# Temperature variability homogenized thermal responses in an ectotherm community along a European longitudinal gradient

Ruining Li, Chun-Sen Ma, Lena Jego, Cecile Le Lann, Joan van Baaren, Gang Ma

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Ruining Li, Chun-Sen Ma, Lena Jego, Cecile Le Lann, Joan van Baaren, et al.. Temperature variability homogenized thermal responses in an ectotherm community along a European longitudinal gradient. Ecography, 2025, pp.e08194. 10.1002/ecog.08194. hal-05421734

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Distributed under a Creative Commons Attribution 4.0 International License
Research Article

Temperature variability homogenized thermal responses in an
ectotherm community along a European longitudinal gradient

Ruining LiID<fnref n="1" />,<fnref n="2" />,<fnref n="3" />, Chun-Sen MaID<fnref n="1" />, Léna Jego<fnref n="3" />, Cécile Le Lann<fnref n="3" />, Joan Van Baaren<fnref n="3" /> and Gang MaID<fnref n="2" />

Correspondence: Chun-Sen Ma (machunsen@caas.cn); Gang MA (magang@caas.cn)

Ecography

2025: e08194

doi: 10.1002/ecog.08194

Subject Editor: Sarah Diamond

Editor-in-Chief: Miguel Araújo

Accepted 7 November 2025

<figure><img src="image_2.png" /></figure>

Climate change, through rising temperatures, greater variability, and more frequent extremes, is reshaping insect phenology and thermal niches, with profound effects for pest outbreaks. Predicting these impacts requires a clear understanding of species and communities' responses across geographic gradients. We assessed thermal tolerance (CTmax, CTmin, CCRT) of three cereal aphid species *Sitobion avenae*, *Rhopalosiphum padi*, *Metopolophium dirhodum* from 30 populations along a 1200 km longitudinal gradient in Europe, comparing autumn and spring collections. We measured guild-level functional diversity to assess thermal trait patterns along the longitudinal gradient. We tested whether 1) eastern populations experiencing greater seasonality and harsher winters exhibited broader thermal ranges, 2) autumn aphids were more cold-tolerant and less heat-tolerant than spring aphids, and 3) stronger seasonality facilitated thermal trait convergence within the guild. Across the longitudinal gradient, autumn populations in eastern Europe exhibited broader thermal ranges, supporting the climatic variability hypothesis (CVH). In contrast, spring populations displayed a counter-gradient pattern, with stronger cold tolerance in the milder western winters, likely reflecting differences in overwintering strategies (active adults in the west versus diapausing eggs in the east). Additionally, autumn aphids were less heat-tolerant than spring individuals. Eastern communities exhibited trait convergence driven by large intraspecific variation, whereas western communities showed interspecific divergence, indicating differential environmental filtering. Increasing climate variability may drive thermal traits homogenization (negative standardized effect size of functional diversity for all traits) in ectotherm communities. Climate change may intensify season-dependent physiological changes, shift eastward the geographic range of aphids overwintering as active adults, and homogenize thermal niches, potentially altering pest dynamics and diminishing the effectiveness of integrated pest management strategies. Our study underscores the importance of integrating seasonal dynamics and intraspecific trait

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<footnote n="1">¹Climate Change Biology Research Group, School of Life Science, Hebei University, Baoding, China</footnote>

<footnote n="2">²Climate Change Biology Research Group, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China</footnote>

<footnote n="3">³UMR CNRS 6553 Ecosystems, Biodiversity, Evolution (ECOBIO), University of Rennes, Rennes, France</footnote>
variations when predicting climate change responses, highlighting how changes in temperature variability – not just warming – may reshape ectotherm communities during the growing season.

Keywords: aphid, climate change, critical thermal limits, longitudinal gradient, thermal tolerance

**Introduction**

Climate change has led to rising mean temperatures, greater
temperature variability, and more frequent and intense
extreme temperature events (IPCC 2023). These changes
increase the likelihood of temperature exceeding the thermal
optimum for species during growing season (Stocker et al.
2024), or alter the proportion of non-diapause individuals
during non-growing seasons (Ma et al. 2025). Ectotherms'
responses to extreme temperatures are often assessed using
basal thermal tolerance, defined as an organism's physiolog-
cal limits to high/low temperatures without acclimation or
short-term physiological adjustments (Bowler and Terblanche
2008, Ma et al. 2021). If thermal tolerance cannot adapt
quickly enough to keep pace with climate change, species
may face increased risks of population decline or extinction
(Huey et al. 2012, Bennett et al. 2021, Martin et al. 2023).

Ectotherms inhabit various geographic regions, where seasonal temperature means and fluctuations shape locally dynamic thermal environments. Higher latitude and altitude are generally associated with lower mean temperatures and tend to amplify diurnal and seasonal fluctuations. However, along longitudinal gradients (increasing distance from the coast at the same latitude), the range of temperature fluctuations increases while photoperiod and mean temperature remain constant (Jego et al. 2023), particularly in Europe. Local climates impose distinct selective pressures along these gradients (Elmendorf et al. 2015), and ectotherms often exhibit adaptive variation in thermal traits. Thus, by examining thermal tolerance across a longitudinal gradient, we can gain broader insights into how changes in temperature variability affect ectotherms, a critical component for predicting the global consequences of climate change.

