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\begin{document}

\title{Beyond the Fast-Slow Continuum of Life Histories}



\author[1]{Iain Stott}%
\author[1]{Rob Salguero-Gómez}%
\author[1]{Owen Jones}%
\author[1]{Thomas Ezard}%
\author[1]{Jean-Michel Gaillard}%
\author[1]{Marlène Gamelon}%
\author[1]{Dave Hodgson}%
\author[1]{Shelly Lachish}%
\author[1]{Jean-Dominique Lebreton}%
\author[1]{Emily G Simmonds}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{March 14, 2025}


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\section*{Beyond the Fast-Slow Continuum of Life
Histories}

{\label{beyond-the-fast-slow-continuum-of-life-histories}}

\emph{A manuscript in preparation as an invited Opinion for TREE}

Iain Stott\textsuperscript{1,2,*}, Roberto
Salguero-G\selectlanguage{ngerman}ómez\textsuperscript{3,*}, Owen R Jones\textsuperscript{2},
Thomas HG Ezard\textsuperscript{4}, Jean-Michel
Gaillard\textsuperscript{5}, Marlène Gamelon\textsuperscript{5,6,7},
Shelly Lachish\textsuperscript{3}, Jean-Dominique
Lebreton\textsuperscript{8}, Emily G Simmonds\textsuperscript{7,9}, and
Dave J Hodgson\textsuperscript{10}

*Shared first authors

\textsuperscript{1}~Department of Life Sciences, University~of Lincoln,
Lincoln LN6 7TS, UK

\textsuperscript{2}~Department of Biology, University of Southern
Denmark, Odense 5230, Denmark

\textsuperscript{3}~Department of Biology, University of Oxford, Oxford,
OX1 3SZ, UK

\textsuperscript{4}~School of Ocean and Earth Science, University of
Southampton, European Way, Southampton SO14 3ZH, UK

\textsuperscript{5}~Laboratoire de Biométrie et Biologie Evolutive UMR
5558, CNRS, Université Claude Bernard Lyon 1, 69622, Villeurbanne,
France

\textsuperscript{6}~Department of Biology, Norwegian University of
Science and Technology, 7491, Trondheim, Norway

\textsuperscript{7}~Centre for Biodiversity Dynamics, Norwegian
University of Science and Technology, 7491, Trondheim, Norway

\textsuperscript{8}~Centre d'Ecologie Fonctionelle \& Evolutive, CNRS
Montpellier, Montpellier 34293, France

\textsuperscript{9~}Department of Mathematical Sciences, Norwegian
University of Science and Technology, 7034, Trondheim, Norway

\textsuperscript{10}~Department of Ecology \& Evolution, University of
Exeter, Penryn TR10 9FE, UK.

\par\null

\textbf{Keywords}

Comparative demography, life history theory, life history strategy, life
history trait, reproductive tactic, phylogenetic comparative analyses

\par\null

\textbf{HIGHLIGHTS}

\selectlanguage{english}* Open-access life history data have proliferated dramatically in recent
years and macro-ecological studies are using these data to examine
patterns in life histories across the tree of life.

\selectlanguage{english}* These studies support the fast-slow continuum being the dominant --
but not unique -- axis structuring life history variation: other
important axes associated with development, reproductive tactics, and
demographic buffering have emerged.

\selectlanguage{english}* Here, we make recommendations for characterising life history through
data choices and analytical methods of dimensionality reduction, with
recourse to a clear model of life history.

\selectlanguage{english}* We use our framework to advocate that future empirical studies of life
history evolution take hypothesis-driven approaches that investigate
classical and emerging life history theories, to reveal novel axes,
clusters, and boundaries of life history variation across all taxa.

\par\null

\textbf{ABSTRACT}

Life history theory is key to understanding Earth's diverse life forms.
Indeed, schedules of survival, development, and reproduction are the
filters through which natural selection acts on species' heritable
traits to shape fitness. Prevailing wisdom places species' life history
strategies along a fast-slow axis of variation. Challenges to the
unicity of this axis are increasingly frequent, given proliferation of
life history databases and availability of sophisticated multivariate
statistical techniques. However, these empirical approaches often lack
concrete theoretical or hypothetical foundations. We advocate for using
standardised traits, targeting data gaps and overcoming focus on
taxonomic siloes, to facilitate a hyopthesis-driven approach in future
examination of drivers of life history variation across the whole Tree
of Life.

