# Species interactions affect dispersal: a meta-analysis

Elvire Bestion, Delphine Legrand, Celina B Baines, Dries Bonte, Aurelie Coulon, Maxime Dahirel, Maria M Delgado, Jhelam N Deshpande, Alison B Duncan, Emanuel A Fronhofer, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Elvire Bestion, Delphine Legrand, Celina B Baines, Dries Bonte, Aurelie Coulon, et al. Species interactions affect dispersal: a meta-analysis. Philosophical Transactions of the Royal Society B: Biological Sciences, 2024, Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics, 379 (1907), pp.20230127. 10.1098/rstb.2023.0127. hal-04622660

HAL Id: hal-04622660

https://hal.science/hal-04622660

Submitted on 24 Jun 2024

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<figure><img src="image_2.png" /></figure>
# Species interactions affect dispersal: a meta-analysis

Elvire Bestion, Delphine Legrand, Celina B Baines, Dries Bonte, Aurélie Coulon, Maxime Dahirel, Maria M Delgado, Jhelam N Deshpande, Alison B Duncan, Emanuel A Fronhofer, et al.

► To cite this version:

Elvire Bestion, Delphine Legrand, Celina B Baines, Dries Bonte, Aurélie Coulon, et al. Species interactions affect dispersal: a meta-analysis. Philosophical Transactions of the Royal Society B: Biological Sciences, 2024, Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics, 379 (1907), pp.20230127. 10.1098/rstb.2023.0127. hal-04622660

HAL Id: hal-04622660

https://hal.science/hal-04622660v1

Submitted on 24 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
SPECIES INTERACTIONS AFFECT DISPERSAL: A META-ANALYSIS

Elvire Bestion<fnref n="1" />*, Delphine Legrand<fnref n="1" />, Celina B. Baines<fnref n="2" />, Dries Bonte<fnref n="3" />, Aurelie Coulon<fnref n="4" />,<fnref n="5" />,
Maxime Dahirel<fnref n="3" />, Maria M. Delgado<fnref n="6" />, Jhelam N. Deshpande<fnref n="7" />, Alison B. Duncan<fnref n="7" />, Emanuel
A. Fronhofer<fnref n="7" />, Isabelle Gounand<fnref n="8" />, Staffan Jacob<fnref n="1" />, Oliver Kaltz<fnref n="7" />, François Massol<fnref n="9" />, Erik
Matthysen<fnref n="10" />, Thomas Parmentier<fnref n="3" />,<fnref n="11" />, Camille Saade<fnref n="7" />, Nicolas Schtickzelle<fnref n="12" />, Giacomo
Zilio<fnref n="5" />,<fnref n="7" />, & Julien Cote<fnref n="13" />

*Philosophical Transactions of the Royal Society B, 2024, 379:20230127,*
*doi:10.1098/rstb.2023.0127*

Part of the Special Issue: “Diversity-dependence of dispersal interspecific interactions determine spatial dynamics”

**Keywords:** context-dependent dispersal, metacommunity dynamics, predator-prey interactions, competition, host-parasite interactions, biotic interactions

ABSTRACT

Context-dependent dispersal allows organisms to seek and settle in habitats improving their fitness. Despite the importance of species interactions in determining fitness, a quantitative synthesis of how they affect dispersal is lacking. We present a meta-analysis asking (1) whether the interaction experienced and/or perceived by a focal species (detrimental interaction with predators, competitors, parasites, or beneficial interaction with resources, hosts, mutualists) affects its dispersal, (2) how the species' ecological and biological background affects the direction and strength of this interaction-dependent dispersal. After a systematic search focusing on actively dispersing species, we extracted 397 effect sizes from 118 empirical studies encompassing 221 species pairs; arthropods were best represented, followed by vertebrates, protists and others. Detrimental species interactions increased the focal species' dispersal (adjusted effect: 0.33 [0.06,0.60]), while beneficial interactions decreased it (-0.55 [-0.92,-0.17]). The effect depended on the dispersal phase, with detrimental interactors having opposite impacts on emigration and transience. Interaction-dependent dispersal was negatively related to species' interaction strength, and depended on the global community composition, with cues of presence having stronger effects than the presence of the interactor, and the ecological complexity of the community. Our work demonstrates the importance of interspecific interactions on dispersal plasticity with consequences for metacommunity dynamics.

<footnote n="1">¹ Station d'Ecologie Théorique et Expérimentale, CNRS, UAR 2029, F-09200 Moulis, France</footnote>

<footnote n="2">² University of Toronto, Department of Ecology and Evolutionary Biology, 25 Willcocks St., Toronto, Canada, M5S 3B2</footnote>

<footnote n="3">³ Terrestrial Ecology Unit (TEREC), Department of Biology, Ghent University, K.L. Ledeganckstraat 35, B-9000 Ghent, Belgium</footnote>

<footnote n="4">⁴ Centre d'Ecologie et des Sciences de la Conservation (CESCO), MNHN, CNRS, Sorbonne University, Paris, Concarneau, France</footnote>

<footnote n="5">⁵ Centre d'Ecologie Fonctionelle et Evolutive (CEFE), University of Montpellier, CNRS, Montpellier, France.</footnote>

<footnote n="6">⁶ Biodiversity Research Institute (IMIB), CSIC/UO/PA, Campus de Mieres, Edificio de Investigación, 33600 Mieres (Asturias), Spain</footnote>

<footnote n="7">⁷ ISEM, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France</footnote>

<footnote n="8">⁸ Institut d'écologie et des sciences de l'environnement (iEES Paris), Sorbonne Université, CNRS, UPEC, CNRS, IRD, INRA, 75005 Paris, France.</footnote>

<footnote n="9">⁹ Institut Pasteur de Lille, Univ. Lille, CNRS, Inserm, CHU Lille, U1019 - UMR 9017 - CIL - Center for Infection and Immunity of Lille, Lille, France</footnote>

<footnote n="10">¹⁰ University of Antwerp, B-2610 Wilrijk, Belgium</footnote>

<footnote n="11">¹¹ Research Unit of Environmental and Evolutionary Biology, Namur Institute of Complex Systems, and Institute of Life, Earth, and the Environment, University of Namur, Rue de Bruxelles 61, 5000 Namur, Belgium</footnote>

<footnote n="12">¹² Earth and Life Institute, UCLouvain, B-1348 Louvain-la-Neuve, Belgium</footnote>

<footnote n="13">¹³ Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), UMR 5300 CNRS-IRD-TINP-UT3, Université Toulouse III – Paul Sabatier, Bât. 4R1, 118 route de Narbonne, 31062 Toulouse, France</footnote>

<footnote>*Correspondence to: elvire.bestion, Station d'Ecologie Théorique et Expérimentale, F-09200 Moulis, France
elvire.bestion@sete.cnrs.fr</footnote>

<footnote>ORCID IDs: 0000-0001-5622-7907 (EB), 0000-0002-6918-3648 (CBB), 0000-0002-3320-7505 (DB), 0000-0001-8077-7765 (MDa), 0000-0002-2219-784X (EAF), 0000-0002-0675-3973 (IG), 0000-0003-1956-9646 (SJ), 0000-0002-4098-955X (FM), 0000-0002-7521-9248 (EM), 0000-0002-4082-0922 (TP), 0000-0001-7829-5361 (NS), 0000-0002-4448-3118, (GZ), 0000-0002-4453-5969 (JC)</footnote>
SPECIES INTERACTIONS AFFECT DISPERSAL: A META-ANALYSIS

Elvire Bestion<fnref n="1" />*, Delphine Legrand<fnref n="1" />, Celina B. Baines<fnref n="2" />, Dries Bonte<fnref n="3" />, Aurelie Coulon<fnref n="4" />,<fnref n="5" />,
Maxime Dahirel<fnref n="3" />, Maria M. Delgado<fnref n="6" />, Jhelam N. Deshpande<fnref n="7" />, Alison B. Duncan<fnref n="7" />, Emanuel
A. Fronhofer<fnref n="7" />, Isabelle Gounand<fnref n="8" />, Staffan Jacob<fnref n="1" />, Oliver Kaltz<fnref n="7" />, François Massol<fnref n="9" />, Erik
Matthysen<fnref n="10" />, Thomas Parmentier<fnref n="3" />,<fnref n="11" />, Camille Saade<fnref n="7" />, Nicolas Schtickzelle<fnref n="12" />, Giacomo
Zilio<fnref n="5" />,<fnref n="7" />, & Julien Cote<fnref n="13" />

Philosophical Transactions of the Royal Society B, 2024, 379:20230127,
doi:10.1098/rstb.2023.0127

Part of the Special Issue: "Diversity-dependence of dispersal interspecific interactions determine spatial dynamics"

**Keywords:** context-dependent dispersal, metacommunity dynamics, predator-prey interactions, competition, host-parasite interactions, biotic interactions

ABSTRACT

Context-dependent dispersal allows organisms to seek and settle in habitats improving their fitness. Despite the importance of species interactions in determining fitness, a quantitative synthesis of how they affect dispersal is lacking. We present a meta-analysis asking (1) whether the interaction experienced and/or perceived by a focal species (detrimental interaction with predators, competitors, parasites, or beneficial interaction with resources, hosts, mutualists) affects its dispersal, (2) how the species' ecological and biological background affects the direction and strength of this interaction-dependent dispersal. After a systematic search focusing on actively dispersing species, we extracted 397 effect sizes from 118 empirical studies encompassing 221 species pairs; arthropods were best represented, followed by vertebrates, protists and others. Detrimental species interactions increased the focal species' dispersal (adjusted effect: 0.33 [0.06,0.60]), while beneficial interactions decreased it (-0.55 [-0.92,-0.17]). The effect depended on the dispersal phase, with detrimental interactors having opposite impacts on emigration and transience. Interaction-dependent dispersal was negatively related to species' interaction strength, and depended on the global community composition, with cues of presence having stronger effects than the presence of the interactor, and the ecological complexity of the community. Our work demonstrates the importance of interspecific interactions on dispersal plasticity with consequences for metacommunity dynamics.

<footnote n="1">¹ Station d'Ecologie Théorique et Expérimentale, CNRS, UAR 2029, F-09200 Moulis, France</footnote>

<footnote n="2">² University of Toronto, Department of Ecology and Evolutionary Biology, 25 Willcocks St., Toronto, Canada, M5S 3B2</footnote>

<footnote n="3">³ Terrestrial Ecology Unit (TEREC), Department of Biology, Ghent University, K.L. Ledeganckstraat 35, B-9000 Ghent, Belgium</footnote>

<footnote n="4">⁴ Centre d'Ecologie et des Sciences de la Conservation (CESCO), MNHN, CNRS, Sorbonne University, Paris, Concarneau, France</footnote>

<footnote n="5">⁵ Centre d'Ecologie Fonctionelle et Evolutive (CEFE), University of Montpellier, CNRS, Montpellier, France.</footnote>

<footnote n="6">⁶ Biodiversity Research Institute (IMIB), CSIC/UO/PA, Campus de Mieres, Edificio de Investigación, 33600 Mieres (Asturias), Spain</footnote>

<footnote n="7">⁷ ISEM, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France</footnote>

<footnote n="8">⁸ Institut d'écologie et des sciences de l'environnement (iEES Paris), Sorbonne Université, CNRS, UPEC, CNRS, IRD, INRA, 75005 Paris, France.</footnote>

<footnote n="9">⁹ Institut Pasteur de Lille, Univ. Lille, CNRS, Inserm, CHU Lille, U1019 - UMR 9017 - CIL - Center for Infection and Immunity of Lille, Lille, France</footnote>

<footnote n="10">¹⁰ University of Antwerp, B-2610 Wilrijk, Belgium</footnote>

<footnote n="11">¹¹ Research Unit of Environmental and Evolutionary Biology, Namur Institute of Complex Systems, and Institute of Life, Earth, and the Environment, University of Namur, Rue de Bruxelles 61, 5000 Namur, Belgium</footnote>

<footnote n="12">¹² Earth and Life Institute, UCLouvain, B-1348 Louvain-la-Neuve, Belgium</footnote>

<footnote n="13">¹³ Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), UMR 5300 CNRS-IRD-TINP-UT3, Université Toulouse III - Paul Sabatier, Bât. 4R1, 118 route de Narbonne, 31062 Toulouse, France</footnote>

<footnote>*Correspondence to: elvire.bestion, Station d'Ecologie Théorique et Expérimentale, F-09200 Moulis, France
elvire.bestion@sete.cnrs.fr</footnote>

<footnote>ORCID IDs: 0000-0001-5622-7907 (EB), 0000-0002-6918-3648 (CBB), 0000-0002-3320-7505 (DB), 0000-0001-8077-7765 (MDa), 0000-0002-2219-784X (EAF), 0000-0002-0675-3973 (IG), 0000-0003-1956-9646 (SJ), 0000-0002-4098-955X (FM), 0000-0002-7521-9248 (EM), 0000-0002-4082-0922 (TP), 0000-0001-7829-5361 (NS), 0000-0002-4448-3118, (GZ), 0000-0002-4453-5969 (JC)</footnote>
INTRODUCTION

The dynamics of local and regional communities result
from the distribution and abundance of interacting
species [1]. At a local scale, the strength and direction
of these interactions affect coexistence and
community dynamics. At a regional scale, local
communities are linked by the dispersal of potentially
interacting species among local habitats [1]. Species
dispersal links local and regional community dynamics
(i.e., metacommunity dynamics) and influences local
population dynamics and community composition [1].
A better understanding of the dispersal process, and its
eco-evolutionary drivers, is therefore crucial for
understanding its influences on community dynamics
at different landscape scales [2,3].

Theoretical and empirical studies have demonstrated that dispersal depends on the experienced and anticipated biotic and abiotic environment, referred to as context-dependent dispersal [4–6]. Indeed, organisms do not move randomly through a landscape, the propensity, direction, and distance moved can be plastically adjusted to the local abiotic and biotic conditions encountered before and while moving [4]. Theory shows that individuals can disperse adaptively as emigration and immigration could depend on the expected fitness in the arrival patch available for colonization relative to the fitness expected in the departure patch [7]. Theory also predicts that such context-dependent dispersal can evolve in spatially and temporally heterogeneous habitats [8]. Further, the evolution of context-dependent dispersal depends on the reliability and cost of information acquisition [9]. Among the many factors theoretically found to drive the evolution of context-dependent dispersal are patch size and quality, and fluctuation in population density or kin competition [8,10,11]. Such context-dependent dispersal can itself affect eco-evolutionary dynamics, including source-sink population dynamics or species ranges, and can further affect metacommunity functioning and stability through changes in species abundances and community composition [12].

The direction and strength of local interactions
are major determinants of fitness and therefore
expected to be among the most important proximal
drivers of dispersal [3]. Bottom-up and top-down
controls of dispersal are now well documented (e.g.,
[13]) and evidence is accumulating for the context-
dependency of dispersal in other types of species
interactions (e.g., interspecific competition, host-
symbiont interactions [14,15]). Further, certain species

can only disperse through other species' movements, such as animal-dispersed seeds or symbionts dispersing through their hosts' movements [16,17]. Despite the importance of species interactions, it remains unclear how their nature actually shapes dispersal plasticity within and among taxonomic groups. After a systematic review of the existing literature, we performed a meta-analysis of published studies investigating species interactions as proximal drivers of dispersal to discuss the potential consequences for metacommunity dynamics and identify important research gaps with respect to the development of a more mechanistic metacommunity theory. To do so, we extracted data from 118 observational and experimental studies focusing on either the emigration stage, the transience stage or the full dispersal process from emigration to immigration, excluding studies focusing only on colonisation. We focused on mobile and actively dispersing species, and thus excluded studies on sessile or passive dispersing species (e.g., plants). Our database, which was taxonomically biased towards animals (essentially arthropods and vertebrates), encompassed 221 species pairs and six types of interactions experienced by the focal species, i.e., detrimental interactions with predators, consumers or parasites, and beneficial interactions with resources, hosts or mutualists. We quantified how the presence/absence or the abundance of an interacting species influenced the dispersal of a focal species through plastic changes across interaction types, and identified key moderators of this context-dependent dispersal (Fig. 1). We especially addressed the following questions:

Does the impact of interacting species on
dispersal depend on the nature of species
interactions?

Species interactions can be first described through
their general effects on the focal species (Fig. 1).
Beneficial interactors are resources, hosts, and
mutualists that should increase the fitness of the focal
species. Detrimental interactors are predators,
parasites, and competitors that should decrease the
fitness of the focal species. If dispersal depends on
fitness expectations [7], detrimental interactions,
endured or expected, should increase species dispersal
propensity away from local habitats containing the
interactor while the opposite should be true for
beneficial interactions, even if this effect should
depend on species ecological traits (see below).
Evolution of such interaction-dependent dispersal has
been theoretically and empirically shown for predator-
prey, host-parasite, and interspecific competition
interactions [6,14,18], showing for example that
# INTRODUCTION

The dynamics of local and regional communities result from the distribution and abundance of interacting species [1]. At a local scale, the strength and direction of these interactions affect coexistence and community dynamics. At a regional scale, local communities are linked by the dispersal of potentially interacting species among local habitats [1]. Species dispersal links local and regional community dynamics (i.e., metacommunity dynamics) and influences local population dynamics and community composition [1]. A better understanding of the dispersal process, and its eco-evolutionary drivers, is therefore crucial for understanding its influences on community dynamics at different landscape scales [2,3].

Theoretical and empirical studies have demonstrated that dispersal depends on the experienced and anticipated biotic and abiotic environment, referred to as context-dependent dispersal [4–6]. Indeed, organisms do not move randomly through a landscape, the propensity, direction, and distance moved can be plastically adjusted to the local abiotic and biotic conditions encountered before and while moving [4]. Theory shows that individuals can disperse adaptively as emigration and immigration could depend on the expected fitness in the arrival patch available for colonization relative to the fitness expected in the departure patch [7]. Theory also predicts that such context-dependent dispersal can evolve in spatially and temporally heterogeneous habitats [8]. Further, the evolution of context-dependent dispersal depends on the reliability and cost of information acquisition [9]. Among the many factors theoretically found to drive the evolution of context-dependent dispersal are patch size and quality, and fluctuation in population density or kin competition [8,10,11]. Such context-dependent dispersal can itself affect eco-evolutionary dynamics, including source-sink population dynamics or species ranges, and can further affect metacommunity functioning and stability through changes in species abundances and community composition [12].

The direction and strength of local interactions are major determinants of fitness and therefore expected to be among the most important proximal drivers of dispersal [3]. Bottom-up and top-down controls of dispersal are now well documented (e.g., [13]) and evidence is accumulating for the context-dependency of dispersal in other types of species interactions (e.g., interspecific competition, host-symbiont interactions [14,15]). Further, certain species

can only disperse through other species' movements, such as animal-dispersed seeds or symbionts dispersing through their hosts' movements [16,17]. Despite the importance of species interactions, it remains unclear how their nature actually shapes dispersal plasticity within and among taxonomic groups. After a systematic review of the existing literature, we performed a meta-analysis of published studies investigating species interactions as proximal drivers of dispersal to discuss the potential consequences for metacommunity dynamics and identify important research gaps with respect to the development of a more mechanistic metacommunity theory. To do so, we extracted data from 118 observational and experimental studies focusing on either the emigration stage, the transience stage or the full dispersal process from emigration to immigration, excluding studies focusing only on colonisation. We focused on mobile and actively dispersing species, and thus excluded studies on sessile or passive dispersing species (e.g., plants). Our database, which was taxonomically biased towards animals (essentially arthropods and vertebrates), encompassed 221 species pairs and six types of interactions experienced by the focal species, i.e., detrimental interactions with predators, consumers or parasites, and beneficial interactions with resources, hosts or mutualists. We quantified how the presence/absence or the abundance of an interacting species influenced the dispersal of a focal species through plastic changes across interaction types, and identified key moderators of this context-dependent dispersal (Fig. 1). We especially addressed the following questions:

## Does the impact of interacting species on dispersal depend on the nature of species interactions?

Species interactions can be first described through their general effects on the focal species (Fig. 1). Beneficial interactors are resources, hosts, and mutualists that should increase the fitness of the focal species. Detrimental interactors are predators, parasites, and competitors that should decrease the fitness of the focal species. If dispersal depends on fitness expectations [7], detrimental interactions, endured or expected, should increase species dispersal propensity away from local habitats containing the interactor while the opposite should be true for beneficial interactions, even if this effect should depend on species ecological traits (see below). Evolution of such interaction-dependent dispersal has been theoretically and empirically shown for predator-prey, host-parasite, and interspecific competition interactions [6,14,18], showing for example that
models of density-dependent dispersal could be
transferred from a single species to a multispecies
context [14].

