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81. Cote *J et al.* 2022 Dispersal syndromes in challenging environments: A cross-species experiment. *Ecol. Lett.* **25**, 2675–2687. (doi:10.1111/ele.14124)

82. Cote J, Fogarty S, Tymen B, Sih A, Brodin T. 2013 Personality-dependent dispersal cancelled under predation risk. *Proc. R. Soc. B Biol. Sci.* **280**, 20132349. (doi:10.1098/rspb.2013.2349)
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1149–1158. (doi:10.1007/s00442-015-3303-9)

63. Ventura L, Smith DR, Lubin Y. 2017 Crowding leads to fitness benefits and reduced dispersal in a colonial spider. *Behav. Ecol.* **28**, 1384–1392. (doi:10.1093/beheco/arx106)

64. Sloggett JJ, Weisser WW. 2002 Parasitoids induce production of the dispersal morph of the pea aphid, *Acyrtosiphon pisum*. *Oikos* **98**, 323–333. (doi:10.1034/j.1600-0706.2002.980213.x)

65. Tremmel M, Steinitz H, Kliot A, Harari A, Lubin Y. 2020 Dispersal, endosymbiont abundance and fitness-related consequences of inbreeding and outbreeding in a social beetle. *Biol. J. Linn. Soc.* **129**, 717–727. (doi:10.1093/biolinnean/blz204)

66. Nørgaard LS, Zilio G, Saade C, Gougat-Barbera C, Hall MD, Fronhofer EA, Kaltz O. 2021 An evolutionary trade-off between parasite virulence and dispersal at experimental invasion fronts. *Ecol. Lett.* **24**, 739–750. (doi:10.1111/ele.13692)

67. Holomuzki JR, Pillsbury RW, Khandwala SB. 1999 Interplay between dispersal determinants of larval hydropsychid caddisflies. *Can. J. Fish. Aquat. Sci.* **56**, 2041–2050. (doi:10.1139/f99-141)

68. Kaster JL. 1989 Observations of predator-prey interaction on dispersal of an oligochaete prey, Limnodrilus hoffmeisteri. *Hydrobiologia* **180**, 191–193. (doi:10.1007/BF00027552)

69. Reim E, Baguette M, Günter F, Fischer K. 2018 Emigration propensity and flight performance are decoupled in a butterfly. *Ecosphere* **9**, e02502. (doi:10.1002/ecs2.2502)

70. Achiano KA, Giliomee JH. 2008 Food-,
temperature-and crowding-mediated laboratory
dispersal of Carcinops pumilio (Erichson)
(Coleoptera: Histeridae), a predator of house fly
(Diptera: Muscidae) eggs and larvae. *Afr. Entomol.*
**16**, 115–121. (doi:10.4001/1021-3589-16.1.115)

71. Matthysen E. 2012 Multicausality of dispersal: a review. In *Dispersal Ecology and Evolution* (eds J Clobert, M Baguette, TG Benton, JM Bullock), pp. 3–18. Oxford University Press.

72. Schmitz OJ, Miller JRB, Trainor AM, Abrahms B. 2017 Toward a community ecology of landscapes: predicting multiple predator-prey interactions

across geographic space. *Ecology* **98**, 2281–2292. (doi:10.1002/ecy.1916)

73. Miyasaka H, Nakano S. 2001 Drift dispersal of mayfly nymphs in the presence of chemical and visual cues from diurnal drift- and nocturnal benthic-foraging fishes. *Freshw. Biol.* **46**, 1229–1237. (doi:10.1046/j.1365-2427.2001.00745.x)

74. Wooster D, Sih A. 1995 A Review of the Drift and Activity Responses of Stream Prey to Predator Presence. *Oikos* **73**, 3–8. (doi:10.2307/3545718)

75. Baines CB, Diab S, McCauley SJ. 2020 Parasitism Risk and Infection Alter Host Dispersal. *Am. Nat.* **196**, 119–131. (doi:10.1086/709163)

76. Zelnik YR, Barbier M, Shanafelt DW, Loreau M, Germain RM. 2024 Linking intrinsic scales of ecological processes to characteristic scales of biodiversity and functioning patterns. *Oikos* **2024**, e10514. (doi:10.1111/oik.10514)

77. Hunt JJFG, Bonsall MB. 2009 The effects of colonization, extinction and competition on coexistence in metacommunities. *J. Anim. Ecol.* **78**, 866–879. (doi:10.1111/j.1365-2656.2009.01532.x)

78. Selonen V, Fey K, Hämäläinen S, Turkia T, Korpimäki E. 2018 Do predators modify context-dependent dispersal of red squirrels? *Behav. Ecol. Sociobiol.* **72**, 136. (doi:10.1007/s00265-018-2554-y)

79. Selonen V, Wistbacka R, Korpimäki E. 2016 Food abundance and weather modify reproduction of two arboreal squirrel species. *J. Mammal.* **97**, 1376–1384. (doi:10.1093/jmammal/gyw096)

80. Selonen V, Wistbacka R. 2016 Siberian flying squirrels do not anticipate future resource abundance. *BMC Ecol.* **16**, 51. (doi:10.1186/s12898-016-0107-7)

81. Cote *J et al.* 2022 Dispersal syndromes in challenging environments: A cross-species experiment. *Ecol. Lett.* **25**, 2675–2687. (doi:10.1111/ele.14124)

82. Cote J, Fogarty S, Tymen B, Sih A, Brodin T. 2013 Personality-dependent dispersal cancelled under predation risk. *Proc. R. Soc. B Biol. Sci.* **280**, 20132349. (doi:10.1098/rspb.2013.2349)
83. Debeffe L, Morellet N, Verheyden-Tixier H, Hoste H, Gaillard J-M, Cargnelutti B, Picot D, Sevila J, Hewison AJM. 2014 Parasite abundance contributes to condition-dependent dispersal in a wild population of large herbivore. *Oikos* **123**, 1121–1125. (doi:10.1111/oik.01396)

84. Bestion E, Teyssier A, Aubret F, Clobert J, Cote J. 2014 Maternal exposure to predator scents: offspring phenotypic adjustment and dispersal. *Proc. R. Soc. B Biol. Sci.* **281**, 20140701. (doi:10.1098/rspb.2014.0701)

85. Terui A, Ooue K, Urabe H, Nakamura F. 2017 Parasite infection induces size-dependent host dispersal: consequences for parasite persistence. *Proc. R. Soc. B Biol. Sci.* **284**, 20171491. (doi:10.1098/rspb.2017.1491)

86. Baines CB, McCauley SJ, Rowe L. 2015 Dispersal depends on body condition and predation risk in the semi-aquatic insect, Notonecta undulata. *Ecol. Evol.* **5**, 2307–2316. (doi:10.1002/ece3.1508)

87. Bonte D, Dahirel M. 2017 Dispersal: a central and independent trait in life history. *Oikos* **126**, 472–479. (doi:10.1111/oik.03801)
83. Debeffe L, Morellet N, Verheyden-Tixier H, Hoste H, Gaillard J-M, Cargnelutti B, Picot D, Sevila J, Hewison AJM. 2014 Parasite abundance contributes to condition-dependent dispersal in a wild population of large herbivore. *Oikos* **123**, 1121–1125. (doi:10.1111/oik.01396)

84. Bestion E, Teyssier A, Aubret F, Clobert J, Cote J. 2014 Maternal exposure to predator scents: offspring phenotypic adjustment and dispersal. *Proc. R. Soc. B Biol. Sci.* **281**, 20140701. (doi:10.1098/rspb.2014.0701)

85. Terui A, Ooue K, Urabe H, Nakamura F. 2017 Parasite infection induces size-dependent host dispersal: consequences for parasite persistence. *Proc. R. Soc. B Biol. Sci.* **284**, 20171491. (doi:10.1098/rspb.2017.1491)

86. Baines CB, McCauley SJ, Rowe L. 2015 Dispersal depends on body condition and predation risk in the semi-aquatic insect, Notonecta undulata. *Ecol. Evol.* **5**, 2307–2316. (doi:10.1002/ece3.1508)

87. Bonte D, Dahirel M. 2017 Dispersal: a central and independent trait in life history. *Oikos* **126**, 472–479. (doi:10.1111/oik.03801)
SUPPLEMENTARY MATERIAL

Box S1: Full description of the keywords for the meta-analysis.

The search was conducted on Web of Science on the 20th of October 2021 and yielded 21499 results.

TS=(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*)

AND TI=(dispers* OR migrat* OR emigrat* OR immigrat* OR colonis* OR coloniz* OR nomadi*)

NOT TI=("seasonal migration" NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=("vertical migration" NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=("autumn migration" NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=("fall migration" NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=("spring migration" NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=(catadrom* NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=(anadrom* NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=(dispersion OR dispersible)

NOT TS=(refugees OR "international migration" OR "domestic migration" OR "human migration" OR asylum OR illegal)

AND (SJ==("ENVIRONMENTAL SCIENCES ECOLOGY" OR "ZOOLOGY" OR "BEHAVIORAL SCIENCES" OR "BIODIVERSITY CONSERVATION" OR "INFECTIOUS DISEASES" OR "PLANT SCIENCES" OR "PHYSIOLOGY" OR "LIFE SCIENCES BIOMEDICINE OTHER TOPICS" OR "AGRICULTURE" OR "MARINE FRESHWATER BIOLOGY" OR "MICROBIOLOGY" OR "PATHOLOGY" OR "EVOLUTIONARY BIOLOGY" OR "DEMOGRAPHY" OR "ENTOMOLOGY" OR "PARASITOLOGY" OR "FORESTRY" OR "FISHERIES"))

NOT (WC==("PUBLIC ENVIRONMENTAL OCCUPATIONAL HEALTH" OR "ETHNIC STUDIES" OR "ECONOMICS" OR "PHARMACOLOGY PHARMACY" OR "FOOD SCIENCE TECHNOLOGY" OR "REGIONAL URBAN PLANNING" OR "CHEMISTRY ANALYTICAL" OR "GREEN SUSTAINABLE SCIENCE TECHNOLOGY" OR "ANTHROPOLOGY" OR "GASTROENTEROLOGY HEPATOLOGY" OR LAW OR "PSYCHOLOGY BIOLOGICAL" OR "ENERGY FUELS" OR "MEDICINE RESEARCH EXPERIMENTAL" OR ONCOLOGY OR "DENTISTRY ORAL SURGERY MEDICINE" OR "RADIOLOGY NUCLEAR MEDICINE MEDICAL IMAGING" OR "NUTRITION DIETETICS" OR "CHEMISTRY PHYSICAL" OR "RESPIRATORY SYSTEM" OR PALEONTOLOGY OR SOCIOLOGY OR "PERIPHERAL VASCULAR DISEASE" OR "PEDIATRICS" OR "SOCIAL SCIENCES BIOMEDICAL" OR "ASTRONOMY ASTROPHYSICS" OR "BIOPHYSICS"))
SUPPLEMENTARY MATERIAL

Box S1: Full description of the keywords for the meta-analysis.

The search was conducted on Web of Science on the 20th of October 2021 and yielded 21499 results.

TS=(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*)

AND TI=(dispers* OR migrat* OR emigrat* OR immigrat* OR colonis* OR coloniz* OR nomadi*)

NOT TI=("seasonal migration" NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=("vertical migration" NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=("autumn migration" NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=("fall migration" NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=("spring migration" NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=(catadrom* NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=(anadrom* NOT dispers* NOT emigrat* NOT immigrat* NOT colonis* NOT coloniz*)

NOT TI=(dispersion OR dispersible)

NOT TS=(refugees OR "international migration" OR "domestic migration" OR "human migration" OR asylum OR illegal)

AND (SJ==("ENVIRONMENTAL SCIENCES ECOLOGY" OR "ZOOLOGY" OR "BEHAVIORAL SCIENCES" OR "BIODIVERSITY CONSERVATION" OR "INFECTIOUS DISEASES" OR "PLANT SCIENCES" OR "PHYSIOLOGY" OR "LIFE SCIENCES BIOMEDICINE OTHER TOPICS" OR "AGRICULTURE" OR "MARINE FRESHWATER BIOLOGY" OR "MICROBIOLOGY" OR "PATHOLOGY" OR "EVOLUTIONARY BIOLOGY" OR "DEMOGRAPHY" OR "ENTOMOLOGY" OR "PARASITOLOGY" OR "FORESTRY" OR "FISHERIES"))

NOT (WC==("PUBLIC ENVIRONMENTAL OCCUPATIONAL HEALTH" OR "ETHNIC STUDIES" OR "ECONOMICS" OR "PHARMACOLOGY PHARMACY" OR "FOOD SCIENCE TECHNOLOGY" OR "REGIONAL URBAN PLANNING" OR "CHEMISTRY ANALYTICAL" OR "GREEN SUSTAINABLE SCIENCE TECHNOLOGY" OR "ANTHROPOLOGY" OR "GASTROENTEROLOGY HEPATOLOGY" OR LAW OR "PSYCHOLOGY BIOLOGICAL" OR "ENERGY FUELS" OR "MEDICINE RESEARCH EXPERIMENTAL" OR ONCOLOGY OR "DENTISTRY ORAL SURGERY MEDICINE" OR "RADIOLOGY NUCLEAR MEDICINE MEDICAL IMAGING" OR "NUTRITION DIETETICS" OR "CHEMISTRY PHYSICAL" OR "RESPIRATORY SYSTEM" OR PALEONTOLOGY OR SOCIOLOGY OR "PERIPHERAL VASCULAR DISEASE" OR "PEDIATRICS" OR "SOCIAL SCIENCES BIOMEDICAL" OR "ASTRONOMY ASTROPHYSICS" OR "BIOPHYSICS"))
Box S2: Full model equations for both the full dataset and the beneficial/detrimental interactor dataset

The models were run with the *metafor* v 4.2-0 package [47] in R v4.3.1. We used the *dredge* function from the *MuMln* v 1.47.5 package to select models with the best fixed structure from the full models, and averaged models within ΔAICc<2.

