DOI: 10.5061/dryad.zcrjdfnm4
title: Data and code for: The role of indirect interspecific effects in the stochastic dynamics of a simple trophic system

description: Understanding indirect interspecific effects (IIEs) on population dynamics
 is key for predicting community dynamics. Yet, empirically teasing apart
 IIEs from other interactions and population drivers is data-demanding. We
 used stochastic population models parameterized with long-term vital rate
 time series to simulate population trajectories and examine IIEs in a
 high-arctic vertebrate trophic chain: Svalbard reindeer, its scavenger
 (Arctic fox), and a migratory fox prey (barnacle goose). Reindeer carcass
 supply shaped fox abundance fluctuations, subsequently affecting goose
 fluctuations. Yet reindeer and goose population growth rates were only
 weakly correlated, probably due to stochasticity, density dependence, and
 life history traits. However, by isolating the effects of individual
 processes within our simulation model, we demonstrate the presence of
 strong IIEs on goose population fluctuations and extinction probability.
 Thus, we highlight the long-term impact of species interactions, including
 IIEs, on species coexistence and communities, beyond immediate effects and
 short-term fluctuations.
description: # Data and code for: The role of indirect interspecific effects in the
 stochastic dynamics of a simple trophic system ## Bartra-Cabré L; Hansen
 BB; Lee A; Layton-Matthews K; Loonen M; Fuglei E; Loe LE, Grøtan V.
 \############ R version used for the scripts: R version 4.3.1 (2023-06-16
 ucrt). We recommend opening the scripts in Rstudio as this will allow to
 automatically install the packages used. \############ CONTENT
 \############ \- A.Simulations.R: main R code that runs simulations for
 the three species. \- B.SimFunctions.R: R code with simulation functions
 needed to run script A. \- C.FoxModelEstimations.R: R code running
 survival and reproduction models for the arctic fox, using the posterior
 samples from Nater et al. (2021). \- D.GooseModelEstimations.R: R code
 running survival and reproduction models for the barnacle goose, using
 (slightly modified) models from Layton-Matthews et al. 2019; 2020. \-
 E.Figure2_code.R: R code for reproducing Figure 2. Note that figure was
 edited in Adobe Illustrator. \- F.Figure3_code.R: R code for reproducing
 Figure 3. Note that figure was edited in Adobe Illustrator. \-
 G.Figure4_code.R: R code for reproducing Figure 4. Note that figure was
 edited in Adobe Illustrator. \- H.extract_winterlength.R: R code
 extracting winter length from daily mean temperature in Svalbard Airport
 needed to run simulations in script A.Simulations.R. \- data: folder with
 3 subfolders, one for each species data needed to run the scripts. ```
 NOTE: prepare here the files needed to run the scripts. \- Reindeer Data:
 * download from Peeters et al. “Harvesting can stabilise population
 fluctuations and buffer the impacts of extreme climatic events”. In:
 Ecology Letters 25 (4 Apr. 2022), pp. 863–875.
 https://doi.org/10.1111/ele.13963. Source code and data (Versión
 v1).Zenodo. https://doi.org/10.5281/zenodo.5803068 \- coeffic.Rdata:
 parameter estimates for 9090 posterior models of age-specific survival,
 age-specific effects of interactions between ROS and density. \-
 coefficF.Rdata: parameter estimates for 9090 posterior models of
 age-specific fecundity, age-specific effects of interactions between ROS
 and density. \- SigmaInt.Rdata: covariance matrices between age-specific
 survival and fecundity for 9090 posterior models \- ROS.proj.Rdata: array
 with simulations of ROS trajectories: 6000 simulations of 100 time steps
 for each of 5 scenarios (very low to very high frequencies of ROS; see
 Hansen et al. 2019 Nature Communications). Note that we only used the
 medium scenario ROS. \- AgeStruc.Rdata: age structures of 0 to 13+ year
 old female reindeer during 1994-2014. These were estimated based on cohort
 analyses as the IPM estimated annual population sizes for six age classes.
