Impacts of larval environment on the adult dispersal syndrome

Impacts of larval environment on the adult dispersal syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impacts of larval environment on the adult dispersal syndrome Delphine Legrand, Elise Mazé-Guilmo, Fabien Aubret, Michel Baguette, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6839133/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Nov, 2025 Read the published version in Evolutionary Ecology → Version 1 posted 9 You are reading this latest preprint version Abstract Dispersal is a fundamental process usually facilitated by suites of correlated traits. Phenotypic plasticity has the potential to change the structure of these dispersal syndromes depending upon the environmental context. Plasticity can take different forms, including time-delayed effects generating phenotypic variation dependent on past environmental conditions. Such carry-over effects have to be considered when estimating the lability of dispersal syndromes and the adaptiveness of dispersal. Here, we exposed caterpillars of the Large white butterfly Pieris brassicae from eight families to four density levels and three diet types in a full-crossed experimental design. We measured both larvae’s immediate physiological and behavioral plasticity and carry-over effects on adults’ traits involved in a dispersal syndrome. Significant immediate plasticity was detected on all caterpillars’ traits, whereas only a single marginal carry-over effect was detected on adult traits. Yet, we observed a dependency on larval environment of the correlations among adult’s traits and so of the dispersal syndrome. We relate these results to the species characteristics and discuss their consequences for spatial dynamics. Phenotypic syndrome dispersal plasticity carry-over effects metabolism mobility exploration morphology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Phenotypic plasticity, the phenotypic modifications of one genotype according to the environment, is a ubiquitous mechanism that can be observed within and between generations ( e.g. , Auge et al. 2017 ). It takes two forms according to the reversibility of the phenotypic changes (Stager et al. 2024 ). Phenotypic flexibility, also called reversible plasticity, defines unfixed phenotypic changes that can repeatedly occur through life (Piersma and Drent 2003 ; Piersma and van Gils 2011 ), like body size regulation in function of population density in the sea urchin Diadema antillarum (Levitan 1988 ). Developmental plasticity defines irreversible phenotypic changes arriving at a specific point in development, for instance caste determination in Apis mellifera honey bees as a function of jelly quantity used to feed larvae (Wright et al. 2018 ). The timing at which phenotypic changes occur as compared with the speed of environmental fluctuations is key in the adaptiveness of plasticity (Dupont et al. 2024 ). Plastic changes that occur shortly after the perception of environmental stimulus rely on reduced lag phases, the time delay before the onset of the plastic change, and fast rates of plasticity, the speed at which a plastic trait changed towards its new value (Burton et al. 2022 ; Dupont et al. 2024 ). This is the case of many behavioral traits, for instance activity adjustments of the treefrog Hyla intermedia in response to predator exposure (Castellano et al. 2021 ). Lag phases can also extend and/or plasticity rates be slow creating latent situations where the environment experienced early in life modifies the expression of traits in subsequent habitats, life stages or generations. This is the case of tail length adjustment in juveniles of the lizard Zooteca vivipara after maternal exposure to predator scents three-four months after birth (Bestion et al. 2014 ), or of progeny performance depending on parents’ experience in a butterfly (Ducatez et al. 2012a ). More generally, situations where past exposure to a given environment translates into phenotypic values independent from the present environment (either through flexibility or developmental plasticity) define carry-over effects. These effects can include plastic by-products of early-stages metabolic variation (Allen and Marshall 2010 ), called “silver spoon effects” when beneficial (Mainwaring et al. 2023 ), and directional adaptive changes. Carry-over effects have been widely documented across the tree of life (see review in O’Connor et al. 2014 ) and (Moore and Ryan 2019 ), and both theoretical and empirical approaches have emphasized their pivotal role in eco-evolutionary dynamics ( e.g. , Harrison et al. 2011 ; Van Allen and Rudolf 2013 ; Betini et al. 2013 ; Kristensen et al. 2018 ; Mahendra et al. 2025). However, cases where both immediate and carry-over effects are documented in response to complex environments ( i.e. , several interacting factors) remain poorly documented. Dispersal, the movement of individuals potentially leading to gene flow (Ronce 2007 ), is a key eco-evolutionary process notably allowing individuals to escape unsuitable environments (Clobert et al. 2012 ). It is highly sensitive to the environmental context at its three stages, emigration, transience, settlement (Clobert et al. 2009 ; Bonte et al. 2012 ; McTavish et al. 2013 ; Fattebert et al. 2019 ; Campana et al. 2022 ), and is generally associated with suites of covarying morphological, physiological and behavioral traits, the so-called dispersal syndromes, usually facilitating dispersal (Clobert et al. 2009 ). Dispersal syndromes are ubiquitous across the tree of life (Stevens et al. 2014 ; Valenta and Nevo 2020 ; Cote et al. 2022 ), can be plastic (Cote et al. 2017 ; Jacob et al. 2020 ; Junker et al. 2021 ; Nicolaus et al. 2022 ), and have strong impact on eco-evolutionary dynamics (Cote et al. 2017 ; Jacob et al. 2019 ; Raffard et al. 2022 ; Nicolaus et al. 2022 ; Zilio et al. 2024 ). To be adaptive, plastic facilitators of dispersal have to (i) be expressed before irreversible harmful effects caused by an unsuitable environment, (ii) limit the costs during the transience phase, (iii) ensure sufficient survival and reproductive capacities for dispersing individuals in the new habitat. Understanding the mechanisms behind dispersal and dispersal syndrome plasticity, and their dynamics in the face of different environmental contexts, including the existence of carry-over effects (Benard and McCauley 2008 ), is thus of utmost importance to state on their adaptive values. The phenotypic changes underlying dispersal plasticity can result from any of the above-mentioned forms of plasticity. For instance, cell shape and movement flexibility explain rapid changes in the ciliate Tetrahymena thermophila dispersal rates in response to density (Pennekamp et al. 2014 ; Jacob et al. 2016 ), while developmental plasticity switches larvae of Caenorhabditis elegans worms and males of Cardiocondyla ants into dispersal morphs in response to temperature fluctuation (Golden and Riddle 1984 ; Cremer and Heinze 2003 ). In Erigone atra spiders, adults adjust their dispersal distance according to the thermal environment experienced by juveniles through carry-over effects (Bonte et al. 2008 ). Carry-over effects are also present in many insects where larval dietary restriction usually reduces flight capability and exploratory skills in adults, two dispersal proxies (Saastamoinen and Rantala 2013 ; Scofield and Mattila 2015 ; Reim et al. 2019 ; Niitepõld and Boggs 2022 ). In a few studies, the presence of both immediate and delayed effects has been described. For instance, Cornu aspersum snails adjust their immigration choices according to congeners’ density within settlement patches, but also to the one experienced earlier at the emigration step (Dahirel et al. 2016 ). Despite this accumulation of empirical data, it is unclear whether the co-existence of plastic mechanisms is common in the context of complex environments, if plasticity mechanisms influence each other, and if they can concomitantly influence dispersal-related traits covariations. To address these questions, we used the large white butterfly Pieris brassicae to experimentally study (i) caterpillars’ immediate physiological and behavioral plasticity in response to variation in density, diet quality, and their interplay (ii) the existence and magnitude of carry-over effects on emerging adults’ dispersal-related traits, and (iii) if carry-over effects potentially detected in adults were indeed mediated by the immediate plasticity affecting larval traits. We choose P. brassicae for three main reasons. Firstly, in insect with complete metamorphosis, larvae are often spatially-constrained due to limited mobility, meaning that time-delayed escaping strategies expressed at the more mobile adult stage might be the primary target of natural selection (Stuligross and Williams 2021 ). Indeed, caterpillars of P. brassicae have short-distance prospection abilities (Le Masurier 1994 ) while adults can travel (hundreds of) kilometers (Feltwell 1982 ). Secondly, dispersal is context-dependent in P. brassicae : weather, vegetation cover, and population sex ratio were shown to influence emigration decisions when measured in a semi-natural mesocosm (Legrand et al. 2012 , 2015 ; Trochet et al. 2013 ). Thirdly, a dispersal syndrome linking emigration decisions with flight performance and wing length has been described in the same semi-natural mesocosm (Legrand et al. 2015 ), these two traits themselves covarying with exploration, flight direction at emergence and copulation dynamics (Ducatez et al. 2012b ; Larranaga et al. 2013 , 2019 ). Dispersing individuals generally displayed better flight performances and exploratory skills, longer wings, and better oriented trajectories at emergence than residents. Interestingly, it has been suggested that P. brassicae ’s dispersal syndrome could have high evolutionary potential and thus be disrupted under specific environmental conditions (Legrand et al. 2015 , 2016 ). It might then be possible to observe divergent plastic responses between traits involved in this syndrome. Finally, we focused on density and resource type as these are good indicators of intraspecific competition and habitat quality, which are among the main external drivers of dispersal (Clobert et al. 2009 ). In general, poor resource increases dispersal (Bengtsson et al. 1994 ; Fronhofer et al. 2018 ; Kreuzinger-Janik et al. 2022 ), while density-dependent dispersal is variable (Matthysen 2005 ; Pennekamp et al. 2014 ; Rutherford et al. 2023 ). In P. brassicae , larvae are gregarious: females lay batches of tens to a few hundreds of eggs on host plants’ leaves and caterpillars remain grouped during until they search for a pupation site (Feltwell 1982 ; Le Masurier 1994 ). Previous studies showed that larval density had no effect on their mortality (Le Masurier 1994 ), but had a positive effect on their growth (Blackwell 1988 ). We thus predicted poor larval diet and low density to generally favor the expression of trait values facilitating adults’ dispersal (higher flight performance, exploration skills, longer wings). However, as we also expected poor density and resource type to deteriorate larval condition, our predictions might turn into the opposite pattern, i.e. , small individuals with poor flight performance when density is low and diet poor, in case of extensive detrimental larval legacy. Material and Methods Study organism and breeding conditions Pieris brassicae is a common multivoltine butterfly (typically 2 to 4 generations per year) in the Palearctic referenced as a facultative migratory species able to travel hundreds of kilometers although little is known about this behavior supposed to vary substantially across its range (Spieth and Kaschuba-Holtgrave 1996 ). At the landscape scale, this species has a nomadic strategy of space use (Feltwell 1982 ; Mueller and Fagan 2008 ) and is a facultative long-distance disperser (Baguette et al. 2014 ). Fertilized females lay batches of eggs on a diversity of host plants although they exhibit taxa preferences like for Brassica species (Feltwell 1982 ). Eggs of a single clutch can be considered as full-sibs because females usually mate once before ovipositing (David and Gardiner 1961 ; Wiklund et al. 2001 ; Larranaga et al. 2019 ). After hatching, gregarious caterpillars develop in their natal site in five successive larval stages and finally separate at the end of the last stage in the search for pupation sites. This fifth stage is the more mobile stage with travels that can reach up to 350m (Feltwell 1982 ). In June 2011, we collected eight clutches on cabbage leaves from a private garden in Orgibet (Ariège, France) that we brought to the Theoretical and Experimental Ecology Station in Moulis (Ariège, France). Each clutch was individually placed in 10 x 10 x 7 cm plastic boxes whose lids were centrally replaced by fine-meshed nets to ensure respiration and limit humidity accumulation. Boxes were then placed in a climatic chamber with a 14:10 photoperiod during which temperatures of 23°C and 18°C were respectively fixed to impede diapause (Guyot 2009 ). Manual spraying was daily performed to maintain ~ 60% of humidity. Caterpillars were fed ad libitum with Brassicae oleracea leaves and transferred into clean boxes each time dejections were too important. At the fifth larval stage, and at minimum one day after molting, caterpillars were submitted to the experimental treatments (see below), and thereafter placed again in the same boxes and breeding conditions as small groups of the same clutch fed ad libitum . After pupation, chrysalids were individually placed in smaller boxes (5 x 5 x 3 cm). Experimental treatments We used four density levels, 1, 2, 6, 12 caterpillars, and three diet types, no food, salad (lettuce) as a repellant plant, and B. oleracea as high-quality food (Feltwell 1982 ), in a full-crossed design. A total of 12 experimental treatments were thus performed. As we could not expose each individual to the 12 treatments, plasticity was measured by subsampling the appropriate number of caterpillars for the 12 treatments among each of the eight clutches, which were thus our level of replication. We considered that phenotypic variation in response to environmental variation between full-sibs can be mainly attributed to plasticity (Scheiner 1993 , see an example in P. brassiace in Chaput-Bardy et al. 2014 ). This means that 63 fifth stage caterpillars were randomly chosen from each clutch, and then randomly dispatched between the 12 treatments, for a total of 504 tested caterpillars. Treatments were imposed to caterpillars in an experimental device allowing to immediately measure their phenotypic flexibility (see below). Caterpillar phenotypic traits measurements At the larval stage, we decided to measure the flexibility of mobility, exploration and metabolism traits, because they might all be affected by our treatments and influence adults’ dispersal syndrome. To acquire larval data traits, we adapted well-described settings aimed at measuring mobility, exploration and/or dispersal in small organisms composed of two arenas connected by a small corridor ( e.g. , (Aragón et al. 2006 ; Fjerdingstad et al. 2007 ; Cote and Clobert 2007 ). We constructed 12 systems to run all treatments simultaneously for a given clutch in the same 23°C regulated room (all trials were performed between the 11th and the 21th of July 2011). Each system was composed of two 900 cm 2 arenas connected by a 38 x 2 cm long corridor. We placed the diet treatment in the center of the two arenas (Fig. 1 A), or left them empty in the no food treatment. To prevent caterpillars’ escape from arenas, we installed 3 cm height walls composed of a 0.5 cm large white plastic on which we fixed a transparent film. The corridor length was above the perception distance of P. brassicae ’s caterpillars (Costa 2006 ), meaning that inter-arena movements can be attributed to high exploratory skills. Individual recognition was obtained by gluing a 1 cm diameter piece of distinguishable color papers (following Noldus et al. 2001 ) on the back of each caterpillar. These paper pieces are thereafter displaced at the posterior extremity of chrysalids by the pupation process itself, facilitating individual recognition between larval and adult phases. As our maximal experimental density was 12, we selected 12 colored papers that could be unambiguously distinguished during the tracking phase. The 12 colors were randomly attributed to each caterpillar in the density 12 treatment, and for the other densities, we used the same 1, 2, and 6 colors randomly attributed to caterpillars for all replicates. We added an individual number as a double marking system on the paper pieces. One hour before introducing caterpillars on top of the food treatment in one of the two arenas (the other being initially caterpillar-free), we weighted them (including their paper mark) at the nearest 0.001g (Adventurer Pro AV213C), and 30 min before experiments, we placed them at 8°C in a dark climatic chamber to limit their movement during the installation phase. As soon as caterpillars were placed in their treatment, we started the recording of 90 min videos with Canon cameras fixed on the roof’s room. After these 90 min, we weighted again each caterpillar and used the mass loss (difference between the masses before and after trials) as a proxy of treatments’ metabolic impacts. Mobility and exploration were obtained from the videos using