Beyond their expected effects on fitness, different
species interactions might actually lead to different
dispersal strategies to mitigate fitness effects (Fig. 1).
Some interaction types might not be efficiently
mitigated by species movements or might induce high
dispersal costs. Dispersing away from local habitats
might therefore fail to improve expected fitness. For
example, interspecific competition negatively affects a
competitor's fitness by reducing acquired energy, thus
increasing the reward to move while reducing the
energy available for movement. Thus, it may result in
more complex - quadratic or threshold-like -
relationships between the expected fitness
consequences of interactions, the actual strength of
interactions, and emigration propensity. Such
relationships may vary with energy availability,
movement costs and niche overlap between
interacting species. Complex effects of competition
have been found for intraspecific competition, with a
systematic review showing that intraspecific density
could either positively, negatively or non-linearly affect
dispersal [19]. Predation risk on the other hand
intrinsically reduces prey fitness, making movement
out of habitats highly rewarding but also often highly
risky [20], with potentially opposite effects on dispersal
depending on the risk level. Similarly, by diverting their
hosts' resources, parasites can constrain their dispersal
[21]. However, hosts may increase their dispersal
propensity to escape an infested habitat [15], and
parasites themselves can manipulate their host's
dispersal to increase contact rate and the transmission
of parasites [21]. We expect the type of interactions to
modulate the dependency of dispersal on interaction
strength (i.e., interaction-dependent dispersal) and
this effect should further depend on a suite of
biological and ecological factors (Fig. 1).

Does the dispersal response depend on the
spatiotemporal dynamics of interacting
species?

Dispersal is not always the best strategy to escape
adverse interactions or to search for beneficial ones. Its
efficiency should depend on temporal synchrony and
spatial overlap of interacting species ([6], Fig. 1). First,
dispersing may not lead to changes in encounters with
an interacting species if that species has a wider spatial
distribution. In such a case, dispersing individuals
would bear more of the energy, time, and opportunity
costs of moving [22] than its benefits. For example,
predators often have larger home and movement
ranges than their prey [23,24], but are also found at

lower densities. Prey species may thus need to disperse
over longer distances to reduce predation risk.
However, transience is a highly vulnerable stage when
prey are exposed to predators [20], which may result
in predators having different impacts on the focal prey
species' emigration phase or transience depending on
the spatiotemporal dynamics [13] and the asymmetry
in space use between predator and prey. When
dispersal does not efficiently influence the adverse or
beneficial effects of interactions, species may opt for
alternative mitigation strategies, such as switching
their activity period or their microhabitat use. We first
compared studies investigating interaction-dependent
emigration (through emigration rate) and transience
(through dispersal distance) for the different types of
interactions. We expected interacting species to
influence dispersal distances more than emigration
probability when interacting partners differ in their
spatial distribution (e.g., consumer-resource
interaction). Following the same rationale, we
expected stronger influences of an interaction in
(typically experimental) studies when only the focal
species could disperse compared with cases where
both species could disperse. Furthermore, we
expected the relative home range of interacting
species to affect the dispersal response (Fig. 1).

Second, the temporal dynamics of interacting species
may modulate the efficiency of dispersal in mitigating
species interactions. Interacting species may differ in 1)
the daily and seasonal profiles of activity and 2) in the
temporal autocorrelation of their population sizes [13].
For instance, in aphids, the evolution of delayed
predator-induced dispersal, where predator presence
induces the production of winged offspring, depends
on the temporal correlation in predation risk [6]. We
were not able to gather information on the daily and
seasonal profiles of activity, but we investigated this
temporal aspect by comparing studies in which the
encounter with the interacting species and the
dispersal responses occur within the same generation
or across different generations, as well as studying the
impact of the duration of the experiment and of
species' generation time (Fig. 1).

Do the interaction strength and ecological
complexity affect the dispersal response?

One species' impact on the dispersal of another
interacting species may depend on their interaction
strength [14], as interaction strengths should affect
fitness expectations. Assessing interaction strength
between species is a non-trivial endeavour, both for
the researcher [25], and possibly for the interacting
organisms, which may rely on several types of cues to
determine it. The interaction strength perceived by a
models of density-dependent dispersal could be
transferred from a single species to a multispecies
context [14].

Beyond their expected effects on fitness, different
species interactions might actually lead to different
dispersal strategies to mitigate fitness effects (Fig. 1).
Some interaction types might not be efficiently
mitigated by species movements or might induce high
dispersal costs. Dispersing away from local habitats
might therefore fail to improve expected fitness. For
example, interspecific competition negatively affects a
competitor's fitness by reducing acquired energy, thus
increasing the reward to move while reducing the
energy available for movement. Thus, it may result in
more complex - quadratic or threshold-like -
relationships between the expected fitness
consequences of interactions, the actual strength of
interactions, and emigration propensity. Such
relationships may vary with energy availability,
movement costs and niche overlap between
interacting species. Complex effects of competition
have been found for intraspecific competition, with a
systematic review showing that intraspecific density
could either positively, negatively or non-linearly affect
dispersal [19]. Predation risk on the other hand
intrinsically reduces prey fitness, making movement
out of habitats highly rewarding but also often highly
risky [20], with potentially opposite effects on dispersal
depending on the risk level. Similarly, by diverting their
hosts' resources, parasites can constrain their dispersal
[21]. However, hosts may increase their dispersal
propensity to escape an infested habitat [15], and
parasites themselves can manipulate their host's
dispersal to increase contact rate and the transmission
of parasites [21]. We expect the type of interactions to
modulate the dependency of dispersal on interaction
strength (i.e., interaction-dependent dispersal) and
this effect should further depend on a suite of
biological and ecological factors (Fig. 1).

Does the dispersal response depend on the
spatiotemporal dynamics of interacting
species?

Dispersal is not always the best strategy to escape
adverse interactions or to search for beneficial ones. Its
efficiency should depend on temporal synchrony and
spatial overlap of interacting species ([6], Fig. 1). First,
dispersing may not lead to changes in encounters with
an interacting species if that species has a wider spatial
distribution. In such a case, dispersing individuals
would bear more of the energy, time, and opportunity
costs of moving [22] than its benefits. For example,
predators often have larger home and movement
ranges than their prey [23,24], but are also found at

lower densities. Prey species may thus need to disperse
over longer distances to reduce predation risk.
However, transience is a highly vulnerable stage when
prey are exposed to predators [20], which may result
in predators having different impacts on the focal prey
species' emigration phase or transience depending on
the spatiotemporal dynamics [13] and the asymmetry
in space use between predator and prey. When
dispersal does not efficiently influence the adverse or
beneficial effects of interactions, species may opt for
alternative mitigation strategies, such as switching
their activity period or their microhabitat use. We first
compared studies investigating interaction-dependent
emigration (through emigration rate) and transience
(through dispersal distance) for the different types of
interactions. We expected interacting species to
influence dispersal distances more than emigration
probability when interacting partners differ in their
spatial distribution (e.g., consumer-resource
interaction). Following the same rationale, we
expected stronger influences of an interaction in
(typically experimental) studies when only the focal
species could disperse compared with cases where
both species could disperse. Furthermore, we
expected the relative home range of interacting
species to affect the dispersal response (Fig. 1).

Second, the temporal dynamics of interacting species
may modulate the efficiency of dispersal in mitigating
species interactions. Interacting species may differ in 1)
the daily and seasonal profiles of activity and 2) in the
temporal autocorrelation of their population sizes [13].
For instance, in aphids, the evolution of delayed
predator-induced dispersal, where predator presence
induces the production of winged offspring, depends
on the temporal correlation in predation risk [6]. We
were not able to gather information on the daily and
seasonal profiles of activity, but we investigated this
temporal aspect by comparing studies in which the
encounter with the interacting species and the
dispersal responses occur within the same generation
or across different generations, as well as studying the
impact of the duration of the experiment and of
species' generation time (Fig. 1).

Do the interaction strength and ecological
complexity affect the dispersal response?

One species' impact on the dispersal of another
interacting species may depend on their interaction
strength [14], as interaction strengths should affect
fitness expectations. Assessing interaction strength
between species is a non-trivial endeavour, both for
the researcher [25], and possibly for the interacting
organisms, which may rely on several types of cues to
determine it. The interaction strength perceived by a
<figure><img src="image_3.png" /><figcaption>Figure 1: The relationships between species interactions and dispersal</figcaption></figure>

The dispersal of a focal species depends upon its biotic context, especially if the presence/abundance of an interacting species affects its fitness (grey arrows). In this meta-analysis, we extracted data from the literature to determine how the presence/abundance of the interacting species affects the dispersal of a focal species (black arrow). The relationship between interacting species' presence/abundance and dispersal depends on the nature and strength of the biological interactors. Detrimental interactors such as predators, parasites or competitors (in red) negatively affect the fitness of the focal species (red - sign on the grey arrow). Because there is a negative relationship between fitness and dispersal (black - sign on the grey arrow), they should thus positively affect its dispersal (red + sign on the black arrow). On the other hand, beneficial interactors such as resources, hosts and mutualists (in blue) should positively affect the fitness (blue + sign on the grey arrow), and through the negative link between fitness and dispersal (black - sign), negatively affect dispersal (blue - sign on the black arrow). The effect of these interactions should depend on numerous biological regulators, related to the spatiotemporal co-dynamics and the ecological complexity, which affect the strength of interaction between species and thus the strength of interaction-dependent dispersal (green arrows). We translated these biological regulators into a number of statistical moderators (gold arrows) to explain the dependency of dispersal to the presence of an interacting species. By testing for these moderator effects on the relationship between the presence/abundance of the interacting species and dispersal of the focal species (gold and green arrows), we attempted to make inferences about the way fitness is affected by the interacting species and the way it affects the dispersal of the focal species (green arrows).

given individual may depend on various moderators
including the abundances of the interacting partners,
the actual presence or the perception of cues, the
niche overlap and generalism of interacting partners,
as well as the overall ecological complexity. For
example, we can expect cues of the presence of
interacting species to elicit a weaker perception of
interaction strength and a weaker dispersal response
than the actual presence of the interactor. Conversely,
the fear of predators has long been considered to have
stronger impacts on prey than their actual presence
and consumption [26]. The effect of an interaction on
species fitness and dispersal may further depend on
community complexity (Fig. 1), i.e., the diversity of
interacting partners within the same and across
interaction types. Multiple host species for a single
parasite may for example decrease the interaction
strength with each host species through a dilution
effect that decreases disease risk [27]. A similar
prediction can be made for any interaction type.

Comparing studies using a two- or a multi-species
network may therefore provide information on the
role of interaction strength, with the prediction that a
design with pairs of species may overestimate the
dependency of dispersal on interaction strength.
Similarly, experimental studies may reduce ecological
complexity, but have better detection of interaction
strength, than observational studies. The strength of
interaction may also depend on the level of generalism
of interacting species, with more specialist interactors
likely having stronger impacts on the focal species and
hence their dispersal. Most of the studies investigating
interaction-dependent dispersal lacked an assessment
of interaction strength between species; however,
several aspects of the environmental context could be
used as a cue of interaction strength. We thus
investigated modulating effects of metrics related to
community complexity, types of manipulation (i.e.,
cues vs. actual presence or abundance of interacting
species), type of studies (observational vs.
<figure><img src="image_2.png" /><figcaption>Figure 1: The relationships between species interactions and dispersal</figcaption></figure>

The dispersal of a focal species depends upon its biotic context, especially if the presence/abundance of an interacting species affects its fitness (grey arrows). In this meta-analysis, we extracted data from the literature to determine how the presence/abundance of the interacting species affects the dispersal of a focal species (black arrow). The relationship between interacting species' presence/abundance and dispersal depends on the nature and strength of the biological interactors. Detrimental interactors such as predators, parasites or competitors (in red) negatively affect the fitness of the focal species (red - sign on the grey arrow). Because there is a negative relationship between fitness and dispersal (black - sign on the grey arrow), they should thus positively affect its dispersal (red + sign on the black arrow). On the other hand, beneficial interactors such as resources, hosts and mutualists (in blue) should positively affect the fitness (blue + sign on the grey arrow), and through the negative link between fitness and dispersal (black - sign), negatively affect dispersal (blue - sign on the black arrow). The effect of these interactions should depend on numerous biological regulators, related to the spatiotemporal co-dynamics and the ecological complexity, which affect the strength of interaction between species and thus the strength of interaction-dependent dispersal (green arrows). We translated these biological regulators into a number of statistical moderators (gold arrows) to explain the dependency of dispersal to the presence of an interacting species. By testing for these moderator effects on the relationship between the presence/abundance of the interacting species and dispersal of the focal species (gold and green arrows), we attempted to make inferences about the way fitness is affected by the interacting species and the way it affects the dispersal of the focal species (green arrows).

given individual may depend on various moderators
including the abundances of the interacting partners,
the actual presence or the perception of cues, the
niche overlap and generalism of interacting partners,
as well as the overall ecological complexity. For
example, we can expect cues of the presence of
interacting species to elicit a weaker perception of
interaction strength and a weaker dispersal response
than the actual presence of the interactor. Conversely,
the fear of predators has long been considered to have
stronger impacts on prey than their actual presence
and consumption [26]. The effect of an interaction on
species fitness and dispersal may further depend on
community complexity (Fig. 1), i.e., the diversity of
interacting partners within the same and across
interaction types. Multiple host species for a single
parasite may for example decrease the interaction
strength with each host species through a dilution
effect that decreases disease risk [27]. A similar
prediction can be made for any interaction type.

Comparing studies using a two- or a multi-species
network may therefore provide information on the
role of interaction strength, with the prediction that a
design with pairs of species may overestimate the
dependency of dispersal on interaction strength.
Similarly, experimental studies may reduce ecological
complexity, but have better detection of interaction
strength, than observational studies. The strength of
interaction may also depend on the level of generalism
of interacting species, with more specialist interactors
likely having stronger impacts on the focal species and
hence their dispersal. Most of the studies investigating
interaction-dependent dispersal lacked an assessment
of interaction strength between species; however,
several aspects of the environmental context could be
used as a cue of interaction strength. We thus
investigated modulating effects of metrics related to
community complexity, types of manipulation (i.e.,
cues vs. actual presence or abundance of interacting
species), type of studies (observational vs.
experimental) and level of generalism of both the focal
and interacting species in dispersal studies. Further, we
used a second literature search to estimate expected
interaction strengths between pairs of species present
in our database, which was possible for a majority of
species pairs but involved a large variation between
the ecological contexts of dispersal and interaction
strength studies.

METHODS

Systematic literature review

We compiled a database of existing studies quantifying
how the presence/absence or the abundance of an
interacting species influences the dispersal of a focal
species. Our database focused on actively dispersing
species to examine the relationship between the
dispersal response and the interaction strength, but
not the numerical and behavioural effects of dispersal
vectors. We defined active dispersal as dispersal in
which the organism either actively moves during the
transience phase, or actively initiates the emigration
phase (e.g., ballooning dispersal in which the wind is
the vector of the transience, but the organism actively
initiates dispersal by its tiptoeing behaviour [28], or
dispersal by a biological vector where the organism
actively climbs on top of the vector [29]). Organisms
that are passively picked up and carried by the wind or
a dispersal vector, such as most cases of seed dispersal,
were thus excluded. In addition, we excluded studies
that focused only on the immigration/colonization
phase of dispersal, because the habitat choices of focal
species depending on local interactors (the purpose of
the present study) could not be distinguished from the
viability of focal species depending on local interactors
(i.e., colonization success). However, immigration
decisions with respect to species interaction are
expected to mirror emigration decisions and so we
expect our conclusions to apply to immigration
decisions as well [5]. In the end, our literature search
included emigration decisions, dispersal distance, the
timing of dispersal, and any type of species interactions
(interspecific competition, consumer-resource
interactions, parasitism, and mutualism). Fig. S1 shows
the process of study identification, selection and the
data extracted from each relevant article. We aimed at
following the preferred reporting items for systematic
reviews and meta-analyses (PRISMA, [30]) as well as its
extension for ecology and evolutionary biology [31].

We conducted the literature search on Web of
Knowledge in October 2021 (timespan: 1975-2021)
including keywords for 1) dispersal in the title (dispers*
OR migrat* OR emigrat* OR immigrat* OR colonis* OR
coloniz* OR nomadi*) and excluding other types of

movements, i.e., seasonal migration (fall, autumn,
spring migration, vertical migration, catadrom*,
anadrom*), human migrations (i.e., refugees,
international migration, domestic migration, human
migration, asylum, illegal), or particle dispersion, and
2) species interaction in the title and/or subject
(predat* OR resource* OR prey* OR parasit* OR host*
OR compet* OR facilitat* OR mutualis* OR cooperat*
OR phore* OR commensal* OR interspeci* OR
heterospeci* OR context* OR condition* OR
metacommunit* OR "mutual inhibition" OR
ammensalis* OR symbio*). We focused on the Web of
Science core collection, and filtered results by Web of
Science categories relevant to our broad thematic, see
supplementary Box S1 for a full description of the
keywords and categories. During the search process,
we checked for the inclusion of known relevant articles
in the database to ensure that we did not miss
important articles. The search yielded 21,499 articles.

Inclusion criteria

We examined each title and abstract to determine
whether articles met the criteria for inclusion in our
overview of the literature with the *metagear* package
v0.7 [32] in R. Criteria for inclusion comprised (a) the
presence, abundance or density of an interacting
species was quantified and varied experimentally or in
observational studies, (b) the rate of emigration or the
dispersal distance or another dispersal metric of a focal
species that was quantified in different contexts of
species interactions, (c) the effect of a single
interacting species on a focal species can be isolated
when more than two species were studied and (d) the
two species were interacting species in natural
environments, excluding artificial biotic elements (e.g.,
unnatural resource or predator species). At times, the
title and abstract were too vague to positively assess
these three criteria of inclusion, and the articles were
kept for further detailed reading of the text; they could
be thus excluded in a second filtering session. All
examiners went through a training process in which
they screened abstracts and titles for a set of 150
articles that were pre-selected by the first author to
contain studies both easy and more difficult to rate as
relevant or irrelevant. We estimated the inter-
examiner accuracy, yielding a Cohen's Kappa of 0.40,
corresponding to a fair to moderate agreement [33].
This first batch allowed to identify points of
misunderstanding and better explain the criteria to
select an article. Then, all examiners went through 70
new articles that were deemed especially difficult to
rate and we checked for potential discrepancies.
Although the batch was more difficult, inter-examiner
accuracy went up (K = 0.43). This second batch allowed
us to clarify the final difficult points before splitting the
experimental) and level of generalism of both the focal
and interacting species in dispersal studies. Further, we
used a second literature search to estimate expected
interaction strengths between pairs of species present
in our database, which was possible for a majority of
species pairs but involved a large variation between
the ecological contexts of dispersal and interaction
strength studies.

METHODS

Systematic literature review

We compiled a database of existing studies quantifying
how the presence/absence or the abundance of an
interacting species influences the dispersal of a focal
species. Our database focused on actively dispersing
species to examine the relationship between the
dispersal response and the interaction strength, but
not the numerical and behavioural effects of dispersal
vectors. We defined active dispersal as dispersal in
which the organism either actively moves during the
transience phase, or actively initiates the emigration
phase (e.g., ballooning dispersal in which the wind is
the vector of the transience, but the organism actively
initiates dispersal by its tiptoeing behaviour [28], or
dispersal by a biological vector where the organism
actively climbs on top of the vector [29]). Organisms
that are passively picked up and carried by the wind or
a dispersal vector, such as most cases of seed dispersal,
were thus excluded. In addition, we excluded studies
that focused only on the immigration/colonization
phase of dispersal, because the habitat choices of focal
species depending on local interactors (the purpose of
the present study) could not be distinguished from the
viability of focal species depending on local interactors
(i.e., colonization success). However, immigration
decisions with respect to species interaction are
expected to mirror emigration decisions and so we
expect our conclusions to apply to immigration
decisions as well [5]. In the end, our literature search
included emigration decisions, dispersal distance, the
timing of dispersal, and any type of species interactions
(interspecific competition, consumer-resource
interactions, parasitism, and mutualism). Fig. S1 shows
the process of study identification, selection and the
data extracted from each relevant article. We aimed at
following the preferred reporting items for systematic
reviews and meta-analyses (PRISMA, [30]) as well as its
extension for ecology and evolutionary biology [31].

We conducted the literature search on Web of
Knowledge in October 2021 (timespan: 1975-2021)
including keywords for 1) dispersal in the title (dispers*
OR migrat* OR emigrat* OR immigrat* OR colonis* OR
coloniz* OR nomadi*) and excluding other types of

movements, i.e., seasonal migration (fall, autumn,
spring migration, vertical migration, catadrom*,
anadrom*), human migrations (i.e., refugees,
international migration, domestic migration, human
migration, asylum, illegal), or particle dispersion, and
2) species interaction in the title and/or subject
(predat* OR resource* OR prey* OR parasit* OR host*
OR compet* OR facilitat* OR mutualis* OR cooperat*
OR phore* OR commensal* OR interspeci* OR
heterospeci* OR context* OR condition* OR
metacommunit* OR "mutual inhibition" OR
ammensalis* OR symbio*). We focused on the Web of
Science core collection, and filtered results by Web of
Science categories relevant to our broad thematic, see
supplementary Box S1 for a full description of the
keywords and categories. During the search process,
we checked for the inclusion of known relevant articles
in the database to ensure that we did not miss
important articles. The search yielded 21,499 articles.

