Sylvie Estrela
email: sylvie.estrela@yale.edu

Eric Libby

Jeremy Van Cleve

Florence Débarre

Maxime Deforet

William R Harcombe

Jorge Peña

Sam P Brown

Michael E Hochberg
email: mhochber@univ-montp2.fr

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Keywords: social evolution, niche construction, microbes, dispersal, spatial structure, ecology

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The role of environmental intermediates in social interactions has received increasing attention in recent years due to the growing realization that explicitly considering environmental dimensions is critical to capturing realistic system complexity [START_REF] Odling-Smee | Niche construction: the neglected process in evolution[END_REF][START_REF] Brown | The Durability of Public Goods Changes the Dynamics and Nature of Social Dilemmas[END_REF][START_REF] Mcnally | Building the microbiome in health and disease: niche construction and social conflict in bacteria[END_REF][START_REF] Kümmerli | Molecular and regulatory properties of a public good shape the evolution of cooperation[END_REF][START_REF] Weitz | An oscillating tragedy of the commons in replicator dynamics with game-environment feedback[END_REF][START_REF] Benincà | Species fluctuations sustained by a cyclic succession at the edge of chaos[END_REF]. For example, if cooperation (or helping) among organisms improves the environment or prevents its degradation, then oscillations can emerge where cooperators and defectors alternately dominate as the environment cycles between replete and deplete states [START_REF] Weitz | An oscillating tragedy of the commons in replicator dynamics with game-environment feedback[END_REF]. Thus, a more realistic and complex theory of social evolution that includes these important feedbacks needs to account for niche construction effects [START_REF] Odling-Smee | Niche construction: the neglected process in evolution[END_REF].

At present, theoretical work on environmentally-mediated social interactions has largely focused on simple non-spatial settings (except, notably, work on biofilms; e.g, [START_REF] Kreft | Biofilms promote altruism[END_REF][START_REF] Xavier | Cooperation and conflict in microbial biofilms[END_REF][START_REF] Nadell | Emergence of spatial structure in cell groups and the evolution of cooperation[END_REF][16][START_REF] Nadell | Spatial structure, cooperation and competition in biofilms[END_REF]), while the broader field of social evolution has long recognized the importance of spatial structure and its role in generating genetic relatedness that can lead to the evolutionary success of cooperation and altruism (e.g., [START_REF] Hamilton | The genetical evolution of social behaviour[END_REF][START_REF] Lehmann | How life history and demography promote or inhibit the evolution of helping behaviours[END_REF][START_REF] Taylor | Altruism in viscous populations -an inclusive fitness model[END_REF][START_REF] Lehmann | The evolution of helping and harming on graphs: the return of the inclusive fitness effect[END_REF]). Mathematical models have shown the influence of spatial structure in determining the success of cooperative behaviours particularly in the face of non-linear group benefits (e.g., [START_REF] Peña | Evolutionary dynamics of collective action in spatially structured populations[END_REF]), repeated interactions (e.g., [START_REF] Akçay | Behavioral responses in structured populations pave the way to group optimality[END_REF][START_REF] Van Cleve | Pathways to social evolution: reciprocity, relatedness, and synergy[END_REF]), or when individuals can move [START_REF] Hauert | Spatial structure often inhibits the evolution of cooperation in the snowdrift game[END_REF][START_REF] Aktipis | Know when to walk away: contingent movement and the evolution of cooperation[END_REF][START_REF] Aktipis | Is cooperation viable in mobile organisms? Simple Walk Away rule favors the evolution of cooperation in groups[END_REF][START_REF] Hochberg | The coevolution of cooperation and dispersal in social groups and its implications for the emergence of multicellularity[END_REF][START_REF] Parvinen | Joint evolution of altruistic cooperation and dispersal in a metapopulation of small local populations[END_REF]. Yet, these models often do not explicitly consider environmental dimensions. The few models that do consider explicit environmental variables suggest that when cooperation improves the local environment, selection for cooperation can be very strong [29][START_REF] Pepper | Simple models of assortment through environmental feedback[END_REF][START_REF] Lehmann | The evolution of cooperation and altruism -A general framework and a classification of models[END_REF][START_REF] Kéfi | Evolution of local facilitation in arid ecosystems[END_REF]. For example, Pepper and Smuts used an agent-based spatial model with conditional movement and environmental feedbacks to demonstrate that cooperation can be maintained in the presence of defection when cooperators improve the environment. Individuals are then less likely to leave locations with good environmental conditions [29].