Several hypotheses have been proposed to explain geo-
graphic variations in thermal responses in ectotherms, at
both population and species levels. The thermal adapta-
tion hypothesis (TAH) suggests that populations in warmer
regions evolve higher thermal limits but narrower thermal
ranges, whereas cold tolerance increases with latitude and
altitude (Angilletta 2009). The climate extremes hypothesis
(CEH) proposes that extreme climatic events define species'
thermal limits and distributions (Pither 2003), and predicts
that cold tolerance increases as minimum annual tempera-
tures decline along latitudinal and altitudinal gradients at
both population (Bozinovic et al. 2014) and species levels
(Bujan et al. 2020a). The CVH predicts that ectotherms
exposed to more variable thermal environments evolve
broader thermal ranges, with support reported across pop-
ulations (Addo-Bediako et al. 2000, Birrell et al. 2023) as
well as at the species level (Sunday et al. 2011). However,

studies conducted along latitudinal or altitudinal gradients
often conflate the effects of mean temperature and variability,
whereas longitudinal gradients allow to disentangle these fac-
tors (Jego et al. 2023).

Ectotherms can also adjust thermal tolerance seasonally within species (Chevin et al. 2010, Sgrò et al. 2016). For instance, in Oklahoma (USA), ant populations sampled in winter had the lowest heat tolerance (Bujan et al. 2020b). The fruit moths *Grapholita molesta* benefited from high summer temperature through accelerated growth, but experienced negative effects of heat in autumn due to disrupted dormancy (Zhu et al. 2025). Seasonal changes in thermal tolerance should therefore be considered alongside geographic gradients.

Moving beyond single-species perspectives, consider-
ing species within guilds offers a more integrative view of
thermal responses. A guild refers to species that coexist and
exploit similar resources (Simberloff and Dayan 1991).
Guilds can be viewed as functional subsets of a community,
offering a framework to investigate how different functional
groups contribute to the overall structure of the community
(Simberloff and Dayan 1991). Environmental filtering, a key
mechanism shaping communities or guilds, involves both
external filters (e.g. climate) and internal filters (e.g. com-
petition and trophic interactions) (Outreman et al. 2017).
These filters often act simultaneously to determine which
species can establish and persist, thereby shifting community
structures across time and space (Violle et al. 2012, Ma et al.
2015, Outreman et al. 2017, Zhu et al. 2021). When inter-
nal filters dominate, intense competition typically results in
phenotypic divergence (Blondel 2003), as illustrated by two
closely related sympatric parasitoid species: *Aphidius avenae*
tolerates heat but not cold, whereas *A. rhopalosiphii* shows
the opposite, which enables seasonal niche partitioning on
the same host (Le Lann et al. 2011a). In contrast, when
external filters dominate, strong selection pressure such as
climatic variability can result in functional trait convergence
among species within the same guild (Le Lann et al. 2014,
Outreman et al. 2017). For example, some parasitoid guilds
in western Europe exhibited similar life history traits in
response to mean warming (Le Lann et al. 2014) and liz-
ard guilds in Florida (USA) converged in cold tolerance in
response to one extreme cold episode in 2020 (Stroud et al.
2020). However, it remains unclear how increasing tempera-
ture variability under climate change might shape thermal
traits within communities.

Trait-based homogenization within communities has
been identified under global change. These reductions in
trait diversity are often caused by the replacement of special-
ist species by generalists that are better adapted to disturbed
environments (Clavel et al. 2011). Functional diversity within fish communities is declining due to global warming (McLean et al. 2021, Scherer et al. 2023). Extreme climatic events can abruptly restructure community composition and drive shifts in functional groups, as observed in the tropicalization of fish communities following a marine heatwave (Wernberg et al. 2013). Although these studies did not directly measure thermal tolerance, the functional traits they assessed, such as body size, growth rate, and reproductive strategies, are strongly influenced by temperature and can serve as indirect indicators of thermal responses. However, understanding how thermal trait diversity is maintained within communities requires considering not only interspecific but also intraspecific sources of variation.

Most studies have assessed trait diversity by averaging trait values at the species level, while few have examined it at the individual level. However, intraspecific variation in thermal sensitivity can fundamentally alter population-level responses to environmental change, but is often overlooked in vulnerability assessments (Bennett et al. 2019). Focusing on intraspecific trait variation and its importance in biotic interactions within gradients could provide further insights

into the effects of temperature variability on community dynamics (Siefert et al. 2015).

Here, we compare the spatiotemporal thermal tolerance of three cereal aphid species, collected in autumn and spring, along a 1200 km longitudinal gradient from western France to the Czech Republic. The gradient spans from oceanic climates in the west, with mild winters and cool summers, to continental climates in the east, with harsh winters and warm summers. Specifically, we test three hypotheses.

H1: Individuals from western populations collected in both spring and autumn exhibit lower cold and heat tolerances and thus a narrower thermal range compared to those in eastern Europe (Fig. 1A).

H2: Individuals sampled in autumn display higher cold tolerance but lower heat tolerance than those sampled in spring across the gradient (Fig. 1A).

H3: At the guild level, the weaker seasonality in western Europe facilitates thermal trait divergence among the three aphid species while the larger seasonality in eastern Europe promotes thermal trait convergence (Fig. 1B).

