A species interaction that has a strong effect on population sizes and other factors in many

Abstract

Inter- and intraspecific interactions are important drivers of distribution patterns, local community assemblies and evolutionary changes. Subterranean communities are species poor, have fewer between species interactions than in surface communities and are hence easier to study. We review the state of the art on interspecific interactions in subterranean communities. We revise the evidence for predation, cannibalism, parasitism, interspecific competition, and eco-evolutionary effects these interactions have on the biodiversity patterns in the subterranean realm. Finally, we discuss evidenced, yet unstudied cases of host-epibiont species interactions.

Landscape Structure and Interspecific Interactions

Interspecific interactions are important determinants of population dynamics, and landscape structure can influence these interactions. All species interact with predators, parasites, competitors, and so on as the biotic portion of their environment. In breeding birds of forests, nest predation rates are affected by both landscape-level and within-patch habitat patterns (Lloyd et al., 2005). Predation rates increase as patch sizes decline and the landscape-wide abundance of nonforest habitats increases. Moreover, landscape structure influences the movement patterns of different predator species so that the risk of encountering a specific predator species depends on the precise location in the landscape (Bergin et al., 2000). Conversely, spatial complexity can promote coexistence between predators and prey. “Refugia” are habitats (or microhabitats) where preys are relatively safe from predators. When predators are abundant, refugia prevent them from driving their prey to extinction.

Spatial heterogeneity is one of the principal explanations for the coexistence of competing species (Amarasekare, 2003) in community ecology. Coexistence can be achieved by niche partitioning when species specialize on different habitats or resources. Habitat heterogeneity within and among patches promotes coexistence. Spatial structure and heterogeneity through time are also necessary for the coexistence by the competition–colonization trade-off (Tilman, 1994). In this mechanism, competing species differ in their competitive abilities and ability to colonize new habitat patches. The superior competitors have low colonizing abilities, and the better colonizers are poor competitors. Disturbance periodically kills off local populations of the superior competitor, and for a brief time those sites or patches are empty. The superior colonizer (but poor competitor) quickly occupies the empty sites and holds them until the superior competitor arrives. Regular, localized disturbances permit coexistence by continually providing habitat for the poor competitor (e.g., Debout et al., 2009). Coexistence is possible when the landscape is a shifting mosaic of disturbed patches and sites at different stages of recovery from prior disturbances (Figure 7). Disturbance and its effects on landscape structure through time are a recurring research theme in landscape ecology.

Question 4 Do Distinct Types of Species Interactions Influence Invasion Success in Different Ways?

Interspecific interactions can be antagonistic (beneficial for one partner but detrimental for the other one, as in predator–prey interactions), mutualistic (beneficial for both partners, as in plant–pollinator interactions), or competitive (detrimental to both species). Interactions can also be transitory (e.g. predation events), long-term and sustained (e.g. lifetime mutualistic symbioses), or can lie anywhere along the continuum between these two durations (Fig. 1). Invasive species spanning the entirety of this continuum have successfully integrated into ecological networks in the recipient community (i.e. Eastwood et al., 2007; Traveset and Richardson, 2014). To date, most studies of invasive species focus on a single type of interaction at a time, and the main hypotheses for how species interactions influence the likelihood of invasion success are related to either competition (e.g. the biotic resistance hypothesis), predation and parasitism (e.g. antagonistic interactions, the enemy release hypothesis), or mutualism (e.g. some examples of invasion facilitation, i.e. Green et al., 2011). However, invasion success and associated consequences for communities and ecosystems are likely to result from the joint effect of different types of interspecific interactions (Inderjit and van der Putten, 2010; Mitchell et al., 2006).

Several studies have shown that ecological networks have different structures depending on the type and strength of interaction (Bellay et al., 2015; Chagnon et al., 2016; Fontaine et al., 2011; Thébault and Fontaine, 2010). Interaction type also influences the relationship between network structure and community dynamics—greater connectance and nestedness increase species persistence in mutualistic webs while they decrease persistence in antagonistic networks (Thébault and Fontaine, 2010). It is thus likely that distinct types of interaction networks might respond differently to invasions due to their varying structures and dynamics. The nature of this response is easier to predict for some interaction types than others. Mutualisms have strong impacts on the success of potential invaders and subsequent population dynamics of species in the invaded network (see Amsellem et al., 2017 and Médoc et al., 2017). Invaders that successfully disrupt existing mutualisms may increase their likelihood of establishment in novel habitats and likely create important shifts in ecosystem functioning and effects on native species (Brouwer et al., 2015). Facultative mutualisms may promote invasions of novel species more easily than obligate mutualisms, which require strong dependencies between partners (Rodríguez-Echeverría and Traveset, 2015; Traveset and Richardson, 2014). On the other hand, the effects of antagonistic relationships for invasion success often vary. Exotic species may spread their parasites in novel ecosystems (Carpentier et al., 2007; Roy et al., 2008, 2011) or acquire novel parasites (Sheath et al., 2015). The enemy release hypothesis posits that alien species will experience increased invasion success in novel habitats that are devoid of the ‘natural enemies’ found in their original habitats. However, a review of studies of this hypothesis found mixed evidence for this—invasive species did encounter a reduced diversity of enemies in their introduced compared to native range, but the impact of enemies in the invaded community was similar for native and introduced species (Colautti et al., 2004).