\par\null

\textbf{MAIN TEXT}

Any individual is born, survives, develops, possibly reproduces, and
ultimately dies. There are countless pathways through life, and as such
there is a tremendous diversity of \textbf{life history strategies} (see
Glossary) across species. However, such strategies are not always
associated with species' evolutionary history or environment (Box 1).
Central aims of evolutionary ecology include describing and explaining
the diversity of life histories across the Tree of Life {[}1{]}. In this
context, the \textbf{fast-slow continuum} {[}2{]} has become the
standard framework. Indeed, the fast-slow continuum describes a high
amount of life history variation across species in most taxonomic groups
studied so far {[}3--9{]}. However, beyond this fast-slow continuum,
many comparative studies have identified various other important axes of
variation, often linked to developmental or reproductive tactics.

Around the inception of the fast-slow continuum, Stearns {[}2{]}
suggested that there was not yet a \emph{``general and reliable theory
of life history evolution''} and that the field was
not\emph{``empirically sufficient''} to test life history theory. With
the emergence of large global databases of demographic and trait data
{[}10--18{]}, we are now much closer in several taxonomic groups to the
required sufficiency to test general theories of life history evolution.
However, the challenges of identifying, quantifying, explaining, and
inferring life history diversity has quickly become lost in a fog of
data types, trait currencies, statistical confounds, non-independence,
mathematical artefacts, and eco-evolutionary feedbacks.

So although the fast-slow continuum is the main structuring axis of life
history variation across species studied to date, it is not by itself an
adequate \emph{universal} theory of life history, and there remains
little consensus on how to measure life history {[}19{]} and which
interspecific patterns deserve explanation. As such, we still lack
a\emph{``general and reliable''} {[}2{]} understanding of life history
diversity.

Here, we reflect on the state-of-the-art in life history theory and the
potential for general theories in the context of further axes of life
history variation beyond the fast-slow continuum. We identify several
empirical challenges that must be overcome to properly test such
theories across the Tree of Life. These challenges include filling
taxonomic and biogeographic data gaps, standardising trait units and
developing novel analytical techniques to facilitate moving toward
hypothesis-based over exploratory analyses. Without universal
benchmarks, it will be difficult to determine whether the life history
strategies we currently observe in the wild are shaped by allometry,
relatedness, physiologies, \textbf{bauplan} , behaviours, lifestyles,
trophic levels, or biogeographic realms.

\textbf{LIFE HISTORY THEORY AND THE FAST-SLOW CONTINUUM}

The life history of an organism determines its fitness, as life history
dictates the numerical representation of future generations {[}1{]}.
Life history is therefore the filter via which selection on phenotypic
traits operates, and via which environmental variation generates
population, community, and ecosystem dynamics. This idea is underpinned
by the canonical Euler-Lotka identity~\(1=\int_{x=1}^{\omega}{\lambda^{-x}l_{x}m_{x}\text{dx}}\), with
fundamental components describing the schedules of survival
(\(l_{x}\)) and reproduction (\(m_{x}\)) to the
maximum age~\emph{x} =\selectlanguage{greek}\selectlanguage{english}\emph{ω}{[}1{]}. This identity characterises how
survival and reproduction over the life course (\emph{x} = age)
determines population growth (\(\lambda\)). However, this
equation tells us nothing about potential covariation between survival
and reproduction, between early-life and late-life reproduction, or
whether age is the best predictor of fitness; indeed, it has been
recently argued that size is a better predictor in many species
{[}20{]}. A comprehensive theory of life history strategies must at the
very least describe measurable directions and strengths of these
potential trade-offs, but in reality, life history is much more
complicated (Box 2).

Schedules of survival and reproduction can be measured in many ways.
Examples include \textbf{life expectancy} , longevity, age at first
reproduction, \textbf{lifetime reproductive output} , measures
of\textbf{actuarial} and \textbf{reproductive senescence} ,
and\textbf{generation time} . Although some of these demographic metrics
often do a good job as proxies for life history continua {[}21{]}, no
single one of these \textbf{life history traits} adequately captures
every characteristic of the demographic schedules \(l_{x}\)
and\(m_{x}\). Therefore, comparative studies often use several
life history traits and apply methods of dimension reduction to yield
emergent, composite measures. The nonlinearity of these schedules,
together with the influence of density dependence and stochasticity
{[}22{]}, complicate measurement of their covariance. These
complications remain even if the conceptual framework describing life
history structures and competing processes of energy allocation is
relatively well known (Box 2).

The fast-slow continuum {[}2{]} remains the standard framework for
understanding life history diversity across the Tree of Life and is
characterised using various life history traits mentioned above. `Fast'
creatures tend to have quick development and early-life allocation of
resources to profuse reproduction over survival. `Slow' creatures have
protracted development, delayed maturation, and allocate resources to
survival over reproduction. The fast-slow continuum continues to emerge
as an important explanator of eco-evolutionary dynamics across taxa, and
more recently in the fields of trophic {[}23{]} and disease ecology
{[}24{]} too.

