Data from: Effects of predator recovery and climate change on the long-term demography of a flagship herbivore
Data files
Sep 03, 2026 version files 16.17 KB
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loonam_etal_2026_ipm_data.rdata
9.47 KB
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README.md
6.70 KB
Abstract
Large herbivores play important roles in ecosystems across the globe, but limitations to study and inference have left gaps in predictive theories of their population dynamics, particularly following carnivore recoveries. Studies using spatial variation have explored the broad patterns of top-down and bottom-up forces in large herbivores. However, temporal changes in a population provide the most direct evidence for predicting future effects, and long-term studies spanning variation in top-down and bottom-up forces remain rare.
We used 36 years of data on elk (Cervus canadensis) in the western United States in an integrated population model to test the effects of top-down (Puma concolor recovery) and bottom-up (drought) forces on pregnancy rates and age-based survival across a range of elk densities in the context of human harvest. We then quantified the proportion of variance in population growth attributable to those forces relative to the influences of harvest, age-structure, and unmodelled variation in demographic rates.
Both top-down and bottom-up forces had measurable effects on elk population dynamics, with puma recovery reducing survival in the first two years of life and drought reducing pregnancy probability and survival at all ages. We also found negative density dependence in pregnancy, consistent with bottom-up control, and positive density dependence in survival of young, consistent with predator swamping. Harvest explained the greatest proportion of variation in population growth (26%). Of the non-harvest parameters, drought severity had the largest effect, explaining as much variation (16%) as all of the unmodelled changes in demographic rates. Puma recovery explained 13% of the variation, and density dependence explained 8%.
Without more long-term studies spanning sufficient ecological variation, the predictive abilities of large herbivore ecology will remain limited. With ongoing anthropogenic changes to the forces acting on large herbivores around the world, improving predictions will be invaluable to conservation.
Dataset DOI: 10.5061/dryad.msbcc2gds
Description of the data and file structure
These data were primarily collected as part of a long term monitoring project at Starkey Experimental Forest and Range. The elk population there is jointly managed by the United States Department of Agriculture Forest Service Pacific Northwest Research Station and by Oregon Department of Fish and Wildlife. All of the elk data was either collected from mandatory hunter check ins or from the elk that voluntarily enter a wintering area between November and March of each year. It is augmented by covariate data obtained from the PRISM climate group at Oregon State University and previously unpublished estimates of puma abundance from Oregon Department of Fish and Wildlife. The data was collated to support an integrated population model spanning 36 years.
Files and variables
File: loonam_etal_2026_ipm_data.rdata
Abbreviations in variable names:
a or ad = adult
j = juvenile
c or ca = calf
m = male
f = female
Variables
- r.xx = Number of each class released each year in the capture/recapture data. xx = age and sex (e.g., r.af)
- ma.xx = M-array for each age class. Rows are release year, columns are recapture year (alive or harvested).
xxfollows class abbreviations. Odd columns (col name ends ina) are individuals encountered alive. Even columns (col name ends inh) are harvested individuals. For example, {ma.af[1,1] = 10} indicates that 10 adult females were released alive in 1988 and next observed alive in 1989, while {ma.af[1,4] = 11} indicates that 11 females were released alive in 1988 and next observed as harvested individuals in 1990. - xx_yri = Indicator variable for whether survival from the capture/recapture module is trustworthy. If 0, it is not used in the process model (untrustworthy). We consider the data to be untrustworthy only when it has known flaws. The known flaws were caused by individuals being removed from the population without having individual ids recorded. Survival estimates are thus biased low if used in a capture-recapture model.
- nt = Number of years (for loop indexing)
- rec_dt = Data for binomial recruitment model. [,(year, calves, females)]. Missing years are from gaps in the historic records. Data from Starkey feed-ground attendance (see data source readme).
- nr = Number of rows of recruitment data (for indexing).
- prg_dt = Pregnancy data matrix. [,(year, age, number pregnant, number observed)]. Missing years represent gaps in the record. Missing age classes within a year mean that no individuals of that age class were checked that year. Data from Starkey handling records (see description of variable "elk_data_sources" in this README or in the readme portion of the RData file).
- np = Number of rows in pregnancy matrix.
- n_x_mov = Net number of each class moved into the population each year. ad refers to adult, ca to calf. Females are in row 1 and males in row 2.
- n_year = Number of years (for loop indexing).
- na = Number of age classes tracked. Each age class represents an integer number of years-old. The final age class is all individuals at least na-years-old.
- n_har = Number of each age harvested each year. Dimensions follow [age, sex, year]. Age is equal to the position in the first dimension. In the second dimension, females are in the first position and males in the second.
- min_xx = Minimum adults (ad) / calves (ca) known alive. Females are in the first row and males in the second.
- xx_count = Count of each class. af = adult female, am = adult male, ca = calf
- nn_xx = Number of counts available for each class. af = adult female, am = adult male, ca = calf
- har_i = Indicator turning harvest on/off each year. Indexing follows [age, sex, year]. For age, position 1 is calf and 2 is adult. For sex, position 1 is female and 2 is male. 1 = harvest occurred, 0 = no harvest
- min_xx1 = Minimum starting population. nf1 = females, nm1 = males
- est_xx1 = Estimated starting population. nf1 = females, nm1 = males
- cd_mean/tau = Mean and precision for puma random draw covariate.
- covariates = Covariate values, with n_adj the density dependence and clim is SPEI. cdens is the data from an unpublished puma population reconstruction conducted by Oregon Department of Fish and Wildlife. All covariates are centered and scaled.
- class_abbreviations = Throughout the data, am refers to adult male, af refers to adult female, and ca refers to calves. When calves are divided by sex, the first dimension (e.g. rows) divides the sexes, with row 1 being female calves and row 2 being males.
- year_start_stop = Throughout the data, year counts from 1 to 36. These represent 1988(1) to 2023(36).
- notes_on_NAs = Two types of NAs exist in the data. When an entire column is filled with NAs, they are simply placeholders, and artifact of coding choices. They were included to allow statements such as
estimates are in column 4even when the intervening columns were empty. When NAs are intermixed with data, they represent missing values that were imputed within an MCMC sampler. For future use, they should be treated as missing. - elk_data_sources = All data comes from Starkey Experimental Forest and Range, the elk in which are jointly managed by the United States Forest Service and Oregon Department of Fish and Wildlife. Harvest records are from mandatory checks each hunting season. Recruitment data and counts are from attendance on a winter feeding pasture. Pregnancy data is obtained by handling a fraction of the overwintering elk using blood tests for pregnancy specific protein B.
- covariate_sources = SPEI was obtained from the website of the PRISM Climate Group at Oregon State University. Elk density was obtained from a null run of the IPM this data was collated for. Puma density was collated from ODFW sources that have not been otherwise published.
Code/software
The file is an .rdata file, which is the native data file type for the programing language R. It has no package dependencies and can be opened with the open source standalone version of the R program or by any number of text editors running R, such as RStudio.
Access information
Other publicly accessible locations of the data:
- None have a comprehensive or curated set, but much of the Starkey data is available on the Forest Service Research Data Archive.
Data was derived from the following sources:
- United States Forest Service
- Oregon Department of Fish and Wildlife
- PRISM Climate Group (Oregon State University)