Here, we seek to address questions concerning how different types of social behaviour can be maintained under the more realistic conditions of non-random (structured) interactions, explicit environmental currencies, and dispersal (or migration) of individuals (Figure 1). This is particularly relevant to cooperative behaviours, which have been shown to require one or more non-homogeneous selective structures to persist (e.g., [START_REF] Van Cleve | Pathways to social evolution: reciprocity, relatedness, and synergy[END_REF][START_REF] Lehmann | The evolution of cooperation and altruism -A general framework and a classification of models[END_REF][33][START_REF] Nowak | Five Rules for the Evolution of Cooperation[END_REF][START_REF] Fletcher | A simple and general explanation for the evolution of altruism[END_REF][START_REF] Frank | Genetics of mutualism: the evolution of altruism between species[END_REF]). For instance, can 'helper' organisms sufficiently co-localize with the products of their helping activities to survive free-loaders? Can 'harming' organisms escape the environmental damage they create? Given the complexity of spatial systems of interacting organisms and environments, the answers to these and other related questions are far from straightforward and require explicit mathematical models and controlled experiments (see Outstanding Questions).

Environmental resources and the tragedy of the commons

Social dilemmas are loosely characterized by the conflict between the individual costs (or benefits) of certain actions and the benefits (or costs) of those actions when performed by enough members of a collective. In many cases, these actions involve the production or consumption of environmental resources valuable for the survival and reproduction of individuals in a group, where groups are defined by the ability of individual members to access those resources. These accessible resources are typically depletable, such as glucose concentration in a bacterial batch culture, and are referred to as 'public goods' or 'common pool resources'. The social dilemma involved in maintaining public goods is often called the 'tragedy of the commons' [START_REF] Hardin | The tragedy of the commons[END_REF]. The idea is that if members of a social group use a shared limiting resource ('the commons') selfishly without restraint, then the commons will be depleted and eventually destroyed. Since organisms across the tree of life exhibit a variety of mechanisms involved in the production, acquisition, or maintenance of environmental resources and public goods, they all potentially face this dilemma [START_REF] Rankin | The tragedy of the commons in evolutionary biology[END_REF][START_REF] Levin | Public goods in relation to competition, cooperation, and spite[END_REF]. Yet, the degree to which evolution averts the tragedy of the commons is often a complex function of individual and collective interactions with the environment. Next, we present a classification of these interactions.

Figure 1. Variety of potential interactions between organisms and their environment.

For simplicity, models of social evolution have often assumed that interactions between individuals are random, the physico-chemical environment is implicit, and/or individuals cannot move within a patch or disperse between patches (top scenarios).

Here we focus on the more realistic conditions where interactions are non-random, the environmental dimension is explicit, and individuals can migrate (bottom scenarios) to investigate how these combined effects may influence the ecology and evolution of social behaviours. Note that we use the terms migration and dispersal interchangeably.

A classification of environmentally mediated social behaviours

Here, we present a framework for classifying social dilemmas based on their environmental interactions that is inspired by Hamilton's individual-based classification [START_REF] Hamilton | The genetical evolution of social behaviour[END_REF][START_REF] Hamilton | The genetical evolution of social behaviour[END_REF][START_REF] Hamilton | Selfish and spiteful behaviour in an evolutionary model[END_REF]. Our first step is to recognize that helping and harming behaviours can be classified as one of two distinct environmental interactions: (1) when the actor produces a helpful or harmful environmental factor, and (2) when the actor consumes a harmful or helpful factor (Figure 2). In our classification, a focal individual (actor) that produces a collectively beneficial good is referred to as an 'enricher', whereas an actor that degrades a damaging environmental factor is a 'detoxifier'. Under the classical classification of social traits, both would be viewed as expressing a 'helping' behaviour (with further categorization as either altruistic or mutualistic depending on the degree to which the benefits return to the focal actor). Similarly, our new classification breaks down conventional 'harming' behaviours into 'pollute' and 'deplete', depending on whether the harming activity produces an environmental 'bad' or consumes a shared environmental 'good'. Thus, what appears as the same social behaviour under the classical taxonomy of social evolution, may actually be the result of opposing consume/ produce strategies when the environment is explicitly considered. Therefore, to understand how social traits mediated through the environment evolve, it is crucial to explicitly consider the contrast between producing and consuming environmental goods and bads, and how this influences the outcome of social dilemmas on both temporal and spatial scales. Below, we discuss examples of our 4-way classification with a focus on microbes.

Microbial examples of the proposed classification

Microbes affect each other in many ways. These effects may arise through direct physical contact (e.g. attachment, toxin injection), or indirectly by simply sharing the same chemical environment. Such environmental intermediates not only shape microbial composition ('who is there') but also the nature of their interaction ('what they do to each other'). Here, we go beyond the traditional 'helping' versus 'harming' classification of social behaviour. We discuss empirical and theoretical examples of our 4-way classification -enrich, detox, pollute, and deplete -to illustrate how considering explicit organism density and environmental dimensions can be important to understand the ecology and evolution of social dilemmas.