# full model for the full dataset

```r
full_mod_all = rma.mv(yi=es, v=var,
  mods=~ type_interactor * dispersal_phase + type_interactor * type_study + type_interactor *
  type_manipulation_interactor + type_interactor * type_community + type_interactor * possibility_interactor_disperse +
  type_interactor * generation_similarity +
  type_interactor * I(level_generalism_focal - mean(level_generalism_focal)) + type_interactor *
  I(level_generalism_interactor - mean(level_generalism_interactor)) + type_interactor * I(log_generation_time_focal -
  mean(log_generation_time_focal)) + type_interactor * I(log_duration_experiment - mean(log_duration_experiment)),

random = list(~1 | study_ID, ~1 | focal_ID, ~1 | interactor_ID), R = list(focal.phylo=focal.phylogenetic_tree),

method='ML', test='t', data= full_dataset)
```

# full model for beneficial interactors

```r
full_mod_beneficial = rma.mv(yi=es, v=var,
  mods=~ nature_interactor + dispersal_phase + type_study + type_manipulation_interactor + type_community +
  possibility_interactor_disperse + generation_similarity +
  I(level_generalism_focal - mean(level_generalism_focal)) + I(level_generalism_interactor -
  mean(level_generalism_interactor)) + I(log_generation_time_focal - mean(log_generation_time_focal)) +
  I(log_duration_experiment - mean(log_duration_experiment)),

random = list(~1 | study_ID, ~1 | focal_ID, ~1 | interactor_ID), R = list(focal.phylo=focal.phylogenetic_tree),

method='ML', test='t', data= beneficial_interactors_dataset)
```

# full model for detrimental interactors

```r
full_mod_detrimental = rma.mv(yi=es, v=var,
  mods=~ nature_interactor + dispersal_phase + type_study + type_manipulation_interactor + type_community +
  possibility_interactor_disperse + generation_similarity +
  I(level_generalism_focal - mean(level_generalism_focal)) + I(level_generalism_interactor -
  mean(level_generalism_interactor)) + I(log_generation_time_focal - mean(log_generation_time_focal)) +
  I(log_duration_experiment - mean(log_duration_experiment)),

random = list(~1 | study_ID, ~1 | focal_ID, ~1 | interactor_ID), R = list(focal.phylo=focal.phylogenetic_tree),

method='ML', test='t', data= detrimental_interactors_dataset)
```
Box S2: Full model equations for both the full dataset and the beneficial/detrimental interactor dataset

The models were run with the *metafor* v 4.2-0 package [47] in R v4.3.1. We used the *dredge* function from the *MuMln* v 1.47.5 package to select models with the best fixed structure from the full models, and averaged models within ΔAICc<2.

# full model for the full dataset

```r
full_mod_all = rma.mv(yi=es, v=var,

    mods=~ type_interactor * dispersal_phase + type_interactor * type_study + type_interactor *
    type-manipulation_interactor + type_interactor * type_community + type_interactor * possibility_interactor_disperse +
    type_interactor * generation_similarity +
    type_interactor * I(level_generalism_focal - mean(level_generalism_focal)) + type_interactor *
    I(level_generalism_interactor - mean(level_generalism_interactor)) + type_interactor * I(log_generation_time_focal -
    mean(log_generation_time_focal)) + type_interactor * I(log_duration_experiment - mean(log_duration_experiment)),

    random= list(~1 | study_ID, ~1 | focal_ID, ~1 | interactor_ID), R = list(focal.phylo=focal.phylogenetic_tree),

    method='ML', test='t', data= full_dataset)
```

# full model for beneficial interactors

```r
full_mod_beneficial = rma.mv(yi=es, v=var,

    mods=~ nature_interactor + dispersal_phase + type_study + type_manipulation_interactor + type_community +
    possibility_interactor_disperse + generation_similarity +
    I(level_generalism_focal - mean(level_generalism_focal)) + I(level_generalism_interactor -
    mean(level_generalism_interactor)) + I(log_generation_time_focal - mean(log_generation_time_focal)) +
    I(log_duration_experiment - mean(log_duration_experiment)),

    random= list(~1 | study_ID, ~1 | focal_ID, ~1 | interactor_ID), R = list(focal.phylo=focal.phylogenetic_tree),

    method='ML', test='t', data= beneficial_interactors_dataset)
```

# full model for detrimental interactors

```r
full_mod_detrimental = rma.mv(yi=es, v=var,

    mods=~ nature_interactor + dispersal_phase + type_study + type_manipulation_interactor + type_community +
    possibility_interactor_disperse + generation_similarity +
    I(level_generalism_focal - mean(level_generalism_focal)) + I(level_generalism_interactor -
    mean(level_generalism_interactor)) + I(log_generation_time_focal - mean(log_generation_time_focal)) +
    I(log_duration_experiment - mean(log_duration_experiment)),

    random= list(~1 | study_ID, ~1 | focal_ID, ~1 | interactor_ID), R = list(focal.phylo=focal.phylogenetic_tree),

    method='ML', test='t', data= detrimental_interactors_dataset)
```
<figure><img src="image_9.png" /><figcaption>Figure S1: Prisma plot</figcaption></figure>

Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA, [30]) diagram showing
how records were assessed, screened, and included in the meta-analysis.
<figure><img src="image_8.png" /><figcaption>Figure S1: Prisma plot</figcaption></figure>

Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA, [30]) diagram showing
how records were assessed, screened, and included in the meta-analysis.
<figure><img src="image_10.png" /><figcaption>Figure S2: Phylogeny of the focal taxon</figcaption></figure>

The taxonomy of the focal taxa extracted from the GBIF database (and other databases when missing) is used as a surrogate for a true phylogenetic tree, with Graphen's method for compiling branch length [46].
<figure><img src="image_9.png" /><figcaption>Figure S2: Phylogeny of the focal taxon</figcaption></figure>

The taxonomy of the focal taxa extracted from the GBIF database (and other databases when missing) is used as a surrogate for a true phylogenetic tree, with Graphen's method for compiling branch length [46].
<figure><img src="image_11.png" /><figcaption>Figure S3: Taxonomic description of the taxa involved in the analysis</figcaption></figure>

(a) Sankey diagram of the number of effect sizes (k) per focal taxon phylum, interacting taxon phylum, type of interactor (beneficial/detrimental) and nature of interactor, coloured by the nature of interactor (red: resource, pink: predator, dark blue: parasite, teal blue: mutualist, forest green: host, yellow-green: competitor). (b) Sankey diagram this time per focal class and interacting taxon class.
<figure><img src="image_10.png" /><figcaption>Figure S3: Taxonomic description of the taxa involved in the analysis</figcaption></figure>

(a) Sankey diagram of the number of effect sizes (k) per focal taxon phylum, interacting taxon phylum, type of interactor (beneficial/detrimental) and nature of interactor, coloured by the nature of interactor (red: resource, pink: predator, dark blue: parasite, teal blue: mutualist, forest green: host, yellow-green: competitor). (b) Sankey diagram this time per focal class and interacting taxon class.
<figure><img src="image_12.png" /><figcaption>Figure S4: Number of effect sizes by moderator level for the main moderators</figcaption></figure>

Sankey diagrams of the number of effect sizes (k, in white inside the bars) per moderator for all of the moderators of interest, i.e. the type of interactor (detrimental or beneficial interactor), the nature of interactor (competitor, host, mutualist, parasite, predator, resource), the dispersal phase (emigration, full dispersal or transience, the type of manipulation of the interactor (presence, cues of presence, abundance), the level of community complexity (pair of species, simplified community or complex community), the possibility for the interactor to disperse, the similarity in generations between the interaction and the dispersal (same or different generation), the level of generalism of the focal species and of the interactor (rated from 1 to 4), and last the type of study (experimental or observational). Colours corresponds to the nature of interactor (red: resource, pink: predator, dark blue: parasite, teal blue: mutualist, forest green: host, yellow-green: competitor).
<figure><img src="image_11.png" /><figcaption>Figure S4: Number of effect sizes by moderator level for the main moderators</figcaption></figure>

Sankey diagrams of the number of effect sizes (k, in white inside the bars) per moderator for all of the moderators of interest, i.e. the type of interactor (detrimental or beneficial interactor), the nature of interactor (competitor, host, mutualist, parasite, predator, resource), the dispersal phase (emigration, full dispersal or transience, the type of manipulation of the interactor (presence, cues of presence, abundance), the level of community complexity (pair of species, simplified community or complex community), the possibility for the interactor to disperse, the similarity in generations between the interaction and the dispersal (same or different generation), the level of generalism of the focal species and of the interactor (rated from 1 to 4), and last the type of study (experimental or observational). Colours corresponds to the nature of interactor (red: resource, pink: predator, dark blue: parasite, teal blue: mutualist, forest green: host, yellow-green: competitor).
<figure><img src="image_13.png" /><figcaption>Figure S5: Funnel plot of the model with all moderators represented in the averaged model</figcaption></figure>

The funnel plot on the model containing all moderators represented in the averaged model does not evidence any strong asymmetry, which is confirmed by the Egger's test that shows that the intercept for the residuals by precision linear model crosses zero (intercept [95% CI] = 0.13 [-0.03, 0.28], t = 1.6, df = 395, p = 0.119). Together with the very high Rosenberg fail-safe number (N =47929), this suggests that there is no evidence of publication bias.
<figure><img src="image_12.png" /><figcaption>Figure S5: Funnel plot of the model with all moderators represented in the averaged model</figcaption></figure>

The funnel plot on the model containing all moderators represented in the averaged model does not evidence any strong asymmetry, which is confirmed by the Egger's test that shows that the intercept for the residuals by precision linear model crosses zero (intercept [95% CI] = 0.13 [-0.03, 0.28], t = 1.6, df = 395, p = 0.119). Together with the very high Rosenberg fail-safe number (N =47929), this suggests that there is no evidence of publication bias.
<figure><img src="image_14.png" /><figcaption>Figure S6: Funnel plots of the models for the two data subsets with all moderators represented in the averaged models</figcaption></figure>

(a) Funnel plot for detrimental interactions, (b) Funnel plot for beneficial interactions. The funnel plot for detrimental interactions reveals some asymmetry, which is confirmed by the Egger's test that shows a non-zero intercept for the residual (intercept [95% CI] = 0.30 [0.11, 0.48], t = 3.2, df = 243, p = 0.002), suggesting potential publication bias. However, the Rosenberg fail-safe number was very high (N = 30,695), suggesting the converse. For beneficial interactions, the Funnel plot does not evidence any strong asymmetry, which is confirmed by the Egger's test (intercept [95% CI] = --0.21 [-0.64, 0.22], t = -0.98, df = 150, p = 0.33). Together with the high Rosenberg fail-safe number (N = 17,746), this suggests that there is no evidence of publication bias.
<figure><img src="image_13.png" /><figcaption>Figure S6: Funnel plots of the models for the two data subsets with all moderators represented in the averaged models</figcaption></figure>

(a) Funnel plot for detrimental interactions, (b) Funnel plot for beneficial interactions. The funnel plot for detrimental interactions reveals some asymmetry, which is confirmed by the Egger's test that shows a non-zero intercept for the residual (intercept [95% CI] = 0.30 [0.11, 0.48], t = 3.2, df = 243, p = 0.002), suggesting potential publication bias. However, the Rosenberg fail-safe number was very high (N = 30,695), suggesting the converse. For beneficial interactions, the Funnel plot does not evidence any strong asymmetry, which is confirmed by the Egger's test (intercept [95% CI] = --0.21 [-0.64, 0.22], t = -0.98, df = 150, p = 0.33). Together with the high Rosenberg fail-safe number (N = 17,746), this suggests that there is no evidence of publication bias.
<figure><img src="image_15.png" /><figcaption>Figure S7: The effect of interactors on dispersal varies with the duration of the experiment and the generation time</figcaption></figure>

Effect of (a) the log duration experiment or (b) the log generation time 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). 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 minimum, Q1, median, mean, and maximum values of log duration.
<figure><img src="image_14.png" /><figcaption>Figure S7: The effect of interactors on dispersal varies with the duration of the experiment and the generation time</figcaption></figure>

Effect of (a) the log duration experiment or (b) the log generation time 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). 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 minimum, Q1, median, mean, and maximum values of log duration.
<caption>Table S1: Effect size transformation depending on the type of data.</caption>

The preferred type of data extracted was either pair of means, contingency tables or correlation (depending on the type of study), followed by test parameters from t-tests, F-tests or other types of tests.