EthoVision XT version 8 (Noldus Information Technology, Wageningen, The Netherlands). Individual trajectories were analyzed through a sampling of two pictures by second and by setting the position of the two arenas and the corridors for each video. From these trajectories, we extracted the time before the first movement (s), the second arena exploration (binary variable yes/no), the proportion of time moving , the total movement distance (s), and the mean and max velocities (cm/s). Butterflies phenotypic trait measurement Within the six days after emergence, we performed the ‘tunnel’ and the ‘vortex’ tests (Fig. 1 B-C) as described in (Ducatez et al. 2012b ). We always performed the vortex test at least one day after the tunnel test. The tunnel test consists in kindly releasing a butterfly in the air at the entrance of a 3m long opaque pipe of 80cm diameter with a light source at its end in a dark room regulated at 23 ± 1°C. During the test, butterflies can fly and/or walk to entirely or partly cross the pipe, or stay at the entrance. In such case, the test was stopped after 10s of inactivity. Several traits were recorded: the willingness of the butterfly to cross the tunnel (binary tunnel cross variable), the time before the first movement after the release (s), the first behavior (stay at the entrance/fly/walk), the cross time (s), the time spent flying and the time spent walking . Crossing the tunnel by always flying can be considered as an especially bold behavior, while staying at the entrance can be considered as the shiest behavior. In between, butterflies can adopt mixed locomotion strategies to cross or partially cross the tunnel. Obviously, individuals with enhanced mobility should cross more rapidly the tunnel, but only if they are willing to explore challenging environments. The tunnel test is highly repeatable in P. brassicae (91%, Ducatez et al. 2012), we thus decided to perform it only once. The vortex test has been designed to test performance under stressful conditions. Butterflies are individually placed in a 25 x 10 x 10 cm plastic chamber maintained by hand by the experimenter on top of a vortex. After a 1 min period of acclimation in the chamber, the vortex is turned on during 1 min so as to strongly shake the chamber and prevent butterflies from perching on chamber’s walls. Butterflies able to fly during at least 50s were qualified as good performers, the others, which rested at least 10s seconds on the bottom of the chamber while being strongly shaken were qualified as bad performers (binary flight performance variable). After the vortex text, we anesthetized butterflies using nitric oxide in a 10 x 10 x 10 cm box (Inject + Matic Sleeper TAS®) just before the same experimenter measured their wing length (mm) using a caliper. In total, 192 butterflies emerged, 160 performed the tunnel test, and 103 the vortex test and wing length measurement (butterflies could die throughout the end of the phenotyping sequences). Statistical analyses We performed all statistical analyses on R version 4.2.2 (R Core Team 2024 ), using the packages lme4 (Bates et al. 2015 ) to run Linear Models, emmeans to perform pairwise contrast tests (Lenth 2025 ), partR2 (Stoffel et al. 2021 ) to estimate the part of variance explained by each significant variable in linear models, corrplot (Wei and Simko 2017 ) to build correlation matrices, and FactoMineR (Lê et al. 2008 ) to run Principal Component Analyses (PCA) with missMDA (Josse and Husson 2016 ) to account for missing data. To test for the existence of immediate plasticity or carry-over effects in response to the treatments, we built Linear Mixed Models (LMM) in case of normally-distributed response variables or a Generalized LMM (GLMM) using a binomial error and a logit link in case of binary response variables. In all models, phenotypic traits were implemented as the response variables, density, diet type and their interaction as explanatory variables, and clutch identity (equivalent of family) as a random intercept. Caterpillar traits were used as the response to study immediate plasticity and butterfly traits were used to study carry-over effects. We performed a backward selection model procedure to select the best model by considering p-values > 0.1 after a Likelihood Ratio Test for the threshold exclusion to include marginal effects. Tukey tests were performed to determine the diet treatments differing from the others in case of a significant effect implying the diet type. In all final models, we checked the distribution of residuals. As the five caterpillars’ variables obtained in the double arena were all significantly correlated (Figure S1 A), we summarized them by the two first axes of a PCA, which accounted for 60% of the total variance (Figure S1 B and C). Mobility variables contributed mainly to Axis 1 (43.23%, renamed caterpillar mobility axis ) with high values referring to caterpillars with tendency to move for long times and distances, and at high mean and max velocities. Exploration variables contributed mainly to Axis 2 (16.85%, renamed caterpillar exploration axis ), high values describing individuals with the tendency to explore the second arena, and move immediately and for a long time after their release in the first arena before potentially exploring the second arena. As the six tunnel variables were also significantly correlated to at least another (Figure S2A), we summarized them by the two first axes of a second PCA (Figure S2B to D), which accounted for 58% of the total variance. High values on axis 1 (34.8%, renamed butterfly exploration axis ) referred to butterflies that did not entirely cross the tunnel (Figure S2C), primarily walked or stayed immobile after release instead of flying (Figure S2D). High values on axis 2 (22.83%, renamed butterfly mobility axis ) referred to long times to cross the tunnel (only recorded for butterflies that crossed entirely the tunnel) and long times to start moving. To complement our search of carry-over effects, we also used a fitness proxy: adult emergence (coded as a binary variable yes/no for each caterpillar). To test for the existence of carry-over effects on adults’ traits indirectly mediated by immediate plastic changes of mass, mobility and exploration expressed at the larval stage, we added the three larval traits ( mass loss , caterpillar mobility axis , caterpillar exploration axis ) as explanatory variables to the final LMMs and GLMMs retained after the selection procedure of the carry-over effect analyses. Cases where previously significant effects of density, diet, or their interaction would disappear in favor of caterpillar traits suggest carry-over effects mediated by phenotypic changes at the larval stage. Usually, the distinction between direct and indirect effects are studied with Structural Equation Models (SEM, Stein et al. 2012 ). In our case, these models were difficult to apply because we were studying an interaction between a continuous and a categorial variable that cannot easily be transformed as an ordinated continuous variable. We thus decided to apply the statistical procedure described above. Finally, to explore further the effects of the larval treatments on adults’ dispersal syndrome, we built correlation matrices between wing length , butterfly exploration axis , butterfly mobility axis , and flight performance during the vortex test for each density level across all diets, and for each diet type across all densities. We decided to explore only these conditions as building and comparing correlation matrices for the 12 treatments seems too complex, and yields to insufficient sample sizes in some treatments. Results Immediate plasticity We observed a significant effect of diet type, density or their interaction for all measured caterpillar traits (Table 1 ). Firstly, caterpillars lost less mass in presence of cabbage (p < 10 − 5 , Table 1 , Fig. 2 A). While density had no effect on mass loss for the salad and cabbage treatment, caterpillars lost more mass at the lowest densities in the absence of food (p = 0.01 for diet x density interaction, Table 1 ). Secondly, diet type and density significantly impacted caterpillar mobility axis (p < 10 − 11 in both cases, Table 1 ). Mobility was enhanced in the absence or food or when salad was provided as compared with the cabbage treatment (Fig. 2 B), and marginally enhanced in presence of salad as compared with the no food treatment (Fig. 2 B). For density, we revealed higher mobility at the highest densities (Fig. 2 C). Finally, caterpillar exploration was affected only by the diet type (p < 10 − 3 , Table 1 ), with higher tendencies to immediately explore the double arena system when salad was proposed, although the difference with the no food treatment was marginal (Fig. 2 D). The part of phenotypic variance explained by the significant treatments ranged between 2.8 and 13% (Table 1 ). Carry-over effects Contrarily to immediate plasticity, only few treatments significantly affected adults’ traits. Over the four tested traits involved in P. brassicae ’s dispersal syndrome, only the butterfly mobility axis was marginally affected by the interaction between density and diet (p = 0.06, 6.8% of variance explained, Table 1 ). We observed a tendency towards higher values on butterfly mobility axis as density increased in the salad treatment (increased time to cross the tunnel and longer time to decide to move at high densities, Fig. 3 A), while density had the reverse effect in presence of cabbage (decreased time to cross the tunnel and longer time to decide to move at high densities). When looking at emergence, we observed a significant interaction between diet type and density (p < 10 − 4 , 3.7% of variance explained, Table 1 ). The number of emerging butterflies increased with density in presence of salad and cabbage, while it decreased with density in absence of food (Fig. 3 B). Indirect effects on adults caused by changes on caterpillar traits None of the significant carry-over effects detected ( butterfly mobility axis and emergence ) were due to indirect effects of the treatments on caterpillars. Indeed, when adding larval traits as explanatory variables on the previously described best models (Table 1 ), model structures were all unchanged (Table S1 ). In addition, none of the larval traits significantly impacted the other adult traits ( wing length , butterfly exploration axis , flight performance ). Effects of larval treatment on the architecture of P. brassicae ’s dispersal syndrome Matrix correlation between adults’ traits over all butterflies showed significant negative correlation between butterfly exploration axis and flight performance (Fig. 4 ), meaning that individuals that succeeded at the vortex test were more often those that crossed the tunnel, mainly by flying. There was also a marginal negative correlation between wing length and butterfly exploration axis , meaning that butterflies that crossed the tunnel had generally shorter wings. When comparing matrices between the diet types (Fig. 4 ), we observed that correlations changed, with a greater link between butterfly exploration axis and flight performance in the salad treatment, while it disappeared in the other diet types, and a positive correlation between wing length and butterfly mobility axis becoming significant in absence of food (butterflies with shorter wings moved rapidly after the beginning of the test and crossed the tunnel rapidly). Looking at the effect of density, we observed that the negative correlation between butterfly exploration axis and flight performance was weaker as density increased. Discussion In this study, we investigated if and how two biotic factors and their interaction impacted the expression of a series of both larvae and adults’ phenotypic traits. Immediate plastic effects were highlighted on all tested larvae’s traits (metabolism, mobility and exploration proxies), while carry-over effects were evidenced on one dispersal-related trait (marginal effect) and one fitness proxy. Despite these limited time-delayed effects on mean adults’ trait expression, we revealed that their correlations varied according to the diet type and the density. This means that P. brassicae ’s dispersal syndrome can be altered by prior environmental conditions, with potential consequences on the species spatial dynamics. We discuss all these points hereafter. Caterpillars plastic response to diet and density variation For all tested trait, we detected significant effects of density, diet or their interaction. Firstly, and as expected, the diet type impacted the loss of caterpillars’ mass during the double arena test, with a higher metabolic cost in absence of food, but to a lesser extent at the highest densities. The effects on mass were identical between the cabbage (host plant naturally eaten by caterpillars) and the salad (repellant plant usually not ingested) treatments. Therefore, either caterpillars consumed the repellant plant as much as the natural host plant, or the presence of a plant itself provided a metabolic reward, such as resistance to desiccation and/or enhanced thermoregulation in the same manner as larval aggregation does (Klok and Chown 1999 ; Qian et al. 2024 ). The reward effect of density on metabolism in the absence of food mentioned above supports previous studies showing that increased larval density has a positive effect on caterpillar growth (Blackwell 1988 ). We did not observe this density effect in presence of a plant, maybe because the metabolic cost during the test was limited in presence of a plant whatever the density. Alternatively, the information of both high density and absence of food could induce a kind of terminal investment response in metabolism. Secondly, we showed that the diet type influenced both mobility and exploration of caterpillars. In presence of cabbage, caterpillars moved less and less rapidly. This agrees with their lifestyle as they usually stay on leaves of their host plant until their complete consumption or when they move to find a pupation site (Feltwell 1982 ). Mobility was also marginally enhanced in presence of salad as compared with the absence of food, confirming the repelling effect of salad on P. brassicae (Feltwell 1982 ). This effect matches those observed in the second tested caterpillar behavior as exploration was especially enhanced in the salad treatment. This suggests that while the absence of food seems to favor short-distance foraging movement, it does not trigger exploration at longer distances as the repellant plant does. Whatever the diet type, caterpillars tended to move less and less rapidly at the lowest densities, but density did not affect their willingness to explore the second arena and to initiate movements. For a gregarious species, very small groups can be considered as stressful situations, with increased metabolic costs as stated earlier. In addition, species laying batches of eggs have often developed communication skills at early-developmental stages increasing individuals’ fitness (Aubret et al. 2016 ). Another non-exclusive hypothesis could be the existence of social mechanisms enhancing movements in this species, but this remains to be tested, as for many aspects of caterpillar collective behaviors (McLellan and Montgomery 2023 ). Overall, results on immediate plasticity agreed with our knowledge of P. brassicae ’s lifestyle and opened the possibility to observe not only carry-over effects of diet type and density on adults’ traits, but also indirect carry-over effects of the treatments passing through immediate plasticity acting on larvae. Unexpected carry-over effects on the adult’s dispersal syndrome With the exception of the marginal interactive effect of diet type and density on butterflies’ mobility in the tunnel, there was a general absence of carry-over effects of larval environmental conditions on adults’ trait involved in P. brassicae ’s dispersal syndrome. This result did not match our predictions. We expected the most stressful conditions (no food, salad and low-density) to generally favor the expression of trait values facilitating adults’ dispersal, i.e. , higher flight performance, exploration skills and longer wings (Legrand et al. 2015 ). Another hypothesis was to observe lower flight performance, wing length and exploration skills if the stressful conditions had incurred too strong metabolic impacts on larvae. On the contrary, mean wing length and performance in the tunnel and vortex tests were the same over all treatments, and we did not detect carry-over effects mediated by changes on larval traits. While these results could be explained by decoupled selective pressures between late larval stages and imagoes leading to distinct reaction norms, another possibility would be the existence of plastic effects on traits without major changes on mean values. Analysis of the correlation matrices between the dispersal syndrome traits across diets and densities agrees with this second scenario. Indeed, the positive correlation between flight performance and the willingness to cross the tunnel (as previously found in Ducatez et al. 2012) was especially high in the most stressful diets and densities. Changes in trait correlations despite flat reaction norms for mean trait values can be observed if there is variation in the plastic responses of dispersal among individuals. Here, some individuals could have increased both their flight performance and willingness to cross the tunnel while others could have