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Quantifying trade‐offs within populations is important in life‐history theory. However, most studies focusing on life‐history trade‐offs focus on two traits and assume trade‐offs to be static. Our work provides a framework for understanding covariation among multiple traits and how population density influences the traits. Using detailed individual‐based data for Soay sheep, we find density strongly shapes life‐history trade‐offs and distribution of lifetime reproductive success (LRS). At low density, a trade‐off between juvenile survival and growth structures life‐history variation, whereas at equilibrium density, trade‐off between reproduction and juvenile survival is the major structuring axes. Contrary to Lomnicki's prediction, we find that at high density, there is little variation in the LRS over the sizes (large juveniles and adults) that contribute to reproduction. Our results advance an understanding of dynamic nature of trade‐offs offer insights into how high‐density limits diversity of individual life histories and have implications for evolution via density‐dependent selection. [ABSTRACT FROM AUTHOR]

Environmental stochasticity is a key determinant of population viability. Decades of work exploring how environmental stochasticity influences population dynamics have highlighted the ability of some natural populations to limit the negative effects of environmental stochasticity, one of the strategies being demographic buffering. Whilst various methods exist to quantify demographic buffering, we still do not know which environmental components and demographic mechanisms are most responsible for the demographic buffering observed in natural populations. Here, we introduce a framework to explore the relative impacts of environmental components (i.e., temporal autocorrelation and variance in demographic rates) on demographic buffering and the demographic mechanisms that underly these impacts (i.e., population structure and demographic rates). Using integral projection models, we show how demographic buffering is more sensitive to environmental variance relative to environmental autocorrelation. In addition, environmental autocorrelation and variance impact demographic buffering through distinct demographic mechanisms-i.e., population structure and demographic rates, respectively., (© 2025 The Author(s). Ecology Letters published by John Wiley & Sons Ltd.)