Inclusion criteria

We examined each title and abstract to determine
whether articles met the criteria for inclusion in our
overview of the literature with the *metagear* package
v0.7 [32] in R. Criteria for inclusion comprised (a) the
presence, abundance or density of an interacting
species was quantified and varied experimentally or in
observational studies, (b) the rate of emigration or the
dispersal distance or another dispersal metric of a focal
species that was quantified in different contexts of
species interactions, (c) the effect of a single
interacting species on a focal species can be isolated
when more than two species were studied and (d) the
two species were interacting species in natural
environments, excluding artificial biotic elements (e.g.,
unnatural resource or predator species). At times, the
title and abstract were too vague to positively assess
these three criteria of inclusion, and the articles were
kept for further detailed reading of the text; they could
be thus excluded in a second filtering session. All
examiners went through a training process in which
they screened abstracts and titles for a set of 150
articles that were pre-selected by the first author to
contain studies both easy and more difficult to rate as
relevant or irrelevant. We estimated the inter-
examiner accuracy, yielding a Cohen's Kappa of 0.40,
corresponding to a fair to moderate agreement [33].
This first batch allowed to identify points of
misunderstanding and better explain the criteria to
select an article. Then, all examiners went through 70
new articles that were deemed especially difficult to
rate and we checked for potential discrepancies.
Although the batch was more difficult, inter-examiner
accuracy went up (K = 0.43). This second batch allowed
us to clarify the final difficult points before splitting the
abstracts between examiners so that each examiner read and rated ~1100 abstracts. During this second phase, inter-examiner accuracy was examined again on 20 articles per reviewer, yielding a K of 0.73, considered a substantial agreement [33]. The filtering process led to a selection of 1,539 studies from the original search that fitted the scope of this overview.

## Effect size extraction

Selected articles were reviewed in full to determine whether they fit our inclusion criteria, contained relevant data, and whether the results were presented with sufficient clarity to extract effect sizes (Fig S1). During this step, we excluded (a) studies in which the focal species dispersed passively and in which there was no active choice to emigrate, (b) studies focused on the colonisation process only, keeping studies on emigration, transience, or the ones monitoring the full dispersal process, (c) studies in which the second species did not interact with the focal species, (d) studies in which the effect of an interacting species on the dispersal of the focal species was compared to a control with a second interacting species instead of no interacting species or different abundances of interacting species. This step led to the further exclusion of 1,420 articles from the first search. In the final set of 119 studies, we extracted 403 effect sizes and collected information on several moderators to investigate our questions of interest (See supplementary reference list). Data to calculate effect sizes were extracted preferentially from raw data when available, from figures using the *juicr v 0.1* R package, or directly from the paper (tables or main text). Effect sizes were calculated with *esc v0.5.1* and *effectsize v0.8.5* R packages, and computed as Hedges' *d* from pairs of means (282 effect sizes, *es*), or converted to Hedges' *d* from contingency tables (41 *es*), correlations (15 *es*), log odds ratios (19 *es*), from different test metrics reported in the text (46 *es*, calculated from χ², *F* or *t* values, non-parametric test values, or partial *eta*² from ANOVA-like tables), see references [34–40] and Table S1 for a list of equations used. Depending on the study, the sign of the effect sizes had to be reversed to be comparable among studies (e.g., effect sizes of remaining rate instead of departure rate, time to dispersal where increased time suggests decreased dispersal propensity). When studies presented several effect sizes for the same dispersal metric, e.g., when the impact of the interacting species was crossed with the effect of another biotic or abiotic factor, we combined groups following [35] when there was no significant interaction between the effect of species interaction and the second factor. Several effect sizes were missing variance information (n = 16), thus we used an imputation procedure to fill-in back-

transformed variance from the predicted values from a model of log(variance) by log(study sample size). Further, several effect sizes had an extremely large variance (n = 6 *es* with variance > 2) which led to instability in meta-analysis results, and we decided to drop these effect sizes, leading to a final sample size of 397 effect sizes from 118 studies (exclusion of one study).

Aside from the effect sizes, we extracted several aspects of the experimental design (Table S2). First, we included the taxonomic identity for the focal taxon and interacting taxon, i.e., either the species or the taxonomic level described (e.g., genus, family, order; for simplicity we will refer to the focal species and interacting species throughout the text, but higher taxonomic levels may be used depending on the study, Table S2). Second, we added the type of interactor, i.e., whether the interacting species is a detrimental interactor such as a predator, resource, competitor, or a beneficial interactor such as a host, parasite, mutualist (see the details of each category in Table S2). Third, we detailed the dispersal phase studied (emigration, transience, or full dispersal process from emigration to settlement). Fourth, we included the type of manipulation (or variation in the case of observational studies) of the interactor: manipulation of presence vs. abundance, manipulation of actual physical presence vs. cues of presence, level of community complexity (pairs of species, simple community, complex community), possibility for the interactor to disperse during the experiment, similarity of generation for interaction and dispersal (same generation, or different generation, e.g., next generation for the effect of predation risk experienced by parents on offspring dispersal, or previous generation for the effect of nest predation risk on parent dispersal). Fifth, we added the type and characteristics of study: experimental vs. observational study, laboratory, semi-natural or field study, as well as the duration of the study in days.

In addition to the information extracted from the studies, we further searched within the literature for information on the focal species' generation time, the home range of the two interacting species [41–45] (unfortunately only found for 17 *es*), the level of generalism of the two interacting species (rated from 1 to 4 on whether they interact with only one species, with species from the same genus or family, with species from the same order, or with species from different orders), and interaction strengths between pairs of species (Table S2). These interaction strengths between pairs of species found in the database are given as standardized effects of the presence or
abstracts between examiners so that each examiner read and rated ~1100 abstracts. During this second phase, inter-examiner accuracy was examined again on 20 articles per reviewer, yielding a K of 0.73, considered a substantial agreement [33]. The filtering process led to a selection of 1,539 studies from the original search that fitted the scope of this overview.

Effect size extraction

Selected articles were reviewed in full to determine
whether they fit our inclusion criteria, contained
relevant data, and whether the results were presented
with sufficient clarity to extract effect sizes (Fig S1).
During this step, we excluded (a) studies in which the
focal species dispersed passively and in which there
was no active choice to emigrate, (b) studies focused
on the colonisation process only, keeping studies on
emigration, transience, or the ones monitoring the full
dispersal process, (c) studies in which the second
species did not interact with the focal species, (d)
studies in which the effect of an interacting species on
the dispersal of the focal species was compared to a
control with a second interacting species instead of no
interacting species or different abundances of
interacting species. This step led to the further
exclusion of 1,420 articles from the first search. In the
final set of 119 studies, we extracted 403 effect sizes
and collected information on several moderators to
investigate our questions of interest (See
supplementary reference list). Data to calculate effect
sizes were extracted preferentially from raw data when
available, from figures using the *juicr v 0.1* R package,
or directly from the paper (tables or main text). Effect
sizes were calculated with *esc v0.5.1* and *effectsize*
v0.8.5 R packages, and computed as Hedges' *d* from
pairs of means (282 effect sizes, es), or converted to
Hedges' *d* from contingency tables (41 es), correlations
(15 es), log odds ratios (19 es), from different test
metrics reported in the text (46 es, calculated from χ²,
F or t values, non-parametric test values, or partial *eta*²
from ANOVA-like tables), see references [34–40] and
Table S1 for a list of equations used. Depending on the
study, the sign of the effect sizes had to be reversed to
be comparable among studies (e.g., effect sizes of
remaining rate instead of departure rate, time to
dispersal where increased time suggests decreased
dispersal propensity). When studies presented several
effect sizes for the same dispersal metric, e.g., when
the impact of the interacting species was crossed with
the effect of another biotic or abiotic factor, we
combined groups following [35] when there was no
significant interaction between the effect of species
interaction and the second factor. Several effect sizes
were missing variance information (n = 16), thus we
used an imputation procedure to fill-in back-

transformed variance from the predicted values from a
model of log(variance) by log(study sample size).
Further, several effect sizes had an extremely large
variance (n = 6 es with variance > 2) which led to
instability in meta-analysis results, and we decided to
drop these effect sizes, leading to a final sample size of
397 effect sizes from 118 studies (exclusion of one
study).

Aside from the effect sizes, we extracted
several aspects of the experimental design (Table S2).
First, we included the taxonomic identity for the focal
taxon and interacting taxon, i.e., either the species or
the taxonomic level described (e.g., genus, family,
order; for simplicity we will refer to the focal species
and interacting species throughout the text, but higher
taxonomic levels may be used depending on the study,
Table S2). Second, we added the type of interactor, i.e.,
whether the interacting species is a detrimental
interactor such as a predator, resource, competitor, or
a beneficial interactor such as a host, parasite,
mutualist (see the details of each category in Table S2).
Third, we detailed the dispersal phase studied
(emigration, transience, or full dispersal process from
emigration to settlement). Fourth, we included the
type of manipulation (or variation in the case of
observational studies) of the interactor: manipulation
of presence vs. abundance, manipulation of actual
physical presence vs. cues of presence, level of
community complexity (pairs of species, simple
community, complex community), possibility for the
interactor to disperse during the experiment, similarity
of generation for interaction and dispersal (same
generation, or different generation, e.g., next
generation for the effect of predation risk experienced
by parents on offspring dispersal, or previous
generation for the effect of nest predation risk on
parent dispersal). Fifth, we added the type and
characteristics of study: experimental vs. observational
study, laboratory, semi-natural or field study, as well as
the duration of the study in days.

In addition to the information extracted from
the studies, we further searched within the literature
for information on the focal species' generation time,
the home range of the two interacting species [41-45]
(unfortunately only found for 17 es), the level of
generalism of the two interacting species (rated from 1
to 4 on whether they interact with only one species,
with species from the same genus or family, with
species from the same order, or with species from
different orders), and interaction strengths between
pairs of species (Table S2). These interaction strengths
between pairs of species found in the database are
given as standardized effects of the presence or
abundance of the interacting species on a fitness-
related trait in the focal species (survival, fecundity,
body condition, body growth, or abundance), found
either directly in the screened articles (166 es) or in a
larger literature search (191 es, interaction strength
was missing for 40 es). A single author (JC) searched for
the strength of interaction and was different from the
author calculating effect sizes for interaction effects on
dispersal (EB) to reduce, without fully preventing,
biases in the estimation of strength and its covariation
with dispersal response. There was also a large
variation in the experimental conditions of dispersal
and fitness-related traits studies, which should result in
a more conservative approach. As we were unable to
extract generation time for two taxa, we imputed the
median generation time of their closest taxa in the
database.

We used taxonomy as a proxy for phylogenetic
relatedness, using the *taxize* package v0.9.100 to
gather taxonomic information from the NCBI database.
When the taxon considered was absent from the NCBI
database, we used the GBIF or ITIS databases. We then
calculated a taxonomic phylogenetic tree from the
taxonomic data (kingdom-phylum-class-order-family-
genus-species), and used Grafen's method of
computing branch length [46] with the *ape* package v
5.1-1 (Fig S2).

Statistical analyses

We studied the impact of interacting species on
dispersal through multilevel mixed effects meta-
analytic models via the rma.mv function of the *metafor*
v 4.2-0 package [47] in R v4.3.1. We used a model
selection approach to investigate our questions, thus
the global model contains moderators for all questions
of interest.

In a first step, we investigated whether the type of
interactor [i.e., beneficial interactor (resource, host
and mutualist) vs. detrimental interactor (predator,
competitor and parasite)] interacted with our
moderators of interest to drive dispersal response to
interacting species presence or abundance (Fig. 1). To
do so, we ran a full model with:

- all pairwise interaction between the type of interactor (beneficial/detrimental) and the dispersal phase, the level of generalism of the focal species, the level of generalism of the interactor, the type of manipulation of the interacting species, the level of community complexity, the possibility for the interacting species to disperse, the similarity in generation between the interaction and dispersal phase, the type of study (observational vs. experimental), the centered log

duration of the experiment, and the centered log
generation time of the focal species as moderators of
interest, and

- the study ID, the focal species ID, the focal species shared phylogenetic relatedness (branch length from the taxonomic phylogenetic tree), and the interacting species ID as random effects (see Box S2 for the full model equation).

We then used the dredge function from the *MuMln* v
1.47.5 package to select models with the best fixed
structure, and averaged models within ΔAICc<2. We
calculated the averaged conditional estimates from
this model with the sum of weights of the metric across
the models (SW, aka relative importance). We further
refitted a model using all of the moderators present in
the averaged best model to assess heterogeneity to
confirm sources of variance across the dataset and
computed the heterogeneity statistics I² at each level,
corresponding to the ratio of true heterogeneity to
total observed variation. To understand whether there
was publication bias, we visualized effect sizes using
funnel plots of the residuals of this model, and
calculated Rosenberg's fail-safe number [48], which
estimates the number of missing values averaging a z-
value of zero needed to make effect sizes statistically
insignificant. Third, we ran Egger's regressions using
the meta-analytic residuals as the response variable,
and the precision (defined as the square root of the
inverse of variance) as the moderator. Intercepts of
this regression that do not overlap zero are evidence of
publication bias [49]. Finally, we analysed temporal
trends in effect sizes that could indicate time-lag bias
[49] by adding the interaction between time and the
consequence of the interaction on fitness to the model
containing all moderators present in the best model.

In a second step, we assessed the effects of
moderators for beneficial and detrimental interactions
separately because the effect sizes of their effects on
dispersal are of opposite signs (i.e., beneficial
interactions reduce dispersal propensity while
detrimental interactions increase it). We divided the
dataset into beneficial and detrimental interactors,
and for each data subset, we fitted a full model with
the same random effects as the above model and the
exact nature of the interaction (i.e., resource, host and
mutualist for the beneficial interactors subset and
predator, parasite and competitor for the detrimental
interactors subset), the dispersal phase, the level of
generalism of the focal species, the level of generalism
of the interactor, the type of manipulation of the
interacting species, the level of community complexity,
the possibility for the interacting species to disperse,
abundance of the interacting species on a fitness-
related trait in the focal species (survival, fecundity,
body condition, body growth, or abundance), found
either directly in the screened articles (166 es) or in a
larger literature search (191 es, interaction strength
was missing for 40 es). A single author (JC) searched for
the strength of interaction and was different from the
author calculating effect sizes for interaction effects on
dispersal (EB) to reduce, without fully preventing,
biases in the estimation of strength and its covariation
with dispersal response. There was also a large
variation in the experimental conditions of dispersal
and fitness-related traits studies, which should result in
a more conservative approach. As we were unable to
extract generation time for two taxa, we imputed the
median generation time of their closest taxa in the
database.

We used taxonomy as a proxy for phylogenetic
relatedness, using the *taxize* package v0.9.100 to
gather taxonomic information from the NCBI database.
When the taxon considered was absent from the NCBI
database, we used the GBIF or ITIS databases. We then
calculated a taxonomic phylogenetic tree from the
taxonomic data (kingdom-phylum-class-order-family-
genus-species), and used Grafen's method of
computing branch length [46] with the *ape* package v
5.1-1 (Fig S2).

Statistical analyses

We studied the impact of interacting species on
dispersal through multilevel mixed effects meta-
analytic models via the rma.mv function of the *metafor*
v 4.2-0 package [47] in R v4.3.1. We used a model
selection approach to investigate our questions, thus
the global model contains moderators for all questions
of interest.

In a first step, we investigated whether the type of
interactor [i.e., beneficial interactor (resource, host
and mutualist) vs. detrimental interactor (predator,
competitor and parasite)] interacted with our
moderators of interest to drive dispersal response to
interacting species presence or abundance (Fig. 1). To
do so, we ran a full model with:

- all pairwise interaction between the type of interactor (beneficial/detrimental) and the dispersal phase, the level of generalism of the focal species, the level of generalism of the interactor, the type of manipulation of the interacting species, the level of community complexity, the possibility for the interacting species to disperse, the similarity in generation between the interaction and dispersal phase, the type of study (observational vs. experimental), the centered log

duration of the experiment, and the centered log
generation time of the focal species as moderators of
interest, and

- the study ID, the focal species ID, the focal species shared phylogenetic relatedness (branch length from the taxonomic phylogenetic tree), and the interacting species ID as random effects (see Box S2 for the full model equation).

We then used the dredge function from the *MuMln* v
1.47.5 package to select models with the best fixed
structure, and averaged models within ΔAICc<2. We
calculated the averaged conditional estimates from
this model with the sum of weights of the metric across
the models (SW, aka relative importance). We further
refitted a model using all of the moderators present in
the averaged best model to assess heterogeneity to
confirm sources of variance across the dataset and
computed the heterogeneity statistics I² at each level,
corresponding to the ratio of true heterogeneity to
total observed variation. To understand whether there
was publication bias, we visualized effect sizes using
funnel plots of the residuals of this model, and
calculated Rosenberg's fail-safe number [48], which
estimates the number of missing values averaging a z-
value of zero needed to make effect sizes statistically
insignificant. Third, we ran Egger's regressions using
the meta-analytic residuals as the response variable,
and the precision (defined as the square root of the
inverse of variance) as the moderator. Intercepts of
this regression that do not overlap zero are evidence of
publication bias [49]. Finally, we analysed temporal
trends in effect sizes that could indicate time-lag bias
[49] by adding the interaction between time and the
consequence of the interaction on fitness to the model
containing all moderators present in the best model.

In a second step, we assessed the effects of
moderators for beneficial and detrimental interactions
separately because the effect sizes of their effects on
dispersal are of opposite signs (i.e., beneficial
interactions reduce dispersal propensity while
detrimental interactions increase it). We divided the
dataset into beneficial and detrimental interactors,
and for each data subset, we fitted a full model with
the same random effects as the above model and the
exact nature of the interaction (i.e., resource, host and
mutualist for the beneficial interactors subset and
predator, parasite and competitor for the detrimental
interactors subset), the dispersal phase, the level of
generalism of the focal species, the level of generalism
of the interactor, the type of manipulation of the
interacting species, the level of community complexity,
the possibility for the interacting species to disperse,
the similarity in generation between the interaction
and dispersal phase, the type of study (observational
vs experimental), the centered log duration of the
experiment, and the centered log generation time of
the focal species as moderators of interest (Box S2).
We used the same model selection and model
averaging procedure as above to select the best
averaged model. We further refitted models using all
of the moderators present in the averaged best models
to assess heterogeneity, study publication bias, and
finally plot results by moderator. We used the
emmeans package v1.8.8 to calculate the overall
adjusted effects, as well as the estimated marginal
means for different levels of each moderator.

Lastly, because we were unable to gather home range
and interaction strength information for most pairs of
species, we ran two separate analyses for these two
questions on the subset of studies for which we had
information (7 studies with 17 effect sizes for home
range, 108 studies with 357 effect sizes for interaction
strength). Models used the study ID, focal species ID,
and interactor ID as random effects and either the
centered log ratio of home ranges between the focal
species and the interactor or the interaction strength
as a fixed moderator.

RESULTS

Summary of the dataset

We identified 118 studies that met our inclusion
criteria with 397 effect sizes for 144 focal species and
165 interacting species. Focal species spanned a wide
range of taxa, with Arthropoda being the most
represented (236 effect sizes), followed by Chordata
(70) and Ciliophora (47), and then Nematoda,
Mollusca, Annelida, and Cryptophora (Fig. S2-S3). As
we restricted our search to actively dispersing species,
plants were excluded. Our database further reflected a
taxonomic bias in dispersal publications, with the
number of effect sizes per taxonomic group
significantly deviating from the frequencies expected
from the diversity of species in the Catalogue of Life
[50,51] through the recorded number of species in
each group (χ² = 354, df = 4, p < 2e16, Table S3).
Specifically, Chromista were overrepresented, and
among Animalia, Arthropoda represented 68 % of our
effect sizes but were still underrepresented (83 %
under the null expectation) and Chordata were
overrepresented (20 % of observed effect sizes
compared to 5 % expected, Table S3). Such
overrepresentation of Chordata is common in
ecological studies, and the magnitude of
overrepresentation is much less in our database than
in behavioural studies for instance, where Chordata

represent up to 71% of publications [50]. The database
included 152 effect sizes for interactions with
beneficial interactors including resources (125),
mutualists (21), and hosts (6), and 245 effect sizes for
interactions with detrimental interactors including
predators (116), competitors (73), and parasites (56).
Studies also varied for the dispersal phase considered,
with 275 considering emigration, 48 transience, and 74
monitoring the full dispersal process, as well as for the
method to manipulate the interacting species (actual
presence or absence of interacting species, varying
abundance, or cues of presence; see Fig. S3-S4 and
Table S2 for a further exploration of the moderators).

Description of the models and publication
bias

We first studied how the type of interactor (beneficial
vs. detrimental interactor) and moderators affected
the context-dependency of dispersal on species
interactions using the full dataset. Our averaged best
model included several pairwise interactions between
the type of interactor (beneficial or detrimental) and
the dispersal phase (emigration, transience, or full
dispersal), the type of study, the duration of the study,
and with other moderators having weaker effects
(Table 1). This model found important heterogeneity
among studies (I² study = 63.2 %), as well as some
heterogeneity related to the focal species’ identity
(I² focal = 8.3 %), phylogeny (I² focal = 8.3 %) and
interactor’s identity (I² interactor = 16.8 %), for a total
heterogeneity of I² = 96.7 %. Such high heterogeneity
is expected in ecology and evolution meta-analyses,
with the mean heterogeneity being 91.7 % [52]. Funnel
plots, Egger’s test and the Rosenberg fail-safe number
showed no significant publication bias; funnel plots did
not reveal significant asymmetry (Fig. S5), Egger’s test
showed an intercept that crossed zero for the residuals
intercept (estimate [95% CI] = 0.13 [-0.03, 0.28], t = 1.6,
df = 395, p = 0.119), and the Rosenberg fail-safe
number was very high (N = 47,929). Additionally, we
found that the effect size changed over time,
suggesting a time-lag effect that was only present for
beneficial interactors (Table S4).

