Eco-evolution from deep time to contemporary dynamics: the role of timescales
and rate modulators

Emanuel A. Fronhofer1, Dov Corenblit2,3, Jhelam N. Deshpande1, Lynn Govaert4,
Philippe Huneman5, Frédérique Viard1, Philippe Jarne6 and Sara Puijalon7

  1. 1.

ISEM, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France

  2. 2.

Université Clermont Auvergne, CNRS, GEOLAB – F-63000 Clermont-Ferrand, France

  3. 3.

CNRS, Laboratoire écologie fonctionnelle et environnement, Université Paul
Sabatier, CNRS, INPT, UPS, F-31062 Toulouse, France

  4. 4.

Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm
310, 12587 Berlin, Germany

  5. 5.

Institut d’Histoire et de Philosophie des Sciences et des Techniques (CNRS/
Université Paris I Sorbonne)

  6. 6.

CEFE, UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry
Montpellier - IRD - EPHE, 1919 route de Mende, 34293 Montpellier Cedex 5,
France

  7. 7.

Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA,
F-69622, Villeurbanne, France

Running title:  Eco-evolution, timescales and modulators

Keywords:  eco-evolutionary feedback, geomorphology, contemporary evolution,
key innovation, multilayer networks, ecological opportunity, speciation,
global change, emergence, ecosystem genetics

Author contributions:  Conceptualization: E.A.F., P.J., S.P., D.C., F.V.;
Visualization: E.A.F., P.J., L.G., D.C.; Writing – original draft: E.A.F.,
P.J., J.N.D., P.H., D.C., S.P.; Writing – review & editing: all authors.

Correspondence Details
Emanuel A. Fronhofer

Institut des Sciences de l’Evolution de Montpellier, UMR5554

Université de Montpellier, CC065, Place E. Bataillon, 34095 Montpellier Cedex
5, France

phone: +33 (0) 4 67 14 31 82

email<EMAIL_ADDRESS>
###### Abstract

Eco-evolutionary dynamics, or eco-evolution for short, are thought to involve
rapid demography (ecology) and equally rapid phenotypic changes (evolution)
leading to novel, emergent system behaviours. This focus on contemporary
dynamics is likely due to accumulating evidence for rapid evolution, from
classical laboratory microcosms and natural populations, including the iconic
Trinidadian guppies. We argue that this view is too narrow, preventing the
successful integration of ecology and evolution. While maintaining that eco-
evolution involves emergence, we highlight that this may also be true for slow
ecology and evolution which unfold over thousands or millions of years, such
as the feedbacks between riverine geomorphology and plant evolution. We
thereby integrate geomorphology and biome-level feedbacks into eco-evolution,
significantly extending its scope. Most importantly, we emphasize that eco-
evolutionary systems need not be frozen in state-space: We identify modulators
of ecological and evolutionary rates, like temperature or sensitivity to
mutation, which can synchronize or desynchronize ecology and evolution. We
speculate that global change may increase the occurrence of eco-evolution and
emergent system behaviours which represents substantial challenges for
prediction. Our perspective represents an attempt to integrate ecology and
evolution across disciplines, from gene-regulatory networks to geomorphology
and across timescales, from contemporary dynamics to deep time.

## Introduction

That evolutionary and ecological change can happen on similar timescales has
been known since the mid of the 20th century (Pimentel, 1961; Chitty, 1967;
Antonovics, 1976). Interestingly, this “old” idea has only recently been
revived thanks to conceptual advances (e.g., the genotype-phenotype map),
long-term studies and advances in mathematical modelling which have made it
operational (Huneman, 2019). Starting in the early 2000s, eco-evolutionary
dynamics and feedbacks, or eco-evolution for short, have experienced an
important hype (Bassar _et al._ , 2021). Accordingly, reviews, perspectives
(Fussmann _et al._ , 2007; Kokko & López-Sepulcre, 2007; Pelletier _et al._ ,
2009; Post & Palkovacs, 2009; Lion, 2018), special issues (BES special issue
“Eco-evolutionary dynamics across scales” 2019) and entire books (Hendry,
2017; McPeek, 2017) have been written on the topic. However, one major
question keeps coming back: What is actually an eco-evolutionary interaction?

Hendry (2017) proposes five categories: the first two are defined as eco-
evolutionary “dynamics” where an ecological (evolutionary) change influences
an evolutionary (ecological) change, but not the other way around. The third
and fourth category are “feedbacks” which may be broad sense feedbacks where
the starting and the end point of the feedback need not be identical. Fifth,
the core or narrow sense eco-evolutionary feedback sensu Hendry involves
identical starting and end-points. For instance, plant seeds could exhibit
morphological traits that protect them from avian seed predation. This could
lead to the evolution of new beak morphologies in the birds, which may
ultimately feed back on plant evolution. Hendry (2017) also states that all
these dynamics should be happening in “contemporary time”. We would like to
note that the word “change” has to be used with caution. As McShea & Brandon
(2010) have argued, the null expectation for a biological system is continuous
change rather than permanence. Variation keeps occurring, so that permanence,
like the continued existence of some taxa for millions of years, is something
worth explaining and may require stabilising natural selection as a cause.
Therefore, in the context of eco-evolution we should understand “change” in
its most general sense that also includes permanence, that is, zero change, as
a special case.

Bassar _et al._ (2021) argue that the most correct and useful definition of
eco-evolution is restricted to Hendry’s broad and narrow sense feedbacks with
an emphasis on there being “no separation in time between ecological and
evolutionary dynamics”. They also emphasize the identical ecological and
evolutionary timescales implicitly assuming that both are fast. In their view,
this is the only case where truly novel dynamics will emerge that cannot be
explained by classical models which, as they write, usually assume a
separation of timescales and weak selection, that is, small phenotypic effects
of mutations (Lion, 2018). This definition of eco-evolution has led to studies
examining the conditions promoting rapid evolution, such as the genetic
architecture of the traits involved (Rudman _et al._ , 2017; Yamamichi, 2022).

But what is “rapid” evolution? And why is ecology considered to be always
fast? Timescales sensu Slobodkin (1961) can be divided into ecological, which
comprises time in the 10s of generations, versus evolutionary, which lies more
in the 100,000s of generations. More focused on evolutionary dynamics,
Gingerich (2001, 2009) differentiates between a generational timescale, which
is the most fundamental timescale, and then microevolutionary and
macroevolutionary timescales. Following Gingerich (2001), micro- and
macroevolutionary timescales are timescales of observation and not of the
actual process of evolution which happens on the generational timescale.