decreased both their flight performance and willingness to cross the tunnel. This would reflect the persistence of intraspecific variability in dispersal strategies despite stressful conditions, i.e., the co-existence of residents and dispersers. This scenario corresponds to the exacerbation of residents’ phenotypic attributes limiting the costs of movement in the hope of better future conditions, concomitantly with the exacerbation of dispersers attributes to favor efficient escaping movements. We also found a marginal positive correlation between wing morphology and butterfly exploration over all conditions and between wing morphology and butterfly mobility in absence of food (butterflies with shorter wings flew more rapidly to cross the tunnel). A correlation between wing morphology and performance in the tunnel test has been previously described, but in the opposite direction (Ducatez et al. 2012b ). Although difficult to explain, this contradictory result could be representative of a general impact of experiencing the arena test on the link between morphology and mobility and/or exploration. The fact that we observed no significant correlation between wing morphology and flight performance as measured in the vortex test supports this hypothesis as a link between these two traits, either positive or negative, was recurrently observed (Ducatez et al. 2012b ; Trochet et al. 2013 ; Legrand et al. 2016 ), but in all previous studies, larvae never experienced experimental tests. Overall, these studies highlight complex links between wing morphology on the one hand, and mobility or exploration on the other hand. To complement our results on dispersal-related traits, we tested for the existence of carry-over effects acting on adult emergence, a fitness component. The number of successful metamorphoses increased at high densities in presence of a plant (either cabbage or salad). This positive carry-over effect of larval density on fitness corroborates previous results on immediate plasticity (Blackwell 1988 , see above). However, the decrease of emergence at the highest densities when food was absent, is more enigmatic. Interestingly, emergence was not especially high in the cabbage treatment as compared with the two other resource treatments, meaning that we did not observe a “silver spoon effect” in the best food condition, i.e. , a higher fitness lately in the development caused by a beneficial environment experienced earlier in the development. It is possible that the energy stored before the fifth larval stage during breeding and after the test until the pupation ( ad libitum conditions) was sufficient to buffer the energetic cost of the food unavailability during the 90 min of the test. Complementary experiments on earlier caterpillar stages would help understanding in further details how the diet impacts emergence probability. Potential impact on spatial dynamics The fact that larval experience can change the intensity of correlations between adults’ dispersal related traits can have strong consequences on spatial dynamics. When larvae and adults’ requirements match, and when the larval environment provides reliable cues on the adult’s one, we expect a strong benefit of such carry-over effects at the landscape scale. In the case of P. brassicae , larvae and adults’ requirements generally match: like caterpillars, adults can be found in high-density groups, and adults can feed on a variety of flowers including those of the preferred caterpillar plant species (Feltwell 1982 ). The revealed carry-over effects on the species dispersal syndrome structure might thus result from a selective process optimizing the species’ spatial dynamics. Accordingly, we observed that the most stressful conditions reinforced some correlations between traits linked to the dispersal status of individuals, as previously observed (Cote et al. 2022 ). One future question will be to unravel the potential interactions between pre- and post-metamorphosis effects on dispersal syndromes, especially when they bring contradictory information. Further, it could be highly relevant to formally measure the consequences of such labile dispersal syndrome on metapopulation and metacommunity stability given their pivotal roles on many eco-evolutionary dynamics (Jacob et al. 2015 , 2019 ; Cote et al. 2017 ; Raffard et al. 2022 ; Nicolaus et al. 2022 ; Zilio et al. 2024 ). This would provide a better appreciation of the adaptive value of carry-over effects acting on dispersal traits (Benard and McCauley 2008 ), and improve our integration of dispersal variability on the functioning of ecological networks (Baguette et al. 2013 ). Overall, our results revealed atypical carry-over effects (few changes in mean values but changes in correlations between treatments) able to modify the structure of a dispersal syndrome. While the plasticity of dispersal syndromes has been empirically described (for instance in Jacob et al. 2020 ), examples involving carry-over effects are rare. A key point is that the harshness of the environment impacted the structure of the studied dispersal syndrome, echoing back to a previous study showing a strengthening of the link between dispersal and some phenotypic traits with environmental harshness across 15 species, albeit no information on the underlying mechanisms were available (Cote et al. 2022 ). Whatever the origin of the link between the structure of dispersal syndromes (genetic and/or plastic, immediate and/or carry-over effects) and the harshness of the environment, data accumulate in favor of an inter-species phenomena. Finally, a major lesson raised by our study is the complexity of the plastic effects that could be observed, according to both the traits and developmental stages, as the two environmental dimensions had either no, independent or interactive effects on traits’ expression and/or correlation. Integrating the multidimensionality of environments in dispersal studies is thus mandatory for future studies on its plasticity. Declarations Acknowledgements We thank Michèle Huet for the sampling of the egg clutches, and Olivier Calvez for his help to breed caterpillars and butterflies. The SETE and CRBE labs are supported by the French Laboratory of Excellence project “TULIP” (ANR-10-LABX-41). JC was supported by the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 817779). References Allen RM, Marshall DJ (2010) The larval legacy: cascading effects of recruit phenotype on post-recruitment interactions. Oikos 119:1977–1983. https://doi.org/10.1111/j.1600-0706.2010.18682.x Aragón P, Clobert J, Massot M (2006) Individual dispersal status influences space use of conspecific residents in the common lizard, Lacerta vivipara. Behav Ecol Sociobiol 60:430–438. https://doi.org/10.1007/s00265-006-0183-3 Aubret F, Blanvillain G, Bignon F, Kok PJR (2016) Heartbeat, embryo communication and hatching synchrony in snake eggs. Sci Rep 6:23519. https://doi.org/10.1038/srep23519 Auge GA, Leverett LD, Edwards BR, Donohue K (2017) Adjusting phenotypes via within‐ and across‐generational plasticity. New Phytologist 216:343–349. https://doi.org/10.1111/nph.14495 Baguette M, Blanchet S, Legrand D, et al (2013) Individual dispersal, landscape connectivity and ecological networks. Biological Reviews 88:310–326. https://doi.org/10.1111/brv.12000 Baguette M, Stevens VM, Clobert J (2014) The pros and cons of applying the movement ecology paradigm for studying animal dispersal Bates D, Mächler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Soft 67:. https://doi.org/10.18637/jss.v067.i01 Benard MF, McCauley SJ (2008) Integrating across Life‐History Stages: Consequences of Natal Habitat Effects on Dispersal. The American Naturalist 171:553–567. https://doi.org/10.1086/587072 Bengtsson G, Hedlund K, Rundgren S (1994) Food- and Density-Dependent Dispersal: Evidence from a Soil Collembolan. The Journal of Animal Ecology 63:513. https://doi.org/10.2307/5218 Bestion E, Teyssier A, Aubret F, et al (2014) Maternal exposure to predator scents: offspring phenotypic adjustment and dispersal. Proc R Soc B 281:20140701. https://doi.org/10.1098/rspb.2014.0701 Betini GS, Griswold CK, Norris DR (2013) Carry-over effects, sequential density dependence and the dynamics of populations in a seasonal environment. Proc R Soc B 280:20130110. https://doi.org/10.1098/rspb.2013.0110 Blackwell A (1988) The disruption of aggregation behaviour in Pieris brassicae larvae by chlordimeform. Entomologia Exp Applicata 48:141–147. https://doi.org/10.1111/j.1570-7458.1988.tb01157.x Bonte D, Travis JMJ, De Clercq N, et al (2008) Thermal conditions during juvenile development affect adult dispersal in a spider. Proc Natl Acad Sci USA 105:17000–17005. https://doi.org/10.1073/pnas.0806830105 Bonte D, Van Dyck H, Bullock JM, et al (2012) Costs of dispersal. Biological Reviews 87:290–312 Burton T, Ratikainen II, Einum S (2022) Environmental change and the rate of phenotypic plasticity. Global Change Biology 28:5337–5345. https://doi.org/10.1111/gcb.16291 Campana JLM, Raffard A, Chaine AS, et al (2022) Dispersal plasticity driven by variation in fitness across species and environmental gradients. Ecology Letters 25:2410–2421. https://doi.org/10.1111/ele.14101 Castellano S, Racca L, Friard O (2021) Plasticity and flexibility in the anti-predator responses of treefrog tadpoles. Behav Ecol Sociobiol 75:142. https://doi.org/10.1007/s00265-021-03078-1 Chaput-Bardy A, Ducatez S, Legrand D, Baguette M (2014) Fitness Costs of Thermal Reaction Norms for Wing Melanisation in the Large White Butterfly (Pieris brassicae). PLoS ONE 9:e90026. https://doi.org/10.1371/journal.pone.0090026 Clobert J, Baguette M, Benton TG, Bullock JM (2012) Dispersal ecology and evolution, Oxford University Press. Oxford Clobert J, Le Galliard J, Cote J, et al (2009) Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecology Letters 12:197–209. https://doi.org/10.1111/j.1461-0248.2008.01267.x Costa JT (2006) The other insect societies, Belknap Press. Harvard University Press, Cambridge, Massachussetts Cote J, Bestion E, Jacob S, et al (2017) Evolution of dispersal strategies and dispersal syndromes in fragmented landscapes. Ecography 40:56–73. https://doi.org/10.1111/ecog.02538 Cote J, Clobert J (2007) Social information and emigration: lessons from immigrants. Ecology Letters 10:411–417. https://doi.org/10.1111/j.1461-0248.2007.01032.x Cote J, Dahirel M, Schtickzelle N, et al (2022) Dispersal syndromes in challenging environments: A cross‐species experiment. Ecology Letters 25:2675–2687. https://doi.org/10.1111/ele.14124 Cremer S, Heinze J (2003) Stress Grows Wings: Environmental Induction of Winged Dispersal Males in Cardiocondyla Ants. Current Biology 13:219–223 Dahirel M, Vardakis M, Ansart A, Madec L (2016) Density-dependence across dispersal stages in a hermaphrodite land snail: insights from discrete choice models. Oecologia 181:1117–1128. https://doi.org/10.1007/s00442-016-3636-z David WAL, Gardiner BOC (1961) The mating behaviour of Pieris brassicae (L.) in a laboratory culture. Bull Entomol Res 52:263–280. https://doi.org/10.1017/S0007485300055401 Ducatez S, Baguette M, Stevens VM, et al (2012a) Complex interactions between paternal and maternal effects: parental experience and age at reproduction affect fecundity and offspring performance in a butterfly. Evolution 66:3558–3569. https://doi.org/10.1111/j.1558-5646.2012.01704.x Ducatez S, Legrand D, Chaput‐Bardy A, et al (2012b) Inter‐individual variation in movement: is there a mobility syndrome in the large white butterfly Pieris brassicae ? Ecological Entomology 37:377–385. https://doi.org/10.1111/j.1365-2311.2012.01375.x Dupont L, Thierry M, Zinger L, et al (2024) Beyond reaction norms: the temporal dynamics of phenotypic plasticity. Trends in Ecology & Evolution 39:41–51. https://doi.org/10.1016/j.tree.2023.08.014 Fattebert J, Perrig M, Naef-Daenzer B, Grüebler MU (2019) Experimentally disentangling intrinsic and extrinsic drivers of natal dispersal in a nocturnal raptor. Proc R Soc B 286:20191537. https://doi.org/10.1098/rspb.2019.1537 Feltwell J (1982) Large white butterfly: the biology, biochemistry, and physiology of Pieris brassicae (Linnaeus), Dr W. Junk Publishers. Springer Dordrecht, The Hague Fjerdingstad EJ, Schtickzelle N, Manhes P, et al (2007) Evolution of dispersal and life history strategies – Tetrahymena ciliates. BMC Evol Biol 7:133. https://doi.org/10.1186/1471-2148-7-133 Fronhofer EA, Legrand D, Altermatt F, et al (2018) Bottom-up and top-down control of dispersal across major organismal groups. Nat Ecol Evol 2:1859–1863. https://doi.org/10.1038/s41559-018-0686-0 Golden JW, Riddle DL (1984) A pheromone-induced developmental switch in Caenorhabditis elegans: Temperature-sensitive mutants reveal a wild-type temperature-dependent process. Proc Natl Acad Sci USA 81:819–823. https://doi.org/10.1073/pnas.81.3.819 Guyot H (2009) Fiche technique d’élevage : la Piéride du Chou. Insectes 19–22 Harrison XA, Blount JD, Inger R, et al (2011) Carry-over effects as drivers of fitness differences in animals. Journal of Animal Ecology 80:4–18 Jacob S, Bestion E, Legrand D, et al (2015) Habitat matching and spatial heterogeneity of phenotypes: implications for metapopulation and metacommunity functioning. Evol Ecol 29:851–871. https://doi.org/10.1007/s10682-015-9776-5 Jacob S, Chaine AS, Huet M, et al (2019) Variability in Dispersal Syndromes Is a Key Driver of Metapopulation Dynamics in Experimental Microcosms. The American Naturalist 194:613–626. https://doi.org/10.1086/705410 Jacob S, Laurent E, Morel‐Journel T, Schtickzelle N (2020) Fragmentation and the context‐dependence of dispersal syndromes: matrix harshness modifies resident‐disperser phenotypic differences in microcosms. Oikos 129:158–169. https://doi.org/10.1111/oik.06857 Jacob S, Wehi P, Clobert J, et al (2016) Cooperation-mediated plasticity in dispersal and colonization. Evolution 70:2336–2345. https://doi.org/10.1111/evo.13028 Josse J, Husson F (2016) missMDA : A Package for Handling Missing Values in Multivariate Data Analysis. J Stat Soft 70:. https://doi.org/10.18637/jss.v070.i01 Junker AD, Jacob S, Philippe H, et al (2021) Plastic cell morphology changes during dispersal. iScience 24:102915. https://doi.org/10.1016/j.isci.2021.102915 Klok CJ, Chown SL (1999) Assessing the benefits of aggregation: thermal biology and water relations of anomalous Emperor Moth caterpillars. Functional Ecology 13:417–427. https://doi.org/10.1046/j.1365-2435.1999.00324.x Kreuzinger‐Janik B, Gansfort B, Traunspurger W, Ptatscheck C (2022) It’s all about food: Environmental factors cause species‐specific dispersal. Ecosphere 13:e4251. https://doi.org/10.1002/ecs2.4251 Kristensen NP, Johansson J, Chisholm RA, et al (2018) Carryover effects from natal habitat type upon competitive ability lead to trait divergence or source–sink dynamics. Ecology Letters 21:1341–1352. https://doi.org/10.1111/ele.13100 Larranaga N, Baguette M, Calvez O, et al (2013) Intra- and inter-individual variations in flight direction in a migratory butterfly co-vary with individual mobility. Journal of Experimental Biology 216:3156–3163 Larranaga N, Baguette M, Calvez O, Legrand D (2019) Mobility affects copulation and oviposition dynamics in Pieris brassicae in seminatural cages. Insect Science 26:743–752. https://doi.org/10.1111/1744-7917.12568 Le Masurier AD (1994) Costs and Benefits of Egg Clustering in Pieris brassicae. The Journal of Animal Ecology 63:677. https://doi.org/10.2307/5233 Lê S, Josse J, Husson F (2008) FactoMineR : An R Package for Multivariate Analysis. J Stat Soft 25:. https://doi.org/10.18637/jss.v025.i01 Legrand D, Guillaume O, Baguette M, et al (2012) The Metatron: an experimental system to study dispersal and metaecosystems for terrestrial organisms. Nat Methods 9:828–833. https://doi.org/10.1038/nmeth.2104 Legrand D, Larranaga N, Bertrand R, et al (2016) Evolution of a butterfly dispersal syndrome. Proc R Soc B 283:20161533. https://doi.org/10.1098/rspb.2016.1533 Legrand D, Trochet A, Moulherat S, et al (2015) Ranking the ecological causes of dispersal in a butterfly. Ecography 38:822–831. https://doi.org/10.1111/ecog.01283 Lenth RV (2025) emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.11.1-00001 Levitan DR (1988) Density-dependent size regulation and negative growth in the sea urchin Diadema antillarum Philippi. Oecologia 76:627–629 Mahendra, Pandey DN, Kumar A (2025) Dynamics of autonomous and nonautonomous Leslie–Gower model for the impacts of fear and its carry‐over effects including predator harvesting. Math Methods in App Sciences 48:3082–3113. https://doi.org/10.1002/mma.10476 Mainwaring MC, Tobalske BW, Hartley IR (2023) Born without a Silver Spoon: A Review of the Causes and Consequences of Adversity during Early Life. Integrative And Comparative Biology 63:742–757. https://doi.org/10.1093/icb/icad061 Matthysen E (2005) Density‐dependent dispersal in birds and mammals. Ecography 28:403–416. https://doi.org/10.1111/j.0906-7590.2005.04073.x McLellan CF, Montgomery SH (2023) Towards an integrative approach to understanding collective behaviour in caterpillars. Phil Trans R Soc B 378:20220072. https://doi.org/10.1098/rstb.2022.0072 McTavish MJ, Basiliko N, Sackett TE (2013) Environmental factors influencing immigration behaviour of the invasive earthworm Lumbricus terrestris. Can J Zool 91:859–865. https://doi.org/10.1139/cjz-2013-0153 Moore MP, Ryan MA (2019) On the evolution of carry-over effects. Journal of Animal Ecology 88:1832–1844 Mueller T, Fagan WF (2008) Search and navigation in dynamic environments – from individual behaviors to population distributions. Oikos 117:654–664. https://doi.org/10.1111/j.0030-1299.2008.16291.x Nicolaus M, Wang X, Lamers KP, et al (2022) Unravelling the causes and consequences of dispersal syndromes in a wild passerine. Proc R Soc B 289:20220068. https://doi.org/10.1098/rspb.2022.0068 Niitepõld K, Boggs CL (2022) Carry‐over effects of larval food stress on adult energetics and life history in a nectar‐feeding butterfly. Ecological Entomology 47:391–399. https://doi.org/10.1111/een.13124 Noldus LPJJ, Spink AJ, Tegelenbosch RAJ (2001) EthoVision: A versatile video tracking system for automation of behavioral experiments. Behavior Research Methods, Instruments, & Computers 33:398–414. https://doi.org/10.3758/BF03195394 O’Connor CM, Norris DR, Crossin GT, Cooke SJ (2014) Biological carryover effects: linking common concepts and mechanisms in ecology and evolution. Ecosphere 5:1–11. https://doi.org/10.1890/ES13-00388.1 Pennekamp F, Mitchell KA, Chaine A, Schtickzelle N (2014) Dispersal propensity in Tetrahymena thermophila ciliates - a reaction norm perspective. Evolution 68:2319–2330. https://doi.org/10.1111/evo.12428 Piersma T, Drent J (2003) Phenotypic flexibility and the evolution of organismal design. Trends in Ecology & Evolution 18:228–233. https://doi.org/10.1016/S0169-5347(03)00036-3 Piersma T, van Gils JA (2011) The Flexible Phenotype: A Body-Centred Integration of Ecology, Physiology, and Behavio. Oxford University Press, Oxford, UK Qian C, Wen C, Guo X, et al (2024) Gregariousness in lepidopteran larvae. Insect Science 1744-7917.13312. https://doi.org/10.1111/1744-7917.13312 R Core Team (2024) R: A Language and Environment for Statistical Computing Raffard A, Bestion E, Cote J, et al (2022) Dispersal syndromes can link intraspecific trait variability and meta-ecosystem functioning. Trends in Ecology & Evolution 37:322–331. https://doi.org/10.1016/j.tree.2021.12.001 Reim E, Eichhorn D, Roy JD, et al (2019) Nutritional stress reduces flight performance and exploratory behavior in a butterfly. Insect Science 26:897–910. https://doi.org/10.1111/1744-7917.12596 Ronce O (2007) How Does It Feel to Be Like a Rolling Stone? Ten Questions About Dispersal Evolution. Annu Rev Ecol Evol Syst 38:231–253. https://doi.org/10.1146/annurev.ecolsys.38.091206.095611 Rutherford KL, Cottenie K, Denomme-Brown ST (2023) To go or not to go: variable density-dependent dispersal in small mammals. Journal of Mammalogy 104:783–791. https://doi.org/10.1093/jmammal/gyac117 Saastamoinen M, Rantala MJ (2013) Influence of Developmental Conditions on Immune Function and Dispersal-Related Traits in the Glanville Fritillary (Melitaea cinxia) Butterfly. PLoS ONE 8:e81289. https://doi.org/10.1371/journal.pone.0081289 Scheiner SM (1993) Genetics and Evolution of Phenotypic Plasticity. Annu Rev Ecol Syst 24:35–68. https://doi.org/10.1146/annurev.es.24.110193.000343 Scofield HN, Mattila HR (2015) Honey Bee Workers That Are Pollen Stressed as Larvae Become Poor Foragers and Waggle Dancers as Adults. PLoS ONE 10:e0121731. https://doi.org/10.1371/journal.pone.0121731 Spieth HR, Kaschuba‐Holtgrave A (1996) A new experimental approach to investigate migration in Pieris brassicae L. Ecological Entomology 21:289–294. https://doi.org/10.1111/j.1365-2311.1996.tb01246.x Stager M, Velotta JP, Cheviron ZA, Senner NR (2024) Assessing the evidence for treating developmental plasticity and phenotypic flexibility as different phenomena. Functional Ecology 1365-2435.14590. https://doi.org/10.1111/1365-2435.14590 Stein CM, Morris NJ, Nock NL (2012) Structural Equation Modeling. In: Elston RC, Satagopan JM, Sun S (eds) Statistical Human Genetics. Humana Press, Totowa, NJ, pp 495–512 Stevens VM, Whitmee S, Le Galliard J, et al (2014) A comparative analysis of dispersal syndromes in terrestrial and semi‐terrestrial animals. Ecology Letters 17:1039–1052. https://doi.org/10.1111/ele.12303 Stoffel MA, Nakagawa S, Schielzeth H (2021) partR2 : partitioning R2 in generalized linear mixed models. PeerJ 9:e11414. https://doi.org/10.7717/peerj.11414 Stuligross C, Williams NM (2021) Past insecticide exposure reduces bee reproduction and population growth rate. Proc Natl Acad Sci USA 118:e2109909118. https://doi.org/10.1073/pnas.2109909118 Trochet A, Legrand D, Larranaga N, et al (2013) Population sex ratio and dispersal in experimental, two‐patch metapopulations of butterflies. Journal of Animal Ecology 82:946–955. https://doi.org/10.1111/1365-2656.12082 Valenta K, Nevo O (2020) The dispersal syndrome hypothesis: How animals shaped fruit traits, and how they did not. Functional Ecology 34:1158–1169. https://doi.org/10.1111/1365-2435.13564 Van Allen BG, Rudolf VHW (2013) Ghosts of Habitats Past: Environmental Carry-Over Effects Drive Population Dynamics in Novel Habitat. The American Naturalist 181:596–608. https://doi.org/10.1086/670127 Wei T, Simko V (2017) R package “corrplot”: Visualization of a Correlation Matrix Wiklund C, Karlsson B, Leimar O (2001) Sexual conflict and cooperation in butterfly reproduction: a comparative study of polyandry and female fitness. Proc R Soc Lond B 268:1661–1667. https://doi.org/10.1098/rspb.2001.1719 Wright GA, Nicolson SW, Shafir S (2018) Nutritional Physiology and Ecology of Honey Bees. Annu Rev Entomol 63:327–344. https://doi.org/10.1146/annurev-ento-020117-043423 Zilio G, Deshpande JN, Duncan AB, et al (2024) Dispersal evolution and eco-evolutionary dynamics in antagonistic species interactions. Trends in Ecology & Evolution 39:666–676. https://doi.org/10.1016/j.tree.2024.03.006 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6839133","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480404051,"identity":"38c6da4d-069c-45b2-8cf3-8771a8e5351e","order_by":0,"name":"Delphine Legrand","email":"data:image/png;base64,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","orcid":"","institution":"Centre National de la Recherche Scientifique (CNRS), Station d’Ecologie Théorique et Expérimentale (SETE)","correspondingAuthor":true,"prefix":"","firstName":"Delphine","middleName":"","lastName":"Legrand","suffix":""},{"id":480404052,"identity":"00249872-0508-456f-a252-fd47b0ccbf66","order_by":1,"name":"Elise Mazé-Guilmo","email":"","orcid":"","institution":"Centre National de la Recherche Scientifique (CNRS), Station d’Ecologie Théorique et Expérimentale (SETE)","correspondingAuthor":false,"prefix":"","firstName":"Elise","middleName":"","lastName":"Mazé-Guilmo","suffix":""},{"id":480404053,"identity":"4717b37c-def6-4b58-9f14-fb9337afbf79","order_by":2,"name":"Fabien Aubret","email":"","orcid":"","institution":"Centre National de la Recherche Scientifique (CNRS), Station d’Ecologie Théorique et Expérimentale (SETE)","correspondingAuthor":false,"prefix":"","firstName":"Fabien","middleName":"","lastName":"Aubret","suffix":""},{"id":480404054,"identity":"d2c41862-805d-4667-bf4e-e5e246c6a455","order_by":3,"name":"Michel Baguette","email":"","orcid":"","institution":"Centre National de la Recherche Scientifique (CNRS), Station d’Ecologie Théorique et Expérimentale (SETE)","correspondingAuthor":false,"prefix":"","firstName":"Michel","middleName":"","lastName":"Baguette","suffix":""},{"id":480404055,"identity":"b9c29faf-6172-448f-a28b-73671a58e524","order_by":4,"name":"Julien Cote","email":"","orcid":"","institution":"Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), Institut National Polytechnique de Toulouse (TINP), Université Toulouse 3 (UT3), UMR 5300","correspondingAuthor":false,"prefix":"","firstName":"Julien","middleName":"","lastName":"Cote","suffix":""}],"badges":[],"createdAt":"2025-06-06 18:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6839133/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6839133/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10682-025-10363-2","type":"published","date":"2025-11-07T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86028427,"identity":"fe5a6b0f-974e-4f29-a058-3fe2101123e5","added_by":"auto","created_at":"2025-07-04 13:39:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":202720,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental design and phenotyping of \u003cem\u003eP. brassicae\u003c/em\u003e across development. A) Larvae were introduced with the appropriate density (1, 2, 6, 12) in a one arena connected to a second arena by a corridor, the having the appropriate diet treatments (brassica leaves, salad, no food). Mobility and exploration were obtained through video analysis. B) The tunnel test was performed on adults by observing exploration and mobility after release next to the entrance of an opaque pipe with an artificial light placed at the end. C) In a third time, butterflies were exposed to the vortex test during which their flight ability over a 60 s period while shaking a plastic chamber was noted.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6839133/v1/bef2507150c082420242daa9.png"},{"id":86029343,"identity":"7dac18db-c8bb-4225-bfbb-35b1597b198d","added_by":"auto","created_at":"2025-07-04 13:55:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":500917,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs of the significant effects of experimental treatments on larval traits (immediate plasticity). A) Linear regression with a 95% confidence interval of \u003cem\u003emass loss\u003c/em\u003e as a function of density for each diet type. B) and C) represents the effects of diet type (violin plot) and density (linear regression with a 95% confidence interval) respectively on the \u003cem\u003ecaterpillar\u003c/em\u003e \u003cem\u003emobility axis\u003c/em\u003e. D) represents the effect of the diet type on \u003cem\u003ecaterpillar\u003c/em\u003e \u003cem\u003eexploration axis\u003c/em\u003e through a violin plot. On A), B) and D), significant contrast tests (Tukey) at a 0.05 threshold are indicated by two asterisks, marginal effects at a 0.1 threshold are indicated by a single asterisk.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6839133/v1/c96440f9daba298dc4bcf842.png"},{"id":86028778,"identity":"19858de3-5a91-44a9-8409-60a9bd3f8b1d","added_by":"auto","created_at":"2025-07-04 13:47:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":242813,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs of the significant effects of experimental treatments on adults’ traits (carry-over effects). A) Linear regression with a 95% confidence interval of \u003cem\u003ebutterfly mobility axis \u003c/em\u003eas a function of density for each diet type. Notice that one outlier had a value of \u003cem\u003ebutterfly mobility axis \u003c/em\u003eabove 10 in the density 12 x salad treatment and was removed from the graph to better observe the regression lines. B) Bar plots representing the proportion of emerging butterflies in function of density for each diet type. On the two graphs, significant contrast tests (Tukey) at a 0.05 threshold are indicated by two asterisks, marginal effects at a 0.1 threshold are indicated by a single asterisk.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6839133/v1/9235231c048cbff520cda6b2.png"},{"id":86028426,"identity":"adbcc882-0766-475c-a646-719f79717ef2","added_by":"auto","created_at":"2025-07-04 13:39:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":532703,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation matrices between adults’ traits involved in the species dispersal syndrome. Significant correlation at a 0.05 threshold are indicated by two asterisks, marginal correlations at a 0.1 threshold are indicated by a single asterisk. Absence of correlation between \u003cem\u003ebutterfly\u003c/em\u003e \u003cem\u003eexploration \u003c/em\u003eand \u003cem\u003emobility\u003c/em\u003e \u003cem\u003eaxes\u003c/em\u003e are expected as the two variables correspond to the two first axes of the same PCA.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6839133/v1/b9b2c4d0ebf6759d1234dda5.png"},{"id":95564121,"identity":"564cdb0f-8043-4489-93e0-920299d87575","added_by":"auto","created_at":"2025-11-10 16:08:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2213078,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6839133/v1/645434e7-d71e-4b8a-abbe-72bf332431a4.pdf"},{"id":86028412,"identity":"0fa3df2a-49f9-43ec-907b-dcbebe7526a8","added_by":"auto","created_at":"2025-07-04 13:39:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":593098,"visible":true,"origin":"","legend":"","description":"","filename":"legrandetalcarryovereffectssupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-6839133/v1/b48f0a60d6b7a5ca5b846c71.docx"},{"id":86028776,"identity":"b752dbed-3442-4326-8a5e-0f90a8fda8e8","added_by":"auto","created_at":"2025-07-04 13:47:13","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19536,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6839133/v1/bc3328fe7a511b8faf835e97.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impacts of larval environment on the adult dispersal syndrome","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhenotypic plasticity, the phenotypic modifications of one genotype according to the environment, is a ubiquitous mechanism that can be observed within and between generations (\u003cem\u003ee.g.\u003c/em\u003e, Auge et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It takes two forms according to the reversibility of the phenotypic changes (Stager et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Phenotypic flexibility, also called reversible plasticity, defines unfixed phenotypic changes that can repeatedly occur through life (Piersma and Drent \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Piersma and van Gils \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), like body size regulation in function of population density in the sea urchin \u003cem\u003eDiadema antillarum\u003c/em\u003e (Levitan \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Developmental plasticity defines irreversible phenotypic changes arriving at a specific point in development, for instance caste determination in \u003cem\u003eApis mellifera\u003c/em\u003e honey bees as a function of jelly quantity used to feed larvae (Wright et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe timing at which phenotypic changes occur as compared with the speed of environmental fluctuations is key in the adaptiveness of plasticity (Dupont et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Plastic changes that occur shortly after the perception of environmental stimulus rely on reduced lag phases, the time delay before the onset of the plastic change, and fast rates of plasticity, the speed at which a plastic trait changed towards its new value (Burton et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dupont et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is the case of many behavioral traits, for instance activity adjustments of the treefrog \u003cem\u003eHyla intermedia\u003c/em\u003e in response to predator exposure (Castellano et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Lag phases can also extend and/or plasticity rates be slow creating latent situations where the environment experienced early in life modifies the expression of traits in subsequent habitats, life stages or generations. This is the case of tail length adjustment in juveniles of the lizard \u003cem\u003eZooteca vivipara\u003c/em\u003e after maternal exposure to predator scents three-four months after birth (Bestion et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), or of progeny performance depending on parents\u0026rsquo; experience in a butterfly (Ducatez et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e). More generally, situations where past exposure to a given environment translates into phenotypic values independent from the present environment (either through flexibility or developmental plasticity) define carry-over effects. These effects can include plastic by-products of early-stages metabolic variation (Allen and Marshall \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), called \u0026ldquo;silver spoon effects\u0026rdquo; when beneficial (Mainwaring et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and directional adaptive changes. Carry-over effects have been widely documented across the tree of life (see review in O\u0026rsquo;Connor et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and (Moore and Ryan \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and both theoretical and empirical approaches have emphasized their pivotal role in eco-evolutionary dynamics (\u003cem\u003ee.g.\u003c/em\u003e, Harrison et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Van Allen and Rudolf \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Betini et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kristensen et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mahendra et al. 2025). However, cases where both immediate and carry-over effects are documented in response to complex environments (\u003cem\u003ei.e.\u003c/em\u003e, several interacting factors) remain poorly documented.