Life Table Response Experiment
Life Table Response Experiment (LTRE) · Also known as: LTRE, demographic analysis, vital rate contribution, elasticity analysis
Life Table Response Experiments (LTRE) decompose observed temporal changes in population growth rate (lambda) into contributions from changes in specific vital rates (survival, reproduction). Developed by Caswell (2000) and applied extensively by Wisdom and colleagues, LTRE reveals which demographic changes drove observed population dynamics. For example, LTRE can show whether a population's decline was primarily due to reduced survival of juveniles, reduced fecundity of adults, or changes in other life stages. This guides targeted conservation or management.
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When to use it
Use LTRE to understand temporal changes in wild populations, identify demographic drivers of population decline or growth, or evaluate which management targets (e.g., protecting juveniles vs. enhancing reproduction) would most effectively improve population growth.
Strengths & limitations
- Reveals mechanistic drivers of population dynamics: partitions change into contributions from individual vital rates
- Directly comparable: ranks vital rate contributions on a common scale
- Guides management: identifies which demographic parameters most strongly drive population change
- Applicable to any population with available demographic data
- Requires accurate vital rate estimates from two or more time periods; errors compound
- Linear decomposition is approximate; may over-simplify nonlinear interactions among vital rates
- Does not reveal causation: LTRE shows correlation between vital rate change and lambda change, not causality
- Assumes vital rates are independent; real vital rates often covary in response to environmental conditions
Frequently asked
Should I use elasticity or sensitivity for management decisions?
Elasticity (proportional sensitivity) is more useful for management because it accounts for the scale of vital rates (survival is bounded 0-1, fecundity is unbounded). A vital rate with high elasticity benefits most from proportional improvement. Use elasticity for ranking management priorities.
How do I account for uncertainty in vital rate estimates?
Propagate uncertainty through LTRE using bootstrap or Bayesian resampling. Recompute vital rate contributions for each bootstrap replicate, generating confidence intervals. Report confidence intervals for vital rate contributions to reflect data uncertainty.
Can I use LTRE to forecast future population dynamics?
LTRE is retrospective: it explains past changes given observed vital rate changes. To forecast, combine LTRE with predictions of future vital rate changes (e.g., from climate models). Project matrices forward with predicted vital rates to forecast lambda.
Sources
- Caswell, H. (2019). Sensitivity Analysis: Matrix Methods in Demography and Ecology. Springer. DOI: 10.1007/978-3-030-10534-1 ↗
- Caswell, H. (2000). Matrix population models. Sinauer Associates. link ↗
- Wisdom, M. J., Mills, L. S., & Doak, D. F. (2000). Life stage simulation analysis: estimating vital-rate effects on population growth for conservation. Ecology, 81(3), 628-641. DOI: 10.1890/0012-9658(2000)081[0628:LSSAEV]2.0.CO;2 ↗
How to cite this page
ScholarGate. (2026, June 3). Life Table Response Experiment (LTRE). ScholarGate. https://scholargate.app/en/ecology/life-table-response-experiment
Which method?
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