One of the emerging challenges is to identify how rapid evolution is relative
to ecology in some pattern of interest, leading to the design of eco-
evolutionary partitioning approaches (Hairston _et al._ , 2005; Collins &
Gardner, 2009; Stoks _et al._ , 2015; Govaert _et al._ , 2016). Interestingly,
quantification of evolutionary rates showed that these rates tend to be higher
than one used to think, especially if measured on short timescales (Hendry &
Kinnison, 1999). All rates can be projected onto the same generation-to-
generation rates if analysed correctly and evolution only seems slow on long
timescales, as mentioned above (Gingerich, 2009). Most recently, DeLong _et
al._ (2016) quantitatively showed, using a dataset encompassing a wide array
of organisms from protozoans to humans, that evolution (rate of phenotypic
change; we here remain on the level of patterns, we will discuss processes
below) can be fast, but is usually slightly slower (by a factor $<$ 10) than
ecological dynamics (rate of population change). These studies focus on
quantitative changes of phenotypes, but qualitative changes, such as key
innovations (Hunter, 1998; Wagner, 2011), might require more fundamental
changes in metabolic pathways or the bodyplan, and occur less frequently,
especially if historical contingencies are involved (Blount _et al._ , 2008).
One may therefore again distinguish between two evolutionary timescales, more
or less coinciding with the classical micro- vs. macroevolutionary
differentiation.

Most of the work we have discussed so far on eco-evolution has a strong
background in evolutionary biology and influences from functional or ecosystem
ecology seem weak. This lack of synthesis is apparent in Hendry (2017)’s book,
for example, and clearly biases the existing work and hinders a full
integration of ecology and evolution. While this was already noted over 10
years ago (Matthews _et al._ , 2011), limited progress seems to have been made
(for exceptions, see Kylafis & Loreau, 2008; Bassar _et al._ , 2010; El-
Sabaawi _et al._ , 2014; Matthews _et al._ , 2016). Most often, ecology, i.e.,
the environment, serves as a “theatre” for the gene-centred “evolutionary
play” (Hutchinson, 1965) because ecology and evolution have more or less
divorced since Darwin, and the Modern Synthesis had little interest in ecology
(Huneman, 2019). Even the “ecological genetics” school (Whitham _et al._ ,
2006) was not really interested in ecology, and no eco-evolutionary feedbacks
were envisaged. Furthermore, ecology is often understood as, or reduced to,
pure demography. This focus is obvious apparent in current eco-evolutionary
analyses conducted by evolutionary biologists, beginning with Pimentel (1961)
and Chitty (1967). Conversely, it also seems that functional ecologists have
had little interest in evolutionary processes (Loreau, 2010; Huneman, 2019).

If we want to take a more ecosystem-level perspective, we must ask whether
fluxes of matter and energy, both in trophic networks and the physical
environment (Corenblit _et al._ , 2009), may also play a role in eco-
evolution. This implies that we need to understand on which timescales such
fluxes happen. Using the meta-ecosystem ecology framework, Gounand _et al._
(2018b) have summarized available information for carbon fluxes, and show that
fluxes vary widely and across orders of magnitude. Yet, often, timescales are
within years, implying that ecosystem dynamics and demographic rates are
comparable, and might even be intrinsically linked. Interestingly, spatial
flows of matter and energy are often mediated by spatial behaviour of
organisms (movement, foraging, seasonal migrations, dispersal; Gounand _et
al._ , 2018a) which provides a mechanistic link between metacommunity and
metaecosystem ecology (Loreau _et al._ , 2003; Massol _et al._ , 2011b). This
behavioural link is especially true for taxonomically similar ecosystems
(e.g., two lake ecosystems) that are more linked by dispersal than ecosystems
that are biotically dissimilar (e.g., terrestrial-aquatic linkages) which may
be more linked by flows of resources (Gounand _et al._ , 2018a). Of course,
different organisms within an ecosystem may experience different timescales,
such as bacteria versus large mammals. While interesting and potentially
relevant for ecosystem dynamics this is beyond the scope of the current
article.

At a more fundamental level, the abiotic environment is defined by the
geological and geomorphological settings that impact abiotic ecosystem
properties (e.g., via physico-chemical conditions, leaching of nutrients,
fluxes and organization of mineral matter) and also impact the biotic
component because geology and geomorphology provide the environmental matrix
in which (meta)ecosystem dynamics play out (Phillips, 2021). In systems in
which geology and geomorphology are the “pacemakers”, ecological dynamics will
occur at much longer timescales than demography.

Given the central role of timescales and lack of true integration of ecology
and evolution, we here propose to conceptualize eco-evolution within a two-
dimensional time space. At the extremes of this time space we can identity
four system states (Fig. 1): A) fast eco-evolution sensu Bassar _et al._
(2021) where evolution is fast enough to impact fast ecology (demography); B)
classical ecology and evolutionary ecology, where evolution is too slow to
immediately impact fast ecological dynamics. Note that even if the ecological
dynamics results from past evolutionary changes (and vice versa), the
timescales do not match; C) evolution is faster than ecology which could imply
rapid adaptation to relatively slower environmental changes (rapid adaptation
to anthropogenic disturbance Chakravarti & van Oppen, 2018; Lagerstrom _et
al._ , 2022) and, ultimately, neutral evolutionary dynamics; and finally D)
both are slow such as when geomorphological conditions provide an ecological
opportunity for a rare key innovation which then feeds back and impacts
geomorphology (Corenblit _et al._ , 2011; Butterfield, 2017; Pausas & Bond,
2022). We recognize that these four states are artificially discrete
categories, and that real situations might better be described along a
continuum.

Figure 1: Eco-evolutionary system states describing extremes along continuous
variation of matching or mismatching ecological and evolutionary timescales.
Transitions in and out of states and between states are possible via the
action of modulators of ecological and evolutionary rates which slow down or
speed up ecology or evolution, respectively.

With these four extreme system states in mind (Fig. 1), we explore below eco-
evolution across slow and fast timescales, bringing to the fore two aspects:
1) What is slow eco-evolution? 2) How is it possible to move between states,
or, in other words, are there modulators that speed up or slow down ecological
and evolutionary dynamics (or both)? We first discuss fast and slow eco-
evolution, before moving to modulators of ecological and evolutionary rates
and their interaction, highlighting the connection between environmental
factors, demography and evolutionary processes, including the role of
stochasticity. The general objective is to build an eco-evolution framework
that can be used across timescales, and to derive predictions in times of
environmental change.

