Diapause Is Not Selected As a Bet-Hedging Strategy in Insects: a Meta-Analysis of Reaction Norm Shapes
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bioRxiv preprint doi: https://doi.org/10.1101/752881; this version posted September 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Diapause is not selected as a bet-hedging strategy in insects: a meta-analysis of reaction norm shapes. Jens Joschinski1*†, Dries Bonte1‡ 1 Terrestrial Ecology Unit (TEREC), Department of Biology, Ghent University, Ghent, Belgium. * [email protected] † Twitter: @jensjoschi, ORCID: 0000-0001-7828-3336 ‡ Twitter: @bontedries, ORCID: 0000-0002-3320-7505 Keywords: Phenotypic plasticity, diversified bet-hedging, conservative bet-hedging, insects, phenotypic variance, critical day length, photoperiodism, phenology, climate change bioRxiv preprint doi: https://doi.org/10.1101/752881; this version posted September 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Abstract 2 Many organisms escape from lethal climatological conditions by entering a resistant resting 3 stage called diapause, and it is essential that this strategy remains optimally timed with seasonal 4 change. Climate change therefore exerts selection pressure on traits that regulate phenology, 5 which is expected to cause the evolution of mean diapause timing, but also phenotypic 6 plasticity and bet-hedging strategies. Especially the latter as a strategy to cope with 7 unpredictability is so far little considered in the context of climate change, and it is unknown 8 whether it can readily evolve. 9 Contemporary patterns of phenological strategies across a geographic range may provide 10 information about their future evolvability. We thus extracted 447 diapause reaction norms 11 from 57 studies. First, we correlated mean diapause timing with mean winter onset. Then we 12 partitioned the reaction norm variance into a temporal component (phenotypic plasticity) and 13 among-offspring variance (diversified bet-hedging) and correlated this variance composition 14 with predictability of winter onset. Contrary to our expectation, mean diapause timing 15 correlated only weakly with mean winter onset, as populations at high latitudes failed to track 16 early onsets. Variance among offspring was also limited and correlated only weakly with 17 environmental predictability, indicating little scope for bet-hedging. We conclude that 18 evolutionary constraints limit the evolvability of phenological traits in a rapidly changing 19 climate. 1 bioRxiv preprint doi: https://doi.org/10.1101/752881; this version posted September 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 20 Introduction 21 Anthropogenic greenhouse gas emissions change the environment at an unprecedented rate 22 (IPCC, 2014), and the majority of species faces extinction risks from climate change (Urban, 23 2015). One of the most commonly observed responses to climate change is a shift in phenology, 24 i.e. in the seasonal timing of an organism (Parmesan & Yohe, 2003). Changes in tree leaf-out 25 (Badeck et al., 2004) and bird egg-laying dates (Dunn & Winkler, 1999) in spring are among 26 the most famous examples of phenology shifts, but shifts in timing have been documented 27 across nearly the whole tree of life (e.g. cyanobacteria, Zhang et al., 2012; fungi, Kauserud et 28 al., 2008; cnidarians, Van Walraven et al., 2015; insects, Bell et al., 2015). Phenological shifts 29 that match an organism’s life cycle with novel conditions clearly provide fitness benefits 30 (Visser & Gienapp, 2019). Nevertheless, there is increasing doubt that they will remain 31 sufficient in a rapidly changing climate (Visser, 2008), so it is essential to infer the evolutionary 32 potential of phenological strategies. 33 Phenology is a complex compound of traits that is subject to multiple selection pressures, 34 predictions on the evolvability of phenological traits are therefore not straightforward. For 35 example, variation in the extent of phenology shifts among interacting species may create 36 phenological mismatches (Thackeray et al., 2016; Visser & Gienapp, 2019), thus selecting for 37 covarying phenologies. Moreover, novel correlations of temperature and day length may 38 impose physiological constraints, such as day length limitations for diurnal animals (Dunbar & 39 Shi, 2013; Joschinski, Kiess & Krauss, 2019) and plants (Saikkonen et al., 2012; but see Zohner 40 et al., 2016) – relying on developmental cues may then constitute an evolutionary trap (van 41 Dyck et al., 2015; Snell-Rood et al., 2018). The genetic architecture that underlies phenological 42 traits is elaborate (Robinet & Roques, 2010; Brachi et al., 2010), making such constraints by 43 pleiotropic effects likely. Therefore it is not clear whether a complex trait such as phenology 44 can evolve an optimal response to changing local conditions. A longitudinal analysis across 2 bioRxiv preprint doi: https://doi.org/10.1101/752881; this version posted September 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 45 species and habitats may, however, provide information about the evolvability of phenological 46 traits. 