<figure><img src="image_4.png" /><figcaption>Figure 1. Conceptual diagram for the three tested hypotheses along a longitudinal gradient (increasing distance from the coast at the same latitude). (A) Along the gradient, insect heat and cold tolerances are predicted to vary in accordance with local temperature extremes (Fig. 2 for temperature data) (H1). At each location, it is expected that heat tolerance is higher in spring populations and cold tolerance is higher in autumn populations, showing seasonal variation (H2). (B) At the guild level, both heat and cold tolerances are predicted to be convergent with increasing external selection pressure generated by extreme temperatures (H3)</figcaption></figure>
# Material and methods

## Focal species and sampling locations

The English grain aphid *Sitobion avenae*, the bird cherry-oat aphid *Rhopalosiphum padi*, and the rose grain aphid *Metopolophium dirhodum* (Hemiptera: Aphididae) are three major cereal pests worldwide, causing important yield loss (Van den Eynde et al. 2020). These species transmit barley and cereal yellow dwarf viruses (BYDV and CYDV) but differ in the types and efficiency of virus transmission (Miller and Rasochová 1997, Van den Eynde et al. 2020). Their broad distribution, rapid reproduction, and frequent co-occurrence in temperate wheat systems make them ideal models for studying interspecific and intraspecific interactions and thermal adaptation in a context of changing climatic variability (Ma et al. 2015, Zhu et al. 2021).

In 2022, individuals of the three species were collected from five locations along a longitudinal gradient in Europe: western France (eLTER ZAAr), northern France, Belgium, Germany, and the Czech Republic (Supporting information), with eLTER ZAAr being a long-term socio-ecological research site in France (https://www.za-inee.org/fr/reseau). Sampling took place in spring (May–June) and autumn (November–December). At each location, 3–7 fields, separated by at least 1 km, were surveyed. At least 30 individuals per species were collected per field. One adult female of each species per field was reared separately, and all progeny produced by this female (same genetic background) were treated as one clone. To minimize the confounding effects of environmental variation and plasticity, all clones were reared for at least three generations (~20 days, ~6–7 days per generation at 20°C) under controlled laboratory conditions in a common garden design (20 ± 1°C, 60 ± 10% RH, 16:8 h L:D; MIR-153, Sanyo, USA) before conducting thermal tolerance assays. These conditions approximated favorable spring–early summer environments, allowing rapid generation turnover without inducing stress or diapause (Le Lann et al. 2011b). A total of 106 clones were established; *M. dirhodum* was scarce in autumn samples.

## Climatic variables

We obtained daily temperature data from the E-OBS dataset (1 January 1980 – 30 June 2023), a gridded observational dataset (Cornes et al. 2018). Hourly data were obtained from ERA5 (1 January 2013 – 31 December 2022), which combines model data with observations globally (Hersbach et al. 2020, Bell et al. 2021). Both datasets provide values at 2 m above ground. Daily data captured long-term trends, and hourly data quantified exposure to extremes relevant for insect thermal stress, because thermal limits are influenced more strongly by extremes than by mean values (Ma et al. 2015). To ensure sufficient replicates, the time series was aggregated into 5-year intervals. We then calculated annual mean temperatures, mean summer maxima (June–August), mean winter minima (December–February), and their standard deviations for each interval and each location.

Seasonality was estimated at 5-year intervals following Phillips et al. (2016). A 30-day polynomial smoother was fitted to daily maximum temperatures, and the difference between total daily variance and monthly variance was used to calculate the seasonality index. High values indicate strong seasonal contrasts, whereas low values reflect relatively consistent year-round temperatures.

To assess heat severity, we calculated the monthly cumulative hours above 30°C (hereafter 'Above30') from ERA5 data (2013–2023). This threshold is biologically relevant for aphids, as their reproduction and survival decline beyond this level (Asín and Pons 2001, Ma and Ma 2012, Ciss et al. 2014).

Climatic heterogeneity among locations was summarized using principal component analysis (PCA). To reduce collinearity, four representative variables were retained: summer Tmax, Above30, winter Tmin, and seasonality. Differences among locations in annual mean temperature, summer Tmax, Above30, winter Tmin, and seasonality were assessed using linear models with location as a fixed factor, followed by pairwise comparisons using the lsmeans function (Lenth 2016).

## Critical thermal limits measurements and their link to climatic variables.

Critical thermal maximum (CTmax) and critical thermal minimum (CTmin) were measured as indicators of heat and cold tolerance, respectively. CTmax was assessed as the temperature at which aphids became immobile (Zhao et al. 2017). For each clone, 20 pre-reproductive adults were placed individually in Eppendorf tubes, attached together, and inserted into a double-walled chamber connected to a cryostat (Supporting information). The chamber temperature, recorded at 0.01°C resolution every 10 s, was held at 20°C for 10 min and then increased at 0.5°C min⁻¹ until all individuals were immobile. In total, CTmax was measured for 2120 individuals (*S. avenae*: 840; *R. padi*: 700; *M. dirhodum*: 580).

CTmin was defined as the temperature at which aphids fell from the host plants, while chill coma recovery time (CCRT) quantified the time needed to resume spontaneous walking after cold exposure (Ørsted et al. 2022). Using the same device, 20 adults per clone were cooled from 20°C at 0.5°C min⁻¹ to –6°C, then transferred to 22 ± 0.5°C to record recovery time (Alford et al. 2014). CTmin could not be determined for *R. padi*, which does not exhibit falling behavior; for this species, only CCRT was recorded. Overall, CTmin was measured for 1240 individuals (*S. avenae*: 720; *M. dirhodum*: 520), and CCRT was measured for 2020 individuals (*S. avenae*: 820; *R. padi*: 640; *M. dirhodum*: 560).

Thermal range, defined as CTmax minus CTmin, provides an integrated measure of tolerance (Bujan et al. 2020a). Because CTmax and CTmin were measured from different individuals for each clone, data could not be directly paired. To avoid pseudoreplication, we applied a bootstrap procedure by constructing a 20 × 20 matrix of observed values,
resampling with replacement to generate distributions of thermal range and 95% confidence intervals for each clone (Sørensen et al. 2015).