As well as being a canonic ordination of life history variation, the
fast-slow continuum predicts species' response to global change.
Compared to fast-lived species, slow-lived species show higher
sensitivity of population growth to sea temperatures in fishes {[}25{]},
higher extinction rates in mammals and birds {[}26{]}, and more negative
responses of mammals to human disturbances {[}27{]}. These
characteristics render long-living species less resilient than
fast-living species to environmental insults, making the populations of
the former more likely to decline (the so-called ``malediction of
long-lived species'' {[}28{]}). However, fast species are more sensitive
to increases in temporal autocorrelation of the environment {[}29{]}.
Such patterns have wider implications for future ecosystem function
{[}26{]} and conservation success {[}30{]}.

\textbf{MULTIPLE AXES TO GRIND}

The fast-slow continuum is almost consistently supported as the dominant
but by no means the sole factor structuring life history variation,
including in mammals {[}3--5{]}, birds {[}3{]}, insects {[}6,7{]}, fish
{[}8{]} and plants {[}9{]} (although the extent of support is sensitive
to the life history traits analysed {[}31{]}). Exceptions to the
fast-slow `rule' exist, with species having strategies characterised by
both high survival and reproduction, such as marine turtles or trees
{[}32{]}, being especially common. Many such organisms have vital rates
closely related to size and development, for which age is a poor
predictor {[}20{]}. These life history strategies are instead defined
using a second reproductive axis of variation: \emph{how} species
allocate to reproduction, rather than their reproductive output\emph{per
se} . The second most dominant axis of variation thus usually relates to
reproductive and developmental tactics, such as from\textbf{altricial}
\emph{vs.} \textbf{precocial} offspring {[}5{]}; from high to low
recruitment rates {[}33{]}, high to low frequency of reproduction
{[}7{]} or from \textbf{semelparity} to\textbf{iteroparity} {[}3,9{]}.
In some cases, axes other than reproduction explain substantial life
history variation, such as age-specific distributions of mortality and
reproduction {[}34{]}. The exact meaning of additional axes is hindered
by the vast heterogeneity of life history traits analysed in this
context {[}31{]}.

Additional axes may be required to explain additional peculiarities in
post-maturation distributions of age-specific survival and reproduction,
such as species that reverse development using shrinkage
or\textbf{retrogression} , or pause development using seed banks or
diapause {[}9{]}.

\textbf{LIFE HISTORY DATA SOURCES}

Quality data for quantifying life histories takes much effort to collect
as life histories cannot be described simply by observing static
phenotypes. Measuring life history traits can be challenging: some
infrequent, brief life history events can be at best rarely observed.
Examples include reproduction in semelparous perennial species, or
mortality in long-lived species. Much effort has been devoted to the
inference of unobserved or imperfectly detected life history events in
population studies {[}35{]}. The demographic context of traits like
generation time and life expectancy means that they must be measured
using samples of individuals within populations or species. Hence, life
history trait data vary greatly in their abundance, accessibility, and
quality (Table 1) {[}12,36{]}, even among charismatic species {[}12{]}.

The gold standard in demographic data is detailed schedules of age- or
stage-specific \textbf{vital rates} . These rates of survival,
development, and reproduction are becoming increasingly available
through open databases of demographic data (e.g.,
{[}10,11,16--18,31,37,38{]}). Despite the diversity of model dimensions
and complexity, life history traits can be derived from these schedules,
yielding a set of common traits across species. This derivation
overcomes some of the issues with simple trait data outlined in Table 1.
Demographic quantities such as stationary {[}39{]} and transient
{[}40{]} population dynamics can also be calculated from life history
schedules, as can selection pressures {[}41{]}. However, although these
databases are accessible, models vary in quality and data remain
taxonomically biased {[}36{]}. Moreover, the traits derived from these
models are mathematical functions of the same underlying schedules of
survival, development, and reproduction, hence correlations among them
are a combination of the real and the artefactual {[}42{]}.

