# Eco-evolutionary responses of a cold-water fish (Salmo trutta) to the combined effects of an emerging pathogen and temperature

Eloïse Duval, Simon Blanchet, Lisa Jacquin, Charlotte Veyssière, Géraldine

Loot, Erwan Quéméré

► To cite this version:

Eloïse Duval, Simon Blanchet, Lisa Jacquin, Charlotte Veyssière, Géraldine Loot, et al.. Eco-evolutionary responses of a cold-water fish (Salmo trutta) to the combined effects of an emerging pathogen and temperature. Functional Ecology, 2025, 39 (8), pp.2096 - 2110. 10.1111/1365-2435.70105. hal-05311467

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# Eco-evolutionary responses of a cold-water fish (*Salmo trutta*) to the combined effects of an emerging pathogen and temperature

Eloïse Duval<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID
Géraldine Loot<fnref n="4" />,<fnref n="5" />

Simon Blanchet<fnref n="1" /> ID
Erwan Quéméré<fnref n="2" />

Lisa Jacquin<fnref n="4" />,<fnref n="5" />

Charlotte Veyssière<fnref n="4" />

## Correspondence

Erwan Quéméré
Email: erwan.quemere@inrae.fr

## Funding information

Office Français de la Biodiversité; Institut Universitaire de France (IUF); Agence Nationale de la Recherche (ANR), Grant/Award Number: project MULTIPAT ANR-21-CE34-0018-01; Laboratoire d'Excellence TULIP (LabEx TULIP)

Handling Editor: Katie E. Marshall

## Abstract

1. How wildlife populations respond to the different components of global change and their interactions is still poorly understood. For instance, emerging pathogens can directly impact the genetic and phenotypic attributes of their hosts through directional selection or plasticity, whereas climatic stress can either mitigate or exacerbate the infection outcomes. Studying the adaptive response of wild populations to both pathogens and environmental stressors is thus critical for predicting their short- and long-term persistence.

2. We investigated the combined impact of water temperature and infection by *Tetracapsuloides bryosalmonae*, a myxozoan pathogen causing the salmonid proliferative kidney disease (PKD), on wild brown trout *Salmo trutta* populations. We compared the density, the genetic and phenotypic characteristics of trout populations sampled along gradients of infection prevalence and water temperature, before and after the period of disease outbreaks (summer).

3. Population density decreased after the disease outbreak with increasing infection prevalence, regardless of the temperature, except in populations that were already heavily infected the previous year. High infection prevalence reduced genetic diversity at an immune-related gene, regardless of the temperature, but not at neutral loci, in line with pathogen-mediated directional selection. We also showed that both temperature and infection negatively affected trout body condition and had an interactive effect on carotenoid-based coloration, likely reflecting short-term plastic responses of phenotypic traits to both stressors.

4. This highlights that different global change stressors (warming and pathogens) can have both negative and interactive effects on fish mortality, genetic diversity and fitness-related traits, and hence on the rapid evolution of wild populations.

## KEYWORDS

emerging infectious disease, micro-evolution, multiple stressors, salmonids, selection, water temperature

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Functional Ecology* published by John Wiley & Sons Ltd on behalf of British Ecological Society.

<figure><img src="image_3.png" /></figure>

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

<footnote n="2">²DECOD (Ecosystem Dynamics and Sustainability), INRAE, Institut Agro, IFREMER, Rennes, France</footnote>

<footnote n="3">³Bureau d'études Fish-Pass, France</footnote>

<footnote n="4">⁴Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 - Paul Sabatier (UT3), Toulouse, France</footnote>

<footnote n="5">⁵Institut Universitaire de France, Paris, France</footnote>
# 1 | INTRODUCTION

Wild organisms are increasingly confronted by human-induced environmental changes (HIEC) and exposed to multiple biotic and abiotic stressors. The combined impacts of these multiple stressors are rarely assessed, especially at the population level (Côté et al., 2016). Understanding the ecological and evolutionary responses of wild populations to changing environments is nonetheless essential to predict their short-and long-term persistence, and to guide management decisions (Fox et al., 2019). However, wild populations often display high levels of intraspecific variability, and the evolutionary responses of heterogeneous populations to multiple environmental changes remain poorly understood.

Intraspecific variation is at the foundation of many ecological and evolutionary processes promoting population dynamics, species interactions and ecosystem functions (Mimura et al., 2017; Raffard et al., 2019). Combined stressors may lead to large declines in wild populations and to the loss of (or shift in) intraspecific variation at the genetic and/or phenotypic level (Canale & Henry, 2010; Pauls et al., 2013). However, few studies have compared patterns of intraspecific variation in wildlife populations before and after a sudden change in biotic or abiotic conditions (but see, e.g. Blondel et al., 2021; Campbell-Staton et al., 2017), and they focused on the direct effects of extreme climatic events, without examining associated changes in species' range and biotic interactions. Our understanding of rapid eco-evolutionary responses to HIEC is thus limited since climatic and biotic factors may interact in complex and unexpected ways, leading to additive, synergistic or antagonistic effects on intraspecific variation (Côté et al., 2016; Jackson et al., 2016).

Emerging infectious diseases represent a special case of sudden change in biotic conditions that may cause severe mortalities in wild host populations (Hoyt et al., 2021). Several mutually non-exclusive mechanisms can operate and drive genetic and/or phenotypic shifts in host populations. Changes in genetic composition following disease outbreaks may occur due to demographic crashes leading to an erosion of the global genetic diversity (Albert et al., 2015). Concomitantly, a shift in genotypes may arise from the directional selection of individuals carrying genotypes that provide better resistance or tolerance to the infection. Under pathogen-mediated selection, a stronger change in genetic diversity is expected at non-neutral (adaptive) loci involved in pathogen resistance/tolerance such as the major histocompatibility complex (MHC) genes compared to neutral loci (e.g. Savage & Zamudio, 2016; Westerdahl et al., 2004). Consistently, several studies showed genetic shifts after a disease outbreak at putative loci for disease resistance, for example, in Tasmanian devils for facial tumour disease (Epstein et al., 2016).

Phenotypic changes are also frequently observed after disease outbreaks. Pathogen infection may favour shifts in fitness-related traits such as body mass or condition (Vander Wal et al., 2014), but also indirectly affect traits linked to individual quality and health such as ornamental traits (Ezenwa & Jolles, 2008). Two main mechanisms are often invoked. First, when a trait is positively correlated with survival, viability selection may cause a shift in its average value

after the infection event (viability selection hypothesis) (Hoekstra et al., 2001). For instance, house finches *Haemorhous mexicanus* surviving mycoplasma conjunctivitis had a smaller mean size, and male survivors were redder than deceased males (Nolan et al., 1998). Alternatively, plastic responses could lead to changes in phenotypic traits to maintain fitness and to potential trade-offs between traits involved in stress resistance and performance in populations exposed to pathogens (*plasticity hypothesis*). Indeed, the cost of the immune response during infection may decrease the amount of resources allocated to other traits such as body condition or ornamental traits (Sheldon & Verhulst, 1996), leading to a shift towards lower mean values after infection. For instance, infection results in lower horn growth in bighorn sheep (Martin et al., 2022) and lower coloration signals in blackbirds (Baeta et al., 2008).

To better understand host responses to parasitism and identify potential selection on phenotypes and underlying genotypes, as well as phenotypic plasticity (Linnen & Hoekstra, 2009), we thus need to record concomitant variations in demography, genetic and phenotypic traits during epidemic outbreaks.

In addition, the impact of pathogen infection on wild populations may be exacerbated by concurrent environmental changes such as warming temperatures that may influence together host susceptibility and pathogen life cycles, thereby altering their interactions (Altizer et al., 2013; Wolinska & King, 2009). The pathogens combined with other environmental stressors may have synergetic effects on intraspecific variation, strengthening the overall selective pressure exerted on the host population and favouring the emergence of disease outbreaks (Johnson & Penaluna, 2019). While modelling studies and literature reviews have investigated the role of changing temperatures on parasites and host-parasite interactions (Altizer et al., 2013; Marcogliese, 2016; Poulin, 2006), empirical studies investigating the effects of multiple stressors in heterogeneous wild populations exposed to pathogens, particularly in the context of temperature changes, are still rare.

A particularly striking case study is the proliferative kidney disease (PKD) in salmonid fish, caused by the myxozoan parasite *Tetracapsuloides bryosalmonae* (Canning et al., 1999). Over the past two decades, PKD has caused mass mortality events (up to 95% reported in fish farms; Okamura et al., 2011) and led to significant declines in wild salmonid populations worldwide (Hutchins et al., 2021; Waldner et al., 2020). The disease triggers severe kidney inflammation and swelling, impairing erythropoiesis and leading to acute anaemia. PKD severity is strongly temperature-dependent, with peak incidence occurring during the warmest periods of the summer (Duval et al., 2024; Okamura et al., 2011). Summer outbreaks could potentially lead to strong pathogen-induced directional selection over short periods of time. Water temperature is also a major determinant of salmonids' ecological niche, and high water temperature (>19°C) induces physiological stress that may decrease growth or impair immune function (Elliott & Elliott, 2010). Besides, *T. bryosalmonae* infection at warm temperatures (>15°C) may cause severe anaemia resulting in a lower ability to acquire oxygen, exacerbating the negative effects of warm temperatures (Bruneaux et al., 2017). Controlled experiments in the laboratory
have indeed demonstrated that warmer temperatures favour the de- velopment of *T. bryosalmonae*, while decreasing trout tolerance to in- fection, therefore enhancing PKD symptoms and mortality (Waldner et al., 2021; see also Duval et al., 2024), which suggests synergetic effects of both stressors in the wild. This underscores the importance of studying the impacts of combined stressors on wild populations to better predict and anticipate the effects of emerging diseases on wild- life health and population persistence in the context of global changes (Altizer et al., 2013).

