Adaptation Potential of the Copepod Eurytemora Affinis to a Future Warmer Baltic Sea Ecology and Evolution

http://www.diva-portal.org This is the published version of a paper published in Ecology and Evolution. Citation for the original published paper (version of record): Karlsson, K., Winder, M. (2020) Adaptation potential of the copepod Eurytemora affinis to a future warmer Baltic Sea Ecology and Evolution https://doi.org/10.1002/ece3.6267 Access to the published version may require subscription. N.B. When citing this work, cite the original published paper. Permanent link to this version: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171506 Received: 27 November 2019 | Revised: 25 February 2020 | Accepted: 20 March 2020 DOI: 10.1002/ece3.6267 ORIGINAL RESEARCH Adaptation potential of the copepod Eurytemora affinis to a future warmer Baltic Sea Konrad Karlsson1,2 | Monika Winder2 1Department of Arctic Biology, University Centre in Svalbard, Svalbard, Norway Abstract 2Department of Ecology, Environment To predict effects of global change on zooplankton populations, it is important to un- and Plant Sciences, Stockholm University, derstand how present species adapt to temperature and how they respond to stress- Stockholm, Sweden ors interacting with temperature. Here, we ask if the calanoid copepod Eurytemora Correspondence affinis from the Baltic Sea can adapt to future climate warming. Populations were Konrad Karlsson, Department of Arctic Biology, University Centre in Svalbard, sampled at sites with different temperatures. Full sibling families were reared in the Svalbard, Norway. laboratory and used in two common garden experiments (a) populations crossed over Email: [email protected] three temperature treatments 12, 17, and 22.5°C and (b) populations crossed over Funding information temperature in interaction with salinity and algae of different food quality. Genetic Svenska Forskningsrådet Formas; European Commission correlations of the full siblings’ development time were not different from zero between 12°C and the two higher temperatures 17 and 22.5°C, but positively correlated between 17 and 22.5°C. Hence, a population at 12°C is unlikely to adapt to warmer temperature, while a population at ≥17°C can adapt to an even higher temperature, that is, 22.5°C. In agreement with the genetic correlations, the population from the warmest site of origin had comparably shorter development time at high temperature than the populations from colder sites, that is, a cogradient variation. The population with the shortest development time at 22.5°C had in comparison lower survival on low quality food, illustrating a cost of short development time. Our results suggest that populations from warmer environments can at present indirectly adapt to a future warmer Baltic Sea, whereas populations from colder areas show reduced adaptation potential to high temperatures, simply because they experience an environment that is too cold. KEYWORDS genotype by environment interaction, global warming, life history, multiple stressors, phenotypic plasticity, trade-off 1 | INTRODUCTION 2016); however, the magnitude of its effect will ultimately depend on the populations’ adaptation potential to changing environmen- Global warming affects the distribution and ecology of popula- tal conditions (Aitken, Yeaman, Holliday, Wang, & Curtis-McLane, tions (De Meester, Stoks, & Brans, 2018; Kratina, Greig, Thompson, 2008; Merilä & Hendry, 2014; Urban, Richardson, & Freidenfelds, Carvalho-Pereira, & Shurin, 2012; Parmesan, 1996; Urban et al., 2014). Both genetic and plastic variation may facilitate retention of 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. © 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Ecology and Evolution. 2020;00:1–17. www.ecolevol.org | 1 2 | KARLSSON anD WINDER populations that otherwise would go extinct or be in need of migra- for population growth rates of zooplankton; however, they are relation to colder areas (Davis & Shaw, 2001; Foden et al., 2013; Hughes tively less important than the time lag between generations (Allan, et al., 2003). Evidence exists for some populations that have adapted 1976). to high temperatures (Lonsdale & Levinton, 1985; Yampolsky, Typically, populations with a short development time are com- Schaer, & Ebert, 2014), and if species exhibit adaptations at pres- parably smaller when they reach maturity than populations with ent, it is likely that they will in the future as well (Merilä & Hendry, longer development time (Kingsolver, Massie, Ragland, & Smith, 2014; Stoks, Geerts, & De Meester, 2014). However, there are still 2007; Merilä, Laurila, & Lindgren, 2004; Sniegula, Golab, Drobniak, important topics to address on how species may adapt to climate & Johansson, 