\u003c/p\u003e \u003cp\u003eDispersal, the movement of individuals potentially leading to gene flow (Ronce \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), is a key eco-evolutionary process notably allowing individuals to escape unsuitable environments (Clobert et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It is highly sensitive to the environmental context at its three stages, emigration, transience, settlement (Clobert et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bonte et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; McTavish et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fattebert et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Campana et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and is generally associated with suites of covarying morphological, physiological and behavioral traits, the so-called dispersal syndromes, usually facilitating dispersal (Clobert et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Dispersal syndromes are ubiquitous across the tree of life (Stevens et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Valenta and Nevo \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cote et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), can be plastic (Cote et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jacob et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Junker et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nicolaus et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and have strong impact on eco-evolutionary dynamics (Cote et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jacob et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Raffard et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nicolaus et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zilio et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To be adaptive, plastic facilitators of dispersal have to (i) be expressed before irreversible harmful effects caused by an unsuitable environment, (ii) limit the costs during the transience phase, (iii) ensure sufficient survival and reproductive capacities for dispersing individuals in the new habitat. Understanding the mechanisms behind dispersal and dispersal syndrome plasticity, and their dynamics in the face of different environmental contexts, including the existence of carry-over effects (Benard and McCauley \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), is thus of utmost importance to state on their adaptive values.\u003c/p\u003e \u003cp\u003eThe phenotypic changes underlying dispersal plasticity can result from any of the above-mentioned forms of plasticity. For instance, cell shape and movement flexibility explain rapid changes in the ciliate \u003cem\u003eTetrahymena thermophila\u003c/em\u003e dispersal rates in response to density (Pennekamp et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jacob et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while developmental plasticity switches larvae of \u003cem\u003eCaenorhabditis elegans\u003c/em\u003e worms and males of \u003cem\u003eCardiocondyla\u003c/em\u003e ants into dispersal morphs in response to temperature fluctuation (Golden and Riddle \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Cremer and Heinze \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In \u003cem\u003eErigone atra\u003c/em\u003e spiders, adults adjust their dispersal distance according to the thermal environment experienced by juveniles through carry-over effects (Bonte et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Carry-over effects are also present in many insects where larval dietary restriction usually reduces flight capability and exploratory skills in adults, two dispersal proxies (Saastamoinen and Rantala \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Scofield and Mattila \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Reim et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Niitep\u0026otilde;ld and Boggs \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In a few studies, the presence of both immediate and delayed effects has been described. For instance, \u003cem\u003eCornu aspersum\u003c/em\u003e snails adjust their immigration choices according to congeners\u0026rsquo; density within settlement patches, but also to the one experienced earlier at the emigration step (Dahirel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite this accumulation of empirical data, it is unclear whether the co-existence of plastic mechanisms is common in the context of complex environments, if plasticity mechanisms influence each other, and if they can concomitantly influence dispersal-related traits covariations.\u003c/p\u003e \u003cp\u003eTo address these questions, we used the large white butterfly \u003cem\u003ePieris brassicae\u003c/em\u003e to experimentally study (i) caterpillars\u0026rsquo; immediate physiological and behavioral plasticity in response to variation in density, diet quality, and their interplay (ii) the existence and magnitude of carry-over effects on emerging adults\u0026rsquo; dispersal-related traits, and (iii) if carry-over effects potentially detected in adults were indeed mediated by the immediate plasticity affecting larval traits. We choose \u003cem\u003eP. brassicae\u003c/em\u003e for three main reasons. Firstly, in insect with complete metamorphosis, larvae are often spatially-constrained due to limited mobility, meaning that time-delayed escaping strategies expressed at the more mobile adult stage might be the primary target of natural selection (Stuligross and Williams \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Indeed, caterpillars of \u003cem\u003eP. brassicae\u003c/em\u003e have short-distance prospection abilities (Le Masurier \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) while adults can travel (hundreds of) kilometers (Feltwell \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Secondly, dispersal is context-dependent in \u003cem\u003eP. brassicae\u003c/em\u003e: weather, vegetation cover, and population sex ratio were shown to influence emigration decisions when measured in a semi-natural mesocosm (Legrand et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Trochet et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thirdly, a dispersal syndrome linking emigration decisions with flight performance and wing length has been described in the same semi-natural mesocosm (Legrand et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), these two traits themselves covarying with exploration, flight direction at emergence and copulation dynamics (Ducatez et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e; Larranaga et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Dispersing individuals generally displayed better flight performances and exploratory skills, longer wings, and better oriented trajectories at emergence than residents. Interestingly, it has been suggested that \u003cem\u003eP. brassicae\u003c/em\u003e\u0026rsquo;s dispersal syndrome could have high evolutionary potential and thus be disrupted under specific environmental conditions (Legrand et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It might then be possible to observe divergent plastic responses between traits involved in this syndrome. Finally, we focused on density and resource type as these are good indicators of intraspecific competition and habitat quality, which are among the main external drivers of dispersal (Clobert et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In general, poor resource increases dispersal (Bengtsson et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Fronhofer et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kreuzinger-Janik et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while density-dependent dispersal is variable (Matthysen \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pennekamp et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rutherford et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In \u003cem\u003eP. brassicae\u003c/em\u003e, larvae are gregarious: females lay batches of tens to a few hundreds of eggs on host plants\u0026rsquo; leaves and caterpillars remain grouped during until they search for a pupation site (Feltwell \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Le Masurier \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Previous studies showed that larval density had no effect on their mortality (Le Masurier \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), but had a positive effect on their growth (Blackwell \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). We thus predicted poor larval diet and low density to generally favor the expression of trait values facilitating adults\u0026rsquo; dispersal (higher flight performance, exploration skills, longer wings). However, as we also expected poor density and resource type to deteriorate larval condition, our predictions might turn into the opposite pattern, \u003cem\u003ei.e.\u003c/em\u003e, small individuals with poor flight performance when density is low and diet poor, in case of extensive detrimental larval legacy.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy organism and breeding conditions\u003c/h2\u003e \u003cp\u003e \u003cem\u003ePieris brassicae\u003c/em\u003e is a common multivoltine butterfly (typically 2 to 4 generations per year) in the Palearctic referenced as a facultative migratory species able to travel hundreds of kilometers although little is known about this behavior supposed to vary substantially across its range (Spieth and Kaschuba-Holtgrave \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). At the landscape scale, this species has a nomadic strategy of space use (Feltwell \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Mueller and Fagan \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and is a facultative long-distance disperser (Baguette et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Fertilized females lay batches of eggs on a diversity of host plants although they exhibit taxa preferences like for Brassica species (Feltwell \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Eggs of a single clutch can be considered as full-sibs because females usually mate once before ovipositing (David and Gardiner \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1961\u003c/span\u003e; Wiklund et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Larranaga et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). After hatching, gregarious caterpillars develop in their natal site in five successive larval stages and finally separate at the end of the last stage in the search for pupation sites. This fifth stage is the more mobile stage with travels that can reach up to 350m (Feltwell \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn June 2011, we collected eight clutches on cabbage leaves from a private garden in Orgibet (Ari\u0026egrave;ge, France) that we brought to the Theoretical and Experimental Ecology Station in Moulis (Ari\u0026egrave;ge, France). Each clutch was individually placed in 10 x 10 x 7 cm plastic boxes whose lids were centrally replaced by fine-meshed nets to ensure respiration and limit humidity accumulation. Boxes were then placed in a climatic chamber with a 14:10 photoperiod during which temperatures of 23\u0026deg;C and 18\u0026deg;C were respectively fixed to impede diapause (Guyot \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Manual spraying was daily performed to maintain\u0026thinsp;~\u0026thinsp;60% of humidity. Caterpillars were fed \u003cem\u003ead libitum\u003c/em\u003e with \u003cem\u003eBrassicae oleracea\u003c/em\u003e leaves and transferred into clean boxes each time dejections were too important. At the fifth larval stage, and at minimum one day after molting, caterpillars were submitted to the experimental treatments (see below), and thereafter placed again in the same boxes and breeding conditions as small groups of the same clutch fed \u003cem\u003ead libitum\u003c/em\u003e. After pupation, chrysalids were individually placed in smaller boxes (5 x 5 x 3 cm).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental treatments\u003c/h3\u003e\n\u003cp\u003eWe used four density levels, 1, 2, 6, 12 caterpillars, and three diet types, no food, salad (lettuce) as a repellant plant, and \u003cem\u003eB. oleracea\u003c/em\u003e as high-quality food (Feltwell \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), in a full-crossed design. A total of 12 experimental treatments were thus performed. As we could not expose each individual to the 12 treatments, plasticity was measured by subsampling the appropriate number of caterpillars for the 12 treatments among each of the eight clutches, which were thus our level of replication. We considered that phenotypic variation in response to environmental variation between full-sibs can be mainly attributed to plasticity (Scheiner \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, see an example in \u003cem\u003eP. brassiace\u003c/em\u003e in Chaput-Bardy et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This means that 63 fifth stage caterpillars were randomly chosen from each clutch, and then randomly dispatched between the 12 treatments, for a total of 504 tested caterpillars. Treatments were imposed to caterpillars in an experimental device allowing to immediately measure their phenotypic flexibility (see below).\u003c/p\u003e\n\u003ch3\u003eCaterpillar phenotypic traits measurements\u003c/h3\u003e\n\u003cp\u003eAt the larval stage, we decided to measure the flexibility of mobility, exploration and metabolism traits, because they might all be affected by our treatments and influence adults\u0026rsquo; dispersal syndrome. To acquire larval data traits, we adapted well-described settings aimed at measuring mobility, exploration and/or dispersal in small organisms composed of two arenas connected by a small corridor (\u003cem\u003ee.g.\u003c/em\u003e, (Arag\u0026oacute;n et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fjerdingstad et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cote and Clobert \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). We constructed 12 systems to run all treatments simultaneously for a given clutch in the same 23\u0026deg;C regulated room (all trials were performed between the 11th and the 21th of July 2011). Each system was composed of two 900 cm\u003csup\u003e2\u003c/sup\u003e arenas connected by a 38 x 2 cm long corridor. We placed the diet treatment in the center of the two arenas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), or left them empty in the no food treatment. To prevent caterpillars\u0026rsquo; escape from arenas, we installed 3 cm height walls composed of a 0.5 cm large white plastic on which we fixed a transparent film. The corridor length was above the perception distance of \u003cem\u003eP. brassicae\u003c/em\u003e\u0026rsquo;s caterpillars (Costa \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), meaning that inter-arena movements can be attributed to high exploratory skills. Individual recognition was obtained by gluing a 1 cm diameter piece of distinguishable color papers (following Noldus et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) on the back of each caterpillar. These paper pieces are thereafter displaced at the posterior extremity of chrysalids by the pupation process itself, facilitating individual recognition between larval and adult phases. As our maximal experimental density was 12, we selected 12 colored papers that could be unambiguously distinguished during the tracking phase. The 12 colors were randomly attributed to each caterpillar in the density 12 treatment, and for the other densities, we used the same 1, 2, and 6 colors randomly attributed to caterpillars for all replicates. We added an individual number as a double marking system on the paper pieces. One hour before introducing caterpillars on top of the food treatment in one of the two arenas (the other being initially caterpillar-free), we weighted them (including their paper mark) at the nearest 0.001g (Adventurer Pro AV213C), and 30 min before experiments, we placed them at 8\u0026deg;C in a dark climatic chamber to limit their movement during the installation phase. As soon as caterpillars were placed in their treatment, we started the recording of 90 min videos with Canon cameras fixed on the roof\u0026rsquo;s room. After these 90 min, we weighted again each caterpillar and used the \u003cem\u003emass loss\u003c/em\u003e (difference between the masses before and after trials) as a proxy of treatments\u0026rsquo; metabolic impacts. Mobility and exploration were obtained from the videos using EthoVision XT version 8 (Noldus Information Technology, Wageningen, The Netherlands). Individual trajectories were analyzed through a sampling of two pictures by second and by setting the position of the two arenas and the corridors for each video. From these trajectories, we extracted the \u003cem\u003etime before the first movement\u003c/em\u003e (s), the \u003cem\u003esecond arena exploration\u003c/em\u003e (binary variable yes/no), the \u003cem\u003eproportion of time moving\u003c/em\u003e, the \u003cem\u003etotal movement distance\u003c/em\u003e (s), and the \u003cem\u003emean\u003c/em\u003e and \u003cem\u003emax velocities\u003c/em\u003e (cm/s).