# Why we cannot always expect life history strategies to directly inform on sensitivity to environmental change --- The dataset provides the code to run DEB-IPM models used in the study in MATLAB and analyze PCA-results. ## Global description of the data and file structure The PCA zip.file contains a data table and R code to run the PCA analysis presented in the manuscript. The MATLAB zip.file contains all matlab files required to run the DEB-IPMS and perform perturbation analysis presented in the manuscript. **Detail PCA zip.file description** * PCA_table.xlsx contains the trait data and sensitivity values for all model species used as input for the PCA analysis. * PhyloPCA_Fish.R contains the code to run a PCA analysis using PCA_table.xlsx as input and correcting for phylogeny. * PhyloPCA_Fish_No_Phyl.R contains the code to run a PCA analysis using PCA_table.xlsx as input without correcting for phylogeny. * PhyloPCA_Fish_Body_size_correction.R contains the code to run a PCA analysis using PCA_table.xlsx as input and correcting for phylogeny and body size. * **Detail MATLAB zip.file description** * The subfolder Model_and_Parameter_files contains fifteen matlab files: * M1_parameters.m contains code describing the trait values for all iteroparous obligate breeders * M2_parameters.m contains code describing the trait values for all iteroparous skip breeders * M3_parameters.m contains code describing the trait values for all semelparous skip breeders * M1_iteroparous_obligate.m contains the code describing the life history functions for iteroparous obligate breeders * M2_iteroparous_skip.m contains the code describing the life history functions for iteroparous skip breeders * M3_semelparous_skip.m contains the code describing the life history functions for semelparous skip breeders * M1_stochseq_DEBIPM.m contains code describing the functions to estimate population growth rates for all iteroparous obligate breeders * M2_stochseq_DEBIPM.m contains code describing the functions to estimate population growth rates for all iteroparous skip breeders * M3_stochseq_DEBIPM.m contains code describing the functions to estimate population growth rates for all semelparous skip breeders * M1_stochseq_DEBIPMsens.m contains code describing the functions to estimate population sensitivity for all iteroparous obligate breeders * M2_stochseq_DEBIPMsens.m contains code describing the functions to estimate population sensitivity for all iteroparous skip breeders * M3_stochseq_DEBIPMsens.m contains code describing the functions to estimate population sensitivity for all semelparous skip breeders * * habit_matrix.m contains the code to initialize the habitat transition matrix * linspencer.m contains the code ensuring different colors per species in output figures * MeanVar.m contains the code to calculate the mean and variance of trait values in perturbation analysis * The subfolder Model_Run.m contains five matlab files to run model analysis using the files in the Model_and_Parameter_files folder as input * M1_stoch_run.m computes and generates figures of the population growth of all iteroparous obligate breeders over environmental autocorrelation. * M2_stoch_run.m computes and generates figures of the population growth of all iteroparous skip breeders over environmental autocorrelation. * M3_stoch_run.m computes and generates figures of the population growth of all semelparous skip breeders over environmental autocorrelation. * Perturb.m runs a perturbation analysis on trait importance for all species in all three DEB-IPM models. * MR_Functional_trait_response.m visualizes the outputs of Perturb.m ## Sharing/Access information This is a section for linking to other ways to access the data, and for linking to sources the data is derived from, if any. Links to other publicly accessible locations of the data: *NONE **Data collection** Functional trait data was derived from scientific literature and a full reference set is provided in table 1 of the manuscript. ## Code/Software This is an optional, freeform section for describing any code in your submission and the software used to run it. -MATLAB 2021A was used to run the DEB-IPMS -RStudio with R 4.2.1 was used to run R.

Speed of life and reproductive strategy form the two major axes that organize variation in life history strategies across plant and animal species. The position of a species along these axes can inform on their sensitivity to environmental change. This provides a tantalizing link between sets of traits and population responses to change, contained in a highly generalizable theoretical framework. The underlying mechanisms are assumed to be governed by life history tradeoffs at the individual level. Examples include the tradeoff between current and future reproductive success, and investing energy into growth versus reproduction. But the importance of such tradeoffs in structuring population-level responses to environmental change remains understudied. We aim to increase our understanding of the link between individual-level life history tradeoffs and the structuring of life history strategies across species, and if they link to population responses to environmental change. We find that the classical association between life history strategies and population responses to environmental change breaks down when accounting for individual-level tradeoffs and reproductive decisions. Projecting population responses to environmental change can therefore not always be inferred based on a limited set of species traits alone. We summarize our perspective and a way forward in a conceptual framework.