To evaluate variation in thermal traits, we fitted linear mixed models with species, season, and location as fixed factors and clone as a random factor (R package ‘lme4’, Bates et al. 2015). Model significance was tested with ANOVA, and variance explained by fixed effects was extracted with the R package ‘r2glmm’ (Jaeger et al. 2016), to disentangle contributions of location, season, and species in the full model. Links between species traits and climate variables were then assessed: CTmax with Above30 (heat severity), CTmin and CCRT with winter Tmin, and thermal range with seasonality. For each, we used mixed models with species, season, and the relevant climatic variable as fixed effects and clone as a random factor. For each trait–environment combination, we fitted both linear models and quadratic models, and selected the best model based on lowest AIC and highest R². When species differences were significant, we further fitted species-specific models with climatic variables as predictors.

### Calculation of the functional diversity index

To assess whether thermal traits diverged or converged within a guild, we quantified functional diversity (FD) using Rao's quadratic entropy (Botta-Dukát 2005), which measures the expected dissimilarity between two randomly selected individuals within a guild (i.e. a field). FD was calculated for all thermal traits (CTmax, CTmin, thermal range, CCRT) in each field (Supporting information):

$$FD = \sum_{i,j}^{s} \delta_{ij} p_i p_j \qquad (1)$$

where S denotes the number of species, δᵢⱼ is the Gower distance between the mean trait values of the ith and jth clones (ranging from 0=identical to 1=maximally different). pᵢ and pⱼ indicate the proportion/abundance of specific clones within a given guild.

To ensure that observed patterns in functional diversity were not artifacts of random chance or spatial variation but instead reflected ecological processes, we applied a null model approach (Gotelli and McCabe 2002). Expected FD (FD_null) was estimated from 999 random permutations of the clone-trait matrix across all fields, and the mean and standard deviation of the expected values were calculated. Then standardized effect size (SES_FD) was then calculated as:

$$SES\_FD = \frac{\text{observedFD} - \text{meanofexpectedFD}}{\text{SDexpectedFD}} \qquad (2)$$

SES_FD was computed separately for each trait and each field. The abundance dataset was a presence-absence matrix of clones (Le Lann et al. 2014). FD was computed using the *rao.diversity* function in the R package ‘SYNCSA’ (Debastiani and Pillar 2012), and null models were generated with the R package ‘picante’ (Kembel et al. 2010).

To assess whether thermal tolerances diverged or converged in each location, we then averaged SES_FD values across all fields within each location from the two seasons to compare functional diversity among sites. Only fields with more than two co-occurring species were included. Positive SES_FD values indicate functional overdispersion (trait divergence), while negative values suggest functional clustering (trait convergence), associated with environmental filtering (Botta-Dukát and Czúcz 2016, Bricca et al. 2022). To see if SES_FD differed among locations, differences among locations were tested with linear models, with SES_FD as the response variable and location as a fixed factor.

### Variance partitioning of inter- and intra-specific variation

To partition inter- and intra-specific contributions to trait variation while excluding location effects, we pooled spring and autumn data for each site and fitted linear mixed models for each trait, with species as the fixed factor and clone as the random factor. Marginal and conditional R² values were extracted with the R package ‘MuMln’ (Barton 2009). Marginal R² represented the variance explained by species (inter-specific variation), whereas the difference between conditional and marginal R² represented the variance explained by clones (intra-specific variation) (Nakagawa et al. 2017). All statistical analyses were conducted in R ver. 4.2.2 (www.r-project.org).

## Results

### Climatic variables along the gradients

Climatic variables exhibited a longitudinal gradient, characterized by seasonality and winter minimum temperatures (Fig. 2A, Dim1). Seasonality increased with longitude (df=4, F=301, p < 0.001), ranging from 36.7°C² in western France to 76.7°C² in the Czech Republic (Fig. 2B). Winter minimum temperatures decreased along the longitudinal gradient (df=4, F=1227, p < 0.001), from 3.0 ± 0.4°C in western France to 1.0 ± 0.3°C in northern France and Belgium, -1.7 ± 0.3°C in Germany, and -2.7 ± 0.4°C in the Czech Republic (Fig. 2C). During summer (June–August), monthly accumulated hours above 30°C differed significantly among locations (df=4, F=2.49, p=0.04), with 8 ± 2 h in western France and Belgium, 12 ± 3 h in northern France and Germany, and 16 ± 3 h in the Czech Republic (Fig. 2D). Mean maximum summer temperatures ranged from 25 to 27°C and did not differ significantly among locations (df=4, F=1.32, p=0.26) (Fig. 2E). Annual mean temperatures ranged from approximately 12°C in western France to 8.9°C in the Czech Republic, resulting in a modest difference of 3°C across the gradient (Supporting information).

### Thermal tolerance among locations, seasons and species

Heat tolerance, measured as CTmax, differed significantly between locations and seasons, but only weakly among species (Table 1). The interactions between location and season
<figure><img src="image_5.png" /></figure>

Figure 2. Climatic variables among five locations along a European gradient (1980.01.01–2023.06.30, E-OBS; 2013.01.01–2022.12.31, ERA5). Different colors refer to the different locations. (A) Plot of principle component analysis (PCA) with means of summer maxima temperatures and of winter minimal temperatures (ERA5), cumulative hours above 30°C (E-OBS) and seasonality (daily variance minus monthly variance, ERA5). Each circle represents one of the five locations. Means (and SE) of seasonality (B), winter minimal temperatures (C), cumulative hours above 30°C (D) and summer maxima temperatures (E). Different letters refer to the results of Tukey post hoc test (*p* < 0.05).

and between season and species were also significant, with the variance explained mostly by season (R²=62.1%), followed by location (R²=50.9%). CTmax increased along the longitudinal gradient, and spring clones exhibited significantly higher values than autumn clones (Fig. 3A, B).

