# Studying interspecific population synchrony: current status and future perspectives

Ragnhild Bjørkås, Ivar Herfindal, Marlène Gamelon, Aline Magdalena Lee

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Ragnhild Bjørkås, Ivar Herfindal, Marlène Gamelon, Aline Magdalena Lee. Studying interspecific population synchrony: current status and future perspectives. Ecography, 2025, 10.1002/ecog.07682. hal-05370223

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ECOGRAPHY

Review

Studying interspecific population synchrony: current status and
future perspectives

Ragnhild Bjørkås ID1,2, Ivar Herfindal ID1,2, Marlène Gamelon ID3 and Aline Magdalena Lee ID1,2

**Correspondence:** Ragnhild Bjørkås (ragnhild.bjorkas@outlook.com)

Ecography

2025: e07682

doi: 10.1002/ecog.07682

Subject Editor:

F. Guillaume Blanchet

Editor-in-Chief: Robert D. Holt

Accepted 19 September 2025

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Interspecific population synchrony, or co-fluctuations in the population dynamics and demographic parameters of different species, is an important ecological phenomenon with major implications for the stability of communities and ecosystems. It is also central in the context of biodiversity loss, as interspecific synchrony can influence how ecological communities are affected by anthropogenic stressors. Studies of interspecific synchrony are therefore important for understanding fundamental mechanisms underlying the biodiversity changes occurring worldwide. Interspecific synchrony has received increased interest in recent years, and studies have focused on synchrony in abundances, growth rates and vital rates at species, community and metacommunity levels. However, there is little cohesiveness in the literature, as studies focused on different levels of biological organization are largely separate from each other. Still, synchrony at these levels of biological organization is likely interconnected in nature. Understanding these connections would greatly benefit our understanding of interspecific synchrony and its implications for populations and communities. Here, we provide an overview of the current status and future perspectives of interspecific synchrony research, highlighting major knowledge gaps. We show how interspecific synchrony at different levels of biological organization is conceptually linked and present an accessible overview of the terminology and methods used to study it. By providing a common understanding of the meaning and applicability of terms and an overview of commonly used methods, this overview will serve as a point of departure for integrating research on interspecific synchrony. Such integration is important to fully understand the impacts of environmental change on species and ecological communities.

Keywords: anti-synchrony, asynchrony, compensatory dynamics, concordant fluctuations, multi-species synchrony, species covariances

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<footnote n="1">¹Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway</footnote>

<footnote n="2">²Gjærevoll Centre for Biodiversity Foresight Analyses, Norwegian University of Science and Technology, Trondheim, Norway</footnote>

<footnote n="3">³Université Claude Bernard Lyon 1, CNRS, VetAgro Sup, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive (LBBE), 69622 Villeurbanne Cedex, France</footnote>
**Introduction**

Understanding how and why the abundances of differ-
ent species co-fluctuate over time has engaged ecologists
for at least a century (Elton 1924). Such co-fluctuations
among species are known as interspecific synchrony and
can occur not only in abundances but also in other metrics
that vary through time, such as growth rates or demographic
parameters (Liebhold et al. 2004). Interspecific synchrony
has been observed in many taxa, including phytoplank-
ton (da Silva et al. 2021), birds (Koenig 2001), crusta-
ceans and fish (Mutshinda et al. 2009), bivalves (Beukema
and Dekker 2019), insects (van Klink et al. 2019), mam-
mals (Hansen et al. 2013) and plants (Lepš et al. 2018).
Interspecific synchrony is often seen when different species
show similar responses to changes in their environment,
such as weather or climate, causing their dynamics to be syn-
chronized (Loreau and de Mazancourt 2008). The degree of
interspecific synchrony among species can also be affected
by interspecific interactions such as competition (Lee et al.
2020) and predation (Korpimäki et al. 2005). Therefore, the
degree of synchrony among species can help us understand
both how the species respond to their shared environment,
and how they interact.

Interspecific synchrony occurs among pairs of species, but also at the aggregate community level (e.g. measured as average synchrony across all studied species in the community). The degree of synchrony at the aggregate community level has a central role in determining several aspects of community stability (Loreau 2010), including variability (Loreau and de Mazancourt 2013) and resistance to environmental perturbations (Sæther et al. 2024). Less synchronous population dynamics have often been associated with more stable community properties such as total abundance or biomass (Yachi and Loreau 1999, Loreau 2010, Loreau and de Mazancourt 2013; but see Srednick and Swearer (2024)). Interspecific synchrony is thus a central ecological phenomenon at both the species and community levels, with major implications for community responses to changes in their environment (such as climate and habitat changes).

As anthropogenic activities are causing biodiversity to
change at an unprecedented rate through habitat loss, over-
exploitation, pollution, invasive species and climate change
(Pereira et al. 2012), understanding the implications of inter-
specific synchrony for populations and communities has
become more important than ever. Interspecific synchrony
can be affected by stressors such as extreme weather events, a
phenomenon expected to increase in frequency with climate
change (IPCC 2023). One example of such extreme weather
events affecting synchrony is found in High Arctic Svalbard,
where the dynamics of several herbivore species were syn-
chronized by rain-on-snow events affecting the availability of
forage plants (Hansen et al. 2013). Interspecific synchrony
can also modulate the effects of other anthropogenic stress-
ors imposed on biodiversity, for example by influencing the
impact of invasive species on communities (Davidson and

Shoemaker 2023). In addition, knowledge about patterns of interspecific synchrony within a community can be useful in an applied ecological context such as harvest management. An example of this is found in fisheries management, where anti-synchrony among species in a multi-species fishery led to reduced variability of the total income for fishermen (Oken et al. 2021). Understanding the causes and implications of interspecific synchrony is therefore important from both a basic and an applied ecological perspective.

Traditionally, research on interspecific synchrony has focused on synchrony in the abundances or growth rates between pairs of species, or at the aggregate community level. More recently, the focus has been extended to also include synchrony in demographic parameters such as survival or reproduction (Lahoz-Monfort et al. 2011, Beukema and Dekker 2014) at broader spatial scales. For instance, temporal variations in adult survival are surprisingly synchronous across 16 bird species in France (up to 73% of the between-year variance in survival is common across species; Ghislain et al. 2024). A major challenge is now to understand how synchrony in survival and reproduction (or productivity) may scale up to synchrony in population abundance (see Morrison et al. (2025) for a study on European landbirds).

