# Ecological and evolutionary dynamics of interconnectedness and modularity

^{a}Department of Mathematics, University of Bergen, N-5020 Bergen, Norway;^{b}Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544;^{c}Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544;^{d}Parmenides Center for the Conceptual Foundations of Science, D-82049 Pullach, Germany;^{e}MTA-ELTE (Hungarian Academy of Sciences—Eötvös University), Theoretical Biology and Evolutionary Ecology Research Group, Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös University, Budapest, H-1117 Hungary;^{f}Evolutionary Systems Research Group, MTA (Hungarian Academy of Sciences) Ecological Research Centre, Tihany, H-8237 Hungary;^{g}Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, N-0316 Oslo, Norway

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Contributed by Nils C. Stenseth, December 1, 2017 (sent for review September 12, 2017; reviewed by David C. Krakauer and Günter P. Wagner)

## Significance

This work contains two major theoretical contributions: Firstly, we define a general set of measures, referred to as interconnectedness, which generalizes and combines classical notions of diversity and modularity. Secondly, we analyze the temporal evolution of interconnectedness based on a microscale model of ecoevolutionary dynamics. This we do by providing a platform for understanding how and why macroecological phenomena change over long time scales. Such a platform allows us to show how the drivers of the dynamics of macroecological descriptors, such as interconnectedness, can be partitioned into three components: (*i*) ecologically driven change, (*ii*) evolutionarily driven change, and (*iii*) environmentally driven change.

## Abstract

In this contribution, we develop a theoretical framework for linking microprocesses (i.e., population dynamics and evolution through natural selection) with macrophenomena (such as interconnectedness and modularity within an ecological system). This is achieved by developing a measure of interconnectedness for population distributions defined on a trait space (generalizing the notion of modularity on graphs), in combination with an evolution equation for the population distribution. With this contribution, we provide a platform for understanding under what environmental, ecological, and evolutionary conditions ecosystems evolve toward being more or less modular. A major contribution of this work is that we are able to decompose the overall driver of changes at the macro level (such as interconnectedness) into three components: (*i*) ecologically driven change, (*ii*) evolutionarily driven change, and (*iii*) environmentally driven change.

- microevolution
- population biology
- macroecological patterns
- macroevolutionary patterns
- mathematical modeling

In evolutionary biology, as in other branches of science, there is a gap between macroscopic patterns and microscopic dynamics, raising the challenge of relating microevolutionary and macroevolutionary perspectives. Here we aim at contributing toward bridging this gap by using a population-based microevolutionary model (where genetic variation modeled as a diffusion process leads to microevolution) to deduce the long-term dynamics of macroecological features of ecological systems. Specifically, we aim at contributing toward the understanding of how macroecological features change over time as a result of population−biological processes. As our focal macroecological feature, we have chosen the issue of interconnectedness, including a generalization of the notion of network modularity (1⇓–3). Modularity is an important concept in evolutionary biology (4). However, an understanding is lacking of when and how it emerges from ecoevolutionary processes. Essentially, we provide a platform for understanding under what environmental, ecological, and evolutionary conditions ecosystems evolve toward being more or less modular; see also Lorenz et al. (5) for an excellent and complementary approach to the emergence and evolution of modularity.

We focus on interconnectedness, that is, how species within an ecosystem relate to each other, both qualitatively (i.e., competitive, trophic, etc.) and quantitatively (i.e., the strength of the ecological interaction). The concept of interconnectedness bears resemblance to notions of alpha, beta, and gamma diversity in community ecology (6), but expressed in terms of nearness in trait space rather than geographical space. Our approach is linked with the tradition of investigating complexity and stability in ecosystems (7, 8). That topic has traditionally ignored evolutionary processes, focusing just on the ecological dynamics [see, e.g., refs. 9⇓–11]; it leaves open the fundamental question of how evolution will shape emergent properties like modularity and robustness. We address this lack through the inclusion of a genetic operator. We concretize our study of interconnectedness by addressing the overall question of how ecological and microevolutionary processes together impact the macroscopic measures of structure of the ecosystems arising from our model. A refinement of this inquiry is to look for compartmentalization (i.e., modularity) in ecosystems. In food webs, for example, compartments (which are subgroups of taxa with many strong interactions in each subgroup and few weak interactions among them) have been empirically identified and revealed to increase stability (12⇓⇓–15). Asking questions regarding how interconnectedness changes over time corresponds to querying how the structure of the ecological interactions changes over time, on both short and long time scales.

