MUNOZ François's profile

## MUNOZ François

• Laboratoire d'Ecologie Alpine, Université Grenoble Alpes, Grenoble, France
• Biodiversity, Biogeography, Botany, Coexistence, Community ecology, Conservation biology, Eco-evolutionary dynamics, Ecological successions, Evolutionary ecology, Interaction networks, Landscape ecology, Macroecology, Ontogeny, Phylogeny & Phylogeography, Spatial ecology, Metacommunities & Metapopulations, Species distributions, Statistical ecology, Terrestrial ecology, Theoretical ecology, Tropical ecology
• recommender

#### Reviews:  0

Areas of expertise
I currently work at the Laboratoire Interdisciplinaire de Physique (LIPHy), Université Grenoble Alpes. My research concerns Ecology, Biogeography and Evolutionary Biology. It mainly investigates the mechanisms underlying population and community dynamics at multiple spatial and temporal scales. Theoretical and methodological approaches are developped and applied to study tropical and temperate ecosystems (tropical forests in India and New Caledonia, grasslands and weed communities in Europe, high-elevation peatlands in Bolivia...).

## Recommendations:  3

01 Mar 2022

### Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring

#### How to evaluate and interpret the contribution of species turnover and interaction rewiring when comparing ecological networks?

Recommended by based on reviews by Ignasi Bartomeus and 1 anonymous reviewer

A network includes a set of vertices or nodes (e.g., species in an interaction network), and a set of edges or links (e.g., interactions between species). Whether and how networks vary in space and/or time are questions often addressed in ecological research.

Two ecological networks can differ in several extents: in that species are different in the two networks and establish new interactions (species turnover), or in that species that are present in both networks establish different interactions in the two networks (rewiring). The ecological meaning of changes in network structure is quite different according to whether species turnover or interaction rewiring plays a greater role. Therefore, much attention has been devoted in recent years on quantifying and interpreting the relative changes in network structure due to species turnover and/or rewiring.

Poisot et al. (2012) proposed to partition the global variation in structure between networks, $$\beta_{WN}$$ (WN = Whole Network) into two terms: $$\beta_{OS}$$ (OS = Only Shared species) and $$\beta_{ST}$$ (ST = Species Turnover), such as $$\beta_{WN} = \beta_{OS} + \beta_{ST}$$.

The calculation lays on enumerating the interactions between species that are common or not to two networks, as illustrated on Figure 1 for a simple case. Specifically, Poisot et al. (2012) proposed to use a Sorensen type measure of network dissimilarity, i.e., $$\beta_{WN} = \frac{a+b+c}{(2a+b+c)/2} -1=\frac{b+c}{2a+b+c}$$ , where $$a$$ is the number of interactions shared between the networks, while $$b$$ and $$c$$ are interaction numbers unique to one and the other network, respectively. $$\beta_{OS}$$ is calculated based on the same formula, but only for the subnetworks including the species common to the two networks, in the form $$\beta_{OS} = \frac{b_{OS}+c_{OS}}{2a_{OS}+b_{OS}+c_{OS}}$$ (e.g., Fig. 1). $$\beta_{ST}$$ is deduced by subtracting $$\beta_{OS}$$ from $$\beta_{WN}$$ and represents in essence a "dissimilarity in interaction structure introduced by dissimilarity in species composition" (Poisot et al. 2012).

Figure 1. Ecological networks exemplified in Fründ (2021) and discussed in Poisot (2022). a is the number of shared links (continuous lines in right figures), while b+c is the number of edges unique to one or the other network (dashed lines in right figures).

Alternatively, Fründ (2021) proposed to define $$\beta_{OS} = \frac{b_{OS}+c_{OS}}{2a+b+c}$$ and $$\beta_{ST} = \frac{b_{ST}+c_{ST}}{2a+b+c}$$, where $$b_{ST}=b-b_{OS}$$  and $$c_{ST}=c-c_{OS}$$ , so that the components $$\beta_{OS}$$ and $$\beta_{ST}$$ have the same denominator. In this way, Fründ (2021) partitioned the count of unique $$b+c=b_{OS}+b_{ST}+c_{ST}$$ interactions, so that $$\beta_{OS}$$ and $$\beta_{ST}$$ sums to $$\frac{b_{OS}+c_{OS}+b_{ST}+c_{ST}}{2a+b+c} = \frac{b+c}{2a+b+c} = \beta_{WN}$$. Fründ (2021) advocated that this partition allows a more sensible comparison of $$\beta_{OS}$$ and $$\beta_{ST}$$, in terms of the number of links that contribute to each component.