47 Rises in mean temperature are not the only potential cause of climate-change induced 48 biodiversity loss - increasing climate variability imposes further extinction risk (Bathiany et 49 al., 2018). Therefore, the concerted evolution of mean phenology and risk-reduction strategies 50 will be required. There are four general strategies by which organisms can cope with changing 51 environments (Seger & Brockmann, 1987; Starrfelt & Kokko, 2012; Tufto, 2015): Evolution 52 of the mean timing, phenotypic plasticity, and bet-hedging (avoidance of fitness variance). The 53 latter consists of strategies to avoid risk (conservative bet-hedging) and of strategies to spread 54 the risk among one’s offspring (diversified bet-hedging). These strategies are intricately related 55 (Lande, 2009; Starrfelt & Kokko, 2012; Simons, 2014; Snell-Rood et al., 2018), examining 56 their evolvability simultaneously is hence a daunting task. However, for binary traits with only 57 two outcomes, such as the decision to overwinter or to germinate, the strategies can be 58 conveniently separated by studying mean and variance composition of reaction norms (Fig. 59 1A), as the strategies then form the extremes of a three-dimensional continuum (Joschinski & 60 Bonte, 2019): the allocation of variance within vs among environments represents a continuum 61 of diversified bet-hedging and phenotypic plasticity (Fig. 1B, x-axis), their sum fixed vs. 62 flexible development (y-axis), and the mean the trade-off between arithmetic mean 63 optimization and conservative bet-hedging (Fig. 1C). The adaptive value of the strategies is 64 dictated by the environment, which may vary in its mean condition (e.g. mean timing of winter 65 onset), its predictability, and the amplitude of change (i.e. seasonality); thus, on can infer the 66 evolvability of evolutionary strategies by correlating the reaction norm properties with 67 environmental conditions. 68 3 bioRxiv preprint doi: https://doi.org/10.1101/752881; this version posted September 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 69 70 Fig. 1. Relationship of evolutionary strategies with reaction norm properties. Panel A shows a 71 dichotomous reaction norm. The decision to switch phenotypes can be expressed by a steep logistic 72 curve. Reaction norms can divert in various ways from this step function: By changes in the ratio (x- 2 2 73 axis) and sum (y-axis) of the variance components 휎푎푚표푛 and 휎푤푡ℎ푛(Panel B), and by changes in the 74 mean (Panel C). Axes describe the resulting evolutionary strategies. 2 2 ∑(푝퐸−푝̅̅̅퐸̅) 2 75 휎 = squared standard deviation along environments(Ei), ; 휎 = Variance 푎푚표푛 푛 푤푡ℎ푛 2 ∑ 푝퐸∗(1−푃퐸) 휎푎푚표푛 76 of Bernoulli draws, ; r = variance composition, 2 2 ; s = phenotypic variance, 푛 휎푎푚표푛+휎푤푡ℎ푛 2 2 77 휎푎푚표푛 + 휎푤푡ℎ푛. 4 bioRxiv preprint doi: https://doi.org/10.1101/752881; this version posted September 6, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 78 Insect diapause, a resting stage to overwinter, is a dichotomous trait that has historically 79 received much attention (e.g. Danilevskii, 1965; Bradshaw, 1976; Urbanski et al., 2012), and 80 there is ample high-quality data under laboratory conditions available. Moreover, insect 81 diapause is amongst the few well-studied traits that mediate autumn phenology, which is 82 generally understudied (Gallinat, Primack & Wagner, 2015). We collected 447 of these reaction 83 norms from laboratory experiments (57 studies; Supp. S1, Fig. 2), derived their mean, 84 phenotypic variance and the variance composition, and then correlated them with winter onset 85 and winter predictability as derived from climate data (Fig. 2). 86 Because studies are (understandably) biased to temperate climates, nearly no canalized 87 phenotypes were available. We therefore concentrated on the other two axes and predicted that: 88 1) Inflection points