A main contribution of this study is the partitioning of the driving forces behind the evolutionary change of interconnectedness into three components: (*i*) ecologically driven change, (*ii*) evolutionarily driven change, and (*iii*) environmentally driven change. This partitioning is inspired by the foundational contribution of Fisher (16), who showed that the variance of a trait can be partitioned into variance due to the environment and variance due to genetic components, including additive genetic effects, dominance effects, and epistasis.

We first provide the basic dynamic model, a mathematical definition of interconnectedness and modularity, followed by a partitioning into three components of drivers. These results are then discussed specifically with respect to our formulations, as well as with respect to the more general related literature.

## Sources of Change in Macroevolutionary Processes and Patterns

Our overall purpose is to provide a theoretical framework for linking measures of macroevolutionary processes and patterns to microevolutionary processes. For this purpose, we will use a set of measures of interconnectedness as macroscopic descriptors. Specifically, we will aim to achieve an understanding of how interconnectedness depends on, and changes in response to, ecological processes and evolution within populations (i.e., microevolution). Within this context, we carefully assess the concept of interconnectedness, and provide an operational definition of this term. Thereafter, we introduce a general ecoevolutionary model and discuss the microevolutionary and ecological drivers of system-level changes in interconnectedness. In framing our results in terms of continuous distributions, we are basically assuming that the population is infinite, and neglecting genetic drift due to demographic stochasticity.

### Ecological Interaction and Evolution.

To prescribe a reasonably general model for ecological interaction and evolution, we introduce the concept of a high-dimensional space Ω, where the dimensions correspond not just to observable traits but, in a broad sense, to all traits of relevance (17). Specifically, we consider k independent traits (or trait families), which define the characteristics of the population of interest. We consider that all observables are continuous parameters (e.g., length, height, strength), although the case of discrete observables (number of legs, spots, fins, etc.) can be accommodated. As such, any combination of phenotypes can be represented by a position vector *Supporting Information* for an example of this). We note that, while this approach, in principle, requires a high-dimensional space Ω, empirical evidence suggests that, in practice, many ecosystems align themselves on relatively low-dimensional submanifolds (19).

We limit the discussion to a community that can be considered to have negligible physical extent; thus our position vector only relates to trait space. Taking this into consideration, the term “total population density” refers to the summed density of all individuals in the community. In this case, a fairly general model for ecological interaction and evolution can be stated as**1** thus relates the rate of change of the total population density to ecological interactions as represented by the growth/decay rate A (which is dependent on the current system state), and to intergenerational phenotypic (diffusive) change via a second-order differential operator, where g has the interpretation of phenotypic dispersion per time. As such, Eq. **1** is a generalization of reaction−diffusion type equations, and includes common models in ecology and evolution as subsets (see, e.g., refs. 20⇓–22; for a more detailed discussion, see refs. 17 and 23).

In Eq. **1**, the function **1** must be complemented with boundary conditions; we will herein only consider the case where the parameter space Ω either is periodic or is large enough that the solution **1**. The results that follow can be further generalized to diffusive differential operators of fractional order, equivalent to distributions with so-called fat tails. Our use of a diffusion operator differs from the standard diffusion in population genetics, namely, the diffusion of types due to finite populations, whose strength decays to zero as types hit the boundary of frequency space.

The time dependence of the parameter functions reflects changes in environmental conditions. As an example, we can consider a case where environmental conditions are given by a set of parameter functions, which we denote by **1** allows for any prescribed environmental conditions. A special case is where the external conditions themselves satisfy a separate evolution equation (see, e.g., work by refs. 24 and 25), such as, for example,

We understand n in a probabilistic sense, such that it represents a continuous density function (rather than a precise discrete accounting of individuals). Thus, the second term of Eq. **1** represents, in a probabilistic sense, the spread in phenotype space associated with intergenerational variability due to mutations during inheritance. The parameter g thus has the interpretation of trait variability per generation, expressed in continuous time. Evolution arises as the ecological interactions encoded in A provide favorable circumstances for certain parts of the variability introduced through the diffusive term.