More generally, despite efforts to improve our understanding of interspecific synchrony in different ecological contexts, we still know little about how synchrony at the different levels of biological organization (Fig. 1) are connected to each other. Knowledge about such connections is vital. Insights into how a change in a demographic parameter would influence synchrony in abundances at the community level could help us understand how, for instance, a climate change-induced effect on the survival of species would ultimately affect community stability.

To gain such an improved understanding of how syn-
chrony at different levels of biological organization is con-
nected, we need to integrate the available knowledge about
synchrony from different ecological subfields. As the study of
interspecific synchrony has been approached from very dif-
ferent angles and with different questions in mind, a wide
range of terms has been used to discuss related topics, and
a variety of methods has been used to measure synchrony.
This inconsistent use of terminology makes it challenging to
get an overview of the knowledge already obtained on the
subject of interspecific synchrony across research areas, and
thus to make use of the existing knowledge. In this synthesis,
we aim to mitigate this issue by describing where knowledge
already exists about the presence and drivers of interspecific
synchrony at different levels of biological organization and
highlighting remaining knowledge gaps. To support future
research on the subject, we also provide a consolidated over-
view of terminology used when studying the subject (Box 1),
and show different methods available for measuring interspe-
cific synchrony (Box 2). We believe that this synthesis will
constitute a valuable starting point in the important process
of integrating research on, and ultimately gaining a more
complete understanding of, interspecific synchrony.
<figure><img src="image_5.png" /><figcaption>Figure 1. Conceptual links among different levels of synchrony. (a) Abiotic conditions (e.g. weather or other characteristics of the physical environment), predation, interspecific competition and demographic stochasticity can influence interspecific synchrony in various parameters through time (b–c). When including a spatial dimension, dispersal also plays a role, so that spatial synchrony (d–f) is affected by all drivers inside the green box (i.e. all factors in the blue box in addition to dispersal). (b) Interspecific synchrony in demographic parameters of two (or more) species can be affected by the drivers of synchrony in (a). Synchrony in demographic parameters may further translate into synchrony in growth rates and abundances. (c) At the community level, synchrony across all species in the community can occur. (d) Spatial synchrony can occur between populations of the same species, or (e) among populations of different species. (f) Finally, community level characteristics of spatially separated communities can also be synchronized.</figcaption></figure>

Evidence of interspecific synchrony in
nature

Interspecific synchrony at different levels of biological
organization

Interspecific synchrony can occur at different levels of bio-
logical organization and spatial scales (Fig. 1), all of which
have been demonstrated in nature (Beukema and Dekker

2019, van Klink et al. 2019, Vindstad et al. 2019, Lamy et al.
2021, Segrestin and Lepš 2022). Different species can exhibit
synchrony in their demographic rates (such as survival or
reproduction), or in their population growth rates or abun-
dances. Interspecific synchrony can occur among species
pairs (Fig. 1b), but we can also consider synchrony among all
(or most) species in a community (Fig. 1c). Synchrony can
also occur among allopatric populations of the same species,
**Box 1. Terminology in interspecific synchrony research**

A wide range of terms is used to describe interspecific synchrony in different studies (Fig. 2), which can cause confusion if not well defined. In addition, it can hamper the ability to find relevant studies using less well-known keywords, making it difficult to synthesize and generalize knowledge across studies. This overview of commonly used terminology and rarer synonyms is therefore aimed at facilitating literature searches. We also provide some guidance on how to avoid confusing use of terminology.

**Terminology used to describe interspecific synchrony**

Synchrony between different species has been denoted by a large number of synonymous terms (Fig. 2). We recommend *interspecific synchrony* as a unifying term moving forward. This term is general enough that it could have been used in all the papers mentioned here, while also being intuitive and easy to understand. Other more specific terms could be used in addition to *interspecific synchrony*, to specify the kind of interspecific synchrony under consideration. Additional central terms that we promote for describing specific types of interspecific synchrony are *community synchrony* for synchrony at the aggregate community level and *compensatory dynamics* to describe contrasting species-specific fluctuations that stabilize fluctuations at the community level. We recommend including these additional terms whenever relevant.

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Figure 2. Terminology used to describe synchrony among species. Definitions of three commonly used terms to describe synchrony (left side), and examples of synonyms and related terms used in a selection of studies (right side) on synchrony between different species (in general; top) and at the community level (bottom). The term ‘species synchrony’ is used to describe synchrony among species in general, both for species pairs and at the community level (Loreau and de Mazancourt 2008). The term ‘coherence’ is sometimes used interchangeably with interspecific (or intraspecific) synchrony (Rusak et al. 1999, Saulnier et al. 2023) but can also be used to describe phase synchrony (i.e. when the peaks of the population fluctuations of different species are synchronized; Liebhold et al. 2004) with a time lag (Ong et al. 2021). Note that ‘compensatory dynamics’ is sometimes also used in studies of synchrony among pairs of species, see Fig. 3.
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Figure 3. Examples of how different studies use terms to indicate the presence of positive versus negative correlations among species at the community level (above the line) and among species pairs (below the line). The field at zero indicates that the study specifically mentioned zero correlation/independent fluctuations (if there is no field, only terms for positive/negative correlations were specifically mentioned). If the bar is divided in the middle, the study divides specifically between positive and negative correlations, whereas if the bar is continuous there is a gradual transition from positive to negative correlations. The color-coding is used to show where similar words are used (e.g. dark green is used for ‘synchrony’, which has been used to describe both positive and negative correlations), and a gradual transition between two colors indicates that the two terms are used to describe a gradual transition between positive and negative correlation (the same correlation could be described as either ‘more synchronized’ or ‘less asynchronized’). Some authors differentiate between perfect synchrony and partial synchrony (Lepš et al. 2019a). Tredennick et al. (2017) uses compensatory dynamics and asynchrony interchangeably.

Terminology used to describe the degree of synchrony

Terms indicating whether species are positively or negatively correlated, or the degree of synchrony, are used in contra-
dictory ways in different studies (Fig. 3). The ambiguous interpretations of similar terms illustrate the need to carefully
define the terms used, to avoid confusion. For instance, *compensatory dynamics* used among a pair of species indicates
negative correlations, such that the decrease in one species is compensated by an increase in another species (Lepš et al.
2018). At the community level, compensatory dynamics can be interpreted in two ways: either that the community
synchrony is negative (Lepš et al. 2018) or more loosely that some species within the community respond differently to
some environmental fluctuation such that the overall community is not perfectly synchronized (Tredennick et al. 2017).
*Asynchrony* has been used to denote both independent fluctuations and negative correlations among pairs of species, while
denoting gradually decreasing levels of synchrony at the community level.