Cold tolerance, assessed by CTmin and CCRT, varied among locations, seasons, and species. For CTmin, the interaction between location and season had the strongest effect (R²=56.5%), followed by location alone (R²=36%) and species (R²=7%). In spring, clones of *M. dirhodum* exhibited lower CTmin than those of *S. avenae* (MD: 1.2 ± 0.2°C; SA: 1.6 ± 0.3°C). In autumn, the pattern reversed: *M. dirhodum* clones had higher CTmin than *S. avenae* (MD: 3.2 ± 0.7°C; SA: 2.2 ± 0.3°C). CTmin increased along the longitudinal gradient in spring but decreased in autumn (Fig. 3C, D).

For CCRT, interactions among location, season, and species were also significant, with season explaining the largest variance component (R²=49.8%) (Table 1). CCRT increased slightly along the gradient, with aphids from western France recovering fastest (Fig. 3G, H).

Thermal range also differed significantly between locations, seasons, and species. Similar to CTmax and CTmin, variance was explained primarily by season (R²=54.3%), followed by location (R²=22.4%) (Table 1). Thermal range decreased along the longitudinal gradient in spring but increased in autumn, with spring populations showing a broader thermal range than autumn populations (Fig. 3E, F).

## Relationship between thermal tolerance and climatic variables

These geographic patterns in tolerance traits were associated with local climatic conditions (Supporting information). CTmax increased with the cumulative number of hours exceeding 30°C in both spring and autumn clones (Fig. 3A, B). CTmin showed opposite seasonal responses: it declined with increasing winter Tmin in spring but rose with winter Tmin in autumn, with aphids from the Czech Republic exhibiting the lowest CTmin (i.e. greatest cold tolerance) in autumn (Fig. 3C, D). Thermal range also showed contrasting seasonal responses, decreasing with increasing seasonality
<table><caption>Table 1. Effects of location, season, species, and their interactions on thermal tolerance traits of aphids. Traits include critical thermal maximum (CTmax), critical thermal minimum (CTmin), thermal range (CTmax – CTmin), and chill coma recovery time (CCRT). Results are derived from linear mixed model analyses with clone as the random factor. Significant p-values are indicated in bold.</caption><thead><tr><th>Variable</th><th>df</th><th>F-value</th><th>p-value</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td colspan="5"><strong>Full model of CTmax</strong></td></tr><tr><td>Full model</td><td></td><td></td><td></td><td>82</td></tr><tr><td>Species</td><td>2</td><td>4.15</td><td><strong>p &lt; 0.05</strong></td><td>9.5</td></tr><tr><td>Location</td><td>4</td><td>20.47</td><td><strong>p &lt; 0.001</strong></td><td>50.9</td></tr><tr><td>Season</td><td>1</td><td>129.67</td><td><strong>p &lt; 0.01</strong></td><td>62.1</td></tr><tr><td>Species × Location</td><td>8</td><td>2.13</td><td><strong>p &lt; 0.05</strong></td><td>17.7</td></tr><tr><td>Species × Season</td><td>2</td><td>8.19</td><td><strong>p &lt; 0.01</strong></td><td>17.2</td></tr><tr><td>Location × Season</td><td>4</td><td>2.86</td><td><strong>p &lt; 0.05</strong></td><td>12.5</td></tr><tr><td>Species × Location × Season</td><td>5</td><td>0.85</td><td>0.48</td><td>5.1</td></tr><tr><td colspan="5"><strong>Full model of CTmin</strong></td></tr><tr><td>Full model</td><td></td><td></td><td></td><td>77.7</td></tr><tr><td>Species</td><td>1</td><td>4.10</td><td><strong>p &lt; 0.05</strong></td><td>7</td></tr><tr><td>Location</td><td>4</td><td>7.55</td><td><strong>p &lt; 0.001</strong></td><td>36</td></tr><tr><td>Season</td><td>1</td><td>10.04</td><td><strong>p &lt; 0.01</strong></td><td>15.9</td></tr><tr><td>Species × Location</td><td>4</td><td>3.55</td><td><strong>p &lt; 0.01</strong></td><td>21.2</td></tr><tr><td>Species × Season</td><td>1</td><td>0.10</td><td>0.75</td><td>0.2</td></tr><tr><td>Location × Season</td><td>4</td><td>17.48</td><td><strong>p &lt; 0.001</strong></td><td>56.5</td></tr><tr><td colspan="5"><strong>Full model of thermal breadth</strong></td></tr><tr><td>Full model</td><td></td><td></td><td></td><td>79.7</td></tr><tr><td>Species</td><td>1</td><td>4.69</td><td><strong>p &lt; 0.05</strong></td><td>8.1</td></tr><tr><td>Location</td><td>4</td><td>3.81</td><td><strong>p &lt; 0.01</strong></td><td>22.4</td></tr><tr><td>Season</td><td>1</td><td>63.00</td><td><strong>p &lt; 0.001</strong></td><td>54.3</td></tr><tr><td>Species × Location</td><td>4</td><td>1.80</td><td>0.14</td><td>12</td></tr><tr><td>Species × Season</td><td>1</td><td>2.95</td><td>0.09</td><td>5.3</td></tr><tr><td>Location × Season</td><td>4</td><td>14.80</td><td><strong>p &lt; 0.001</strong></td><td>52.8</td></tr><tr><td colspan="5"><strong>Full model of CCRT</strong></td></tr><tr><td>Full model</td><td></td><td></td><td></td><td>76.4</td></tr><tr><td>Species</td><td>2</td><td>4.65</td><td><strong>p &lt; 0.01</strong></td><td>10.7</td></tr><tr><td>Location</td><td>4</td><td>6.83</td><td><strong>p &lt; 0.001</strong></td><td>26.1</td></tr><tr><td>Season</td><td>1</td><td>75.4</td><td><strong>p &lt; 0.001</strong></td><td>49.8</td></tr><tr><td>Species × Location</td><td>8</td><td>3.36</td><td><strong>p &lt; 0.001</strong></td><td>26.1</td></tr><tr><td>Species × Season</td><td>2</td><td>1.85</td><td>0.16</td><td>10.2</td></tr><tr><td>Location × Season</td><td>4</td><td>3.69</td><td><strong>p &lt; 0.05</strong></td><td>16</td></tr><tr><td>Species × Location × Season</td><td>3</td><td>2.13</td><td>0.1</td><td>7.7</td></tr></tbody></table>