Here, we investigated the combined effect of *T. bryosalmonae* in- fection and water temperature on the genetic and phenotypic varia- tion of 15 wild brown trout (*Salmo trutta*, Linnaeus, 1758) populations differently exposed to gradients of water temperature and infection prevalence. We tested whether the pathogen and/or temperature- mediated selection led to significant shifts in abundance, genetic diver- sity and phenotypes during the summer (before/after PKD outbreaks). We used single nucleotide polymorphism (SNPs) and a locus from the MHC (a highly polymorphic complex of genes implicated in patho- gen recognition as part of the acquired immune system; Bernatchez & Landry, 2003) to assess population genetic diversity at neutral and adaptive loci, respectively. We measured fish body condition, as well as melanin and carotenoid-based coloration as phenotypic traits influ- enced by pathogens and related to individual fitness (Côte et al., 2018; Ducrest et al., 2008). As we expected a cumulative or amplifying effect of infection and temperature, we predicted a decrease in population density at sites exposed to high infection prevalence and high tem- perature. In turn, we expected this demographic decline to be asso- ciated with a loss in genetic diversity, more pronounced at the MHC locus than at the SNP loci due to pathogen-mediated directional se- lection on immunity-related genes. Concurrently, we anticipated shifts in the mean phenotypic values of traits related to fitness during the summer (disease outbreak), especially in the warmer sites with higher pathogen prevalence, due to two differing but not mutually exclusive mechanisms. Under the preponderant effect of viability selection, one might expect that individuals surviving the summer would exhibit higher body condition and brighter ornamental traits than those that did not survive, leading to an increase in phenotypic trait values at the population level over the summer. Alternatively, if the plasticity hypothesis is the main mechanism of response to selection, surviving individuals might allocate more resources to pathogen resistance, at the expense of body condition or ornamental traits due to energetic trade-offs, leading to a decrease in phenotypic trait values at the pop- ulation level after the epidemic outbreak over the summer.

## 2 | METHODS

### 2.1 | Replication statement

<table><thead><tr><td>Scale of inference</td><td>Scale at which the factor of interest is applied</td><td>Number of replicates at the appropriate scale</td></tr></thead><tbody><tr><td>Population</td><td>Population</td><td>15</td></tr></tbody></table>

### 2.2 | Brown trout sampling

We sampled brown trout populations in 15 sites at the foothills of the Pyrenean mountains, in southern France (Figure 1). These sites were chosen after a study conducted in 2019 to generate a balanced sampling design with cold, warm, infected and non-infected sites (Duval et al., 2022). We ended up with a fair gradient of *T. bryosalmonae* infection prevalence ranging from 0% to 100%, and a gradi- ent of mean summer temperature (July–August) ranging from 14.5 to 18.2°C (Table 1, Figure 1, Figure S1). These gradients were not significantly correlated (rho = 0.37, p = 0.18).

PKD outbreaks are reported during the summer and may develop upon the first infection (i.e. in naive fish). Fish surviving the infection likely develop an adaptive immune response, preventing disease de- velopment upon reinfection, making young-of-the-year (0+) fish the most sensitive stage (Feist & Longshaw, 2006). Our sampling design therefore included sampling sessions before and after the warm- est period of the summer, at the end of June 2020 (session S₁) and at the end of September 2020 (session S₂) respectively, focusing on 0+ brown trout. Water temperature was continuously recorded at each site using HOBO® loggers (Figure S2). We electro-fished as many fish as possible at each site and session, resulting in a total of 1018 0+ trout (12–52 individuals per session and per site according to local abundances, Table S1). Brown trout density was estimated as the number of trout caught per minute using single-pass electrofishing surveys ('catch-per-unit effort', CPUE; Kruse et al., 1998; Table 1). Authorization to collect brown trout was granted by Prefectoral Decree No. 31-2020-06-12-002 (Haute-Garonne), No. 65-2020-06-09-007 (Haute-Pyrénées) and No. 09-2020-05-05-001 (Ariège). The study did not require ethical approval.

#### 2.2.1 | Infection prevalence

The infection prevalence by *T. bryosalmonae* at each site was assessed during S₂ by examining the infection status of 20 randomly selected fish (less when the trout density was too low, Table S1), assessed with the non-lethal urine DNA (uDNA) method developed in Duval et al. (2021) that quantifies parasite DNA excreted in fish urine. Briefly, fish were placed individually into plastic bottles with commercial min- eral water for 1 hour to let time for infected fish to excrete pathogen spores or DNA through urine. We then filtered the water through 1.2 µm filters and carried out DNA extraction from these filters. Lastly, we used a specific droplet digital PCR assay (ddPCR) to quantify *T. bry- osalmonae* DNA (see Duval et al., 2021, 2022 for details). Population infection prevalence corresponds to the ratio between the number of infected fish and the total number of tested fish.

#### 2.2.2 | Morphometric and coloration measurements

After benzocaine anaesthesia, we measured the length (L, cm) and weight (W, g) of all fish sampled at each site and session, with
<figure><img src="image_4.png" /><figcaption>FIGURE 1 Map showing the 15 sampled sites and their infection prevalence by *Tetracapsuloides bryosalmonae* measured in September 2020. The top-corner inset shows the location of the study area, in Southern France. Mean summer temperature (°C) is indicated in brackets.</figcaption></figure>

precision to the nearest mm and the nearest cg, respectively. We computed fish body condition (K) according to Equation (1) (Fulton, 1904, Table 1).

$$K = \frac{W}{L^3} \times 100 \qquad (1)$$

We adapted a standardized protocol to measure brown trout carotenoid and melanin-based coloration (Jacquin et al., 2017). Briefly, we took a picture of the left side of each fish with a Canon® PowerShot G16 camera, under a white light cage to standardize light conditions. Brightness values were then calibrated using a color checker with standard values (X-Rite® ColorChecker Passport) using GIMP 2.10.8 (The GIMP Development Team, 2019). We then treated pictures with ImageJ 1.52a (Schneider et al., 2012), defining a zone excluding the head and fins and calculated the percentage of fish skin covered by black coloration (melanin-based coloration) and red coloration (carotenoid-based coloration, Table 1).

After all measures (excretion, anaesthesia, pictures, morphometry and fin clip; see below), all fish were put in a bucket with stream water and air pumps to recover before being released at the site of sampling.

## 2.3 | Neutral and adaptive genetic diversity

A small piece of pelvic fin was sampled and stored in 70% ethanol for individual genotyping using two marker types: (1) a set of 173 neutral SNPs evenly spread on the brown trout genome (Saint-Pé et al., 2019) and (2) the DAB-MHC exon 2 locus, a highly polymorphic

gene involved in pathogen resistance in vertebrates, including Salmonids (Dionne et al., 2007).