We encourage careful definition of terms used to describe positive versus negative correlations/covariances. As *synchrony* is most often used for positive correlations, and *anti-synchrony* seems to be the only term for negative correlations not having contradictory interpretations, we recommend always using these when correlations are positive or negative, and reserving the term *asynchrony* for the absence of synchrony (i.e. correlations near zero). Some metrics used for studying community-level synchrony are not well suited to separating between negative correlations and independent fluctuations, which has led to the use of *asynchrony* to mean either negative or no correlations. To avoid confusion, we suggest using the longer but more descriptive ‘*asynchrony or anti-synchrony*’ to describe this situation. Terms such as *compensatory dynamics* (which is generally used in community-level studies focusing on the stability in some community parameters such as total biomass; Jochimsen et al. 2013, Brown et al. 2016), are useful for describing additional characteristics of the dynamics, but should not be used as a replacement for *anti-synchrony* when referring to negative correlations.
Box 2. How to estimate interspecific synchrony – an overview of central methods

A variety of methods exist to measure or quantify interspecific synchrony. An overview of some traditionally used methods and some more recent developments is presented below, along with some discussion on their strengths and weaknesses and points that are important to keep in mind when choosing among them. Subsets of these methods have previously been reviewed in Azovsky et al. (2022), Gonzalez and Loreau (2009), Gouhier and Guichard (2014), Lepš et al. (2019a) and Thibaut and Connolly (2013).

Correlation/covariance-based methods

Correlation or covariance-based methods are commonly used to estimate synchrony, both among pairs of species, and aggregated community synchrony by calculating cross-correlation (or covariance) between time series of abundances or growth rates of different species. Common correlation coefficients include Pearson's r, Spearman's ρ and Kendall's τ (Beukema and Dekker 2014, van Klink et al. 2019, Rocha et al. 2021, Harrod and Rolland 2022). Combining correlations or covariances among pairs of species into a measure of community synchrony can be done in several ways. Maybe the simplest solution is to calculate the mean pairwise correlation among all species in the community (Bjørnstad et al. 1999). However, this metric is not comparable across communities with different numbers of species, because the lower limit of the metric increases as species richness increases (Loreau and de Mazancourt 2008). Kendall's coefficient of concordance (Kendall's W), which can be calculated from the mean of the pairwise Spearman correlations, has a similar weakness in that its value depends on species richness, even if its range does not (Legendre 2005, Gouhier and Guichard 2014). This latter metric also does not differentiate between independent fluctuations and negative correlations (Legendre 2005, Gouhier and Guichard 2014). Other correlation-based metrics have therefore been developed to be more comparable across communities. One of these, Gross' mean correlation coefficient, calculates the average correlation between each species and the sum of all other species in the community (Gross et al. 2014). This metric ranges from -1 to +1, regardless of how many species there are in the community, and is centered at 0 when species fluctuate independently, facilitating interpretation and comparison across systems. An adapted version of this metric weighs species by their relative abundances because this metric was developed in the context of evaluating community stability where the assumption is that the synchrony of common species has a greater impact on stability than that of rarer species (Blüthgen et al. 2016). This illustrates an important point to keep in mind when selecting metrics for interspecific synchrony – the desired characteristics of a metric depend on the goal of the study. For example, most of the metrics described so far are not influenced by differences in variance among species, i.e. increasing the relative variance of a species in the system does not increase that species' impact on the synchrony metric. This has been argued to be both a strength (Gross et al. 2014) and a weakness (Thibaut and Connolly 2013) of these methods. Thus, the 'best' method for quantifying interspecific synchrony will depend on the requirements of a particular study.

Variance ratios

Variance ratios calculate the ratio of the variance of the total community abundance to what would be expected if the dynamics of all species were either perfectly synchronized or fluctuating independently. The focus on total community abundance reflects that these methods have mainly been developed and used to study compensatory dynamics and community stability. Several forms of variance ratios are in use, of which three of the most central are the classical variance ratio (Peterson 1975, Schlüter 1984), the log variance ratio (Lepš et al. 2018) and Loreau and de Mazancourt's φ (2008). These differ somewhat in their interpretations. The log variance ratio ranges from negative numbers (anti-synchrony) through zero (independent fluctuations) to positive numbers (synchrony), and the upper limit depends on species richness. Loreau and de Mazancourt's φ ranges from 0 (maximum anti-synchrony [called asynchrony in their paper] = constant community abundance) to 1 (perfect synchrony) regardless of species richness, but its value can be affected by species richness at intermediate levels of synchrony (Loreau 2010, Thibaut and Connolly 2013, Lepš et al. 2018). These variance ratios can be complemented by a skewness ratio that accounts for the case in which synchrony is higher at higher abundances (Ghosh et al. 2021).

Intra-class correlation

Intra-class correlation (ICC) coefficients are used to calculate the proportion of a species' population fluctuations that is shared with the whole community (Lahoz-Monfort et al. 2011, 2013). This does not specifically measure negative correlations. ICC has often been used in studies of synchrony in demographic parameters (Lahoz-Monfort et al. 2011, 2013, Jansen et al. 2020).
Accounting for non-stationary time series

As in most other types of analyses involving ecological time series, quantification of interspecific synchrony requires con-
sideration of temporal trends. Such trends can be accounted for by decomposing synchrony estimates into parts related
to the trend and a detrended part (e.g. analyzing residuals after detrending), or by calculating variances across moving
windows (Lepš et al. 2019a). These can be combined with other covariance or correlation-based methods described above
(Lepš et al. 2019a).

Often, a community will be simultaneously influenced by several aspects of the environment that fluctuate on dif-
ferent time scales, causing timescale-specific synchrony. Timescale-specific synchrony can also be caused by different life
history characteristics (Shoemaker et al. 2022). This creates a need for methods that can partition out synchrony at dif-
ferent frequencies. Frequency-based methods that use Fourier, wavelet or spectral analyses are particularly suited to this
and have been used in a number of synchrony studies (Keitt and Fischer 2006, Vasseur and Gaedke 2007, Cazelles et al.
2008, Keitt 2008, Zhao et al. 2020). These methods can be used both among pairs of species (Keitt and Fischer 2006,
Walter et al. 2021a) and at the community level (Keitt 2008, Zhao et al. 2020), but they require long time series and a
non-negligible level of technical understanding (Gonzalez and Loreau 2009).