For instance, let us consider the networks 1 and 2 in Figure 1 (left panel) such as $$a_{OS}=2$$ (continuous lines in right panel), $$b_{ST} + c_{ST} = 1$$ and $$b_{OS} + c_{OS} = 1$$ (dashed lines in right panel), and thereby $$a = 2$$, $$b+c=2$$, $$\beta_{WN} = 1/3$$. Fründ (2021) measured $$\beta_{OS}=\beta_{ST}=1/6$$ and argued that it is appropriate insofar as it reflects that the number of unique links in the OS and ST components contributing to network dissimilarity (dashed lines) are actually equal. Conversely, the formula of Poisot et al. (2012) yields $$\beta_{OS}=1/5$$, hence $$\beta_{ST} = \frac{1}{3}-\frac{1}{5}=\frac{2}{15}<\beta_{OS}$$. Fründ (2021) thus argued that the method of Poisot tends to underestimate the contribution of species turnover.

To clarify and avoid misinterpretation of the calculation of $$\beta_{OS}$$ and $$\beta_{ST}$$ in Poisot et al. (2012), Poisot (2022) provides a new, in-depth mathematical analysis of the decomposition of $$\beta_{WN}$$. Poisot et al. (2012) quantify in $$\beta_{OS}$$ the actual contribution of rewiring in network structure for the subweb of common species. Poisot (2022) thus argues that $$\beta_{OS}$$ relates only to the probability of rewiring in the subweb, while the definition of $$\beta_{OS}$$ by Fründ (2021) is relative to the count of interactions in the global network (considered in denominator), and is thereby dependent on both rewiring probability and species turnover. Poisot (2022) further clarifies the interpretation of $$\beta_{ST}$$. $$\beta_{ST}$$ is obtained by subtracting $$\beta_{OS}$$ from $$\beta_{WN}$$ and thus represents the influence of species turnover in terms of the relative architectures of the global networks and of the subwebs of shared species. Coming back to the example of Fig.1., the Poisot et al. (2012) formula posits that $$\frac{\beta_{ST}}{\beta_{WN}}=\frac{2/15}{1/3}=2/5$$, meaning that species turnover contributes two-fifths of change in network structure, while rewiring in the subweb of common species contributed three fifths.  Conversely, the approach of Fründ (2021) does not compare the architectures of global networks and of the subwebs of shared species, but considers the relative contribution of unique links to network dissimilarity in terms of species turnover and rewiring.

Poisot (2022) concludes that the partition proposed in Fründ (2021) does not allow unambiguous ecological interpretation of rewiring. He provides guidelines for proper interpretation of the decomposition proposed in Poisot et al. (2012).

References

Fründ J (2021) Dissimilarity of species interaction networks: how to partition rewiring and species turnover components. Ecosphere, 12, e03653. https://doi.org/10.1002/ecs2.3653

Poisot T, Canard E, Mouillot D, Mouquet N, Gravel D (2012) The dissimilarity of species interaction networks. Ecology Letters, 15, 1353–1361. https://doi.org/10.1111/ele.12002

Poisot T (2022) Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring. EcoEvoRxiv Preprints, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.32942/osf.io/gxhu2

22 Nov 2021

### Beating your neighbor to the berry patch

#### When more competitors means less harvested resource

Recommended by based on reviews by Francois Massol, Jeremy Van Cleve and 1 anonymous reviewer

In this paper, Alan R. Rogers (2021) examines the dynamics of foraging strategies for a resource that gains value over time (e.g., ripening fruits), while there is a fixed cost of attempting to forage the resource, and once the resource is harvested nothing is left for other harvesters. For this model, not any pure foraging strategy is evolutionary stable. A mixed equilibrium exists, i.e., with a mixture of foraging strategies within the population, which is still evolutionarily unstable. Nonetheless, Alan R. Rogers shows that for a large number of competitors and/or high harvesting cost, the mixture of strategies remains close to the mixed equilibrium when simulating the dynamics. Surprisingly, in a large population individuals will less often attempt to forage the resource and will instead “go fishing”. The paper also exposes an experiment of the game with students, which resulted in a strategy distribution somehow close to the theoretical mixture of strategies.

