DOI: 10.5061/dryad.vq83bk3vr
title: Balancing carnivore conservation and sustainable hunting of a key prey species: a case study on the Florida panther and white-tailed deer

description: 1. Large carnivore restoration programs are often promoted as capable of
 providing ecosystem services. However, these programs rarely measure
 effects of successful restoration on other economically and ecologically
 important species. In South Florida, while the endangered Florida panther
 (Puma concolor coryi) population has increased in recent years due to
 conservation efforts, the population of its main prey, the white-tailed
 deer (Odocoileus virginianus), has declined in some regions. The extent to
 which panther predation has affected deer populations has been difficult
 to assess because several other factors have changed during this period,
 including hydrology and hunting regulations. 2. We collected known-fate
 survival data on 241 GPS-collared adult deer (156 females and 85 males)
 from 2015 to 2018 in the Florida Panther National Wildlife Refuge and the
 Big Cypress National Preserve in Florida, USA, to assess effects of
 panther predation on the deer population, while also evaluating the
 impacts of hunting and hydrology. 3. Predation was the primary cause of
 death (110 of 134 mortalities), and 87% of predation events were
 attributed to panthers, a much greater rate than reported by studies
 conducted before the panther genetic restoration effort initiated in 1995.
 One deer was legally harvested, and two were likely killed by poachers.
 Increasing water depth decreased female survival but had little impact on
 male survival, and drowning was never a cause of mortality. 4. Females had
 greater survival probability than males, except during fawning season.
 From 2015 to 2018, annual survival rates increased from 0.61 (0.52-0.70)
 to 0.86 (0.79-0.91) for females, and from 0.45 (95% CI: 0.33-0.58) to 0.79
 (0.69-0.86) for males. 5. Synthesis and applications – High predation
 rates, coupled with previous evidence of low recruitment of deer in South
 Florida, suggest that it will be challenging to meet society’s competing
 demands for large predator restoration and sustainable deer harvest. Deer
 hunting in the area must remain tightly controlled, for now, if it is to
 be sustainable, and managers should seek to mitigate effects of high
 waters and improve deer habitat quality to increase deer population
 viability. Future work should closely monitor the deer population to
 assess if management actions can increase vital rates and abundance in the
 context of high predation rates.
description: We collected known-fate survival data on 241
 GPS-collared adult deer (156 females and 85 males) from 2015 to 2018 in
 the Florida Panther National Wildlife Refuge and the Big Cypress National
 Preserve in Florida, USA<br>
 We obtained hydrological data from the
 Everglades Depth Estimation Network database (Telis, 2006). We downloaded
 daily median water levels from water gages located in our study area
 (BCA1, BCA12, BCA17 and BCA18) for the study period, and corrected these
 values to obtain actual water depth (i.e., water height in relation to the
 ground). Covariates have been
 scaled, scaling factors are indicated in the metadata README.txt
 file.
description: coda.samples.dic.R<br>  R script, alternative to
 coda.sample, while allowing for computation of DIC. Based on
 http://ihrke.github.io/dic_jags.html.<br><br>code.R<br> 
 R script, contains sample code to load data, run best model, and extract
 some information<br><br>covars.RData<br>  R Data, data
 frame containing covariates for the analysis:<br>     $
 deerID        : Deer unique ID<br>     $ day           : Date
 (format: "YYYY-MM-DD")<br>     $ sex           : Sex ;
 "f"=female,"m"=male<br>     $ season        :
 Biological season ; "fawning", "rearing",
 "rut", "postrut"<br>     $ location      : Site
 ; "BCNP": Big Cypress National Park, "FPNWR": Florida
 Panther National Wildlife Refuge<br>     $ waterDepthAvgS: water
 depth (scaled) ;                                Backtransformation: Water
 depth (in m) = (X'*s + c) * k     with s= 1.067989 , c= 1.116642 ,
 and k= 0.3048<br>     $ time          : time since the begining of
 the study (scaled)         Backtransformation: Time (in days) = X'*s
 + c                    with s= 412.3724  and c= 34<br>     $
 days.wetS     : time since the last dry day (scaled)                 
 Backtransformation: Dry time (in days) = X'*s                    with
 s= 228.797<br>     $ days.fawnS    : number of days since/to peak
 fawning (scaled)         Backtransformation: Fawning time (in days) =
 X'*s + c            with s= 52.89494  and c= 92.18487<br>     $
 days.rutS     : number of days since/to peak rut (scaled)            
 Backtransformation: Rut time (in days) =  X'*s + c               with
 s= 52.86728  and c= 90.28011<br>     $ year          : study
 year<br>     $ age           : deer age<br>     $
 age.fac       : deer age as factor ( &lt;2 or &gt;=
 2)<br>     $ age2          : deer age
 (squared)<br><br>data.jags.all.RData<br>  R Data, list
 containing deer survival data for all sources of mortality:<br>    
 $ fateTime: Individual duration in the study (computed as, if Death:
 last-first ; if Right-censored: last-first+1)<br>     $ nDeer   :
 Number of individuals<br>     $ nDays   : Total number of days in
 the study<br>     $ first   : Time of entry in the study for each
 individual<br>     $ last    : Time of exit from the study for each
 individual<br><br>data.jags.panther.RData<br>  R Data,
 list containing deer survival data censoring non-panther related sources
 of mortality:<br>     $ fateTime: Individual duration in the study
 (computed as, if Death by panther: last-first ; if Right-censored:
 last-first+1)<br>     $ nDeer   : Number of
 individuals                                                                                 <br>     $ nDays   : Total number of days in the study                                                                     <br>     $ first   : Time of entry in the study for each individual                                                        <br>     $ last    : Time of exit from the study for each individual<br><br>deer.jags.R<br>  R script, internal function to prep data based on specified model, call JAGS and run analysis<br><br>JPEbledSA1. Model code for JAGS.txt<br>  JAGS code for the survival model used in the paper<br><br>README.txt<br>  This file, contains general information on what each element contains and does.