The incremental impact function

The special case where A is linear leads to

Our approach may be linked with the tradition of investigating complexity and stability in ecosystems (7, 8, 25), a topic traditionally restricted to the ecological time scale. We go further because of the inclusion of the genetic operator. Our model allows us to explore the species and interactions that may coexist, as well as the ecological circumstances that underpin this coexistence. This can be modified to look at compartmentalization (modularity) in ecosystems. In food webs, for example, compartments (subgroups of taxa with many strong interactions in each subgroup and few weak interactions among them) have been empirically identified and revealed to increase stability (12⇓–14). Our model is furthermore connected to the adaptive dynamics approach, in that Eq. **1** can be seen as the continuum limit of the stochastic processes from which the so-called canonical equation of adaptive dynamics is derived (22). In the special case of well-defined and distinct species with small mutation rates (when traits can be approximated as normally distributed), Eq. **1** is an equivalent formulation of the canonical equation.

### Measures of Interconnectedness and Modularity.

Interconnectedness and the closely related concept of modularity are examples of widely used concepts that address structural properties of communities and ecosystems (27⇓–29).

Our intention is to consider a family of related interconnectedness measures, which not only reflect the number and abundance of species within a system but also the variability within each species. It is therefore not sufficient to consider measures based solely on counting species (30); we also need to incorporate measures of both interspecies and intraspecies variability. Our concept of interconnectedness is a functional one, in that we aim for a metric that not only evaluates the individuals present in a population but also honors their interactions in the ecological system. To discuss interconnectedness in a macroevolutionary setting, it is imperative to have definitions that not only capture the current state of the system but also are sensible in the face of speciation and extinction. It follows that a measure in this context must honor both interspecies and intraspecies variability.

With this motivation, we start by considering the total population size, defined as a time-dependent quantity,

A secondary consideration is that it is useful, from a modeling perspective, if measures of interconnectedness and modularity have information content not only as measurements of the state of the system but also as variables internal to the system. To this end, we introduce the concept of “observed interconnectedness” *i*) The observed interconnectedness *ii*) We define *iii*) From an operational perspective, an individual cannot observe any interconnectedness associated with species it does not have any interaction with; thus, for two populations *iv*) The observed interconnectedness cannot decrease with the addition of new individuals to the population, i.e.,

To discuss the difference in structure between differences in traits and strength of interactions, we will use the additional property that (*v*) for a population composed of a single species, interconnectedness should not decrease when the variance of traits increases; that is, if we define a normal distribution with variance ϵ as

When omitting this property, and thus neglecting the differences in traits, we can recover standard measures of diversity as special instances of interconnectedness. This is detailed in *Supporting Information*.

The following functional form is perhaps the simplest that satisfies all of the above relationships, while retaining a reasonable generality:**6** can be interpreted in several ways, including as the observed population (from x) relative to the total population. Secondly, if we consider

We note that both the observed and imposed interconnectedness take on distinct values for each point in Ω, much in analogy to the coordination number or connectedness measures on graphs. From this analogy, we expect that the distributions of **7** and **9**, we obtain, directly, the global interconnectedness measure,**10** and **11**, we see that the interconnectedness measure H reflects both the phenotypic distance between members of the population and the mutual interaction of individuals. If the population is a monotype, or if no individuals have any interaction, the interconnectedness will be zero.

While a much richer notion, the mean interconnectedness can nevertheless be related to measures of diversity such as the Shannon entropy or the Simpson measure by particular (degenerate) choices of *Supporting Information*.

To increase the intuition for the interconnectedness measure, we illustrate the interconnectedness associated with some simple (yet typical) situations in Fig. 1; in Box 1, we further provide some gedanken examples illustrating our measure of interconnectedness. The calculations used to obtain the illustrative values of interconnectedness are based on a discrete number of individuals (as opposed to the general formulation allowing for distribution of traits), as summarized in *Supporting Information*, in particular Eq. **S3**. This allows us to provide an interpretation of the interconnectedness kernel

#### Box 1: Gedanken Examples of Change in Interconnectedness

To provide a better intuitive understanding of the interconnectedness concept, we here provide some gedanken examples as to how some typical ecological and evolutionary changes might lead to changes in the degree of interconnectedness (additional examples may be found in ref. 5).

a. Speciation: During speciation, it is reasonable to assume that the interaction between the two new species is similar to the interaction within the original species. Thus, speciation may be thought of as two populations that are initially at the same phenotypic position, such that

b. Change in the overall environmental conditions: Environmental change may both increase and decrease interconnectedness through the impact on the interaction strength ϕ. When, for instance, two ecosystems that have previously been physically separated merge, previously noninteracting species (

c. Ecological dynamics: Ecological dynamics will continuously change the interconnectedness of a system. For example, an ecosystem with a very dominant species may be seen to have a relatively small interconnectedness, according to Eq. **9** (assuming a relatively constant interaction strength ϕ). After the collapse of the dominant species (through internal or external processes), the resulting system will be more well-balanced, and will, in general, result in a higher degree of interconnectedness. The well-studied hare−lynx system may be seen as such an example of rapid change in relative population sizes (38, 39, 46).