The economist John F. Nash Jr. (1950) gained the Nobel Prize of economy in 1994 for his game theoretical contributions. He gave his name to the “Nash equilibrium”, which represents a set of individual strategies that is reached whenever all the players have nothing to gain by changing their strategy while the strategies of others are unchanged. Alan R. Rogers shows that the mixed equilibrium in the foraging game is such a Nash equilibrium. Yet it is evolutionarily unstable insofar as a distribution close to the equilibrium can invade.

The insights of the study are twofold. First, it sheds light on the significance of Nash equilibrium in an ecological context of foraging strategies. Second, it shows that an evolutionarily unstable state can rule the composition of the ecological system. Therefore, the contribution made by the paper should be most significant to better understand the dynamics of competitive communities and their eco-evolutionary trajectories.

References

Nash JF (1950) Equilibrium points in n-person games. Proceedings of the National Academy of Sciences, 36, 48–49. https://doi.org/10.1073/pnas.36.1.48

Rogers AR (2021) Beating your Neighbor to the Berry Patch. bioRxiv, 2020.11.12.380311, ver. 8 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.11.12.380311

19 Feb 2020

### Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree

#### Growth trajectories, better than organ-level functional traits, reveal intraspecific response to environmental variation

Recommended by based on reviews by Georges Kunstler and François Munoz

Functional traits are “morpho-physio-phenological traits which impact fitness indirectly via their effects on growth, reproduction and survival” [1]. Most functional traits are defined at organ level, e.g. for leaves, roots and stems, and reflect key aspects of resource acquisition and resource use by organisms for their development and reproduction [2]. More rarely, some functional traits can be related to spatial development, such as vegetative height and lateral spread in plants.
Organ-level traits are especially popular because they can be measured in a standard way and easily compared over many plants. But these traits can broadly vary during the life of an organism. For instance, Roggy et al. [3] found that Leaf Mass Area can vary from 30 to 140 g.m^(-2) between seedling and adult stages for the canopy tree Dicorynia guianensis in French Guiana. Fortunel et al. [4] have also showed that developmental stages much contribute to functional trait variation within several Micropholis tree species in lowland Amazonia.
The way plants grow and invest resources into organs is variable during life and allows defining specific developmental sequences and architectural models [5,6]. There is clear ontogenic variation in leaf number, leaf properties and ramification patterns. Ontogenic variations reflect changing adaptation of an individual over its life, depending on the changing environmental conditions.
In this regard, measuring a single functional trait at organ level in adult trees should miss the variation of resource acquisition and use strategies over time. Thus we should built a more integrative approach of ecological development, also called “eco-devo” approach [7].
Although the ecological significance of ontogeny and developmental strategies is now well known, the extent to which it contributes to explain species survival and coexistence in communities is still broadly ignored in functional ecology. Levionnois et al. [8] investigated intraspecific variation of functional traits and growth trajectories in a typical, early-successional tree species in French Guiana, Amazonia. This species, Cecropia obtusa, is generalist regarding soil type and can be found on both white sand and ferralitic soil. The study examines whether there in intraspecific variation in functional traits and growth trajectories of C. obtusa in response to the contrasted soil types.
The tree communities observed on the two types of soils include species with distinctive functional trait values, that is, there are changes in species composition related to different species strategies along the classical wood and leaf economic spectra. The populations of C. obtusa found on the two soils showed some difference in functional traits, but it did not concern traits related to the main economic spectra. Conversely, the populations showed different growth strategies, in terms of spatial and temporal development.
The major lessons we can learn from the study are:
(i) Functional traits measured at organ level cannot reflect well how long-lived plants collect and invest resources during their life. The results show the potential of considering architectural and developmental traits together with organ-level functional traits, to better acknowledge the variation in ecological strategies over plant life, and thus to better understand community assembly processes.
(ii) What makes functional changes between communities differs when considering interspecific and intraspecific variation. Species turnover should encompass different corteges of soil specialists. These specialists are sorted along economic spectra, as shown in tropical rainforests and globally [2]. Conversely, a generalist species such as C. obtusa does occur on contrasted soil, which entails that it can accommodate the contrasted ecological conditions. However, the phenotypic adjustment is not related to how leaves and wood ensure photosynthesis, water and nutrient acquisition, but regards the way the resources are allocated to growth and reproduction over time.
The results of the study stress the need to better integrate growth strategies and ontogeny in the research agenda of functional ecology. We can anticipate that organ-level functional traits and growth trajectories will be more often considered together in ecological studies. The integration should help better understand the temporal niche of organisms, and how organisms can coexist in space and time with other organisms during their life. Recently, Klimešová et al. [9] have proposed standardized protocols for collecting plant modularity traits. Such effort to propose easy-to-measure traits representing plant development and ontogeny, with clear functional roles, should foster the awaited development of an “eco-devo” approach.

