Experimental evidence of population differences in reproductive investment conditional on environmental stochasticity

Highlights

• We place two populations of brown trout under contrasting water flow for reproduction.

• Energetic metabolite variation is used as a cue of reproductive investment.

• In constant flow, both populations show the same reproductive investment.

• In variable flow, only one of the populations modifies its reproductive investment.

• Divergent evolution of reproductive investment is expected under hydrological change.

Abstract

Environmental stochasticity is expected to shape life histories of species, wherein organisms subjected to strong environmental variation should display adaptive response by being able to tune their reproductive investment. For riverine ecosystems, climate models forecast an increase in the frequency and intensity of extreme events such as floods and droughts. The speed and the mechanisms by which organisms may adapt their reproductive investment are therefore of primary importance to understand how species will cope with such radical environmental changes. In the present study, we sampled spawners from two different populations of wild brown trout, originating from two environments with contrasting levels of flow stochasticity. We placed them in sympatry within an experimental channel during reproductive season. In one modality, water flow was maintained constant, whereas in another modality, water flow was highly variable. Reproductive investment of all individuals was monitored using weight and energetic plasma metabolite variation throughout the reproductive season. Only the populations originating from the most variable environment showed a plastic response to experimental manipulation of water flow, the females being able to reduce their weight variation (from 19.2% to 13.1%) and metabolites variations (from 84.2% to 18.6% for triglycerides for instance) under variable flow conditions. These results imply that mechanisms to cope with environmental stochasticity can differ between populations of the same species, where some populations can be plastic whereas other cannot.

Introduction

The principle of energy allocation (Fisher, 1930, Williams, 1966) aims at understanding the response of organisms to environmental stochasticity with respect to reproductive success. It states that, to maximize the lifetime reproductive success, reproductive investment should result from a trade-off between current benefits of reproduction, and future opportunities of reproduction, increased by higher survival. Hence, in an environment where annual reproductive success is more variable than annual survival, individuals should invest more on survival, and reduce their reproductive investment (Schaffer, 1974, McNamara and Houston, 1996, Fischer et al., 2011). This concept has been widely applied and demonstrated, with a special regard to age dependent condition (Clutton-Brock et al., 1982). While trans-generational selection is expected to shape such evolutionary optima, it has also been shown that plasticity is possible, and that individuals may adapt to within lifetime changing environment (Bardsen et al., 2010, Bardsen et al., 2014, Kaiser et al., 2014). The use of environmental cues allows for the allocation process to be regulated in an adaptive way: different populations in different environments do not display the same reproductive effort patterns (Tully and Ferrière, 2008, Smallegange, 2011). At the proximal level, this population-dependence in plasticity may be caused by differences in the perception of environmental cues or in the physiological or behavioral response to the cues. At the ultimate level, the difference in the populations' capacity to cope with increased stochasticity will affect their fate in a changing world.

The fact that long term evolutionary processes (selection) and short term adaptive response (plasticity through perception of environmental cues) both contribute to the tuning of reproductive investment is of major interest for forecasting future evolutionary patterns regarding reproductive investment (Crozier and Hutchings, 2014). This question is especially pregnant in the context of rapid climate change which predicts – and verifies (Milly et al., 2002, IPCC, 2013) – increased stochastic climatic events in temperate areas, with increasing occurrence of extreme rainfalls and droughts. This environmental change is especially worrying for aquatic organisms (Fisher et al., 1982, Poff et al., 1997). In particular, salmonid reproduction is highly sensitive to extreme climatic events, as floods can scour and destroy their nests, which are dug in shallow riffle areas, whereas droughts can dry them out (Ottaway et al., 1981, DeVries, 1997, Armstrong et al., 2003, Riedl and Peter, 2013, Gauthey et al., 2015b). Therefore, it seems that salmonid populations should adjust their reproductive investment in function of the degree of environmental stochasticity, although it remains unknown whether reproductive strategies differ in trout populations subjected to contrasting environments.

To analyze this question we performed a manipulative experiment in which brown trout (Salmo trutta L.) from two populations naturally subject to different degrees of hydrological variability were kept under either constant or stochastic flow regime. We hypothesized that, within a single reproductive season, the two populations may show different investment strategies when placed in each of these two contrasted regimes. To estimate reproductive investment we measured the variations in plasma metabolites and in weight through the reproductive season, as Gauthey et al. (2015a) recently demonstrated that weight variation is a good proxy of gametic investment, whereas variation of metabolic status indicates investment in reproductive behavioral activity such as intrasexual competition, intersexual preference, and parental care. Building on these recent results, we now therefore investigate how population origin conditions the reproductive investment in relationship to water flow patterns. We then browse the options for the potential evolutionary mechanisms at work and their effect on population dynamics in a changing environment.