In a second step, we split the dataset according to the
previously defined beneficial and detrimental
interactor types, replacing these broad types with the
exact nature of the interaction (predators, parasites,
competitors, resources, hosts, or mutualist) as a
moderator on the two data subsets. The two analyses
did not retain the same moderators, suggesting that
the importance of a moderator depended on the broad
type of interaction (Table 2, see below for description).
Both models showed important heterogeneity (I² =
93.9 vs. 95.5 % respectively for beneficial vs.
the similarity in generation between the interaction
and dispersal phase, the type of study (observational
vs experimental), the centered log duration of the
experiment, and the centered log generation time of
the focal species as moderators of interest (Box S2).
We used the same model selection and model
averaging procedure as above to select the best
averaged model. We further refitted models using all
of the moderators present in the averaged best models
to assess heterogeneity, study publication bias, and
finally plot results by moderator. We used the
emmeans package v1.8.8 to calculate the overall
adjusted effects, as well as the estimated marginal
means for different levels of each moderator.

Lastly, because we were unable to gather home range
and interaction strength information for most pairs of
species, we ran two separate analyses for these two
questions on the subset of studies for which we had
information (7 studies with 17 effect sizes for home
range, 108 studies with 357 effect sizes for interaction
strength). Models used the study ID, focal species ID,
and interactor ID as random effects and either the
centered log ratio of home ranges between the focal
species and the interactor or the interaction strength
as a fixed moderator.

RESULTS

Summary of the dataset

We identified 118 studies that met our inclusion
criteria with 397 effect sizes for 144 focal species and
165 interacting species. Focal species spanned a wide
range of taxa, with Arthropoda being the most
represented (236 effect sizes), followed by Chordata
(70) and Ciliophora (47), and then Nematoda,
Mollusca, Annelida, and Cryptophora (Fig. S2-S3). As
we restricted our search to actively dispersing species,
plants were excluded. Our database further reflected a
taxonomic bias in dispersal publications, with the
number of effect sizes per taxonomic group
significantly deviating from the frequencies expected
from the diversity of species in the Catalogue of Life
[50,51] through the recorded number of species in
each group (χ² = 354, df = 4, p < 2e16, Table S3).
Specifically, Chromista were overrepresented, and
among Animalia, Arthropoda represented 68 % of our
effect sizes but were still underrepresented (83 %
under the null expectation) and Chordata were
overrepresented (20 % of observed effect sizes
compared to 5 % expected, Table S3). Such
overrepresentation of Chordata is common in
ecological studies, and the magnitude of
overrepresentation is much less in our database than
in behavioural studies for instance, where Chordata

represent up to 71% of publications [50]. The database
included 152 effect sizes for interactions with
beneficial interactors including resources (125),
mutualists (21), and hosts (6), and 245 effect sizes for
interactions with detrimental interactors including
predators (116), competitors (73), and parasites (56).
Studies also varied for the dispersal phase considered,
with 275 considering emigration, 48 transience, and 74
monitoring the full dispersal process, as well as for the
method to manipulate the interacting species (actual
presence or absence of interacting species, varying
abundance, or cues of presence; see Fig. S3-S4 and
Table S2 for a further exploration of the moderators).

Description of the models and publication
bias

We first studied how the type of interactor (beneficial
vs. detrimental interactor) and moderators affected
the context-dependency of dispersal on species
interactions using the full dataset. Our averaged best
model included several pairwise interactions between
the type of interactor (beneficial or detrimental) and
the dispersal phase (emigration, transience, or full
dispersal), the type of study, the duration of the study,
and with other moderators having weaker effects
(Table 1). This model found important heterogeneity
among studies (I² study = 63.2 %), as well as some
heterogeneity related to the focal species’ identity
(I² focal = 8.3 %), phylogeny (I² focal = 8.3 %) and
interactor’s identity (I² interactor = 16.8 %), for a total
heterogeneity of I² = 96.7 %. Such high heterogeneity
is expected in ecology and evolution meta-analyses,
with the mean heterogeneity being 91.7 % [52]. Funnel
plots, Egger’s test and the Rosenberg fail-safe number
showed no significant publication bias; funnel plots did
not reveal significant asymmetry (Fig. S5), Egger’s test
showed an intercept that crossed zero for the residuals
intercept (estimate [95% CI] = 0.13 [-0.03, 0.28], t = 1.6,
df = 395, p = 0.119), and the Rosenberg fail-safe
number was very high (N = 47,929). Additionally, we
found that the effect size changed over time,
suggesting a time-lag effect that was only present for
beneficial interactors (Table S4).

In a second step, we split the dataset according to the
previously defined beneficial and detrimental
interactor types, replacing these broad types with the
exact nature of the interaction (predators, parasites,
competitors, resources, hosts, or mutualist) as a
moderator on the two data subsets. The two analyses
did not retain the same moderators, suggesting that
the importance of a moderator depended on the broad
type of interaction (Table 2, see below for description).
Both models showed important heterogeneity (I² =
93.9 vs. 95.5 % respectively for beneficial vs.
detrimental interactions), but the distribution of the heterogeneity varied to a large extend among datasets, with a more important among-study effect for beneficial interactions ($I^2_{study}$ = 88.0 vs 45.8 %), and a larger importance of species identity and species phylogeny for detrimental interactions ($I^2_{focal}$ = 2.6 vs 13.8 %, $I^2_{phylo}$ = 2.6 vs 13.8 % and $I^2_{interactor}$ = 0.7 vs 22.1%). When looking at the publication bias of those models with funnel plots and Egger's test, we found some asymmetry for the detrimental interaction dataset (Fig. S6, Egger's test intercept [95% CI] = 0.30 [0.11, 0.48], t = 3.2, df = 243, p = 0.002, Rosenberg fail-safe N = 30,695), suggesting some publication bias, but not for the beneficial interaction dataset (Fig S6, Egger's test intercept [95% CI] = -0.21 [-0.64, 0.22], t = -0.98, df = 150, p = 0.33, Rosenberg fail-safe N = 17,746).

Does the impact of interacting species on dispersal depend on the nature of species interactions?

Our global model found that the type of interactor and its interaction with various moderators affected the dispersal response to interacting species (Table 1). Overall, the marginal effect size of this global model crossed zero (-0.029 [-0.267, 0.209] adjusted effect [95%CI]), which was explained by the mix of decreasing and increasing effects on the dispersal of beneficial and detrimental interactions (Table 1). When we divided the dataset into beneficial and detrimental interactors, dispersal propensity indeed decreased (adjusted effect: -0.55 [-0.92, -0.17]) and increased (adjusted effect: 0.33 [0.06, 0.59]) respectively when beneficial interactors and detrimental interactors were present or more abundant (Fig. 2a).

<caption>Table 1: Summary from the averaged best model investigating the effect of moderators on impacts of interacting species on focal species dispersal</caption>

The results are issued from a model selection from a full model described in Box S2, averaging estimates from models where $\Delta AICc<2$. The results show the conditional averaged estimates and SE, z and p-values, as well as the sum of weights of the moderator (SW).

<table><thead><tr><th>Moderator</th><th>Level</th><th>Estimate</th><th>SE</th><th>z-value</th><th>p-value</th><th>SW</th></tr></thead><tbody><tr><td>Intercept</td><td></td><td>-0.36</td><td>0.19</td><td>1.94</td><td>0.053.</td><td></td></tr><tr><td>Dispersal phase</td><td>full dispersal</td><td>0.03</td><td>0.23</td><td>0.12</td><td>0.902</td><td>1.00</td></tr><tr><td></td><td><strong>transience</strong></td><td><strong>-1.30</strong></td><td><strong>0.36</strong></td><td><strong>3.64</strong></td><td><strong>&lt;0.001***</strong></td><td><strong>1.00</strong></td></tr><tr><td>Centered log duration</td><td></td><td>-0.13</td><td>0.04</td><td>3.28</td><td>0.001**</td><td>1.00</td></tr><tr><td>Centered level of generalism focal species</td><td></td><td>0.23</td><td>0.11</td><td>2.09</td><td>0.037*</td><td>1.00</td></tr><tr><td>Type of community</td><td><strong>simple community</strong></td><td>0.89</td><td>0.24</td><td>3.69</td><td>&lt;0.001***</td><td>1.00</td></tr><tr><td></td><td><strong>complex community</strong></td><td>0.73</td><td>0.29</td><td>2.51</td><td>0.012*</td><td>1.00</td></tr><tr><td>Type of interactor</td><td>beneficial</td><td>-0.29</td><td>0.20</td><td>1.46</td><td>0.145</td><td>1.00</td></tr><tr><td>Type of study</td><td>observation</td><td>0.16</td><td>0.31</td><td>0.53</td><td>0.597</td><td>1.00</td></tr><tr><td>Type of interactor:Dispersal phase</td><td>beneficial:full dispersal</td><td>0.58</td><td>0.36</td><td>1.61</td><td>0.107</td><td>1.00</td></tr><tr><td></td><td><strong>beneficial :transience</strong></td><td><strong>1.39</strong></td><td><strong>0.42</strong></td><td><strong>3.27</strong></td><td><strong>0.001**</strong></td><td><strong>1.00</strong></td></tr><tr><td>Type of interactor:Centered log duration</td><td><strong>beneficial</strong></td><td>0.23</td><td>0.06</td><td>3.83</td><td>&lt;0.001***</td><td>1.00</td></tr><tr><td>Type of interactor:Type of study</td><td><strong>beneficial:observation</strong></td><td>-1.44</td><td>0.52</td><td>2.75</td><td>0.006**</td><td>1.00</td></tr><tr><td>Centered level of generalism interactor</td><td></td><td>-0.21</td><td>0.11</td><td>2.02</td><td>0.044*</td><td>0.92</td></tr><tr><td>Type of manipulation of interactor</td><td>abundance</td><td>-0.14</td><td>0.11</td><td>1.33</td><td>0.185</td><td>0.69</td></tr><tr><td></td><td>cues of presence</td><td>0.28</td><td>0.18</td><td>1.50</td><td>0.134</td><td>0.69</td></tr><tr><td>Centered log generation time</td><td></td><td>0.06</td><td>0.04</td><td>1.28</td><td>0.200</td><td>0.31</td></tr><tr><td>Type of interactor:Centered log generation time</td><td>beneficial</td><td>-0.09</td><td>0.06</td><td>1.60</td><td>0.109</td><td>0.20</td></tr><tr><td>Similarity generation dispersal-interaction</td><td>different generation</td><td>0.26</td><td>0.29</td><td>0.90</td><td>0.369</td><td>0.17</td></tr><tr><td>Type of interactor:Centered level of generalism focal species</td><td>beneficial</td><td>0.12</td><td>0.17</td><td>0.67</td><td>0.505</td><td>0.08</td></tr></tbody></table>
detrimental interactions), but the distribution of the heterogeneity varied to a large extend among datasets, with a more important among-study effect for beneficial interactions ($I^2_{study}$ = 88.0 vs 45.8 %), and a larger importance of species identity and species phylogeny for detrimental interactions ($I^2_{focal}$ = 2.6 vs 13.8 %, $I^2_{phylo}$ = 2.6 vs 13.8 % and $I^2_{interactor}$ = 0.7 vs 22.1%). When looking at the publication bias of those models with funnel plots and Egger's test, we found some asymmetry for the detrimental interaction dataset (Fig. S6, Egger's test intercept [95% CI] = 0.30 [0.11, 0.48], t = 3.2, df = 243, p = 0.002, Rosenberg fail-safe N = 30,695), suggesting some publication bias, but not for the beneficial interaction dataset (Fig S6, Egger's test intercept [95% CI] = -0.21 [-0.64, 0.22], t = -0.98, df = 150, p = 0.33, Rosenberg fail-safe N = 17,746).

Does the impact of interacting species on dispersal depend on the nature of species interactions?

Our global model found that the type of interactor and its interaction with various moderators affected the dispersal response to interacting species (Table 1). Overall, the marginal effect size of this global model crossed zero (-0.029 [-0.267, 0.209] adjusted effect [95%CI]), which was explained by the mix of decreasing and increasing effects on the dispersal of beneficial and detrimental interactions (Table 1). When we divided the dataset into beneficial and detrimental interactors, dispersal propensity indeed decreased (adjusted effect: -0.55 [-0.92, -0.17]) and increased (adjusted effect: 0.33 [0.06, 0.59]) respectively when beneficial interactors and detrimental interactors were present or more abundant (Fig. 2a).

<caption>Table 1: Summary from the averaged best model investigating the effect of moderators on impacts of interacting species on focal species dispersal</caption>

The results are issued from a model selection from a full model described in Box S2, averaging estimates from models where $\Delta AICc<2$. The results show the conditional averaged estimates and SE, z and p-values, as well as the sum of weights of the moderator (SW).

<table><thead><tr><th>Moderator</th><th>Level</th><th>Estimate</th><th>SE</th><th>z-value</th><th>p-value</th><th>SW</th></tr></thead><tbody><tr><td>Intercept</td><td></td><td>-0.36</td><td>0.19</td><td>1.94</td><td>0.053.</td><td></td></tr><tr><td>Dispersal phase</td><td>full dispersal</td><td>0.03</td><td>0.23</td><td>0.12</td><td>0.902</td><td>1.00</td></tr><tr><td></td><td><strong>transience</strong></td><td><strong>-1.30</strong></td><td><strong>0.36</strong></td><td><strong>3.64</strong></td><td><strong>&lt;0.001***</strong></td><td><strong>1.00</strong></td></tr><tr><td>Centered log duration</td><td></td><td>-0.13</td><td>0.04</td><td>3.28</td><td>0.001**</td><td>1.00</td></tr><tr><td>Centered level of generalism focal species</td><td></td><td>0.23</td><td>0.11</td><td>2.09</td><td>0.037*</td><td>1.00</td></tr><tr><td>Type of community</td><td><strong>simple community</strong></td><td>0.89</td><td>0.24</td><td>3.69</td><td>&lt;0.001***</td><td>1.00</td></tr><tr><td></td><td><strong>complex community</strong></td><td>0.73</td><td>0.29</td><td>2.51</td><td>0.012*</td><td>1.00</td></tr><tr><td>Type of interactor</td><td>beneficial</td><td>-0.29</td><td>0.20</td><td>1.46</td><td>0.145</td><td>1.00</td></tr><tr><td>Type of study</td><td>observation</td><td>0.16</td><td>0.31</td><td>0.53</td><td>0.597</td><td>1.00</td></tr><tr><td>Type of interactor:Dispersal phase</td><td>beneficial:full dispersal</td><td>0.58</td><td>0.36</td><td>1.61</td><td>0.107</td><td>1.00</td></tr><tr><td></td><td><strong>beneficial:transience</strong></td><td><strong>1.39</strong></td><td><strong>0.42</strong></td><td><strong>3.27</strong></td><td><strong>0.001**</strong></td><td><strong>1.00</strong></td></tr><tr><td>Type of interactor:Centered log duration</td><td><strong>beneficial</strong></td><td>0.23</td><td>0.06</td><td>3.83</td><td>&lt;0.001***</td><td>1.00</td></tr><tr><td>Type of interactor:Type of study</td><td><strong>beneficial:observation</strong></td><td>-1.44</td><td>0.52</td><td>2.75</td><td>0.006**</td><td>1.00</td></tr><tr><td>Centered level of generalism interactor</td><td></td><td>-0.21</td><td>0.11</td><td>2.02</td><td>0.044*</td><td>0.92</td></tr><tr><td>Type of manipulation of interactor</td><td>abundance</td><td>-0.14</td><td>0.11</td><td>1.33</td><td>0.185</td><td>0.69</td></tr><tr><td></td><td>cues of presence</td><td>0.28</td><td>0.18</td><td>1.50</td><td>0.134</td><td>0.69</td></tr><tr><td>Centered log generation time</td><td></td><td>0.06</td><td>0.04</td><td>1.28</td><td>0.200</td><td>0.31</td></tr><tr><td>Type of interactor:Centered log generation time</td><td>beneficial</td><td>-0.09</td><td>0.06</td><td>1.60</td><td>0.109</td><td>0.20</td></tr><tr><td>Similarity generation dispersal-interaction</td><td>different generation</td><td>0.26</td><td>0.29</td><td>0.90</td><td>0.369</td><td>0.17</td></tr><tr><td>Type of interactor:Centered level of generalism focal species</td><td>beneficial</td><td>0.12</td><td>0.17</td><td>0.67</td><td>0.505</td><td>0.08</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>Figure 2: The effect of interacting species on dispersal of the focal species depends on the type of interactor</figcaption></figure>

Effect of the (a) type of interactor (red: detrimental interactors, blue: beneficial interactors) and (b) the nature of
interactor (predators, competitors, parasites, resources, hosts, mutualists) on the effect of interacting species on
dispersal (positive Hedges' d values: increased dispersal in the presence of interactors, negative: decreased dispersal).
Violin plots of raw effect sizes, with point and error bars corresponding to the marginal effect means and 95% CI from
the two meta-analytic models for the subsets (i.e., beneficial and detrimental interactions, Table 2, Table S5), and labels
corresponding to the number of effect sizes across categorical moderators (k) and the number of studies across
moderators (n).

The exact nature of the beneficial interactor (i.e., resources, hosts, or mutualist) had an important effect on dispersal propensity (SW = 0.83), while the nature of the detrimental interactor (i.e., predators, parasites or competitors) had only weak effects (SW = 0.15, Table 2). While beneficial interactions generally reduced dispersal propensity, parasites tended to increase their dispersal propensity when their host was present or more abundant (Table 2, Table S5, Fig. 2b). On the contrary, all detrimental interactions had similar effects on dispersal, with competitors tending to have a stronger effect (Table 2, Table S5, Fig. 2b).

Does the dispersal response depend on the
spatiotemporal dynamics of interacting
species?

Moderators related to spatiotemporal dynamics influenced the strength of interaction-dependent dispersal. In the full dataset, the interactions between the type of interactors and the dispersal phase had a strong impact on dispersal (SW = 1, Table 1). This was confirmed with data subsets on beneficial and detrimental interactors (SW ≥ 0.91, Table 2). The presence or abundance of detrimental interactors increased the rates of emigration and full dispersal while it decreased dispersal distance (i.e., transience phase, Fig. 3, Table S5). Beneficial interactions had a stronger negative effect on dispersal propensity at the

emigration phase than for the transience phase and
the full dispersal process (Fig. 3, Table S5).

<figure><img src="image_5.png" /><figcaption>Figure 3: The effect of interactors on focal species dispersal varies depending on the dispersal phase considered</figcaption></figure>

Effect of the dispersal phase and the type of interactor
(red: detrimental interactors, i.e. predators,
competitors, parasites, blue: beneficial interactors, i.e.
resources, hosts, mutualists) on the effect of interacting
species on dispersal (positive Hedges' d values:
increased dispersal in the presence of interactors,
negative: decreased dispersal). Violin plots of raw
effect sizes, with point and error bars corresponding to
<figure><img src="image_3.png" /><figcaption>Figure 2: The effect of interacting species on dispersal of the focal species depends on the type of interactor</figcaption></figure>

Effect of the (a) type of interactor (red: detrimental interactors, blue: beneficial interactors) and (b) the nature of
interactor (predators, competitors, parasites, resources, hosts, mutualists) on the effect of interacting species on
dispersal (positive Hedges' d values: increased dispersal in the presence of interactors, negative: decreased dispersal).
Violin plots of raw effect sizes, with point and error bars corresponding to the marginal effect means and 95% CI from
the two meta-analytic models for the subsets (i.e., beneficial and detrimental interactions, Table 2, Table S5), and labels
corresponding to the number of effect sizes across categorical moderators (k) and the number of studies across
moderators (n).

The exact nature of the beneficial interactor (i.e., resources, hosts, or mutualist) had an important effect on dispersal propensity (SW = 0.83), while the nature of the detrimental interactor (i.e., predators, parasites or competitors) had only weak effects (SW = 0.15, Table 2). While beneficial interactions generally reduced dispersal propensity, parasites tended to increase their dispersal propensity when their host was present or more abundant (Table 2, Table S5, Fig. 2b). On the contrary, all detrimental interactions had similar effects on dispersal, with competitors tending to have a stronger effect (Table 2, Table S5, Fig. 2b).

Does the dispersal response depend on the
spatiotemporal dynamics of interacting
species?