in spring but increasing in autumn (Fig. 3E, F). For CCRT, recovery times rose along the geographic gradient and varied with winter minimum temperatures. Species-specific differences were significant: in spring, *R. padi* recovered faster than the other two species in western France, whereas *S. avenae* recovered faster in Germany and the Czech Republic. In autumn, however, CCRT was relatively consistent across species and locations (Fig. 3G, H).

## Functional diversity and variance partition along the gradient

We calculated SES FD values for all four traits (CTmax, CTmin, thermal range, and CCRT) at the field level, where negative values indicate trait convergence and positive values indicate divergence. Only fields with more than two co-occurring species were included to ensure reliable estimates of functional diversity. The mean SES FD across all fields at

each location was then used to represent the site-level SES FD. Across all traits, we observed a weak negative trend in SES FD values from western France to the Czech Republic, suggesting an increasing tendency toward trait convergence along the longitudinal gradient (Fig. 4A). All examined traits showed a pattern of divergence in western and northern France and Belgium, and convergence in Germany and the Czech Republic. However, functional diversity did not differ significantly among locations (CTmax: df=4, F=1.22, p=0.31; CTmin: df=4, F=0.76, p=0.65; thermal range: df=4, F=0.82, p=0.70; CCRT: df=4, F=1.8, p=0.07) (Fig. 4A).

We therefore partitioned the inter- and intra-specific variances at each location for different thermal traits. For CTmax, intraspecific variance accounted for more than 80% of the total across all sites. For CTmin and thermal range, intraspecific variance was also the dominant component. For CCRT, interspecific variance was higher than intraspecific variance
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

Species

● *Metopolophium dirhodum*

▲ *Sitobion avenae*

♦ *Rhopalosiphum padi*

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

Species

— *Metopolophium dirhodum* (MD)

— ▪ *Sitobion avenae* (SA)

— □ *Rhopalosiphum padi* (RP)

Figure 3. Relationships between thermal tolerance traits of the aphid guild and climatic variables (hours above 30°C, winter minimal temperature, seasonality) for each season: (A–B) critical thermal maximum (CTmax); (C–D) critical thermal minimum (CTmin); (E–F) thermal range (CTmax – CTmin); (G–H) chill coma recovery time (CCRT). Clones were sampled in spring (A, C, E, G) and autumn (B, D, F, H). Small grey points: each represents the mean trait of a clone in one field. Big colored points: direct measurements (CTmax, CTmin, CCRT) show population means ± SE, while bootstrap estimates of thermal range (E–F) show means ± 95% CI. Lines or curves show model-predicted trait–climate relationships (linear or quadratic) at the guild level, with shaded areas indicating SE. For CCRT, line types denote species-specific relationships (solid: *Metopolophium dirhodum*; dashed: *Sitobion avenae*; dotted: *Rhopalosiphum padi*) at population level (G–H). Different shapes indicate species (circle: *M. dirhodum*; triangle: *S. avenae*; diamond: *R. padi*).
<figure><img src="image_14.png" /><figcaption>Figure 4. Functional diversity (A) and variance partitioning of intra- and interspecific variance along the longitudinal gradient (B). X-axes are identical across panels (FR(W)–CZ) and shown only once for clarity; arrows indicate increasing seasonality from west to east. (A) Standardized effect size of functional diversity (SES_FD) of thermal tolerance traits – critical thermal maximum (CTmax), critical thermal minimum (CTmin), thermal range (CTmax–CTmin), and chill coma recovery time (CCRT) – at different locations along the longitudinal gradient in Europe. Small circles show SES_FD values for each guild (each field, n = 39), and bars on large points indicate mean SES_FD ± SE at each location. Blue lines show the decreasing trend of mean SES_FD. Negative mean values (red) indicate trait convergence, whereas positive mean values (blue) indicate trait divergence. (B) Variance partitioning between species (triangles with dashed lines: inter-specific variation) and within species (squares with solid lines: intraspecific variation) among locations.</figcaption></figure>

in western France (25 versus 17%), whereas the opposite pattern was observed in northern France (12 versus 18%). At the remaining locations, intraspecific variance dominated (Fig. 4B). Across all traits, the proportion of variance attributable to interspecific differences declined from western France toward the Czech Republic, suggesting a reduction in interspecific divergence along the longitudinal gradient. When considering functional diversity and variance components together, regions that exhibited trait divergence (e.g. western France) also showed greater variation at the interspecific level.

