How do animals adapt to stressful environments? Consequences for species interactions and extinction risk

Animals live in increasingly variable environments where conditions can change rapidly. In cases where environmental change is predictable, such as seasonal changes in temperature and precipitation, animals are adapted to cope with this variation. However, unpredictable, abrupt or extreme changes in the environment, which are increasingly characteristic of climate change, can have profound effects on species. Those species that fail to move or adapt face an increasing risk of extinction so understanding how some animals adapt to rapid changes in the environment and some don’t is a key question in ecology, evolution and conservation biology. There is ample evidence that individual species have adapted to recent environmental change, but how they will cope with future change is not understood. Furthermore, of particular concern is how trophic interactions will respond because these critical interactions, such as predator-prey or plant-herbivore interactions, are key for ecosystem functioning and ecosystem services.

One clue to understanding how species respond to rapidly changing environments may lie in the molecular and/or genetic mechanisms underpinning these responses. Adaptation may take advantage of existing genetic diversity within the population. Higher levels of genetic diversity provide more variation in phenotypes, some of which may be better adapted to the new or fluctuating environmental conditions. Variation in ecologically relevant traits can also occur through epigenetic mechanisms, such as DNA methylation, in the absence of genetic variation. These epigenetic marks don’t alter the DNA sequence permanently, and the effects may be transient, lasting for a single generation, or remarkably they may be passed between generations. One of the aims of this project would be to examine whether environmental information passed between generations facilitates adaptation to new environments.

This project will focus on insects, which are the most abundant and species-rich group of animals on the planet. They have fundamental roles in all terrestrial and many aquatic ecosystems and are critical for ecological functions including pollination and pest control. The effects of short and long-term effects of environmental variations (e.g. temperature) that mimic future climate change can be carried out in model laboratory systems and this project would build on a well-established trophic system, the Indian meal moth (Plodia interpunctella) and the parasitic wasp (Venturia canescens) (e.g. 1,2). Model systems, such as PlodiaVenturia, have been used for decades to answer questions in ecology and evolution that are extremely difficult to address in the natural world. Recent work in the Sait lab has shown that different frequencies of variation cause phenotypic changes over short time scales in these species, affecting population dynamics of the host and parasitoid (2). However, little is known about the molecular and/or genetic mechanisms underpinning these responses (e.g. 3). Using this system we will combine measures of host and parasitoid life history traits with molecular methods (e.g. 3-5) to understand how the host and parasitoid adapt to changing environments over both short and long-time periods of environmental change, within and across multiple generations.

This project will be based in the School of Biology at the University of Leeds and combines the skills of both Steve Sait ( and Elizabeth Duncan ( to amalgamate ecological studies with population studies of genetic variation. In the context of the current biodiversity crisis, slowing, and ideally halting biodiversity loss is of paramount importance. A key aspect of this is identifying factors that predict resilience of species to environmental perturbation. This project will begin to address how species adapt to short- and long-term environmental change, and whether these mechanisms can act as predictors for adaptability to global change in other species.

1) Sait SM et al. (2000), Nature 405, 448-50.

2) Mugabo M et al. (2019) Journal of Animal Ecology 88, 1657-1669.

3) Duncan, EJ et al. (2014) Journal of Experimental Zoology B (Mol. Dev. Evol.) 322, 208-20.

4) Schield DR et al. (2016) Methods in Ecology and Evolution 7, 60-69.

5) Davey JW & Baxter ML (2010) Briefings in Functional Genomics 9, 416-23.