[Paper Review] Eco-evolution from deep time to contemporary dynamics: the role of timescales and rate modulators

Emanuel A. Fronhofer, Dov Corenblit+6 more|arXiv (Cornell University)|Oct 21, 2022

Evolution and Genetic Dynamics4 citations

TL;DR

This paper redefines eco-evolutionary dynamics by integrating ecology and evolution across all timescales—from deep time to contemporary processes—arguing that emergence arises not only from rapid changes but also from slow, long-term feedbacks. It identifies rate modulators like temperature and genetic architecture as key drivers that synchronize or desynchronize ecological and evolutionary dynamics, especially under global change, challenging the narrow focus on 'rapid' evolution and calling for integrative, mechanistic models to improve predictability in non-analog conditions.

ABSTRACT

Eco-evolutionary dynamics, or eco-evolution for short, are thought to involve rapid demography (ecology) and equally rapid phenotypic changes (evolution) leading to novel, emergent system behaviours. This focus on contemporary dynamics is likely due to accumulating evidence for rapid evolution, from classical laboratory microcosms and natural populations, including the iconic Trinidadian guppies. We argue that this view is too narrow, preventing the successful integration of ecology and evolution. While maintaining that eco-evolution involves emergence, we highlight that this may also be true for slow ecology and evolution which unfold over thousands or millions of years, such as the feedbacks between riverine geomorphology and plant evolution. We thereby integrate geomorphology and biome-level feedbacks into eco-evolution, significantly extending its scope. Most importantly, we emphasize that eco-evolutionary systems need not be frozen in state-space: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can synchronize or desynchronize ecology and evolution. We speculate that global change may increase the occurrence of eco-evolution and emergent system behaviours which represents substantial challenges for prediction. Our perspective represents an attempt to integrate ecology and evolution across disciplines, from gene-regulatory networks to geomorphology and across timescales, from contemporary dynamics to deep time.

Motivation & Objective

• To expand the scope of eco-evolutionary dynamics beyond contemporary, rapid changes to include slow, deep-time processes such as riverine geomorphology and plant evolution.
• To challenge the prevailing assumption that only fast ecological and evolutionary changes produce emergent system behaviors.
• To identify rate modulators—such as temperature and genetic architecture—that can synchronize or desynchronize ecological and evolutionary dynamics.
• To address the limitations of current correlative models in predicting emergent behaviors under global change by advocating for mechanistic, multilayer network-based modeling.
• To promote a more integrative framework linking gene-regulatory networks, ecosystems, and geological processes across disciplines and timescales.

Proposed method

• Proposes a conceptual framework that unifies ecology and evolution across timescales, from generational to macroevolutionary, using the concept of emergence as a unifying principle.
• Introduces rate modulators—such as temperature, mutation sensitivity, and genetic decanalization—as key factors that can alter the relative pace of ecological and evolutionary processes.
• Applies the lens of multilayer networks to represent eco-evolutionary feedbacks from gene-regulatory networks to ecosystem-level interactions.
• Uses long-term empirical evidence (e.g., Trinidadian guppies, coral systems) and theoretical models to demonstrate that rapid evolution is not a prerequisite for eco-evolutionary feedbacks.
• Analyzes how global change factors—like warming, habitat fragmentation, and pollution—act as environmental modulators that may push systems into new eco-evolutionary regimes.
• Advocates for mechanistic models incorporating species interactions, demography, physiology, and evolution to overcome the limitations of purely correlative approaches in non-analog conditions.

Experimental results

Research questions

• RQ1 How can eco-evolutionary dynamics be meaningfully extended beyond contemporary, rapid changes to include slow, deep-time processes?
• RQ2 What role do rate modulators—such as temperature and genetic architecture—play in synchronizing or desynchronizing ecological and evolutionary dynamics?
• RQ3 In what ways can geomorphological feedbacks, such as those between riverine systems and plant evolution, constitute eco-evolutionary processes over long timescales?
• RQ4 How do global change drivers like warming and habitat fragmentation influence the likelihood of emergent eco-evolutionary behaviors?
• RQ5 What kind of modeling frameworks are needed to predict emergent system behaviors in non-analog, rapidly changing environments?

Key findings

• Eco-evolutionary dynamics are not limited to rapid changes; slow, long-term feedbacks—such as those between riverine geomorphology and plant evolution—can also produce emergent system behaviors.
• Rate modulators such as temperature can decouple ecological and evolutionary rates, with warming potentially slowing ecology while accelerating evolution, increasing the likelihood of eco-evolutionary feedbacks.
• Global change factors like land-use change, urbanization, and pollution can act as strong selective pressures, prompting fast evolutionary responses and pushing systems into novel eco-evolutionary regimes.
• The concept of 'emergence' applies equally to slow and fast dynamics, challenging the assumption that only rapid changes yield novel system behaviors.
• Current correlative models based on machine learning and AI may fail under non-analog conditions due to lack of mechanistic underpinning, necessitating integrative, multilayer network models.
• The integration of gene-regulatory networks, ecosystem networks, and geological processes through multilayer network frameworks offers a promising path toward more predictive eco-evolutionary science.