## Discussion

Through large-scale field sampling and clonal-level laboratory experiments, we tested three hypotheses. H1 proposed that western populations, sampled in both spring and autumn, would show weaker cold and heat tolerance and narrower thermal ranges than eastern populations. This was partly supported: in autumn, eastern (Czech) populations exhibited lower CTmin and higher CTmax, consistent with the climate

extremes hypothesis (CEH), which indicates that cold tolerance increases with minimum annual temperatures, and the CVH, which predicts broader thermal ranges under greater climatic variability. However, spring populations deviated from H1, with western populations showing unexpectedly higher cold tolerance. H2 predicted that autumn populations would have higher cold tolerance but lower heat tolerance than spring populations. Heat tolerance followed this pattern, with spring populations generally exhibiting higher CTmax, but seasonal shifts in cold tolerance varied among locations. H3 proposed that weaker seasonality in western Europe would favor interspecific divergence, whereas stronger seasonality in eastern Europe would promote convergence. This was supported by thermal trait convergence observed in regions with pronounced seasonality, such as Germany and the Czech Republic.

### Seasonal variations shape thermal tolerance across the longitudinal gradient

Our results demonstrate that thermal tolerance is strongly season-dependent, with autumn and spring populations
showing contrasting relationships with climatic gradients. Autumn-sampled populations showed thermal adaptation, with CTmax, CTmin, and thermal range positively correlated with annual heat severity (hours ≥ 30°C), annual Tmin, and seasonality, respectively. However, spring populations exhibited distinct patterns: CTmax correlated with heat severity, whereas CTmin and thermal range decreased with increasing winter Tmin, indicating counter-gradient trends. These patterns reflect regional overwintering strategies. In western Europe, aphids overwinter as active adults, requiring cold tolerance to deal with low temperatures. By contrast, during harsh winters (e.g. in Czech Republic), aphids overwinter as diapausing eggs, with their sexual parental adults dying postoviposition (Langer and Hance 2000, Dedryver et al. 2001, Honek et al. 2018). Measuring CTmin in adults rather than diapausing eggs may explain the observed counter-gradient patterns. Earlier spring growth in mild regions can expose adults to late frosts, potentially selecting for increased cold tolerance despite generally mild conditions.

CTmin showed clearer geographic variation than CTmax, likely because minimum winter temperatures vary more among locations than maximum summer temperatures. This climatic asymmetry likely explains stronger divergence in cold than heat tolerance. Insects employ diverse overwintering strategies – including diapause, seasonal migration, freeze tolerance, and freeze avoidance – depending on physiological limits and winter severity (Sinclair et al. 2003, Bale and Hayward 2010). Most temperate pests, including cereal aphids, rely on freeze avoidance, combining physiological and behavioral mechanisms. For example, insects may compensate for physiological limits through behavior. The Colorado potato beetle *Leptinotarsa decemlineata* digs deeper in colder regions, while the Asian corn borer *Ostrinia furnacalis* shifts between freeze tolerance and intolerance depending on winter conditions, which demonstrates that cold tolerance reflects interactions among physiology, behavior, and overwintering strategy (Izzo et al. 2014, Xie et al. 2015).

It is also worth noting that *M. dirhodum* was largely absent in most autumn samples, reflecting its natural seasonal distribution. This absence is ecologically informative but limits analysis of seasonal thermal trait dynamics at the community level. Future studies with more balanced seasonal sampling would help assess how thermal traits vary among species and seasons.

## Large temperature variability tends to induce thermal trait convergence

Across a longitudinal gradient of more than 14°, annual mean temperature showed only moderate variation, in contrast to latitudinal ranges where differences often exceed 10°C (De Frenne et al. 2013). This moderate variation allowed us to examine patterns of thermal adaptation while minimizing confounding effects of mean temperature and focused on seasonality.

Thermal traits converged in regions with greater seasonality and harsher winters. Similar convergence phenomena have been reported in aquatic systems. In the North Sea, fish

communities diverged taxonomically but converged functionally over the past three decades, shifting toward smaller, faster-growing species with higher thermal tolerance as waters warmed (McLean et al. 2019). Likewise, reductions in functional diversity have been observed in temperate French streams (Buisson et al. 2013) and tropical basins such as the Amazon and Paraná (Scherer et al. 2023). Compared to freshwater environments, terrestrial systems experience greater temperature variability, and global climate change has amplified these fluctuations (Bathiany et al. 2018). Yet few studies have examined trait convergence in terrestrial communities.

Our results contribute to filling this gap, suggesting that increasing temperature fluctuations along longitudinal gradients facilitate convergence in thermal tolerance, especially in the regions with pronounced seasonality. Such convergence may undermine community stability by reducing functional diversity and synchronizing pest population dynamics, thereby amplifying outbreak and disease risks.

## The sources of functional variability within communities along the gradient

Trait convergence in the eastern regions of the gradient, particularly in the Czech Republic, was primarily driven by intra-specific variation, whereas interspecific divergence was more pronounced for CCRT in French communities. Overall, the contribution of interspecific variance declined from west to east, reflecting a shift from interspecific divergence in the west to convergence in the east.

