Simple traits, based on expert natural historian knowledge and
opinion.
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Easy to measure.
Available for most widely-known species.
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Difficult to verify.
Often missing measurement types (e.g., mean, median or mode).
Usually lack measures of variation.
Terminology and meaning is often taxon-specific.
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Field guides, books.
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Typically lifespan, age-at-maturity, gestation interval, clutch size,
frequency of reproduction, mass at birth and at maturity.
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Simple traits, derived from published measurements.
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Verifiable using primary literature.
Often available in open databases.
Often include population-level replicates or estimates of variation.
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Available for fewer species, especially in more charismatic taxa.
Verifiable sources may be difficult to find.
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Primary literature, open databases.
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Databases containing many thousands of species for mammals, fish,
reptiles, birds, amphibians, flowering plants [13,15–17].
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Life cycle models with age- or stage-based schedules of survival
(lx) and reproduction
(mx), e.g., life tables, projection matrices,
integral projection models.
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Quantifies whole lifespan
Popular for both plants and animals
Verifiable using primary literature
Often available in open databases
Often include population-level replicates or estimates of variation
Large toolbox of methods to derive diverse life history trait
measurements
Can generate derived life history traits (see below).
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Not available for most species, mostly concerns tetrapods with many
broad taxa neglected.
Data labour-intensive to collect
Often synthesised from multiple sources (sometimes even
interspecific).
Vital rates measured with variable precision and often contain errors
in inference or parameterisation.
Vary in length / dimension.
Require expertise to handle data and calculate derived measures
(usually programming).
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Primary literature, open databases.
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Databases available for plants and animals [10,11,18], and detailed
data for humans [63].
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Life history traits derived from life cycle models using algebraic and
computational methods.
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Benefit from all advantages of life cycle models as above
Overcome the issue that models vary in length / dimension.
Standardised sets of measurements amenable to comparative analysis.
Measures include entire life cycle
Measures include many which are not observable (e.g., life
expectancy, generation time).
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Suffer from disadvantages of life cycle models as above: taxonomic
breath, data requirements and sources, measurement error.
Often assume conditions not met in real systems, e.g.,
density-independent population growth, stable age/stage structure.
Possible to conflate life-history and demographic traits derived from
models, e.g., asymptotic or transient population growth.
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Derivation from life cycle models.
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[39]
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Selection pressures on traits, describing the “importance” of vital
rates to fitness using the derivative of the latter with respect to the
former, e.g., elasticity or sensitivity.
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Infer the filter that converts vital rates into fitness and imposes
selective outcomes.
Provide well-established framework for life history variation in
plants.
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Selection pressures are not life history traits per se.
Often assume the same conditions as life cycle models as above.
Elasticities usually have sum-constraints across vital rates, hence
life-history trade-offs are inevitable.
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Derivation from life cycle models.
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[41]
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