Latent variable models

Latent variable models have become popular for joint modeling of the abundances of multiple species (Warton et al. 2015). Latent variable models include dynamic factor analysis (Zuur et al. 2003) which is often used to study synchrony in fisheries data (Gorecki et al. 2022, Saulnier et al. 2023) and joint dynamic species distribution models (JDSDMs), which can be used to measure synchrony in spatio-temporal data (Thorson et al. 2016, Omori and Thorson 2022, Cao et al. 2024). An advantage of using latent variable modeling in the context of interspecific synchrony is that the models can be tailored to specific studies and built to account for complexities such as the non-stationary time series discussed above. In theory, nearly any metric of interspecific synchrony could be extracted from an appropriate latent variable model (e.g. as done in Mutshinda et al. (2011) and Thorson et al. (2016)), and this is a method that we expect to become more common going forward. In the longer term one can envisage the possibility of flexible pre-built models that could be used without specialized knowledge of modeling.

Synchrony at the metacommunity level

In recent years, several measures have been developed for quantifying different types of synchrony among communities within a metacommunity. Synchrony in temporal change in community structure can be calculated using the structural synchrony metric by Azovsky et al. (2022), and spatial synchrony in species richness by a metric from Walter et al. (2021b). Another option is to measure spatial synchrony in community composition using a metric by Lamy et al. (2021), which informs on the relationship between variability in community composition in local communities and variability in metacommunity composition (Lamy et al. 2021). Finally, spatial synchrony in total community biomass can be measured using a metric similar to Loreau and de Mazancourt's φ (Wang and Loreau 2014).

Through recently developed frameworks to measure metacommunity stability it is also possible to measure synchrony
across several levels of biological organization, partitioned into several of the synchrony types shown in Fig. 1, including
community synchrony, spatial population synchrony (Wang et al. 2019) and synchrony among different species in differ-
ent localities (Hammond et al. 2020, Segrestin and Lepš 2022). We consider this an important step towards integrating
synchrony at different levels of biological organization.

known as spatial population synchrony (Fig. 1d), where
populations that are geographically close often fluctuate
more synchronously than distant populations (Hansen et al.
2020). This is a widespread phenomenon reviewed thor-
oughly elsewhere (Liebhold et al. 2004, Hansen et al. 2020,
Herfindal et al. 2022, Reuman et al. 2023). Spatial synchrony
can also occur between spatially separated populations of
different species (Fig. 1e; Hammond et al. 2020, Segrestin
and Lepš 2022). At regional scales, multiple spatially sepa-
rated populations connected by dispersal of individuals can
be considered as a *metapopulation* (Hanski 1998), and mul-
tiple spatially separated communities can be considered as a
*metacommunity* (Leibold et al. 2004). Among the constituent

communities of a metacommunity (Fig. 1f), synchrony can
occur in species richness (Walter et al. 2021b), total com-
munity biomass (Wang and Loreau 2014), community
composition (Lamy et al. 2021) or community structure
(Azovsky et al. 2022).

Demographic parameters

Current knowledge about synchrony in demographic parame-
ters comes mostly from studies on birds (Lahoz-Monfort et al.
2011, 2013, 2017, Robinson et al. 2013, Jansen et al. 2020,
Youngflesh et al. 2021, Harrod and Rolland 2022, Payo-
Payo et al. 2022, Weegman et al. 2022, Ghislain et al. 2024),
but also on fish (Feiner et al. 2019, Pregler et al. 2023) and
bivalves (Beukema et al. 2001, Beukema and Dekker 2014, 2019). Some of these studies investigated synchrony in lower-level demographic parameters such as hatching success or breeding investment (measured as the size of the eggs) that may in turn affect vital rates such as recruitment and juvenile or adult survival (Lahoz-Monfort et al. 2013, Harrod and Rolland 2022, Payo-Payo et al. 2022), whereas others study vital rates directly.

Empirical evidence for synchrony in demographic parameters differs among systems and demographic parameters: high levels of synchrony in adult survival was found among species in a seabird community (Lahoz-Monfort et al. 2011), among songbirds (Ghislain et al. 2024) and in a fish community (Pregler et al. 2023), while survival fluctuated independently among species in a bivalve community (Beukema and Dekker 2019). Synchrony in recruitment (measured as number of offspring per female, breeding attempt or site) was observed for communities of bivalves (Beukema and Dekker 2019), ducks and geese (Péron and Koons 2012, Weegman et al. 2022), but not for most species in some studied seabird communities (Lahoz-Monfort et al. 2013, Youngflesh et al. 2021). It should, however, be noted that it is challenging to directly compare findings from different studies as they often use measures of synchrony that are not directly comparable (Box 2): synchrony measured using intra-class correlations (Lahoz-Monfort et al. 2011) is, for instance, not directly comparable to synchrony measured using pairwise correlations (Beukema and Dekker 2019).

## Abundances and growth rates

### Synchrony among pairs of species

Interspecific synchrony in growth rates or abundances has been studied and demonstrated for pairs of species (Fig. 1b) within many taxa and on most continents. This includes insects (Mutshinda et al. 2011, van Klink et al. 2019), fish (Cheal et al. 2007, Mutshinda et al. 2009, Copeland and Meyer 2011), phytoplankton (da Silva et al. 2021), crustaceans (Mutshinda et al. 2009), plants (Lepš et al. 2018), mammals (Hansen et al. 2013, Bierhoff et al. 2024) and birds (Koenig 2001, Mutshinda et al. 2009, Michel et al. 2016).

Within a community, the extent to which species pairs are synchronized in their dynamics tends to vary a lot. For instance, a study on beetle communities found that pairs of species were on average positively correlated, but with individual pairs exhibiting a wide range of correlations – spanning from highly positive to highly negative – in their temporal population fluctuations (van Klink et al. 2019). Similar findings have also been reported from other communities of fish and birds (Mutshinda et al. 2009). This variation could be expected, given that species within a community typically differ both in their environmental requirements and in how they interact with other species (see the following section about drivers of synchrony). However, some studies have also reported exclusively positive correlations among species, for instance among annual densities in an ungulate community (Bierhoff et al. 2024).