Several mechanisms may explain this pattern. First, genetic structure can influence trait variability. Aphid reproductive mode shapes genetic diversity: obligate parthenogenesis favors dominant 'super clones' in mild oceanic climates (e.g. *R. padi* in France; *S. avenae* in the UK and Chile), while cyclical parthenogenesis maintains higher variation in harsher continental climates, as seen in the Russian wheat aphid *Diuraphis noxia* in Russia and western Asia (Puterka et al. 1993, Llewellyn et al. 2003, Gilabert et al. 2015, Barrios-SanMartín et al. 2016, Figueroa et al. 2018). Thus, we hypothesize relatively homogeneous genotypes in western Europe may limit intraspecific variation, whereas diverse genotypes and stronger seasonality in eastern Europe may enhance it. Second, microhabitat heterogeneity can influence trait variation. Fine-scale thermal mosaics, altered by climate change and local factors such as urbanization, affect ectotherm performance and may promote convergence in thermal traits (Pincebourde and Casas 2015, Woods et al. 2015, Diamond et al. 2024).

## Variable climate change scenarios along geographic gradients alter community dynamics

Though Europe is the fastest-warming continent, warming rates vary markedly between summer and winter across regions, leading to different trends in seasonality along longitudinal gradients. The greatest increases are projected in north-eastern Europe, whereas western Europe is expected to experience the smallest rise in mean temperature (IPCC 2023, European Environment Agency 2024). Along the
longitudinal gradient, France is warming more rapidly in summer than in winter, a trend that will likely enhance seasonality (De Winter et al. 2024, Arguez et al. 2024). This increase in seasonality may also expand insect thermal ranges, consistent with the CVH. Longer summers further increase late-summer and early-autumn heatwave likelihood (Fischer and Schär 2009), which may promote convergence in heat tolerance among aphid species.

In contrast, the Czech Republic is expected to warm at similar rates in summer and winter, suggesting stable seasonality (Brázdil et al. 2009, Arguez et al. 2024). However, ecological pressures remain significant. Intensifying summer heatwaves (Fischer et al. 2012) may drive convergence in heat tolerance and push temperatures beyond thermal limits of ectotherms, thereby increasing local extinction risks. Concurrently, milder winters may allow more aphids to overwinter as active adults, accelerating spring population growth and potentially increasing pest pressure in early seasons.

These climatic contrasts are expected to shape BYDV transmission dynamics. In France, longer summers, broader insect thermal ranges, and convergence in heat tolerance may increase the temporal overlap of aphid population peaks, thereby enhancing co-infection risks and potentially amplifying disease impacts (Seabloom et al. 2009). Such synchronization could also reduce the efficiency of species-specific control strategies, as multiple species reach high densities simultaneously. In the Czech Republic, higher overwintering survival may advance seasonal colonization, lengthening the BYDV transmission period. These region-specific outcomes underscore the need for tailored management strategies, such as late-season population suppression in France and a combination of winter surveillance and early-season controls in the Czech Republic.

## Conclusion

Our results supported the CVH and the climatic extremes hypothesis (CEH) for autumn populations, as they showed broader thermal ranges and greater tolerance to temperature extremes, but not for spring populations. This highlights the importance of considering seasonal effects and overwintering strategies in thermal adaptation along geographic gradients. In regions with stronger seasonality and harsher winters, interspecific convergence in thermal limits suggests that climate acts as an environmental filter shaping community composition. Longitudinal gradients provide valuable insights into how insect communities may respond to increasing temperature variability. Under ongoing climate change, seasonal-dependent physiological shifts are likely to intensify, aphids overwintering as adults may expand eastward, and pest communities may become increasingly homogeneous in thermal niches. These dynamics could alter outbreak risks and reduce the effectiveness of biological control and integrated pest management strategies, emphasizing the need for adaptive and region-specific interventions.

**Acknowledgements** – We would like to thank Agathe Depercenaire, Sarah Carpentier, Emilie Akoka, François Louart, Emma Maunoury, Youna Cochet, Julie Besnard, Matteo Pean, Anna Sediva, Ana Larbata Wienand, Emmanuel Da Costa Beauregard for helping field sampling. We also appreciate Maleaume Voirnesson for helping lab experiments. We thank Bingxin Wang for the suggestions on data analysis. We wish to thank the administrative staff of ECOBIO.

**Funding** – This research was financially supported by National Natural Science Foundation of China [32330090, 32471597], the IRP ‘GRADIENTS’ project from the French National Center for Scientific Research (CNRS), National Key R&D Program of China [2022YFD1400400, 2023YFD1401400], Hebei Natural Science Foundation [C2022201042].

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

## Author contributions

**Ruining Li:** Conceptualization (equal); Data curation (equal); Formal analysis (equal); Methodology (equal); Validation (equal); Visualization (equal); Writing – original draft (equal); Writing – review and editing (equal). **Chun-Sen Ma:** Conceptualization (equal); Funding acquisition (equal); Supervision (equal); Validation (equal); Writing – review and editing (equal). **Léna Jego:** Methodology (equal); Validation (equal); Writing – review and editing (equal). **Cécile Le Lann:** Conceptualization (equal); Supervision (equal); Validation (equal); Writing – review and editing (equal). **Joan Van Baaren:** Conceptualization (equal); Funding acquisition (equal); Resources (equal); Supervision (equal); Validation (equal); Writing – review and editing (equal). **Gang Ma:** Conceptualization (equal); Supervision (equal); Validation (equal); Writing – review and editing (equal);

## Transparent peer review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/ecog.08194.

## Data availability statement

Data are available from the Figshare https://figshare.com/s/522b633e166f40d428c0. (Li et al. 2025).

## Supporting information

The Supporting information associated with this article is available with the online version.

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