d. Ghost of competition past: Connell (32) argues that measuring the apparent overlap of species in nature is a misleading way to measure the extent to which they compete. Indeed, overlap may imply the absence of competition because species have been displaced to coexist. The degree of interconnectedness naturally differentiates these cases.

e. Autocatalytic sets and ecosystem functioning:Ecological systems develop syntrophy and other mutualistic interactions. These interactions link species together into autocatalytic modules that are self-sustaining and interact perhaps more weakly with other modules, as suggested by Paine (15). Over evolutionary time, some linkages tighten, while others weaken. The modularity of the system can be derived from the interconnectedness measures, as exposed in Eq. **15**.

Referring to Fig. 1, we note that, in general, the highest value of interconnectedness would be obtained by a population that is proportional to the eigenfunction of **11**, we note that

Although we do not include classical genetics, we are considering hereditary phenotypes. However, if we had included genetics explicitly, we would have another component of interconnectedness. Two genotypes with the same phenotype can (*i*) have different evolutionary potential or (*ii*) become distinct phenotypes when the biotic or the abiotic environment changes.

Interconnectedness as defined above is closely related to modularity (2) by using the interpretation introduced above of the interconnectedness kernel as the connectivity map of a graphon. More precisely, we consider the population

Modularity has proven to be useful in investigating the substructure of populations. Thus, for any division of the population into two subpopulations, identified by a partition function

We obtain an explicit link between the interconnectedness measure **10** and the ecoevolutionary system stated in Eqs. **1**–**3** by assuming that the interaction strength ϕ is dependent on the impact function α. To be concrete, we will take the interaction strength to be equal to the magnitude of the incremental impact function, such that**3**, **10**, and **11**.

### Drivers of Change in Interconnectedness.

A principal goal of our work is to determine the ecological and microevolutionary processes that drive changes in interconnectedness, and thereby also modularity. Taking Eq. **10** as a starting point, we can use the general ecoevolutionary model from Eq. **1** to evaluate the temporal change in interconnectedness as it responds to the ecological system. While we, in this section, deal with the mean interconnectedness *Supporting Information* for details)

Similarly, **17** represent the main drivers for change in interconnectedness. For a system in equilibrium (i.e., stasis), Eq. **20** implies that **17** that, while both

The environmental term **4**, where **16**. Loosely speaking, we understand that environmental and population changes that lead to weaker interactions in the ecosystem (e.g., a relative abundance of resources, due to either external factors or a reduction in population) reduce the interconnectedness measure, while environmental conditions which lead to a strengthening of interactions lead to an increase in interconnectedness. This interpretation generalizes to weakly nonlinear systems. When, furthermore, the external conditions are constant and a linear model is considered (in the sense of Eq. **4**, as was the case in ref. 17),

In contrast, both *Supporting Information*, we conclude that

To gain an understanding of the ecological driver **4**. Furthermore, we let *Supporting Information*.

In *Supporting Information*, we also show that, for the linear system, strong contributions to interconnectedness occur when, for two trait combinations x and *i*) there is an abundance of individuals in the sense that *ii*) the interactions are strong in the sense of *ii* and *iii*, in light of the definition given in Eq. **11**, we understand that these two conditions will hold together if the distance *Supporting Information*.

This also emphasizes the difference between measures of interconnectedness and diversity, wherein the latter generally do not include the concepts of trait distance, and, as such, do not capture the nuances associated with the similarity of species identified above.

## Discussion and Conclusion

Our model relates to the tradition of Simpson (33), with regard to phenotypic and adaptive landscapes that are dynamically changing (see, e.g., refs. 22 and 34⇓–36). These kinds of landscapes have been proposed to conceptually bridge microevolution and macroevolution (37). We submit that our model is a dynamical manifestation of this goal, in that we account for the coevolutionarily and environmentally driven movement of the fitness optima.

In this contribution, we have emphasized the fundamental structure of interconnectedness and the ecological, evolutionary, and abiotic drivers for changes in this metric. The interconnectedness concept naturally applies to biological systems, in particular when interaction strengths have been determined. A particularly relevant example is the present-day vegetation−hare−lynx system of the Canadian boreal forest, for which interaction coefficients have been determined explicitly from time series analysis (38, 39). To understand the changes in interconnectedness over time, longer records are needed, and analysis of paleontological data, based on assumptions of correlations between interaction and species overlap, provides a promising avenue (40); see also Box 1.