### 2.3.1 | SNP markers

After DNA extraction, SNP genotyping was performed using the KASPar allele-specific fluorescent genotyping system provided by the LGC® Group (UK). To identify potential genotyping errors or signals of selection, we first tested for Hardy-Weinberg equilibrium using *Genepop* (Rousset, 2008) and removed from the analyses loci that departed from the equilibrium in all the populations. Additionally, three ‘outlier loci’ potentially under purifying/balancing selection were identified using *BayeScan* v2.1 with default parameters, and a false discovery rate (FDR) correction of 5% (Foll & Gaggiotti 2008). These loci were also removed, leaving 141 polymorph neutral SNPs. Because at least one of the populations (GARVil) could be affected by hatchery fish stocking, we used *Structure* 2.3.4 (Pritchard et al., 2000) to compare each population with the genotype of individuals from neighboring hatcheries to identify individuals that were recently stocked. The area encompasses three administrative departments (Ariège, Haute-Garonne, Haute-Pyrénées) with each their own stocking strategies: in the Ariège department (SALTau, ARGPon, ARGFoi, ARZDur, ARIVar, ARZPon) stocking has ceased 10-15 years ago and we do not expect strong captive-bred ancestry (Prunier et al., 2022), in the Haute-Garonne (GARVil, GERAsp, JOBEnc, ARBMan, GERPoi, ARBBar) and in the Haute-Pyrénées (TORTuz, BOUSer, ARRGou) departments stocking still occurs. In these departments stocking is taken in charge by the federal angling
<table><caption>TABLE 1 Sampled sites and their measured parameters for the two sampling sessions, ranked by decreasing infection prevalence.</caption><thead><tr><th>Site</th><th>Infection prevalence (%)</th><th>Mean summer temperature (°C)</th><th>N</th><th>Density (N/min)</th><th>N<sub>SNPs</sub></th><th>N<sub>MHC</sub></th><th>H<sub>e-SNPs</sub></th><th>H<sub>e-MHC</sub></th><th>F<sub>ST-SNPs</sub></th><th>F<sub>ST-MHC</sub></th><th>Mean K</th><th>Mean carotenoid area (%)</th><th>Mean melanin area (%)</th></tr></thead><tbody><tr><td>BOUSeR</td><td>100</td><td>17.75</td><td>43</td><td>1.43</td><td>256</td><td>12</td><td>0.228</td><td>0.820</td><td>0.000</td><td>0.000</td><td>1.27</td><td>0.9</td><td>37</td></tr><tr><td></td><td></td><td></td><td>36</td><td>1.06</td><td>251</td><td>12</td><td>0.225</td><td>0.788</td><td>0.000</td><td>0.000</td><td>1.17</td><td>1.0</td><td>45</td></tr><tr><td>ARRGou</td><td>100</td><td>16.63</td><td>37</td><td>0.74</td><td>216</td><td>16</td><td>0.165</td><td>0.866</td><td>0.000</td><td>0.000</td><td>1.23</td><td>1.6</td><td>47</td></tr><tr><td></td><td></td><td></td><td>27</td><td>0.50</td><td>211</td><td>12</td><td>0.162</td><td>0.832</td><td>0.000</td><td>0.000</td><td>1.17</td><td>2.5</td><td>43</td></tr><tr><td>JOBEnc</td><td>90</td><td>16.49</td><td>44</td><td>0.63</td><td>228</td><td>19</td><td>0.185</td><td>0.87</td><td>0.000</td><td>0.000</td><td>1.22</td><td>1.6</td><td>43</td></tr><tr><td></td><td></td><td></td><td>39</td><td>0.72</td><td>229</td><td>17</td><td>0.188</td><td>0.90</td><td>0.000</td><td>0.000</td><td>1.21</td><td>2.3</td><td>47</td></tr><tr><td>TORTuz</td><td>89</td><td>17.44</td><td>31</td><td>0.69</td><td>223</td><td>17</td><td>0.198</td><td>0.89</td><td>0.000</td><td>0.000</td><td>1.27</td><td>0.8</td><td>41</td></tr><tr><td></td><td></td><td></td><td>23</td><td>0.30</td><td>229</td><td>14</td><td>0.202</td><td>0.85</td><td>0.000</td><td>0.000</td><td>1.11</td><td>2.6</td><td>55</td></tr><tr><td>ARZDur</td><td>85</td><td>17.41</td><td>36</td><td>1.13</td><td>218</td><td>9</td><td>0.166</td><td>0.82</td><td>0.003</td><td>0.000</td><td>1.19</td><td>1.7</td><td>40</td></tr><tr><td></td><td></td><td></td><td>20</td><td>0.31</td><td>212</td><td>8</td><td>0.165</td><td>0.80</td><td>0.000</td><td>0.000</td><td>1.24</td><td>2.2</td><td>41</td></tr><tr><td>GARVil</td><td>78</td><td>17.21</td><td>20</td><td>0.36</td><td>224</td><td>15</td><td>0.198</td><td>0.88</td><td>0.000</td><td>0.000</td><td>1.21</td><td>0.7</td><td>41</td></tr><tr><td></td><td></td><td></td><td>12</td><td>0.17</td><td>218</td><td>13</td><td>0.199</td><td>0.90</td><td>0.000</td><td>0.000</td><td>1.25</td><td>2.4</td><td>40</td></tr><tr><td>ARIVar</td><td>67</td><td>17.33</td><td>32</td><td>0.53</td><td>258</td><td>16</td><td>0.229</td><td>0.88</td><td>0.000</td><td>0.000</td><td>1.22</td><td>1.0</td><td>51</td></tr><tr><td></td><td></td><td></td><td>18</td><td>0.23</td><td>252</td><td>16</td><td>0.227</td><td>0.90</td><td>0.000</td><td>0.000</td><td>1.24</td><td>4.4</td><td>42</td></tr><tr><td>SALTau</td><td>53</td><td>17.62</td><td>33</td><td>0.63</td><td>221</td><td>17</td><td>0.199</td><td>0.86</td><td>0.004</td><td>0.000</td><td>1.20</td><td>0.7</td><td>45</td></tr><tr><td></td><td></td><td></td><td>15</td><td>0.18</td><td>225</td><td>10</td><td>0.207</td><td>0.88</td><td>0.000</td><td>0.000</td><td>1.23</td><td>2.8</td><td>39</td></tr><tr><td>ARBMan</td><td>50</td><td>18.23</td><td>33</td><td>0.70</td><td>229</td><td>19</td><td>0.191</td><td>0.90</td><td>0.000</td><td>0.0043</td><td>1.25</td><td>3.3</td><td>42</td></tr><tr><td></td><td></td><td></td><td>39</td><td>0.38</td><td>223</td><td>21</td><td>0.189</td><td>0.92</td><td>0.000</td><td>0.0043</td><td>1.25</td><td>3.3</td><td>44</td></tr><tr><td>GERPoi</td><td>42</td><td>17.84</td><td>21</td><td>0.30</td><td>227</td><td>21</td><td>0.204</td><td>0.93</td><td>0.000</td><td>0.000</td><td>1.17</td><td>1.9</td><td>44</td></tr><tr><td></td><td></td><td></td><td>22</td><td>0.24</td><td>223</td><td>17</td><td>0.196</td><td>0.92</td><td>0.000</td><td>0.000</td><td>1.19</td><td>3.7</td><td>39</td></tr><tr><td>ARBBar</td><td>22</td><td>15.29</td><td>34</td><td>0.97</td><td>224</td><td>10</td><td>0.186</td><td>0.819</td><td>0.015*</td><td>0.03*</td><td>1.23</td><td>0.5</td><td>47</td></tr><tr><td></td><td></td><td></td><td>31</td><td>0.56</td><td>221</td><td>15</td><td>0.192</td><td>0.856</td><td>0.000</td><td>0.001</td><td>1.30</td><td>2.6</td><td>51</td></tr><tr><td>ARGFoi</td><td>20</td><td>17.00</td><td>52</td><td>1.04</td><td>239</td><td>18</td><td>0.218</td><td>0.891</td><td>0.000</td><td>0.001</td><td>1.10</td><td>0.8</td><td>48</td></tr><tr><td></td><td></td><td></td><td>45</td><td>1.10</td><td>244</td><td>21</td><td>0.212</td><td>0.923</td><td>0.000</td><td>0.000</td><td>1.13</td><td>4.7</td><td>43</td></tr><tr><td>ARGPon</td><td>5</td><td>15.79</td><td>30</td><td>0.50</td><td>222</td><td>14</td><td>0.180</td><td>0.903</td><td>0.005</td><td>0.000</td><td>1.14</td><td>1.2</td><td>41</td></tr><tr><td></td><td></td><td></td><td>28</td><td>0.35</td><td>228</td><td>18</td><td>0.195</td><td>0.886</td><td>0.000</td><td>0.003</td><td>1.26</td><td>11.4</td><td>54</td></tr><tr><td>ARZPon</td><td>5</td><td>16.76</td><td>44</td><td>0.88</td><td>212</td><td>8</td><td>0.155</td><td>0.699</td><td>0.000</td><td>0.003</td><td>1.21</td><td>1.5</td><td>45</td></tr><tr><td></td><td></td><td></td><td>35</td><td>0.58</td><td>241</td><td>10</td><td>0.160</td><td>0.782</td><td>0.000</td><td>0.003</td><td>1.20</td><td>6.0</td><td>46</td></tr><tr><td>GERAsp</td><td>0</td><td>14.51</td><td>52</td><td>2.17</td><td>230</td><td>15</td><td>0.185</td><td>0.859</td><td>0.000</td><td>0.000</td><td>1.13</td><td>0.2</td><td>52</td></tr><tr><td></td><td></td><td></td><td>43</td><td>1.95</td><td>226</td><td>12</td><td>0.180</td><td>0.832</td><td>0.000</td><td>0.000</td><td>1.29</td><td>3.3</td><td>50</td></tr></tbody></table>

Note: The infection prevalence was measured during S<sub>2</sub> in September 2020. Density (N/min): Number of trout caught per minute using single-pass electrofishing surveys. N<sub>SNPs</sub>, N<sub>MHC</sub>: Total number of alleles at the SNPs and MHC loci, respectively. H<sub>e-SNPs</sub>, H<sub>e-MHC</sub>: Expected heterozygosity at the SNPs and MHC loci, respectively. K: body condition factor. F<sub>ST-SNPs</sub>, F<sub>ST-MHC</sub>: Pairwise F<sub>ST</sub> values between S1 and S2 for each site at the SNPs and MHC loci, respectively.

*Denotes significant F<sub>ST</sub> value (p < 0.05).
association (Fédération de Pêche et de Protection des Milieux Aquatiques) with a unique hatchery for each department (Soueich and Cauterets hatcheries for Haute-Garonne and Haute-Pyrénées, respectively). Individuals from the hatcheries were sampled and used as comparative baseline in *Structure*. *Structure* was run three times with a burn-in period of 5000 replications and a run length of 50,000 Markov Chain Monte Carlo iterations, for K=1 to K=4 clusters. We kept the results for K=2 and K=3 (ARGFoi, BOUSer) according to the homogeneity of the hatchery cluster. The individuals with a proportion >0.75 were considered as stocked individuals, which constitutes a conservative threshold. This analysis revealed that few individuals came from stocked populations (43 individuals, Appendix S2), and they were removed from the dataset to ensure that only wild individuals (N=975) were analysed. These corresponded either to fish stocked in the year of sampling (as juveniles) or descendants of hatchery genitors having been stocked years before sampling. We did not exclude potential hybrids as they genetically contribute to the local populations. Neutral genetic diversity at each site in S₁ and S₂ was evaluated by computing the total number of alleles (N<sub>SNP</sub>) and the expected heterozygosity (H<sub>e-SNP</sub>) with the poppr package (Kamvar et al., 2014; Table 1). We used the number of alleles in a population rather than the allelic richness because it directly reflects the loss or appearance of genetic variants. To assess change in allele frequencies over the summer, pairwise F<sub>ST</sub> values (i.e. coefficients of population differentiation) between S1 and S2 were also computed at each site using *Genetix* 4.05 (Belkhir et al., 2004), and their significance was tested by performing permutation tests (100 permutations).