Although the mechanisms of enrich, detox, pollute, and deplete are discussed separately, they are clearly often intertwined and interdependent, contributing to the rich and complex diversity of microbe-microbe metabolic interactions found in nature.

Enrich

Microbes can modify their environment by producing extracellular molecules that promote the growth of the focal individual and its neighbouring conspecifics. Examples include exoenzymes that break down complex substrates into simple digestible compounds (e.g. glycoside hydrolases and proteases) [START_REF] Rakoff-Nahoum | The evolution of cooperation within the gut microbiota[END_REF], scavenging molecules that bind specific compounds in the environment and transport them back into the cell (e.g. iron-scavenging siderophores [START_REF] Griffin | Cooperation and competition in pathogenic bacteria[END_REF][START_REF] Cordero | Public good dynamics drive evolution of iron acquisition strategies in natural bacterioplankton populations[END_REF]), and structural compounds in biofilms that protect microbes from chemical or biological attacks [START_REF] Nadell | Extracellular matrix structure governs invasion resistance in bacterial biofilms[END_REF][START_REF] Vidakovic | Dynamic biofilm architecture confers individual and collective mechanisms of viral protection[END_REF][45] or place them near other important resources such as oxygen [46]. In some cases, such exoenzymes benefit the focal producer and conspecifics by directly harming others such as antibiotics and toxins that kill or inhibit the growth of other interspecific organisms [START_REF] Riley | The ecological role of bacteriocins in bacterial competition[END_REF]. Production of such beneficial compounds are often costly and will only evolve if it sufficiently benefits the focal producer ('enricher'), as otherwise it would be selected against and eventually lost from the population. Any benefits leaked to other neighbouring organisms depend on how public or private these traits are.

Detox

A long-overlooked but increasingly important factor shaping microbial interactions is detoxification [START_REF] Estrela | From metabolism to ecology: cross-feeding interactions shape the balance between polymicrobial conflict and mutualism[END_REF][START_REF] Estrela | Private benefits and metabolic conflicts shape the emergence of microbial interdependencies[END_REF][START_REF] Yurtsev | Oscillatory dynamics in a bacterial cross-protection mutualism[END_REF][START_REF] Morris | Dependence of the cyanobacterium Prochlorococcus on hydrogen peroxide scavenging microbes for growth at the ocean's surface[END_REF][52][53][START_REF] O'brien | No effect of intraspecific relatedness on public goods cooperation in a complex community[END_REF][START_REF] O'brien | Social evolution of toxic metal bioremediation in Pseudomonas aeruginosa[END_REF][START_REF] Frost | Cooperation, competition and antibiotic resistance in bacterial colonies[END_REF][START_REF] Estrela | Community interactions and spatial structure shape selection on antibiotic resistant lineages[END_REF][58]. Examples include the production of enzymes such as beta-lactamases that degrade beta-lactam antibiotics [53,[START_REF] Frost | Cooperation, competition and antibiotic resistance in bacterial colonies[END_REF], catalases that degrade hydrogen peroxide [START_REF] Morris | Dependence of the cyanobacterium Prochlorococcus on hydrogen peroxide scavenging microbes for growth at the ocean's surface[END_REF]58], and metal-chelating siderophores that reduce toxic metal levels [START_REF] O'brien | No effect of intraspecific relatedness on public goods cooperation in a complex community[END_REF][START_REF] O'brien | Social evolution of toxic metal bioremediation in Pseudomonas aeruginosa[END_REF].

Detoxification not only benefits the focal producer ('detoxifier') but also protects neighbouring cells living in the environment. Because enzyme production is generally costly, detoxification is expected to have evolved to benefit the producer, with the benefits to others depending on the privatization level of detoxification [START_REF] Estrela | Private benefits and metabolic conflicts shape the emergence of microbial interdependencies[END_REF][START_REF] Morris | Black Queen evolution: the role of leakiness in structuring microbial communities[END_REF]. Detoxification can nevertheless also be 'incidental' (i.e., not selected for), such as when an organism feeds on compounds that are toxic to others but not to itself [START_REF] Estrela | From metabolism to ecology: cross-feeding interactions shape the balance between polymicrobial conflict and mutualism[END_REF][START_REF] Cowan | Commensal interactions in a dual-species biofilm exposed to mixed organic compounds[END_REF].