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eButterflies phenotypic trait measurement\u003c/h3\u003e\n\u003cp\u003eWithin the six days after emergence, we performed the \u0026lsquo;tunnel\u0026rsquo; and the \u0026lsquo;vortex\u0026rsquo; tests (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C) as described in (Ducatez et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e). We always performed the vortex test at least one day after the tunnel test. The tunnel test consists in kindly releasing a butterfly in the air at the entrance of a 3m long opaque pipe of 80cm diameter with a light source at its end in a dark room regulated at 23\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C. During the test, butterflies can fly and/or walk to entirely or partly cross the pipe, or stay at the entrance. In such case, the test was stopped after 10s of inactivity. Several traits were recorded: the willingness of the butterfly to cross the tunnel (binary \u003cem\u003etunnel cross\u003c/em\u003e variable), the \u003cem\u003etime before the first movement\u003c/em\u003e after the release (s), the \u003cem\u003efirst behavior\u003c/em\u003e (stay at the entrance/fly/walk), the \u003cem\u003ecross time\u003c/em\u003e (s), the \u003cem\u003etime spent flying\u003c/em\u003e and the \u003cem\u003etime spent walking\u003c/em\u003e. Crossing the tunnel by always flying can be considered as an especially bold behavior, while staying at the entrance can be considered as the shiest behavior. In between, butterflies can adopt mixed locomotion strategies to cross or partially cross the tunnel. Obviously, individuals with enhanced mobility should cross more rapidly the tunnel, but only if they are willing to explore challenging environments. The tunnel test is highly repeatable in \u003cem\u003eP. brassicae\u003c/em\u003e (91%, Ducatez et al. 2012), we thus decided to perform it only once. The vortex test has been designed to test performance under stressful conditions. Butterflies are individually placed in a 25 x 10 x 10 cm plastic chamber maintained by hand by the experimenter on top of a vortex. After a 1 min period of acclimation in the chamber, the vortex is turned on during 1 min so as to strongly shake the chamber and prevent butterflies from perching on chamber\u0026rsquo;s walls. Butterflies able to fly during at least 50s were qualified as good performers, the others, which rested at least 10s seconds on the bottom of the chamber while being strongly shaken were qualified as bad performers (binary \u003cem\u003eflight performance\u003c/em\u003e variable). After the vortex text, we anesthetized butterflies using nitric oxide in a 10 x 10 x 10 cm box (Inject\u0026thinsp;+\u0026thinsp;Matic Sleeper TAS\u0026reg;) just before the same experimenter measured their \u003cem\u003ewing length\u003c/em\u003e (mm) using a caliper. In total, 192 butterflies emerged, 160 performed the tunnel test, and 103 the vortex test and wing length measurement (butterflies could die throughout the end of the phenotyping sequences).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eWe performed all statistical analyses on R version 4.2.2 (R Core Team \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), using the packages lme4 (Bates et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) to run Linear Models, emmeans to perform pairwise contrast tests (Lenth \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), partR2 (Stoffel et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to estimate the part of variance explained by each significant variable in linear models, corrplot (Wei and Simko \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to build correlation matrices, and FactoMineR (L\u0026ecirc; et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) to run Principal Component Analyses (PCA) with missMDA (Josse and Husson \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to account for missing data.\u003c/p\u003e \u003cp\u003eTo test for the existence of immediate plasticity or carry-over effects in response to the treatments, we built Linear Mixed Models (LMM) in case of normally-distributed response variables or a Generalized LMM (GLMM) using a binomial error and a logit link in case of binary response variables. In all models, phenotypic traits were implemented as the response variables, density, diet type and their interaction as explanatory variables, and clutch identity (equivalent of family) as a random intercept. Caterpillar traits were used as the response to study immediate plasticity and butterfly traits were used to study carry-over effects. We performed a backward selection model procedure to select the best model by considering \u003cem\u003ep-values\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1 after a Likelihood Ratio Test for the threshold exclusion to include marginal effects. Tukey tests were performed to determine the diet treatments differing from the others in case of a significant effect implying the diet type. In all final models, we checked the distribution of residuals.\u003c/p\u003e \u003cp\u003eAs the five caterpillars\u0026rsquo; variables obtained in the double arena were all significantly correlated (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA), we summarized them by the two first axes of a PCA, which accounted for 60% of the total variance (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB and C). Mobility variables contributed mainly to Axis 1 (43.23%, renamed \u003cem\u003ecaterpillar mobility axis\u003c/em\u003e) with high values referring to caterpillars with tendency to move for long times and distances, and at high mean and max velocities. Exploration variables contributed mainly to Axis 2 (16.85%, renamed \u003cem\u003ecaterpillar exploration axis\u003c/em\u003e), high values describing individuals with the tendency to explore the second arena, and move immediately and for a long time after their release in the first arena before potentially exploring the second arena.\u003c/p\u003e \u003cp\u003eAs the six tunnel variables were also significantly correlated to at least another (Figure S2A), we summarized them by the two first axes of a second PCA (Figure S2B to D), which accounted for 58% of the total variance. High values on axis 1 (34.8%, renamed \u003cem\u003ebutterfly exploration axis\u003c/em\u003e) referred to butterflies that did not entirely cross the tunnel (Figure S2C), primarily walked or stayed immobile after release instead of flying (Figure S2D). High values on axis 2 (22.83%, renamed \u003cem\u003ebutterfly mobility axis\u003c/em\u003e) referred to long times to cross the tunnel (only recorded for butterflies that crossed entirely the tunnel) and long times to start moving. To complement our search of carry-over effects, we also used a fitness proxy: adult \u003cem\u003eemergence\u003c/em\u003e (coded as a binary variable yes/no for each caterpillar).\u003c/p\u003e \u003cp\u003eTo test for the existence of carry-over effects on adults\u0026rsquo; traits indirectly mediated by immediate plastic changes of mass, mobility and exploration expressed at the larval stage, we added the three larval traits (\u003cem\u003emass loss\u003c/em\u003e, \u003cem\u003ecaterpillar mobility axis\u003c/em\u003e, \u003cem\u003ecaterpillar exploration axis\u003c/em\u003e) as explanatory variables to the final LMMs and GLMMs retained after the selection procedure of the carry-over effect analyses. Cases where previously significant effects of density, diet, or their interaction would disappear in favor of caterpillar traits suggest carry-over effects mediated by phenotypic changes at the larval stage. Usually, the distinction between direct and indirect effects are studied with Structural Equation Models (SEM, Stein et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In our case, these models were difficult to apply because we were studying an interaction between a continuous and a categorial variable that cannot easily be transformed as an ordinated continuous variable. We thus decided to apply the statistical procedure described above.\u003c/p\u003e \u003cp\u003eFinally, to explore further the effects of the larval treatments on adults\u0026rsquo; dispersal syndrome, we built correlation matrices between \u003cem\u003ewing length\u003c/em\u003e, \u003cem\u003ebutterfly exploration axis\u003c/em\u003e, \u003cem\u003ebutterfly mobility axis\u003c/em\u003e, and \u003cem\u003eflight performance\u003c/em\u003e during the vortex test for each density level across all diets, and for each diet type across all densities. We decided to explore only these conditions as building and comparing correlation matrices for the 12 treatments seems too complex, and yields to insufficient sample sizes in some treatments.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eImmediate plasticity\u003c/h2\u003e\n \u003cp\u003eWe observed a significant effect of diet type, density or their interaction for all measured caterpillar traits (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Firstly, caterpillars lost less mass in presence of cabbage (p\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). While density had no effect on \u003cem\u003emass loss\u003c/em\u003e for the salad and cabbage treatment, caterpillars lost more mass at the lowest densities in the absence of food (p\u0026thinsp;=\u0026thinsp;0.01 for diet x density interaction, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Secondly, diet type and density significantly impacted \u003cem\u003ecaterpillar mobility axis\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e in both cases, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Mobility was enhanced in the absence or food or when salad was provided as compared with the cabbage treatment (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB), and marginally enhanced in presence of salad as compared with the no food treatment (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). For density, we revealed higher mobility at the highest densities (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Finally, caterpillar exploration was affected only by the diet type (p\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), with higher tendencies to immediately explore the double arena system when salad was proposed, although the difference with the no food treatment was marginal (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). The part of phenotypic variance explained by the significant treatments ranged between 2.8 and 13% (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eCarry-over effects\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eContrarily to immediate plasticity, only few treatments significantly affected adults\u0026rsquo; traits. Over the four tested traits involved in \u003cem\u003eP. brassicae\u003c/em\u003e\u0026rsquo;s dispersal syndrome, only the \u003cem\u003ebutterfly mobility axis\u003c/em\u003e was marginally affected by the interaction between density and diet (p\u0026thinsp;=\u0026thinsp;0.06, 6.8% of variance explained, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). We observed a tendency towards higher values on \u003cem\u003ebutterfly mobility axis\u003c/em\u003e as density increased in the salad treatment (increased time to cross the tunnel and longer time to decide to move at high densities, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA), while density had the reverse effect in presence of cabbage (decreased time to cross the tunnel and longer time to decide to move at high densities). When looking at emergence, we observed a significant interaction between diet type and density (p\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, 3.7% of variance explained, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The number of emerging butterflies increased with density in presence of salad and cabbage, while it decreased with density in absence of food (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eIndirect effects on adults caused by changes on caterpillar traits\u003c/h2\u003e\n \u003cp\u003eNone of the significant carry-over effects detected (\u003cem\u003ebutterfly mobility axis\u003c/em\u003e and \u003cem\u003eemergence\u003c/em\u003e) were due to indirect effects of the treatments on caterpillars. Indeed, when adding larval traits as explanatory variables on the previously described best models (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), model structures were all unchanged (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). In addition, none of the larval traits significantly impacted the other adult traits (\u003cem\u003ewing length\u003c/em\u003e, \u003cem\u003ebutterfly exploration axis\u003c/em\u003e, \u003cem\u003eflight performance\u003c/em\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eEffects of larval treatment on the architecture of\u003c/em\u003e P. brassicae\u003cem\u003e\u0026rsquo;s dispersal syndrome\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eMatrix correlation between adults\u0026rsquo; traits over all butterflies showed significant negative correlation between \u003cem\u003ebutterfly exploration axis\u003c/em\u003e and \u003cem\u003eflight performance\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), meaning that individuals that succeeded at the vortex test were more often those that crossed the tunnel, mainly by flying. There was also a marginal negative correlation between \u003cem\u003ewing length\u003c/em\u003e and \u003cem\u003ebutterfly exploration axis\u003c/em\u003e, meaning that butterflies that crossed the tunnel had generally shorter wings. When comparing matrices between the diet types (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), we observed that correlations changed, with a greater link between \u003cem\u003ebutterfly exploration axis\u003c/em\u003e and \u003cem\u003eflight performance\u003c/em\u003e in the salad treatment, while it disappeared in the other diet types, and a positive correlation between \u003cem\u003ewing length\u003c/em\u003e and \u003cem\u003ebutterfly mobility axis\u003c/em\u003e becoming significant in absence of food (butterflies with shorter wings moved rapidly after the beginning of the test and crossed the tunnel rapidly). Looking at the effect of density, we observed that the negative correlation between \u003cem\u003ebutterfly exploration axis\u003c/em\u003e and \u003cem\u003eflight performance\u003c/em\u003e was weaker as density increased.\u003c/p\u003e\n\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated if and how two biotic factors and their interaction impacted the expression of a series of both larvae and adults\u0026rsquo; phenotypic traits. Immediate plastic effects were highlighted on all tested larvae\u0026rsquo;s traits (metabolism, mobility and exploration proxies), while carry-over effects were evidenced on one dispersal-related trait (marginal effect) and one fitness proxy. Despite these limited time-delayed effects on mean adults\u0026rsquo; trait expression, we revealed that their correlations varied according to the diet type and the density. This means that \u003cem\u003eP. brassicae\u003c/em\u003e\u0026rsquo;s dispersal syndrome can be altered by prior environmental conditions, with potential consequences on the species spatial dynamics. We discuss all these points hereafter.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCaterpillars plastic response to diet and density variation\u003c/h2\u003e \u003cp\u003eFor all tested trait, we detected significant effects of density, diet or their interaction. Firstly, and as expected, the diet type impacted the loss of caterpillars\u0026rsquo; mass during the double arena test, with a higher metabolic cost in absence of food, but to a lesser extent at the highest densities. The effects on mass were identical between the cabbage (host plant naturally eaten by caterpillars) and the salad (repellant plant usually not ingested) treatments. Therefore, either caterpillars consumed the repellant plant as much as the natural host plant, or the presence of a plant itself provided a metabolic reward, such as resistance to desiccation and/or enhanced thermoregulation in the same manner as larval aggregation does (Klok and Chown \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Qian et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The reward effect of density on metabolism in the absence of food mentioned above supports previous studies showing that increased larval density has a positive effect on caterpillar growth (Blackwell \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). We did not observe this density effect in presence of a plant, maybe because the metabolic cost during the test was limited in presence of a plant whatever the density. Alternatively, the information of both high density and absence of food could induce a kind of terminal investment response in metabolism. Secondly, we showed that the diet type influenced both mobility and exploration of caterpillars. In presence of cabbage, caterpillars moved less and less rapidly. This agrees with their lifestyle as they usually stay on leaves of their host plant until their complete consumption or when they move to find a pupation site (Feltwell \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Mobility was also marginally enhanced in presence of salad as compared with the absence of food, confirming the repelling effect of salad on \u003cem\u003eP. brassicae\u003c/em\u003e (Feltwell \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). This effect matches those observed in the second tested caterpillar behavior as exploration was especially enhanced in the salad treatment. This suggests that while the absence of food seems to favor short-distance foraging movement, it does not trigger exploration at longer distances as the repellant plant does. Whatever the diet type, caterpillars tended to move less and less rapidly at the lowest densities, but density did not affect their willingness to explore the second arena and to initiate movements. For a gregarious species, very small groups can be considered as stressful situations, with increased metabolic costs as stated earlier. In addition, species laying batches of eggs have often developed communication skills at early-developmental stages increasing individuals\u0026rsquo; fitness (Aubret et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Another non-exclusive hypothesis could be the existence of social mechanisms enhancing movements in this species, but this remains to be tested, as for many aspects of caterpillar collective behaviors (McLellan and Montgomery \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Overall, results on immediate plasticity agreed with our knowledge of \u003cem\u003eP. brassicae\u003c/em\u003e\u0026rsquo;s lifestyle and opened the possibility to observe not only carry-over effects of diet type and density on adults\u0026rsquo; traits, but also indirect carry-over effects of the treatments passing through immediate plasticity acting on larvae.