## Fast eco-evolution

To set the scene, we first discuss one of the most classical examples of fast
eco-evolution with novel dynamics emerging that has been called the “smoking
gun” of eco-evolutionary feedbacks. This work has been synthesized by Hiltunen
_et al._ (2014) and has been associated with eco-evolutionary interactions
since the foundational work of Pimentel (1961, 1968): Cycling predator-prey
dynamics, which are characterized by a quarter-phase lag between prey and
predator cycles, select for (costly) defence mechanisms in the prey. As
predation pressure increases with increasing numbers of predators, the prey
evolves a defence mechanism, while, due to the associated costs, the
undefended prey will start dominating when predator numbers are low again.
This oscillation between defended and undefended prey (evolutionary change) is
as fast as the demography of the predator-prey system (ecological change)
which leads to an eco-evolutionary feedback in the narrow sense (eco-
evolution) and a novel, emergent, system property sensu Bassar _et al._
(2021): the predator-prey system now does not oscillate with a quarter-phase
lag any more but it shows anti-phase dynamics. This was first reported by
Yoshida _et al._ (2003) for rotifers and alga, and shown to be a relatively
common but overlooked feature of a lot of predator-prey time-series (Hiltunen
_et al._ , 2014), from bacteria-phage systems to insects and their
parasitoids, hinting at the ubiquity of eco-evolution. Due to the similar
timescales, Bassar _et al._ (2021) conclude that there is a very specific
domain of applicability of eco-evolution: strong selection (large mutational
effects Lion, 2018), non-negligible phenotypic variances and large genetic
effects on ecological variables.

Work on the Trinidadian guppy system (reviewed by El-Sabaawi _et al._ , 2014;
Reznick & Travis, 2019) goes beyond a pure focus on demography and includes
ecosystem-level processes in the form of nutrient recycling. This body of work
shows that in low predation conditions guppy population density is higher than
under high predation (Reznick, 1982), which leads to modifications in the
ecosystem through grazing, affecting algae and invertebrates, and excretion
rates, which in turn imposes selection on guppy traits (Bassar _et al._ ,
2010, 2012; Reznick & Travis, 2019).

## Slow eco-evolution

While the above-mentioned examples of fast eco-evolution are well known, we
will now discuss examples of slow eco-evolution, that is, system state D in
Fig. 1 where both, ecological and evolutionary dynamics are slow.
Stereotypically, this will imply that slow ecosystem-level processes linked to
the abiotic compartment (e.g., geology and geomorphology) act as a “pacemaker”
for ecology and that evolutionary answers to these selection pressures are
slow or rare such as in key innovations that require fundamental changes in
metabolic pathways or the body plan.

Going far back in time, the dynamics of oxygen on Earth provide a good example
(Judson, 2017), especially two major oxidation events: the “Great Oxidation
Event” slightly more than 2 Ga ago and the phase just before the Cambrian
(Holland, 2006). The “Great Oxidation Event” is most likely due to
cyanobacteria evolving oxygenic photosynthesis as a key innovation which
raised oxygen levels in the atmosphere. This new ecological opportunity
eventually led to wide-sense slow eco-evolution and permitted the emergence of
eukaryotes and land plants (for a more detailed discussion see Judson 2017;
but see Mills _et al._ (2022) for a recent argument decoupling eukaryogenesis
and oxygen levels). The rise in oxygen probably set the conditions for the
explosion of animal body plans and the rise of wasteful species (i.e., species
that are energetically more efficient when using resources, but are releasing
more waste; Vermeij 2017). Both aspects changed the face of the Earth since
the new life forms enlarged interaction networks (e.g., food chains and webs,
see Butterfield, 2007) with associated adaptations to predatory lifestyles and
a major impact on the environment via bioturbation. Bioturbation is known to
have driven slow eco-evolution in the oceans and on land by influencing key
geomorphological and physicochemical components (Johnson, 2002; Meysman _et
al._ , 2006; Murray _et al._ , 2008; Phillips, 2015; Butterfield, 2017).

Figure 2: Slow eco-evolutionary feedbacks between riparian vegetation and
fluvial geomorphology during the Palaeozoic Era. From top to bottom: a)
Feedbacks between geomorphology and vegetation; the squares represent
geomorphology and the circles vegetation; arrow widths represent the
importance of the relationships; abiotic effects are shown in grey, biotic
effects in green and the dark-green colour indicates feedbacks. b) Changes in
the percentage of fluvial patterns and appearance of novel micro-habitat
conditions promoting selection in plant traits. c) Major aspects of plant
evolution. The letters represent taxa listed in the legend (right). Adapted
from Nikas _et al._ (1985) and Davies & Gibling (2013).

Another well-documented example of slow eco-evolution involves riparian
ecosystems which cover large areas in almost all terrestrial biomes. Plants
establishing in riparian areas are subject to strong selection pressures
related to flooding, drought, sediment erosion, transport and burial. At the
same time, plants have an important impact on river morphodynamics via the
stabilization of the substrate by their belowground organs (rhizomes, roots)
and the modulation of water flow properties and sediment dynamics by their
aboveground organs (Gurnell, 2014). As a consequence, plant traits and
assemblages, on the one side, and river morphology on the other, impact each
other in a feedback loop (Corenblit _et al._ , 2015). More specifically, plant
colonization of the continents began in the late Ordovician and early Silurian
(444–416 Ma), with small tracheophytes colonizing coastal areas. Initially,
plants evolved traits that allowed them to live in intertidal areas, followed
by traits related to the new hydrodynamic, geomorphological, and physiological
constraints they encountered while spreading into river corridors (Bashforth
_et al._ , 2011). The gradual evolution of riparian plant traits in the face
of disturbances and stresses inherent to the coastal and fluvial environment
led in turn to drastic and irreversible changes in river morphodynamics across
continents throughout the Paleozoic Era (Fig. 2; Bashforth _et al._ , 2011;
Davies & Gibling, 2011; Gibling _et al._ , 2014; Davies _et al._ , 2021).
Paleozoic changes in fluvial morphodynamics led to the development of fluvial
landforms that were rare or absent in the Cambrian or before, such as
elevated, muddy floodplains incorporating confined sinuous channels showing at
their margins steles of levees and crevasse splays (Gibling _et al._ , 2014).
It was during this pivotal period in the evolution of the biosphere that
meandering and anastomosing geomorphological fluvial patterns first developed
in close relation to the evolution of riparian trees with robust and deep root
systems. The stabilizing and constructing effect of riparian vegetation on the
floodplains fed back and caused major changes in riparian ecosystem structure
and function (Falcon-Lang _et al._ , 2011; Gibling & Davies, 2012). The
meandering and anastomosing fluvial patterns in particular provided new
opportunities for plants and animals to evolve in a variety of patchy habitats
such as main and side channels, oxbows, and to move from wet coastal to dry
upland conditions, ultimately leading to the spread of seed plants and animals
on hillslopes during the Carboniferous period (Greb _et al._ , 2006; Davies &
Gibling, 2013). The fossil record indicates that the Devonian period (419 to
359 Ma ago) already encompassed numerous non-vertebrates, vertebrates, and
plant communities forming complex ecosystems (Labandeira, 1998; DiMichele _et
al._ , 2005; Kennedy _et al._ , 2012; Wilson _et al._ , 2020).

Other examples involve the building of past and actual reefs, creating new
geological carbonate structures in the ocean, that have subsequently served as
substrate for life and the development of new ecosystems over millions of
years (bioherms). Of course, reef building is a lot more complex and varied
than what we can discuss here (Kiessling _et al._ , 1999).

