Trophic interactions are at the heart of community ecology. Herbivores consume plants, predators consume herbivores, and pathogens and parasites infect, and sometimes kill, individuals of all species in a food web. Given the ubiquity of trophic interactions, it is no surprise that ecologists and evolutionary biologists strive to accurately characterize them.
The outcome of an interaction between individuals of different species depends upon numerous factors such as the age, sex, and even phenotype of the individuals involved and the environment in which they are in. Despite this complexity, biologists often simplify an interaction down to a single number, an interaction coefficient that describes the average outcome of interactions between members of the populations of the species. Models of interacting species tend to be very simple, and interaction coefficients are often estimated from time series of population sizes of interacting species. Although biologists have long known that this approach is often approximate and sometimes unsatisfactory, work on estimating interaction strengths in more complex scenarios, and using ecological data beyond estimates of abundance, is still in its infancy.
In their paper, Matthieu Paquet and Frederic Barraquand (2023) develop a demographic model of a predator and its prey. They then simulate demographic datasets that are typical of those collected by ecologists and use integrated population modelling to explore whether they can accurately retrieve the values interaction coefficients included in their model. They show that they can with good precision and accuracy. The work takes an important step in showing that accurate interaction coefficients can be estimated from the types of individual-based data that field biologists routinely collect, and it paves for future work in this area.
As if often the case with exciting papers such as this, the work opens up a number of other avenues for future research. What happens as we move from demographic models of two species interacting such as those used by Paquet and Barraquand to more realistic scenarios including multiple species? How robust is the approach to incorrectly specified process or observation models, core components of integrated population modelling that require detailed knowledge of the system under study?
Integrated population models have become a powerful and widely used tool in single-species population ecology. It is high time the techniques are extended to community ecology, and this work takes an important step in showing that this should and can be done. I would hope the paper is widely read and cited.
Paquet, M., & Barraquand, F. (2023). Assessing species interactions using integrated predator-prey models. EcoEvoRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/X2RC7W
DOI or URL of the preprint: https://doi.org/10.32942/X2RC7W
Version of the preprint: 1
We have recieved two reviews on the manuscript, and both are positive. The manuscript requires a relatively minor revision that will not require re-review -- the co-recommenders can make the call. We consequently expect to accept the revised manuscript.
The manuscript simulates data typical of those collected in a demographic field study of interacting species and then uses integrated population modelling (IPM) to recover the parameters used to simulate the data. The work is sound, and the manuscript is well written. The reviewers have identified some minor edits that will aid clarity.
One reviewer raises two slighly more major concerns. First, the reliance on a previous publication. This manuscript builds on that work, so I am not overly concerned by this. However, a few edits to make this manuscript a little more stand alone would not go amiss.
The second issue is one would hope that IPMs would recover parameters from simulations when the same model structure is used to simulate and estimate the process model in an IPM. These tests are consequently not particularly challenging. I do think this needs more acknolwedement in the discussion. In reality, we never know the generating process when we collect data, and we have to make decisions on what the process and error models will look like in an IPM. Get one of the wrong, and results can be unreliable. I am not going to request more simulations here, but in future work it might be interesting to select a broader range of models that differ from the simulation for the process model of the IPM. Perhaps stress this point more.