Pollute

Microbial cells commonly excrete metabolic waste products such as lactic acid, acetate and hydrogen. These metabolic by-products may become toxic, especially when at high concentrations, suppressing the growth of not only the producer cell but also of any nearby susceptible cells (e.g., by changing the pH [START_REF] Ratzke | Modifying and reacting to the environmental pH can drive bacterial interactions[END_REF]). In some cases, however, such metabolic waste can be a resource for another species, allowing both polluters (waste producers) and detoxifiers (cross-feeders) to potentially benefit from the interaction if the mutual benefits of food for detoxification outweigh the costs of competition for shared resources [START_REF] Estrela | From metabolism to ecology: cross-feeding interactions shape the balance between polymicrobial conflict and mutualism[END_REF]. Such syntrophic interactions are common between hydrogen-producing bacteria and methanogens.

While the build-up of hydrogen inhibits the growth of the producing bacteria, methanogens are able to use hydrogen for growth, and thus keep hydrogen levels low, allowing their hydrogen-producing partner to grow [62,[START_REF] Hillesland | Rapid evolution of stability and productivity at the origin of a microbial mutualism[END_REF].

Deplete

Competition over resources -occurring when organisms feed on shared, limiting resources (also known as 'exploitation competition') -is a key factor shaping microbial interactions [START_REF] Ghoul | The Ecology and Evolution of Microbial Competition[END_REF]. Although resource competition is often initially weak, competition increases as cells grow and resources become depleted. In an unstructured environment with a single resource, theory predicts that the more competitive strain displaces the other strains, thus resulting in competitive exclusion [START_REF] Hardin | The competitive exclusion principle[END_REF]. However, the presence of trade-offs [START_REF] Bohannan | Trade-offs and coexistence in microbial microcosms[END_REF][START_REF] Litchman | Microbial resource utilization traits and trade-offs: implications for community structure, functioning, and biogeochemical impacts at present and in the future[END_REF], such as 'fastgrowing but low-yield' vs 'slow-growing but high-yield', can promote species coexistence even when organisms compete for a single resource. From a social evolution perspective, a slow-growing and high-yield strategy can be viewed as a form of cooperation because resources are used in a more prudent way compared to a fast-growing and low-yield strategy. Thus, the evolutionary dilemma is how to maintain prudent (cooperative) resource use in the face of rapacious (non-cooperative) individuals that eventually deplete the commons. A solution to this dilemma is spatial structure that enables cooperators to preferentially interact with other cooperators and remain protected from the local effects of non-cooperators [START_REF] Kreft | Biofilms promote altruism[END_REF][START_REF] Pfeiffer | Cooperation and competition in the evolution of ATPproducing pathways[END_REF][START_REF] Kerr | Local migration promotes competitive restraint in a hostpathogen"tragedy of the commons[END_REF][START_REF] Nahum | Evolution of restraint in a structured rock-paper-scissors community[END_REF].

The value of explicit environmental variables

Models of resource competition have often assumed that resources are implicit (e.g., Lotka-Volterra models of competition, [START_REF] Volterra | Variations and Fluctuations of the Number of Individuals in Animal Species living together[END_REF]) or explicit but with a single resource pool (e.g. classic consumer-resource models with a single limiting resource, [72]). Microbial cells, however, are often surrounded by a variety of genotypes with different resource-use strategies and diverse resource pools (supplied exogenously and/or as by-products of cell metabolism). Classic population ecology models or resource-based models with a single resource can therefore lead to an oversimplified depiction of organism-organism interactions and, in some cases, fail to predict the dynamics of microbial interactions [START_REF] Momeni | Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions[END_REF][START_REF] Goldford | Emergent simplicity in microbial community assembly[END_REF]. Work combining these and other explicit metabolic models with spatially-explicit environmental models [START_REF] Harcombe | Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics[END_REF][START_REF] Chacón | The spatial and metabolic basis of colony size variation[END_REF][START_REF] Xavier | A framework for multidimensional modelling of activity and structure of multispecies biofilms[END_REF] have recently received more attention and promise to yield a wealth of insights into more realistic representations of ecological interactions in nature.