### 2.3.2 | MHC marker

Genes of the MHC are involved in vertebrates' adaptive immunity, and their ability to trigger a rapid microevolutionary response to pathogen-mediated selective pressures has been demonstrated experimentally (Eizaguirre et al., 2012). Parasite-mediated selection acting on MHC genes is expected to modulate allelic frequencies in a different way than when only neutral evolutionary forces (drift, mutation and migration) are acting. Therefore, comparing patterns of genetic diversity at the MHC to that of supposedly neutral loci (the SNPs) may inform on the parasite-mediated selective pressure (Spurgin & Richardson, 2010; Stet et al., 2002). We amplified and sequenced the second exon of the DAB-MHC locus (255 pb, one copy in salmoids), encoding the ligand-binding domain of the protein. We used the primers TVS4501 5'-TCTGTATTATGTTTTCCTTCC-3' (Langefors et al., 2000) and ÅL1002 3'-CACCTGTCTTGTCCAGTATG-5' (OIsén et al., 1998), with the addition of a partial overhang Illumina sequencing primers in 5'-end. We used a two-step PCR approach with dual-index paired-end sequencing (see Portanier et al., 2019) following the procedure detailed in Appendix S1. We used *obitools* (Boyer et al., 2016) to sort sequences, identify the haplotypes, discard artefactual variants and genotype the individuals. We used the program codeML implemented in *pamlX* 1.3.1 software (Xu & Yang, 2013)

to identify codon sites under positive selection (Schwensow et al., 2007). We integrated and aligned 100 sequences retrieved on GenBank and identified 22 positively selected codon sites that were used to define a total of 118 MHC alleles in our dataset. As for the SNPs, we computed the number of alleles (N<sub>MHC</sub>) and the expected heterozygosity (H<sub>e-MHC</sub>) at each site in S₁ and S₂ (Table 1), as well as pairwise F<sub>ST</sub> values between S1 and S2.

## 2.4 | Statistical analyses

To evaluate the variation of each parameter (population density, body condition, melanin and carotenoid-based coloration, N<sub>SNP</sub>, H<sub>e-SNP</sub>, N<sub>MHC</sub> and H<sub>e-MHC</sub>) between S₁ and S₂ at each site, we used a standardized delta statistic computed as follows:

$$ \Delta_{\text{parameter}} = \frac{\text{parameter S}_2 - \text{parameter S}_1}{\text{parameter S}_1} \times 100 \quad (2) $$

A negative delta value indicates a decrease in the parameter during the summer, whereas a positive delta value indicates an increase. Then, to investigate the potential effects of water temperature, infection prevalence and their interaction on these deltas and on pairwise F<sub>ST</sub> (on either SNP or MHC markers), we used linear models of the following formula:

$$ \Delta_{\text{parameter}} (\text{or } F_{\text{ST}}) \sim \text{temperature} + \text{prevalence} + \text{temperature: prevalence} \quad (3) $$

For each parameter, we applied a two-step approach to evaluate the significance of terms (temperature, prevalence and the two-term interaction). First, we used a model simplification procedure based on the small sample size corrected Akaike Information Criterion (AICc; Burnham & Anderson, 2002) with the *MuMln* package (Barton, 2020). Following this approach, we retained only the best model—the one with the lowest AICc value (Burnham & Anderson, 2002)—to simplify each model and limit the number of terms tested, given our relatively small sample size (n=15). Second, from this simplified model, we assessed the significance of each term using a Type III ANOVA (F-test) with a significance threshold of α=0.05. Note that when the null model was identified as the best model (i.e. with the lowest AICc at this step), we did not test the significance of terms, as this indicated that neither temperature nor prevalence could explain parameter variance. Fixed continuous predictors were scaled to the mean before analyses. All statistical analyses were conducted in the R environment (R 4.1.2; R Core Team, 2021).

# 3 | RESULTS

## 3.1 | Variation in population density

Population density tended to decrease over the summer in the studied populations (Δ<sub>density</sub> = -35% ± 25% (mean ± SD)). This decrease in population density was not explained neither by the mean summer
water temperature nor by the infection prevalence (none of the terms was selected in the simplified model based on AICc, Table 2). However, the three populations with the highest infection rates from the previous year (infection prevalence >90% in 2019), clearly appeared as ‘outliers’ in this relationship (see Figure 2a, Figure S1). After removing these three populations, the simplified model included the infection prevalence as an explicative term, and there was a marked and significant negative effect of the infection prevalence on the variation in density (Table 2; Figure 2b). There was thus a marked decrease in brown trout density with increasing prevalence, but only for populations moderately or not infected the year before (2019).

## 3.2 | Variation in genetic diversity

We did not observe any effect of infection prevalence, water temperature, and their interaction on the variation in neutral genetic diversity (i.e. $N_{SNP}$, $H_{e-SNP}$) between sessions (the null model was identified as the best model based on AICc, Table 2, Figure S3). Contrastingly, we identified likely relationships between the variation in $N_{MHC}$ or $H_{e-MHC}$ (indices of adaptive genetic diversity) and the infection prevalence (this term was selected in the two best models based on AICc, Table 2). Specifically, the number of alleles at MHC loci ($N_{MHC}$) significantly decreased with infection prevalence (Table 2, Figure 3a, *p*=0.048) whereas there was a tendency towards a decrease in $H_{e-MHC}$ as prevalence increased, although this term was not significant (Table 2, Figure 3b, *p*=0.098). Additionally, we found that $F_{ST}$ (for both SNPs and MHC) was associated with water temperature as this parameter was selected in the best model for both SNPs and MHC (Table 2). In both cases, $F_{ST}$ values decrease over the summer as the water temperature increases, although this tendency was significant only for MHC (Table 2, *p*=0.102 and *p*=0.036 for SNPs and MHC respectively, Figure S4).

## 3.3 | Variation in body condition and coloration

Variation in body condition was related to both the infection prevalence and the mean summer water temperature, although the two terms selected using AICc were only marginally significant (Table 2, *p*=0.059 and *p*=0.071 for water temperature and infection prevalence respectively). Fish body condition tended to decrease in sites with high infection prevalence (Figure 4a) and in sites with high summer water temperature (Figure 4b). Carotenoid-based coloration was affected by infection prevalence, mean summer water temperature and their interaction (Table 2, interaction term; *p*=0.022). More specifically, in cold sites, fish carotenoid-based coloration sharply increased during the summer in non-infected sites, and was affected by infection prevalence (Figure 4c, in blue). In warmer sites, there was a much smaller increase in carotenoid-based coloration in non-infected fish and no effect of infection prevalence (Figure 4c in red). In contrast, melanin-based coloration was not affected by either stressor (Table 2, Figure S3).

<table><caption>TABLE 2 Summary of the ANOVA tests (type III Anova, F-test) used to estimate the significance of terms selected after model simplification (based on AICc).</caption><thead><tr><th></th><th>Density</th><th>Density (12 sites)<sup>a</sup></th><th>Number of SNP alleles</th><th>SNP heterozygosity</th><th>F<sub>ST</sub> SNPs</th><th>Number of MHC alleles</th><th>MHC heterozygosity</th><th>F<sub>ST</sub> MHC</th><th>Body condition</th><th>Carotenoid area</th><th>Melanin area</th></tr></thead><tbody><tr><td>Water temperature</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>3.095</td><td>NI</td><td>NI</td><td>5.494*</td><td>4.357</td><td>1.476</td><td>NI</td></tr><tr><td>Infection prevalence</td><td>NI</td><td>12.873**</td><td>NI</td><td>BI</td><td>NI</td><td>4.755*</td><td>3.145</td><td>NI</td><td>3.930</td><td>8.607*</td><td>NI</td></tr><tr><td>Infection × temperature</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>NI</td><td>7.013*</td><td>NI</td></tr></tbody></table>

Note: Explained parameters are indicated as columns and potential explicative terms are rows. ‘NI’ stems for terms that were not included in the simplified model. Values are F-values associated to terms that were included in the simplified model. Bolded values indicate significant terms at $\alpha$=0.05 and stars their categorized level of significance (‘*’ < 0.05; ‘**’ < 0.01).

<sup>a</sup>Density delta after removing three populations with very high infection prevalence in 2019.
<figure><img src="image_5.png" /><figcaption>FIGURE 2 Variation in brown trout density between S₁ and S₂ according to the infection prevalence in the different sites (a) including all the sampled sites and (b) after removing the sites that were highly infected in 2019 (>90%, Figure S1; the 3 red points in (a)). The grey shaded areas represent the 95% confidence intervals (CIs).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>FIGURE 3 Variation in the genetic diversity at the MHC locus according to the infection prevalence. (a) Variation in the number of alleles. (b) Variation in expected heterozygosity.</figcaption></figure>

4 | DISCUSSION

We investigated the combined effect of summer water temperature and pathogen infection on wild brown trout population density and intraspecific variation at the genetic and phenotypic levels. Brown trout density decreased with increasing *T. bryosalmonae* infection prevalence, but only for populations that were not already highly infected the year before. The absence of decline in trout density in these populations suggests tolerance induced by past infection events. We found a decline of immune MHC gene diversity after high infection prevalence by *T. bryosalmonae*, which was not observed in

neutral genetic markers. We further identified additive and/or inter-
active impacts of infection and water temperature on phenotypic
traits associated with trout fitness.