\textbf{CURRENCIES OF LIFE HISTORY TRAITS}

We identify four key ways to quantify life history data (Box 2). These
ways are often linked to hierarchies of biological organisation and the
researcher's own perspective. These four life history
\emph{currencies}define how an organism acquires and allocates resources
{[}43{]}, and the consequences for its vital rates. Evolutionary
ecologists typically see the world through an adaptive lens and might
study selection strength by measuring how schedules of life history
traits affect fitness. In contrast, behavioural ecologists perceive
evolution of life history in light of an organism's use of energy or
space in acquiring resources, whereas developmental biologists focus on
energy allocation to ontogenetic cellular and organic processes. From a
demographer's perspective, life histories consist of events measured in
time or frequency, as these events capture or directly affect survival
and/or reproduction over age or stage. However, even within these broad
perspectives and currencies, the units of life history trait measurement
often vary. For example, demographic measures can be confused by mixing
rates, durations, ages, frequencies, and times-to-events. Currencies can
also be confused by measuring traits in one currency as proxies for
processes in another. Examples include using morphological or
behavioural sexual displays as proxies for allocation of energy to
reproduction {[}44{]}, or body size as an allometric proxy for survival,
reproduction, metabolic rate, and use of space {[}3,5,45,46{]}). Past
comparative analyses have often been performed on rather heterogeneous
life history trait currencies, including the very first one published by
Stearns 40 years ago. The mixture of currencies confounds the
interpretation of the structuring axes of life history variation across
the Tree of Life. As such, we urge to standardise the currency in future
analyses to enable cross-study, cross-taxonomic fair comparisons.

\textbf{STATISTICAL ANALYSIS OF LIFE HISTORY TRAIT DATA}

We argue that alternatives to popular analytical approaches in life
history theory should be considered more often {[}47{]}, and judiciously
applied. Tractable generalisations of life history variation frequently
require analyses to collapse multivariate trait datasets and associated
covariates into a smaller set of latent variables. Common statistical
means of reducing dimensionality have both strengths and drawbacks (Box
3). The most popular approach in this context is the Principal
Components Analysis (PCA). Although PCA is a well-established
exploratory tool, it should not be used na\selectlanguage{ngerman}ïvely {[}48{]}.

First, researchers should be aware of their own interpretations and
biases when interpreting dimension reduction analyses. Prior to
analysis, careful consideration should be given to the aim of the study:
for example, PCA informs on correlative rather than causative
relationships between variables. Interpreting results of multivariate
analyses can be challenging, as composite axes are combinations of
underlying traits. Second, careful consideration should be given to how
the chosen data may affect outcomes. Indeed, sets of life history traits
that are derived algebraically from vital rate schedules may be
correlated due to mathematical relationships as much as natural
covariation {[}49{]}. If a trait set includes measures that are a
function of multiple vital rates (\emph{e.g} ., generation time), or
that are measured in different units (\emph{e.g} ., energy for metabolic
rate and time for mortality), structuring axes from PCAs (principal
components) may be challenging to determine. Even if the multivariate
trait set is carefully chosen, it is likely to be difficult to draw fair
comparisons between studies that use different trait sets and therefore
have component axes with different orientations. Third, the statistical
shortcomings of analyses must be carefully considered. In particular, a
weakness of PCA is that, in its simplest form, it does not consider
measurement errors (but see {[}50{]}). Fourth, PCAs do not account for
non-linearities among life history traits {[}45{]}. Finally, and perhaps
most importantly, inquiry should be hypothesis-driven: PCA is designed
to inform on how variation in multivariate data is partitioned, rather
than to test associations between specific traits (Box 3).

\textbf{RECOMMENDATIONS FOR AN HYPOTHESIS-DRIVEN, EMPIRICALLY SUFFICIENT
ERA}

Recent increases in available life history trait data unlock our ability
to further understand the prevailing patterns of global variation in
life history strategies, their evolutionary and ecological drivers and
constraints, and their consequences for the extinction risk,
invasiveness, and ecosystem functioning. We expand on Stearns' {[}2{]}
original conditions to identify a number of contemporary challenges
concerning life history data and analyses, as we strive to attain the
``empirical sufficiency'' required to test modern theories of life
history evolution.

\textbf{Moving towards universal life history traits, derived from
demographic schedules of survival, reproduction \emph{and}
development.}Such data can be implicitly measured in a single currency
of rates per standard unit of time {[}51{]}. This unit harmonisation
will strengthen the links between life history, demography, and fitness
{[}1{]}, given their explicit treatment of time in the canonical
Euler-Lotka equation. Such rates still need credible transformation
during statistical analysis {[}52{]}. We further encourage the
development of theories and methods to drive our understanding of life
history evolution using state variables that are \emph{not} measured
using time but linked to other currencies, with energy being a primary
candidate.