While not being a comprehensive list, these examples highlight the existence
of slow eco-evolution. The recognition that organisms affect abiotic
conditions in their environment in cumulative or recurrent ways leads to many
opportunities to more explicitly consider feedbacks between genes, organisms
and the environment. On a slow evolutionary timescale, this may involve large-
effect mutations and key innovations (Hunter, 1998; Wagner, 2011) and major
transitions (Maynard Smith & Szathmáry, 1995; Szathmáry & Maynard Smith,
1995), adaptive radiations and the occupation of new ecological opportunities
after extinctions (Stroud & Losos, 2016; Vermeij, 2017).

All of the above mentioned examples imply that the biotic compartment impacts
the abiotic compartment. This impact is often referred to as “niche
construction”, which directly connects (ecosystem) ecology and evolution.
Labelled “ecosystem engineering” by ecologists (e.g., Jones _et al._ , 1994),
this interaction across generations changes selective pressures exerted upon
the niche-constructing species and therefore induces evolution (Odling-Smee
_et al._ , 2003). More precisely, ecosystem engineering is related to the
effect of a species on the structure and function of the ecosystem through
modification of the physical environment without an evolutionary feedback. The
latter is described by the concept of niche construction. Debates are raging
about the specificity of niche construction, and its reducibility to the
ordinary selective process where an allele affects the environmental
parameters of the focal species (Laland _et al._ , 1999; Lehmann, 2007).
According to the latter view, niche construction would just be a form of
extended phenotype (Dawkins, 1982) which has led to notable debates (Laland,
2004; Dawkins, 2004; Laland _et al._ , 2016).

The concepts of ecosystem engineering and niche construction can also be found
in the Earth System Sciences literature (Butterfield, 2011) since ecosystems
can be seen as complex adaptive systems (Levin, 1998; Solé & Levin, 2022). A
more detailed discussion of the relationship between evolutionary biology,
ecology and Earth sciences is unfortunately beyond the scope of this paper.

## Change of system states: what modulates ecological and evolutionary rates?

As mentioned earlier, the four system states identified in Fig. 1 are extremes
along a continuum of matching or mismatching rates of ecological and
evolutionary processes. In reality, many situations may exist in between. We
might even expect transitions between these states (Fig. 1) or at least
movement along the axes when ecological and evolutionary rates change. For
example, stressful environmental factors may decrease birth rates and thereby
slow down ecological dynamics. By analogy, mutation rates may be impacted, for
example, by mutagens, and speed up evolutionary dynamics in response to a
given selection pressure. We may even imagine transitions from the slow to the
fast eco-evolution regime. In the following, we will refer to the factors
responsible for changes in the ecological or evolutionary rates as “rate
modulators” (Fig. 3). While certain processes may modulate both ecological and
evolutionary rates, we first focus on distinguishing between modulators of
ecological rates and modulators of evolutionary rates (Fig. 3) and will
discuss interactions later. We here do not provide an exhaustive list of all
potential rate modulators, but rather want to illustrate the role of rate
modulation for eco-evolution.

Figure 3: Eco-evolutionary feedback loop with pacemakers (red) and modulators
of ecological and evolutionary rates (blue). Pacemakers will define whether
the fundamental dynamics are slow or fast. For instance, if slow geological
processes, such as nutrient leaching, determine the pace of ecology, ecology
will be overall slow. The analogous is true for evolutionary responses: if the
focal evolutionary response is, for instance, linked to key innovations and
speciation, this process may be overall slow. These broadly defined paces can
be modulated by rate modulators. Some of these are external (e.g.,
temperature) or internal such as species interactions, mutation rate evolution
or changes in the genotype-phenotype map. Note that the feedback loop depicted
here highlights the importance of the individual level and of basic processes.
We have added some emergent features such as speciation, hybridization or
extinctions (grey) for clarity. Adapted from Govaert _et al._ (2019).

### Modulators of ecological rates: changing the speed of ecology

Overall, ecological rates will either be determined by demographic rates
(rates of birth, death, and dispersal) or by abiotic components (current
environmental conditions but also physico-chemical conditions, leaching of
nutrients, fluxes and organization of mineral matter) as discussed above (Fig.
3).

Ecological rate modulators are well-known, and involve classical stressors
such as temperature, pH, salinity, precipitation, and other environmental
factors. These will be relevant for fast eco-evolution, but oxygen levels in
the atmosphere, for example, are also an ecological rate modulator which have
impacted the evolution of biodiversity in deep time (see above).
Interestingly, oxygen is a rate modulator that is produced and consumed by
organisms, which may hint at complex feedbacks. The effect of these stressors
may increase after natural catastrophic events (e.g., asteroid impacts,
volcanic activity), changes caused by human activities (e.g., increases in pH
or temperature) or due to slower tectonic or astronomic forcing (e.g.,
orogenesis or continental fragmentation and Milankovitch cycles at the basis
of long-term climatic variations that deeply impact biomes and their
distribution; also long-term volcanic activity, e.g., the formation of Deccan
Traps and their potential role in the Cretaceous – Paleogene extinction
event). Importantly, the form of their effects can vary broadly, from linear,
monotonic to unimodal or u-shaped. If effects are extremely non-linear, these
have usually been described as tipping points implying alternative stable
states (Scheffer _et al._ , 2001; Drake _et al._ , 2020). We start with
discussing what modulates demographic rates and then focus on modulation via
the abiotic environment (Fig. 3). As ecology, if defined by demographic rates,
may generally be seen as (slightly) faster than evolution, any ecological rate
modulator that slows down ecological dynamics may lead to an increased
likelihood of eco-evolution and, therefore, emergent system properties.

#### Modulation of birth and death rates

Temperature is a well known ecological rate modulator as it regulates the
metabolism of organisms, and hence can alter birth and death rates (Brown _et
al._ , 2004; Gillooly _et al._ , 2001). Biological rates may scale
exponentially within a certain range of temperature, but will globally scale
with a unimodal relationship. If temperature alters birth and death rates, it
can alter the ecological dynamics governed by these processes and consequently
alter the rate of demographic processes. This is highly relevant in the
current context of ongoing global changes, in which temperatures are expected
to rise quickly and globally (Perkins-Kirkpatrick & Lewis, 2020; Masson-
Delmotte _et al._ , 2021), and may thus push systems into alternative eco-
evolutionary states by changing ecological rates. Global change-induced rises
in temperature may speed up or slow down ecological processes, depending on
whether environmental conditions are above or below the focal organism’s
thermal optimum. For example, in consumer-resource systems, both foraging rate
and consumer-resource interaction strength depends on temperature (Dell _et
al._ , 2014; Gilbert _et al._ , 2014; Hamann _et al._ , 2020; Synodinos _et
al._ , 2021). If higher temperatures speed up birth rates of prey or predator
species, this could lead to fast eco-evolution or, on the contrary, disrupt
fast eco-evolution, depending on the rate of evolution. More generally, if a
system already has matching ecological and evolutionary rates, the consequence
will be a reduction of the potential for eco-evolution because ecological
timescales will become faster or slower than evolutionary ones.