 \- Fox Data: * dowload ArcticFox_IPM_PosteriorSamples.RData from Nater et
 al. “Contributions from terrestrial and marine resources stabilize
 predator populations in a rapidly changing climate”. In: Ecosphere 12
 (2021), e03546. https://doi.org/10.1002/ecs2.3546 \* extract Pink-footed
 goose annual population size in the overwintering ground from Pedersen et
 al. “Climate-Ecological Observatory for Arctic Tundra (COAT)”. In: 2020,
 pp. 58–83
 https://www.coat.no/Portals/coat/FIles/Papporter/SESS_2019_02_COAT.pdf \*
 extract Monthly sea ice extent in Isforden from Dahlke et al. “The
 observed recent surface air temperature development across Svalbard and
 concurring footprints in local sea ice cover”. In: International Journal
 of Climatology 40 (12 Oct. 2020), pp. 5246–5265.
 https://doi.org/10.1002/joc.6517. \* Prepare a dataset (.RData file)
 containing fox covariates \- fox_covariates:RData: R dataset containing
 fox covariates year = covariate year SeIce = Annual sea ice extent (from
 monthly sea ice extent above) pfgeese = pink-footed goose annual
 population size in the overwintering ground (from above) Den_occupancy =
 annual proportion of occupied fox dens extracted from Nater et al. (2021)
 S3: Fig. S10 \- Goose Data: already including goose data and covariates \-
 goose_recruitment.txt: Individual-level data (only adult females) used for
 estimation of reproductive stages (H,G,F)*. year = observation year surv =
 survived to following year repG = female reproduced at least one gosling
 (1/0) repF = female reproduced at least one fledging(1/0) Ngoslings =
 number of goslings per female with a brood Nfledgings = number of
 fledglings per female with a brood \- goose_survival.txt: Individual-level
 data (only females) used to create individual capture histories (CMR
 data)*. id = unique bird identification number age = age
 class(0=fledgling, 1=adult) year = observation year surv = survived to
 following year *The owner and curator of this data (both
 goose_recruitment.txt and goose_survival.txt) is: Maarten J.J.E. Loonen
 Faculty of Arts Arctic and Antarctic studies — Faculty Board A-weg 30 9718
 CW Groningen The Netherlands Email: m.j.j.e.loonen@rug.nl) For details on
 the models see Layton- Matthews, Kate, et al. "Density-dependent
 population dynamics of a high Arctic capital breeder, the barnacle
 goose." Journal of Animal Ecology 88.8 (2019): 1191-1201.
 https://doi.org/10.1111/1365-2656.13001. \- Env_covariates: Covariate data
 use as predictors in the models of survival and reproductive rates. year =
 annual covariate year scot_tmin = Mean minumum temperature in Scotland
 scot_pop = Population counts in Scotland SO = Ordinal day of spring onset
 t_mjunmjul = Mean june-july temperature on Svalbard helg_p_aprmay = Mean
 rainfall at Helgeland pop_ad = number of adults in Svalbard estimated in
 the IPM (Layton-Matthews et al. 2019) fox = percentage of fox den occupied
 in Ny-Ålesund - data from the annual Arctic fox monitoring program of the
 Norwegian Polar Institute (mosk.no) p_mjulaug = Mean july-august rainfall
 at Svalbard for details on the covariates see Layton-Matthews, Kate, et
 al. "Contrasting consequences of climate change for migratory geese:
 Predation, density dependence and carryover effects offset benefits of
 high‐arctic warming." Global Change Biology 26.2 (2020): 642-657.
 https://doi.org/10.1111/gcb.14773. Layton-Matthews, Kate et al.
 “Density-dependent population dynamics of a high Arctic capital breeder,
 the barnacle goose”. In: Journal of Animal Ecology 88 (2019), pp.
 1191–1201. https://doi.org/10.1111/1365-2656.13001. ``` \- tmp_data:
 folder with data files creaded in this study: fox and goose parameter
 estimates and covariance matrices, and simulation outputs: ``` -
 fox_estimates_varcov.RData: R object that includes reproduction and
 survival estimated parameters and variance covariance matrices obtained
 when running C.FoxModelEstimations.R \- goose_surv_estimates.RData: R
 object that includes estimated survival parameters obtained when running
 C.FoxModelEstimations.R \- Reindeer_SIMS.RData: simulated projections of
 reindeer population size (N, for each age class) and reindeer carcass (RC)
 in the baseline model. This object is obtained using the script
 A.Simulations.R \- Fox_SIMS.RData: simulated projections of fox population
 size (N, for each age class) and fox breeding females (B) in the baseline
 model and adjusted models. This object is obtained using the script
 A.Simulations.R \- Goose_SIMS.RData: simulated projections of goose
 population size (N, for each age class) and number of fledglings per
 female (fledg) in the baseline model and adjusted models. This object is
 obtained using the script A.Simulations.R∅ ```