\textbf{Filling gaps in life history trait data across the Tree of Life,
especially for microbes, fungi, and invertebrates} . There are
challenges inherent to this recommendation, since for many organisms we
lack a good working definition of life cycles and life histories, let
alone what constitutes an individual, death, or reproduction. Variation
in lifestyle (\emph{e.g.,} sedentary \emph{vs.} mobile, diet, habitat),
bauplan (\emph{e.g.} , modular \emph{vs.} unitary, degree of mobility,
brain development), growth pattern (\emph{e.g.,} determinate
\emph{vs.}indeterminate growth) and reproductive modes (\emph{e.g.,}
sexual\emph{vs.} asexual) further complicate the comparative landscape.
Attempt to fill data gaps should prioritise measures that will
facilitate broad comparisons of life history across taxa. As information
gaps across species fill in, it is worthwhile to consider the need for
data on vital rate variation within species {[}53{]}. Moreover, time
series vital rate data exist for a relatively small number of species,
and so comprehensive assessments of density-dependent mechanisms driving
vital rate variation remain rare.

\textbf{Embracing life history traits comparative across broad taxa and
levels of biological organisation} . Besides some exceptions
{[}54,55{]}, multivariate studies seeking to understand axes of life
history variation across kingdoms of life emerged only recently
{[}29,56,57{]}. Previous studies that have encompassed a broad taxonomic
range were limited to bivariate analyses {[}55{]}. Naturally, continuing
taxon-specific lines of enquiry will play an important role, especially
in taxa with rich data quality. However, limiting analysis only to
separate groups of organisms implies a perceived wisdom that gross
differences in the morphology, physiology, and lifestyle of different
groups will inevitably create different selection pressures on their
life history strategies. This presumption should be backed up with
empirical analyses of whether and how the dominant axes of life history
variation change among taxa. Existing evidence is they do not {[}29{]}.

\textbf{Achieving consensus regarding evolutionary ancestry across all
species to implement robust phylogenetic analyses.} Recent advances in
phylogenetics have yielded trees describing the relationships among
species across ever larger taxonomic groups {[}58--60{]}. Despite these
advances, the details of ancestral relationships in many parts of these
trees, and particularly in deeper evolutionary time, remain debated and
with multiple gaps to fill. We require better consensus on the best way
to deal with phylogenetic covariance. The classic assumption of Brownian
trait evolution is often not supported, with increasing recognition for
the need to incorporate evolution towards trait optima and to identify
and account for shifts in trait optima across taxa {[}61{]}. There is a
strong potential to find patterning in life history strategies common
across all species, but we should be ready to be surprised by
deep-rooted differences in how natural selection has shaped strategies
in particular clades.

\textbf{Adopting analytical approaches that infer explicit links between
life history traits and emergent axes of life history variation.} We see
a role for the wider use of Factor Analysis {[}47{]} to help discover
life history axes as latent factors of observed vital rates. Further
development of FA algorithms will be key to help test hypotheses derived
from life history theory, and particularly phylogenetically-controlled
confirmatory FA methods. We also urge for the development and
application of Canonical Correlation Analysis to reveal associations
between the multivariate life history traits of species and their
multivariate suites of demographic, phenotypic, and ecological features.

\textbf{CONCLUDING REMARKS: ARE ALL SPECIES FAST OR SLOW?}

Hypothesis-driven research, rather than exploratory data analyses,
should guide the study of multivariate life history traits and the
eco-evolutionary drivers and limits that shape them. Prevailing evidence
shows that the fast-slow continuum is the major structuring axis of life
history variation in several taxa, but also that this continuum alone
does not fully describe life history variation. We advocate for the
development of further theory to test hypotheses regarding which axes,
clusters, syndromes, and constraints shape said variation. Likely
candidates include different reproductive or developmental tactics (see
Outstanding Questions). As we make further progress in the era of
``empirical sufficiency'', we must also take more mechanistic
approaches. These approaches must be coupled with higher-quality data
and robust analytical methods. Ultimately, we argue that more time spent
on theory and hypothesis development will lead to more targeted efforts
to better understand how life history strategies are shaped by the
environment, ancestry, and bauplan. Concurrently, the search for
universal syndromes of life history across all currencies of measurement
must continue, encompassing the whole Tree of Life.

\par\null

\textbf{ACKNOWLEDGEMENTS}

T. Coulson provided constructive feedback on an earlier version of this
work. IS received funding from the EU Horizon 2020 research and
innovation programme under Marie Sk\selectlanguage{polish}ł\selectlanguage{english}odowska-Curie Award (MSCA) grant
agreement WHYAGE 746235. RSG was supported by a NERC Independent
Research Fellowship (NE/M018458/1). MG was supported by the Research
Council of Norway through its Centres of Excellence funding scheme
(223257), by the CNRS, and by the ``PURE'' ANR JCJC program.



