The role of interaction structure in eco-evolutionary dynamics (EcoEvoInteract)
Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions
Final Report Abstract
Within all levels of biology, units affect other units. For example, cells affect other cells, individuals affect other individuals, and populations affect other populations. Such interactions are central to almost all questions in biology. What is rarely considered is how units at one level might affect those at another level. This project firstly outlined a general framework of hierarchical embeddedness in biological networks, and secondly to explore some of the ecological and evolutionary consequences of such embeddedness. The framework describes how different levels of biological organisation (e.g. cellular vs individual vs populations) create structure in the within-level social networks arising from interactions between units. For example, each person hosts a rich diversity of microbes, each of which interact with other microbes. When two people interact, this then creates links between their communities of microbes. Thus, at the microbial level, interactions are shaped not only by how microbes interact with other microbes within an organism, but also how organisms interact with other organisms. This hierarchical, or nested, structure represents a unique dimension, and our framework formally describes this for a broad biological audience. Building on the framework that was developed, we then explored two key questions. The first is the consequences of behaviour to affect eco-evolutionary dynamics. Eco-evolutionary dynamics are the feedback between ecological and evolutionary processes, where ecological processes influence evolutionary change, which then feedbacks onto the ecology of the system. Such dynamics are generally considered at the population or community level (i.e. upper levels of our framework / biological organisation). However, each individual in a population or community has the opportunity to make decisions, such as when to forage or how to interact with others. If individuals vary in their behaviour, then this variation can scale up to affect community-level processes and feedback onto the population or (through community dynamics) onto other populations. We outlined examples of such effects using classical models of behavioural ecology. The study of structure in the linkages between populations (e.g. via dispersal) is the well-established field of meta-populations. Multitudes of studies have shown that the structure of meta-populations (i.e. the network connecting populations) has major implications on evolutionary outcomes. However, few studies consider explicitly how meta-population structure arises. For example, underlying habitat configuration (e.g. connectivity between patches) can inherently shape meta-population structure. This leads to a key question—are some populations pre-destined to express particular evolutionary outcomes due to the underlying abiotic factors that shape their connectivity to other populations? PhD student Peng He completed his thesis building on our framework to address this question, starting with developing models for generating habitat networks and presently by testing how habitat configuration shapes both ecological (e.g. disease spread) and evolutionary (e.g. trait diversity) outcomes. Finally, we explored how biological communities affect evolutionary dynamics in metapopulations. Specifically, we asked whether the evolution of traits that emerge and spread through a connected set of populations could be affected by the feedback that evolution has on the environment that all populations experience. We modelled populations that evolve traits that bring anti-predator benefits to individuals, and that subsequently affect the foraging of predators that span across multiple populations. Predators reduce foraging on populations with anti-predator traits, but subsequently increase their foraging effort on populations that have no such traits). In doing so, the evolution of a trait in one population changes the environment for other populations by increasing predator pressure. This can then alter the trajectory of trait evolution when it finally reaches those populations, and also change the final stable outcomes.
Publications
- (2018). Linking the fine‐scale social environment to mating decisions: a future direction for the study of extra‐pair paternity. Biological Reviews, 93(3), 1558-1577
Maldonado‐Chaparro, A. A., Montiglio, P. O., Forstmeier, W., Kempenaers, B., & Farine, D. R.
(See online at https://doi.org/10.1111/brv.12408) - (2018). The potential impacts of the songbird trade on mixedspecies flocking. Biological Conservation, 222, 222-231
Marthy, W., & Farine, D. R.
(See online at https://doi.org/10.1016/j.biocon.2018.04.015) - (2019). Animal behavior facilitates eco-evolutionary dynamics
Gotanda, K. M., Farine, D. R., Kratochwil, C. F., Laskowski, K. L., & Montiglio, P. O.
(See online at https://doi.org/10.48550/arXiv.1912.09505) - (2020). Hierarchically embedded interaction networks represent a missing link in the study of behavioral and community ecology. Behavioral Ecology, 31(2), 279-286
Montiglio, P. O., Gotanda, K. M., Kratochwil, C. F., Laskowski, K. L., & Farine, D. R.
(See online at https://doi.org/10.1093/beheco/arz168) - (2020). Social barriers in ecological landscapes: the social resistance hypothesis. Trends in Ecology & Evolution, 35(2), 137-148
Armansin, N. C., Stow, A. J., Cantor, M., Leu, S. T., Klarevas-Irby, J. A., Chariton, A. A., & Farine, D. R.
(See online at https://doi.org/10.1016/j.tree.2019.10.001) - (2021). The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions. Oecologia 196, 649-665
He, P., Montiglio, P. O., Somveille, M., Cantor, M., & Farine, D. R.
(See online at https://doi.org/10.1007/s00442-021-04967-y)