Moderators related to spatiotemporal dynamics influenced the strength of interaction-dependent dispersal. In the full dataset, the interactions between the type of interactors and the dispersal phase had a strong impact on dispersal (SW = 1, Table 1). This was confirmed with data subsets on beneficial and detrimental interactors (SW ≥ 0.91, Table 2). The presence or abundance of detrimental interactors increased the rates of emigration and full dispersal while it decreased dispersal distance (i.e., transience phase, Fig. 3, Table S5). Beneficial interactions had a stronger negative effect on dispersal propensity at the

emigration phase than for the transience phase and
the full dispersal process (Fig. 3, Table S5).

<figure><img src="image_4.png" /><figcaption>Figure 3: The effect of interactors on focal species dispersal varies depending on the dispersal phase considered</figcaption></figure>

Effect of the dispersal phase and the type of interactor
(red: detrimental interactors, i.e. predators,
competitors, parasites, blue: beneficial interactors, i.e.
resources, hosts, mutualists) on the effect of interacting
species on dispersal (positive Hedges' d values:
increased dispersal in the presence of interactors,
negative: decreased dispersal). Violin plots of raw
effect sizes, with point and error bars corresponding to
the marginal effect means and 95% CI from the two meta-analytic models for the subsets (i.e., beneficial and detrimental interactions, Table 2, Table S5), and

labels corresponding to the number of effect sizes across categorical moderators (k) and the number of studies across moderators (n).

<caption>Table 2: Summary from the averaged best model investigating the effect of moderators on impacts of interacting species on focal species dispersal subsetting the data by type of interactor</caption>

The results are issued from a model selection from a full model, averaging estimates from models where ΔAICc<2. The results show the conditional averaged estimates and SE, z and p-values, as well as the sum of weights of the moderator (SW).

<table><thead><tr><th>Data subset</th><th>Moderator</th><th>Level</th><th>Estimate</th><th>SE</th><th>z-value</th><th>p-value</th><th>SW</th></tr></thead><tbody><tr><td rowspan="10">Detrimental</td><td>Intercept</td><td></td><td>-0.12</td><td>0.24</td><td>0.51</td><td>0.610</td><td></td></tr><tr><td>Dispersal phase</td><td>full dispersal</td><td>0.06</td><td>0.22</td><td>0.28</td><td>0.780</td><td>1.00</td></tr><tr><td></td><td><strong>transience</strong></td><td><strong>-1.18</strong></td><td><strong>0.34</strong></td><td><strong>3.49</strong></td><td><strong>&lt;0.001***</strong></td><td>1.00</td></tr><tr><td><strong>Centered log duration</strong></td><td></td><td><strong>-0.12</strong></td><td><strong>0.04</strong></td><td><strong>2.77</strong></td><td><strong>0.006**</strong></td><td>1.00</td></tr><tr><td>Type of manipulation of interactor</td><td>abundance</td><td>-0.17</td><td>0.13</td><td>1.29</td><td>0.196</td><td>1.00</td></tr><tr><td></td><td><strong>cues of presence</strong></td><td><strong>0.58</strong></td><td><strong>0.25</strong></td><td><strong>2.36</strong></td><td><strong>0.018*</strong></td><td>1.00</td></tr><tr><td><strong>Level of community complexity</strong></td><td><strong>simple community</strong></td><td><strong>0.54</strong></td><td><strong>0.26</strong></td><td><strong>2.12</strong></td><td><strong>0.034*</strong></td><td>0.78</td></tr><tr><td></td><td><strong>complex community</strong></td><td><strong>0.69</strong></td><td><strong>0.28</strong></td><td><strong>2.43</strong></td><td><strong>0.015*</strong></td><td>0.78</td></tr><tr><td></td><td>Centered level generalism focal</td><td></td><td>0.27</td><td>0.16</td><td>1.71</td><td>0.087.</td><td>0.70</td></tr><tr><td></td><td>Centered level generalism interactor</td><td></td><td>-0.17</td><td>0.11</td><td>1.47</td><td>0.143</td><td>0.39</td></tr><tr><td></td><td>Type of study</td><td>observation</td><td>0.42</td><td>0.30</td><td>1.40</td><td>0.161</td><td>0.25</td></tr><tr><td></td><td>Centered log generation time</td><td></td><td>0.06</td><td>0.04</td><td>1.30</td><td>0.194</td><td>0.24</td></tr><tr><td></td><td>Possibility for interactor to disperse</td><td>yes</td><td>0.31</td><td>0.28</td><td>1.13</td><td>0.256</td><td>0.21</td></tr><tr><td></td><td>Similarity in generations between dispersal and interaction</td><td>different generation</td><td>0.54</td><td>0.38</td><td>1.43</td><td>0.154</td><td>0.20</td></tr><tr><td></td><td><strong>Nature of interactor</strong></td><td><strong>competitor</strong></td><td><strong>0.76</strong></td><td><strong>0.37</strong></td><td><strong>2.04</strong></td><td><strong>0.041*</strong></td><td>0.15</td></tr><tr><td></td><td></td><td>parasite</td><td>0.49</td><td>0.32</td><td>1.55</td><td>0.121</td><td>0.15</td></tr><tr><td rowspan="10">Beneficial</td><td>Intercept</td><td></td><td><strong>-0.71</strong></td><td><strong>0.22</strong></td><td><strong>3.29</strong></td><td><strong>&lt;0.001***</strong></td><td></td></tr><tr><td>Dispersal phase</td><td>full dispersal</td><td><strong>0.45</strong></td><td><strong>0.18</strong></td><td><strong>2.47</strong></td><td><strong>0.013*</strong></td><td>0.91</td></tr><tr><td></td><td>transience</td><td>0.08</td><td>0.24</td><td>0.34</td><td>0.733</td><td>0.91</td></tr><tr><td><strong>Nature of interactor</strong></td><td><strong>host</strong></td><td><strong>1.73</strong></td><td><strong>0.70</strong></td><td><strong>2.48</strong></td><td><strong>0.013*</strong></td><td>0.83</td></tr><tr><td></td><td>mutualist</td><td>0.51</td><td>0.82</td><td>0.62</td><td>0.537</td><td>0.83</td></tr><tr><td>Centered level generalism interactor</td><td></td><td>-0.46</td><td>0.26</td><td>1.80</td><td>0.072.</td><td>0.79</td></tr><tr><td>Centered log duration</td><td></td><td>0.06</td><td>0.04</td><td>1.60</td><td>0.110</td><td>0.74</td></tr><tr><td>Type of study</td><td>observation</td><td>-0.60</td><td>0.42</td><td>1.40</td><td>0.161</td><td>0.21</td></tr><tr><td>Centered level generalism focal</td><td></td><td>0.13</td><td>0.14</td><td>0.94</td><td>0.345</td><td>0.10</td></tr></tbody></table>

In addition, longer experiments showed weaker effects of interactions on dispersal and this attenuation was stronger for detrimental than for beneficial interactions (Table 1 & 2, SW = 1 vs 0.74, resp., Fig. S7). The generation time of the focal species further had a very weak effect for detrimental interactions only, with a slightly stronger positive effect of detrimental interactors on dispersal for species with longer generation times (SW = 0.24, Table 2, Fig. S7).

We found that increasing ratios of home ranges between the focal species and the interactor tended to decrease the positive effect of detrimental interactors on dispersal, although the effects are to be taken with caution due to the very low sample size (only 7 studies on detrimental interactions with 17 effect sizes, see Methods and Table S6).
the marginal effect means and 95% CI from the two meta-analytic models for the subsets (i.e., beneficial and detrimental interactions, Table 2, Table S5), and

labels corresponding to the number of effect sizes across categorical moderators (k) and the number of studies across moderators (n).

<caption>Table 2: Summary from the averaged best model investigating the effect of moderators on impacts of interacting species on focal species dispersal subsetting the data by type of interactor</caption>

The results are issued from a model selection from a full model, averaging estimates from models where ΔAICc<2. The results show the conditional averaged estimates and SE, z and p-values, as well as the sum of weights of the moderator (SW).

<table><thead><tr><th>Data subset</th><th>Moderator</th><th>Level</th><th>Estimate</th><th>SE</th><th>z-value</th><th>p-value</th><th>SW</th></tr></thead><tbody><tr><td rowspan="12">Detrimental</td><td>Intercept</td><td></td><td>-0.12</td><td>0.24</td><td>0.51</td><td>0.610</td><td></td></tr><tr><td>Dispersal phase</td><td>full dispersal</td><td>0.06</td><td>0.22</td><td>0.28</td><td>0.780</td><td>1.00</td></tr><tr><td></td><td><strong>transience</strong></td><td><strong>-1.18</strong></td><td><strong>0.34</strong></td><td><strong>3.49</strong></td><td><strong>&lt;0.001***</strong></td><td>1.00</td></tr><tr><td><strong>Centered log duration</strong></td><td></td><td><strong>-0.12</strong></td><td><strong>0.04</strong></td><td><strong>2.77</strong></td><td><strong>0.006**</strong></td><td>1.00</td></tr><tr><td>Type of manipulation of interactor</td><td>abundance</td><td>-0.17</td><td>0.13</td><td>1.29</td><td>0.196</td><td>1.00</td></tr><tr><td></td><td><strong>cues of presence</strong></td><td><strong>0.58</strong></td><td><strong>0.25</strong></td><td><strong>2.36</strong></td><td><strong>0.018*</strong></td><td>1.00</td></tr><tr><td rowspan="3"><strong>Level of community complexity</strong></td><td><strong>simple community</strong></td><td><strong>0.54</strong></td><td><strong>0.26</strong></td><td><strong>2.12</strong></td><td><strong>0.034*</strong></td><td>0.78</td></tr><tr><td><strong>complex community</strong></td><td><strong>0.69</strong></td><td><strong>0.28</strong></td><td><strong>2.43</strong></td><td><strong>0.015*</strong></td><td>0.78</td></tr><tr><td></td><td></td><td>0.27</td><td>0.16</td><td>1.71</td><td>0.087.</td><td>0.70</td></tr><tr><td>Centered level generalism focal</td><td></td><td></td><td>-0.17</td><td>0.11</td><td>1.47</td><td>0.143</td><td>0.39</td></tr><tr><td>Centered level generalism interactor</td><td></td><td></td><td>0.42</td><td>0.30</td><td>1.40</td><td>0.161</td><td>0.25</td></tr><tr><td>Type of study</td><td>observation</td><td></td><td>0.06</td><td>0.04</td><td>1.30</td><td>0.194</td><td>0.24</td></tr><tr><td>Centered log generation time</td><td></td><td></td><td>0.31</td><td>0.28</td><td>1.13</td><td>0.256</td><td>0.21</td></tr><tr><td>Possibility for interactor to disperse</td><td>yes</td><td></td><td>0.54</td><td>0.38</td><td>1.43</td><td>0.154</td><td>0.20</td></tr><tr><td>Similarity in generations between dispersal and interaction</td><td>different generation</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2"><strong>Nature of interactor</strong></td><td><strong>competitor</strong></td><td><strong>0.76</strong></td><td><strong>0.37</strong></td><td><strong>2.04</strong></td><td><strong>0.041*</strong></td><td>0.15</td></tr><tr><td>parasite</td><td>0.49</td><td>0.32</td><td>1.55</td><td>0.121</td><td>0.15</td></tr><tr><td rowspan="12">Beneficial</td><td>Intercept</td><td></td><td><strong>-0.71</strong></td><td><strong>0.22</strong></td><td><strong>3.29</strong></td><td><strong>&lt;0.001***</strong></td><td></td></tr><tr><td>Dispersal phase</td><td>full dispersal</td><td><strong>0.45</strong></td><td><strong>0.18</strong></td><td><strong>2.47</strong></td><td><strong>0.013*</strong></td><td>0.91</td></tr><tr><td></td><td>transience</td><td>0.08</td><td>0.24</td><td>0.34</td><td>0.733</td><td>0.91</td></tr><tr><td rowspan="2"><strong>Nature of interactor</strong></td><td><strong>host</strong></td><td><strong>1.73</strong></td><td><strong>0.70</strong></td><td><strong>2.48</strong></td><td><strong>0.013*</strong></td><td>0.83</td></tr><tr><td>mutualist</td><td>0.51</td><td>0.82</td><td>0.62</td><td>0.537</td><td>0.83</td></tr><tr><td>Centered level generalism interactor</td><td></td><td>-0.46</td><td>0.26</td><td>1.80</td><td>0.072.</td><td>0.79</td></tr><tr><td>Centered log duration</td><td></td><td>0.06</td><td>0.04</td><td>1.60</td><td>0.110</td><td>0.74</td></tr><tr><td>Type of study</td><td>observation</td><td>-0.60</td><td>0.42</td><td>1.40</td><td>0.161</td><td>0.21</td></tr><tr><td>Centered level generalism focal</td><td></td><td>0.13</td><td>0.14</td><td>0.94</td><td>0.345</td><td>0.10</td></tr></tbody></table>

In addition, longer experiments showed weaker effects of interactions on dispersal and this attenuation was stronger for detrimental than for beneficial interactions (Table 1 & 2, SW = 1 vs 0.74, resp., Fig. S7). The generation time of the focal species further had a very weak effect for detrimental interactions only, with a slightly stronger positive effect of detrimental interactors on dispersal for species with longer generation times (SW = 0.24, Table 2, Fig. S7).

We found that increasing ratios of home ranges between the focal species and the interactor tended to decrease the positive effect of detrimental interactors on dispersal, although the effects are to be taken with caution due to the very low sample size (only 7 studies on detrimental interactions with 17 effect sizes, see Methods and Table S6).
<figure><img src="image_6.png" /><figcaption>Figure 4: For detrimental interactions, the effect of interacting species on focal species dispersal varies depending on the biotic context</figcaption></figure>

Effect of (a) the type of manipulation of the interactor (i.e. presence, cues of presence or abundance) and (b) the level of community complexity (pairs of species, simple community, complex community) on the effect of detrimental interactors on dispersal (positive Hedges' d values: increased dispersal in the presence of interactors, negative: decreased dispersal). Violin plots of raw effect sizes, with point and error bars corresponding to the marginal effect means and 95% CI from the meta-analytic model for the detrimental interactors data subset (Table 2, Table S5), and labels corresponding to the number of effect sizes across categorical moderators (k) and the number of studies across moderators (n).

<figure><img src="image_7.png" /><figcaption>Figure 5: The effect of interactors on focal species dispersal varies depending on both species' level of generalism</figcaption></figure>

Effect of the generalism of (a) the focal species or (b) the interactor and type of interactor (red: detrimental, blue: beneficial) on the effect of interacting species on dispersal (positive Hedges' d values: increased dispersal in the presence of interactors, negative: decreased dispersal). The level of generalism is rated from 1 to 4 on whether species
<figure><img src="image_5.png" /><figcaption>Figure 4: For detrimental interactions, the effect of interacting species on focal species dispersal varies depending on the biotic context</figcaption></figure>

Effect of (a) the type of manipulation of the interactor (i.e. presence, cues of presence or abundance) and (b) the level of community complexity (pairs of species, simple community, complex community) on the effect of detrimental interactors on dispersal (positive Hedges' d values: increased dispersal in the presence of interactors, negative: decreased dispersal). Violin plots of raw effect sizes, with point and error bars corresponding to the marginal effect means and 95% CI from the meta-analytic model for the detrimental interactors data subset (Table 2, Table S5), and labels corresponding to the number of effect sizes across categorical moderators (k) and the number of studies across moderators (n).

<figure><img src="image_6.png" /><figcaption>Figure 5: The effect of interactors on focal species dispersal varies depending on both species' level of generalism</figcaption></figure>

Effect of the generalism of (a) the focal species or (b) the interactor and type of interactor (red: detrimental, blue: beneficial) on the effect of interacting species on dispersal (positive Hedges' d values: increased dispersal in the presence of interactors, negative: decreased dispersal). The level of generalism is rated from 1 to 4 on whether species
interact only with one species (1), with species from the same genus or family (2), with species from the same order (3), or with species from different orders (4). Points represent the effect sizes, and lines and confidence intervals are displayed from marginal means and 95 % CI from the two meta-analytic models calculated at the four levels of generalism.

Do the interaction strength and ecological
complexity affect the dispersal response?

Moderators related to the perception of interaction strength by an individual and the ecological complexity influenced the strength of interaction-dependent dispersal. The full database showed a marked importance of the community complexity level (SW = 1) and a weaker importance of the way the interactor was manipulated (SW = 0.69, Table 1). Subsetting by interactor types showed that these effects were mainly found for detrimental interactions (SW = 1 and 0.78 resp. for community complexity level and type of manipulation, Table 2). More complex systems with three or more species or natural communities showed stronger positive effects (Fig. 4, Table S5), while manipulation of cues of presence showed stronger positive effects than manipulation of presence (Fig. 4, Table S5). Whether the study was experimental or observational was unlikely to have an effect (SW ≤ 0.25, Table 2).

The level of generalism of both species was evidenced
as important (SW ≥ 0.92, Table 1). Subsetting beneficial
and detrimental interactions showed that higher levels
of generalism of the focal species led to slightly
stronger positive effect of detrimental interactions on
dispersal (SW = 0.70, Table 2, Fig. 5, Table S5) and more
generalist beneficial interactors had slightly stronger
negative effects on dispersal (SW = 0.79, Table 2, Fig.
5, Table S5).

The possibility for the interactor to disperse and the
similarity of generation between interaction and
dispersal were unlikely to have a strong effect on
context-dependent dispersal (SW ≤ 0.21, Table 2, Table
S5).

We further gathered a continuous estimate of
interaction strengths between pairs of species present
in our database from the literature (193 pairs of species
and 357 effect sizes). We found a strong negative
relationship between measured interaction strength
and dispersal response to interaction (Table S7, Fig. 6).
While matching the effect of the binary interaction
type (see above), this continuous interaction strength
was a better predictor (Table S7, Fig. 6). When
restricting this analysis to studies measuring
concomitantly the dispersal response to interaction
and the interaction strength (166 effect sizes), the
impact of interaction strength was an even better

<figure><img src="image_8.png" /><figcaption>Figure 6: The effect of interactors on focal species dispersal varies on the strength of interaction between species</figcaption></figure>

Effect of the interaction strength between pair of species and type of interactor (red: detrimental, blue: beneficial) on the effect of interacting species on dispersal (positive Hedges' d values: increased dispersal in the presence of interactors, negative: decreased dispersal), for the 357 effect sizes for which we could retrieve interaction strength. Coloured points represent interaction strength as a function of Hedges'd, for either the subset of data for which interaction strength was measured concomitantly to dispersal (“strict database”, triangles, 166 es), for which the interaction strength was found in the secondary literature (upside-down triangles, 191 es), or for which the interaction strength was not found at all and thus is only categorized as positive or negative (crosses, 40 es). Lines and ribbons represent the effect of interaction strength on dispersal from the meta-analytic model, either on the strict database (dashed line, Table S7), or on all measures of interaction strength (full line, Table S7).

DISCUSSION

Species do not move randomly among habitats in a
landscape [53]. On the contrary, a number of external
cues about abiotic and biotic contexts, and internal
cues about physiological states influence individuals'
interact only with one species (1), with species from the same genus or family (2), with species from the same order (3), or with species from different orders (4). Points represent the effect sizes, and lines and confidence intervals are displayed from marginal means and 95 % CI from the two meta-analytic models calculated at the four levels of generalism.

Do the interaction strength and ecological
complexity affect the dispersal response?

Moderators related to the perception of interaction strength by an individual and the ecological complexity influenced the strength of interaction-dependent dispersal. The full database showed a marked importance of the community complexity level (SW = 1) and a weaker importance of the way the interactor was manipulated (SW = 0.69, Table 1). Subsetting by interactor types showed that these effects were mainly found for detrimental interactions (SW = 1 and 0.78 resp. for community complexity level and type of manipulation, Table 2). More complex systems with three or more species or natural communities showed stronger positive effects (Fig. 4, Table S5), while manipulation of cues of presence showed stronger positive effects than manipulation of presence (Fig. 4, Table S5). Whether the study was experimental or observational was unlikely to have an effect (SW ≤ 0.25, Table 2).

The level of generalism of both species was evidenced
as important (SW ≥ 0.92, Table 1). Subsetting beneficial
and detrimental interactions showed that higher levels
of generalism of the focal species led to slightly
stronger positive effect of detrimental interactions on
dispersal (SW = 0.70, Table 2, Fig. 5, Table S5) and more
generalist beneficial interactors had slightly stronger
negative effects on dispersal (SW = 0.79, Table 2, Fig.
5, Table S5).

The possibility for the interactor to disperse and the
similarity of generation between interaction and
dispersal were unlikely to have a strong effect on
context-dependent dispersal (SW ≤ 0.21, Table 2, Table
S5).