### Aggregate community synchrony

Community synchrony has been studied and demonstrated within many systems and taxa, and on most continents. This includes marine fish (Saulnier et al. 2023), plankton (Jochimsen et al. 2013, Brown et al. 2016), freshwater invertebrates (Ferreira et al. 2022, Larsen et al. 2024), plants (Houlahan et al. 2007, Hallett et al. 2014, Ma et al. 2017, Lepš et al. 2018, Valencia et al. 2020, Wang et al. 2020, Ghosh et al. 2021, Gu et al. 2023), birds (Blüthgen et al. 2016, Li et al. 2021, Barraquand et al. 2022), small mammals (Houlahan et al. 2007) and reptiles (Houlahan et al. 2007). These studies often focus on the relationship between community synchrony and community stability (Hallett et al. 2014, Blüthgen et al. 2016, Valencia et al. 2020), sometimes with a focus on compensatory dynamics (Fig. 2; Gonzalez and Loreau 2009) rather than synchrony per se, and often considering synchrony in biomass of species (as opposed to abundance).

### Spatial interspecific synchrony

Although spatial interspecific synchrony (Fig. 1e–f) is a more recent research focus, some empirical evidence exists for synchrony at these levels of biological organization in nature. An example of synchrony among spatially separated populations of different species (Fig. 1e) is found in grassland communities in the Czech Republic, where populations of different species of grass showed synchronized dynamics across communities (Fig. 1e; Segrestin and Lepš 2022). An example of synchrony in aggregate community parameters across communities within a metacommunity (Fig. 1f) was found by Walter et al. (2021b), where synchrony in species richness was observed among communities in several marine and terrestrial metacommunities.

## Drivers of synchrony at different levels of biological organization

The degree of synchrony among species at the different levels of biological organization can be influenced by both abiotic and biotic factors (Fig. 1a). Coexisting species are exposed to similar fluctuations in their abiotic environment. If different species respond in a similar manner to these fluctuations, implying that they have partly overlapping niches, this can cause interspecific synchrony in their population fluctuations (Loreau and de Mazancourt 2008). Niche overlap can, on the other hand, also cause competition among species. Strong competition has been theoretically shown to decrease synchrony among competing species (Loreau and de Mazancourt 2008, 2013, Lee et al. 2020), indicating that environmental drivers and competition may have counteracting effects on interspecific synchrony. Other types of interspecific interactions can also affect synchrony, as predation by a shared predator has been found to act synchronizing (Marcström et al. 1988, Raimondo et al. 2004b, Korpimäki et al. 2005). At small population sizes, population dynamics are also affected by demographic stochasticity, which acts independently on populations of different species and can decrease interspecific synchrony (Loreau and de Mazancourt 2008). This means
that species richness also can play a role in interspecific syn-
chrony, if population sizes decrease when species richness
increases, making the population fluctuations more affected
by demographic stochasticity (Loreau and de Mazancourt
2008). These factors can together affect synchrony in the
demographic parameters, growth rates and abundances of
different species through time (Fig. 1b).

Synchrony in space (Fig. 1d–f) can be affected by spatially
correlated environmental variation (known as the Moran
effect; Moran 1953): interactions with other spatially syn-
chronized species such as predators (Ims and Steen 1990,
Ims and Andreassen 2000) or competitors (Jarillo et al.
2018), and dispersal of individuals between populations
(Lande et al. 1999, Vindstad et al. 2019). In addition, har-
vesting can affect the spatial scale of population synchrony,
but whether harvesting increases or decreases the spatial scale
depends on the harvest strategy chosen (Engen et al. 2018,
Jarillo et al. 2018, Gamelon et al. 2019).

Synchrony among spatially separated populations of dif-
ferent species (Fig. 1e) can happen when the population
dynamics of two species in a specific location are synchro-
nized by their abiotic environment or competition, and
dispersal of individuals causes synchrony also between popu-
lations of these species in other locations (Lee et al. 2020).
Additionally, in the same way as the Moran effect can cause
spatial synchrony in spatially separated populations of a single
species (Moran 1953, Hansen et al. 2020), the same could be
expected for spatially separated populations of different spe-
cies if they respond similarly to a synchronized environment.
However, as mentioned above, there are as yet few studies
testing these ideas in real systems.

Demographic parameters

The degree of synchrony in demographic parameters can
depend on which mechanisms are driving the synchrony.
For example, recruitment may be more synchronized than
survival in cases where species associate more (are found in
the same location and thus depend on the same environmen-
tal factors) in the breeding season than in the nonbreeding
season (Péron and Koons 2012). Low synchrony in recruit-
ment could indicate that the offspring production in dif-
ferent species is not similarly affected by the environmental
factors they are exposed to (Feiner et al. 2019), supported by
the finding that certain climatic conditions contributed to
lower synchrony in adult survival among species in a seabird
community (Lahoz-Monfort et al. 2011). Thus, studies of
interspecific synchrony in demographic parameters can give
valuable insights into the factors driving synchrony in growth
rates or abundances, by indicating if the environmental con-
ditions during the breeding season or nonbreeding season are
more important.

Abundances and growth rates

Synchrony among pairs of species

Several empirical studies have highlighted environmen-
tal conditions as an important driver of the observed syn-
chrony among species, indicating that species within a

community often show some degree of similar responses
to their environment (Mutshinda et al. 2009, Hansen et al.
2013, Tredennick et al. 2017, Kawatsu et al. 2020). In some
cases, interspecific competition has been found to have little
impact on interspecific synchrony (Mutshinda et al. 2009,
2011). However, in another study, the expected, desynchro-
nizing effect of competition was found (Lepš et al. 2019b;
among pairs of functional groups). This could reflect differ-
ences in the relative impact of shared environmental fluc-
tuations (tending to increase synchrony) and interspecific
competition (tending to decrease synchrony) in different
systems.

It could be expected that species with higher degrees of
niche overlap (e.g. due to more similar functional traits)
should respond more similarly to their environment and
consequently have more synchronized population dynamics
(as reviewed by de Bello et al. (2021)). Indeed, species' pairs
with a higher degree of overall trait similarity were in some
studies more synchronized than pairs with lower degrees
of trait similarity (Rocha et al. 2011, Lepš et al. 2018, van
Klink et al. 2019, Powell et al. 2024). However, this rela-
tionship is not unequivocal, as another study found no such
relationship between functional distance and synchrony
among phytoplankton (da Silva et al. 2021). For single
traits, a relationship between trait similarity and pairwise
synchrony has been found for certain species and traits: for
instance, fish with more similar body lengths (Rocha et al.
2021), and lepidoptera larvae with similar feeding strategy
(Kawatsu et al. 2020) or that were present at the same time
of year (Raimondo et al. 2004a) showed higher levels of
synchrony in their abundances. However, other traits stud-
ied were not related to the degree of interspecific synchrony
among species pairs (Raimondo et al. 2004a, Rocha et al.
2021, Powell et al. 2024). Collectively, these findings show
that similarity in species traits can sometimes be linked to
increased synchrony among pairs of species. In cases where
we find no relationship between synchrony and traits, it is
important to remember that there could be other (not inves-
tigated) traits that could be important for synchrony. The
relationship between synchrony and species traits has, to our
knowledge, only been investigated for abundances or growth
rates, and not for demographic parameters.