Environment-mediated organism-organism interactions

If we abstract away the environment and consider two organisms and the various positive and negative interactions they can exert on themselves and each other, we find ten possible scenarios after removing symmetries (Figure 3). Six of these interaction scenarios can occur via a single environmental compound (A, D-G,J). Interestingly, some reduced (environmentally-implicit) systems map to two different environmentally-mediated social interactions A,D,E,J), while others map to only one (F,G). Furthermore, the mappings from the reduced system to the environmentally-explicit system are not always identical. For instance, scenario J in the reduced system corresponds to a case where each organism negatively affects itself and the other organism. In the environmentallyexplicit model, this can be either deplete/deplete or pollute/pollute. In the case of deplete/deplete, both organisms consume the same resource and hence negatively affect themselves and each other by removing the resource. In the case of pollute/pollute, both organisms produce the same waste product that negatively affects everyone. Importantly, these two scenarios produce different dynamical models, especially regarding the dynamics of E. It is also worth noting that while some implicit models where O 1 and O 2 have the same strategy lead to explicit models where O 1 and O 2 also have the same strategy (J), in other cases O 1 and O 2 must have different strategies (F, G). Note that here we remove symmetries, that is, when species O 1 and O 2 are interchangeable. Six of the reduced systems can be used to describe all ten unique scenarios involving a single environmental intermediate (E 1 ) and combinations of the environmental modifications enrich, detox, pollute, deplete (A, D-G, J). The other four reduced systems cannot be described with a single environmental factor and at least two are needed (B, C, H, I).

Note that here we only represent environmental variables that directly mediate the interaction between the two organisms. Environmental factors that affect the growth of one organism but not the other are omitted.

Spatial and temporal dynamics of environmentally-mediated social dilemmas

While it is well known in social evolution theory that the spatial scale at which cooperation and conflict over resources occur can have a large impact on evolved levels of cooperation [START_REF] Allison | Cheaters, diffusion and nutrients constrain decomposition by microbial enzymes in spatially structured environments[END_REF][START_REF] Driscoll | Theory for the evolution of diffusible external goods[END_REF][START_REF] Allen | Spatial dilemmas of diffusible public goods[END_REF][81], it is possible that unexpected social evolutionary effects emerge as a consequence of interactions between environmental, spatial, and temporal parameters. To illustrate these possibilities, we describe below how the viscosity (spatial scale) and persistence (temporal scale) of environmental factors can affect the evolution of cooperative traits, and in turn, the spatial arrangement of cooperators and non-cooperators.

Environmental viscosity: cell dispersal and exoproduct diffusion

Environmental mediation allows for social interactions to take place at larger spatial scales via diffusible compounds. Whether or not spatial dynamics favour enriching strategies, depends on environmental viscosity and its effect on the levels of cell dispersal and public goods diffusion [START_REF] Allen | Spatial dilemmas of diffusible public goods[END_REF][START_REF] Kümmerli | Habitat structure and the evolution of diffusible siderophores in bacteria[END_REF]. In the case of high viscosity where both cell dispersal and public goods diffusion are limited, the benefits of public goods can be channeled between adjacent, and likely related organisms. This acts to privatize the produced goods and can promote cooperation (see e.g., [START_REF] Kümmerli | Habitat structure and the evolution of diffusible siderophores in bacteria[END_REF][START_REF] Kümmerli | Viscous medium promotes cooperation in the pathogenic bacterium Pseudomonas aeruginosa[END_REF]). However, if viscosity is such that cell dispersal is limited while public goods diffusion remains high, then the spatial scale over which cooperative interactions occur becomes larger than the spatial scale of competition [START_REF] Platt | Kin competition and the evolution of cooperation[END_REF] and viscosity will not favour the evolution of cooperation (see e.g., [START_REF] Le Gac | Environmental viscosity does not affect the evolution of cooperation during experimental evolution of colicigenic bacteria[END_REF]).

The interplay of social interactions and environmental viscosity has received particular attention in the study of microbial biofilms [START_REF] Nadell | Spatial structure, cooperation and competition in biofilms[END_REF]. To capture realistic features of microbial biofilms as well as the chemical-physical properties of the environment spatially and mechanistically explicit individual-based models have shown to be a powerful approach [START_REF] Nadell | Emergence of spatial structure in cell groups and the evolution of cooperation[END_REF]. Such models can provide important insights into how factors such as exoproduct diffusion, population growth, and social interactions affect the eco-evolutionary dynamics of these spatially-structured systems [START_REF] Nadell | Spatial structure, cooperation and competition in biofilms[END_REF][START_REF] Steenackers | Experimental evolution in biofilm populations[END_REF]. For instance, the extracellular matrix that generates biofilms was initially thought of as a public good that could be exploited by non-producers. However, individual-based models that incorporated nutrient diffusion and cell limited dispersal demonstrated that polymer secretion is often a competitive strategy that lifts producer genotypes into favourable environments while suffocating non-producer genotypes [START_REF] Xavier | Cooperation and conflict in microbial biofilms[END_REF]. Further, individual-based models have revealed how social interactions shape the arrangement of genotypes, and therefore, selection in structured systems. While cells with cooperative genotypes will mix as they grow and divide, competing genotypes become segregated as a result of growth [START_REF] Estrela | Metabolic and demographic feedbacks shape the emergent spatial structure and function of microbial communities[END_REF]88]. This can lead to social insulation of cooperative genotypes from cheating mutants and enhance selection for cooperation [16,89].