<table><thead><tr><th>Type of data</th><th>Data extraction</th><th>Reference and R functions used</th><th>Formula</th></tr></thead><tbody><tr><td>Pair of means</td><td>From raw data, from tables or text, from figures with means and SE, means and SD, or boxplots [18]</td><td>[37]<br>esc::esc_mean_sd</td><td>d = <sup>Y<sub>1</sub> - Y<sub>2</sub></sup>&frasl;<sub>&radic;( (n<sub>1</sub> - 1)s<sub>1</sub><sup>2</sup> + (n<sub>2</sub> - 1)s<sub>2</sub><sup>2</sup> )</sub> J,<br>where J = 1 - <sup>3</sup>&frasl;<sub>4(n<sub>1</sub> + n<sub>2</sub> - 2) - 1</sub><br>and v<sub>d</sub> = <sup>n<sub>1</sub> + n<sub>2</sub></sup>&frasl;<sub>n<sub>1</sub>n<sub>2</sub></sub> + <sup>d<sup>2</sup></sup>&frasl;<sub>2(n<sub>1</sub> + n<sub>2</sub>)</sub></td></tr><tr><td>Contingency table or log odds ratio</td><td>From raw data, tables or text, occasionally from figures. Either number of dispersers (A: treatment, B: control) and residents (C: treatment, D: control) per category, or log odds ratio and variance extracted from summary data from logistic models and logistic mixed models</td><td>[34,37]<br>esc::esc_2x2</td><td><sup>AD</sup>&frasl;<sub>BC'</sub>,<br>With v<sub>lnOR</sub> = <sup>1</sup>&frasl;<sub>A</sub> + <sup>1</sup>&frasl;<sub>B</sub> + <sup>1</sup>&frasl;<sub>C</sub> + <sup>1</sup>&frasl;<sub>D'</sub><br>Converted to Hedges' d following:<br>d = <sup>lnln( )√3</sup>&frasl;<sub>π</sub><br>And<br>v<sub>d</sub> = <sup>3v<sub>lnOR</sub></sup>&frasl;<sub>π<sup>2</sup></sub></td></tr><tr><td>Correlation</td><td>From raw data, figures, tables or text</td><td>[34]</td><td>Conversion to Hedge's d following<br>d = <sup>2r</sup>&frasl;<sub>√(1 - r<sup>2</sup>)</sub><br>And<br>v<sub>d</sub> = <sup>4v<sub>r</sub></sup>&frasl;<sub>(1 - r<sup>2</sup>)<sup>3</sup></sub></td></tr><tr><td>Student t test, F-test from one-way ANOVA</td><td>From tables or text</td><td>[37,38]<br>esc::esc_t</td><td>|r| = √<sup>t<sup>2</sup></sup>&frasl;<sub>t<sup>2</sup> + df</sub> or |r| = √<sup>F</sup>&frasl;<sub>F + df</sub></td></tr></tbody></table>
Table S1: Effect size transformation depending on the type of data.

The preferred type of data extracted was either pair of means, contingency tables or correlation (depending on the type of study), followed by test parameters from t-tests, F-tests or other types of tests.

<table><thead><tr><th>Type of data</th><th>Data extraction</th><th>Reference and R functions used</th><th>Formula</th></tr></thead><tbody><tr><td>Pair of means</td><td>From raw data, from tables or text, from figures with means and SE, means and SD, or boxplots [18]</td><td>[37]<br>esc::esc_mean_sd</td><td>d = <sup>Y<sub>1</sub> - Y<sub>2</sub></sup>&frasl;<sub>&radic;( (n<sub>1</sub> - 1)s<sub>1</sub><sup>2</sup> + (n<sub>2</sub> - 1)s<sub>2</sub><sup>2</sup> )</sub> J,<br>where J = 1 - <sup>3</sup>&frasl;<sub>4(n<sub>1</sub> + n<sub>2</sub> - 2) - 1</sub><br>and v<sub>d</sub> = <sup>n<sub>1</sub> + n<sub>2</sub></sup>&frasl;<sub>n<sub>1</sub>n<sub>2</sub></sub> + <sup>d<sup>2</sup></sup>&frasl;<sub>2(n<sub>1</sub> + n<sub>2</sub>)</sub></td></tr><tr><td>Contingency table or log odds ratio</td><td>From raw data, tables or text, occasionally from figures. Either number of dispersers (A: treatment, B: control) and residents (C: treatment, D: control) per category, or log odds ratio and variance extracted from summary data from logistic models and logistic mixed models</td><td>[34,37]<br>esc::esc_2x2</td><td><sup>AD</sup>&frasl;<sub>BC'</sub>,<br>With v<sub>lnOR</sub> = <sup>1</sup>&frasl;<sub>A</sub> + <sup>1</sup>&frasl;<sub>B</sub> + <sup>1</sup>&frasl;<sub>C</sub> + <sup>1</sup>&frasl;<sub>D'</sub><br>Converted to Hedges' d following:<br>d = <sup>lnln( )√3</sup>&frasl;<sub>π</sub><br>And<br>v<sub>d</sub> = <sup>3v<sub>lnOR</sub></sup>&frasl;<sub>π<sup>2</sup></sub></td></tr><tr><td>Correlation</td><td>From raw data, figures, tables or text</td><td>[34]</td><td>Conversion to Hedge's d following<br>d = <sup>2r</sup>&frasl;<sub>√(1 - r<sup>2</sup>)</sub><br>And<br>v<sub>d</sub> = <sup>4v<sub>r</sub></sup>&frasl;<sub>(1 - r<sup>2</sup>)<sup>3</sup></sub></td></tr><tr><td>Student t test, F-test from one-way ANOVA</td><td>From tables or text</td><td>[37,38]<br>esc::esc_t</td><td>|r| = √<sup>t<sup>2</sup></sup>&frasl;<sub>t<sup>2</sup> + df</sub> or |r| = √<sup>F</sup>&frasl;<sub>F + df</sub></td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td>Followed by a conversion from r to Hedge's d (see above)</td></tr><tr><td>X<sup>2</sup> coefficient from 2x2 frequency tables</td><td>From tables or text</td><td>[37]<br>esc::esc_chisq</td><td>|r| = &radic;<span style="text-decoration: overline;">X<sup>2</sup>/n</span><br>Followed by a conversion from r to Hedge's d (see above)</td></tr><tr><td>Mann-Whitney Z-score, Wilcoxon signed-rank test for paired samples</td><td>From tables or text</td><td>[38]<br>effectsize::z_to_d</td><td>r = <span style="text-decoration: overline;">Z</span>/&radic;n<br>Followed by a conversion from r to Hedge's d (see above)</td></tr><tr><td>Partial eta<sup>2</sup> from multiple factor ANOVA, from multiple factor regression, from linear mixed models, eta<sup>2</sup> from Kruskal-Wallis H-test</td><td>From tables or text</td><td>[38-40]<br>esc::cohens_d</td><td>η<sub>p</sub><sup>2</sup> = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">SS<sub>effect</sub></div><div>SS<sub>effect</sub>+SS<sub>error</sub></div></div><br>η<sub>p</sub><sup>2</sup> = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">F*df<sub>effect</sub></div><div>F*df<sub>effect</sub>+F*df<sub>error</sub></div></div><br>η<sub>p</sub><sup>2</sup> = &radic;<span style="text-decoration: overline;">t<sup>2</sup>/t<sup>2</sup>+df</span><br>η<sub>h</sub><sup>2</sup> = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">H-k+1</div><div>n-k</div></div></td></tr></table>
<table><tr><td></td><td></td><td></td><td>Followed by a conversion from r to Hedge's d (see above)</td></tr><tr><td>X<sup>2</sup> coefficient from 2x2 frequency tables</td><td>From tables or text</td><td>[37] esc::esc_chisq</td><td>|r| = &radic;<span style="text-decoration: overline;">X<sup>2</sup>/n</span><br>Followed by a conversion from r to Hedge's d (see above)</td></tr><tr><td>Mann-Whitney Z-score, Wilcoxon signed-rank test for paired samples</td><td>From tables or text</td><td>[38] effectsize::z_to_d</td><td>r = <span style="text-decoration: overline;">Z</span>/&radic;n<br>Followed by a conversion from r to Hedge's d (see above)</td></tr><tr><td>Partial eta<sup>2</sup> from multiple factor ANOVA, from multiple factor regression, from linear mixed models, eta<sup>2</sup> from Kruskal-Wallis H-test</td><td>From tables or text</td><td>[38-40] esc::cohens_d</td><td>η<sub>p</sub><sup>2</sup> = <div style="display: inline-block; vertical-align: middle; text-align: center;"><span style="display: block; border-bottom: 1px solid black; padding: 0 0.2em;">SS<sub>effect</sub></span><span style="display: block; padding: 0 0.2em;">SS<sub>error</sub></span></div> / (SS<sub>effect</sub>+SS<sub>error</sub>)<br>η<sub>p</sub><sup>2</sup> = <div style="display: inline-block; vertical-align: middle; text-align: center;"><span style="display: block; border-bottom: 1px solid black; padding: 0 0.2em;">F*df<sub>effect</sub></span><span style="display: block; padding: 0 0.2em;">F*df<sub>error</sub></span></div> / (F*df<sub>effect</sub>+F*df<sub>error</sub>)<br>η<sub>p</sub><sup>2</sup> = &radic;<span style="text-decoration: overline;">(t<sup>2</sup>)/(t<sup>2</sup>+df)</span><br>η<sub>h</sub><sup>2</sup> = <div style="display: inline-block; vertical-align: middle; text-align: center;"><span style="display: block; border-bottom: 1px solid black; padding: 0 0.2em;">H-k+1</span><span style="display: block; padding: 0 0.2em;">n-k</span></div></td></tr></table>
Table S2: Types of moderators and simplification of information

For each type of moderator/random effect, we describe the levels and the number of effect sizes (es) per level, as well explain the choices made to create the moderator.

<table><thead><tr><td>Name of moderator/random effect</td><td>Levels</td><td>Choices on moderator</td></tr></thead><tbody><tr><td>Study ID</td><td>118 levels, the ID of the study</td><td></td></tr><tr><td>Focal taxon identity, taxonomy and phylogeny</td><td>Focal taxon identity: 144 levels, genus: 116 levels, family: 94 levels, order: 41 levels, class: 16 levels, phylum: 7 levels, kingdom: 2 levels.<br>Correlation matrix between focal taxa from phylogeny.</td><td>Focal taxon identity gathered from the study, normally species ID but can be genus (17 effect sizes) or family (12 es) depending on the study.<br>Focal taxon taxonomy gathered from the NCBI database with taxize R package v0.9.100; when missing from GBIF or ITIS databases.<br>Focal taxon phylogeny created from surrogate taxonomic tree with grafens method to compute branch length.</td></tr><tr><td>Interacting taxon identity, taxonomy</td><td>Interacting taxon identity: 165 levels, genus: 125 levels, family: 103 levels, order: 69 levels, class: 32 levels, phylum: 20 levels, kingdom 7 levels.</td><td>Interacting taxon identity gathered from the study, normally species ID but the taxon can be determined at several taxonomic levels up to the kingdom or include undefined groups (e.g., dead wood, microalgae). We gathered interacting taxon taxonomy from the same databases as focal taxon, and to avoid missing data we replaced all NA by « undetermined ». Because of the number of undetermined data, as well as the number of groups defined to higher taxonomic levels, we did not provide a phylogeny.</td></tr><tr><td>Nature of interactor</td><td>6 levels, resource (125 es), competitor (73 es), host (6 es), mutualist (21 es), parasite (56 es), predator (116 es). Reference level: resource</td><td>We grouped parasites, parasitoid and virus as the « parasite » category, we grouped prey and resource as « resource », and endosymbiont and defensive mutualist as « mutualist ».</td></tr><tr><td>Type of interactor</td><td>2 levels, detrimental (245 es) or beneficial (152 es). Reference level: detrimental</td><td>We assumed that competitors, parasites and predators reduce the fitness of the focal species and are thus detrimental interactors, while resources, hosts and mutualists enhance the fitness and are thus beneficial</td></tr></tbody></table>
Table S2: Types of moderators and simplification of information

For each type of moderator/random effect, we describe the levels and the number of effect sizes (es) per level, as well explain the choices made to create the moderator.