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\par\null

\textbf{OUTSTANDING QUESTIONS}

\selectlanguage{english}* Is there a universal characterisation of life histories across the
tree of life?

\selectlanguage{english}* Does the fast-slow continuum explain the majority of life-history
variation across all taxa? It does in Tetrapods (especially mammals and
birds), which are relatively over-studied, and only in recent years has
the fast-slow continuum been shown to be important in sessile and clonal
organisms (vascular plants, corals). Whether the fast-slow continuum is
the major structuring axis of life history variation has not been
studied in most invertebrates other than insects, where the fast-slow
continuum was identified {[}6{]}, but seems to be less structuring than
in tetrapods {[}7{]}.

\selectlanguage{english}* Are other axes of life history variation consistent among taxa? Traits
relating to reproductive or developmental tactics have repeatedly
emerged as the second most important axis of life history variation.
However, heterogeneity in the set of traits analysed so far prevents us
from concluding taxonomic universality.

\selectlanguage{english}* What, if any, are the implications of particular choices of data and
analytical approaches? Certainly the choice of multivariate data to
analyse, and the availability of particular measures by taxa, will vary.
If the impact of these choices on research outcomes is significant,
cognisance of such consequences is of paramount importance.

\selectlanguage{english}* Are there clearly defined clusters and boundaries of life history
variation across taxa that occupy distinct spaces in multidimensional
life history trait space? Biomechanical constraints limit the range of
covariation among life history traits {[}62{]}. Intuitively, certain
life history strategies should not exist if they defy physical or
evolutionary laws. How will human actions and climate change alter these
boundaries and shape rates of extinction/invasion?

\par\null\par\null

\textbf{TABLE 1}~- Comparison of different types of demographic data in
the context of life history strategy comparative analyses.

\par\null\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Data Type} & \textbf{~~~~~~~~Strengths} &
\textbf{~~~~~~~~~~~~Weaknesses} & \textbf{~~~~~~~~~~~~~~~~~~~~Source(s)}
& \textbf{Examples/references}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Simple traits, based on expert natural historian knowledge and opinion.
& Easy to measure. Available for most widely-known species. & Difficult
to verify. Often missing measurement types (\emph{e.g.}, mean, median or
mode). Usually lack measures of variation. Terminology and meaning is
often taxon-specific. & Field guides, books. & Typically lifespan,
age-at-maturity, gestation interval, clutch size, frequency of
reproduction, mass at birth and at maturity.\tabularnewline
Simple traits, derived from published measurements. & Verifiable using
primary literature. Often available in open databases. Often include
population-level replicates or estimates of variation. & Available for
fewer species, especially in more charismatic taxa. Verifiable sources
may be difficult to find. & Primary literature, open databases. &
Databases containing many thousands of species for mammals, fish,
reptiles, birds, amphibians, flowering plants
{[}13,15--17{]}.\tabularnewline
Life cycle models with age- or stage-based schedules of survival
(\emph{l\textsubscript{x}}) and reproduction
(\emph{m\textsubscript{x}}), e.g., life tables, projection matrices,
integral projection models. & 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).
& 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). &
Primary literature, open databases. & Databases available for plants and
animals {[}10,11,18{]}, and detailed data for humans
{[}63{]}.\tabularnewline
Life history traits derived from life cycle models using algebraic and
computational methods. & 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 (\emph{e.g.}, life expectancy, generation time). &
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. & Derivation from life cycle models. &
{[}39{]}\tabularnewline
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. & Infer the filter that
converts vital rates into fitness and imposes selective outcomes.
Provide well-established framework for life history variation in plants.
& Selection pressures are not life history traits~\emph{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. & Derivation from life cycle models. &
{[}41{]}\tabularnewline
\bottomrule
\end{longtable}

\par\null\par\null

\textbf{BOX 1. LIFE HISTORY STRATEGIES}

Are carrots (\emph{Daucus carota} ) similar to blue ringed octopuses
(\emph{Hapalochlaena lunulata} )? They both start life small, grow fast
but few survive to adulthood, produce many offspring in one breeding
event at around 1 years old, and are \textbf{semelparous} . In contrast,
humans (\emph{Homo sapiens sapiens} ) and blue whales
(\emph{Balaenoptera musculus} ) grow slowly, mature late but are likely
to survive to adulthood, usually produce one offspring every 2-3 years
and have remarkably similar lifespans: the oldest known living human,
Jeanne Calment, reached 122 years of age, and blue whales may live up to
110 {[}64{]}. The sacred fig tree (\emph{Ficus religiosa} ) and giant
barrel sponge (\emph{Xestospongia muta} ) are like carrots and blue
ringed octopuses in that they start life small, are unlikely to survive
to adulthood and, once mature, produce many offspring.
\emph{Xestospongia muta} and \emph{F. religiosa} are also like humans
and blue whales, however, as they are \textbf{iteroparous} over long
lifetimes: one sacred fig ``Jaya Sri Maha Bohi'' in Sri Lanka is the
oldest known human-planted tree, which at around 2,300 years old is
similar in age to the oldest known Caribbean giant barrel sponge
{[}65{]}.