Environmental persistence: organism lifespan and exoproduct durability

Another important consequence of environmental mediation is that when modified environmental factors persist for long enough, their effects can be transmitted across generations [START_REF] Brown | The Durability of Public Goods Changes the Dynamics and Nature of Social Dilemmas[END_REF][START_REF] Kümmerli | Molecular and regulatory properties of a public good shape the evolution of cooperation[END_REF] (so that there is ecological inheritance sensu [START_REF] Odling-Smee | Niche construction: the neglected process in evolution[END_REF][START_REF] Odling-Smee | Niche Construction[END_REF]). This means that actors interact not only with existing relatives, but also with relatives living in the future who are not in direct competition with actors in the present [START_REF] Lehmann | The evolution of trans-generational altruism: Kin selection meets niche construction[END_REF][START_REF] Lehmann | The adaptive dynamics of niche constructing traits in spatially subdivided populations: evolving posthumous extended phenotypes[END_REF]. This decouples kin selected benefits and costs due to kin competition and can increase selection pressure on helping traits, including altruistic behaviours that would be selected against when actors and recipients only interact during the same generation [START_REF] Lehmann | The evolution of trans-generational altruism: Kin selection meets niche construction[END_REF]. For durable public goods that are non-rival (i.e. not consumed by use), investments by earlier generations can lead to loss of cooperative genotypes in subsequent generations -unless cooperative behaviours are under regulatory control [START_REF] Brown | The Durability of Public Goods Changes the Dynamics and Nature of Social Dilemmas[END_REF][START_REF] Kümmerli | Molecular and regulatory properties of a public good shape the evolution of cooperation[END_REF]. For microbial interactions, these observations suggest that not only the viscosity of the medium is important, but also the durability of secreted compounds relative to the lifespan of organisms. In the case where exoproduct durability is sufficiently larger than the lifespan of the focal producer, ecological inheritance can operate, thus paving the way for posthumous niche construction [START_REF] Lehmann | The adaptive dynamics of niche constructing traits in spatially subdivided populations: evolving posthumous extended phenotypes[END_REF].

Environmentally-mediated conditional dispersal

The production and consumption of environmental factors (as in the enrich, detox, pollute, deplete scenarios) are one of two ways organisms can engage in niche-constructing activities.

The other way is the active movement of organisms within a patch and dispersal between patches. By dispersing, an individual necessarily changes the patch from which it dispersed, and the patch to which it migrates [START_REF] Odling-Smee | Niche construction: the neglected process in evolution[END_REF]. One inclusive fitness benefit of dispersal is the ability of an individual to avoid competing for resources with its relatives in its natal patch. Though this topic has received considerable theoretical attention [START_REF] Hochberg | The coevolution of cooperation and dispersal in social groups and its implications for the emergence of multicellularity[END_REF][START_REF] Hamilton | Dispersal in stable habitats[END_REF][START_REF] Taylor | How to Make a Kin Selection Model[END_REF][95][START_REF] Perrin | Is sociality driven by the costs of dispersal or the benefits of philopatry? A role for kin-discrimination mechanisms[END_REF][START_REF] Galliard | Adaptive evolution of social traits: Origin, trajectories, and correlations of altruism and mobility[END_REF][START_REF] Mouden | Nice natives and mean migrants: the evolution of dispersal-dependent social behaviour in viscous populations[END_REF][START_REF] Lehmann | Altruism, dispersal, and phenotype-matching kin recognition[END_REF][100], previous work mostly considers the environment implicitly and does not detail the helpful or harmful environmental factors that affect survival and reproduction (i.e., our classification above). One crucial consequence of omitting these environmental factors is the inability to consider how dispersal can evolve to be conditional on the state of the local environment [101], which is itself affected by organismal niche construction. Since many social interactions can be produced by multiple explicit interactions with the environment (see Figure 3), the effect of dispersal conditional on environmental state is likely to depend on these explicit interactions.

To see how dispersal conditional on environmental variables may be important, consider scenario J in Figure 3, whose implicit representation consists of negative effects of each organism on itself and on the other organism. Scenario J can be represented by two explicit environmental interactions: (i) both organisms negatively affect the environmental compound which in turn positively affects them, as occurs in a 'deplete' scenario, or (ii) both organisms positively affect the environment which negatively affects them, as occurs in a 'pollute' scenario. Organismal growth rates increase with greater concentrations of the environmental compound in the 'deplete' scenario and decrease in the 'pollute' scenario. Thus, all else equal, dispersal rates away from the current patch may evolve to be higher for low concentrations of the environmental product in the 'deplete' scenario and lower for high concentrations of the product in the 'pollute' scenario (Box 1). Thus, a single implicit social interaction can generate different dispersal mechanisms depending on the explicit environmental interactions. For a more complex scenario, see Box 1.

