DOI: 10.5061/dryad.2382q
title: Data from: Bayesian estimates of male and female African lion mortality for future use in population management

description: The global population size of African lions is plummeting, and many small
 fragmented populations face local extinction. Extinction risks are
 amplified through the common practice of trophy hunting for males, which
 makes setting sustainable hunting quotas a vital task. Various demographic
 models evaluate consequences of hunting on lion population growth.
 However, none of the models use unbiased estimates of male age-specific
 mortality because such estimates do not exist. Until now, estimating
 mortality from resighting records of marked males has been impossible due
 to the uncertain fates of disappeared individuals: dispersal or death. We
 develop a new method and infer mortality for male and female lions from
 two populations that are typical with respect to their experienced levels
 of human impact. We found that mortality of both sexes differed between
 the populations and that males had higher mortality across all ages in
 both populations. We discuss the role that different drivers of lion
 mortality may play in explaining these differences and whether their
 effects need to be included in lion demographic models. Synthesis and
 applications. Our mortality estimates can be used to improve lion
 population management and, in addition, the mortality model itself has
 potential applications in demographically informed approaches to the
 conservation of species with sex-biased dispersal.
description: Individual-level
 survival data for Serengeti and Hwange lionsThe structure of the
 data in datSH.txt by columns is as follows: 1) "id" = individual
 identifier: sequence from 1 to 4754; 2) "sex" = sex of
 indvidual: male, female, unknown (m/f/u); 3) "dead" = binary
 indicator of whether death was confirmed at dissapearance: surely dead?
 (yes/no); 4) "cens" = censored observation: individual still
 alive at end of study? (yes/no); 5) "xli" = last seen age of
 individual i in years; 6) "xti" = left-truncation age of
 individual i (i.e. “first seen”) in years; 7) "loc" = location:
 observation from Serengeti or Hwange (s/h); 8) "potEm" =
 potential emigrant: characteristics of individual at last seen age
 indicate that the indivdiual may have dispersed (yes/no); 9)
 "im" = immigrant: individual immigrated into the study area
 (yes/no); 10) "unsex" = unsexed: did the indvidual die before
 sex was determined? (yes/no); 11) "outMigr" = out-migrant:
 expert opinion on whether individual may have dispersed (yes/no), only
 relevant for Serengeti data. Note that for the binary indicators 1
 indicates "yes", 0 indicates "no". Data are based on
 mark-resighting records of the Hwange and Serengeti lion population,
 respectively. For details on the data collection protocol, please see the
 "Materials and methods" section in the associated paper. Further
 questions can be addressed to Andrew Loveridge
 (andrew.loveridge@zoo.ox.ac.uk) for the Hwange data and to Craig Packer
 (packer@umn.edu) for the Serengeti data.datSH.txtR code to fit the mortality modelR code to fit the Siler mortality model to re-sighting data of male and female lions from two populations (i.e. Hwange National Park and Serengeti National Park, respectively). For running the analysis that is presented in the associated paper, download this file "runsModel.R" and the corresponding functions file "fcts01.R". Furthermore, please download the data file "datSH.txt". Save all files to the same folder. Set your R working directory to that folder using the corresponding line in "runsModel.R". Run the model by running "runsModel.R". When the model is finished, the model output will be automatically stored as an Rdata file to the working directory folder. There will be three output objects, which store the output pertaining to the three model start ages. The three output objects will be a composite name of the nature: outFrom"model start age"_"version".Rdata (e.g. outFrom1_01.Rdata for start age x = 1 and model version 01). Please see the documentation/comments in the code for further details on the starting objects, model fitting process, and the output objects.runsModel.RR script containing the functions for running the modelThis R script contains the functions to set up the starting objects, run the model, and process the model outputs. This R script is sourced by "runsModel.R".fcts01.R