We further gathered a continuous estimate of
interaction strengths between pairs of species present
in our database from the literature (193 pairs of species
and 357 effect sizes). We found a strong negative
relationship between measured interaction strength
and dispersal response to interaction (Table S7, Fig. 6).
While matching the effect of the binary interaction
type (see above), this continuous interaction strength
was a better predictor (Table S7, Fig. 6). When
restricting this analysis to studies measuring
concomitantly the dispersal response to interaction
and the interaction strength (166 effect sizes), the
impact of interaction strength was an even better

<figure><img src="image_7.png" /><figcaption>Figure 6: The effect of interactors on focal species dispersal varies on the strength of interaction between species</figcaption></figure>

Effect of the interaction strength between pair of species and type of interactor (red: detrimental, blue: beneficial) on the effect of interacting species on dispersal (positive Hedges' d values: increased dispersal in the presence of interactors, negative: decreased dispersal), for the 357 effect sizes for which we could retrieve interaction strength. Coloured points represent interaction strength as a function of Hedges'd, for either the subset of data for which interaction strength was measured concomitantly to dispersal (“strict database”, triangles, 166 es), for which the interaction strength was found in the secondary literature (upside-down triangles, 191 es), or for which the interaction strength was not found at all and thus is only categorized as positive or negative (crosses, 40 es). Lines and ribbons represent the effect of interaction strength on dispersal from the meta-analytic model, either on the strict database (dashed line, Table S7), or on all measures of interaction strength (full line, Table S7).

DISCUSSION

Species do not move randomly among habitats in a
landscape [53]. On the contrary, a number of external
cues about abiotic and biotic contexts, and internal
cues about physiological states influence individuals'
decision to leave their habitats, to orient themselves in
a landscape, and to settle in a novel habitat [4].
Context-dependent dispersal is now well-supported by
accumulating empirical evidence and theoretically
bolstered by the adaptive dispersal theory
[5,7,13,19,54–58]. Accordingly, individuals should
leave habitats with adverse conditions and search for
better ones, as long as the energy, time, and risk
barriers to dispersal (i.e., dispersal costs) do not offset
the benefit [22]. Using a systematic review approach,
we investigated the relationship between species
interactions, a key driver of dispersal suffering from an
absence of global synthesis in the literature, and
dispersal plasticity including all types of species
interactions, across diverse taxonomic groups,
focusing on actively dispersing organisms.

Our first main result is that the interaction-dependence
of dispersal matches the expected fitness
consequences of the interaction. The presence or
abundance of detrimental interactors (consumers,
parasites or competitors) increased the dispersal
propensity of a focal species. Conversely, the presence
or abundance of beneficial interactors like resources,
hosts and mutualists, decreased the dispersal
propensity of a focal species. While clearly fitting basic
adaptive dispersal theory, there is a large variation
around this general pattern that is explained by
dependencies on the dispersal phase studied. We
found that the impacts of detrimental interactions
changed from positive to negative depending on
whether the study focused on the emigration or
transience phase, and we found that the dependencies
on the duration of the experiment, on the complexity
of the community studied, and on other moderators
related to ecological complexity, interaction strength,
and spatiotemporal dynamics.

We expected the context-dependency of dispersal to
depend on the exact nature of the interaction, with for
example responses to predators not being the same as
responses to parasites due to e.g., differences in the
efficiency of dispersal to mitigate these detrimental
interactions (see the introduction). This last result was
only partially supported, with only weak differences
between species responses to predators, parasites and
competitors for detrimental interactions and a
difference between responses to hosts and resources
for beneficial interactions. This weak variation is likely
not explained by a potential scarcity of studies
involving predators, as the sample size per interaction
is relatively high (32 studies and 117 effect sizes),
competitors (12 studies and 73 es), and parasites (25
studies and 56 es). Similarly, the difference between
responses to host and resource presence was

unexpected, although the sample size for hosts was
very low. Consumers logically decreased their dispersal
propensity away from places with available resources,
while parasites tended to disperse away from habitats
with more hosts. We posit that this surprising result
may be explained by underlying positive effects of host
occurrence on the population growth and dynamics of
parasites leading to intertwined effects of inter- and
intraspecific interactions. However, the number of
studies investigating both inter- and intraspecific
interactions and the population growth of interacting
species was too limited [59] to test this hypothesis.

Similar to the reported effects of intraspecific density
[19], we showed that the consequence of interspecific
interactors' presence and abundance led to complex
responses on dispersal. Species have been shown to
respond to intraspecific density through emigration in
diverse ways, with 30 % of the 145 studies reviewed
showing no density-dependent dispersal, 36 %
showing positive density-dependent dispersal, and 25
% negative density-dependent dispersal, with 9%
representing nonlinear responses [19]. Our meta-
analysis similarly showed important heterogeneity in
dispersal responses to interspecific interactions which
is not surprising given the dependency of interaction
strength itself on biotic and abiotic context [60]. The
strength of biotic interactions was shown to
particularly vary along abiotic gradients, with spatial
and temporal gradients showing lower variation, and
to particularly vary in laboratory studies, while
mesocosm and field studies showed lower variation
[60]. Chamberlein and colleagues [60] even reported
changes in the sign of the interaction across contexts
within a study, with an important proportion of
changes among mutualists and competitors. It is thus
possible that context-dependent variation in the
strength of biotic interactions, due to experimental
design, abiotic or biotic variation, would in return lead
to variation in the context-dependency of dispersal.
Overall, our literature search did not yield many
examples of variation in the effect of heterospecifics
on dispersal with other abiotic and biotic context (but
see some studies on the interplay between
intraspecific and interspecific conditions [61–66],
between habitat quality and predation risk [67,68],
between abiotic conditions (e.g. sunshine,
temperature) and diverse biotic conditions [69,70]).
The interplay between biotic and abiotic
environmental dimensions, phenotype, and dispersal
can further make the relationship between
interspecific interactions and dispersal complicated
[59]. Dispersal is indeed a multi-causal process [71],
responding to multiple internal and external cues that
are themselves intertwined. For example, species
decision to leave their habitats, to orient themselves in
a landscape, and to settle in a novel habitat [4].
Context-dependent dispersal is now well-supported by
accumulating empirical evidence and theoretically
bolstered by the adaptive dispersal theory
[5,7,13,19,54–58]. Accordingly, individuals should
leave habitats with adverse conditions and search for
better ones, as long as the energy, time, and risk
barriers to dispersal (i.e., dispersal costs) do not offset
the benefit [22]. Using a systematic review approach,
we investigated the relationship between species
interactions, a key driver of dispersal suffering from an
absence of global synthesis in the literature, and
dispersal plasticity including all types of species
interactions, across diverse taxonomic groups,
focusing on actively dispersing organisms.

Our first main result is that the interaction-dependence
of dispersal matches the expected fitness
consequences of the interaction. The presence or
abundance of detrimental interactors (consumers,
parasites or competitors) increased the dispersal
propensity of a focal species. Conversely, the presence
or abundance of beneficial interactors like resources,
hosts and mutualists, decreased the dispersal
propensity of a focal species. While clearly fitting basic
adaptive dispersal theory, there is a large variation
around this general pattern that is explained by
dependencies on the dispersal phase studied. We
found that the impacts of detrimental interactions
changed from positive to negative depending on
whether the study focused on the emigration or
transience phase, and we found that the dependencies
on the duration of the experiment, on the complexity
of the community studied, and on other moderators
related to ecological complexity, interaction strength,
and spatiotemporal dynamics.

We expected the context-dependency of dispersal to
depend on the exact nature of the interaction, with for
example responses to predators not being the same as
responses to parasites due to e.g., differences in the
efficiency of dispersal to mitigate these detrimental
interactions (see the introduction). This last result was
only partially supported, with only weak differences
between species responses to predators, parasites and
competitors for detrimental interactions and a
difference between responses to hosts and resources
for beneficial interactions. This weak variation is likely
not explained by a potential scarcity of studies
involving predators, as the sample size per interaction
is relatively high (32 studies and 117 effect sizes),
competitors (12 studies and 73 es), and parasites (25
studies and 56 es). Similarly, the difference between
responses to host and resource presence was

unexpected, although the sample size for hosts was
very low. Consumers logically decreased their dispersal
propensity away from places with available resources,
while parasites tended to disperse away from habitats
with more hosts. We posit that this surprising result
may be explained by underlying positive effects of host
occurrence on the population growth and dynamics of
parasites leading to intertwined effects of inter- and
intraspecific interactions. However, the number of
studies investigating both inter- and intraspecific
interactions and the population growth of interacting
species was too limited [59] to test this hypothesis.

Similar to the reported effects of intraspecific density
[19], we showed that the consequence of interspecific
interactors' presence and abundance led to complex
responses on dispersal. Species have been shown to
respond to intraspecific density through emigration in
diverse ways, with 30 % of the 145 studies reviewed
showing no density-dependent dispersal, 36 %
showing positive density-dependent dispersal, and 25
% negative density-dependent dispersal, with 9%
representing nonlinear responses [19]. Our meta-
analysis similarly showed important heterogeneity in
dispersal responses to interspecific interactions which
is not surprising given the dependency of interaction
strength itself on biotic and abiotic context [60]. The
strength of biotic interactions was shown to
particularly vary along abiotic gradients, with spatial
and temporal gradients showing lower variation, and
to particularly vary in laboratory studies, while
mesocosm and field studies showed lower variation
[60]. Chamberlein and colleagues [60] even reported
changes in the sign of the interaction across contexts
within a study, with an important proportion of
changes among mutualists and competitors. It is thus
possible that context-dependent variation in the
strength of biotic interactions, due to experimental
design, abiotic or biotic variation, would in return lead
to variation in the context-dependency of dispersal.
Overall, our literature search did not yield many
examples of variation in the effect of heterospecifics
on dispersal with other abiotic and biotic context (but
see some studies on the interplay between
intraspecific and interspecific conditions [61–66],
between habitat quality and predation risk [67,68],
between abiotic conditions (e.g. sunshine,
temperature) and diverse biotic conditions [69,70]).
The interplay between biotic and abiotic
environmental dimensions, phenotype, and dispersal
can further make the relationship between
interspecific interactions and dispersal complicated
[59]. Dispersal is indeed a multi-causal process [71],
responding to multiple internal and external cues that
are themselves intertwined. For example, species
interactions strongly influence the density of
interacting species. Predation, by decreasing prey
density, would thereby decrease intraspecific
competition [6] and potentially increase inbreeding.
On the other hand, the response to intraspecific
competition itself can vary with predation risk, as is the
case in *Notonecta undulata* where the intraspecific
density threshold for dispersal was shifted downwards
when fish predator cues were present [61]. The effect
of predation risk can further depend on the structure
of the habitat, such as in hydropsychid larvae where
the use of drift dispersal to avoid predators was
favoured in structurally simple habitats, while habitats
with a dense periphyton mat allowed sufficient
microhabitat refugia from predators to decrease
dispersal [67]. The resulting dispersal responses to this
blend of central dispersal drivers are difficult to
predict. Interestingly, studies manipulating only
detrimental interaction cues led to a greater positive
impact on the focal species' dispersal than the ones
studying the actual presence of interacting species.
Such stronger effects of cues can be related to the
notion of landscape of fear [26,72], where the mere
presence of predators can have stronger impacts than
actual prey consumption. The type of cues affecting
the dispersal response can vary depending on the
species and their specific biology, such as on *Baetis
thermicus* larvae where larvae detected the risk of
predation by diurnal foragers such as salmon mainly
through visual cues, increasing drift rate, while
predation of the nocturnal sculpin was mainly detected
through chemical cues [73].

The actual interaction strength between species,
perceived within the actual study conditions, should
therefore better predict the effect of the interactor's
presence on dispersal [14]. However, only 43% of
studies quantified the interaction strength along with
dispersal responses, and thus we used indirect
measures of interaction strength through the
generalism of interacting partners towards each other
(e.g. single or multiple predator species for a focal prey
species). We found that the level of generalism of
interacting species affected the dispersal outcome.
Counterintuitively, the level of generalism of the focal
species was mainly important for detrimental
interactions, where positive effects of interactor
presence on dispersal were strongest for more
generalist focal species, while the level of generalism
of the interacting species was more important for
beneficial interactions, with a stronger negative impact
for more generalist interactors. We were expecting the
level of interactor generalism to be of stronger
importance for detrimental interactors, as for example
prey might have a higher motivation to escape highly

specialized predators than generalist predators.
Further, we were expecting that more specialized focal
species may pose a broader burden on their resource,
increasing demographic fluctuations and thus
extinction probability of the focal species, leading to a
stronger effect on dispersal [17].

We further screened articles and the broader literature
to gather information about interaction strength
between pairs of species, resulting in variation in the
populations or study condition used (densities and
type of species manipulation) in the studies on
dispersal and interaction strength, a potential source
of discrepancies [60]. Further, the interaction strength
was measured through fitness-related traits that varied
among pairs of interacting species (i.e., survival,
fecundity, body condition and abundance). While this
result should be taken with caution, although the
approach should be conservative, the estimated
strength of interactions between species predicted the
dispersal responses to interaction well, and even
better than using the binary beneficial/detrimental
type of interaction (Table S7). When restricting our
dataset to studies jointly measuring the dispersal
response and interaction strength, we even better
estimated the effect of interaction strength on
dispersal. These results demonstrate the importance of
assessing interaction strengths and fitness
consequences concomitantly with dispersal
dependencies, in single and multiple conditions, to
better understand interaction-dependent dispersal
and why it varies among species, interaction types, and
ecosystems (Fig. 1).

Our results further showed that the effects of
interactors varied with the dispersal phase studied.
Detrimental interactions had stronger positive impacts
on emigration than on full dispersal, and even had
negative impacts on transience. We expected
predators, parasites, and competitors to increase the
dispersal of the focal species to avoid the potential
negative consequences on fitness, and this was the
case for emigration and full dispersal [15,74]. However,
transience is a particularly vulnerable stage where the
risk of predation may be heightened; thus, a decrease
in dispersal distance for instance may be expected.
Similarly, hosts may increase their dispersal behaviour
to escape an infested habitat [15], and parasites
themselves can manipulate their hosts' dispersal to
increase contact rate and the transmission of parasites
[21]. However, by diverting their hosts' resources,
parasites can constrain their ability to move [21]. Such
contrasted effects of parasitism can be illustrated by a
study on *Notonecta*, where parasite infection reduced
*Notonecta* dispersal ability, but parasitism risk
interactions strongly influence the density of
interacting species. Predation, by decreasing prey
density, would thereby decrease intraspecific
competition [6] and potentially increase inbreeding.
On the other hand, the response to intraspecific
competition itself can vary with predation risk, as is the
case in *Notonecta undulata* where the intraspecific
density threshold for dispersal was shifted downwards
when fish predator cues were present [61]. The effect
of predation risk can further depend on the structure
of the habitat, such as in hydropsychid larvae where
the use of drift dispersal to avoid predators was
favoured in structurally simple habitats, while habitats
with a dense periphyton mat allowed sufficient
microhabitat refugia from predators to decrease
dispersal [67]. The resulting dispersal responses to this
blend of central dispersal drivers are difficult to
predict. Interestingly, studies manipulating only
detrimental interaction cues led to a greater positive
impact on the focal species' dispersal than the ones
studying the actual presence of interacting species.
Such stronger effects of cues can be related to the
notion of landscape of fear [26,72], where the mere
presence of predators can have stronger impacts than
actual prey consumption. The type of cues affecting
the dispersal response can vary depending on the
species and their specific biology, such as on *Baetis
thermicus* larvae where larvae detected the risk of
predation by diurnal foragers such as salmon mainly
through visual cues, increasing drift rate, while
predation of the nocturnal sculpin was mainly detected
through chemical cues [73].

The actual interaction strength between species,
perceived within the actual study conditions, should
therefore better predict the effect of the interactor's
presence on dispersal [14]. However, only 43% of
studies quantified the interaction strength along with
dispersal responses, and thus we used indirect
measures of interaction strength through the
generalism of interacting partners towards each other
(e.g. single or multiple predator species for a focal prey
species). We found that the level of generalism of
interacting species affected the dispersal outcome.
Counterintuitively, the level of generalism of the focal
species was mainly important for detrimental
interactions, where positive effects of interactor
presence on dispersal were strongest for more
generalist focal species, while the level of generalism
of the interacting species was more important for
beneficial interactions, with a stronger negative impact
for more generalist interactors. We were expecting the
level of interactor generalism to be of stronger
importance for detrimental interactors, as for example
prey might have a higher motivation to escape highly

specialized predators than generalist predators.
Further, we were expecting that more specialized focal
species may pose a broader burden on their resource,
increasing demographic fluctuations and thus
extinction probability of the focal species, leading to a
stronger effect on dispersal [17].

We further screened articles and the broader literature
to gather information about interaction strength
between pairs of species, resulting in variation in the
populations or study condition used (densities and
type of species manipulation) in the studies on
dispersal and interaction strength, a potential source
of discrepancies [60]. Further, the interaction strength
was measured through fitness-related traits that varied
among pairs of interacting species (i.e., survival,
fecundity, body condition and abundance). While this
result should be taken with caution, although the
approach should be conservative, the estimated
strength of interactions between species predicted the
dispersal responses to interaction well, and even
better than using the binary beneficial/detrimental
type of interaction (Table S7). When restricting our
dataset to studies jointly measuring the dispersal
response and interaction strength, we even better
estimated the effect of interaction strength on
dispersal. These results demonstrate the importance of
assessing interaction strengths and fitness
consequences concomitantly with dispersal
dependencies, in single and multiple conditions, to
better understand interaction-dependent dispersal
and why it varies among species, interaction types, and
ecosystems (Fig. 1).

Our results further showed that the effects of
interactors varied with the dispersal phase studied.
Detrimental interactions had stronger positive impacts
on emigration than on full dispersal, and even had
negative impacts on transience. We expected
predators, parasites, and competitors to increase the
dispersal of the focal species to avoid the potential
negative consequences on fitness, and this was the
case for emigration and full dispersal [15,74]. However,
transience is a particularly vulnerable stage where the
risk of predation may be heightened; thus, a decrease
in dispersal distance for instance may be expected.
Similarly, hosts may increase their dispersal behaviour
to escape an infested habitat [15], and parasites
themselves can manipulate their hosts' dispersal to
increase contact rate and the transmission of parasites
[21]. However, by diverting their hosts' resources,
parasites can constrain their ability to move [21]. Such
contrasted effects of parasitism can be illustrated by a
study on *Notonecta*, where parasite infection reduced
*Notonecta* dispersal ability, but parasitism risk
increased dispersal distance [75]. We thus expected
factors influencing emigration decisions and dispersal
distance to be fundamentally different, with
emigration decisions varying solely with local
conditions and dispersal distance varying with the
ability to disperse and the spatial scale of the
interaction. The impact of beneficial interactions also
depended on the dispersal phase studied, with strong
negative impacts on emigration, slightly weaker
impacts on transience and no effect on full dispersal.
The difference between effects of beneficial and
detrimental interactors might reflect a dependency on
both the local and the broader spatial context for
detrimental interactors, while the local context was the
main driver of dispersal decisions for beneficial
interactors. Again, a more precise estimate of the scale
and decay of interaction strength through the
landscape and its effects on focal species fitness at
each dispersal phase may help resolve this question.
The information we could gather on species home
range was fairly limited, but we found that the ratio of
home ranges influenced the focal species dispersal,
with a tendency for larger home ranges for focal
species relative to the interactor decreasing the
dependency of dispersal on detrimental interactions.
This larger home range may allow focal species to
escape detrimental interactors through changes in
spatial distribution within their habitat, a likely less
costly strategy than dispersing away. The interplay
between species interaction and dispersal at different
spatial scales should play an important role for
metacommunity dynamics. Indeed, a recent
theoretical study has shown that the spatial scales of
dispersal and species interactions have opposite
effects on ecosystem functioning [76]. A modelling
effort illustrating the impacts of context-dependent
dispersal in simple three-level food chain communities
shows that simultaneous resource- and predator-
dependent emigration reduced local fluctuations of
population dynamics through time, and increased
metacommunity stability [13]. In competitive
communities of two *Callosobruchus* species,
subdivision of the habitat into patches within a
metacommunity structure allows for new niche
differentiating mechanism through a dispersal-
competition trade off allowing species coexistence
[77]. Further, the dependency of dispersal on spatial
scale should itself depend on the spatiotemporal
autocorrelation of population dynamics between
interacting species across scales [6,13,18], potentially
leading to the co-evolution of dispersal of interacting
species [17].

We were also expecting the impact of the interacting
species to depend on the larger biotic context.

Specifically, we expected that more complex
communities could lead to a “dilution effect” where
the cues and the impact of each interacting species on
the focal species’ dispersal may be weaker when
diluted in a larger number of species interactions. We
observed the opposite effect, with stronger positive
effects of detrimental interactors being observed in
more complex communities than for studies on pairs of
interacting species. This could be explained by
methodological differences between studies on pairs
of interacting species, very often laboratory studies,
and studies on more complex communities, often
corresponding to field studies or semi-natural
mesocosm studies. Oversimplified set-ups may actually
buffer the effects of interaction on dispersal through
acclimation to threats and an overall lower perception
of risk. Because of the inherent nestedness of
community complexity and experimental set-up, we
were not able to put both moderators in the models.
An alternative explanation rests on a confounded
effect of multiple species interactions. As mentioned
above, field studies often lead to difficulties in
disentangling effects on survival from effects on
dispersal and in properly isolating the effect of a single
species. For example, the abundances of a prey species
and its different predator species are often correlated,
leading to difficulties in isolating the effect of each
predator species without intensive monitoring of all
species at play [78]. The same reasoning can be made
for other interacting species like resources for
consumers, as in flying squirrels which depend on
multiple resources (spruce, birch and alder catkin), that
likely covary and can alternatively appear as the main
driver of fitness [79,80], potentially hampering the
estimate of their respective effects on dispersal.