Similar to temperature, other physicochemical stressors (e.g., pH, salinity,
humidity, pollution) that have linear or non-linear relationships with
demographic rates may shift eco-evolutionary system states. Predicting whether
such stressors act as positive (speeding up) or negative (slowing down) rate
modulators will depend on the shape of the response curve, but, if all
stressors ultimately have a negative impact on population growth, the
consequences in terms of matching or mismatching eco-evolutionary timescales
will depend on whether the stressor acts via decreasing birth rates (slowing
down ecology) or increasing death rates (speeding up ecology; see also Boyce
_et al._ 2006, Lawson _et al._ 2015).

Stressors may of course exist outside of the global change context. Especially
gradients of stress, such as large scale, latitudinal or altitudinal
temperature gradients may be responsible for determining species ranges
exactly via ecological rate modulation. For instance, the “Species-
Interactions Abiotic-Stress Hypothesis” (SIASH, reviewed by Louthan _et al._
2015) states that stressful range edges are defined by abiotic forces and that
non-stressful edges are more defined by species interactions (for a focus on
facilitative interactions see the “Stress Gradient Hypothesis” by Bertness &
Callaway 1994 which has somewhat opposing predictions). One potential
mechanism for the SIASH is the reduction of inter-individual interactions at
stressful margins via reduced densities, which leads to a spatial gradient in
ecological rates. As a consequence, such large-scale gradients may lead to
geographical hot- and cold-spots of eco-evolution. Patterns can of course be
complexified due to dispersal, for instance.

Beyond the abiotic environment, biotic interactions themselves may act as rate
modulators (Fig. 3). For example, the presence of predators may induce
variation in morphological and life-history traits, as shown by studies on the
effect of crayfish on maturation age in freshwater snails (Hoverman _et al._ ,
2005; Covich, 2010). More generally, predator defence mechanisms or
competitive ability may be linked to demographic rates directly (via density
regulation in the case of competition; see e.g., Fronhofer _et al._ 2020,
Siepielski _et al._ 2020) or indirectly (via costs of anti-predator
mechanisms, for example; Urban 2007) and modulate eco-evolutionary rates.

#### Modulation of dispersal

External factors may also impact the third demographic rate: dispersal.
Fragmentation, for instance, and loss of habitat in general, may reduce
effective dispersal rates and thereby reduce the speed of ecological dynamics
(Legrand _et al._ , 2017). Conversely, the rewiring of dispersal networks
(Bullock _et al._ , 2018) may facilitate dispersal and speed up ecological
dynamics. Dispersal may also be modulated via biotic interactions. Theory
indicates that dispersal should globally increase with intra- and
interspecific competition (Metz & Gyllenberg, 2001; Poethke & Hovestadt,
2002). Empirical evidence indicates that, in a food-web context, both bottom-
up and top-down effects will determine dispersal rates (Fronhofer _et al._ ,
2018; Cote _et al._ , accepted).

#### Modulation via the abiotic environment

In the above sections we have highlighted processes that may directly modulate
demographic rates which may be most relevant when ecological dynamics are
globally fast. For modulation of globally slow ecological processes we propose
to differentiate three broad categories: 1) Slow ecological processes with
external slow forcing, e.g., glaciations over the last three million years.
Here, forcing was external via the position of the Earth and temperature, and
other environmental parameters, changed slowly allowing organisms to
potentially track climatic zones geographically and occupied “refuges”. The
slowness of these processes likely prevented eco-evolution, while local
adaptation to refuges was possible. Another example is the colonization of
land ca. 500 Ma ago where the abiotic environment was defined by available
space, which led to ecological opportunities and subsequent major key
innovations (Vermeij, 2017) leading to slow eco-evolution. 2) Slow processes
with internal (biological) slow forcing which includes the great oxidation
events discussed earlier. The rise in oxygen was due to internal forcing with
bacterial activity slowly rising and coinciding with the rate of key
innovations which implies slow eco-evolution. 3) Finally, slow processes
following catastrophic events, such as events that happened at the Cretaceous
– Paleogene boundary and the massive extinction of non-avian dinosaurs and
many other taxonomic groups, leaving room for birds and mammals (Longrich _et
al._ , 2012). Similarly to the internal forcing example, here, ecological
opportunities arise paving the way for key innovations and further slow eco-
evolution.

### Modulators of evolutionary rates: changing the speed of evolution

Evolutionary changes are determined by four processes: selection, genetic
drift, gene flow (including across species through hybridization and
subsequent introgression) and mutation (including recombination). At the
species-level one may also mention extinctions and speciation. While we do not
intend to wade into a discussion on the link between processes that are
classically understood as microevolutionary and processes labelled as
macroevolutionary, we here assume that species-level patterns, such as
speciation are driven by underlying processes such as (the lack of) gene flow,
for example.

All of these processes clearly influence the rate of evolution. However, a
discussion of evolutionary rate modulators is complicated by the fact that
such changes in evolutionary rates are also mediated by changes in ecological
rates, particularly, in the case of selection, gene flow and genetic drift
(Fig. 3). This is because selection pressures, gene flow and drift are the
evolutionary forces that are most likely directly defined by ecology (Fig. 3).
Note that this is not completely true as discussed, for example by Futuyma
(2010) who points out that selection may also be “internal” via developmental
effects. In Fig. 3 this may be captured by the genotype-phenotype map. Gene
flow is further complicated by the fact that it is mediated by dispersal which
is an ecological rate but at the same time a trait with a genetic basis
(Saastamoinen _et al._ , 2018) which allows for complex, second-order
evolutionary processes which we will discuss below.

In general, the rate of an evolutionary response is contingent on the amount
of heritable phenotypic variation that aligns with the optimum that is
selected for, potentially determined by ecological change (Schluter, 1996) and
the ability of a biological system to show an adaptive response. We will here
focus on modulation of evolutionary rates in the context of evolvability, and
its evolution (Payne & Wagner, 2019). We do not discuss epigenetic variation
in any detail but some of the reasoning below may also apply.

The term “evolvability” has been used differently depending on timescale and
context (Pigliucci, 2008; Riederer _et al._ , 2022). On short timescales,
evolvability is defined as standing genetic variation (Houle, 1992). If such
variation is already present, then a rapid evolutionary response is expected
in response to rapidly changing ecological conditions, that is, fast eco-
evolution. A well-documented example is the Atlantic killifish which was shown
to have adapted extremely rapidly (and repeatedly) to high levels of
industrial pollutants in estuaries of the north-eastern coast of America (Reid
_et al._ , 2016; Lee & Coop, 2017).