Aggregate community synchrony

Aggregated community-level synchrony (Thibaut and Connolly 2013) reflects the overall level of interspecific synchrony among all species in the community (Loreau and de Mazancourt 2008). Community synchrony can consequently also be affected by environmental conditions, species interactions and demographic stochasticity (Tredennick et al. 2017).

Empirical evidence for the importance of environmental
drivers in affecting community synchrony is found in several
systems. Temperature has been highlighted as important for
interspecific synchrony, although it has different effects on
synchrony in different systems: bird communities in areas
with higher average temperatures showed lower community
synchrony than communities in areas with lower average
temperatures (Li et al. 2021) while some plant communities
exposed to experimental warming were more synchronized
than the control communities (Ma et al. 2017, Gu et al.
2023). Changes in rainfall regime had contrasting effects on
grassland communities, where more variable rainfall caused
higher community synchrony and lower community stability
in one study (Wang et al. 2020), while the opposite relation-
ship was found in another community (Hallett et al. 2014). A
third study found that change in precipitation did not affect
community synchrony (Ma et al. 2017). Large-scale climatic
indices such as the North Atlantic Oscillation synchronize
weather patterns over large areas and have been found to influ-
ence community synchrony both at a local scale and at the
regional, metacommunity scale (Larsen et al. 2024), which
can be taken as further, indirect evidence that local envi-
ronmental factors are important for community synchrony.
Other important environmental factors include changes in
pH and nutrient availability, which can influence the degree
of community synchrony in plankton and plant communi-
ties (Keitt 2008, Lepš et al. 2018, Buttay et al. 2022). Taken
together, environmental factors clearly play an important role
for community synchrony, but the details of this relationship
are context dependent and differ among systems. In some
communities, the majority of species could respond posi-
tively to a certain environmental condition, whereas in other
communities most species may respond negatively. Such
community-specific differences could occur because different
communities hold different sets of species, but also because
the responses to a certain condition can depend on the aver-
age condition that the community experiences. When dis-
cussing the role of the environment, it is important to keep in
mind the difference between short-term temperature fluctua-
tions that drive fluctuations in abundance and longer-term
trends in background environmental conditions, as these play
different (but not necessarily separate) roles.

The composition of species' traits within the community
is one factor that can influence how the community responds
to environmental variation. Communities with more diverse
traits or higher functional diversity have in several cases been
found to be less synchronous than communities with lower
functional diversity (van Klink et al. 2019, White et al. 2023,
Larsen et al. 2024). These findings may be attributed to larger
niche differentiation and thus differential responses to the
environment, which corresponds well to some of the results
from the species-level studies observing higher synchrony
among pairs of species with high degrees of niche overlap/
similar traits (Lepš et al. 2018, Powell et al. 2024).

Interspecific interactions and demographic stochastic-
ity can influence synchrony also at the community level,
and telling apart each of these effects in natural communi-
ties can be challenging (Box 2; Ranta et al. 2008, Gonzalez
and Loreau 2009). Tredennick et al. (2017) used simulations
together with data from grassland communities to tease apart
the relative effects of environmental fluctuations, interspe-
cific interactions and demographic stochasticity on com-
munity synchrony, finding that environmental stochasticity
had the largest (synchronizing) effect – and that removing
environmental fluctuations caused up to 48% reduction in

the community synchrony in their studied communities.
Demographic stochasticity had a weaker, counteracting,
effect – and the effect was most pronounced when popula-
tions were small. In the same study, Tredennick et al. (2017)
found interspecific interactions to be of little importance for
synchrony. However, in another study from experimental
grassland and algal communities, competition was found to
decrease community synchrony (although the desynchro-
nizing effect of competition was not equally strong in all
communities; Gross et al. 2014). The effect of interspecific
competition on population dynamics may also interact with
the environmental conditions, so that competition is more
important in periods with more limited resources (Chesson
2000, Gonzalez and Loreau 2009). Overall, the multiple
factors influencing community synchrony may have oppos-
ing effects, so that predicting the outcome of one without
accounting for the others is challenging.

Empirically linking synchrony across levels
of biological organization

Having looked at evidence for interspecific synchrony in
demographic parameters, growth rates and abundances
among pairs of species and at the community level, the
next important question is how synchrony in these differ-
ent levels of biological organization is connected in empirical
systems. Understanding how synchrony in lower levels (i.e.
demographic parameters) translates into synchrony in higher
levels (i.e. abundances/growth rates among pairs of species,
and at the community level) would be extremely useful, as it
could shed light on how changes in the drivers of synchrony
(caused by for instance climate change) would affect com-
munity synchrony and stability through their effects on the
lower-level parameters.

From lower to higher levels of biological
organization

How interspecific synchrony in demographic parameters
scales up to interspecific synchrony in growth rates or abun-
dances has still not been the subject of much investigation.
The few existing studies find varied relationships. For exam-
ple, four species of bivalves showed synchrony in recruitment,
while their survival and total biomass (a proxy for abun-
dance) were in most cases not synchronized between species
(Beukema and Dekker 2019). A pair of goose species was, on
the other hand, synchronized in both their survival, offspring
production and population growth rates (Weegman et al.
2022). Looking to research on spatial population synchrony,
some empirical evidence exists for the relationship between
synchrony in demographic parameters and growth rates. For
instance, synchrony in demographic parameters of barn swal-
lows *Hirundo rustica* was mostly higher than synchrony in
population growth rates, and the demographic parameters
contributing most to population growth were also driving
the population synchrony (Schaub et al. 2015). Such a rela-
tionship between synchrony in the demographic parameter
with the highest elasticity (i.e. the demographic parameter
with the largest contribution to the population growth rate;
Kroon et al. 1986) and synchrony in population growth can
also be expected for interspecific synchrony. However, species'
life history strategies can be an important factor to account
for when trying to understand the link between synchrony in
demographic rates and population sizes, as the demographic
rate with the highest elasticity differs, depending on species'
life history strategies (Sæther and Bakke 2000). In fact, the
effect of demographic rates on spatial population synchrony
has been theoretically shown to differ depending on species'
life history traits, where fast-lived species' synchrony was more
affected by fecundity, while slow-lived species' synchrony was
more affected by survival (Martin 2023). Since interspecific
synchrony occurs between species that often differ in life
history traits, the link between synchrony in demographic
parameters and synchrony in growth rates or abundances
is not straightforward. Additionally, since growth rates and
abundances can depend on more than one demographic
parameter, synchrony in growth rates may also be higher or
lower than the synchrony in a single demographic parameter,
depending on the degree of synchronization of the differ-
ent demographic parameters. This is further complicated by
trade-offs between demographic parameters, such as repro-
duction and survival (Hamel et al. 2010), that may further
weaken the link between interspecific synchrony at different
organismal levels. More research is therefore needed to iden-
tify how interspecific synchrony in demographic parameters
scales up to interspecific synchrony in growth rates or abun-
dances, and to find to what extent such patterns are generaliz-
able across species and systems.