Finally, we showed that the duration of the study
modulates the effects of interactions on the dispersal
of focal species. The longer the study lasted the closer
the effect sizes were to zero for detrimental
interactions, and to a lower extent for beneficial
interactions. This effect may reflect an acclimation to
the interacting species in experimental studies,
reducing the sensitivity of individuals to stressful
interactions and therefore their dispersal propensity to
avoid them. In addition, not all individuals are equally
likely to disperse [4] and/or to respond through
dispersal to adverse abiotic and biotic conditions
[55,81]. Shorter experiments may capture the most
responsive individuals and the longer the study lasts
the weaker the overall response should be. However,
one can alternatively argue that less responsive
individuals would also react and disperse only after
long-lasting experiments. Unfortunately, dispersal
studies are often limited in time or space and might
increased dispersal distance [75]. We thus expected factors influencing emigration decisions and dispersal distance to be fundamentally different, with emigration decisions varying solely with local conditions and dispersal distance varying with the ability to disperse and the spatial scale of the interaction. The impact of beneficial interactions also depended on the dispersal phase studied, with strong negative impacts on emigration, slightly weaker impacts on transience and no effect on full dispersal. The difference between effects of beneficial and detrimental interactors might reflect a dependency on both the local and the broader spatial context for detrimental interactors, while the local context was the main driver of dispersal decisions for beneficial interactors. Again, a more precise estimate of the scale and decay of interaction strength through the landscape and its effects on focal species fitness at each dispersal phase may help resolve this question. The information we could gather on species home range was fairly limited, but we found that the ratio of home ranges influenced the focal species dispersal, with a tendency for larger home ranges for focal species relative to the interactor decreasing the dependency of dispersal on detrimental interactions. This larger home range may allow focal species to escape detrimental interactors through changes in spatial distribution within their habitat, a likely less costly strategy than dispersing away. The interplay between species interaction and dispersal at different spatial scales should play an important role for metacommunity dynamics. Indeed, a recent theoretical study has shown that the spatial scales of dispersal and species interactions have opposite effects on ecosystem functioning [76]. A modelling effort illustrating the impacts of context-dependent dispersal in simple three-level food chain communities shows that simultaneous resource- and predator-dependent emigration reduced local fluctuations of population dynamics through time, and increased metacommunity stability [13]. In competitive communities of two *Callosobruchus* species, subdivision of the habitat into patches within a metacommunity structure allows for new niche differentiating mechanism through a dispersal-competition trade off allowing species coexistence [77]. Further, the dependency of dispersal on spatial scale should itself depend on the spatiotemporal autocorrelation of population dynamics between interacting species across scales [6,13,18], potentially leading to the co-evolution of dispersal of interacting species [17].

We were also expecting the impact of the interacting
species to depend on the larger biotic context.

Specifically, we expected that more complex
communities could lead to a “dilution effect” where
the cues and the impact of each interacting species on
the focal species’ dispersal may be weaker when
diluted in a larger number of species interactions. We
observed the opposite effect, with stronger positive
effects of detrimental interactors being observed in
more complex communities than for studies on pairs of
interacting species. This could be explained by
methodological differences between studies on pairs
of interacting species, very often laboratory studies,
and studies on more complex communities, often
corresponding to field studies or semi-natural
mesocosm studies. Oversimplified set-ups may actually
buffer the effects of interaction on dispersal through
acclimation to threats and an overall lower perception
of risk. Because of the inherent nestedness of
community complexity and experimental set-up, we
were not able to put both moderators in the models.
An alternative explanation rests on a confounded
effect of multiple species interactions. As mentioned
above, field studies often lead to difficulties in
disentangling effects on survival from effects on
dispersal and in properly isolating the effect of a single
species. For example, the abundances of a prey species
and its different predator species are often correlated,
leading to difficulties in isolating the effect of each
predator species without intensive monitoring of all
species at play [78]. The same reasoning can be made
for other interacting species like resources for
consumers, as in flying squirrels which depend on
multiple resources (spruce, birch and alder catkin), that
likely covary and can alternatively appear as the main
driver of fitness [79,80], potentially hampering the
estimate of their respective effects on dispersal.

Finally, we showed that the duration of the study
modulates the effects of interactions on the dispersal
of focal species. The longer the study lasted the closer
the effect sizes were to zero for detrimental
interactions, and to a lower extent for beneficial
interactions. This effect may reflect an acclimation to
the interacting species in experimental studies,
reducing the sensitivity of individuals to stressful
interactions and therefore their dispersal propensity to
avoid them. In addition, not all individuals are equally
likely to disperse [4] and/or to respond through
dispersal to adverse abiotic and biotic conditions
[55,81]. Shorter experiments may capture the most
responsive individuals and the longer the study lasts
the weaker the overall response should be. However,
one can alternatively argue that less responsive
individuals would also react and disperse only after
long-lasting experiments. Unfortunately, dispersal
studies are often limited in time or space and might
therefore misestimate the effect of interaction-
dependent dispersal at the whole population scale.

Our systematic literature review focused only on actively dispersing species, and thus excluded passively dispersed taxa such as plants. It yielded a majority of articles on insects, with other groups like spiders, birds, nematodes, mammals, or protists being less represented. While our dataset was taxonomically biased, it matched the taxonomic coverage of the systematic review on intraspecific density [19], and showed much lesser taxonomic bias than behavioural studies for instance [50]. This highlights the importance of covering a wide range of taxa across the phylogenetic tree in dispersal studies to account for the diversity of dispersal behaviours and species interactions. Another knowledge gap highlighted by our literature search is the relative lack of studies on the interplay between species interaction and dispersal syndromes (but see [82–86] in our database). Given the suggested role of dispersal syndromes in fitness-dispersal relationship and eco-evolutionary dynamics of metacommunities [55,81], we would suggest that research groups integrate a more detailed characterisation of the phenotype and its relationship to dispersal into their studies. Further, a meta-analysis focusing on passively dispersing species would help to understand whether processes influencing interaction-dependent dispersal are similar to those affecting active dispersers. Finally, we want to highlight the following conclusions and ideas to stimulate further research on dispersal and species interaction:

(1) **Methodological issues :**

(a) The **experimental set-up** matters. Oversimplified set-ups may buffer effects of interactions on dispersal through acclimation to threats. On the other hand, field studies often prevent disentangling effects of biotic conditions on survival and dispersal. Further, they can struggle to isolate properly the effect of each species interaction on the dispersal of a focal species because interactions among different interacting species (e.g., interaction of a prey species with multiple predator species) often covary in time or space. Those issues are at the core of discussions on **experimental vs. observational approaches**. Large-scale experiments in more realistic ecological conditions monitored over time as well as a more holistic investigation of biotic and abiotic drivers in the field might improve our understanding of the relationship between dispersal and species interactions.

(b) **The duration of the experiment** largely influences the estimated relationship between dispersal and species interaction. It does not mean we should run shorter experiments to increase the likelihood of finding larger effects because species interactions and dispersal phases last longer in nature than in an experiment. However, we should consider the temporal aspect as an additional factor when running an experiment manipulating species interactions on varying time windows and select appropriate durations relative to species' generation time. In addition, natural contexts often imply the **integration of cues** on interacting species **over a larger temporal window** within and between generations, thereby providing a more realistic estimate of information acquisition in stable and predictable environments and a poor estimate of enduring species interaction in changing environments.

(c) **Cues** might lead to stronger effects than the **actual presence** of an interacting species. A potential reason to consider is that the actual presence often mixes direct effects of the interaction, including numerical effects of it (i.e., predators consume prey), and species behavioural responses. For example, prey may intend to respond to cues about predation risk by leaving its habitat, but this intent might be repressed by the immediate lethal costs of doing so when actual predators are present. Such impacts of cues can be related to the landscape of fear, whereby predators have a disproportionately strong effect on prey redistribution over the landscape compared to their direct consumptive effect [26,72]. This landscape of fear effect also implies that measuring interaction strength by fitness effects alone might underestimate its ecological impact mediated by behaviour.

(2) **Ecological aspects of interaction-dependent dispersal**

(d) The numerical effects of species interactions further influence **intraspecific competition** through changes in population density. This main driver likely influences species fitness and dispersal propensity in an opposite direction to species interactions. Beneficial interactions likely increase population density while detrimental interactions decrease it [6]. The resulting consequence for dispersal calls for study designs testing the respective and
therefore misestimate the effect of interaction-
dependent dispersal at the whole population scale.

Our systematic literature review focused only on actively dispersing species, and thus excluded passively dispersed taxa such as plants. It yielded a majority of articles on insects, with other groups like spiders, birds, nematodes, mammals, or protists being less represented. While our dataset was taxonomically biased, it matched the taxonomic coverage of the systematic review on intraspecific density [19], and showed much lesser taxonomic bias than behavioural studies for instance [50]. This highlights the importance of covering a wide range of taxa across the phylogenetic tree in dispersal studies to account for the diversity of dispersal behaviours and species interactions. Another knowledge gap highlighted by our literature search is the relative lack of studies on the interplay between species interaction and dispersal syndromes (but see [82–86] in our database). Given the suggested role of dispersal syndromes in fitness-dispersal relationship and eco-evolutionary dynamics of metacommunities [55,81], we would suggest that research groups integrate a more detailed characterisation of the phenotype and its relationship to dispersal into their studies. Further, a meta-analysis focusing on passively dispersing species would help to understand whether processes influencing interaction-dependent dispersal are similar to those affecting active dispersers. Finally, we want to highlight the following conclusions and ideas to stimulate further research on dispersal and species interaction:

(1) **Methodological issues :**

(a) The **experimental set-up** matters. Oversimplified set-ups may buffer effects of interactions on dispersal through acclimation to threats. On the other hand, field studies often prevent disentangling effects of biotic conditions on survival and dispersal. Further, they can struggle to isolate properly the effect of each species interaction on the dispersal of a focal species because interactions among different interacting species (e.g., interaction of a prey species with multiple predator species) often covary in time or space. Those issues are at the core of discussions on **experimental vs. observational approaches**. Large-scale experiments in more realistic ecological conditions monitored over time as well as a more holistic investigation of biotic and abiotic drivers in the field might improve our understanding of the relationship between dispersal and species interactions.

(b) The **duration of the experiment** largely influences the estimated relationship between dispersal and species interaction. It does not mean we should run shorter experiments to increase the likelihood of finding larger effects because species interactions and dispersal phases last longer in nature than in an experiment. However, we should consider the temporal aspect as an additional factor when running an experiment manipulating species interactions on varying time windows and select appropriate durations relative to species' generation time. In addition, natural contexts often imply the **integration of cues** on interacting species **over a larger temporal window** within and between generations, thereby providing a more realistic estimate of information acquisition in stable and predictable environments and a poor estimate of enduring species interaction in changing environments.

(c) **Cues** might lead to stronger effects than the **actual presence** of an interacting species. A potential reason to consider is that the actual presence often mixes direct effects of the interaction, including numerical effects of it (i.e., predators consume prey), and species behavioural responses. For example, prey may intend to respond to cues about predation risk by leaving its habitat, but this intent might be repressed by the immediate lethal costs of doing so when actual predators are present. Such impacts of cues can be related to the landscape of fear, whereby predators have a disproportionately strong effect on prey redistribution over the landscape compared to their direct consumptive effect [26,72]. This landscape of fear effect also implies that measuring interaction strength by fitness effects alone might underestimate its ecological impact mediated by behaviour.

(2) **Ecological aspects of interaction-dependent dispersal**

(d) The numerical effects of species interactions further influence **intraspecific competition** through changes in population density. This main driver likely influences species fitness and dispersal propensity in an opposite direction to species interactions. Beneficial interactions likely increase population density while detrimental interactions decrease it [6]. The resulting consequence for dispersal calls for study designs testing the respective and
interactive effects in a fully crossed manner
[59].

(e) A clear pattern is the effects of **dispersal phase**. It has been pointed out repeatedly [4,5] that aside from our naïve expectation that emigration and immigration decisions should mirror each other, the risk and benefits of species interaction likely vary between initial habitat, environments crossed during transience and new habitats to settle in. It appears important to investigate the fitness and dispersal consequences of species interactions through the three movement phases. A further review should focus on the consequences of interspecific interactions at the colonisation stage.

(f) We need to improve our understanding of the **spatiotemporal dynamics** of interacting species. Indeed, dispersing from its current or natal habitat is a behavioural decision with large and lasting consequences for individual life history [87]. Therefore, alternative responses to a threat might be favoured when the population dynamics of interacting species give room to those alternatives (e.g., asynchrony in predator and prey population dynamics). Modelling studies have shown the importance of spatiotemporal autocorrelation for dispersal dynamics [6,13,18]

(g) The dispersal response depends on the **interaction strength**. We have shown that beneficial and detrimental interactions lead to opposite dispersal responses, but the effect can be modulated by the type of interaction, the level of specialization between interactors, and the dilution of the interaction by community diversity. More studies should aim at characterising this interaction strength, from the local community level to higher spatial scales in a metacommunity context, to better understand its impacts on dispersal.

(3) **Ecological and evolutionary consequences for metacommunities**

(h) **Moving from pairs of species to complex metacommunities** may be a difficult task. We have shown that the importance of context-dependent dispersal depends on the overall community context, thus experiments with pairwise interactions may fail to capture the complexity of the dispersal response and studies in natural communities may fail to tease apart the respective and interactive

effects of multiple interactions. Nevertheless, modelling studies show that with even simple three-level communities, context-dependent dispersal between pairs of species has a strong impact on metacommunity dynamics [13].

(i) **Context-dependent dispersal should shape metacommunity dynamics.** When studying the consequences of dispersal at the landscape level, studies often treat dispersal as a constitutive trait of species and/or landscape features [76]. Our results evidence a dispersal plasticity in response to biotic interactions, particularly in the context of the landscape of fear [26,72], which should influence the species distribution and their match to local ecological conditions within a landscape.

(j) We need to better understand how **evolving landscapes can affect the context-dependency of dispersal**. Eco-evolutionary dynamics of species interactions should influence the evolution of both context-independent [17] and context-dependent dispersal [6,55], and conversely the eco-evolutionary dynamics of dispersal should influence species interactions and metacommunity dynamics [55]. These two evidenced assertions hamper the accurate predictions of metacommunity dynamics in a greatly and rapidly changing world.

**Data and materials availability:** Data and code are available on Zenodo (http://dx.doi.org/10.5281/zenodo.10940162).

**Author contributions:** The idea of the article originally emerged from a dispersal workshop organised by EAF, in which EB and JC proposed to lead the article. EB, JC, DL, DB, JND, ABD, EAF, OK, FM, TP, CS, NS, GZ discussed the original search terms, and EB, JC and DL refined the search. EB and JC defined the abstract screening strategy, with the help of DL, and all authors participated in abstract screening. EB, JC and DL defined the full text screening strategy and the type of data to be extracted. EB screened each article's full text with the help of JC. JC and DL extracted complementary data from the literature. EB extracted effect sizes with the help of JC. EB ran the analyses. EB and JC wrote the first draft of the manuscript, DL contributed to early draft revisions, and all authors contributed to revisions.

**Competing interests:** The authors declare no competing interests.

**Acknowledgements:** The idea of the study originated from a workshop on dispersal led by Emanuel A. Fronhofer supported by a grant from the Agence Nationale de la Recherche (No.: ANR-19-CE02-0015) to EAF. We thank all participants of the workshop for fruitful discussions. This
interactive effects in a fully crossed manner
[59].

(e) A clear pattern is the effects of **dispersal phase**. It has been pointed out repeatedly [4,5] that aside from our naïve expectation that emigration and immigration decisions should mirror each other, the risk and benefits of species interaction likely vary between initial habitat, environments crossed during transience and new habitats to settle in. It appears important to investigate the fitness and dispersal consequences of species interactions through the three movement phases. A further review should focus on the consequences of interspecific interactions at the colonisation stage.

(f) We need to improve our understanding of the **spatiotemporal dynamics** of interacting species. Indeed, dispersing from its current or natal habitat is a behavioural decision with large and lasting consequences for individual life history [87]. Therefore, alternative responses to a threat might be favoured when the population dynamics of interacting species give room to those alternatives (e.g., asynchrony in predator and prey population dynamics). Modelling studies have shown the importance of spatiotemporal autocorrelation for dispersal dynamics [6,13,18]

(g) The dispersal response depends on the **interaction strength**. We have shown that beneficial and detrimental interactions lead to opposite dispersal responses, but the effect can be modulated by the type of interaction, the level of specialization between interactors, and the dilution of the interaction by community diversity. More studies should aim at characterising this interaction strength, from the local community level to higher spatial scales in a metacommunity context, to better understand its impacts on dispersal.

(3) **Ecological and evolutionary consequences for metacommunities**

(h) **Moving from pairs of species to complex metacommunities** may be a difficult task. We have shown that the importance of context-dependent dispersal depends on the overall community context, thus experiments with pairwise interactions may fail to capture the complexity of the dispersal response and studies in natural communities may fail to tease apart the respective and interactive

effects of multiple interactions. Nevertheless, modelling studies show that with even simple three-level communities, context-dependent dispersal between pairs of species has a strong impact on metacommunity dynamics [13].

(i) **Context-dependent dispersal should shape metacommunity dynamics.** When studying the consequences of dispersal at the landscape level, studies often treat dispersal as a constitutive trait of species and/or landscape features [76]. Our results evidence a dispersal plasticity in response to biotic interactions, particularly in the context of the landscape of fear [26,72], which should influence the species distribution and their match to local ecological conditions within a landscape.

(j) We need to better understand how **evolving landscapes can affect the context-dependency of dispersal**. Eco-evolutionary dynamics of species interactions should influence the evolution of both context-independent [17] and context-dependent dispersal [6,55], and conversely the eco-evolutionary dynamics of dispersal should influence species interactions and metacommunity dynamics [55]. These two evidenced assertions hamper the accurate predictions of metacommunity dynamics in a greatly and rapidly changing world.

**Data and materials availability:** Data and code are available on Zenodo (http://dx.doi.org/10.5281/zenodo.10940162).

**Author contributions:** The idea of the article originally emerged from a dispersal workshop organised by EAF, in which EB and JC proposed to lead the article. EB, JC, DL, DB, JND, ABD, EAF, OK, FM, TP, CS, NS, GZ discussed the original search terms, and EB, JC and DL refined the search. EB and JC defined the abstract screening strategy, with the help of DL, and all authors participated in abstract screening. EB, JC and DL defined the full text screening strategy and the type of data to be extracted. EB screened each article's full text with the help of JC. JC and DL extracted complementary data from the literature. EB extracted effect sizes with the help of JC. EB ran the analyses. EB and JC wrote the first draft of the manuscript, DL contributed to early draft revisions, and all authors contributed to revisions.

**Competing interests:** The authors declare no competing interests.

**Acknowledgements:** The idea of the study originated from a workshop on dispersal led by Emanuel A. Fronhofer supported by a grant from the Agence Nationale de la Recherche (No.: ANR-19-CE02-0015) to EAF. We thank all participants of the workshop for fruitful discussions. This
work was supported by LABEX TULIP (ANR-10-LABX-0041) and the EVENET network. This is publication ISEM-2024-125 of the Institut des Sciences de l'Evolution - Montpellier. JuCo was supported by a funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 817779), MMD was supported by Ayudas de Incorporación Científico Titular' (#2022301042; CSIC), OK was supported by a grant from the Agence Nationale de la Recherche (ANR-20-CE02-0023-01).

REFERENCES

1. Leibold MA *et al*. 2004 The metacommunity concept: a framework for multi-scale community ecology. *Ecol. Lett.* **7**, 601–613.
(doi:10.1111/j.1461-0248.2004.00608.x)

2. Baguette M, Blanchet S, Legrand D, Stevens VM, Turlure C. 2013 Individual dispersal, landscape connectivity and ecological networks. *Biol. Rev.* **88**, 310–326. (doi:10.1111/brv.12000)

3. Kubisch A, Holt RD, Poethke H-J, Fronhofer EA. 2014 Where am I and why? Synthesizing range biology and the eco-evolutionary dynamics of dispersal. *Oikos* **123**, 5–22. (doi:10.1111/j.1600-0706.2013.00706.x)

4. Clobert J, Le Galliard J-F, Cote J, Meylan S, Massot M. 2009 Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. *Ecol. Lett.* **12**, 197–209. (doi:10.1111/j.1461-0248.2008.01267.x)

5. Bowler DE, Benton TG. 2005 Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics. *Biol. Rev.* **80**, 205–225.
(doi:10.1017/S1464793104006645)

6. Poethke HJ, Weisser WW, Hovestadt T. 2010 Predator-Induced Dispersal and the Evolution of Conditional Dispersal in Correlated Environments. *Am. Nat.* **175**, 577–586. (doi:10.1086/651595)

7. Armsworth PR, Roughgarden JE, Losos EJB, Perrin AEN. 2005 The Impact of Directed versus Random Movement on Population Dynamics and Biodiversity Patterns. *Am. Nat.* **165**, 449–465. (doi:10.1086/428595)

8. McPeek MA, Holt RD. 1992 The Evolution of Dispersal in Spatially and Temporally Varying Environments. *Am. Nat.* **140**, 1010–1027.
(doi:10.1086/285453)

9. Bocedi G, Heinonen J, Travis JMJ, Berger AEU, Bronstein EJL. 2012 Uncertainty and the Role of Information Acquisition in the Evolution of Context-Dependent Emigration. *Am. Nat.* **179**, 606–620. (doi:10.1086/665004)

10. Poethke HJ, Pfenning B, Hovestadt T. 2007 The relative contribution of individual and kin selection to the evolution of density-dependent dispersal rates. *Evol. Ecol. Res.* **9**, 41–50.