2016). Hence, a fitness trade-off between size (via fe- change, such as how contemporary populations can adapt to future cundity) and development may influence the evolution of thermal conditions. This can be estimated by quantifying indirect selection reaction norms. Although, exceptions from the typical trade-off between present and future environments, which is revealed by the exists where populations can maintain both fast development and sign and strength of genetic correlations. Moreover, many studies large size at maturity (Gotthard, Nylin, & Wiklund, 1994; Tang, He, focus on one factor at a time (Todgham & Stillman, 2013), and hence, Chen, Fu, & Xue, 2017). However, maximizing both traits involves much less is known about the effect of multiple factors interacting increased growth rates and can result in higher susceptibility to star- simultaneously with temperature (Stoks et al., 2014). vation (Gotthard et al., 1994; Stoks, Block, & McPeek, 2006). Thus, Adaptive potential is essentially genetic variance (Foden et al., overcoming one trade-off includes a new trade-off. This is important 2013; Urban et al., 2014), from which a series of estimates related in a scenario where other stressors may change in addition to tem- to selection and adaptation can be calculated. For example, if a trait perature and indirectly affect organisms’ response to temperature. is measured on groups of full siblings, the proportion of phenotypic The calanoid copepod Eurytemora affinis is at places one of variance that is caused by between-group variance is the heritability, the dominating zooplankton species in terms of number and mass which is a predictor between direct selection and adaptation. More in both freshwater and coastal estuaries, and hence an important so, if the same trait is measured across different environments, the grazer and prey for plankton feeding fish (Diekmann, Clemmesen, correlation of between-group variances in each environment is the John, Paulsen, & Peck, 2012; Hernroth & Ackefors, 1979; Rajasilta, genetic correlation, which is a predictor between indirect selection Hänninen, & Vuorinen, 2014). In the Baltic Sea, E. affinis forms large and adaptation. Indirect selection depends on the sign of the cor- transitory populations that typically peak in late summer (Hernroth relation and may either reinforce, antagonize, or have no effect on & Ackefors, 1979). Given this opportunistic (r) life strategy, it is ex- adaptation (Etterson & Shaw, 2001). For example, if a high value of pected that E. affinis has a development time that is as short as phys- the same trait is of benefit in two environments, such as extant and iologically possible when conditions are favorable. Eurytemora affinis future conditions, and the genetic correlation between the trait in consists of a species complex with a widespread distribution in the both environments is positive, then selection at extant conditions northern Hemisphere (Lee, 2016). Within the complex, both devel- will render a high value also in future conditions through indirect se- opment time and body size differ between populations (Karlsson, lection. Hence, genetic correlations are highly relevant for inferences Puiac, & Winder, 2018; Karlsson & Winder, 2018). More so, the of local adaptation and for the adaptation potential of populations. populations are highly variable in diverse traits, such as morphol- The difference in trait value between two or more environ- ogy, habitat use, ecological effects, and salinity tolerance (Favier & ments is the phenotypic plasticity; this is an environmentally in- Winkler, 2014; Karlsson & Winder, 2018; Lee, Remfert, & Gelembiuk, duced change in the phenotype that enables a single genotype to 2003). Clades and lineages are also spread outside their native range respond differently to various environmental conditions (Via et al., because of maritime traffic and introduced into other environments 1995). Plasticity may also vary between genotypes in response to (Sukhikh, Souissi, Souissi, & Alekseev, 2013; Winkler, Souissi, Poux, the environment, that is, an interaction between the genotype and & Castric, 2011). However, the rapid adaptations recorded in this the environment (Falconer & Mackay, 1996; Lee, 2002; Saltz et al., species complex support that even invasive populations might be lo- 2018). The variance between genotypes in different environments cally adapted to their new environments (Lee, 2002; Lee, Posavi, & may reveal if selection in one environment will have a correlated, Charmantier, 2012; Lee, Remfert, & Chang, 2007). indirect, response in another environment. Hence, there is a formal The Baltic Sea is one of the marine areas with the highest re- link

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