At intermediate timescales, mechanisms that generate standing genetic
variation, termed variability (potential to vary; Wagner & Altenberg 1996),
contribute to evolvability. The potential to generate variation depends not
only on mutation rates but also how these mutation rates impact fitness
(Riederer _et al._ , 2022). Thus, mechanisms that generate, deplete or
maintain variation are critical to understanding how evolutionary rates are
modulated. We will discuss modulators of evolutionary rates in two sections,
first focusing on external modulators, such as temperature or stressors and,
second, highlighting internal modulators, for instance, related to genetic
architecture.

#### External modulation

External modulation of evolutionary rates may be due to processes very similar
to those discussed above for ecological modulators. Focusing on variability,
temperature, for instance, has been shown to impact spontaneous mutation rates
in a u-shaped manner, with increased mutation rates at both low and high
temperatures (Waldvogel & Pfenninger, 2021). Other stressors, such as UV or
chemical contaminants, are also well known to impact mutation rates (see e.g.,
López-Barea & Pueyo, 1998; Somers _et al._ , 2002; Bickham, 2011; Saaristo _et
al._ , 2018).

In analogy to ecological rate modulation and focusing on variation, influx of
novel genes or genotypes via gene flow will be modulated negatively by
fragmentation and positively by rewiring of dispersal networks (Bullock _et
al._ , 2018). Gene flow may not only bring new alleles, but also lead to new
allelic combinations through hybridization and possible introgression.
However, the question is whether such inflow will increase or decrease the
rate of evolution. Isolation may indeed promote local adaptation, while high
gene flow may reduce local adaptation and could slow down evolutionary
responses (Massol _et al._ , 2011a; Laroche _et al._ , 2016).

Finally, in the context of slow eco-evolution, ecological opportunities, which
may often be provided by previous (mass) extinction events, can promote key
innovations, diversification and speciation. Therefore, such extinctions
events may also be seen as external modulators of evolutionary rates. Large-
scale latitudinal gradients of speciation rates as reported by Weir & Schluter
(2007) or Rabosky _et al._ (2018), for instance, also provide examples of
evolutionary rate modulation, potentially driven by associated major
environmental factors.

#### Internal modulation

Modulation of evolutionary rates may however be a lot more complex.
Specifically, evolution of mutation rates, as well as changes in the structure
of the genotype-phenotype map, can change evolutionary rates.

Mutation rates are subject to evolution with, for example, selection favouring
overall low mutation rates in stable environments for well adapted populations
because the majority of mutations have harmful effects (see also Wielgoss _et
al._ , 2012, for hypermutator fates in E. coli). Evolution of mutation rates
might be regulated by the physiological cost of maintaining mutations at low
level or by genetic drift (Sniegowski _et al._ , 2000; Lynch _et al._ , 2016).
Evolution of mutation rates is probably rather to be expected on longer
timescales or in alternating bouts of high and low mutation rates (Giraud,
2001). This is not the case for recombination and hybridization which may
modulate rates on both short and long timescales impacting adaptation rates
but also potentially leading to fast innovation. Of course, the effect of
mutations will depend on their effect size, with large effect mutations having
the potential to lead to innovations.

Recombination rate may evolve under stressful environmental conditions, most
notably in species with flexible sexuality including transition from asexual
to sexual reproduction (Burke & Bonduriansky, 2017; Gerber _et al._ , 2018;
Moerman _et al._ , 2020). Of course, asexuality can also be triggered by
demographic conditions such as low density, for example, and not because of
selection. In addition, hybridization processes (i.e., interspecific gene
flow) can speed up evolutionary rates, which is illustrated by the Gulf
killifish. This species rapidly adapted to the extremely polluted environment
of the Houston harbour thanks to an adaptive introgression by the Atlantic
killifish. This introgression occurred because of secondary contact between
the two killifish species most probably due to human-assisted transport
(Oziolor _et al._ , 2019). This latter example also shows that evolutionary
rates can be modulated by ecological rate modulators, here human-driven
dispersal.

Ultimately, the fitness impact of mutations depends on the structure of the
genotype-to-phenotype map which includes mutation effects, pleiotropy and
epistasis. The structure and properties of this map (Nichol _et al._ , 2019)
are thus relevant to defining evolutionary rates at varying timescales. Since
in realistic maps multiple genotypes can correspond to one phenotype,
robustness to mutation can emerge. While the potential to generate more
variation might lead to rapid evolutionary responses at intermediate
timescales, on longer timescales, robustness to mutation (genetic
canalization) can lead to large leaps in evolution. This is because
mutationally robust genotypes can neutrally explore genotypic space without
any changes in the phenotype (see Ciliberti _et al._ , 2007). On longer
timescales, modularity in developmental systems promotes innovations (Wagner &
Altenberg, 1996). Hence robustness on short timescales allows for evolvability
in longer timescales (Wagner, 2011). Evolvability, in turn, allows species to
persist in the midst of ecological change. Thus, when taken into account in
evolutionary modelling, the genotype-phenotype map explains a form of short-
timescale robustness, which, in turn, stabilises ecological change at larger
scales due to the evolvability of the species. This mutational robustness acts
as an evolutionary rate modulator. Under conditions of rapid ecological
change, a loss of robustness (decanalization) can speed up evolutionary
responses (Deshpande & Fronhofer, 2022). Further, phenotypic plasticity can
also modulate evolutionary rates (genetic assimilation or the accumulation of
cryptic genetic variation; e.g., Van Gestel & Weissing 2016).

A majority of the above-mentioned evolutionary rate modulators allow to speed
up the pace of evolution. Since evolution seems to be slightly slower than
ecology (DeLong _et al._ , 2016), these processes may bias dynamics towards
the fast eco-evolution sensu Bassar _et al._ (2021).

### Interactions between ecological and evolutionary rate modulators

The action of rate modulators is likely a lot more complex than what we have
described above, in particular because they may simultaneously impact
ecological and evolutionary change (Fig. 3).

One example is again temperature which we have discussed in both ecological
and evolutionary contexts. Interestingly, it will modulate ecological rates
globally following a concave relationship (classical hump-shaped thermal
performance curve) while the evolutionary modulation of mutation rates may be
convex (u-shaped; Waldvogel & Pfenninger 2021; for recombination see Morgan
_et al._ 2017) as discussed above (see Fig. 4). Considering that evolutionary
rates are on average smaller than ecological ones (DeLong _et al._ , 2016)
this leads to a transition from slow ecology with fast evolution at low and
high temperatures to fast ecology and slow evolution at intermediate
temperatures, close to the optimal temperature. Strong eco-evolution can
therefore be predicted to occur at the intersection of these curves, namely to
the left and the right of the mode of the thermal performance curve (Fig. 4).
In a climate change context, with extreme warming and temperatures beyond an
organism’s optimum, we can therefore predict the increased occurrence of eco-
evolution.