4.1 | Decline of density in infected populations

We found a strong decrease in brown trout density after the
summer period in populations with high infection prevalence, ex-
cept in the three most infected populations (prevalence between
90% and 100%) that only slightly varied over the summer. These
<figure><img src="image_7.png" /><figcaption>**FIGURE 4** Variation in phenotypic traits related to the individual fitness. Variation in the mean body condition according to (a) the infection prevalence (with fixed value for water temperature) and to (b) the water temperature (with fixed value for infection prevalence). (a) and (b) illustrate the same model output ($\Delta_{\text{body condition}}$ ~ infection prevalence + water temperature, $R^2_{adj}=0.53$, $p = 0.004$). (c) Variation in the mean area of carotenoids according to the infection prevalence and its interaction with the water temperature.</figcaption></figure>

three populations have densities in the higher range of the studied populations, and we found no correlation between change in fish density across summer and brown trout density measured at $S_1$ ($\rho=0.13$; $p=0.65$). However, contrary to other populations, these three populations were already highly infected in 2019 (infection prevalence >90%), suggesting that the pathogen may have stronger impacts on density in 'naive' populations (i.e. populations that had little or no exposure to the pathogen the year before). Although preliminary, this is an important observation suggesting that *T. bryosalmonae*-infected populations may first suffer large declines (Schager et al., 2007), followed by stabilization in subsequent years, as often observed in wild populations in response to emerging diseases (Hastings, 2004). For example, after a major decline due to the facial tumour disease, Tasmanian devil populations rapidly stabilized

and even regenerated after reduction of the pool of susceptible individuals (Wells et al., 2019). Several non-exclusive mechanisms could explain this transient dynamics. First, these populations may have genetically adapted, with strong directional pathogen-mediated selection favouring more resistant or tolerant breeders, leading to an increased resistance/tolerance in their offspring in the following years (Mazé-Guilmo et al., 2014). Second, the induction of immune response in juveniles could have been facilitated by epigenetic responses and/or by maternal transfer of immunity to offspring (Gasparini et al., 2002; Hasselquist & Nilsson, 2009; Boulinier & Staszewski, 2008), a mechanism already reported in fish (e.g. Oshima et al., 1996). Although important for the conservation and management of infected populations, this observation requires further validation through mechanistic approaches.
## 4.2 | Erosion of immune gene variation in infected populations

We did not detect any loss of neutral genetic diversity over the summer. This suggests that the decline in density in infected populations described above was not strong enough to affect the neutral genetic variation, or that they might have been offset by migration as discussed below. Accordingly, other studies in the wild have found that population declines due to emerging diseases were not systematically accompanied by a loss in neutral genetic diversity (e.g. Vazquez et al., 2019).

By contrast, we observed a significant decrease in the number of immune-related MHC alleles in highly infected populations, but not in populations suffering high water temperature. This likely illustrates a potential directional selection of advantageous alleles by pathogens. This result is consistent with previous studies showing rapid and adaptive evolution of MHC genes in response to pathogen-mediated selective pressures (Eizaguirre et al., 2012), and that *T. bryosalmonae* can lead to rapid natural selection for resistance (S. trutta, Debes et al., 2017). Nonetheless, the observed erosion of MHC diversity may have detrimental consequences for the long-term viability of trout populations, as MHC variants are involved in multiple biological functions, including immune recognition, individual odours, mating preferences, kin recognition or cooperation (Sommer, 2005). In particular, this could compromise the resistance to other pathogen infections (Phillips et al., 2021). Alternatively, such a selection on immune-related genes could be favourable upon transmission to the next generations, that could likely be more resistant or tolerant to the infection (McKnight et al., 2017). However, it is important to keep in mind that because highly polymorphic multiallelic markers—such as the MHC-DAB locus—harbour many low-frequency haplotypes, using the total number of alleles as a diversity metric provides greater sensitivity to detecting even subtle declines in genetic diversity, whereas bi-allelic SNP metrics often remain unchanged until common alleles are lost.

As expected given the short time frame, $F_{ST}$ values between S1 and S2 remained relatively low in most sites for both SNPs, indicating no measurable shift in allele frequencies during the summers and MHC loci. $F_{ST}$ is primarily sensitive to changes in common alleles and may remain unchanged when only rare alleles are lost. Thus, the significant reductions in allelic richness that we detected are most likely attributable to the loss of low-frequency alleles rather than to shifts in overall allele frequency distributions. We found a significant relationship between changes in allele frequencies over the summer and water temperature. Allele frequencies tended to diverge more in rivers with relatively low water temperatures, but this relationship is largely driven by a single population (ARBBar). This pattern was consistent for both SNPs and MHC loci, indicating that it is likely driven by a demographic (neutral) process rather than selection. One possible explanation is that in colder rivers, where population density is generally high, juvenile trout dispersal (emigration due to space competition) plays a more significant role than in warm water rivers, leading to greater population divergence over time. ARBBar

is located downstream of the confluence of two streams, which may amplify this effect.

## 4.3 | Changes in phenotypic traits associated with water temperature and pathogen infection

Summer is an important period for food intake and growth in brown trout (Kelly-Quinn & Bracken, 1990), as well as for carotenoid acquisition, as their availability is expected to increase due to enhanced river productivity (Price et al., 2008).

Accordingly, juvenile brown trout showed a substantial increase in body condition across the summer in the coldest non-infected sites. In contrast, we observed a sharp decrease in body condition at the warmest sites. This likely reflects energetic costs associated with thermoregulatory behaviour and/or physiological stress caused by elevated temperatures (Alfonso et al., 2021; Hein & Keirsted, 2012). Indeed, elevated summer water temperature (>19.5°C) may decrease feeding efficiency (Elliott & Elliott, 2010), as trout spent more time inactive or shifted their feeding activity during periods that are less favourable for resource acquisition (Blanchet et al., 2008). We also found an independent negative effect of pathogen infection on body condition. This could reflect an energetic trade-off between costly immunity and maintenance (Rauw, 2012). Such a negative impact of *T. bryosalmonae* infection on body condition throughout summer has already been observed in farmed Atlantic salmon (Cauwelier et al., 2010) but this is—up to our knowledge—the first demonstration in wild populations.

In addition, we found complex effects of temperature and pathogen on carotenoid-based coloration. In warmer sites, juvenile trout showed a limited increase in carotenoid-based coloration during summer, suggesting a reduced feeding activity during warmer days and/or an enhanced use of carotenoid for antioxidant defences, for instance (Metcalfe & Alonso-Alvarez, 2010; Monaghan et al., 2009). Further physiological analyses measuring oxidative stress and defences would be necessary to test these hypotheses. *Tetracapsuloides bryosalmonae*'s infection also had a negative impact on carotenoid coloration, suggesting a trade-off between immunity and ornamentation traits (e.g. Baeta et al., 2008). Therefore, infected individuals might have a lower amount of carotenoid pigments to allocate to their pigmentation. Interestingly, this pattern was only visible at colder sites. At warmer sites, carotenoid coloration remained low regardless of the infection prevalence. This suggests that increased water temperature may have limited carotenoid coloration and/or masked the detrimental effect of infection on coloration (antagonistic interaction between temperature and infection, Jackson et al., 2016). Melanin-based coloration was however not affected, probably because melanin-based coloration is more genetically determined and less condition-dependent than carotenoid-based coloration (e.g. Roulin, 2016).

Taken together, these results on phenotypic traits support the hypothesis that phenotypic plasticity played a predominant role in the resistance/tolerance to both high temperature and
T. bryosalmonae's infection through energetic trade-offs, although the underpinning physiological mechanisms remain unknown. Although we cannot completely rule out a concomitant role of viability selection (differential survival of individuals) without a mark-recapture analysis, this mechanism likely played a minor role, as it does not appear significant enough to offset the observed decrease in body condition and carotenoid condition. Plasticity may allow juvenile trout to reallocate their resources towards essential functions for short-term survival, such as immunity, at the expense of body condition and ornamentation. This may, however, have long-term effects on over-winter survival, size at all ages, and adult future reproduction (Biro et al., 2004). Future studies should focus on the long-term impacts of phenotypic changes caused by multiple stressors on fitness, as individuals experiencing poor environmental conditions in their early life could show a reduced fitness later in life (e.g. Vasilieva & Tchabovsky, 2020).

## 4.4 | Dispersal: An unmeasured process

The main drawback of this study is that the potential role of dispersal was not investigated. Indeed, the observed patterns of population variation in density and genetic diversity may also partly reflect emigration or immigration movements during summer. Brown trout juveniles are known to disperse downstream during summer over short distances (~200m; Vøllestad et al., 2012). Therefore, potential mortality due to water temperature and/or pathogen infection could have been partly offset by the colonization of new individuals from neighboring populations (e.g. Carlson et al., 2014), and conversely, a loss of density could merely reflect fish emigration. However, the very small variation in allele frequencies (low $F_{ST}$ values, see above) throughout the summer in most populations suggests limited long-distance dispersal of individuals from genetically divergent populations. On the other hand, it is important to keep in mind that the spatial scale at which the two stressors operate is likely much broader than the range of juvenile trout dispersal. Therefore, it is unlikely that the observed decrease in population density in some infected populations can be solely attributed to small-scale juvenile movements. Moreover, the loss of genetic diversity at the MHC illustrates that the directional selection exerted by pathogen infection could not be compensated by immigration. Further studies quantifying migration between connected populations would be highly valuable to better understand the role of dispersal in the eco-evolutionary rescue from an emerging pathogen threat (Jiao et al., 2020).

## 5 | CONCLUSIONS

This study illustrates the importance of considering the impacts of combined stressors, as they may have contrasting effects on demographic, genetic and phenotypic traits. Different facets of populations can be impacted, and it is therefore important to jointly consider the impact at the genetic and phenotypic levels to

understand the ecological and evolutionary responses of wild populations to changing environments. Here, we showed that wild populations may respond rapidly to temperature and emerging pathogens in a context of global change. Results also suggest an attenuation of the pathogen effects on population density in the years following high infection prevalence, but the mechanisms involved remain to be clarified. Nevertheless, our study emphasizes the importance of standing genetic variation, especially at adaptive loci, to enable selection under pathogen pressure. A population monitoring before and after disease outbreaks for several consecutive years, together with the investigation of small-scale dispersal, may help to disentangle the respective role of plasticity, micro-evolution, genetic exchanges and their long-term effects on individual fitness, including future reproduction (Pigeon & Pelletier, 2018). Further studies would be needed to investigate the adaptive effects of these genetic and plastic phenotypic responses on the demographic trajectory of populations in a context of increasing global warming.