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eUnexpected carry-over effects on the adult\u0026rsquo;s dispersal syndrome\u003c/h2\u003e \u003cp\u003eWith the exception of the marginal interactive effect of diet type and density on butterflies\u0026rsquo; mobility in the tunnel, there was a general absence of carry-over effects of larval environmental conditions on adults\u0026rsquo; trait involved in \u003cem\u003eP. brassicae\u003c/em\u003e\u0026rsquo;s dispersal syndrome. This result did not match our predictions. We expected the most stressful conditions (no food, salad and low-density) to generally favor the expression of trait values facilitating adults\u0026rsquo; dispersal, \u003cem\u003ei.e.\u003c/em\u003e, higher flight performance, exploration skills and longer wings (Legrand et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Another hypothesis was to observe lower flight performance, wing length and exploration skills if the stressful conditions had incurred too strong metabolic impacts on larvae. On the contrary, mean wing length and performance in the tunnel and vortex tests were the same over all treatments, and we did not detect carry-over effects mediated by changes on larval traits. While these results could be explained by decoupled selective pressures between late larval stages and imagoes leading to distinct reaction norms, another possibility would be the existence of plastic effects on traits without major changes on mean values. Analysis of the correlation matrices between the dispersal syndrome traits across diets and densities agrees with this second scenario. Indeed, the positive correlation between flight performance and the willingness to cross the tunnel (as previously found in Ducatez et al. 2012) was especially high in the most stressful diets and densities. Changes in trait correlations despite flat reaction norms for mean trait values can be observed if there is variation in the plastic responses of dispersal among individuals. Here, some individuals could have increased both their flight performance and willingness to cross the tunnel while others could have decreased both their flight performance and willingness to cross the tunnel. This would reflect the persistence of intraspecific variability in dispersal strategies despite stressful conditions, i.e., the co-existence of residents and dispersers. This scenario corresponds to the exacerbation of residents\u0026rsquo; phenotypic attributes limiting the costs of movement in the hope of better future conditions, concomitantly with the exacerbation of dispersers attributes to favor efficient escaping movements. We also found a marginal positive correlation between wing morphology and butterfly exploration over all conditions and between wing morphology and butterfly mobility in absence of food (butterflies with shorter wings flew more rapidly to cross the tunnel). A correlation between wing morphology and performance in the tunnel test has been previously described, but in the opposite direction (Ducatez et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e). Although difficult to explain, this contradictory result could be representative of a general impact of experiencing the arena test on the link between morphology and mobility and/or exploration. The fact that we observed no significant correlation between wing morphology and flight performance as measured in the vortex test supports this hypothesis as a link between these two traits, either positive or negative, was recurrently observed (Ducatez et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e; Trochet et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Legrand et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), but in all previous studies, larvae never experienced experimental tests. Overall, these studies highlight complex links between wing morphology on the one hand, and mobility or exploration on the other hand.\u003c/p\u003e \u003cp\u003eTo complement our results on dispersal-related traits, we tested for the existence of carry-over effects acting on adult emergence, a fitness component. The number of successful metamorphoses increased at high densities in presence of a plant (either cabbage or salad). This positive carry-over effect of larval density on fitness corroborates previous results on immediate plasticity (Blackwell \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1988\u003c/span\u003e, see above). However, the decrease of emergence at the highest densities when food was absent, is more enigmatic. Interestingly, emergence was not especially high in the cabbage treatment as compared with the two other resource treatments, meaning that we did not observe a \u0026ldquo;silver spoon effect\u0026rdquo; in the best food condition, \u003cem\u003ei.e.\u003c/em\u003e, a higher fitness lately in the development caused by a beneficial environment experienced earlier in the development. It is possible that the energy stored before the fifth larval stage during breeding and after the test until the pupation (\u003cem\u003ead libitum\u003c/em\u003e conditions) was sufficient to buffer the energetic cost of the food unavailability during the 90 min of the test. Complementary experiments on earlier caterpillar stages would help understanding in further details how the diet impacts emergence probability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePotential impact on spatial dynamics\u003c/h2\u003e \u003cp\u003eThe fact that larval experience can change the intensity of correlations between adults\u0026rsquo; dispersal related traits can have strong consequences on spatial dynamics. When larvae and adults\u0026rsquo; requirements match, and when the larval environment provides reliable cues on the adult\u0026rsquo;s one, we expect a strong benefit of such carry-over effects at the landscape scale. In the case of \u003cem\u003eP. brassicae\u003c/em\u003e, larvae and adults\u0026rsquo; requirements generally match: like caterpillars, adults can be found in high-density groups, and adults can feed on a variety of flowers including those of the preferred caterpillar plant species (Feltwell \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). The revealed carry-over effects on the species dispersal syndrome structure might thus result from a selective process optimizing the species\u0026rsquo; spatial dynamics. Accordingly, we observed that the most stressful conditions reinforced some correlations between traits linked to the dispersal status of individuals, as previously observed (Cote et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). One future question will be to unravel the potential interactions between pre- and post-metamorphosis effects on dispersal syndromes, especially when they bring contradictory information. Further, it could be highly relevant to formally measure the consequences of such labile dispersal syndrome on metapopulation and metacommunity stability given their pivotal roles on many eco-evolutionary dynamics (Jacob et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cote et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Raffard et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nicolaus et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zilio et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This would provide a better appreciation of the adaptive value of carry-over effects acting on dispersal traits (Benard and McCauley \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and improve our integration of dispersal variability on the functioning of ecological networks (Baguette et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, our results revealed atypical carry-over effects (few changes in mean values but changes in correlations between treatments) able to modify the structure of a dispersal syndrome. While the plasticity of dispersal syndromes has been empirically described (for instance in Jacob et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), examples involving carry-over effects are rare. A key point is that the harshness of the environment impacted the structure of the studied dispersal syndrome, echoing back to a previous study showing a strengthening of the link between dispersal and some phenotypic traits with environmental harshness across 15 species, albeit no information on the underlying mechanisms were available (Cote et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Whatever the origin of the link between the structure of dispersal syndromes (genetic and/or plastic, immediate and/or carry-over effects) and the harshness of the environment, data accumulate in favor of an inter-species phenomena.\u003c/p\u003e \u003cp\u003eFinally, a major lesson raised by our study is the complexity of the plastic effects that could be observed, according to both the traits and developmental stages, as the two environmental dimensions had either no, independent or interactive effects on traits\u0026rsquo; expression and/or correlation. Integrating the multidimensionality of environments in dispersal studies is thus mandatory for future studies on its plasticity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We thank Mich\u0026egrave;le Huet for the sampling of the egg clutches, and Olivier Calvez for his help to breed caterpillars and butterflies. The SETE and CRBE labs are supported by the French Laboratory of Excellence project \u0026ldquo;TULIP\u0026rdquo; (ANR-10-LABX-41). JC was supported by the European Research Council under the European Union\u0026rsquo;s Horizon 2020 research and innovation program (grant agreement no. 817779).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen RM, Marshall DJ (2010) The larval legacy: cascading effects of recruit phenotype on post-recruitment interactions. Oikos 119:1977\u0026ndash;1983. https://doi.org/10.1111/j.1600-0706.2010.18682.x\u003c/li\u003e\n\u003cli\u003eArag\u0026oacute;n P, Clobert J, Massot M (2006) Individual dispersal status influences space use of conspecific residents in the common lizard, Lacerta vivipara. Behav Ecol Sociobiol 60:430\u0026ndash;438. https://doi.org/10.1007/s00265-006-0183-3\u003c/li\u003e\n\u003cli\u003eAubret F, Blanvillain G, Bignon F, Kok PJR (2016) Heartbeat, embryo communication and hatching synchrony in snake eggs. Sci Rep 6:23519. https://doi.org/10.1038/srep23519\u003c/li\u003e\n\u003cli\u003eAuge GA, Leverett LD, Edwards BR, Donohue K (2017) Adjusting phenotypes via within‐ and across‐generational plasticity. New Phytologist 216:343\u0026ndash;349. https://doi.org/10.1111/nph.14495\u003c/li\u003e\n\u003cli\u003eBaguette M, Blanchet S, Legrand D, et al (2013) Individual dispersal, landscape connectivity and ecological networks. Biological Reviews 88:310\u0026ndash;326. https://doi.org/10.1111/brv.12000\u003c/li\u003e\n\u003cli\u003eBaguette M, Stevens VM, Clobert J (2014) The pros and cons of applying the movement ecology paradigm for studying animal dispersal\u003c/li\u003e\n\u003cli\u003eBates D, M\u0026auml;chler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Soft 67:. https://doi.org/10.18637/jss.v067.i01\u003c/li\u003e\n\u003cli\u003eBenard MF, McCauley SJ (2008) Integrating across Life‐History Stages: Consequences of Natal Habitat Effects on Dispersal. The American Naturalist 171:553\u0026ndash;567. https://doi.org/10.1086/587072\u003c/li\u003e\n\u003cli\u003eBengtsson G, Hedlund K, Rundgren S (1994) Food- and Density-Dependent Dispersal: Evidence from a Soil Collembolan. The Journal of Animal Ecology 63:513. https://doi.org/10.2307/5218\u003c/li\u003e\n\u003cli\u003eBestion E, Teyssier A, Aubret F, et al (2014) Maternal exposure to predator scents: offspring phenotypic adjustment and dispersal. Proc R Soc B 281:20140701. https://doi.org/10.1098/rspb.2014.0701\u003c/li\u003e\n\u003cli\u003eBetini GS, Griswold CK, Norris DR (2013) Carry-over effects, sequential density dependence and the dynamics of populations in a seasonal environment. Proc R Soc B 280:20130110. https://doi.org/10.1098/rspb.2013.0110\u003c/li\u003e\n\u003cli\u003eBlackwell A (1988) The disruption of aggregation behaviour in Pieris brassicae larvae by chlordimeform. Entomologia Exp Applicata 48:141\u0026ndash;147. https://doi.org/10.1111/j.1570-7458.1988.tb01157.x\u003c/li\u003e\n\u003cli\u003eBonte D, Travis JMJ, De Clercq N, et al (2008) Thermal conditions during juvenile development affect adult dispersal in a spider. Proc Natl Acad Sci USA 105:17000\u0026ndash;17005. https://doi.org/10.1073/pnas.0806830105\u003c/li\u003e\n\u003cli\u003eBonte D, Van Dyck H, Bullock JM, et al (2012) Costs of dispersal. Biological Reviews 87:290\u0026ndash;312\u003c/li\u003e\n\u003cli\u003eBurton T, Ratikainen II, Einum S (2022) Environmental change and the rate of phenotypic plasticity. Global Change Biology 28:5337\u0026ndash;5345. https://doi.org/10.1111/gcb.16291\u003c/li\u003e\n\u003cli\u003eCampana JLM, Raffard A, Chaine AS, et al (2022) Dispersal plasticity driven by variation in fitness across species and environmental gradients. Ecology Letters 25:2410\u0026ndash;2421. https://doi.org/10.1111/ele.14101\u003c/li\u003e\n\u003cli\u003eCastellano S, Racca L, Friard O (2021) Plasticity and flexibility in the anti-predator responses of treefrog tadpoles. Behav Ecol Sociobiol 75:142. https://doi.org/10.1007/s00265-021-03078-1\u003c/li\u003e\n\u003cli\u003eChaput-Bardy A, Ducatez S, Legrand D, Baguette M (2014) Fitness Costs of Thermal Reaction Norms for Wing Melanisation in the Large White Butterfly (Pieris brassicae). PLoS ONE 9:e90026. https://doi.org/10.1371/journal.pone.0090026\u003c/li\u003e\n\u003cli\u003eClobert J, Baguette M, Benton TG, Bullock JM (2012) Dispersal ecology and evolution, Oxford University Press. Oxford\u003c/li\u003e\n\u003cli\u003eClobert J, Le Galliard J, Cote J, et al (2009) Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecology Letters 12:197\u0026ndash;209. https://doi.org/10.1111/j.1461-0248.2008.01267.x\u003c/li\u003e\n\u003cli\u003eCosta JT (2006) The other insect societies, Belknap Press. Harvard University Press, Cambridge, Massachussetts\u003c/li\u003e\n\u003cli\u003eCote J, Bestion E, Jacob S, et al (2017) Evolution of dispersal strategies and dispersal syndromes in fragmented landscapes. Ecography 40:56\u0026ndash;73. https://doi.org/10.1111/ecog.02538\u003c/li\u003e\n\u003cli\u003eCote J, Clobert J (2007) Social information and emigration: lessons from immigrants. Ecology Letters 10:411\u0026ndash;417. https://doi.org/10.1111/j.1461-0248.2007.01032.x\u003c/li\u003e\n\u003cli\u003eCote J, Dahirel M, Schtickzelle N, et al (2022) Dispersal syndromes in challenging environments: A cross‐species experiment. Ecology Letters 25:2675\u0026ndash;2687. https://doi.org/10.1111/ele.14124\u003c/li\u003e\n\u003cli\u003eCremer S, Heinze J (2003) Stress Grows Wings: Environmental Induction of Winged Dispersal Males in Cardiocondyla Ants. Current Biology 13:219\u0026ndash;223\u003c/li\u003e\n\u003cli\u003eDahirel M, Vardakis M, Ansart A, Madec L (2016) Density-dependence across dispersal stages in a hermaphrodite land snail: insights from discrete choice models. Oecologia 181:1117\u0026ndash;1128. https://doi.org/10.1007/s00442-016-3636-z\u003c/li\u003e\n\u003cli\u003eDavid WAL, Gardiner BOC (1961) The mating behaviour of Pieris brassicae (L.) in a laboratory culture. Bull Entomol Res 52:263\u0026ndash;280. https://doi.org/10.1017/S0007485300055401\u003c/li\u003e\n\u003cli\u003eDucatez S, Baguette M, Stevens VM, et al (2012a) Complex interactions between paternal and maternal effects: parental experience and age at reproduction affect fecundity and offspring performance in a butterfly. Evolution 66:3558\u0026ndash;3569. https://doi.org/10.1111/j.1558-5646.2012.01704.x\u003c/li\u003e\n\u003cli\u003eDucatez S, Legrand D, Chaput‐Bardy A, et al (2012b) Inter‐individual variation in movement: is there a mobility syndrome in the large white butterfly Pieris brassicae ? Ecological Entomology 37:377\u0026ndash;385. https://doi.org/10.1111/j.1365-2311.2012.01375.x\u003c/li\u003e\n\u003cli\u003eDupont L, Thierry M, Zinger L, et al (2024) Beyond reaction norms: the temporal dynamics of phenotypic plasticity. Trends in Ecology \u0026amp; Evolution 39:41\u0026ndash;51. https://doi.org/10.1016/j.tree.2023.08.014\u003c/li\u003e\n\u003cli\u003eFattebert J, Perrig M, Naef-Daenzer B, Gr\u0026uuml;ebler MU (2019) Experimentally disentangling intrinsic and extrinsic drivers of natal dispersal in a nocturnal