<table><thead><tr><td>Name of moderator/random effect</td><td>Levels</td><td>Choices on moderator</td></tr></thead><tbody><tr><td>Study ID</td><td>118 levels, the ID of the study</td><td></td></tr><tr><td>Focal taxon identity, taxonomy and phylogeny</td><td>Focal taxon identity: 144 levels, genus: 116 levels, family: 94 levels, order: 41 levels, class: 16 levels, phylum: 7 levels, kingdom: 2 levels.<br>Correlation matrix between focal taxa from phylogeny.</td><td>Focal taxon identity gathered from the study, normally species ID but can be genus (17 effect sizes) or family (12 es) depending on the study.<br>Focal taxon taxonomy gathered from the NCBI database with taxize R package v0.9.100; when missing from GBIF or ITIS databases.<br>Focal taxon phylogeny created from surrogate taxonomic tree with grafens method to compute branch length.</td></tr><tr><td>Interacting taxon identity, taxonomy</td><td>Interacting taxon identity: 165 levels, genus: 125 levels, family: 103 levels, order: 69 levels, class: 32 levels, phylum: 20 levels, kingdom 7 levels.</td><td>Interacting taxon identity gathered from the study, normally species ID but the taxon can be determined at several taxonomic levels up to the kingdom or include undefined groups (e.g., dead wood, microalgae). We gathered interacting taxon taxonomy from the same databases as focal taxon, and to avoid missing data we replaced all NA by « undetermined ». Because of the number of undetermined data, as well as the number of groups defined to higher taxonomic levels, we did not provide a phylogeny.</td></tr><tr><td>Nature of interactor</td><td>6 levels, resource (125 es), competitor (73 es), host (6 es), mutualist (21 es), parasite (56 es), predator (116 es). Reference level: resource</td><td>We grouped parasites, parasitoid and virus as the « parasite » category, we grouped prey and resource as « resource », and endosymbiont and defensive mutualist as « mutualist ».</td></tr><tr><td>Type of interactor</td><td>2 levels, detrimental (245 es) or beneficial (152 es). Reference level: detrimental</td><td>We assumed that competitors, parasites and predators reduce the fitness of the focal species and are thus detrimental interactors, while resources, hosts and mutualists enhance the fitness and are thus beneficial</td></tr></tbody></table>
<table><tr><td></td><td></td><td>interactors.</td></tr><tr><td>Type of community</td><td>3 levels: pair of species (135 es), simple community (141 es) or complex community (121 es). Reference level: pair of species.</td><td>Studies either manipulated two taxa alone, manipulated a focal taxa and an interacting species in a simplified community composed of a small subset of species (often resources, but sometimes more complex), or manipulated the focal taxa and the interacting taxa in more complex settings including a large number of taxa (e.g., manipulative studies in the wild with addition of predator cues or resources). Further, studies that did not manipulate the interacting species presence or abundance could also be classified in these levels (e.g., observational studies correlating the abundance of predators and the dispersal of the focal taxon).</td></tr><tr><td>Type of manipulation</td><td>3 levels: presence (196 es), cues of presence (84 es), or abundance (117 es). Reference level: presence</td><td>Studies could either manipulate the presence or the cues of presence of the interacting species, in a 0/1 setting, or manipulate the abundance (or abundance of cues) in a gradient or by comparing levels. Because only one study manipulated the cues of abundance, with only 4 es, we merged the effect sizes of direct manipulation of abundance with the cues of abundance effect sizes to create only three levels, presence, cues of presence, or abundance. Observational studies could also be classified in these three levels.</td></tr><tr><td>Possibility for the interactor to disperse</td><td>2 levels: no (249 es) or yes (148 es). Reference level: no</td><td>The question here is can the interacting taxon disperse during the experiment/observation, and not can it disperse overall.</td></tr><tr><td>Dispersal phase</td><td>3 levels: emigration (275 es), transience (48 es) and full dispersal (74 es). Reference level: emigration</td><td>Dispersal phase studied during the experiment/observation, either emigration, transience or full dispersal where individuals were monitored from the emigration to the immigration phase. Studies focusing only on the colonization part were excluded.</td></tr><tr><td>Similarity of generation between interaction and dispersal</td><td>2 levels: same generation (348 es) or different generation (49 es). Reference level: same generation.</td><td>Is the dispersing individual the same as the interacting individual? In most of the cases, the dispersing individual is the individual that has undergone the interaction, but in certain cases, it can be its parent (e.g. risk of predation on the parent and observed dispersal of the offspring) or its offspring (e.g. nest predation where the parent disperses after a predation</td></tr></table>
<table><tr><td></td><td></td><td>interactors.</td></tr><tr><td>Type of community</td><td>3 levels: pair of species (135 es), simple community (141 es) or complex community (121 es). Reference level: pair of species.</td><td>Studies either manipulated two taxa alone, manipulated a focal taxa and an interacting species in a simplified community composed of a small subset of species (often resources, but sometimes more complex), or manipulated the focal taxa and the interacting taxa in more complex settings including a large number of taxa (e.g., manipulative studies in the wild with addition of predator cues or resources). Further, studies that did not manipulate the interacting species presence or abundance could also be classified in these levels (e.g., observational studies correlating the abundance of predators and the dispersal of the focal taxon).</td></tr><tr><td>Type of manipulation</td><td>3 levels: presence (196 es), cues of presence (84 es), or abundance (117 es). Reference level: presence</td><td>Studies could either manipulate the presence or the cues of presence of the interacting species, in a 0/1 setting, or manipulate the abundance (or abundance of cues) in a gradient or by comparing levels. Because only one study manipulated the cues of abundance, with only 4 es, we merged the effect sizes of direct manipulation of abundance with the cues of abundance effect sizes to create only three levels, presence, cues of presence, or abundance. Observational studies could also be classified in these three levels.</td></tr><tr><td>Possibility for the interactor to disperse</td><td>2 levels: no (249 es) or yes (148 es). Reference level: no</td><td>The question here is can the interacting taxon disperse during the experiment/observation, and not can it disperse overall.</td></tr><tr><td>Dispersal phase</td><td>3 levels: emigration (275 es), transience (48 es) and full dispersal (74 es). Reference level: emigration</td><td>Dispersal phase studied during the experiment/observation, either emigration, transience or full dispersal where individuals were monitored from the emigration to the immigration phase. Studies focusing only on the colonization part were excluded.</td></tr><tr><td>Similarity of generation between interaction and dispersal</td><td>2 levels: same generation (348 es) or different generation (49 es). Reference level: same generation.</td><td>Is the dispersing individual the same as the interacting individual? In most of the cases, the dispersing individual is the individual that has undergone the interaction, but in certain cases, it can be its parent (e.g. risk of predation on the parent and observed dispersal of the offspring) or its offspring (e.g. nest predation where the parent disperses after a predation</td></tr></table>
<table><tr><td></td><td></td><td>on its offspring)</td></tr><tr><td>Type of study</td><td>2 levels: experiment (331 es), observation (66 es). Reference level : experiment</td><td>Either experimental studies, or observational studies in the field.</td></tr><tr><td>Experimental setting</td><td>3 levels: laboratory (213 es), semi-natural (74 es) or in natura (110 es). Reference level: laboratory</td><td>Laboratory studies regroup studies in indoors microcosms or mesocosms, dispersal arenas or wind tunnels, while semi natural studies are outdoors mesocosms or some greenhouse and volary studies, and the last category are field studies. Note that because this moderator was nested within the type of community moderator, and thus difficult to disentangle, and because we were more interested in biological questions that methodological ones, we excluded this moderator.</td></tr><tr><td>Duration of the experiment</td><td>Numerical, centered log number of days.</td><td>Duration of the experiment or observation. The number of days varies between -2e-4 days and 1e5 days, with a Q1 of 0.2 days, a median of 2 days, a Q3 of 28 days. We use the log number of days.</td></tr><tr><td>Generation time of the focal species</td><td>Numerical, centered log number of days</td><td>This moderator was either extracted from the studies when available, or more often found in a second step of searching the larger literature. The number of days varies between 0.1 and 7044, with a Q1 of 18, a median of 170 and a Q3 of 365. We use the log number of days.</td></tr><tr><td>Level of generalism of the focal species + Level of generalism of the interactor</td><td>Numerical, range 1-4.</td><td>This moderator was found in a second step of searching the larger literature. We rated focal and interacting species from 1 to 4 on whether they eat only one species, from the same genus or family, from the same order, or from different orders. Most of the species fell into the polyphagous category (rated 4: 292 es for focal species and 315 for interactors), with other ranging from 34 es for focal and 42 for interactors rated 3, 54 es for focal and 26 for interactors rated 2, and 17 es for focal and 14 for interactors rated 1 (monophagous).</td></tr><tr><td>Home range of the focal species + Home range of the interactor</td><td>Numerical</td><td>This moderator was found in a second step of searching the larger literature. Unfortunately, this information was available for very few pair of species, thus it is not included in the main models but tested for separately</td></tr></table>
<table><tr><td></td><td></td><td>on its offspring)</td></tr><tr><td>Type of study</td><td>2 levels: experiment (331 es), observation (66 es). Reference level : experiment</td><td>Either experimental studies, or observational studies in the field.</td></tr><tr><td>Experimental setting</td><td>3 levels: laboratory (213 es), semi-natural (74 es) or in natura (110 es). Reference level: laboratory</td><td>Laboratory studies regroup studies in indoors microcosms or mesocosms, dispersal arenas or wind tunnels, while semi natural studies are outdoors mesocosms or some greenhouse and volary studies, and the last category are field studies. Note that because this moderator was nested within the type of community moderator, and thus difficult to disentangle, and because we were more interested in biological questions that methodological ones, we excluded this moderator.</td></tr><tr><td>Duration of the experiment</td><td>Numerical, centered log number of days.</td><td>Duration of the experiment or observation. The number of days varies between -2e-4 days and 1e5 days, with a Q1 of 0.2 days, a median of 2 days, a Q3 of 28 days. We use the log number of days.</td></tr><tr><td>Generation time of the focal species</td><td>Numerical, centered log number of days</td><td>This moderator was either extracted from the studies when available, or more often found in a second step of searching the larger literature. The number of days varies between 0.1 and 7044, with a Q1 of 18, a median of 170 and a Q3 of 365. We use the log number of days.</td></tr><tr><td>Level of generalism of the focal species + Level of generalism of the interactor</td><td>Numerical, range 1-4.</td><td>This moderator was found in a second step of searching the larger literature. We rated focal and interacting species from 1 to 4 on whether they eat only one species, from the same genus or family, from the same order, or from different orders. Most of the species fell into the polyphagous category (rated 4: 292 es for focal species and 315 for interactors), with other ranging from 34 es for focal and 42 for interactors rated 3, 54 es for focal and 26 for interactors rated 2, and 17 es for focal and 14 for interactors rated 1 (monophagous).</td></tr><tr><td>Home range of the focal species + Home range of the interactor</td><td>Numerical</td><td>This moderator was found in a second step of searching the larger literature. Unfortunately, this information was available for very few pair of species, thus it is not included in the main models but tested for separately</td></tr></table>
<table><tr><td></td><td></td><td>on a small subset of 17 effect sizes. We use the log ratio of home range focal/home range interactor.</td></tr><tr><td>Interaction strength between species</td><td>Numerical</td><td>This moderator measures the interaction strengths between pairs of species found in the database as a standardized effect of the presence or abundance of the interacting species on a fitness-related traits in focal species (survival, fecundity, body condition, body growth, or abundance). The information was found either in the primary literature (studies that measured both dispersal and interaction strength, 166 es) or on the secondary literature (studies on interactions between pairs of species, 191 es). We were not able to find interaction strength data for 40 es in our database. Because we used different response metrics with very different ranges (e.g., survival on a 0-1 scale vs fecundity 10000 eggs), we first centered the dependent variable by dividing by the mean variable value allowing to estimate proportional changes while maintaining metrics' variation. Then, we regressed the response metric depending on the occurrence (0 vs. 1) or the density of the interacting species mean-centered and scaled by standard deviation. The interaction strength ranges between -1.64 and 1.42, with a Q1 of -0.29, a median of -0.08 and a Q3 of 0.14.</td></tr></table>
<table><tr><td></td><td></td><td>on a small subset of 17 effect sizes. We use the log ratio of home range focal/home range interactor.</td></tr><tr><td>Interaction strength between species</td><td>Numerical</td><td>This moderator measures the interaction strengths between pairs of species found in the database as a standardize effect of the presence or abundance of the interacting species on a fitness-related traits in focal species (survival, fecundity, body condition, body growth, or abundance). The information was found either in the primary literature (studies that measured both dispersal and interaction strength, 166 es) or on the secondary literature (studies on interactions between pairs of species, 191 es). We were not able to find interaction strength data for 40 es in our database. Because we used different response metrics with very different ranges (e.g., survival on a 0-1 scale vs fecundity 10000 eggs), we first centered the dependent variable by dividing by the mean variable value allowing to estimate proportional changes while maintaining metrics' variation. Then, we regressed the response metric depending on the occurrence (0 vs. 1) or the density of the interacting species mean-centered and scaled by standard deviation. The interaction strength ranges between -1.64 and 1.42, with a Q1 of -0.29, a median of -0.08 and a Q3 of 0.14.</td></tr></table>
Table S3: Evaluation of the taxonomic bias of the dataset

Comparison of the number of effect sizes per taxonomic level in our database to the actual number of described species in each taxon taken from the Catalogue of Life version 2023-11-24 (https://www.catalogueoflife.org/, [51]). Following [50], we used chi-square analyses to compare the observed numbers of effect sizes extracted from the publications in our database on taxa at two levels, kingdom and phylum, to expected frequencies generated from the recorded number of species in each taxon, under the null hypothesis that the representation of taxa in our database is in direct proportion to the relative number of species in that taxon.

<table><thead><tr><th>Taxonomic level</th><th>Taxon</th><th>Nb of effect sizes</th><th>Nb of species</th><th>Expected frequency</th><th>Observed frequency</th><th>χ<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="2">Kingdom</td><td>Animalia</td><td>349</td><td>1,505,821</td><td>96 %</td><td>87.9 %</td><td rowspan="2">χ<sup>2</sup> = 68, df = 1, p = 2e-16</td></tr><tr><td>Chromista</td><td>48</td><td>62,581</td><td>4 %</td><td>12.1 %</td></tr><tr><td rowspan="5">Phylum (among Animalia)</td><td>Annelida</td><td>4</td><td>17,656</td><td>1.2 %</td><td>1.1 %</td><td rowspan="5">χ<sup>2</sup> = 354, df = 4, p = 2e-75</td></tr><tr><td>Arthropoda</td><td>236</td><td>1,171,751</td><td>82.9 %</td><td>67.6 %</td></tr><tr><td>Chordata</td><td>70</td><td>73,509</td><td>5.2 %</td><td>20.1 %</td></tr><tr><td>Mollusca</td><td>7</td><td>132,305</td><td>9.4 %</td><td>2 %</td></tr><tr><td>Nematoda</td><td>32</td><td>17,590</td><td>1.2 %</td><td>9.2 %</td></tr></tbody></table>
Table S3: Evaluation of the taxonomic bias of the dataset

Comparison of the number of effect sizes per taxonomic level in our database to the actual number of described species in each taxon taken from the Catalogue of Life version 2023-11-24 (https://www.catalogueoflife.org/, [51]). Following [50], we used chi-square analyses to compare the observed numbers of effect sizes extracted from the publications in our database on taxa at two levels, kingdom and phylum, to expected frequencies generated from the recorded number of species in each taxon, under the null hypothesis that the representation of taxa in our database is in direct proportion to the relative number of species in that taxon.

<table><thead><tr><th>Taxonomic level</th><th>Taxon</th><th>Nb of effect sizes</th><th>Nb of species</th><th>Expected frequency</th><th>Observed frequency</th><th>χ<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="2">Kingdom</td><td>Animalia</td><td>349</td><td>1,505,821</td><td>96 %</td><td>87.9 %</td><td rowspan="2">χ<sup>2</sup> = 68, df = 1, p = 2e-16</td></tr><tr><td>Chromista</td><td>48</td><td>62,581</td><td>4 %</td><td>12.1 %</td></tr><tr><td rowspan="5">Phylum (among Animalia)</td><td>Annelida</td><td>4</td><td>17,656</td><td>1.2 %</td><td>1.1 %</td><td rowspan="5">χ<sup>2</sup> = 354, df = 4, p = 2e-75</td></tr><tr><td>Arthropoda</td><td>236</td><td>1,171,751</td><td>82.9 %</td><td>67.6 %</td></tr><tr><td>Chordata</td><td>70</td><td>73,509</td><td>5.2 %</td><td>20.1 %</td></tr><tr><td>Mollusca</td><td>7</td><td>132,305</td><td>9.4 %</td><td>2 %</td></tr><tr><td>Nematoda</td><td>32</td><td>17,590</td><td>1.2 %</td><td>9.2 %</td></tr></tbody></table>
Table S4: Evaluation of time-lag bias for the meta-analytic model

Model investigating the effect of time-lag bias by adding the interaction between the type of interactor (beneficial or detrimental) and the centered publication year to the model with all moderators retained in the averaged model. The effect sizes means, SE, 95 % CI, z-values and p-values are presented.

