IAP2-22-334

Using life histories to forecast how coldblooded animals respond to environmental change

Two major patterns in biology that organize variation in life history traits across plant and animal species are termed the ‘slow-fast speed of life’ and ‘reproductive strategy’ strategies (Salguero-Gómez et al. 2016) (Fig. 1A): the slow-fast continuum has at the slow end species with low mortality, fecundity and development rates and fast life histories with fast characteristics at the other end; the reproductive strategy continuum has species with a single reproductive episode at one end and species with multiple reproductive cycles at the other end. The usefulness of this ‘fast-slow continuum and reproductive strategy’ framework lies in the fact that life history patterns can be linked to a wide range of biological quantities such as demographic performance, responses to environmental change including extreme events (IPCC 2019) and extinction risk (Salguero-Gómez 2017; IPBES 2019; Smallegange et al. 2020). The framework thus has the potential to serve as a powerful predictive tool for biologists and conservation managers.

The framework assumes that the patterning of species along the two life history strategy axes is the outcome of how individual organisms trade off survival and growth against reproduction (fast-slow continuum) or current versus future reproduction (reproductive strategy). Yet, this ecological gambit assumption (Fig. 1B) is not based on theory (Del Giuldice 2020), and recent work suggests we should also consider how ecological constraints, like time or nutrition, shape life history patterns (Cohen et al. 2020). Doing so can lead to new functional insights into how processes and constraints interact to shape life history patterns, which can significantly improve the effectiveness of population viability predictions. In the current biodiversity crisis (IPBES 2019), this is especially urgent for coldblooded animals (reptiles, amphibians, fish and invertebrates) because conservation policies and management measures for animals are often based on the ‘classical’ species groups of birds and mammals.

The aim of this project is to assess how trade-offs and constraints of time and nutrition structure life history patterns of coldblooded animals to then build a mechanistic framework that uses easy-to-obtain information on animal life histories to forecast their responses to environmental change.

Objective 1: To construct the life history framework by identifying the major life history axes that structure variation in life history traits calculated from population models with pre-specified trade-offs and reproductive strategies, for a wide range of coldblooded animals under different time and nutritional constraints.

Objective 2: To test the performance of the life history framework developed under objective 1 by assessing if the position of a species on the two life history axes predicts (i) species conservation status as per the International Union for Conservation of Nature’s (IUCN) Red List of Threatened Species, (ii) species extinction risk as defined by the IUNC Red List, and (iii) population growth rate and the speed of recovery after an extreme event; two important metrics of population performance.

This project aims to deliver a new numerical method that can be used by academics, conservationists and policymakers to forecast from first principles of energy conservation how animals of a wide range of life histories respond to environmental change, including their responses to extreme events and their risk of extinction.

To achieve objective 1, you will use mechanistic population matrix models (Smallegange et al. 2017) to calculate a set of representative life history traits (generation time, mean lifetime reproduction, degree of iteroparity, etc.) that inform on schedules of survival, growth, and reproduction, after which you evaluate the variation in these traits along major axes using phylogenetic principal component analyses. In the population models, energy invested into growth and reproduction trade-off against each other, reproduction can be once or multiple, and constraints are imposed on time available for feeding and on food quality. The eco-physiological data required to parameterise the populations models for coldblooded animals will be sourced from the Add-my-Pet (AmP) database, which is a free online database of referenced data on animal energetics and life history (AmP 2021, Marques et al. 2018) for over 3000 animal species from all major phyla.

Performance testing for objective 2 takes place by statistically analysing (using [ordinal] generalised linear models) if the principal component scores that define the position of a species along the two life history axes significantly correlate with (i) IUCN Red List conservation status categories, (ii) IUCN Red List criterion 4 extinction risk values, and (iii) population growth rates and rate at which populations recover from extreme climate events (IPCC 2019), calculated from the population models.

Year 1

– Conduct a comprehensive literature review.
– Software training including R and MatLab.
– Construct and parameterise populations models to create database.

Year 2

– Run phylogenetic principal component analyses on population models in your database.
– Conduct framework performance test on phylogenetic principal component results.
– Write first manuscript on database (to be published in e.g. Scientific Data) (objective 1).
– Attend national conference to present first findings and network.

Year 3

– Simulate and analyse responses to different extreme climate event scenarios.
– Write second manuscript that presents the new framework (to be published in a high impact journal like PNAS or Ecology Letters) (objective 2).
– Write third manuscript that applies the new framework to the biodiversity consequences of extreme climate events (to be published in e.g. Global Change Biology) (objective 2).

Year 3.5

– Submit manuscripts to peer-reviewed, high-impact journals.
– Attend international conference to present findings.
– Thesis completion.

The prospective student should have a background in population biology, life history theory or demography. A strong interest in computational modelling is essential and any experience therein desirable. University modules and the supervisory team will provide the necessary data analysis and software training. Additional training will be identified to meet the needs throughout the studentship.