11. Gros A, Joachim Poethke H, Hovestadt T. 2006 Evolution of local adaptations in dispersal strategies. *Oikos* **114**, 544–552.
(doi:10.1111/j.2006.0030-1299.14909.x)

12. Jacob S, Bestion E, Legrand D, Clobert J, Cote J. 2015 Habitat matching and spatial heterogeneity of phenotypes: implications for metapopulation and metacommunity functioning. *Evol. Ecol.* **29**, 851–871. (doi:10.1007/s10682-015-9776-5)

13. Fronhofer EA *et al*. 2018 Bottom-up and top-down control of dispersal across major organismal groups. *Nat. Ecol. Evol.* **2**, 1859–1863.
(doi:10.1038/s41559-018-0686-0)

14. Fronhofer EA, Klecka J, Melián CJ, Altermatt F. 2015 Condition-dependent movement and dispersal in experimental metacommunities. *Ecol. Lett.* **18**, 954–963. (doi:10.1111/ele.12475)

15. Boulinier, McCoy, Sorci. 2001 Dispersal and parasitism. In *Dispersal* (eds J Clobert, E Danchin, AA Dhondt, JD Nichols), pp. 169–179. Oxford University Press.

16. Casas RR de, Willis CG, Donohue K. 2012 Plant dispersal phenotypes: a seed perspective of maternal habitat selection. In *Dispersal Ecology and Evolution* (eds J Clobert, M Baguette, TG Benton, JM Bullock), pp. 171–185. Oxford University Press.

17. Zilio G, Deshpande JN, Duncan AB, Fronhofer EA, Kaltz O. 2024 Dispersal evolution and eco-evolutionary dynamics in antagonistic species interactions. *Trends Ecol. Evol.* **0**. (doi:10.1016/j.tree.2024.03.006)

18. Deshpande JN, Kaltz O, Fronhofer EA. 2021 Host–parasite dynamics set the ecological theatre for the evolution of state- and context-dependent dispersal in hosts. *Oikos* **130**, 121–132.
(doi:10.1111/oik.07512)
work was supported by LABEX TULIP (ANR-10-LABX-0041) and the EVENET network. This is publication ISEM-2024-125 of the Institut des Sciences de l'Evolution - Montpellier. JuCo was supported by a funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 817779), MMD was supported by Ayudas de Incorporación Científico Titular' (#2022301042; CSIC), OK was supported by a grant from the Agence Nationale de la Recherche (ANR-20-CE02-0023-01).

REFERENCES

1. Leibold MA et al. 2004 The metacommunity concept: a framework for multi-scale community ecology. *Ecol. Lett.* **7**, 601–613.
(doi:10.1111/j.1461-0248.2004.00608.x)

2. Baguette M, Blanchet S, Legrand D, Stevens VM, Turlure C. 2013 Individual dispersal, landscape connectivity and ecological networks. *Biol. Rev.* **88**, 310–326. (doi:10.1111/brv.12000)

3. Kubisch A, Holt RD, Poethke H-J, Fronhofer EA. 2014 Where am I and why? Synthesizing range biology and the eco-evolutionary dynamics of dispersal. *Oikos* **123**, 5–22. (doi:10.1111/j.1600-0706.2013.00706.x)

4. Clobert J, Le Galliard J-F, Cote J, Meylan S, Massot M. 2009 Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. *Ecol. Lett.* **12**, 197–209. (doi:10.1111/j.1461-0248.2008.01267.x)

5. Bowler DE, Benton TG. 2005 Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics. *Biol. Rev.* **80**, 205–225.
(doi:10.1017/S1464793104006645)

6. Poethke HJ, Weisser WW, Hovestadt T. 2010 Predator-Induced Dispersal and the Evolution of Conditional Dispersal in Correlated Environments. *Am. Nat.* **175**, 577–586. (doi:10.1086/651595)

7. Armsworth PR, Roughgarden JE, Losos EJB, Perrin AEN. 2005 The Impact of Directed versus Random Movement on Population Dynamics and Biodiversity Patterns. *Am. Nat.* **165**, 449–465. (doi:10.1086/428595)

8. McPeek MA, Holt RD. 1992 The Evolution of Dispersal in Spatially and Temporally Varying Environments. *Am. Nat.* **140**, 1010–1027.
(doi:10.1086/285453)

9. Bocedi G, Heinonen J, Travis JMJ, Berger AEU, Bronstein EJL. 2012 Uncertainty and the Role of Information Acquisition in the Evolution of Context-Dependent Emigration. *Am. Nat.* **179**, 606–620. (doi:10.1086/665004)

10. Poethke HJ, Pfenning B, Hovestadt T. 2007 The relative contribution of individual and kin selection to the evolution of density-dependent dispersal rates. *Evol. Ecol. Res.* **9**, 41–50.

11. Gros A, Joachim Poethke H, Hovestadt T. 2006 Evolution of local adaptations in dispersal strategies. *Oikos* **114**, 544–552.
(doi:10.1111/j.2006.0030-1299.14909.x)

12. Jacob S, Bestion E, Legrand D, Clobert J, Cote J. 2015 Habitat matching and spatial heterogeneity of phenotypes: implications for metapopulation and metacommunity functioning. *Evol. Ecol.* **29**, 851–871. (doi:10.1007/s10682-015-9776-5)

13. Fronhofer EA et al. 2018 Bottom-up and top-down control of dispersal across major organismal groups. *Nat. Ecol. Evol.* **2**, 1859–1863. (doi:10.1038/s41559-018-0686-0)

14. Fronhofer EA, Klecka J, Melián CJ, Altermatt F. 2015 Condition-dependent movement and dispersal in experimental metacommunities. *Ecol. Lett.* **18**, 954–963. (doi:10.1111/ele.12475)

15. Boulinier, McCoy, Sorci. 2001 Dispersal and parasitism. In *Dispersal* (eds J Clobert, E Danchin, AA Dhondt, JD Nichols), pp. 169–179. Oxford University Press.

16. Casas RR de, Willis CG, Donohue K. 2012 Plant dispersal phenotypes: a seed perspective of maternal habitat selection. In *Dispersal Ecology and Evolution* (eds J Clobert, M Baguette, TG Benton, JM Bullock), pp. 171–185. Oxford University Press.

17. Zilio G, Deshpande JN, Duncan AB, Fronhofer EA, Kaltz O. 2024 Dispersal evolution and eco-evolutionary dynamics in antagonistic species interactions. *Trends Ecol. Evol.* **0**. (doi:10.1016/j.tree.2024.03.006)

18. Deshpande JN, Kaltz O, Fronhofer EA. 2021 Host–parasite dynamics set the ecological theatre for the evolution of state- and context-dependent dispersal in hosts. *Oikos* **130**, 121–132. (doi:10.1111/oik.07512)
19. Harman RR, Goddard J, Shivaji R, Cronin JT. 2020 Frequency of Occurrence and Population-Dynamic Consequences of Different Forms of Density-Dependent Emigration. *Am. Nat.* **195**, 851–867. (doi:10.1086/708156)

20. Yoder JM, Marschall EA, Swanson DA. 2004 The cost of dispersal: predation as a function of movement and site familiarity in ruffed grouse. *Behav. Ecol.* **15**, 469–476. (doi:10.1093/beheco/arh037)

21. Binning SA, Shaw AK, Roche DG. 2017 Parasites and Host Performance: Incorporating Infection into Our Understanding of Animal Movement. *Integr. Comp. Biol.* **57**, 267–280. (doi:10.1093/icb/icx024)

22. Bonte D et al. 2012 Costs of dispersal. *Biol. Rev.* **87**, 290–312. (doi:10.1111/j.1469-185X.2011.00201.x)

23. Carbone C, Cowlishaw G, Isaac NJB, Rowcliffe JM. 2005 How Far Do Animals Go? Determinants of Day Range in Mammals. *Am. Nat.* **165**, 290–297. (doi:10.1086/426790)

24. Haskell JP, Ritchie ME, Olff H. 2002 Fractal geometry predicts varying body size scaling relationships for mammal and bird home ranges. *Nature* **418**, 527–530. (doi:10.1038/nature00840)

25. Wootton JT, Emmerson M. 2005 Measurement of Interaction Strength in Nature. *Annu. Rev. Ecol. Evol. Syst.* **36**, 419–444. (doi:10.1146/annurev.ecolsys.36.091704.175535)

26. Preisser EL, Bolnick DI, Benard MF. 2005 Scared to death? the effects of intimidation and consumption in predator–prey interactions. *Ecology* **86**, 501–509. (doi:10.1890/04-0719)

27. Strauss AT, Civitello DJ, Cáceres CE, Hall SR. 2015 Success, failure and ambiguity of the dilution effect among competitors. *Ecol. Lett.* **18**, 916–926. (doi:10.1111/ele.12468)

28. Goodacre SL, Martin OY, Bonte D, Hutchings L, Woolley C, Ibrahim K, Thomas CG, Hewitt GM. 2009 Microbial modification of host long-distance dispersal capacity. *BMC Biol.* **7**, 32. (doi:10.1186/1741-7007-7-32)

29. Moore AT, Brown CR. 2014 Dispersing hemipteran vectors have reduced arbovirus

prevalence. *Biol. Lett.* **10**, 20140117. (doi:10.1098/rsbl.2014.0117)

30. Moher D, Liberati A, Tetzlaff J, Altman DG, Group TP. 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. *PLOS Med.* **6**, e1000097. (doi:10.1371/journal.pmed.1000097)

31. O'Dea RE et al. 2021 Preferred reporting items for systematic reviews and meta-analyses in ecology and evolutionary biology: a PRISMA extension. *Biol. Rev.* **96**, 1695–1722. (doi:10.1111/brv.12721)

32. Lajeunesse MJ. 2016 Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for r. *Methods Ecol. Evol.* **7**, 323–330. (doi:10.1111/2041-210X.12472)

33. Landis JR, Koch GG. 1977 The Measurement of Observer Agreement for Categorical Data. *Biometrics* **33**, 159–174. (doi:10.2307/2529310)

34. Borenstein M, editor. 2009 *Introduction to meta-analysis*. Chichester, U.K: John Wiley & Sons.

35. Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, Welch V, editors. 2022 *Cochrane Handbook for Systematic Reviews of Interventions*. Cochrane. See https://training.cochrane.org/handbook/current.

36. Wan X, Wang W, Liu J, Tong T. 2014 Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. *BMC Med. Res. Methodol.* **14**, 1–13. (doi:10.1186/1471-2288-14-135)

37. Koricheva J, Gurevitch J, Mengersen K. 2013 *Handbook of Meta-analysis in Ecology and Evolution*. Princeton, New Jersey: Princeton University Press.

38. Tomczak M, Tomczak E. 2014 The need to report effect size estimates revisited. An overview of some recommended measures of effect size. *Trends Sport Sci.* **1**, 19–25.

39. Correll J, Mellinger C, Pedersen EJ. 2021 Flexible approaches for estimating partial eta squared in mixed-effects models with crossed random factors. *Behav. Res. Methods* **54**, 1626–1642. (doi:10.3758/s13428-021-01687-2)
19. Harman RR, Goddard J, Shivaji R, Cronin JT. 2020 Frequency of Occurrence and Population-Dynamic Consequences of Different Forms of Density-Dependent Emigration. *Am. Nat.* **195**, 851–867. (doi:10.1086/708156)

20. Yoder JM, Marschall EA, Swanson DA. 2004 The cost of dispersal: predation as a function of movement and site familiarity in ruffed grouse. *Behav. Ecol.* **15**, 469–476. (doi:10.1093/beheco/arh037)

21. Binning SA, Shaw AK, Roche DG. 2017 Parasites and Host Performance: Incorporating Infection into Our Understanding of Animal Movement. *Integr. Comp. Biol.* **57**, 267–280. (doi:10.1093/icb/icx024)

22. Bonte D et al. 2012 Costs of dispersal. *Biol. Rev.* **87**, 290–312. (doi:10.1111/j.1469-185X.2011.00201.x)

23. Carbone C, Cowlishaw G, Isaac NJB, Rowcliffe JM. 2005 How Far Do Animals Go? Determinants of Day Range in Mammals. *Am. Nat.* **165**, 290–297. (doi:10.1086/426790)

24. Haskell JP, Ritchie ME, Olff H. 2002 Fractal geometry predicts varying body size scaling relationships for mammal and bird home ranges. *Nature* **418**, 527–530. (doi:10.1038/nature00840)

25. Wootton JT, Emmerson M. 2005 Measurement of Interaction Strength in Nature. *Annu. Rev. Ecol. Evol. Syst.* **36**, 419–444. (doi:10.1146/annurev.ecolsys.36.091704.175535)

26. Preisser EL, Bolnick DI, Benard MF. 2005 Scared to death? the effects of intimidation and consumption in predator–prey interactions. *Ecology* **86**, 501–509. (doi:10.1890/04-0719)

27. Strauss AT, Civitello DJ, Cáceres CE, Hall SR. 2015 Success, failure and ambiguity of the dilution effect among competitors. *Ecol. Lett.* **18**, 916–926. (doi:10.1111/ele.12468)

28. Goodacre SL, Martin OY, Bonte D, Hutchings L, Woolley C, Ibrahim K, Thomas CG, Hewitt GM. 2009 Microbial modification of host long-distance dispersal capacity. *BMC Biol.* **7**, 32. (doi:10.1186/1741-7007-7-32)

29. Moore AT, Brown CR. 2014 Dispersing hemipteran vectors have reduced arbovirus

prevalence. *Biol. Lett.* **10**, 20140117. (doi:10.1098/rsbl.2014.0117)

30. Moher D, Liberati A, Tetzlaff J, Altman DG, Group TP. 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. *PLOS Med.* **6**, e1000097. (doi:10.1371/journal.pmed.1000097)

31. O'Dea RE et al. 2021 Preferred reporting items for systematic reviews and meta-analyses in ecology and evolutionary biology: a PRISMA extension. *Biol. Rev.* **96**, 1695–1722. (doi:10.1111/brv.12721)

32. Lajeunesse MJ. 2016 Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for r. *Methods Ecol. Evol.* **7**, 323–330. (doi:10.1111/2041-210X.12472)

33. Landis JR, Koch GG. 1977 The Measurement of Observer Agreement for Categorical Data. *Biometrics* **33**, 159–174. (doi:10.2307/2529310)

34. Borenstein M, editor. 2009 *Introduction to meta-analysis*. Chichester, U.K: John Wiley & Sons.

35. Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, Welch V, editors. 2022 *Cochrane Handbook for Systematic Reviews of Interventions*. Cochrane. See https://training.cochrane.org/handbook/current.

36. Wan X, Wang W, Liu J, Tong T. 2014 Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. *BMC Med. Res. Methodol.* **14**, 1–13. (doi:10.1186/1471-2288-14-135)

37. Koricheva J, Gurevitch J, Mengersen K. 2013 *Handbook of Meta-analysis in Ecology and Evolution*. Princeton, New Jersey: Princeton University Press.

38. Tomczak M, Tomczak E. 2014 The need to report effect size estimates revisited. An overview of some recommended measures of effect size. *Trends Sport Sci.* **1**, 19–25.

39. Correll J, Mellinger C, Pedersen EJ. 2021 Flexible approaches for estimating partial eta squared in mixed-effects models with crossed random factors. *Behav. Res. Methods* **54**, 1626–1642. (doi:10.3758/s13428-021-01687-2)
40. Lakens D. 2013 Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. *Front. Psychol.* **4**, 863. (doi:10.3389/fpsyg.2013.00863)

41. Auger G, Pottier J, Mathieu J, Jabot F. 2024 Space use of invertebrates in terrestrial habitats: Phylogenetic, functional and environmental drivers of interspecific variations. *Glob. Ecol. Biogeogr.* **33**, e13811. (doi:10.1111/geb.13811)

42. Broekman MJE, Hoeks S, Freriks R, Langendoen MM, Runge KM, Savenco E, ter Harmsel R, Huijbregts MAJ, Tucker MA. 2023 HomeRange: A global database of mammalian home ranges. *Glob. Ecol. Biogeogr.* **32**, 198–205. (doi:10.1111/geb.13625)

43. Tamburello N, Côté IM, Dulvy NK. 2015 Energy and the Scaling of Animal Space Use. *Am. Nat.* **186**, 196–211. (doi:10.1086/682070)

44. Tucker MA, Ord TJ, Rogers TL. 2014 Evolutionary predictors of mammalian home range size: body mass, diet and the environment. *Glob. Ecol. Biogeogr.* **23**, 1105–1114. (doi:10.1111/geb.12194)

45. Pinsky M, McCauley D. 2019 Home range and body size data compiled from the literature for marine and terrestrial vertebrates. (doi:10.1575/1912/bco-dmo.752795.1)

46. Grafen A. 1989 The phylogenetic regression. *Philos. Trans. R. Soc. Lond. B Biol. Sci.* **326**, 119–157. (doi:10.1098/rstb.1989.0106)

47. Viechtbauer W. 2010 Conducting Meta-Analyses in R with the metafor Package. *J. Stat. Softw.* **36**, 1–48. (doi:10.18637/jss.v036.i03)

48. Rosenberg MS. 2005 THE FILE-DRAWER PROBLEM REVISITED: A GENERAL WEIGHTED METHOD FOR CALCULATING FAIL-SAFE NUMBERS IN META-ANALYSIS. *Evolution* **59**, 464–468. (doi:10.1111/j.0014-3820.2005.tb01004.x)

49. Nakagawa S, Santos ESA. 2012 Methodological issues and advances in biological meta-analysis. *Evol. Ecol.* **26**, 1253–1274. (doi:10.1007/s10682-012-9555-5)

50. Rosenthal MF, Gertler M, Hamilton AD, Prasad S, Andrade MCB. 2017 Taxonomic bias in animal behaviour publications. *Anim. Behav.* **127**, 83–89. (doi:10.1016/j.anbehav.2017.02.017)

51. Bánki O et al. 2023 Catalogue of life checklist. (doi:10.48580/dfgnm)

52. Senior AM, Grueber CE, Kamiya T, Lagisz M, O'Dwyer K, Santos ESA, Nakagawa S. 2016 Heterogeneity in ecological and evolutionary meta-analyses: its magnitude and implications. *Ecology* **97**, 3293–3299. (doi:10.1002/ecy.1591)

53. Lowe WH, McPeek MA. 2014 Is dispersal neutral? *Trends Ecol. Evol.* **29**, 444–450. (doi:10.1016/j.tree.2014.05.009)

54. Matthysen E. 2005 Density-dependent dispersal in birds and mammals. *Ecography* **28**, 403–416. (doi:10.1111/j.0906-7590.2005.04073.x)

55. Cote J, Bestion E, Jacob S, Travis J, Legrand D, Baguette M. 2017 Evolution of dispersal strategies and dispersal syndromes in fragmented landscapes. *Ecography* **40**, 56–73. (doi:10.1111/ecog.02538)

56. Holt RD, Barfield M. 2008 Habitat Selection and Niche Conservatism. *Isr. J. Ecol. Evol.* **54**, 295–309. (doi:10.1560/IJEE.54.3-4.295)

57. Ravigné V, Dieckmann U, Olivieri I. 2009 Live Where You Thrive: Joint Evolution of Habitat Choice and Local Adaptation Facilitates Specialization and Promotes Diversity. *Am. Nat.* **174**, E141–E169. (doi:10.1086/605369)

58. Weisser WW. 2001 The effects of predation on dispersal. In *Dispersal* (eds J Clobert, E Danchin, AA Dhondt, JD Nichols), pp. 180–188. Oxford University Press.

59. Thierry M, Cote J, Bestion E, Legrand D, Clobert J, Jacob S. 2024 The interplay between abiotic and biotic factors on dispersal decisions in metacommunities. *Philos. Trans. R. Soc. B-Biol. Sci.* **379**, 20230137. (doi:10.1098/rstb.2023.0137)

60. Chamberlain SA, Bronstein JL, Rudgers JA. 2014 How context dependent are species interactions? *Ecol. Lett.* **17**, 881–890. (doi:10.1111/ele.12279)

61. Baines CB, McCauley SJ, Rowe L. 2014 The interactive effects of competition and predation risk on dispersal in an insect. *Biol. Lett.* **10**, 20140287. (doi:10.1098/rsbl.2014.0287)

62. Hammill E, Fitzjohn RG, Srivastava DS. 2015 Conspecific density modulates the effect of