<table><thead><tr><th>Moderator</th><th>Level</th><th>effect size</th><th>SE</th><th>95% CI</th><th>z-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>intrcpt</td><td>-0.31</td><td>0.19</td><td>[-0.69,0.07]</td><td>-1.62</td><td>0.105</td></tr><tr><td>Dispersal phase</td><td>full dispersal</td><td>0.05</td><td>0.22</td><td>[-0.39,0.48]</td><td>0.21</td><td>0.837</td></tr><tr><td></td><td><strong>transience</strong></td><td><strong>-1.38</strong></td><td><strong>0.35</strong></td><td><strong>[-2.06,-0.70]</strong></td><td><strong>-3.99</strong></td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Centered log duration</td><td></td><td><strong>-0.15</strong></td><td><strong>0.04</strong></td><td><strong>[-0.22,-0.07]</strong></td><td><strong>-3.77</strong></td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Centered level of generalism focal species</td><td></td><td>0.24</td><td>0.14</td><td>[-0.04,0.51]</td><td>1.69</td><td>0.092.</td></tr><tr><td>Type of community</td><td><strong>simple community</strong></td><td><strong>0.69</strong></td><td><strong>0.21</strong></td><td><strong>[0.28,1.11]</strong></td><td><strong>3.25</strong></td><td><strong>0.001**</strong></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>[0.14,1.25]</strong></td><td><strong>2.45</strong></td><td><strong>0.014*</strong></td></tr><tr><td>Type of interactor</td><td>beneficial</td><td>-0.34</td><td>0.19</td><td>[-0.70,0.03]</td><td>-1.81</td><td>0.070.</td></tr><tr><td>Type of study</td><td>observational</td><td>0.24</td><td>0.30</td><td>[-0.35,0.83]</td><td>0.78</td><td>0.433</td></tr><tr><td>Centered level of generalism interactor</td><td></td><td><strong>-0.23</strong></td><td><strong>0.10</strong></td><td><strong>[-0.43,-0.03]</strong></td><td><strong>-2.21</strong></td><td><strong>0.027*</strong></td></tr><tr><td>Type of manipulation of interactor</td><td>abundance</td><td>-0.15</td><td>0.11</td><td>[-0.36,0.06]</td><td>-1.38</td><td>0.168</td></tr><tr><td></td><td><strong>cues of presence</strong></td><td><strong>0.44</strong></td><td><strong>0.19</strong></td><td><strong>[0.06,0.82]</strong></td><td><strong>2.29</strong></td><td><strong>0.022*</strong></td></tr><tr><td>Centered log generation time</td><td></td><td>0.05</td><td>0.04</td><td>[-0.03,0.13]</td><td>1.18</td><td>0.238</td></tr><tr><td>Similarity generation dispersal-interaction</td><td>different generation</td><td>0.27</td><td>0.28</td><td>[-0.28,0.82]</td><td>0.95</td><td>0.341</td></tr><tr><td>Type of interactor:Dispersal phase</td><td>beneficial:full dispersal</td><td>0.66</td><td>0.35</td><td>[-0.03,1.35]</td><td>1.88</td><td>0.060.</td></tr><tr><td></td><td><strong>beneficial:transience</strong></td><td><strong>1.49</strong></td><td><strong>0.41</strong></td><td><strong>[0.67,2.30]</strong></td><td><strong>3.59</strong></td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Type of interactor:Centered log duration</td><td><strong>beneficial:duration</strong></td><td><strong>0.25</strong></td><td><strong>0.06</strong></td><td><strong>[0.14,0.37]</strong></td><td><strong>4.31</strong></td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Type of interactor:Type of study</td><td><strong>beneficial:observation</strong></td><td><strong>-1.48</strong></td><td><strong>0.50</strong></td><td><strong>[-2.47,-0.49]</strong></td><td><strong>-2.94</strong></td><td><strong>0.003**</strong></td></tr><tr><td>Type of interactor:Centered log generation time</td><td>beneficial:generation time</td><td>-0.08</td><td>0.06</td><td>[-0.19,0.03]</td><td>-1.45</td><td>0.146</td></tr><tr><td>Type of interactor:Centered level of generalism focal species</td><td>beneficial:generalism focal</td><td>-0.10</td><td>0.18</td><td>[-0.45,0.24]</td><td>-0.59</td><td>0.557</td></tr><tr><td>Type of interactor:Centered publication year</td><td>detrimental:year</td><td>0.00</td><td>0.01</td><td>[-0.03,0.03]</td><td>-0.19</td><td>0.848</td></tr><tr><td></td><td><strong>beneficial:year</strong></td><td><strong>0.06</strong></td><td><strong>0.02</strong></td><td><strong>[0.03,0.09]</strong></td><td><strong>3.64</strong></td><td><strong>&lt;0.001***</strong></td></tr></tbody></table>
Table S4: Evaluation of time-lag bias for the meta-analytic model

Model investigating the effect of time-lag bias by adding the interaction between the type of interactor (beneficial or detrimental) and the centered publication year to the model with all moderators retained in the averaged model. The effect sizes means, SE, 95 % CI, z-values and p-values are presented.

<table><thead><tr><th>Moderator</th><th>Level</th><th>effect size</th><th>SE</th><th>95% CI</th><th>z-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>intrcpt</td><td>-0.31</td><td>0.19</td><td>[-0.69,0.07]</td><td>-1.62</td><td>0.105</td></tr><tr><td>Dispersal phase</td><td>full dispersal</td><td>0.05</td><td>0.22</td><td>[-0.39,0.48]</td><td>0.21</td><td>0.837</td></tr><tr><td></td><td><strong>transience</strong></td><td><strong>-1.38</strong></td><td><strong>0.35</strong></td><td><strong>[-2.06,-0.70]</strong></td><td><strong>-3.99</strong></td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Centered log duration</td><td></td><td><strong>-0.15</strong></td><td><strong>0.04</strong></td><td><strong>[-0.22,-0.07]</strong></td><td><strong>-3.77</strong></td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Centered level of generalism focal species</td><td></td><td>0.24</td><td>0.14</td><td>[-0.04,0.51]</td><td>1.69</td><td>0.092.</td></tr><tr><td>Type of community</td><td><strong>simple community</strong></td><td><strong>0.69</strong></td><td><strong>0.21</strong></td><td><strong>[0.28,1.11]</strong></td><td><strong>3.25</strong></td><td><strong>0.001**</strong></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>[0.14,1.25]</strong></td><td><strong>2.45</strong></td><td><strong>0.014*</strong></td></tr><tr><td>Type of interactor</td><td>beneficial</td><td>-0.34</td><td>0.19</td><td>[-0.70,0.03]</td><td>-1.81</td><td>0.070.</td></tr><tr><td>Type of study</td><td>observational</td><td>0.24</td><td>0.30</td><td>[-0.35,0.83]</td><td>0.78</td><td>0.433</td></tr><tr><td>Centered level of generalism interactor</td><td></td><td><strong>-0.23</strong></td><td><strong>0.10</strong></td><td><strong>[-0.43,-0.03]</strong></td><td><strong>-2.21</strong></td><td><strong>0.027*</strong></td></tr><tr><td>Type of manipulation of interactor</td><td>abundance</td><td>-0.15</td><td>0.11</td><td>[-0.36,0.06]</td><td>-1.38</td><td>0.168</td></tr><tr><td></td><td><strong>cues of presence</strong></td><td><strong>0.44</strong></td><td><strong>0.19</strong></td><td><strong>[0.06,0.82]</strong></td><td><strong>2.29</strong></td><td><strong>0.022*</strong></td></tr><tr><td>Centered log generation time</td><td></td><td>0.05</td><td>0.04</td><td>[-0.03,0.13]</td><td>1.18</td><td>0.238</td></tr><tr><td>Similarity generation dispersal-interaction</td><td>different generation</td><td>0.27</td><td>0.28</td><td>[-0.28,0.82]</td><td>0.95</td><td>0.341</td></tr><tr><td>Type of interactor:Dispersal phase</td><td>beneficial:full dispersal</td><td>0.66</td><td>0.35</td><td>[-0.03,1.35]</td><td>1.88</td><td>0.060.</td></tr><tr><td></td><td><strong>beneficial:transience</strong></td><td><strong>1.49</strong></td><td><strong>0.41</strong></td><td><strong>[0.67,2.30]</strong></td><td><strong>3.59</strong></td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Type of interactor:Centered log duration</td><td><strong>beneficial:duration</strong></td><td><strong>0.25</strong></td><td><strong>0.06</strong></td><td><strong>[0.14,0.37]</strong></td><td><strong>4.31</strong></td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Type of interactor:Type of study</td><td><strong>beneficial:observation</strong></td><td><strong>-1.48</strong></td><td><strong>0.50</strong></td><td><strong>[-2.47,-0.49]</strong></td><td><strong>-2.94</strong></td><td><strong>0.003**</strong></td></tr><tr><td>Type of interactor:Centered log generation time</td><td>beneficial:generation time</td><td>-0.08</td><td>0.06</td><td>[-0.19,0.03]</td><td>-1.45</td><td>0.146</td></tr><tr><td>Type of interactor:Centered level of generalism focal species</td><td>beneficial:generalism focal</td><td>-0.10</td><td>0.18</td><td>[-0.45,0.24]</td><td>-0.59</td><td>0.557</td></tr><tr><td>Type of interactor:Centered publication year</td><td>detrimental:year</td><td>0.00</td><td>0.01</td><td>[-0.03,0.03]</td><td>-0.19</td><td>0.848</td></tr><tr><td></td><td><strong>beneficial:year</strong></td><td><strong>0.06</strong></td><td><strong>0.02</strong></td><td><strong>[0.03,0.09]</strong></td><td><strong>3.64</strong></td><td><strong>&lt;0.001***</strong></td></tr></tbody></table>
<caption>Table S5: Meta-analytic means and 95 % CI by moderator for each of the two subsets of data</caption>

Marginal means on the two meta-analytic models for beneficial and detrimental interactions, either averaged across all moderators or separated by type of moderator retained in the averaged model. The effect sizes means, SE and 95 % CI are calculated by the emmeans function from the emmeans package v1.8.8 on the models with all moderators maintained in the averaged model. Note for numeric variables, we present either results for the min, median and max values, of in the case of generalism, the min and max values (specialist and generalist).

<table><thead><tr><th>Data subset</th><th>Moderator</th><th>Moderator level</th><th>Effect size</th><th>SE</th><th>95% CI</th></tr></thead><tbody><tr><td rowspan="16">Detrimental</td><td><strong>Marginal means across all Moderators</strong></td><td></td><td><strong>0.328</strong></td><td><strong>0.136</strong></td><td><strong>[0.063,0.594]</strong></td></tr><tr><td rowspan="3">Dispersal phase</td><td>emigration</td><td><strong>0.500</strong></td><td><strong>0.147</strong></td><td><strong>[0.211,0.789]</strong></td></tr><tr><td>full dispersal</td><td><strong>0.513</strong></td><td><strong>0.227</strong></td><td><strong>[0.069,0.958]</strong></td></tr><tr><td>transience</td><td><strong>-0.972</strong></td><td><strong>0.323</strong></td><td><strong>[-1.605,-0.339]</strong></td></tr><tr><td rowspan="3">Log duration</td><td>Min</td><td><strong>1.723</strong></td><td><strong>0.404</strong></td><td><strong>[0.932,2.514]</strong></td></tr><tr><td>Med</td><td><strong>0.324</strong></td><td><strong>0.135</strong></td><td><strong>[0.058,0.589]</strong></td></tr><tr><td>Max</td><td><strong>-0.995</strong></td><td><strong>0.354</strong></td><td><strong>[-1.688,-0.301]</strong></td></tr><tr><td rowspan="2">Type of manipulation</td><td>presence</td><td>0.102</td><td>0.166</td><td>[-0.224,0.427]</td></tr><tr><td>abundance</td><td>-0.057</td><td>0.177</td><td>[-0.404,0.29]</td></tr><tr><td rowspan="2">Level of community complexity</td><td>cues of presence</td><td><strong>0.866</strong></td><td><strong>0.211</strong></td><td><strong>[0.453,1.279]</strong></td></tr><tr><td>pair of species</td><td>0.007</td><td>0.211</td><td>[-0.407,0.42]</td></tr><tr><td rowspan="2">Level of generalism focal</td><td>simple community</td><td><strong>0.434</strong></td><td><strong>0.201</strong></td><td><strong>[0.040,0.829]</strong></td></tr><tr><td>complex community</td><td><strong>0.488</strong></td><td><strong>0.243</strong></td><td><strong>[0.013,0.963]</strong></td></tr><tr><td rowspan="2">Level of generalism interactor</td><td>mono</td><td>-0.358</td><td>0.271</td><td>[-0.890,0.173]</td></tr><tr><td>poly</td><td><strong>1.015</strong></td><td><strong>0.273</strong></td><td><strong>[0.480,1.549]</strong></td></tr><tr><td rowspan="2">Type of study</td><td>mono</td><td><strong>0.601</strong></td><td><strong>0.214</strong></td><td><strong>[0.182,1.020]</strong></td></tr><tr><td>poly</td><td>0.055</td><td>0.230</td><td>[-0.396,0.507]</td></tr><tr><td rowspan="2">Log generation time</td><td>experiment</td><td>0.268</td><td>0.144</td><td>[-0.015,0.550]</td></tr><tr><td>observation</td><td>0.583</td><td>0.301</td><td>[-0.007,1.174]</td></tr><tr><td rowspan="3">Possibility for interactor to disperse</td><td>Min</td><td>-0.125</td><td>0.338</td><td>[-0.787,0.538]</td></tr><tr><td>Med</td><td><strong>0.402</strong></td><td><strong>0.136</strong></td><td><strong>[0.136,0.668]</strong></td></tr><tr><td>Max</td><td><strong>0.665</strong></td><td><strong>0.234</strong></td><td><strong>[0.207,1.123]</strong></td></tr><tr><td rowspan="2"></td><td>no</td><td>0.225</td><td>0.198</td><td>[-0.164,0.613]</td></tr><tr><td>yes</td><td><strong>0.457</strong></td><td><strong>0.187</strong></td><td><strong>[0.092,0.823]</strong></td></tr></tbody></table>
Table S5: Meta-analytic means and 95 % CI by moderator for each of the two subsets of data

Marginal means on the two meta-analytic models for beneficial and detrimental interactions, either averaged across all moderators or separated by type of moderator retained in the averaged model. The effect sizes means, SE and 95 % CI are calculated by the emmeans function from the emmeans package v1.8.8 on the models with all moderators maintained in the averaged model. Note for numeric variables, we present either results for the min, median and max values, of in the case of generalism, the min and max values (specialist and generalist).

