Data and code for: The role of indirect interspecific effects in the stochastic dynamics of a simple trophic system
Cite this dataset
Bartra-Cabré, Laura et al. (2024). Data and code for: The role of indirect interspecific effects in the stochastic dynamics of a simple trophic system [Dataset]. Dryad. https://doi.org/10.5061/dryad.zcrjdfnm4
Abstract
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.
README: 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∅
Funding
The Research Council of Norway, Award: 223257
The Research Council of Norway, Award: 276080
The Research Council of Norway, Award: 343398
University of Groningen