References

[1] Violle, C., Navas, M. L., Vile, D., Kazakou, E., Fortunel, C., Hummel, I., & Garnier, E. (2007). Let the concept of trait be functional!. Oikos, 116(5), 882-892. doi: 10.1111/j.0030-1299.2007.15559.x
[2] Díaz, S. et al. (2016). The global spectrum of plant form and function. Nature, 529(7585), 167-171. doi: 10.1038/nature16489
[3] Roggy, J. C., Nicolini, E., Imbert, P., Caraglio, Y., Bosc, A., & Heuret, P. (2005). Links between tree structure and functional leaf traits in the tropical forest tree Dicorynia guianensis Amshoff (Caesalpiniaceae). Annals of forest science, 62(6), 553-564. doi: 10.1051/forest:2005048
[4] Fortunel, C., Stahl, C., Heuret, P., Nicolini, E. & Baraloto, C. (2020). Disentangling the effects of environment and ontogeny on tree functional dimensions for congeneric species in tropical forests. New Phytologist. doi: 10.1111/nph.16393
[5] Barthélémy, D., & Caraglio, Y. (2007). Plant architecture: a dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny. Annals of botany, 99(3), 375-407. doi: 10.1093/aob/mcl260
[6] Hallé, F., & Oldeman, R. A. (1975). An essay on the architecture and dynamics of growth of tropical trees. Kuala Lumpur: Penerbit Universiti Malaya.
[7] Sultan, S. E. (2007). Development in context: the timely emergence of eco-devo. Trends in Ecology & Evolution, 22(11), 575-582. doi: 10.1016/j.tree.2007.06.014
[8] Levionnois, S., Tysklind, N., Nicolini, E., Ferry, B., Troispoux, V., Le Moguedec, G., Morel, H., Stahl, C., Coste, S., Caron, H. & Heuret, P. (2020). Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree. bioRxiv, 351197, ver. 4 peer-reviewed and recommended by PCI Ecology. doi: 10.1101/351197
[9] Klimešová, J. et al. (2019). Handbook of standardized protocols for collecting plant modularity traits. Perspectives in Plant Ecology, Evolution and Systematics, 40, 125485. doi: 10.1016/j.ppees.2019.125485

## MUNOZ François

• Laboratoire d'Ecologie Alpine, Université Grenoble Alpes, Grenoble, France
• Biodiversity, Biogeography, Botany, Coexistence, Community ecology, Conservation biology, Eco-evolutionary dynamics, Ecological successions, Evolutionary ecology, Interaction networks, Landscape ecology, Macroecology, Ontogeny, Phylogeny & Phylogeography, Spatial ecology, Metacommunities & Metapopulations, Species distributions, Statistical ecology, Terrestrial ecology, Theoretical ecology, Tropical ecology
• recommender

#### Reviews:  0

Areas of expertise
I currently work at the Laboratoire Interdisciplinaire de Physique (LIPHy), Université Grenoble Alpes. My research concerns Ecology, Biogeography and Evolutionary Biology. It mainly investigates the mechanisms underlying population and community dynamics at multiple spatial and temporal scales. Theoretical and methodological approaches are developped and applied to study tropical and temperate ecosystems (tropical forests in India and New Caledonia, grasslands and weed communities in Europe, high-elevation peatlands in Bolivia...).