<table><thead><tr><th>Data subset</th><th>Moderator</th><th>Moderator level</th><th>Effect size</th><th>SE</th><th>95% CI</th></tr></thead><tbody><tr><td rowspan="15">Detrimental</td><td>Marginal means across all Moderators</td><td></td><td><strong>0.328</strong></td><td><strong>0.136</strong></td><td><strong>[0.063,0.594]</strong></td></tr><tr><td rowspan="3">Dispersal phase</td><td>emigration</td><td><strong>0.500</strong></td><td><strong>0.147</strong></td><td><strong>[0.211,0.789]</strong></td></tr><tr><td>full dispersal</td><td><strong>0.513</strong></td><td><strong>0.227</strong></td><td><strong>[0.069,0.958]</strong></td></tr><tr><td>transience</td><td><strong>-0.972</strong></td><td><strong>0.323</strong></td><td><strong>[-1.605,-0.339]</strong></td></tr><tr><td rowspan="3">Log duration</td><td>Min</td><td><strong>1.723</strong></td><td><strong>0.404</strong></td><td><strong>[0.932,2.514]</strong></td></tr><tr><td>Med</td><td><strong>0.324</strong></td><td><strong>0.135</strong></td><td><strong>[0.058,0.589]</strong></td></tr><tr><td>Max</td><td><strong>-0.995</strong></td><td><strong>0.354</strong></td><td><strong>[-1.688,-0.301]</strong></td></tr><tr><td rowspan="2">Type of manipulation</td><td>presence</td><td>0.102</td><td>0.166</td><td>[-0.224,0.427]</td></tr><tr><td>abundance</td><td>-0.057</td><td>0.177</td><td>[-0.404,0.29]</td></tr><tr><td rowspan="2">Level of community complexity</td><td>cues of presence</td><td><strong>0.866</strong></td><td><strong>0.211</strong></td><td><strong>[0.453,1.279]</strong></td></tr><tr><td>pair of species</td><td>0.007</td><td>0.211</td><td>[-0.407,0.42]</td></tr><tr><td rowspan="2">Level of generalism focal</td><td>simple community</td><td><strong>0.434</strong></td><td><strong>0.201</strong></td><td><strong>[0.040,0.829]</strong></td></tr><tr><td>complex community</td><td><strong>0.488</strong></td><td><strong>0.243</strong></td><td><strong>[0.013,0.963]</strong></td></tr><tr><td rowspan="2">Level of generalism interactor</td><td>mono</td><td>-0.358</td><td>0.271</td><td>[-0.890,0.173]</td></tr><tr><td>poly</td><td><strong>1.015</strong></td><td><strong>0.273</strong></td><td><strong>[0.480,1.549]</strong></td></tr><tr><td rowspan="2">Type of study</td><td>mono</td><td><strong>0.601</strong></td><td><strong>0.214</strong></td><td><strong>[0.182,1.020]</strong></td></tr><tr><td>poly</td><td>0.055</td><td>0.230</td><td>[-0.396,0.507]</td></tr><tr><td rowspan="2">Log generation time</td><td>experiment</td><td>0.268</td><td>0.144</td><td>[-0.015,0.550]</td></tr><tr><td>observation</td><td>0.583</td><td>0.301</td><td>[-0.007,1.174]</td></tr><tr><td rowspan="3">Possibility for interactor to disperse</td><td>Min</td><td>-0.125</td><td>0.338</td><td>[-0.787,0.538]</td></tr><tr><td>Med</td><td><strong>0.402</strong></td><td><strong>0.136</strong></td><td><strong>[0.136,0.668]</strong></td></tr><tr><td>Max</td><td><strong>0.665</strong></td><td><strong>0.234</strong></td><td><strong>[0.207,1.123]</strong></td></tr><tr><td rowspan="2"></td><td>no</td><td>0.225</td><td>0.198</td><td>[-0.164,0.613]</td></tr><tr><td>yes</td><td><strong>0.457</strong></td><td><strong>0.187</strong></td><td><strong>[0.092,0.823]</strong></td></tr></tbody></table>
<table><thead><tr><th>Data subset</th><th>Moderator</th><th>Moderator level</th><th>Effect size</th><th>SE</th><th>95% CI</th></tr></thead><tbody><tr><td rowspan="12">Beneficial</td><td rowspan="2">Similarity in generations between dispersal and interaction</td><td>same generation</td><td>0.245</td><td>0.145</td><td>[-0.039,0.530]</td></tr><tr><td><strong>different generation</strong></td><td><strong>1.057</strong></td><td><strong>0.356</strong></td><td><strong>[0.359,1.755]</strong></td></tr><tr><td rowspan="2">Nature of interactor</td><td>predator</td><td>-0.002</td><td>0.205</td><td>[-0.404,0.400]</td></tr><tr><td><strong>competitor</strong></td><td><strong>0.765</strong></td><td><strong>0.289</strong></td><td><strong>[0.198,1.331]</strong></td></tr><tr><td rowspan="2">Marginal means across all Moderators</td><td>parasite</td><td>0.444</td><td>0.248</td><td>[-0.043,0.931]</td></tr><tr><td><strong>emigration</strong></td><td><strong>-0.549</strong></td><td><strong>0.192</strong></td><td><strong>[-0.924,-0.173]</strong></td></tr><tr><td rowspan="2">Dispersal phase</td><td>full dispersal</td><td>-0.132</td><td>0.254</td><td>[-0.629,0.366]</td></tr><tr><td>transience</td><td>-0.555</td><td>0.291</td><td>[-1.125,0.015]</td></tr><tr><td rowspan="2">Nature of interactor</td><td><strong>resource</strong></td><td><strong>-0.720</strong></td><td><strong>0.194</strong></td><td><strong>[-1.100,-0.339]</strong></td></tr><tr><td>host</td><td>1.128</td><td>0.637</td><td>[-0.120,2.375]</td></tr><tr><td rowspan="2">Level of generalism interactor</td><td>mutualist</td><td>-0.011</td><td>0.759</td><td>[-1.499,1.476]</td></tr><tr><td>mono</td><td>0.318</td><td>0.417</td><td>[-0.498,1.135]</td></tr><tr><td rowspan="2">Log duration</td><td><strong>poly</strong></td><td><strong>-1.416</strong></td><td><strong>0.455</strong></td><td><strong>[-2.308,-0.524]</strong></td></tr><tr><td><strong>Min</strong></td><td><strong>-1.536</strong></td><td><strong>0.453</strong></td><td><strong>[-2.424,-0.648]</strong></td></tr><tr><td rowspan="2">Type of study</td><td><strong>Med</strong></td><td><strong>-0.546</strong></td><td><strong>0.192</strong></td><td><strong>[-0.921,-0.170]</strong></td></tr><tr><td>Max</td><td>0.387</td><td>0.433</td><td>[-0.461,1.236]</td></tr><tr><td rowspan="2">Level of generalism focal</td><td><strong>experiment</strong></td><td><strong>-0.470</strong></td><td><strong>0.203</strong></td><td><strong>[-0.868,-0.073]</strong></td></tr><tr><td><strong>observation</strong></td><td><strong>-1.100</strong></td><td><strong>0.415</strong></td><td><strong>[-1.913,-0.287]</strong></td></tr><tr><td rowspan="2"></td><td><strong>mono</strong></td><td><strong>-0.813</strong></td><td><strong>0.280</strong></td><td><strong>[-1.361,-0.265]</strong></td></tr><tr><td>poly</td><td>-0.285</td><td>0.292</td><td>[-0.858,0.288]</td></tr></tbody></table>
<table><thead><tr><th>Data subset</th><th>Moderator</th><th>Moderator level</th><th>Effect size</th><th>SE</th><th>95% CI</th></tr></thead><tbody><tr><td rowspan="10">Beneficial</td><td rowspan="2">Similarity in generations between dispersal and interaction</td><td>same generation</td><td>0.245</td><td>0.145</td><td>[-0.039,0.530]</td></tr><tr><td><strong>different generation</strong></td><td><strong>1.057</strong></td><td><strong>0.356</strong></td><td><strong>[0.359,1.755]</strong></td></tr><tr><td rowspan="2">Nature of interactor</td><td>predator</td><td>-0.002</td><td>0.205</td><td>[-0.404,0.400]</td></tr><tr><td><strong>competitor</strong></td><td><strong>0.765</strong></td><td><strong>0.289</strong></td><td><strong>[0.198,1.331]</strong></td></tr><tr><td rowspan="2">Marginal means across all Moderators</td><td>parasite</td><td>0.444</td><td>0.248</td><td>[-0.043,0.931]</td></tr><tr><td><strong>emigration</strong></td><td><strong>-0.549</strong></td><td><strong>0.192</strong></td><td><strong>[-0.924,-0.173]</strong></td></tr><tr><td rowspan="2">Dispersal phase</td><td>full dispersal</td><td>-0.132</td><td>0.254</td><td>[-0.629,0.366]</td></tr><tr><td>transience</td><td>-0.555</td><td>0.291</td><td>[-1.125,0.015]</td></tr><tr><td rowspan="2">Nature of interactor</td><td><strong>resource</strong></td><td><strong>-0.720</strong></td><td><strong>0.194</strong></td><td><strong>[-1.100,-0.339]</strong></td></tr><tr><td>host</td><td>1.128</td><td>0.637</td><td>[-0.120,2.375]</td></tr><tr><td rowspan="2">Level of generalism interactor</td><td>mutualist</td><td>-0.011</td><td>0.759</td><td>[-1.499,1.476]</td></tr><tr><td>mono</td><td>0.318</td><td>0.417</td><td>[-0.498,1.135]</td></tr><tr><td rowspan="4">Log duration</td><td><strong>poly</strong></td><td><strong>-1.416</strong></td><td><strong>0.455</strong></td><td><strong>[-2.308,-0.524]</strong></td></tr><tr><td><strong>Min</strong></td><td><strong>-1.536</strong></td><td><strong>0.453</strong></td><td><strong>[-2.424,-0.648]</strong></td></tr><tr><td><strong>Med</strong></td><td><strong>-0.546</strong></td><td><strong>0.192</strong></td><td><strong>[-0.921,-0.170]</strong></td></tr><tr><td>Max</td><td>0.387</td><td>0.433</td><td>[-0.461,1.236]</td></tr><tr><td rowspan="2">Type of study</td><td><strong>experiment</strong></td><td><strong>-0.470</strong></td><td><strong>0.203</strong></td><td><strong>[-0.868,-0.073]</strong></td></tr><tr><td><strong>observation</strong></td><td><strong>-1.100</strong></td><td><strong>0.415</strong></td><td><strong>[-1.913,-0.287]</strong></td></tr><tr><td rowspan="2">Level of generalism focal</td><td><strong>mono</strong></td><td><strong>-0.813</strong></td><td><strong>0.280</strong></td><td><strong>[-1.361,-0.265]</strong></td></tr><tr><td>poly</td><td>-0.285</td><td>0.292</td><td>[-0.858,0.288]</td></tr></tbody></table>
Table S6: Effect of home range ratio on the context-dependency of dispersal

Model investigating the effect of interacting species' home range. Because we were not able to gather information on home range for most of the species pair, this is a separate model on the 17 effect sizes for detrimental interactions for which we were able to get the information. The model included the centered log ratio of home ranges (home range of the focal species/ home range of the interactor), as well as the study ID, focal species ID and interactor ID. The model had a total heterogeneity I² of 39.7 %. The Egger's test showed no significant asymmetry (intercept [95% CI] = -0.03 [-0.21, 0.28], t = -0.29, df = 15, p = 0.77). The effect sizes means, SE, 95 % CI, z-values and p-values are presented.