## AUTHOR CONTRIBUTIONS

Simon Blanchet, Erwan Quéméré and Eloïse Duval conceived the ideas and designed the methodology. Eloïse Duval, Simon Blanchet, Charlotte Veyssière, Géraldine Loot, Lisa Jacquin and Erwan Quéméré collected the data. Eloïse Duval and Erwan Quéméré analysed the data. Eloïse Duval, Simon Blanchet, Lisa Jacquin and Erwan Quéméré led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

## ACKNOWLEDGEMENTS

We warmly thank Maxime Lefort, Camille Poésy, Rick Verdonck and all students who assisted with field sampling, as well as the angling associations from Hautes-Pyrénées, Haute-Garonne and Ariège for their various forms of support. We also gratefully acknowledge Anne-Laure Besnard for her valuable help with high-throughput sequencing-based haplotyping of the DAB-MHC locus at DECOD. This work was financially supported by a grant from the French Office for Biodiversity to Simon Blanchet, and by a PhD fellowship from the French Ministry of Higher Education, Research and Innovation (MESRI) to Eloïse Duval. Lisa Jacquin was supported by the Institut Universitaire de France (IUF) and the Agence Nationale de la Recherche ANR (project MULTIPAT ANR-21-CE34-0018-01). The CRBE laboratory is part of the Laboratoire d'Excellence TULIP (LabEx TULIP).

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

## DATA AVAILABILITY STATEMENT

Data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.47d7wm3s5 (Duval et al., 2025).

## ORCID

Eloïse Duval ID https://orcid.org/0000-0002-8762-1090

Simon Blanchet ID https://orcid.org/0000-0002-3843-589X
REFERENCES

Albert, E. M., Fernández-Beaskoetxea, S., Godoy, J. A., Tobler, U., Schmidt, B. R., & Bosch, J. (2015). Genetic management of an amphibian population after a chytridiomycosis outbreak. *Conservation Genetics, 16*(1), 103-111. https://doi.org/10.1007/s10592-014-0644-6

Alfonso, S., Gesto, M., & Sadoul, B. (2021). Temperature increase and its effects on fish stress physiology in the context of global warming. *Journal of Fish Biology, 98*(6), 1496-1508. https://doi.org/10.1111/jfb.14599

Altizer, S., Ostfeld, R. S., Johnson, P. T. J., Kutz, S., & Harvell, C. D. (2013). Climate change and infectious diseases: From evidence to a predictive framework. *Science, 341*(6145), 514-519. https://doi.org/10.1126/science.1239401

Baeta, R., Faivre, B., Motreuil, S., Gaillard, M., & Moreau, J. (2008). Carotenoid trade-off between parasitic resistance and sexual display: An experimental study in the blackbird (*Turdus merula*). *Proceedings of the Royal Society B: Biological Sciences*, 275(1633), 427-434. https://doi.org/10.1098/rspb.2007.1383

Barton, K. (2020). MuMln: Multi-model inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMln

Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N., & Bonhomme, F. (2004). GENETIX 4.05, *logiciel sous Windows pour la génétique des populations*. Laboratoire Génome, Populations, Interactions, CNRS UMR 5000. Université de Montpellier II.

Bernatchez, L., & Landry, C. (2003). MHC studies in nonmodel vertebrates: What have we learned about natural selection in 15 years? MHC studies in nonmodel vertebrates. *Journal of Evolutionary Biology, 16*(3), 363-377. https://doi.org/10.1046/j.1420-9101.2003.00531.x

Biro, P. A., Morton, A. E., Post, J. R., & Parkinson, E. A. (2004). Over-winter lipid depletion and mortality of age-0 rainbow trout (*Oncorhynchus mykiss*). *Canadian Journal of Fisheries and Aquatic Sciences, 61*, 7.

Blanchet, S., Loot, G., Bernatchez, L., & Dodson, J. J. (2008). The effects of abiotic factors and intraspecific versus interspecific competition on the diel activity patterns of Atlantic salmon (*Salmo salar*) fry. *Canadian Journal of Fisheries and Aquatic Sciences, 65*(8), 1545-1553. https://doi.org/10.1139/F08-079

Blondel, L., Paterson, I. G., Bentzen, P., & Hendry, A. P. (2021). Resistance and resilience of genetic and phenotypic diversity to “black swan” flood events: A retrospective analysis with historical samples of guppies. *Molecular Ecology, 30*(4), 1017-1028. https://doi.org/10.1111/mec.15782

Boulinier, T., & Staszewski, V. (2008). Maternal transfer of antibodies: Raising immuno-ecology issues. *Trends in Ecology & Evolution, 23*(5), 282-288. https://doi.org/10.1016/j.tree.2007.12.006

Boyer, F., Mercier, C., Bonin, A., Le Bras, Y., Taberlet, P., & Coissac, E. (2016). OBITOOLS: A UNIX-inspired software package for DNA metabarcoding. *Molecular Ecology Resources, 16*, 176-182.

Bruneaux, M., Visse, M., Gross, R., Pukk, L., Saks, L., & Vasemägi, A. (2017). Parasite infection and decreased thermal tolerance: Impact of proliferative kidney disease on a wild salmonid fish in the context of climate change. *Functional Ecology, 31*(1), 216-226. https://doi.org/10.1111/1365-2435.12701

Burnham, K. P., & Anderson, D. R. (2002). *Model selection and multi-model inference: A practical information-theoretic approach* (2nd ed.). Springer.

Campbell-Staton, S. C., Cheviron, Z. A., Rochette, N., Catchen, J., Losos, J. B., & Edwards, S. V. (2017). Winter storms drive rapid phenotypic, regulatory, and genomic shifts in the green anole lizard. *Science, 357*(6350), 495-498. https://doi.org/10.1126/science.aam5512

Canale, C., & Henry, P. (2010). Adaptive phenotypic plasticity and resilience of vertebrates to increasing climatic unpredictability. *Climate Research, 43*(1), 135-147. https://doi.org/10.3354/cr00897

Canning, E. U., Curry, A., Feist, S. W., Longshaw, M., & Okamura, B. (1999). *Tetracapsula bryosalmonae* n.sp. for PKX organism, the cause

of PKD in Salmonid fish. *Bulletin of the European Association of Fish Pathologists*, 19(5), 203-206.

Carlson, S. M., Cunningham, C. J., & Westley, P. A. H. (2014). Evolutionary rescue in a changing world. *Trends in Ecology & Evolution, 29*(9), 521-530. https://doi.org/10.1016/j.tree.2014.06.005

Cauwelier, E., Gilbey, J., Jones, C., Noble, L., & Verspoor, E. (2010). Genotypic and phenotypic correlates with proliferative kidney disease-induced mortality in Atlantic salmon. *Diseases of Aquatic Organisms, 89*, 125-135. https://doi.org/10.3354/dao02191

Côté, I. M., Darling, E. S., & Brown, C. J. (2016). Interactions among ecosystem stressors and their importance in conservation. *Proceedings of the Royal Society B: Biological Sciences*, 283(1824), 20152592. https://doi.org/10.1098/rspb.2015.2592

Côte, J., Boniface, A., Blanchet, S., Hendry, A. P., Gasparini, J., & Jacquin, L. (2018). Melanin-based coloration and host-parasite interactions under global change. *Proceedings of the Royal Society B: Biological Sciences*, 285(1879), 20180285. https://doi.org/10.1098/rspb.2018.0285

Debes, P. V., Gross, R., & Vasemägi, A. (2017). Quantitative genetic variation in, and environmental effects on, pathogen resistance and temperature-dependent disease severity in a wild trout. *The American Naturalist*, 190(2), 244-265. https://doi.org/10.1086/692536

Dionne, M., Miller, K. M., Dodson, J. J., Caron, F., & Bernatchez, L. (2007). Clinical variation in MHC diversity with temperature: Evidence for the role of host? Pathogen interaction on local adaptation in Atlantic salmon. *Evolution, 61*(9), 2154-2164. https://doi.org/10.1111/j.1558-5646.2007.00178.x

Ducrest, A., Keller, L., & Roulin, A. (2008). Pleiotropy in the melanocortin system, coloration and behavioural syndromes. *Trends in Ecology & Evolution, 23*(9), 502-510. https://doi.org/10.1016/j.tree.2008.06.001

Duval, E., Blanchet, S., Jacquin, L., Loot, G., & Quéméré, E. (2025). Genetic database (SNPs and MHC) and indicators used for the paper 'Co-evolutionary responses of a cold-water fish (*Salmo trutta*) to the combined effects of an emerging pathogen and temperature'. *Dryad Digital Repository*. https://doi.org/10.5061/dryad.47d7wm3s5

Duval, E., Blanchet, S., Quéméré, E., Jacquin, L., Veyssière, C., Lautraite, A., Garmendia, L., Yotte, A., Parthuisot, N., Côte, J., & Loot, G. (2021). Urine DNA (uDNA) as a non-lethal method for endoparasite biomonitoring: Development and validation. *Environmental DNA, 3*, 1035-1045. https://doi.org/10.1002/edn3.228

Duval, E., Blanchet, S., Quéméré, E., Jacquin, L., Veyssière, C., & Loot, G. (2024). When does a parasite become a disease? eDNA unravels complex host-pathogen dynamics across environmental stress gradients in wild salmonid populations. *Science of the Total Environment, 946*, 174367.

Duval, E., Quéméré, E., Loot, G., Jacquin, L., Veyssière, C., & Blanchet, S. (2022). A multifaceted index of population health to detect risk-prone populations and underlying stressors in wildlife. *Biological Conservation, 274*, 109706. https://doi.org/10.1016/j.biocon.2022.109706

Eizaguirre, C., Lenz, T. L., Kalbe, M., & Milinski, M. (2012). Rapid and adaptive evolution of MHC genes under parasite selection in experimental vertebrate populations. *Nature Communications, 3*(1), 621. https://doi.org/10.1038/ncomms1632

Elliott, J. M., & Elliott, J. A. (2010). Temperature requirements of Atlantic salmon *Salmo salar*, brown trout *Salmo trutta* and Arctic charr *Salvelinus alpinus*: Predicting the effects of climate change. *Journal of Fish Biology, 25*, 1793-1817.

