimated as the time elapsed, in days, between the start of a kill kn (considered as the first GPS fix of the cluster) and the start of the next kill kn+1. *month - Month of the first fix of the GLC *n_locs_original - Number of daily fixes used per tracking sequence *h_mod_index_17_period - Human modification index calculated across each tracking sequence (95% MCP; median value); (more details: Appendix S3- Table S3) *esm_prox_period - Distance to settlements calculated across each tracking sequence (95% MCP; median value); (more details: Appendix S3- Table S3) *scavengers_count - Number of scavenger species potentially present at the GLC according to occurrence maps *wildboar_period_prop - Proportion of the area of each tracking sequence (95% MCP) overlapping with wild boar occurrence map *bear_period_prop - Proportion of the area of each tracking sequence (95% MCP) overlapping with bear occurrence map *wolverine_period_prop - Proportion of the area of each tracking sequence (95% MCP) overlapping with wolverine occurrence map *dens_f_edge_period - Forest edge density calculated for each tracking sequence (95% MCP; median value) *NDVI_annual_mean_period - NDVI values (annual mean) extracted across each tracking sequence (95% MCP) *mcp85_hr- 85% MCP of all GPS data for an individual with more than 10 months of tracking data. When an individual did not fit in this criterion, we attributed, as its home range size, the average HR size within the population and sex. * Missing values - there are no missing values in this dataset * This file is the direct input to run the GAMs with the model structure provided in Appendix S4: Table S4. Methods of data collection/generation: see publication for details. --- ## Contact Information * Name: Teresa Oliveira * Affiliations: Biotechnical Faculty, University of Ljubljana, Slovenia * ORCID ID: [https://orcid.org/0000-0001-9751-5198](https://orcid.org/0000-0001-9751-5198) * Email: [mteresaoliveira92@gmail.com](mailto:mteresaoliveira92@gmail.com) --- **Additional Dataset Metadata** **Acknowledgements** * We obtained tracking data through the EUROLYNX network ([https://euromammals.org/eurolynx/](https://euromammals.org/eurolynx/)). This is the paper number 7 in the EUROLYNX series. We would like to thank all EUROLYNX members for the stimulating discussions. We thank Andrea Corradini and Anja Molinari-Jobin for their contributions in the earlier stages of this manuscript. We are extremely grateful to all the personnel involved in data collection (veterinarians, field technicians, researchers, hunters, rangers, volunteers, and students), including lynx capturing and collaring, as well as field-checking of GPS clusters. # Dates and Locations * Dates of data collection: Field data for all study areas collected in several periods between 2008 and 2022. * Geographic locations of data collection: Fieldwork conducted in several areas across a large latitudinal gradient in Europe (see publication and Figure 1 for more details).