raptor. Proc R Soc B 286:20191537. https://doi.org/10.1098/rspb.2019.1537\u003c/li\u003e\n\u003cli\u003eFeltwell J (1982) Large white butterfly: the biology, biochemistry, and physiology of Pieris brassicae (Linnaeus), Dr W. Junk Publishers. Springer Dordrecht, The Hague\u003c/li\u003e\n\u003cli\u003eFjerdingstad EJ, Schtickzelle N, Manhes P, et al (2007) Evolution of dispersal and life history strategies \u0026ndash; Tetrahymena ciliates. BMC Evol Biol 7:133. https://doi.org/10.1186/1471-2148-7-133\u003c/li\u003e\n\u003cli\u003eFronhofer EA, Legrand D, Altermatt F, et al (2018) Bottom-up and top-down control of dispersal across major organismal groups. Nat Ecol Evol 2:1859\u0026ndash;1863. https://doi.org/10.1038/s41559-018-0686-0\u003c/li\u003e\n\u003cli\u003eGolden JW, Riddle DL (1984) A pheromone-induced developmental switch in Caenorhabditis elegans: Temperature-sensitive mutants reveal a wild-type temperature-dependent process. Proc Natl Acad Sci USA 81:819\u0026ndash;823. https://doi.org/10.1073/pnas.81.3.819\u003c/li\u003e\n\u003cli\u003eGuyot H (2009) Fiche technique d\u0026rsquo;\u0026eacute;levage : la Pi\u0026eacute;ride du Chou. Insectes 19\u0026ndash;22\u003c/li\u003e\n\u003cli\u003eHarrison XA, Blount JD, Inger R, et al (2011) Carry-over effects as drivers of fitness differences in animals. Journal of Animal Ecology 80:4\u0026ndash;18\u003c/li\u003e\n\u003cli\u003eJacob S, Bestion E, Legrand D, et al (2015) Habitat matching and spatial heterogeneity of phenotypes: implications for metapopulation and metacommunity functioning. Evol Ecol 29:851\u0026ndash;871. https://doi.org/10.1007/s10682-015-9776-5\u003c/li\u003e\n\u003cli\u003eJacob S, Chaine AS, Huet M, et al (2019) Variability in Dispersal Syndromes Is a Key Driver of Metapopulation Dynamics in Experimental Microcosms. The American Naturalist 194:613\u0026ndash;626. https://doi.org/10.1086/705410\u003c/li\u003e\n\u003cli\u003eJacob S, Laurent E, Morel‐Journel T, Schtickzelle N (2020) Fragmentation and the context‐dependence of dispersal syndromes: matrix harshness modifies resident‐disperser phenotypic differences in microcosms. Oikos 129:158\u0026ndash;169. https://doi.org/10.1111/oik.06857\u003c/li\u003e\n\u003cli\u003eJacob S, Wehi P, Clobert J, et al (2016) Cooperation-mediated plasticity in dispersal and colonization. Evolution 70:2336\u0026ndash;2345. https://doi.org/10.1111/evo.13028\u003c/li\u003e\n\u003cli\u003eJosse J, Husson F (2016) missMDA : A Package for Handling Missing Values in Multivariate Data Analysis. J Stat Soft 70:. https://doi.org/10.18637/jss.v070.i01\u003c/li\u003e\n\u003cli\u003eJunker AD, Jacob S, Philippe H, et al (2021) Plastic cell morphology changes during dispersal. iScience 24:102915. https://doi.org/10.1016/j.isci.2021.102915\u003c/li\u003e\n\u003cli\u003eKlok CJ, Chown SL (1999) Assessing the benefits of aggregation: thermal biology and water relations of anomalous Emperor Moth caterpillars. Functional Ecology 13:417\u0026ndash;427. https://doi.org/10.1046/j.1365-2435.1999.00324.x\u003c/li\u003e\n\u003cli\u003eKreuzinger‐Janik B, Gansfort B, Traunspurger W, Ptatscheck C (2022) It\u0026rsquo;s all about food: Environmental factors cause species‐specific dispersal. Ecosphere 13:e4251. https://doi.org/10.1002/ecs2.4251\u003c/li\u003e\n\u003cli\u003eKristensen NP, Johansson J, Chisholm RA, et al (2018) Carryover effects from natal habitat type upon competitive ability lead to trait divergence or source\u0026ndash;sink dynamics. Ecology Letters 21:1341\u0026ndash;1352. https://doi.org/10.1111/ele.13100\u003c/li\u003e\n\u003cli\u003eLarranaga N, Baguette M, Calvez O, et al (2013) Intra- and inter-individual variations in flight direction in a migratory butterfly co-vary with individual mobility. Journal of Experimental Biology 216:3156\u0026ndash;3163\u003c/li\u003e\n\u003cli\u003eLarranaga N, Baguette M, Calvez O, Legrand D (2019) Mobility affects copulation and oviposition dynamics in Pieris brassicae in seminatural cages. Insect Science 26:743\u0026ndash;752. https://doi.org/10.1111/1744-7917.12568\u003c/li\u003e\n\u003cli\u003eLe Masurier AD (1994) Costs and Benefits of Egg Clustering in Pieris brassicae. The Journal of Animal Ecology 63:677. https://doi.org/10.2307/5233\u003c/li\u003e\n\u003cli\u003eL\u0026ecirc; S, Josse J, Husson F (2008) FactoMineR : An R Package for Multivariate Analysis. J Stat Soft 25:. https://doi.org/10.18637/jss.v025.i01\u003c/li\u003e\n\u003cli\u003eLegrand D, Guillaume O, Baguette M, et al (2012) The Metatron: an experimental system to study dispersal and metaecosystems for terrestrial organisms. Nat Methods 9:828\u0026ndash;833. https://doi.org/10.1038/nmeth.2104\u003c/li\u003e\n\u003cli\u003eLegrand D, Larranaga N, Bertrand R, et al (2016) Evolution of a butterfly dispersal syndrome. Proc R Soc B 283:20161533. https://doi.org/10.1098/rspb.2016.1533\u003c/li\u003e\n\u003cli\u003eLegrand D, Trochet A, Moulherat S, et al (2015) Ranking the ecological causes of dispersal in a butterfly. Ecography 38:822\u0026ndash;831. https://doi.org/10.1111/ecog.01283\u003c/li\u003e\n\u003cli\u003eLenth RV (2025) emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.11.1-00001\u003c/li\u003e\n\u003cli\u003eLevitan DR (1988) Density-dependent size regulation and negative growth in the sea urchin Diadema antillarum Philippi. Oecologia 76:627\u0026ndash;629\u003c/li\u003e\n\u003cli\u003eMahendra, Pandey DN, Kumar A (2025) Dynamics of autonomous and nonautonomous Leslie\u0026ndash;Gower model for the impacts of fear and its carry‐over effects including predator harvesting. Math Methods in App Sciences 48:3082\u0026ndash;3113. https://doi.org/10.1002/mma.10476\u003c/li\u003e\n\u003cli\u003eMainwaring MC, Tobalske BW, Hartley IR (2023) Born without a Silver Spoon: A Review of the Causes and Consequences of Adversity during Early Life. Integrative And Comparative Biology 63:742\u0026ndash;757. https://doi.org/10.1093/icb/icad061\u003c/li\u003e\n\u003cli\u003eMatthysen E (2005) Density‐dependent dispersal in birds and mammals. Ecography 28:403\u0026ndash;416. https://doi.org/10.1111/j.0906-7590.2005.04073.x\u003c/li\u003e\n\u003cli\u003eMcLellan CF, Montgomery SH (2023) Towards an integrative approach to understanding collective behaviour in caterpillars. Phil Trans R Soc B 378:20220072. https://doi.org/10.1098/rstb.2022.0072\u003c/li\u003e\n\u003cli\u003eMcTavish MJ, Basiliko N, Sackett TE (2013) Environmental factors influencing immigration behaviour of the invasive earthworm Lumbricus terrestris. Can J Zool 91:859\u0026ndash;865. https://doi.org/10.1139/cjz-2013-0153\u003c/li\u003e\n\u003cli\u003eMoore MP, Ryan MA (2019) On the evolution of carry-over effects. Journal of Animal Ecology 88:1832\u0026ndash;1844\u003c/li\u003e\n\u003cli\u003eMueller T, Fagan WF (2008) Search and navigation in dynamic environments \u0026ndash; from individual behaviors to population distributions. Oikos 117:654\u0026ndash;664. https://doi.org/10.1111/j.0030-1299.2008.16291.x\u003c/li\u003e\n\u003cli\u003eNicolaus M, Wang X, Lamers KP, et al (2022) Unravelling the causes and consequences of dispersal syndromes in a wild passerine. Proc R Soc B 289:20220068. https://doi.org/10.1098/rspb.2022.0068\u003c/li\u003e\n\u003cli\u003eNiitep\u0026otilde;ld K, Boggs CL (2022) Carry‐over effects of larval food stress on adult energetics and life history in a nectar‐feeding butterfly. Ecological Entomology 47:391\u0026ndash;399. https://doi.org/10.1111/een.13124\u003c/li\u003e\n\u003cli\u003eNoldus LPJJ, Spink AJ, Tegelenbosch RAJ (2001) EthoVision: A versatile video tracking system for automation of behavioral experiments. Behavior Research Methods, Instruments, \u0026amp; Computers 33:398\u0026ndash;414. https://doi.org/10.3758/BF03195394\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Connor CM, Norris DR, Crossin GT, Cooke SJ (2014) Biological carryover effects: linking common concepts and mechanisms in ecology and evolution. Ecosphere 5:1\u0026ndash;11. https://doi.org/10.1890/ES13-00388.1\u003c/li\u003e\n\u003cli\u003ePennekamp F, Mitchell KA, Chaine A, Schtickzelle N (2014) Dispersal propensity in Tetrahymena thermophila ciliates - a reaction norm perspective. Evolution 68:2319\u0026ndash;2330. https://doi.org/10.1111/evo.12428\u003c/li\u003e\n\u003cli\u003ePiersma T, Drent J (2003) Phenotypic flexibility and the evolution of organismal design. Trends in Ecology \u0026amp; Evolution 18:228\u0026ndash;233. https://doi.org/10.1016/S0169-5347(03)00036-3\u003c/li\u003e\n\u003cli\u003ePiersma T, van Gils JA (2011) The Flexible Phenotype: A Body-Centred Integration of Ecology, Physiology, and Behavio. Oxford University Press, Oxford, UK\u003c/li\u003e\n\u003cli\u003eQian C, Wen C, Guo X, et al (2024) Gregariousness in lepidopteran larvae. Insect Science 1744-7917.13312. https://doi.org/10.1111/1744-7917.13312\u003c/li\u003e\n\u003cli\u003eR Core Team (2024) R: A Language and Environment for Statistical Computing\u003c/li\u003e\n\u003cli\u003eRaffard A, Bestion E, Cote J, et al (2022) Dispersal syndromes can link intraspecific trait variability and meta-ecosystem functioning. Trends in Ecology \u0026amp; Evolution 37:322\u0026ndash;331. https://doi.org/10.1016/j.tree.2021.12.001\u003c/li\u003e\n\u003cli\u003eReim E, Eichhorn D, Roy JD, et al (2019) Nutritional stress reduces flight performance and exploratory behavior in a butterfly. Insect Science 26:897\u0026ndash;910. https://doi.org/10.1111/1744-7917.12596\u003c/li\u003e\n\u003cli\u003eRonce O (2007) How Does It Feel to Be Like a Rolling Stone? Ten Questions About Dispersal Evolution. Annu Rev Ecol Evol Syst 38:231\u0026ndash;253. https://doi.org/10.1146/annurev.ecolsys.38.091206.095611\u003c/li\u003e\n\u003cli\u003eRutherford KL, Cottenie K, Denomme-Brown ST (2023) To go or not to go: variable density-dependent dispersal in small mammals. Journal of Mammalogy 104:783\u0026ndash;791. https://doi.org/10.1093/jmammal/gyac117\u003c/li\u003e\n\u003cli\u003eSaastamoinen M, Rantala MJ (2013) Influence of Developmental Conditions on Immune Function and Dispersal-Related Traits in the Glanville Fritillary (Melitaea cinxia) Butterfly. PLoS ONE 8:e81289. https://doi.org/10.1371/journal.pone.0081289\u003c/li\u003e\n\u003cli\u003eScheiner SM (1993) Genetics and Evolution of Phenotypic Plasticity. Annu Rev Ecol Syst 24:35\u0026ndash;68. https://doi.org/10.1146/annurev.es.24.110193.000343\u003c/li\u003e\n\u003cli\u003eScofield HN, Mattila HR (2015) Honey Bee Workers That Are Pollen Stressed as Larvae Become Poor Foragers and Waggle Dancers as Adults. PLoS ONE 10:e0121731. https://doi.org/10.1371/journal.pone.0121731\u003c/li\u003e\n\u003cli\u003eSpieth HR, Kaschuba‐Holtgrave A (1996) A new experimental approach to investigate migration in Pieris brassicae L. Ecological Entomology 21:289\u0026ndash;294. https://doi.org/10.1111/j.1365-2311.1996.tb01246.x\u003c/li\u003e\n\u003cli\u003eStager M, Velotta JP, Cheviron ZA, Senner NR (2024) Assessing the evidence for treating developmental plasticity and phenotypic flexibility as different phenomena. Functional Ecology 1365-2435.14590. https://doi.org/10.1111/1365-2435.14590\u003c/li\u003e\n\u003cli\u003eStein CM, Morris NJ, Nock NL (2012) Structural Equation Modeling. In: Elston RC, Satagopan JM, Sun S (eds) Statistical Human Genetics. Humana Press, Totowa, NJ, pp 495\u0026ndash;512\u003c/li\u003e\n\u003cli\u003eStevens VM, Whitmee S, Le Galliard J, et al (2014) A comparative analysis of dispersal syndromes in terrestrial and semi‐terrestrial animals. Ecology Letters 17:1039\u0026ndash;1052. https://doi.org/10.1111/ele.12303\u003c/li\u003e\n\u003cli\u003eStoffel MA, Nakagawa S, Schielzeth H (2021) partR2 : partitioning R2 in generalized linear mixed models. PeerJ 9:e11414. https://doi.org/10.7717/peerj.11414\u003c/li\u003e\n\u003cli\u003eStuligross C, Williams NM (2021) Past insecticide exposure reduces bee reproduction and population growth rate. Proc Natl Acad Sci USA 118:e2109909118. https://doi.org/10.1073/pnas.2109909118\u003c/li\u003e\n\u003cli\u003eTrochet A, Legrand D, Larranaga N, et al (2013) Population sex ratio and dispersal in experimental, two‐patch metapopulations of butterflies. Journal of Animal Ecology 82:946\u0026ndash;955. https://doi.org/10.1111/1365-2656.12082\u003c/li\u003e\n\u003cli\u003eValenta K, Nevo O (2020) The dispersal syndrome hypothesis: How animals shaped fruit traits, and how they did not. Functional Ecology 34:1158\u0026ndash;1169. https://doi.org/10.1111/1365-2435.13564\u003c/li\u003e\n\u003cli\u003eVan Allen BG, Rudolf VHW (2013) Ghosts of Habitats Past: Environmental Carry-Over Effects Drive Population Dynamics in Novel Habitat. The American Naturalist 181:596\u0026ndash;608. https://doi.org/10.1086/670127\u003c/li\u003e\n\u003cli\u003eWei T, Simko V (2017) R package \u0026ldquo;corrplot\u0026rdquo;: Visualization of a Correlation Matrix\u003c/li\u003e\n\u003cli\u003eWiklund C, Karlsson B, Leimar O (2001) Sexual conflict and cooperation in butterfly reproduction: a comparative study of polyandry and female fitness. Proc R Soc Lond B 268:1661\u0026ndash;1667. https://doi.org/10.1098/rspb.2001.1719\u003c/li\u003e\n\u003cli\u003eWright GA, Nicolson SW, Shafir S (2018) Nutritional Physiology and Ecology of Honey Bees. Annu Rev Entomol 63:327\u0026ndash;344. https://doi.org/10.1146/annurev-ento-020117-043423\u003c/li\u003e\n\u003cli\u003eZilio G, Deshpande JN, Duncan AB, et al (2024) Dispersal evolution and eco-evolutionary dynamics in antagonistic species interactions. Trends in Ecology \u0026amp; Evolution 39:666\u0026ndash;676. https://doi.org/10.1016/j.tree.2024.03.006\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"evolutionary-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evec","sideBox":"Learn more about [Evolutionary Ecology](https://www.springer.com/journal/10682)","snPcode":"10682","submissionUrl":"https://submission.nature.com/new-submission/10682/3","title":"Evolutionary Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Phenotypic syndrome, dispersal plasticity, carry-over effects, metabolism, mobility, exploration, morphology","lastPublishedDoi":"10.21203/rs.3.rs-6839133/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6839133/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDispersal is a fundamental process usually facilitated by suites of correlated traits. Phenotypic plasticity has the potential to change the structure of these dispersal syndromes depending upon the environmental context. Plasticity can take different forms, including time-delayed effects generating phenotypic variation dependent on past environmental conditions. Such carry-over effects have to be considered when estimating the lability of dispersal syndromes and the adaptiveness of dispersal. Here, we exposed caterpillars of the Large white butterfly \u003cem\u003ePieris brassicae\u003c/em\u003e from eight families to four density levels and three diet types in a full-crossed experimental design. We measured both larvae\u0026rsquo;s immediate physiological and behavioral plasticity and carry-over effects on adults\u0026rsquo; traits involved in a dispersal syndrome. Significant immediate plasticity was detected on all caterpillars\u0026rsquo; traits, whereas only a single marginal carry-over effect was detected on adult traits. Yet, we observed a dependency on larval environment of the correlations among adult\u0026rsquo;s traits and so of the dispersal syndrome. We relate these results to the species characteristics and discuss their consequences for spatial dynamics.\u003c/p\u003e","manuscriptTitle":"Impacts of larval environment on the adult dispersal syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-04 13:39:08","doi":"10.21203/rs.3.rs-6839133/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-21T02:30:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T13:12:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T06:12:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141666730667254154091908090244760459955","date":"2025-07-07T16:54:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123562979771833768939203030847161693733","date":"2025-07-03T22:47:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-02T16:51:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T04:56:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-07T01:23:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Evolutionary Ecology","date":"2025-06-06T18:39:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"evolutionary-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evec","sideBox":"Learn more about [Evolutionary Ecology](https://www.springer.com/journal/10682)","snPcode":"10682","submissionUrl":"https://submission.nature.com/new-submission/10682/3","title":"Evolutionary Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f49189d6-52c8-41b2-a547-111ab38a5fff","owner":[],"postedDate":"July 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:04:12+00:00","versionOfRecord":{"articleIdentity":"rs-6839133","link":"https://doi.org/10.1007/s10682-025-10363-2","journal":{"identity":"evolutionary-ecology","isVorOnly":false,"title":"Evolutionary Ecology"},"publishedOn":"2025-11-07 15:57:26","publishedOnDateReadable":"November 7th, 2025"},"versionCreatedAt":"2025-07-04 13:39:08","video":"","vorDoi":"10.1007/s10682-025-10363-2","vorDoiUrl":"https://doi.org/10.1007/s10682-025-10363-2","workflowStages":[]},"version":"v1","identity":"rs-6839133","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6839133","identity":"rs-6839133","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}