Further, dispersal conditional on explicit environmental variables may affect the conditions under which different kinds of social behaviours evolve. For example, models have shown that cooperative interactions that increase group survival or carrying capacity can easily evolve [102,103]. In effect, these cases represent a type of 'enrich' scenario whereby cooperators create larger and more persistent groups. However, if dispersal is allowed to be conditional on local resource concentrations, then non-cooperators are no longer so easily trapped in unproductive groups that go extinct and might preferentially disperse from lowresource patches to high-resource patches with more cooperators. Such conditional dispersal would likely erode the chance that cooperators enrich the environment for other cooperators and diminish selection for cooperation in these scenarios. Interestingly, there also might be selection for conditional dispersal of cooperators away from low-resource patches to highresource patches. Since high-resource concentrations typically cannot be generated by groups composed mostly of non-cooperators, they might serve as an environmental cue of the presence of other cooperators and could create the kind of positive assortment among cooperators necessary to maintain them in the presence of non-cooperators [START_REF] Van Cleve | Pathways to social evolution: reciprocity, relatedness, and synergy[END_REF][START_REF] Lehmann | The evolution of cooperation and altruism -A general framework and a classification of models[END_REF]. In fact, Pepper and Smuts [29] studied this kind of conditional dispersal and found that it can generate significant assortment between cooperators. More generally, conditional dispersal might either enhance or erode cooperation depending on the details of the environmental interactions. Thus, not including explicit environmental interactions could exclude important ecological and evolutionary dynamics in social dilemmas.

Box 1. The value of explicit environment representation in the evolution of dispersal

The interplay between dispersal and environmentally-mediated social dilemmas can benefit from explicit representations of the environment in theoretical models. For example, consider scenario F in Figure 3. In an implicit representation of the environment, organisms O 1 and O 2 appear identical in kind: both help themselves and harm the other. Now suppose that each organism can disperse to a new environment. Under the implicit representation, there is no suggestion that O 1 and O 2 would follow different dispersal strategies. Yet, an explicit representation of the environment shows that they are affected by the environment differently: one enriches the environment while the other detoxifies it. Thus, we would expect each organism to follow a different dispersal strategy as a function of environmental state: the enrich organism has a higher growth rate in higher concentrations of E and so should remain in high E patches (lower dispersal rate from such patches) while the detox organism is harmed by higher concentrations of E and so should move away from high E patches (high dispersal rate) (Figure IA). In a sense, this agrees with the implicit environmental model in that the two organisms should disperse away from one another since they harm each other. Yet, the explicit environmental model suggests a mechanism by which a patchy environment of high/low concentrations of E might facilitate a corresponding spatial assortment of the organisms. Now let's turn to scenario J in Figure 3. Again, in an implicit representation of the environment, organisms O 1 and O 2 appear identical in kind: both harm themselves and harm the other. This time, however, the two organisms interact with the environment in the same way and two different environmentally-mediated interactions are possible: either deplete/deplete or pollute/pollute. We therefore expect each organism to adopt the same dispersal strategy. But importantly, which strategy they adopt -higher or lower dispersal rate-depends on how the explicit environment is represented (Figure IB). Such a contrast would be difficult to capture with an environmentally implicit model. This is also the case in scenario E in Figure 3, in which the two organisms interact with the environment in a different way, and therefore should adopt opposite dispersal strategies. But whether each organism should stay or leave a patch with a particular concentration of E is determined by how the environment is represented (Figure IC). Together, these examples highlight how considering both environmental and social conditions explicitly can provide important insights for understanding and predicting the evolution of conditional dispersal.

Concluding remarks

The typology we describe in Figure 2 aims to catalyse a better mechanistic understanding of the ecological and evolutionary dynamics of environmentally mediated social dilemmas. Nevertheless, the simple typology of social dilemmas initiated by Hamilton that classifies behaviours strictly based on their fitness effects is still an important conceptual tool [START_REF] Hamilton | The genetical evolution of social behaviour[END_REF]. It produces many insights including the role of genetic identity and co-ancestry in the evolution of altruism [START_REF] Hamilton | The genetical evolution of social behaviour[END_REF] and the effect of local competition for resources and other demographic forces in altering selection for or against cooperation [START_REF] Lehmann | How life history and demography promote or inhibit the evolution of helping behaviours[END_REF][START_REF] Taylor | Altruism in viscous populations -an inclusive fitness model[END_REF]102]. Therefore, a crucial task is to develop formal mathematical connections between the environmentally-mediated and classical social dilemma typologies.