Interconnectedness and modularity are fundamental features of ecological systems, and are fundamental determinants of their robustness in the face of endogenous and exogenous threats (41). Simon (42) emphasized the importance of modularity through his parable of two watchmakers, and explored the dynamics of modular systems by demonstrating how they play out on multiple time scales (35) [in evolutionary biology, modularity plays a fundamental role, and arises naturally from microscopic forces (1, 3, 36)]. In this paper, we have developed a mathematical framework to explore the emergence of modularity. Inspired by Fisher’s foundational paper on components of variance, we partition the rate of change in modularity into the parts due to endogenous factors, specifically ecological and evolutionary dynamics, and those due to exogenous environmental changes. Such a partitioning may help us separate ecological and evolutionary drivers of variation of macroecological descriptors, not the least to differentiate between biotic and abiotic drivers of evolutionary dynamics (43). More generally, although we have focused on interconnectedness, the approach may be used to explore influences on other macroscopic descriptors.

## Acknowledgments

Jo Skeie Hermansen, Joshua Plotkin, Sergey Kryazhimskiy, Kjetil Lysne Voje, and Han Wang are all thanked for commenting upon an earlier version of this manuscript. Sari Cunningham is thanked for splendid linguistic and copy-editing work on the manuscript. Funding for this project was primarily covered by Centre for Ecological and Evolutionary Synthesis (core and Colloquium 4) funding and the Research Council of Norway (RCN) project, Red Queen coevolution in multispecies communities: Long-term evolutionary consequences of biotic and abiotic interactions (EVOQUE). In addition, S.A.L. acknowledges support through Simons Foundation Grant 395890 and Army Research Office Grant W911NF-14-1-0431. E.S. acknowledges support through the European Research Council [294332 – EVOEVO (Evolution of Evolvable Systems)], GINOP-2.3.2-15-2016-00057, and Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (National Research, Development and Innovation Office) 119347.

## Footnotes

- ↵
^{1}To whom correspondence should be addressed. Email: n.c.stenseth{at}ibv.uio.no.

Author contributions: J.M.N. and N.C.S. designed research; J.M.N. and N.C.S. performed research; and J.M.N., S.A.L., E.S., and N.C.S. wrote the paper.

Reviewers: D.C.K., Santa Fe Institute; and G.P.W., Yale University.

The authors declare no conflict of interest.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1716078115/-/DCSupplemental.

- Copyright © 2018 the Author(s). Published by PNAS.

This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

## References

- ↵
- Schlosser G,
- Wagner GP

- ↵
- Newman MEJ

- ↵
- ↵
- ↵
- ↵
- Whittaker RH

- ↵
- ↵
- ↵
- ↵
- Gilarranz LJ,
- Rayfield B,
- Liñán-Cembrano G,
- Bascompte J,
- Gonzalez A

- ↵
- Sales-Pardo M

- ↵
- ↵
- Yodzis P

- ↵
- ↵
- ↵
- ↵
- Nordbotten JM,
- Stenseth NC

- ↵
- ↵
- ↵
- ↵
- ↵
- Takeuchi Y,
- Iwasa Y,
- Sato K

- Dieckmann U,
- Brännström Å,
- HilleRisLambers R,
- Ito HC

- ↵
- Levin SA,
- Segel LA

- ↵
- ↵
- Diekmann O

- ↵
- ↵
- Begon M,
- Townsend CR,
- Harper JL

- ↵
- ↵
- Stouffer DB,
- Bascompte J

- ↵
- Pielou E

- ↵
- ↵
- ↵
- Simpson GG

- ↵
- ↵
- ↵
- ↵
- ↵
- Stenseth NC,
- Falck W,
- Bjørnstad ON,
- Krebs CJ

- ↵
- Stenseth NC, et al.

- ↵
- Žliobaitė I,
- Fortelius M,
- Stenseth NC

- ↵
- Levin SA

- ↵
- Simon HA

- Simon HA

- ↵
- Voje KL,
- Holen ØH,
- Liow LH,
- Stenseth NC

- ↵
- Grant PR,
- Grant BR

- ↵
- Barnes I,
- Matheus P,
- Shapiro B,
- Jensen D,
- Cooper A

- ↵
- Krebs CJ,
- Boutin SA,
- Boonstra R

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