Whilst distantly-related organisms may share similar life history
strategies, closely-related organisms can show remarkably different life
histories. The \emph{Dasyuridae} family, to which the Tasmanian devil
(\emph{Sarcophilus harrisii} ) belongs, is one of the most diverse
marsupial families {[}66{]}, with dasyurid species showing different
life history strategies, despite being closely related. Maturity occurs
at \textasciitilde{}11 months but, while in some species all individuals
die by 12 months old following a single synchronous mating, others
reproduce many times annually and live for several years {[}67{]} .
Plant life history strategies are often even more labile than animal
strategies: pine trees (\emph{Pinus} spp.) vary vastly in lifespan,
despite having similar morphology and physiology: the Virginia pine
(\emph{P. virginiana} ) rarely lives over 150 years {[}68{]}, whereas
bristlecone pine (\emph{P. longaeva} ) hold the record of non-clonal
longevity, at 4,850 year old {[}69{]}. Selection for varying longevities
in pines may be a result of the differing fire environments {[}70{]}.

The Tree of Life abounds with examples of distantly related organisms
sharing convergent life history strategies, despite divergent body size
and lifestyle, and \emph{vice versa} . Evolutionary ecologists seek to
understand, across broad taxonomic groups, how and why diverse life
history strategies, such as those exemplified here, are distributed
across phylogenies, space, and time.

\par\null\par\null

\textbf{BOX 2. A MODEL LIFE HISTORY: FROM RESOURCE ACQUISITION TO
ALLOCATION AND FITNESS}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

An organism's life history strategy is shaped by traits that have a
natural hierarchy. The organism's \emph{phenotype} determines how the
individual acquires resources {[}71{]} through morphological,
physiological, and behavioural adaptations. \emph{Development} describes
the allocation of those resources to competing processes: supporting
somatic maintenance \emph{vs.} supporting the germline
\emph{vs.}development in either {[}72{]}. The consequences of these
allocations are \textbf{life history traits} . These traits are
characterised by probability distributions of survival and fertility. As
survival and fertility govern the number of individuals in populations,
life history traits govern not just an organism's \emph{fitness} but
also its\emph{demography} - population size, structure, and dynamics
{[}1{]}. Traits at all hierarchical levels covary with, and feed-back on
one another, and are subject to constraints imposed by evolutionary
history, physical laws, bauplan, and the environment {[}73{]}. Natural
selection acts to favour life history strategies that maximise the
organism's fitness in its environment, given those constraints {[}1{]}.

Trait currencies and their units vary considerably among life history
studies. Moreover, these units do not necessarily correspond with trait
types or hierarchies. Physiological traits often measure
\emph{energy}expenditure (\emph{e.g.,} metabolic rate). Behavioural
traits typically quantify energetic expenditure (\emph{e.g.,} foraging
efficiency) or space usage (\emph{e.g.} , home range size). However,
space units are also common among morphological traits (\emph{e.g.,}
specific root length). Rates of energetic allocation are used to
describe resources given to developmental processes (\emph{e.g} .,
change in body size over time), thus combining energy and time
currencies. Time is commonly used to measure life history traits,
whether as durations (\emph{e.g} ., mean life expectancy), rates
(\emph{e.g} ., number of offspring per year), or temporal probabilities
linked to survival, development, and reproduction (\emph{e.g.} ,
probability of reaching maturity at a given age). Life history traits
are often converted to expectations of life history for populations or
species, which is especially necessary where events occur only once. For
example, an individual only dies once, but the aggregate deaths in a
population yield the distribution of survival for the population's
average phenotype, from which we can estimate \emph{e.g.}mean life
expectancy. \emph{Selection} on phenotypic, developmental, or life
history traits can quantify the trait's contribution to fitness
(\emph{e.g} ., selection pressure). Shaded areas on the figure are
indicative, but not exhaustive, of common currencies used at each level
and their overlap.