Under some circumstances, environmentally implicit approaches will accurately represent social dilemmas and their dynamics. Evidently, this is increasingly the case as habitats become unstructured, social interactions are more direct and less mediated by elements of the environment (as occurs with mechanisms requiring direct cell-to-cell contact, e.g., competitor killing via the type VI secretion systems (T6SS) [104]), and when the environmental dimension is coupled to producers (e.g., when there is a fast turnover of the environmental currency and/or a short lag between changes in focal producer and public good/bad densities [START_REF] Brown | The Durability of Public Goods Changes the Dynamics and Nature of Social Dilemmas[END_REF]). However, we suspect that such simplicity, even if common, is not representative of many environments. Thus, a more explicit framework such as the one presented here is necessary for structured environments and environmentally-mediated social interactions. Attaining both generality and accuracy in such a framework will be challenging, both because of the stunning diversity of social systems [105] and their complex associations with the environment [START_REF] Odling-Smee | Niche construction: the neglected process in evolution[END_REF]. Nevertheless, the ubiquitous importance of social interactions and how dilemmas are mediated by ecology and evolution argue for an environmentally-inclusive framework, and future investigation using mathematical, behavioural, molecular and genomic tools. Although our examples focus on microbial systems, we believe that our four-way classification will also be useful for understanding interactions mediated through the environment in other systems, like social animals, and humans.

Figure 2 .

 2 Figure 2. A classification of environmentally mediated social behaviours. Individual behaviours are categorised into helping / harming others, and also into producing / consuming an environmental factor, yielding a 4-way classification of behaviour. Schematic summaries illustrate the signs of reciprocal organism (O) -environment (E) interactions under each scenario (pointed arrow indicates a positive effect; flat arrow indicates a negative effect). Effects on organisms are generally measured as changes in direct fitness (e.g. growth rate, survival) and effects on environment are measured as changes in the density/amount of environmental factor. For each scenario, all individuals in the population are assumed to have the same behaviour, i.e. they act and are affected by the environment in the same way. Thus, helping (or harming) others means helping (or harming) other individuals of the same type.

Figure 3 .

 3 Figure 3. Environment-mediated organism-organism interactions. Illustrated are the possible combinations of positive (red) and negative (blue) interaction scenarios between two organisms (O 1 and O 2 ) from different types when the environment is implicit and when taking into account the effect of each organism on themselves and individuals of the same type (illustrated as an arrow end pointing towards self) and the effect on others (pointing towards the other type). Note that here we remove symmetries, that is, when species O 1 and O 2 are interchangeable. Six of the reduced systems can be used to describe all ten unique scenarios involving a single environmental intermediate (E 1 ) and combinations of the environmental modifications enrich, detox, pollute, deplete (A, D-G, J). The other four reduced systems cannot be described with a single environmental factor and at least two are needed (B, C, H, I).Note that here we only represent environmental variables that directly mediate the interaction between the two organisms. Environmental factors that affect the growth of one organism but not the other are omitted.

Figure I .

 I Figure I. The value of explicit environment representation for predicting the evolution of dispersal. Plots show the predicted growth rates and dispersal probabilities of two organisms (O 1 and O 2 ) interacting through a shared environmental factor (E) for three of the scenarios represented in Figure 3. The lines are simply meant to represent the direction of the trend (increasing/ decreasing) not the form (depicted as linear, for simplicity). Here dispersal probability of O 1 (or O 2 ) means an organism's probability of moving away from a patch with density E.

  The other four scenarios, however, are impossible to achieve with only one environmental intermediate (B,C,H,I). To see this, consider scenario B in which one organism (O 1 ) positively affects itself and the other organism (O 2 ). This could occur through the removal of a toxic product (detox) or the production of a beneficial product (enrich). In addition, organism O 2 interacts positively with O 1 but negatively with itself (e.g. by producing a waste product that is toxic to itself but on which O 1 can feed, or by feeding on a compound that is toxic to O 1 ). A simple 'O 1 -E 1 -O 2 ' schematic is limited because it cannot capture the possibility that the single environmental compound (E 1 ) that helped O 1 and helped O 2 can also help O 1 and hurt O 2 . Therefore we need a second environmental factor (E 2 ) to be able to capture such scenarios.

Acknowledgements

We wish to thank the Santa Fe Institute for hosting the two working groups in 2016 and 2017, during which this research was conducted, and four anonymous reviewers for helpful suggestions. FD received funding from ANR (ANR-14-ACHN-0003). JP gratefully acknowledges financial support from the ANR-Labex IAST. MEH thanks the McDonnell Foundation (Studying Complex Systems Research Award No. 220020294).