Epstein, B., Jones, M., Hamede, R., Hendricks, S., McCallum, H., Murchison, E. P., Schönfeld, B., Wiench, C., Hohenlohe, P., & Storfer, A. (2016). Rapid evolutionary response to a transmissible cancer in Tasmanian devils. *Nature Communications, 7*(1), 12684. https://doi.org/10.1038/ncomms12684
Ezenwa, V. O., & Jolles, A. E. (2008). Horns honestly advertise parasite infection in male and female African buffalo. *Animal Behaviour, 75*(6), 2013–2021. https://doi.org/10.1016/j.anbehav.2007.12.013

Feist, S. W., & Longshaw, M. (2006). Phylum Myxozoa. In P. T. K. Woo (Ed.), *Fish diseases and disorders* (pp. 230–296). CAB International.

Foll, M., & Gaggiotti, O. (2008). A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. *Genetics, 180*(2), 977–993.

Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T., & Gaitán-Espitia, J. D. (2019). Beyond buying time: The role of plasticity in phenotypic adaptation to rapid environmental change. *Philosophical Transactions of the Royal Society, B: Biological Sciences*, 374(1768), 20180174. https://doi.org/10.1098/rstb.2018.0174

Fulton, T. W. (1904). *The rate of growth of fishes*. Twenty-Second Annual Report (pp. 141–241).

Gasparini, J., McCoy, K. D., Tveraa, T., & Boulinier, T. (2002). Related concentrations of specific immunoglobulins against the Lyme disease agent *Borrelia burgdorferi* sensu lato in eggs, young and adults of the kittiwake (*Rissa tridactyla*). *Ecology Letters, 5*(4), 519–524. https://doi.org/10.1046/j.1461-0248.2002.00345.x

Hasselquist, D., & Nilsson, J.-Å. (2009). Maternal transfer of antibodies in vertebrates: Trans-generational effects on offspring immunity. *Philosophical Transactions of the Royal Society, B: Biological Sciences*, 364(1513), 51–60. https://doi.org/10.1098/rstb.2008.0137

Hastings, A. (2004). Transients: The key to long-term ecological understanding? *Trends in Ecology & Evolution, 19*(1), 39–45. https://doi.org/10.1016/j.tree.2003.09.007

Hein, A. M., & Keirsted, K. E. (2012). The rising cost of warming waters: Effects of temperature on the cost of swimming in fishes. *Biology Letters, 8*(2), 266–269. https://doi.org/10.1098/rsbl.2011.0885

Hoekstra, H. E., Hoekstra, J. M., Berrigan, D., Vignieri, S. N., Hoang, A., Hill, C. E., Beerli, P., & Kingsolver, J. G. (2001). Strength and tempo of directional selection in the wild. *Proceedings of the National Academy of Sciences of the United States of America*, 98(16), 9157–9160. https://doi.org/10.1073/pnas.161281098

Hoyt, J. R., Kilpatrick, A. M., & Langwig, K. E. (2021). Ecology and impacts of white-nose syndrome on bats. *Nature Reviews Microbiology*, 19(3), 196–210. https://doi.org/10.1038/s41579-020-00493-5

Hutchins, P. R., Sepulveda, A. J., Hartikainen, H., Staigmiller, K. D., Opitz, S. T., Yamamoto, R. M., Huttinger, A., Cordes, R. J., Weiss, T., Hopper, L. R., Purcell, M. K., & Okamura, B. (2021). Exploration of the 2016 Yellowstone River fish kill and proliferative kidney disease in wild fish populations. *Ecosphere*, 12, e03436. https://doi.org/10.1002/ecs2.3436

Jackson, M. C., Loewen, C. J. G., Vinebrooke, R. D., & Chimimba, C. T. (2016). Net effects of multiple stressors in freshwater ecosystems: A meta-analysis. *Global Change Biology*, 22, 180–189.

Jacquin, L., Gauthey, Z., Roussille, V., Le Hénaff, M., Tentelier, C., & Labonne, J. (2017). Melanin in a changing world: Brown trout coloration reflects alternative reproductive strategies in variable environments. *Behavioral Ecology*, 28(6), 1423–1434. https://doi.org/10.1093/beheco/arx102

Jiao, J., Gilchrist, M. A., & Fefferman, N. H. (2020). The impact of host metapopulation structure on short-term evolutionary rescue in the face of a novel pathogenic threat. *Global Ecology and Conservation*, 23, e01174. https://doi.org/10.1016/j.gecco.2020.e01174

Johnson, S. L., & Penaluna, B. E. (2019). Climate change and interactions with multiple stressors in Rivers. In *Multiple stressors in river ecosystems* (pp. 23–44). Elsevier. https://doi.org/10.1016/B978-0-12-811713-2.00002-9

Kamvar, Z. N., Tabima, J. F., & Grünwald, N. J. (2014). Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. *Perl*, 2, e281. https://doi.org/10.7717/peerj.281

Kelly-Quinn, M., & Bracken, J. J. (1990). A seasonal analysis of the diet and feeding dynamics of brown trout, *Salmo trutta* L., in a small nursery system. *Aquaculture and Fisheries Management*, 21, 107–124.

Kruse, C. G., Hubert, W. A., & Rahel, F. J. (1998). Single-pass electrofishing predicts trout abundance in mountain streams with sparse habitat. *North American Journal of Fisheries Management*, 18, 940–946.

Langefors, A., Lohm, J., von Schantz, T., & Grahn, M. (2000). Screening of Mhc variation in Atlantic salmon (*Salmo salar*): A comparison of restriction fragment length polymorphism (RFLP), denaturing gradient gel electrophoresis (DGGE) and sequencing. *Molecular Ecology*, 9(2), 215–219. https://doi.org/10.1046/j.1365-294x.2000.00838.x

Linnen, C. R., & Hoekstra, H. E. (2009). Measuring natural selection on genotypes and phenotypes in the wild. *Cold Spring Harbor Symposia on Quantitative Biology*, 74, 155–168. https://doi.org/10.1101/sqb.2009.74.045

Marcogliese, D. J. (2016). The distribution and abundance of parasites in aquatic ecosystems in a changing climate: More than just temperature. *Integrative and Comparative Biology*, 56(4), 611–619. https://doi.org/10.1093/icb/icw036

Martin, A. M., Hogg, J. T., Manlove, K. R., LaSharr, T. N., Shannon, J. M., McWhirter, D. E., Miyasaki, H., Monteith, K. L., & Cross, P. C. (2022). Disease and secondary sexual traits: Effects of pneumonia on horn size of bighorn sheep. *The Journal of Wildlife Management*, 86(1), e22154. https://doi.org/10.1002/jwmg.22154

Mazé-Guilmo, E., Loot, G., Paez, D. J., Lefèvre, T., & Blanchet, S. (2014). Heritable variation in host tolerance and resistance inferred from a wild host-parasite system. *Proceedings of the Royal Society B: Biological Sciences*, 281(1779), 20132567. https://doi.org/10.1098/rspb.2013.2567

McKnight, D. T., Schwarzkopf, L., Alford, R. A., Bower, D. S., & Zenger, K. R. (2017). Effects of emerging infectious diseases on host population genetics: A review. *Conservation Genetics*, 18(6), 1235–1245. https://doi.org/10.1007/s10592-017-0974-2

Metcalfe, N. B., & Alonso-Alvarez, C. (2010). Oxidative stress as a life-history constraint: The role of reactive oxygen species in shaping phenotypes from conception to death. *Functional Ecology*, 24(5), 984–996. https://doi.org/10.1111/j.1365-2435.2010.01750.x

Mimura, M., Yahara, T., Faith, D. P., Vázquez-Domínguez, E., Colautti, R. I., Araki, H., Javadi, F., Núñez-Farfán, J., Mori, A. S., Zhou, S., Hollingsworth, P. M., Neaves, L. E., Fukano, Y., Smith, G. F., Sato, Y.-I., Tachida, H., & Hendry, A. P. (2017). Understanding and monitoring the consequences of human impacts on intraspecific variation. *Evolutionary Applications*, 10(2), 121–139. https://doi.org/10.1111/eva.12436

Monaghan, P., Metcalfe, N. B., & Torres, R. (2009). Oxidative stress as a mediator of life history trade-offs: Mechanisms, measurements and interpretation. *Ecology Letters*, 12(1), 75–92.

Nolan, P. M., Hill, G. E., & Stoehr, A. M. (1998). Sex, size, and plumage redness predict house finch survival in an epidemic. *Proceedings of the Royal Society of London, Series B: Biological Sciences*, 265(1400), 961–965. https://doi.org/10.1098/rspb.1998.0384

Okamura, B., Hartikainen, H., Schmidt-Posthaus, H., & Wahli, T. (2011). Life cycle complexity, environmental change and the emerging status of salmonid proliferative kidney disease. *Freshwater Biology*, 56(4), 735–753. https://doi.org/10.1111/j.1365-2427.2010.02465.x

Olsén, K. H., Grahn, M., Lohm, J., & Langefors, Å. (1998). MHC and kin discrimination in juvenile Arctic charr, *Salvelinus alpinus* (L.). *Animal Behaviour*, 56(2), 319–327. https://doi.org/10.1006/anbe.1998.0837

Oshima, S.-I., Hata, J.-I., Segawa, C., & Yamashita, S. (1996). Mother to fry, successful transfer of immunity against infectious haematopoietic necrosis virus infection in rainbow trout. *Journal of General Virology*, 77(10), 2441–2445. https://doi.org/10.1099/0022-1317-77-10-2441

Pauls, S. U., Nowak, C., Bálint, M., & Pfenninger, M. (2013). The impact of global climate change on genetic diversity within populations and species. *Molecular Ecology*, 22(4), 925–946. https://doi.org/10.1111/mec.12152

Phillips, K. P., Cable, J., Mohammed, R. S., Chmielewski, S., Przesmycka, K. J., Van Oosterhout, C., & Radwan, J. (2021). Functional
immunogenetic variation, rather than local adaptation, predicts ectoparasite infection intensity in a model fish species. *Molecular Ecology*, 30(21), 5588-5604. https://doi.org/10.1111/mec.16135

Pigeon, G., & Pelletier, F. (2018). Direct and indirect effects of early-life environment on lifetime fitness of bighorn ewes. *Proceedings of the Royal Society B: Biological Sciences*, 285(1870), 20171935. https://doi.org/10.1098/rspb.2017.1935

Portanier, E., Garel, M., Devillard, S., Maillard, D., Poissant, J., Galan, M., Benabed, S., Poirel, M.-T., Duhayer, J., Itty, C., & Bourgoin, G. (2019). Both candidate gene and neutral genetic diversity correlate with parasite resistance in female Mediterranean mouflon. *BMC Ecology*, 19(1), 12. https://doi.org/10.1186/s12898-019-0228-x

Poulin, R. (2006). Global warming and temperature-mediated increases in cercarial emergence in trematode parasites. *Parasitology*, 132(1), 143-151.