Figure 4: Possible interactions between modulators of ecological and
evolutionary rates leading to changes in system states from evolution to eco-
evolution via ecology back to eco-evolution and evolution (from left to
right). Concretely, the rate modulator here is temperature which increases,
for example, due to anthropogenic activities from left to right
(alternatively, this could also be a latitudinal gradient). The rate modulator
has a concave (convex) effect on the ecological (evolutionary) rates. For
temperature, the grey (ecological) curve represents a thermal performance
curve and temperature effects on population growth rates, while the green
curve represents the impact of temperature on mutation rates, for example.
Crossing lines indicate similar rates which implies that emergent, eco-
evolutionary phenomena become possible. Of course, rate-modulator
relationships can take different forms which will determine in which state the
ecological system is (see Fig. 1). Note that the example mentioned here is
overall in the fast eco-evolution realm, but analogous patterns in the slow
eco-evolution case are also possible.

More generally, ecological rate modulators will also impact evolution,
depending on whether they primarily act on birth or on death rates. If
stressors mainly act via a decrease in birth rates (see e.g., Aulsebrook _et
al._ , 2020) evolutionary rates will also be decreased because less births
imply less input of novel mutations. By contrast, an effect on death rates
(e.g., Pardo _et al._ , 2017) may have no impact or increase the strength of
selection. Dynamics may get even more complex if one thinks about processes
like senescence, that will increase selection early on in the life-cycle
(Rose, 1991), or trade-offs and trait correlation such as the competition-
colonization trade-off (Cadotte _et al._ , 2006; Cadotte, 2007), for example.
The effect of ecological rate modulators on evolution may also be more
indirect. It has for instance been shown that mutation rates can depend on
population density (Krašovec _et al._ , 2017). Therefore, any ecological rate
modulator that changes emergent population densities also immediately has the
potential to impact mutation rates.

As mentioned above, regardless of what demographic rate is impacted by the
modulators, selection pressures will be changed, thereby indirectly linking
ecology and evolution. One example is the variability of selection pressures
due to frequency-dependent selection. In this case, an ecological rate
modulator may imply a feedback on evolutionary change that decreases the
intensity of selection, which in turn impacts ecological change, slowing it
down. This eventually lets evolutionary change occur in a more constant
ecological setting.

At the intersection between ecological and evolutionary rate modulators,
dispersal and its drivers may become especially prominent. Clearly dispersal
can be a key player in eco-evolution (Govaert _et al._ , 2019): it is itself
an ecological rate, it defines gene flow and therefore the pace of evolution
and, finally, it has a genetic basis (Saastamoinen _et al._ , 2018) and can
itself be subject to evolution (Bowler & Benton, 2005; Ronce, 2007). This
combination of roles may lead to complex eco-evolutionary feedbacks (for a
discussion of temperature and fragmentation effects on eco-evolution, see
Faillace _et al._ , 2021).

Last, we would like to mention that all rates and modulators mentioned above
may be context-dependent, such as dependent on body sizes or complexities of
the ecosystem and therefore exhibit plasticity. While certainly interesting,
these complexities are beyond the scope of this work. In conclusion, changes
in ecological and evolutionary rates are highly coupled, and causation is not
always easy to disentangle, calling for detailed analyses of the mechanisms
involved in eco-evolution and their respective strength (Fig. 3, 4).

### Introducing stochasticity

Up to now, we have not, at least not explicitly, considered stochasticity,
although it plays a key role in both evolution (Lenormand _et al._ , 2009),
hotly discussed in the wake of Gould (1989)’s “replaying the tape of life”
idea, and ecology (Shoemaker _et al._ , 2020), and presumably also for eco-
evolution. Stochasticity is a hallmark of living systems, it can be
demographic or environmental, and we outline some of its impacts below.

Environmental stochasticity is perhaps, in our context, the most
straightforward to consider since it affects both the evolutionary and
ecological dynamics. Environments are often spatially heterogeneous, and may
fluctuate in time in a more or less predictable fashion. Of importance here is
that the abiotic side of environmental stochasticity may affect living
entities, from genes to ecosystems, at various timescales on the fast to slow
continuum of eco-evolution. For example, weather variation or the occurrence
of floods, storms and fires can be sources of stochasticity at short
timescales. Of course, mitigating environmental stochasticity is part of the
selective process at these timescales (Lenormand _et al._ , 2009), for example
through bet-hedging strategies. A less common source of stochasticity comes
from rare large-scale events, such as volcanic eruptions with global effects
or asteroid impacts (Hoffman _et al._ , 1998), leading to extreme consequences
on living beings, such as mass extinctions (Raup & Sepkoski, 1982; Longrich
_et al._ , 2012), and no less extreme evolutionary consequences such as
radiations (Penny & Phillips, 2004).

Ecological rate modulators and changing demographic rates will also directly
impact demographic stochasticity. Even if the equilibrium density is not
impacted, higher underlying birth and death rates will increase demographic
stochasticity due to increased turnover at population equilibrium. Even more
extreme, increasing demographic rates are known in discrete-time systems to
lead to deterministic chaos (Hassell, 1975; Hassell _et al._ , 1976, for an
experimental demonstration of chaos see Becks _et al._ 2005).

Besides these direct impacts on variance in population dynamics, ecological
rate modulators can also have indirect impacts on demographic stochasticity,
via effects on (equilibrium) population sizes. For instance, equilibrium
population sizes have been shown to decrease with increasing temperature
(Bernhardt _et al._ , 2018), which implies that demographic stochasticity will
become relatively more important with increasing temperature, for example.

Beyond obvious consequences for stability, risk of extinction and
predictability of systems, these ecological effects also impact evolution via
increased genetic drift, if population sizes are small enough. We know that
drift may play a (non-directed) positive role in evolution (Lenormand _et al._
, 2009), for example by purging deleterious mutations under specific
conditions (Glémin, 2003) or by impacting mutation rates (Lynch _et al._ ,
2016). Over much longer timescales, drift may shape genome evolution with
differential effects depending on organism size (effective size; Lynch, 2007).
Similarly, genetic diversity in animals seems to be strongly linked to a
slow–fast continuum of life-history strategies, such that demography and drift
might also be at play here (Romiguier _et al._ , 2014). Clearly, at larger
scales, founder effects may play important roles in (island) biogeography, for
example. Especially when coupled with rare, long-distance dispersal such
events can shape biogeographic patterns (Gillespie _et al._ , 2012).

Genetic drift may also be relevant in a spatial context via its spatial
analogue, gene surfing. Gene surfing is a process by which neutral or even
deleterious alleles can increase in frequency at expanding range fronts thanks
to sequential founder events (for a review see Miller _et al._ , 2020).