<table><thead><tr><td>Moderator</td><td>effect size</td><td>SE</td><td>95% CI</td><td>z-value</td><td>p-value</td></tr></thead><tbody><tr><td>Intercept</td><td>0.16</td><td>0.06</td><td>[0.05,0.28]</td><td>2.72</td><td>0.006**</td></tr><tr><td>Centered log ratio of home ranges</td><td>-0.02</td><td>0.01</td><td>[-0.05,0.00]</td><td>-1.73</td><td>0.084.</td></tr></tbody></table>
Table S6: Effect of home range ratio on the context-dependency of dispersal

Model investigating the effect of interacting species' home range. Because we were not able to gather information on home range for most of the species pair, this is a separate model on the 17 effect sizes for detrimental interactions for which we were able to get the information. The model included the centered log ratio of home ranges (home range of the focal species/ home range of the interactor), as well as the study ID, focal species ID and interactor ID. The model had a total heterogeneity I² of 39.7 %. The Egger's test showed no significant asymmetry (intercept [95% CI] = -0.03 [-0.21, 0.28], t = -0.29, df = 15, p = 0.77). The effect sizes means, SE, 95 % CI, z-values and p-values are presented.

<table><thead><tr><td>Moderator</td><td>effect size</td><td>SE</td><td>95% CI</td><td>z-value</td><td>p-value</td></tr></thead><tbody><tr><td>Intercept</td><td>0.16</td><td>0.06</td><td>[0.05,0.28]</td><td>2.72</td><td>0.006**</td></tr><tr><td>Centered log ratio of home ranges</td><td>-0.02</td><td>0.01</td><td>[-0.05,0.00]</td><td>-1.73</td><td>0.084.</td></tr></tbody></table>
Table S7: Effect of interaction strength on the context-dependency of dispersal

Model investigating the effect of interaction strength on the focal species' dispersal, and their comparison to models investigating only binary interaction types (beneficial/detrimental). The models were done on two subset of studies: first on all data for which we were able to gather interaction strength information, from either the primary or the secondary literature ("All interaction strengths" dataset, 357 effect sizes over 108 studies), and second the data for which interaction strength was gathered from the primary literature ("Strict interaction strengths" dataset, 166 effect sizes over 51 studies). We compared through AICc models including the continuous interaction strength as a fixed effect and all random effects (study ID, focal species ID and phylogeny, interacting species ID) to models with the same random effects but binary interaction type as fixed effects. The Egger's tests showed some asymmetry for the dataset on all interaction strengths (p = 0.029 and 0.027 for the intercept of models on interaction strength and type), but not for the strict dataset (p = 0.94 and 0.85). All models had a large heterogeneity (I² = 97.8, 97.5, 97.2 and 96.6 respectively for the models on interaction strength and on interaction type on all interactions, and the models on interaction strength and interaction type on the strict interaction database). The effect sizes means, SE, 95% CI, z-values and p-values, as well as ΔAICc between models, are presented.

<table><thead><tr><th>Type dataset</th><th>Type model</th><th>Moderator</th><th>effect size</th><th>SE</th><th>95% CI</th><th>z-value</th><th>p-value</th><th>ΔAICc</th><th>N</th></tr></thead><tbody><tr><td rowspan="4">All interaction strengths</td><td rowspan="2">Model interaction strength</td><td>Intercept</td><td>-0.08</td><td>0.11</td><td>[-0.29,0.13]</td><td>-0.72</td><td>0.469</td><td rowspan="2">0.0</td><td rowspan="2">357</td></tr><tr><td>Interaction strength</td><td>-0.72</td><td>0.13</td><td>[-0.98,-0.47]</td><td>-5.56</td><td>&lt;0.001***</td></tr><tr><td rowspan="2">Model interaction type</td><td>Intercept</td><td>0.21</td><td>0.12</td><td>[-0.03,0.45]</td><td>1.72</td><td>0.085.</td><td rowspan="2">16.5</td><td rowspan="2"></td></tr><tr><td>Interaction type</td><td>-0.61</td><td>0.16</td><td>[-0.92,-0.30]</td><td>-3.91</td><td>&lt;0.001***</td></tr><tr><td rowspan="4">Strict interaction strengths</td><td rowspan="2">Model interaction strength</td><td>Intercept</td><td>-0.13</td><td>0.16</td><td>[-0.45,0.19]</td><td>-0.81</td><td>0.422</td><td rowspan="2">0.0</td><td rowspan="2">166</td></tr><tr><td>Interaction strength</td><td>-1.24</td><td>0.20</td><td>[-1.64,-0.84]</td><td>-6.12</td><td>&lt;0.001***</td></tr><tr><td rowspan="2">Model interaction type</td><td>Intercept</td><td>0.10</td><td>0.17</td><td>[-0.24,0.44]</td><td>0.58</td><td>0.561</td><td rowspan="2">32.8</td><td rowspan="2"></td></tr><tr><td>Interaction type</td><td>-0.43</td><td>0.24</td><td>[-0.90,0.03]</td><td>-1.83</td><td>0.069.</td></tr></tbody></table>
Table S7: Effect of interaction strength on the context-dependency of dispersal

Model investigating the effect of interaction strength on the focal species' dispersal, and their comparison to models investigating only binary interaction types (beneficial/detrimental). The models were done on two subset of studies: first on all data for which we were able to gather interaction strength information, from either the primary or the secondary literature ("All interaction strengths" dataset, 357 effect sizes over 108 studies), and second the data for which interaction strength was gathered from the primary literature ("Strict interaction strengths" dataset, 166 effect sizes over 51 studies). We compared through AICc models including the continuous interaction strength as a fixed effect and all random effects (study ID, focal species ID and phylogeny, interacting species ID) to models with the same random effects but binary interaction type as fixed effects. The Egger's tests showed some asymmetry for the dataset on all interaction strengths (p = 0.029 and 0.027 for the intercept of models on interaction strength and type), but not for the strict dataset (p = 0.94 and 0.85). All models had a large heterogeneity (I² = 97.8, 97.5, 97.2 and 96.6 respectively for the models on interaction strength and on interaction type on all interactions, and the models on interaction strength and interaction type on the strict interaction database). The effect sizes means, SE, 95% CI, z-values and p-values, as well as ΔAICc between models, are presented.

<table><thead><tr><th>Type dataset</th><th>Type model</th><th>Moderator</th><th>effect size</th><th>SE</th><th>95% CI</th><th>z-value</th><th>p-value</th><th>ΔAICc</th><th>N</th></tr></thead><tbody><tr><td rowspan="4">All interaction strengths</td><td rowspan="2">Model interaction strength</td><td>Intercept</td><td>-0.08</td><td>0.11</td><td>[-0.29,0.13]</td><td>-0.72</td><td>0.469</td><td rowspan="2">0.0</td><td rowspan="2">357</td></tr><tr><td>Interaction strength</td><td>-0.72</td><td>0.13</td><td>[-0.98,-0.47]</td><td>-5.56</td><td>&lt;0.001***</td></tr><tr><td rowspan="2">Model interaction type</td><td>Intercept</td><td>0.21</td><td>0.12</td><td>[-0.03,0.45]</td><td>1.72</td><td>0.085.</td><td rowspan="2">16.5</td><td rowspan="2"></td></tr><tr><td>Interaction type</td><td>-0.61</td><td>0.16</td><td>[-0.92,-0.30]</td><td>-3.91</td><td>&lt;0.001***</td></tr><tr><td rowspan="4">Strict interaction strengths</td><td rowspan="2">Model interaction strength</td><td>Intercept</td><td>-0.13</td><td>0.16</td><td>[-0.45,0.19]</td><td>-0.81</td><td>0.422</td><td rowspan="2">0.0</td><td rowspan="2">166</td></tr><tr><td>Interaction strength</td><td>-1.24</td><td>0.20</td><td>[-1.64,-0.84]</td><td>-6.12</td><td>&lt;0.001***</td></tr><tr><td rowspan="2">Model interaction type</td><td>Intercept</td><td>0.10</td><td>0.17</td><td>[-0.24,0.44]</td><td>0.58</td><td>0.561</td><td rowspan="2">32.8</td><td rowspan="2"></td></tr><tr><td>Interaction type</td><td>-0.43</td><td>0.24</td><td>[-0.90,0.03]</td><td>-1.83</td><td>0.069.</td></tr></tbody></table>
Supplementary reference list: List of all articles included in the meta-analysis

Achiano Ka, Giliomee Jh. 2008 Food-, Temperature- And Crowding-mediated Laboratory Dispersal Of Carcinops Pumilio (erichson) (coleoptera : Histeridae), A Predator Of House Fly (diptera : Muscidae) Eggs And Larvae. *African Entomology*. **16**, 115-121. (doi:10.4001/1021-3589-16.1.115)

Alcalay Y, Tsurim I, Ovadia O. 2018 Female Mosquitoes Disperse Further When They Develop Under Predation Risk. *Behavioral Ecology*. **29**, 1402-1408. (doi:10.1093/beheco/ary113)

Asman K, Ekbom B, Ramert B. 2001 Effect Of Intercropping On Oviposition And Emigration Behavior Of The Leek Moth (lepidoptera : Acrolepiidae) And The Diamondback Moth (lepidoptera : Plutellidae). *Environmental Entomology*. **30**, 288-294. (doi:10.1603/0046-225x-30.2.288)

Brown Cr, Brown Mb. 1992 Ectoparasitism As A Cause Of Natal Dispersal In Cliff Swallows. *Ecology*. **73**, 1718-1723. (doi:10.2307/1940023)

Baines Cb, Diab S, Mccauley Sj. 2020 Parasitism Risk And Infection Alter Host Dispersal. *American Naturalist*. **196**, 119-131. (doi:10.1086/709163)

Baines Cb, Mccauley Sj, Rowe L. 2015 Dispersal Depends On Body Condition And Predation Risk In The Semi-aquatic Insect, Notonecta Undulata. *Ecology And Evolution*. **5**, 2307-2316. (doi:10.1002/ece3.1508)

Baines Cb, Mccauley Sj, Rowe L. 2014 The Interactive Effects Of Competition And Predation Risk On Dispersal In An Insect. *Biology Letters*. **10**. (doi:10.1098/rsbl.2014.0287)

Bartonicka T. 2008 Cimex Pipistrelli (heteroptera, Cimicidae) And The Dispersal Propensity Of Bats: An Experimental Study. *Parasitology Research*. **104**, 163-168. (doi:10.1007/s00436-008-1175-1)

Baudouin A Gatti S Levrero F Genton C Cristescu Rh Billy V Motsch P Pierre Js Le Gouar P Menard N. 2019 Disease Avoidance, And Breeding Group Age And Size Condition The Dispersal Patterns Of Western Lowland Gorilla Females. *Ecology*. **100**. (doi:10.1002/ecy.2786)

Berger-tal R, Berner-aharon N, Aharon S, Tuni C, Lubin Y. 2016 Good Reasons To Leave Home: Proximate Dispersal Cues In A Social Spider. *Journal Of Animal Ecology*. **85**, 1035-1042. (doi:10.1111/1365-2656.12534)

Bestion E, Teyssier A, Aubret F, Clobert J, Cote J. 2014 Maternal Exposure To Predator Scents: Offspring Phenotypic Adjustment And Dispersal. *Proceedings Of The Royal Society B-biological Sciences*. **281**. (doi:10.1098/rspb.2014.0701)

Bodasing M, Crouch T, Slotow R. 2002 The Influence Of Starvation On Dispersal In The Social Spider, Stegodyphus Mimosarum (araneae, Eresidae). *Journal Of Arachnology*. **30**, 373-382. (doi:10.1636/0161-8202(2002)030[0373:tiosod]2.0.co;2)

Brown Gp, Kelehear C, Pizzatto L, Shine R. 2016 The Impact Of Lungworm Parasites On Rates Of Dispersal Of Their Anuran Host, The Invasive Cane Toad. *Biological Invasions*. **18**, 103-114. (doi:10.1007/s10530-015-0993-1)

Buddle Cn, Rypstra Al. 2003 Factors Initiating Emigration Of Two Wolf Spider Species (araneae : Lycosidae) In An Agroecosystem. *Environmental Entomology*. **32**, 88-95. (doi:10.1603/0046-225x-32.1.88)

Buitenhuis R, Shipp L, Scott-dupree C. 2010 Dispersal Of Amblyseius Swirskii Athias-henriot (acari: Phytoseiidae) On Potted Greenhouse Chrysanthemum. *Biological Control*. **52**, 110-114. (doi:10.1016/j.biocontrol.2009.10.007)
Supplementary reference list: List of all articles included in the meta-analysis

Achiano Ka, Giliomee Jh. 2008 Food-, Temperature- And Crowding-mediated Laboratory Dispersal Of Carcinops Pumilio (erichson) (coleoptera : Histeridae), A Predator Of House Fly (diptera : Muscidae) Eggs And Larvae. *African Entomology*. **16**, 115-121. (doi:10.4001/1021-3589-16.1.115)

Alcalay Y, Tsurim I, Ovadia O. 2018 Female Mosquitoes Disperse Further When They Develop Under Predation Risk. *Behavioral Ecology*. **29**, 1402-1408. (doi:10.1093/beheco/ary113)

Asman K, Ekbom B, Ramert B. 2001 Effect Of Intercropping On Oviposition And Emigration Behavior Of The Leek Moth (lepidoptera : Acrolepiidae) And The Diamondback Moth (lepidoptera : Plutellidae). *Environmental Entomology*. **30**, 288-294. (doi:10.1603/0046-225x-30.2.288)

Brown Cr, Brown Mb. 1992 Ectoparasitism As A Cause Of Natal Dispersal In Cliff Swallows. *Ecology*. **73**, 1718-1723. (doi:10.2307/1940023)

Baines Cb, Diab S, Mccauley Sj. 2020 Parasitism Risk And Infection Alter Host Dispersal. *American Naturalist*. **196**, 119-131. (doi:10.1086/709163)

Baines Cb, Mccauley Sj, Rowe L. 2015 Dispersal Depends On Body Condition And Predation Risk In The Semi-aquatic Insect, Notonecta Undulata. *Ecology And Evolution*. **5**, 2307-2316. (doi:10.1002/ece3.1508)