Understanding how synchrony among pairs of species is
connected to community synchrony could inform on how
different groups of species affect community synchrony
and ultimately stability. Some studies have measured both
pairwise synchrony and community synchrony in the same
study, finding for instance a majority of positive pairwise
correlations and positive but low community synchrony in
phytoplankton communities (da Silva et al. 2021) and beetle
communities (van Klink et al. 2019). In an experimental
study on grasslands, Lepš et al. (2018) found mainly posi-
tive pairwise correlations and a mix of negative and positive
community synchrony. Studies measuring both pairwise and
community synchrony are a step forward in understanding
the link between the two synchrony types, but a specific focus
on the relationship itself is necessary in order to shed more
light on the connection between the synchrony at these levels.

In addition to the already mentioned synchrony in demo-
graphic parameters, growth rates and abundances, it should
also be mentioned that synchrony can occur in phenologi-
cal events. Phenological synchrony involves for instance
the coincidence in timing of reproduction within a year in
animal populations as a mechanism of predator avoidance
(reproductive synchrony; Ims 1990, Sinclair et al. 2000)
or within the plant kingdom with e.g. the co-occurrence in
seed production in trees (known as masting; Koenig 2021).
Phenological synchrony can also occur among trophic levels,

for example among the timing of reproduction in birds and
seasonal availability of food (Visser et al. 2004). As pheno-
logical synchrony is well covered elsewhere (Durant et al.
2007, Renner and Zohner 2018), we will not go in detail
here. However, synchrony in phenological events can also
be of importance for synchrony in demographic parameters,
growth rates or abundances. For instance, in a butterfly com-
munity, phenological synchrony was observed among species
at the same trophic level, and a change in phenology was in
many species accompanied by a shift in abundance (although
the abundances were less synchronized than the phenologi-
cal events; Stewart et al. 2020). Similarly, (intraspecific) phe-
nological synchrony in laying dates occurs among spatially
separated populations of passerine bird species across Europe,
where other demographic parameters (clutch size and fledg-
ling number) were also synchronized but to a lower degree
(Vriend et al. 2023). As phenological synchrony also can
affect demographic parameters (Iler et al. 2021), one could
argue that phenological synchrony potentially can act as an
underlying mechanism of interspecific synchrony in demo-
graphic parameters, growth rates and abundances and that
it may thus be considered an even lower level of biological
organization than synchrony in demographic rates (Fig. 1b).

The spatial aspect of synchrony

It is important to keep in mind that communities do not exist
in a vacuum, but rather in a spatial network with other com-
munities. Dispersal among populations or communities can
be important for synchrony, for instance at the single-species
level where dispersal tends to synchronize populations in dif-
ferent geographical locations (Vindstad et al. 2019). At the
community level, dispersal from other communities can be
important for maintaining compensatory dynamics within
the community, and thus community stability (Loreau et al.
2003, Gonzalez and Loreau 2009). This means that dispersal
and spatial mechanisms should be considered when investi-
gating interspecific synchrony at any level. Moving forward,
such an understanding is likely more important than ever, as
habitat changes in the form of loss and degradation are major
threats that species currently face (Pereira et al. 2012).

One type of study that incorporates both interspecific
synchrony and space, is metacommunity synchrony studies
(often carried out in the context of investigating metacom-
munity stability, see: Wang and Loreau 2014, Wilcox et al.
2017, Lamy et al. 2021, Walter et al. 2021b). Such studies
can involve several of the aforementioned forms of synchrony,
including synchrony among populations of single species
(Fig. 1d), community synchrony within local communi-
ties (Fig. 1c), spatial synchrony among local communities
(Fig. 1f), overall synchrony (regionally) at the metacom-
munity level (not shown in Fig. 1; Wang et al. 2019) and
interspecific synchrony among allopatric populations of dif-
ferent species (Fig. 1e; Hammond et al. 2020, Segrestin and
Lepš 2022). Some of the frameworks developed for study-
ing metacommunity stability allow for estimating and com-
paring several of these forms of synchrony simultaneously
(Wang et al. 2019, Hammond et al. 2020, Segrestin and
Lepš 2022). These have, for instance, shown that environmental fluctuations that are perfectly synchronized between some patches and perfectly anti-synchronized between others may decouple anti-synchrony between species, increase population and metapopulation variability, reduce synchrony between patches and enhance stability at the metacommunity level (Firkowski et al. 2022). One study found that the degree of synchrony can increase with spatial scale, so that there can be anti-synchrony in local communities while there is synchrony at the metacommunity level. This likely implies that there are different synchronizing mechanisms acting at the different levels of biological organization (Segrestin and Lepš 2022). However, a different study (using other methods) found the opposite: that synchrony decreased with increasing spatial scale or level of biological organization (Wang et al. 2019). Additionally, simultaneously investigating synchrony at different levels of biological organization has shown that synchrony at different levels may be caused by different environmental drivers (Hammond et al. 2020). These findings highlight that more work on simultaneously investigating synchrony at different levels of biological organization and spatial scales can help us disentangle and understand the different mechanisms driving synchrony and suggests that a closer integration of synchrony research across levels would improve our understanding of the spatio-temporal dynamics of ecological communities.

# How to move on from here: future perspectives on interspecific synchrony research

## Remaining knowledge gaps and how to fill them

We already have a lot of knowledge about interspecific synchrony in natural communities. However, to properly understand the implications of such synchrony and how synchrony is connected across different levels of biological organization, there are some substantial knowledge gaps that need to be filled.

