DEB-IPM project

What is the DEB-IPM project about?

How can we predict how populations respond to the ever greater changes in their environment? Within this project, we want to know which characteristics of organisms relate to population responses to environmental change. One way to find out is to analyse life history patterns using demographic models. However, depending on whether you model individual life histories from phenomenological descriptions (Salguero-Gomez et al. 2016; Paniw et al. 2018; Capdevila et al 2020) or from mechanistic descriptions using energy budget models (Smallegange et al. in press; Smallegange & Berg 2019), different predictions are obtained.

The DEB-IPM project aims to (i) unravel if energy budget descriptions of individual life histories consistently return different predictions on population responses to environmental change compared to when individual life histories are represented by statistical functions, (ii) understand why that is the case, and (iii) identify the most accurate way to predict population responses to novel environmental change. To this end, we support Bachelor, Master and PhD student projects in which students answer their own research questions, while at the same time expanding the DEB-IPM database to ultimately conduct large, cross-taxonomical life history analyses.

What is a DEB-IPM?

Integral projection models (IPMs) have emerged as a powerful tool to investigate population­level processes from an individual-level perspective, partly because the demographic processes of growth, survival and reproduction are estimated using flexible and easy-to-use phenomenological methods such as regression models (Smallegange & Coulson 2013). The downside of these regression models, however, is that they lack a mechanistic representation of the biological processes that give rise to observed survival, growth and reproduction. The DEB-­IPM (Smallegange et al. 2017) explicitly incorporates an energetic description of growth and reproduction into IPMs. IPMs, and also DEB-IPMs, can be analysed using tools from demography. This will allow researchers to investigate ecological and evolutionary patterns such as population dynamics, geographic distributions or evolution oflife-history strategies, from an energy budget perspective on demographic rates, which has traditionally been tackled by using the mathematically more challenging physiological structured population models ( de Roos & Persson 2013).

For a how-to guide to build a DEB-IPM see point 4 of the Appendix of Smallegange et al. 2017.

DEB-IPM database

The number of species for which we have collected data to parameterise a DEB-IPM is increasing. The data are collated in an excel file available on FigShare.

Who is involved

  • Mark Rademakers (PhD student at the NIOZ and IBED/UvA): Can energy budget strategies forecast population growth in fish in temporally autocorrelated environments?
  • Kim Eustache (PhD student at CRIOBE and IBED/UvA): Study of the effects of yearling, juvenile and adult survival on blacktip reef shark ( Carcharhinus melanopterus) population demography.
  • Isabel Smallegange: The role of developmental plasticity in population persistence.
  • There are always BSc and MSc projects available.

Do you want to be involved?

f you are a quantitatively driven mind with interests in life history theory, demography, eco­
evolutionary dynamics, or related topics, please contact me to discuss potential graduate or postdoc opportunities in my group.

Students: Below is a list of current and past student projects conducted within the DEB-IPM
project. Contact me to discuss research questions you would like to tackle.

Postdoctoral researchers: If you have got a project in mind that you would like to develop in my group, please contact me with a brief project proposal, CV and list of funding themes that you are considering for this project ( e.g. Marie Curie).

Completed DEB-IPM student projects

  • Jasmijn Hoevers: Demographic analysis to protect declining marine megafaunal populations against environmental changes (2021).
  • Josje Romeijn: A dynamic budget approach to identify a fast-slow life history continuum in microorganisms (2021).
  • Iris van Rijn: Analyzing life history patterns using the Dynamic Energy Budget Integral Projection Model (DEB-IPM) (2021).
  • Dora Vig (MSc student at Utrecht University): Comparison of population-level life-history patterns of invasive marine species, using dynamic energy budget integral projection models, (2021).
  • Sophie Timmerman: On the paradox in dynamic energy budget population models (201 II
  • Gavin Jansen: Predicting changes in population dynamics using stochastic demographic models (2018).
  • Tom Hopman: An analysis of life-history patterns in the fast-slow continuum using dynamic energy budget theory (2018).
  • Naomi Eeltink: Predicting life history patterns across the fast-slow continuum: A cross-level test using the Dynamic Energy Budget-Integral Projection Model (DEB-IPM) (2017).
  • Marjolein Toorians: her BSc project is part of the paper Smallegange et al. (2017) (see below).

Scientific papers resulting from the DEB-IPM project

  • Smallegange IM, Flotats Avilés M, Eustache K. 2020. Unusually paced life history strategies of marine megafauna drive atypical sensitivities to environmental variability. Frontiers in Marine Science 7:597492
  • Smallegange IM, Berg M. 2019. A functional trait approach to identifying life history patterns in stochastic environments. Ecology and Evolution 9: 9350-9361
  • Smallegange IM, Ens HM. 2018. Trait-based predictions and responses from laboratory mite populations to harvesting in stochastic environments. Journal of Animal Ecology 87: 893-905
  • Smallegange IM, Caswell H, Toorians MEM, de Roos AM. 2017. Mechanistic description of population dynamics using dynamic energy budget theory incorporated into integral projection models. Methods in Ecology and Evolution 8: 146-154.
  • Podcast: Population Biology & Eco-Evolutionary Dynamics From Mites to Manta Rays with Isabel Smallegange
  • o Smallegange IM, Berg MP (2020). A slow pace of life makes animals more sensitive to unpredictable climate variations. Amsterdam Science 11:3
  • Smallegange IM. Big data also need big concepts. Biog post September 2019.

References

Capdevila, P., Beger, M., Blomberg, S. P., Hereu, B., Linares, C., and Salguero-Gómez, R. (2020). Longevity, body dimension and reproductive mode drive differences in aquatic versus terrestrial life history strategies. Funct. Ecol. 34, 1613–1625. doi: 10.1111/1365-2435.13604

Paniw, M., Ozgul, A., and Salguero-Gómez, R. (2018). Interactive life-history traits predict sensitivity of plants and animals to temporal autocorrelation. Ecol. Lett. 21, 275–286. doi: 10.1111/ele.12892

de Roos AM, Persson L. (2013) Population and Community Ecology of Ontogenetic Development (Monographs in Population Biology, 51). Princeton University Press, Princeton, NJ, USA.

Salguero-Gomez R, Jones OR, Jongejans E, Blomberg SP, Hodgson DJ, Mbeau-Ache C. et al. (2016). Fast-slow continuum and reproductive strategies structure plant life-history variation worldwide. Proc. Natl Acad. Sci. 113: 230-235.

Smallegange IM, Coulson T (2013). Towards a general, population-level understanding of eco-evolutionary change. Trends in Ecology & Evolution 28: 143-148.

Smallegange, I. M., Caswell, H., Toorians, M. E. M., and de Roos, A. M. (2017). Mechanistic description of population dynamics using dynamic energy budget theory incorporated into integral projection models. Methods Ecol. Evol. 8, 146–154. doi: 10.1111/2041-210x.12675