Baines Cb, Mccauley Sj, Rowe L. 2014 The Interactive Effects Of Competition And Predation Risk On Dispersal In An Insect. *Biology Letters*. **10**. (doi:10.1098/rsbl.2014.0287)

Bartonicka T. 2008 Cimex Pipistrelli (heteroptera, Cimicidae) And The Dispersal Propensity Of Bats: An Experimental Study. *Parasitology Research*. **104**, 163-168. (doi:10.1007/s00436-008-1175-1)

Baudouin A Gatti S Levrero F Genton C Cristescu Rh Billy V Motsch P Pierre Js Le Gouar P Menard N. 2019 Disease Avoidance, And Breeding Group Age And Size Condition The Dispersal Patterns Of Western Lowland Gorilla Females. *Ecology*. **100**. (doi:10.1002/ecy.2786)

Berger-tal R, Berner-aharon N, Aharon S, Tuni C, Lubin Y. 2016 Good Reasons To Leave Home: Proximate Dispersal Cues In A Social Spider. *Journal Of Animal Ecology*. **85**, 1035-1042. (doi:10.1111/1365-2656.12534)

Bestion E, Teyssier A, Aubret F, Clobert J, Cote J. 2014 Maternal Exposure To Predator Scents: Offspring Phenotypic Adjustment And Dispersal. *Proceedings Of The Royal Society B-biological Sciences*. **281**. (doi:10.1098/rspb.2014.0701)

Bodasing M, Crouch T, Slotow R. 2002 The Influence Of Starvation On Dispersal In The Social Spider, Stegodyphus Mimosarum (araneae, Eresidae). *Journal Of Arachnology*. **30**, 373-382. (doi:10.1636/0161-8202(2002)030[0373:tiosod]2.0.co;2)

Brown Gp, Kelehear C, Pizzatto L, Shine R. 2016 The Impact Of Lungworm Parasites On Rates Of Dispersal Of Their Anuran Host, The Invasive Cane Toad. *Biological Invasions*. **18**, 103-114. (doi:10.1007/s10530-015-0993-1)

Buddle Cn, Rypstra Al. 2003 Factors Initiating Emigration Of Two Wolf Spider Species (araneae : Lycosidae) In An Agroecosystem. *Environmental Entomology*. **32**, 88-95. (doi:10.1603/0046-225x-32.1.88)

Buitenhuis R, Shipp L, Scott-dupree C. 2010 Dispersal Of Amblyseius Swirskii Athias-henriot (acari: Phytoseiidae) On Potted Greenhouse Chrysanthemum. *Biological Control*. **52**, 110-114. (doi:10.1016/j.biocontrol.2009.10.007)
Castillo-neyra R, Barbu Cm, Salazar R, Borrini K, Naquira C, Levy Mz. 2015 Host-seeking Behavior And Dispersal Of Triatoma Infestans, A Vector Of Chagas Disease, Under Semi-field Conditions. *Plos Neglected Tropical Diseases*. 9. (doi:10.1371/journal.pntd.0003433)

Conrad Kf, Willson Kh, Whitfield K, Harvey If, Thomas Cj, Sherratt Tn. 2002 Characteristics Of Dispersing Ischnura Elegans And Coenagrion Puella (odonata): Age, Sex, Size, Morph And Ectoparasitism. *Ecography*. 25, 439-445. (doi:10.1034/j.1600-0587.2002.250406.x)

Cortesero Am, Monge Jp, Huignard J. 1997 Dispersal And Parasitizing Abilities Of Eupelmus Vuilleti (hymenoptera: Eupelmidae) Within A Column Of Cowpea Seeds. *Environmental Entomology*. 26, 1025-1030. (doi:10.1093/ee/26.5.1025)

Cote J, Fogarty S, Tymen B, Sih A, Brodin T. 2013 Personality-dependent Dispersal Cancelled Under Predation Risk. *Proceedings Of The Royal Society B-biological Sciences*. 280. (doi:10.1098/rspb.2013.2349)

Cronin Jt, Haynes Kj, Dillemuth F. 2004 Spider Effects On Planthopper Mortality, Dispersal, And Spatial Population Dynamics. *Ecology*. 85, 2134-2143. (doi:10.1890/03-0591)

Denno Rf, Roderick Gk. 1992 Density-related Dispersal In Planthoppers - Effects Of Interspecific Crowding. *Ecology*. 73, 1323-1334. (doi:10.2307/1940679)

Dahl J, Greenberg L. 1999 Effects Of Prey Dispersal On Predator-prey Interactions In Streams. *Freshwater Biology*. 41, 771-780. (doi:10.1046/j.1365-2427.1999.00422.x)

Dallas T, Melbourne B, Hastings A. 2020 Community Context And Dispersal Stochasticity Drive Variation In Spatial Spread. *Journal Of Animal Ecology*. 89, 2657-2664. (doi:10.1111/1365-2656.13331)

De Meester N, Derycke S, Rigaux A, Moens T. 2016 Active Dispersal Is Differentially Affected By Inter- And Intraspecific Competition In Closely Related Nematode Species (vol 124, Pg 561, 2016). *Oikos*. 125, 893-893. (doi:10.1111/oik.03561)

Debeffe L, Morellet N, Verheyden-tixier H, Hoste H, Gaillard Jm, Cargnelutti B, Picot D, Sevila J, Hewison Ajm. 2014 Parasite Abundance Contributes To Condition-dependent Dispersal In A Wild Population Of Large Herbivore. *Oikos*. 123, 1121-1125. (doi:10.1111/oik.01396)

Dickinson Jl Ferree Ed Stern Ca Swift R Zuckerberg B. 2014 Delayed Dispersal In Western Bluebirds: Teasing Apart The Importance Of Resources And Parents. *Behavioral Ecology*. 25, 843-851. (doi:10.1093/beheco/aru042)

Edelman Aj. 2011 Sex-specific Effects Of Size And Condition On Timing Of Natal Dispersal In Kangaroo Rats. *Behavioral Ecology*. 22, 776-783. (doi:10.1093/beheco/arr050)

Forrester Ge. 1994 Influences Of Predatory Fish On The Drift Dispersal And Local-density Of Stream Insects. *Ecology*. 75, 1208-1218. (doi:10.2307/1937447)

Frumkin R. 1994 Intraspecific Brood-parasitism And Dispersal In Fledgling Sparrowhawks Accipiter-nisus. *Ibis*. 136, 426-433. (doi:10.1111/j.1474-919x.1994.tb01117.x)

Fellous S, Quillery E, Duncan Ab, Kaltz O. 2011 Parasitic Infection Reduces Dispersal Of Ciliate Host. *Biology Letters*. 7, 327-329. (doi:10.1098/rsbl.2010.0862)

Feng Cz, Yang Cc, Liang W. 2019 Nest-site Fidelity And Breeding Dispersal By Common Tailorbirds In A Tropical Forest. *Avian Research*. 10. (doi:10.1186/s40657-019-0185-2)
Castillo-neyra R, Barbu Cm, Salazar R, Borrini K, Naquira C, Levy Mz. 2015 Host-seeking Behavior And Dispersal Of Triatoma Infestans, A Vector Of Chagas Disease, Under Semi-field Conditions. *Plos Neglected Tropical Diseases*. 9. (doi:10.1371/journal.pntd.0003433)

Conrad Kf, Willson Kh, Whitfield K, Harvey If, Thomas Cj, Sherratt Tn. 2002 Characteristics Of Dispersing Ischnura Elegans And Coenagrion Puella (odonata): Age, Sex, Size, Morph And Ectoparasitism. *Ecography*. 25, 439-445. (doi:10.1034/j.1600-0587.2002.250406.x)

Cortesero Am, Monge Jp, Huignard J. 1997 Dispersal And Parasitizing Abilities Of Eupelmus Vuilleti (hymenoptera: Eupelmidae) Within A Column Of Cowpea Seeds. *Environmental Entomology*. 26, 1025-1030. (doi:10.1093/ee/26.5.1025)

Cote J, Fogarty S, Tymen B, Sih A, Brodin T. 2013 Personality-dependent Dispersal Cancelled Under Predation Risk. *Proceedings Of The Royal Society B-biological Sciences*. 280. (doi:10.1098/rspb.2013.2349)

Cronin Jt, Haynes Kj, Dillemuth F. 2004 Spider Effects On Planthopper Mortality, Dispersal, And Spatial Population Dynamics. *Ecology*. 85, 2134-2143. (doi:10.1890/03-0591)

Denno Rf, Roderick Gk. 1992 Density-related Dispersal In Planthoppers - Effects Of Interspecific Crowding. *Ecology*. 73, 1323-1334. (doi:10.2307/1940679)

Dahl J, Greenberg L. 1999 Effects Of Prey Dispersal On Predator-prey Interactions In Streams. *Freshwater Biology*. 41, 771-780. (doi:10.1046/j.1365-2427.1999.00422.x)

Dallas T, Melbourne B, Hastings A. 2020 Community Context And Dispersal Stochasticity Drive Variation In Spatial Spread. *Journal Of Animal Ecology*. 89, 2657-2664. (doi:10.1111/1365-2656.13331)

De Meester N, Derycke S, Rigaux A, Moens T. 2016 Active Dispersal Is Differentially Affected By Inter- And Intraspecific Competition In Closely Related Nematode Species (vol 124, Pg 561, 2016). *Oikos*. 125, 893-893. (doi:10.1111/oik.03561)

Debeffe L, Morellet N, Verheyden-tixier H, Hoste H, Gaillard Jm, Cargnelutti B, Picot D, Sevila J, Hewison Ajm. 2014 Parasite Abundance Contributes To Condition-dependent Dispersal In A Wild Population Of Large Herbivore. *Oikos*. 123, 1121-1125. (doi:10.1111/oik.01396)

Dickinson Jl Ferree Ed Stern Ca Swift R Zuckerberg B. 2014 Delayed Dispersal In Western Bluebirds: Teasing Apart The Importance Of Resources And Parents. *Behavioral Ecology*. 25, 843-851. (doi:10.1093/beheco/aru042)

Edelman Aj. 2011 Sex-specific Effects Of Size And Condition On Timing Of Natal Dispersal In Kangaroo Rats. *Behavioral Ecology*. 22, 776-783. (doi:10.1093/beheco/arr050)

Forrester Ge. 1994 Influences Of Predatory Fish On The Drift Dispersal And Local-density Of Stream Insects. *Ecology*. 75, 1208-1218. (doi:10.2307/1937447)

Frumkin R. 1994 Intraspecific Brood-parasitism And Dispersal In Fledgling Sparrowhawks Accipiter-nisus. *Ibis*. 136, 426-433. (doi:10.1111/j.1474-919x.1994.tb01117.x)

Fellous S, Quillery E, Duncan Ab, Kaltz O. 2011 Parasitic Infection Reduces Dispersal Of Ciliate Host. *Biology Letters*. 7, 327-329. (doi:10.1098/rsbl.2010.0862)

Feng Cz, Yang Cc, Liang W. 2019 Nest-site Fidelity And Breeding Dispersal By Common Tailorbirds In A Tropical Forest. *Avian Research*. 10. (doi:10.1186/s40657-019-0185-2)
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*Oecologia*. **197**, 411-419. (doi:10.1007/s00442-021-05037-z)

Holomuzki Jr, Pillsbury Rw, Khandwala Sb. 1999 Interplay Between Dispersal Determinants Of Larval Hydropsychid Caddisflies. *Canadian Journal Of Fisheries And Aquatic Sciences*. **56**, 2041-2050. (doi:10.1139/cjfas-56-11-2041)

Hopkins Sr, Boyle Lj, Belden Lk, Wojdak Jm. 2015 Dispersal Of A Defensive Symbiont Depends On Contact Between Hosts, Host Health, And Host Size. *Oecologia*. **179**, 307-318. (doi:10.1007/s00442-015-3333-3)

Horky P, Douda K, Maciak M, Zavorka L, Slavik O. 2014 Parasite-induced Alterations Of Host Behaviour In A Riverine Fish: The Effects Of Glochidia On Host Dispersal. *Freshwater Biology*. **59**, 1452-1461. (doi:10.1111/fwb.12357)

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Jenkins Jma, Lesmeister Db, Forsman Ed, Dugger Km, Ackers Sh, Andrews Ls, Mccafferty Ce, Pruett Ms, Reid Ja, Sovern Sg, Horn Rb, Gremel Sa, Wiens Jd, Yang Zq. 2019 Social Status, Forest Disturbance, And Barred Owls Shape Long-term Trends In Breeding Dispersal Distance Of Northern Spotted Owls. *Condor*. **121**. (doi:10.1093/condor/duz055)

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Korpimaki E. 1993 Does Nest-hole Quality, Poor Breeding Success Or Food Depletion Drive The Breeding Dispersal Of Tengmalm Owls. *Journal Of Animal Ecology*. **62**, 606-613. (doi:10.2307/5382)

Kaufman Pe, Long Sj, Rutz Da, Glenister Cs. 2000 Prey- And Density-mediated Dispersal In Carcinops Pumilio (coleoptera : Histeridae), A Predator Of House Fly (diptera : Muscidae) Eggs And Larvae. *Journal Of Medical Entomology*. **37**, 929-932. (doi:10.1603/0022-2585-37.6.929)

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Kobak J, Rachalewski M, Bacela-spychalska K. 2016 Conquerors Or Exiles? Impact Of Interference Competition Among Invasive Ponto-caspian Gammarideans On Their Dispersal Rates. *Biological Invasions*. **18**, 1953-1965. (doi:10.1007/s10530-016-1140-3)

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Levri Ep, Luft R, Li Xs. 2019 Predator Detection And A Possible Dispersal Behavior Of The Invasive New Zealand Mud Snail, Potamopyrgus Antipodarum (gray, 1843). *Aquatic Invasions*. **14**, 417-432. (doi:10.3391/ai.2019.14.3.02)

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