\par\null\par\null

\textbf{BOX 3. STATISTICAL METHODS FOR COMPARATIVE ANALYSES OF LIFE
HISTORIES}

A set of measurable, correlated traits can be described using a smaller
number of emergent, orthogonal variables representing dominant axes of
life history variation, such as the fast-slow continuum. Principal
component analysis (PCA) is one common statistical method to achieve
this goal {[}3--5,7,9,26,27,29,30,33,34,74,75{]}. Although some -largely
arbitrary- guidance exists regarding how many dimensions explain
meaningful amounts of variation {[}48{]}, it is less clear how to
compare dominant axes across independent analyses with data sets
comprising different traits, taxa, and sampling methodologies.

It is also possible to explore relationships between two different
multivariate data sets (\emph{e.g} ., climatic and life history data).
Canonical Correlation Analysis (CCA) yields two sets of emergent
uncorrelated variables by calculating axes with highest correlation
between variable sets. This approach recognises that dominant axes of
variation (\emph{e.g.} , fast-slow) may not yield strong relationships
with other ecological or evolutionary processes.

Unlike both PCA and CCA, Factor Analysis (FA) {[}47,76{]} treats
measured variables as functions of latent variables, with associated
measurement and/or residual error. As FA does not require latent
variables to be uncorrelated, the approach offers solutions to some
problems of PCA and CCA {[}47{]}. We suggest that FA is better suited to
the testing of hypothetical, rather than data-driven, axes of variation.

Dimension-reduction analyses fit axes through data, rather than measure
the multidimensional boundaries of life history variation. Cluster
Analyses (CA) may help understand, after standardising for the
species-specific ranking on the fast-slow continuum (by using\emph{e.g.}
generation time), which and why certain life history strategies do not
exist, as much as the clustered patterning of those that do.
Hierarchical CA has previously been used to identify substructure in
life history variation {[}77{]} and could prove a useful tool to apply
much more widely in comparative life history theory {[}78--82{]}.

Depending on the researcher's perspective, a given environmental,
phylogenetic, or morphological variable may drive life history
variation, or be a nuisance covariate to deal with statistically
{[}83{]}. Comparative analyses must account for the non-independence
from shared evolutionary history {[}9,47{]}. Phylogenetic methods exist
for PCA {[}83{]}, but not yet for FA or CA, forcing researchers to use
phylogenetically independent contrasts. Correlates of life history
traits, particularly organism size, cause further challenges: including
these correlates in analyses risks deriving life history axes defined by
non-life-history traits. Moreover, using residuals in multivariate
analyses drawn from regressions of life history against body size can
introduce statistical biases {[}84{]}.

\par\null\par\null

\textbf{GLOSSARY}

- \textbf{Actuarial} (or \textbf{reproductive} ) \textbf{senescence} :
Decreases in survival (or reproduction) with age after maturity.

- \textbf{Altricial} \emph{vs.} \textbf{precocial} : born helpless and
requiring significant parental care \emph{vs.} born in an advanced state
and able to feed itself and move independently almost immediately.

- \textbf{Bauplan} : the generalised structural body plan that
characterises a group of organisms and especially a major taxon.

- \textbf{Generation time} : mean age of reproduction in a population.
Other definitions exist, such as the average time between two
consecutive generations.

- \textbf{Fast-slow continuum} : pattern of species' life history
strategies structured along two extremes: ``fast'' species develop fast,
reproduce much, and die young, whereas ``slow'' species develop slowly,
reproduce little, and live a long life. In its original inception,
Stearns referred to it as `slow-fast' {[}36{]}, but here we revert it as
this is currently a more widely used term.

- \textbf{Life expectancy} : the average period that an organism may
expect to live in a population.

- \textbf{Life history trait} : key moments along the life cycle of an
organism related to its investment on survival, development, and/or
reproduction. Examples include generation time, age at maturity,
longevity, \emph{etc} .

- \textbf{Life history strategy} : the age- and/or stage-specific
patterns and timings of events that make up an organism's life cycle.
Life history strategies are defined by the combination of life history
traits (e.g., long-lived semelparous).

- \textbf{Lifetime reproductive output} : total number of offspring
produced over the lifetime of an individual.

- \textbf{Retrogression} : the ability to regress to a smaller, younger,
or less developed stage with time.

- \textbf{Semelparity} \emph{vs.} \textbf{iteroparity} : reproductive
schedule whereby a single (\emph{vs.} multiple) reproductive event(s)
occurs during the lifecycle of an organism. In fatal semelparity, death
occurs right after reproduction.

- \textbf{Vital rate} : Key demographic process that shapes the dynamics
of a population. At minimum, these rates include survival, development
(in stage-based models), and reproduction, but can also include
dispersal, dormancy, \emph{etc} .

\selectlanguage{english}
\FloatBarrier
\end{document}