## Discussion and perspectives

The field of eco-evolutionary dynamics has, in recent years, suffered from a
lack of clear definition to the point where every study at the interface
between ecology and evolution has used the label eco-evolutionary dynamics or
feedbacks. Bassar _et al._ (2021) have addressed this problem recently and
have clearly argued under what conditions eco-evolution yields different
predictions and insights in comparison to more classical work in evolutionary
ecology. These authors focus on the emergence of novel patterns that would not
be within the realm of possible explanations in either purely ecological or
purely evolutionary contexts. Note that the term “emergence” is often used
rather loosely implying only the appearance of a pattern of some kind.
Strictly speaking, emergence refers to phenomena or processes that are
underivable from their components, or unpredictable from the laws that govern
basic phenomena (see Bedau, 2008; Huneman, 2008). Grantham (2007), for
example, showed that some biogeographical dynamics are indeed emerging from
regional ecological processes.

Here, we revisit the definition of eco-evolution and argue that, for emergence
to happen, similar timescales are enough, and do not necessarily require fast
ecology and evolution (Fig. 1). We show that eco-evolution can very well
embrace the ecosystem level as well as geology and geomorphology while keeping
its focus on emergence. Indeed, slow eco-evolution allows us to understand
emergent phenomena over longer timescales, such as the trajectories of
ecosystems in which abiotic components are coupled to the biological
activities of ecosystem engineers. In these systems, adaptive changes of
organisms to the environment participate in the formation of emerging patterns
and ecosystem self-organization.

Of course, reciprocal feedbacks can also occur across different timescales
(e.g., Lion, 2018; Govaert _et al._ , 2019, Fig. 1) and interesting phenomena
can be studied in that context. We here do not argue that these lines of
research are not valuable, on the contrary, we would like to call for a
broader integration of ecosystem ecology, geology, paleontology and
evolutionary biology. Such an integrative approach can help, not only
understanding the past and current dynamics of biodiversity, but also to
tackle the challenges of the future associated with massive urbanization and
global change. As described above, emergent phenomena are by definition
underivable from their components which represents important challenges for
theory and modelling and for moving from a descriptive to a more predictive
science of the environment (Mouquet _et al._ , 2015; Urban _et al._ , 2016;
Houlahan _et al._ , 2016; Yates _et al._ , 2018).

Besides expanding the eco-evolution framework across timescales, we highlight
a central point, that is especially relevant in changing environments:
biological systems do not need to be associated with a specific timescale
forever. Rates can be modulated (Fig. 3), that is, sped up or slowed down by
what we term “rate modulators” above, which move biological systems along the
ecological and/or evolutionary timescale axes. While we could not provide a
comprehensive list of such modulators, the main point we would like to
underline is the dynamical nature of eco-evolutionary systems.

Central questions that our work implies are: Is eco-evolution likely, is it an
(evolutionary) repellor or maybe an attractor? Where are real biological
systems in the diagram of states represented in Fig. 1? While we can only
speculate about the answers to these questions, we would like to highlight a
few important takeaways. Global change, such as increased temperature,
landscape fragmentation or sea-level rise, has the potential to impact
biological systems and move them in or out of their current eco-evolution
state. Specifically, temperature increases above an optimum have the potential
to slow down ecology and speed up evolution forming eco-evolutionary feedbacks
and, therefore, making emergent phenomena more likely. At the same time, many
other environmental changes are becoming stronger (e.g., landuse change,
urbanisation, pollution). While many ecologists may assume that these
pressures are occurring too fast and are too strong for evolution to be
relevant, these strong environmental changes actually can exert strong
selection pressures on organisms, prompting fast evolutionary responses (see
Colton _et al._ , 2022, for a discussion of eco-evolution and coral
conservation). Together with evolutionary rate modulators, such as the
capacity of genetic architecture to speed up evolutionary responses via
decanalisation, strong environmental pressures may be exactly creating the
context that moves biological systems into the eco-evolution realm. In
combination with decreasing population sizes and increasing stochasticity,
this represents an important challenge for predicting the future of
ecosystems. On a more speculative note, slow eco-evolution may be relevant
when trying to envision changes over the very long term, such as the future of
the Earth including (or not) humankind after a sixth mass extinction and
potential novel adaptations including radiations of new biological groups.

With the increasing availability of large amounts of data and immense
computing power, machine learning and artificial intelligence have been
advocated as a solution for improving predictability in ecology and evolution
(Peters _et al._ , 2014; Rammer & Seidl, 2019). However, the increased
occurrence of emergent phenomena under eco-evolution implies that these
correlative approaches may reach their limits because of their lack of a
mechanistic underpinning. It is currently unclear what kind of models are
needed in such non-analog, that is, new, conditions (Yates _et al._ , 2018).
Urban _et al._ (2016) argue that mechanistic models including species
interactions, dispersal, demography, physiology, environment and evolution are
promising. Network-based approaches can represent a productive way forward in
this context. As Melián _et al._ (2018) highlight, multilayer networks
(Pilosof _et al._ , 2017) can be used to capture eco-evolution from gene-
networks to networks of ecosystems. While such approaches are regularly used
in ecology they are less often found at the interface between ecology and
evolution (but see e.g., Deshpande & Fronhofer, 2022).

### Conclusions

In conclusion, we have discussed that, while keeping the definition of eco-
evolution by Bassar _et al._ (2021) based on emergence, one can integrate
ecology and evolution more broadly when focusing on the entire spectrum of
timescales of both ecology and evolution. Our perspective therefore highlights
important possibilities for a better integration of ecosystem sciences,
geology, geomorphology and evolutionary biology.

Our timescale-based perspective further shows that biological systems need not
be frozen in state space. Rather, rate modulators may move them into and out
of slow or fast eco-evolution regimes. Especially in the context of global
changes, being aware of the fact that emergent eco-evolutionary behaviours may
arise, or be prevented, is crucial for developing a more robust and predictive
eco-evolutionary science.

Our work highlights two main challenges: 1) understanding current and past
eco-evolution requires bridging across all disciplines studying ecology and
evolution, regardless of whether they focus on the presents or on the past,
which 2) leads to an important challenge for prediction. As a consequence,
future eco-evolutionary work has to be more integrative, including levels of
complexity from gene networks to networks of ecosystems. The speculative
nature of many ideas discussed above clearly calls for more formal theory and
experimentation in order to make concepts operational.

## Acknowledgements

We thank Pierre-Olivier Antoine and Olivia Judson for valuable comments on a
previous manuscript version. P.J., E.A.F, S.P., D.C. and F.V. are supported by
the CNRS Institute for Ecology and Environment. This work was supported by a
grant from the Agence Nationale de la Recherche (No.: ANR-19-CE02-0015) to
EAF. This is publication ISEM-YYYY-XXX of the Institut des Sciences de
l’Evolution — Montpellier.

## Conflict of interest

The authors declare no conflicts of interest.

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