Although interspecific synchrony and its drivers have been studied in a wide range of empirical systems and at different levels of biological organization, the results of these studies are diverse, making it challenging to draw firm conclusions from the current literature. To better understand what causes the patterns of synchrony and drivers observed in different empirical systems, we need more studies that identify specific environmental drivers of synchrony and explicitly link them to species traits and demographic rates, enabling comparisons of the ecological context within which the different results occur. In addition, more work aimed at disentangling the effects of environmental drivers from those of species interactions, as well as empirical investigations of spatial interspecific synchrony (Fig. 1e) and its drivers, would be helpful. Developing a stronger theoretical underpinning to explain and predict patterns of synchrony in different situations, providing testable hypotheses, could help guide this development. Some such theory already exists (Lee et al.

2020, Firkowski et al. 2022, Quévreux and Loreau 2022), but many gaps remain.

We still have little knowledge about how interspecific synchrony in demographic parameters translates into synchrony at higher levels, including synchrony in population growth rates and abundances among pairs of species and at the community/metacommunity level. Such knowledge would greatly benefit our ability to understand the mechanisms driving community stability, and how communities are likely to be affected by future perturbations. One useful step towards gaining this knowledge would be to quantify synchrony at more than one level of biological organization within the same ecological system. For instance, when we find strong patterns of synchrony at one level of biological organization, we could follow this up by investigating the patterns of synchrony at other levels of biological organization within the same community. Ideally, we would eventually have studies of synchrony at every level from phenology to metacommunity within the same empirical study system. Recently developed frameworks for studying metacommunity synchrony (Wang et al. 2019, Hammond et al. 2020, Segrestin and Lepš 2022) have made it possible to partition synchrony at the metacommunity level into contributions from several of the lower biological levels. These frameworks thus provide a particularly promising way forward in linking synchrony from population to metacommunity levels.

How synchrony in phenological events affects synchrony in demographic parameters, growth rates and abundances is an area of research we also know little about. Climate change is one of the major threats that affect the viability of species and communities (Díaz et al. 2019). Since a warming climate often affects the phenology of species (Cohen et al. 2018), knowing whether such phenological changes are likely to affect interspecific synchrony would be of great benefit. Investigating this link both theoretically and empirically should therefore be a prioritized future research step.

Another question of high priority is how interspecific synchrony among pairs of species translates into synchrony at the community level. Some studies measuring both forms of synchrony already exist, but studies specifically investigating how these synchrony types are connected are still needed. Community synchrony is not necessarily just the average of all pairwise correlations in the community. Species with higher variance in their abundances could, for instance, contribute more to the community synchrony than species with smaller variances (as highlighted by Thibaut and Connolly (2013)). Species evenness can also affect community synchrony, as shown empirically in plant communities by Valencia et al. (2020). In their study, low evenness (i.e. when some species are present in higher numbers than others) was connected to lower community synchrony and higher stability. Other characteristics that could potentially influence which species contribute most to community synchrony include their role in the community, for example represented by trophic level and traits. The ability to pinpoint species of greater importance for community synchrony and stability would be extremely useful from a management perspective.
This is a question that can be tackled both mathematically and empirically, and we suggest that both approaches will be important moving forward.

Much of the empirical research proposed above is data-
hungry, requiring time series from many species, preferably
on both life history traits and demographic parameters,
together with corresponding environmental data, and from
multiple locations. Such data are rare and often limited to
a few well-known study sites. Obtaining knowledge from a
wider environmental range will increase our understanding of
the processes involved in interspecific synchrony. Integrating
data obtained through professional research with data from
other sources, such as citizen science data (Mostert et al.
2025), can be a way forward in gaining knowledge from new
regions that historically have been under-studied, but this
requires methodological developments.

Metrics for measuring interspecific synchrony

To close the remaining knowledge gaps, researchers interested
in interspecific synchrony at any level also need to know which
methods and metrics are available to describe and study such
synchrony. Many metrics exist – all with their own benefits
and drawbacks – and the measures of synchrony obtained are
not necessarily comparable across studies. Studies of inter-
specific synchrony from different levels of biological organi-
zation often use different metrics to measure synchrony. In
Box 2, we present an overview of commonly used methods
and newer methodological developments, along with infor-
mation about the types of studies in which they are com-
monly used. We also highlight some benefits and challenges
connected to the different methods. A useful future direc-
tion to facilitate research on interspecific synchrony would
be to rigorously compare the performance of these different
methods and metrics and how they are related to each other.
This would arm researchers with the best tools for measur-
ing synchrony in different situations and facilitate the work
of synthesizing findings across studies. In the meantime, we
recommend reporting values of more than one metric wher-
ever feasible to aid comparison between studies. For example,
when advanced metrics are used to extract more information
from the data than simpler metrics can provide, also report-
ing the simpler metrics (given that they are appropriate for
the data) can facilitate cross-study comparisons.

Concluding remarks

A steadily increasing amount of research is conducted on the
topic of interspecific synchrony, spanning from the species
to the metacommunity level, and focusing on synchrony
in demographic parameters, growth rates or abundances.
Interspecific synchrony has been documented across all lev-
els of biological organization. We have compelling evidence
that synchrony is an important phenomenon in natural com-
munities; however, we lack a clear understanding of how
environmental variation, species interactions and demo-
graphic stochasticity translate into community synchrony.
How synchrony scales up from lower to higher levels of bio-
logical organization is a central missing piece in our overall

knowledge of interspecific synchrony. Filling these knowl-
edge gaps will bring us closer to a cohesive understanding of
interspecific synchrony in natural communities and its driv-
ers, and strengthen our ability to predict how anthropogenic
stressors will impact community synchrony and stability.

*Funding* – This work was supported by the Research Council of Norway through its Centre of Excellence funding scheme, project no. 223257 and the Norwegian University of Science and Technology (NTNU). MG was supported by the “PURE” ANR JCJC program. RB, IH and AML were supported by the Gjærevoll Centre for Biodiversity Foresight Analyses.

*Conflict of interest* – The authors declare no conflict of interest.

Author contributions

**Ragnhild Bjørkås:** Conceptualization (equal); Visualization (lead); Writing – original draft (lead); Writing – review and editing (lead). **Ivar Herfindal:** Conceptualization (equal); Supervision (supporting); Visualization (supporting); Writing – original draft (equal); Writing – review and editing (supporting). **Marlène Gamelon:** Conceptualization (equal); Supervision (supporting); Visualization (supporting); Writing – original draft (equal); Writing – review and editing (supporting). **Aline Magdalena Lee:** Conceptualization (equal); Supervision (lead); Visualization (supporting); Writing – original draft (equal); Writing – review and editing (lead).

Transparent peer review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/eco.g.07682.

Data availability statement

This is a review paper and does not contain any data.

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