Price, A. C., Weadick, C. J., Shim, J., & Rodd, F. H. (2008). Pigments, patterns, and fish behavior. *Zebrafish*, 5(4), 297-307. https://doi.org/10.1089/zeb.2008.0551

Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. *Genetics*, 155(2), 945-959. https://doi.org/10.1093/genetics/155.2.945

Prunier, J. G., Saint-Pé, K., Tissot, L., Poulet, N., Marselli, G., Veyssière, C., & Blanchet, S. (2022). Captive-bred ancestry affects spatial patterns of genetic diversity and differentiation in brown trout (*Salmo trutta*) populations. *Aquatic Conservation: Marine and Freshwater Ecosystems*, 32(9), 1529-1543. https://doi.org/10.1002/aqc.3826

R Core Team. (2021). *R: A language for statistical computing*. R Foundation for Statistical Computing. https://www.R-project.org/

Raffard, A., Cucherousset, J., Prunier, J. G., Loot, G., Santoul, F., & Blanchet, S. (2019). Variability of functional traits and their syndromes in a freshwater fish species (*Phoxinus phoxinus*): The role of adaptive and nonadaptive processes. *Ecology and Evolution*, 9(5), 2833-2846. https://doi.org/10.1002/ece3.4961

Rauw, W. M. (2012). Immune response from a resource allocation perspective. *Frontiers in Genetics*, 3, 267. https://doi.org/10.3389/fgene.2012.00267

Roulin, A. (2016). Condition-dependence, pleiotropy and the handicap principle of sexual selection in melanin-based colouration: Melanin and sexual selection. *Biological Reviews*, 91(2), 328-348. https://doi.org/10.1111/brv.12171

Rousset, F. (2008). *genepop'007: A complete re-implementation of the genepop* software for windows and Linux. *Molecular Ecology Resources*, 8(1), 103-106. https://doi.org/10.1111/j.1471-8286.2007.01931.x

Saint-Pé, K., Leitwein, M., Tissot, L., Poulet, N., Guinand, B., Berrebi, P., Marselli, G., Lascaux, J.-M., Gagnaire, P.-A., & Blanchet, S. (2019). Development of a large SNPs resource and a low-density SNP array for brown trout (*Salmo trutta*) population genetics. *BMC Genomics*, 20, 582.

Savage, A. E., & Zamudio, K. R. (2016). Adaptive tolerance to a pathogenic fungus drives major histocompatibility complex evolution in natural amphibian populations. *Proceedings of the Royal Society B: Biological Sciences*, 283(1827), 20153115. https://doi.org/10.1098/rspb.2015.3115

Schager, E., Peter, A., & Burkhardt-Holm, P. (2007). Status of young-of-the-year brown trout (*Salmo trutta fario*) in Swiss streams: Factors influencing YOY trout recruitment. *Aquatic Sciences*, 69(1), 41-50. https://doi.org/10.1007/s00027-007-0845-x

Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH image to ImageJ: 25 years of image analysis. *Nature Methods*, 9(7), 671-675. https://doi.org/10.1038/nmeth.2089

Schwensow, N., Fietz, J., Dausmann, K. H., & Sommer, S. (2007). Neutral versus adaptive genetic variation in parasite resistance: Importance of major histocompatibility complex supertypes in a free-ranging primate. *Heredity*, 99(3), 265-277. https://doi.org/10.1038/sj.hdy.6800993

Sheldon, B. C., & Verhulst, S. (1996). Ecological immunology: Costly parasite defences and trade-offs in evolutionary ecology. *Trends in Ecology & Evolution*, 11(8), 317-321. https://doi.org/10.1016/0169-5347(96)10039-2

Sommer, S. (2005). The importance of immune gene variability (MHC) in evolutionary ecology and conservation. *Frontiers in Zoology*, 2(1), 16. https://doi.org/10.1186/1742-9994-2-16

Spurgin, L. G., & Richardson, D. S. (2010). How pathogens drive genetic diversity: MHC, mechanisms and misunderstandings. *Proceedings of the Royal Society B: Biological Sciences*, 277(1684), 979-988. https://doi.org/10.1098/rspb.2009.2084

Stet, R., de Vries, B., Mudde, K., Hermsen, T., van Heerwaarden, J., Shum, B., & Grimholt, U. (2002). Unique haplotypes of co-segregating major histocompatibility class II A and class II B alleles in Atlantic salmon (*Salmo salar*) give rise to diverse class II genotypes. *Immunogenetics*, 54(5), 320-331. https://doi.org/10.1007/s00251-002-0477-1

The GIMP Development Team. (2019). GIMP. https://www.gimp.org

Vander Wal, E., Grant, D., & Pelletier, F. (2014). Evolutionary perspectives on wildlife disease: Concepts and applications. *Evolutionary Applications*, 7(7), 715-722. https://doi.org/10.1111/eva.12179

Vasilieva, N. A., & Tchabovsky, A. V. (2020). Early predictors of female lifetime reproductive success in a solitary hibernator: Evidence for "silver spoon" effect. *Oecologia*, 193(1), 77-87. https://doi.org/10.1007/s00442-020-04649-1

Vazquez, A., Gustafson, K. D., Harmeling, B., & Ernest, H. B. (2019). Genetic diversity of yellow-billed magpies (*Pica nutalli*) before and after a west Nile virus epidemic. *Journal of Wildlife Diseases*, 55(2), 316-324. https://doi.org/10.7589/2018-01-023

Vøllestad, L. A., Serbezov, D., Bass, A., Bernatchez, L., Olsen, E. M., & Taugbøl, A. (2012). Small-scale dispersal and population structure in stream-living brown trout (*Salmo trutta*) inferred by mark-recapture, pedigree reconstruction, and population genetics. *Canadian Journal of Fisheries and Aquatic Sciences*, 69(9), 1513-1524. https://doi.org/10.1139/f2012-073

Waldner, K., Bechter, T., Auer, S., Borgwardt, F., El-Matbouli, M., & Unfer, G. (2020). A brown trout (*Salmo trutta*) population faces devastating consequences due to proliferative kidney disease and temperature increase: A case study from Austria. *Ecology of Freshwater Fish*, 29(3), 465-476. https://doi.org/10.1111/eff.12528

Waldner, K., Borkovec, M., Borgwardt, F., Unfer, G., & El-Matbouli, M. (2021). Effect of water temperature on the morbidity of *Tetracapsuloides bryosalmonae* (Myxozoa) to brown trout (*Salmo trutta*) under laboratory conditions. *Journal of Fish Diseases*, 44(7), 1005-1013. https://doi.org/10.1111/jfd.13361

Wells, K., Hamede, R. K., Jones, M. E., Hohenlohe, P. A., Storfer, A., & McCallum, H. I. (2019). Individual and temporal variation in pathogen load predicts long-term impacts of an emerging infectious disease. *Ecology*, 100(3), Article 3.

Westerdahl, H., Hansson, B., Bensch, S., & Hasselquist, D. (2004). Between-year variation of MHC allele frequencies in great reed warblers: Selection or drift?: Temporal variation of MHC allele frequencies. *Journal of Evolutionary Biology*, 17(3), 485-492. https://doi.org/10.1111/j.1420-9101.2004.00711.x

Wolinska, J., & King, K. C. (2009). Environment can alter selection in host-parasite interactions. *Trends in Parasitology*, 25(5), 236-244. https://doi.org/10.1016/j.pt.2009.02.004

Xu, B., & Yang, Z. (2013). PAMLX: A graphical user interface for PAML. *Molecular Biology and Evolution*, 30(12), 2723-2724. https://doi.org/10.1093/molbev/mst179

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /></figure>

**Figure S1.** (a) Infection prevalence in 2020 according to the infection prevalence in 2019, and (b) Water temperature in 2020 according to water temperature in 2019.

**Figure S2.** Continuous recording of water temperature at the 15 sampled sites.

**Figure S3.** Supplementary biplots.

**Figure S4.** Variation in *F*<sub>ST</sub> (genetic differentiation) measured within sites before and after the infection period according to the mean water summer temperature measured at each site.

**Figure S5.** Percentage of time recorded between the 3rd of July and the 14th of September with a temperature >19.5°C, the upper limit for brown trout feeding, according to the mean summer water temperature.

**Table S1.** Number of brown trout individuals at each sampling session, for each measured parameter.

**Appendix S1.** Two-step PCR protocol for the MHC genotyping.

**Appendix S2.** Identification of stocked individuals with population admixture analyses.

**How to cite this article:** Duval, E., Blanchet, S., Jacquin, L., Veyssière, C., Loot, G., & Quéméré, E. (2025). Eco-evolutionary responses of a cold-water fish (*Salmo trutta*) to the combined effects of an emerging pathogen and temperature. *Functional Ecology*, *39*, 2096–2110. https://doi.org/10.1111/1365-2435.70105