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Testing the evolutionary species interactions – abiotic stress hypothesis
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Why do we observe species occurring in certain habitats but not in others? Since the days of Darwin researchers debate this fundamental question and the relative role of environment versus species’ interactions in driving species distribution patterns. Summarized as the “Species Interactions – Abiotic Stress Hypothesis” (SIASH), theory suggests that abiotic forces set range boundaries under stressful conditions, while biotic interaction are most relevant in benign environments.
While central to basic and applied research this hypothesis 1) lacks the integration of contemporary evolution and 2) remains largely untested empirically. Therefore, this project follows two complementary objectives: 1) Expansion and update of theory to include eco-evolutionary feedbacks, i.e., evolution of dispersal, local adaptation and correlated life-history traits, which are central drivers of range dynamics. 2) Experimental testing in microbial model landscapes subject to a temperature gradient using experimental evolution and protists as model organisms.
While central to basic and applied research this hypothesis 1) lacks the integration of contemporary evolution and 2) remains largely untested empirically. Therefore, this project follows two complementary objectives: 1) Expansion and update of theory to include eco-evolutionary feedbacks, i.e., evolution of dispersal, local adaptation and correlated life-history traits, which are central drivers of range dynamics. 2) Experimental testing in microbial model landscapes subject to a temperature gradient using experimental evolution and protists as model organisms.
E-SIASH team
Jhelam Deshpande (MSc)
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Marie-Ange Devillez (T)
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Emanuel Fronhofer (PI)
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Justina Givens (IE)
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Fanny Gonguet (M2)
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Claire Gougat-Barbera (AI)
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Natalie Lewis (M1)
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Sarthak Malusare (PhD)
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Iain Moodie (M1)
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Saismit Naik (MSc)
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Shrinath Narayanan (M1)
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Mellina Sidous (M1)
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E-SIASH publications
[4] Fronhofer E. A., Corenblit D., Deshpande J. N., Govaert L., Huneman P., Viard F. Jarne P. & Puijalon S. (2023) Eco-evolution from deep time to contemporary dynamics: the role of timescales and rate modulators. Ecology Letters 26 (S1): S91-S108.
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We here conceptualise biological systems as occupying a two-dimensional time space along axes that capture the speed of ecology and evolution. Using Hutchinson's analogy: Time is the ‘theatre’ in which ecology and evolution are two interacting ‘players’. Eco-evolutionary systems are therefore dynamic: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can change the speed of ecology and evolution, and hence impact eco-evolution. Environmental change may synchronise the speed of ecology and evolution via these rate modulators, increasing the occurrence of eco-evolution and emergent system behaviours. This represents substantial challenges for prediction, especially in the context of global change.
[3] Rosenbaum B. & Fronhofer E. A. (2023) Confronting population models with experimental microcosm data: from trajectory matching to state-space models. Ecosphere 14: e4503.
More and more, classical models are being confronted with data via fitting to empirical time series for purposes of projections or for estimating model parameters of interest. In order to help empiricists, especially researchers working with experimental laboratory populations in micro- and mesocosms, make informed decisions about the statistical formalism to use, we here compare different error structures one could use when fitting classical deterministic ordinary differential equation (ODE) models to empirical data. We consider a large range of biological scenarios and theoretical models, from single species to community dynamics and trophic interactions. Finally, we provide a comprehensive tutorial for fitting these models in R.
[2] Malusare S. P., Zilio G. & Fronhofer E. A. (2023) Evolution of thermal performance curves: a meta-analysis of selection experiments. Journal of Evolutionary Biology 36: 15-28.
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We performed a systematic literature search and meta-analysis focusing on experimental evolution of thermal performance curves (TPCs). Relative fitness was calculated as differences between TPCs from ancestral and derived populations after thermal selection.
We provide evidence for (i) adaptive potential of TPCs, (ii) trade-off between adaptation to higher temperatures and fitness at lower temperatures, (iii) higher relative fitness for experimental evolution studies that relied on de novo mutations rather than standing genetic variation, and (iv) no support for the "Hotter is better" hypothesis.
We provide evidence for (i) adaptive potential of TPCs, (ii) trade-off between adaptation to higher temperatures and fitness at lower temperatures, (iii) higher relative fitness for experimental evolution studies that relied on de novo mutations rather than standing genetic variation, and (iv) no support for the "Hotter is better" hypothesis.
[1] Deshpande J. N. & Fronhofer E. A. (2022) Genetic architecture of dispersal and local adaptation drives accelerating range expansions. Proceedings of the National Academy of Sciences of the United States of America 119: e2121858119.
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To predict contemporary ecological and evolutionary dynamics, one must take the speed of evolution, that is, the rates of evolutionary processes, into account. These rates depend on how variation for relevant traits is generated or eliminated, which depends on their genetic basis. Here, we model range expansions into environmental gradients, such as temperature, and use gene-regulatory networks to represent dispersal and local adaptation, two key traits for range expansions. Indeed, incorporating an explicit genetic basis for traits leads to predictions different from simpler models: Particularly, gene-regulatory networks evolve greater sensitivity to mutation, increasing the rate of adaptation to the gradient. Importantly, this leads to accelerating range expansions. Our results highlight how assumptions at the genomic level modify predictions of large scale regional processes.