Studies of ecological networks will therefore benefit from considering a diversity of trophic and nontrophic interactions (Kéfi et al., 2012), because interaction types are observed to combine in nonrandom ways and to influence community response to perturbation (Fontaine et al., 2011; Kéfi et al., 2012, 2015; Pocock et al., 2012; Sauve et al., 2014, 2016). For example, the effects of species invasions are more likely to propagate in highly connected interaction networks, and this connectedness can arise from either trophic interactions such as low-intimacy mutualisms (Fontaine et al., 2011) or nontrophic interactions such as competition for space or refuge provisioning (Kéfi et al., 2015). In another example, the dominance of superior competitors and exclusion of inferior competitors predicted by traditional resource ratio coexistence theory (León and Tumpson, 1975; Tilman, 1980, 1982) may not be observed if interference competition (and priority effects) is taken into consideration (Gerla et al., 2009).

Using networks to understand interactions between species requires defining edges, and this becomes complicated when multiple interaction types are combined. Kéfi et al. (2012) review modelling approaches to incorporate nontrophic interactions through their modification of trophic functional responses, and even to consider interactions that do not influence feeding. Kéfi et al. (2016) then successfully reduced a complex web of trophic, competitive, and facilitative interactions into a smaller subset of multilayer ecological functional groups. Another solution may be to use phylogenetic distances between species. Mitchell et al. (2006) developed a coherent framework that integrates enemy release, mutualist facilitation, competitive release, and abiotic environmental suitability, and how their relationship to invader success can be viewed via phylogenetic distance between invasive and native species (Fig. 2). In this framework, species introduced to communities including close relatives should experience high rates of enemy, mutualist, and competitor accumulation and encounter favourable abiotic conditions. Invasion success would therefore be limited by (the lack of) enemy or competitor release and enhanced by favourable habitat filtering or mutualist facilitation. Species introduced to communities without close relatives could experience successful invasions via enemy or competitor release or unsuccessful invasions via unfavourable habitat filtering or loss of beneficial mutualisms. The mechanistic basis of the direction of some of these relationships between interaction type and phylogenetic distance is better understood than others. Related prey species do tend to share more consumers than related consumers share prey species (Cagnolo et al., 2011; Elias et al., 2013; Naisbit et al., 2012), and related plant species tend to share more herbivores than they share pollinators (Fontaine and Thébault, 2015), supporting the hypothesized negative relationship between enemy release and phylogenetic relatedness. The mechanism for the negative relationship between phylogenetic relatedness and competitive release, that more closely related competitors are more likely to be phenotypically similar and thus competitively exclude one another, has mixed support (Jones et al., 2013; see also Question 7: Does the relationship between evolved phenotypic traits of invasive species and invaded networks influence the likelihood of invasions?). However, phylogenetic relatedness remains a promising explanatory variable of the interactions among different members of a network that may help us understand how distinct types of species interactions influence invasion success (see Question 8: How can we use phylogenetic similarity to better understand invasions in networks?).

Conditional and Context-Dependent Outcomes

The outcomes of interspecific interactions are not simply (+, 0, −), but instead vary along a continuum. Mutualism, like predation and competition, is in many cases not a fixed attribute or outcome of the interacting species. For example, the upper half of Figure 1 shows that mutualism can grade into commensalism (+, 0) and then predation (+, −) as the effect on one of the two partners changes. This variation in the strength and outcome has become known as conditionality or context dependency of mutualism. Mutualistic outcomes can vary depending upon numerous factors, including the abundance of predators and competitors, the supply of resources such as nutrients, the density and distribution of mutualists, and the size, stage, or age classes of interacting species. All of these factors can lead to spatial and temporal variation in the community and environmental context of mutualistic interactions. Gradation of mutualism into other interaction outcomes arises mechanistically via changes in the relative magnitudes of benefits and costs associated with spatial and temporal changes in these above factors.

Mutualisms are often contingent upon external factors, such as the availability of limiting resources or the presence and/or density of a predator or competitor. The protection mutualism between ants and treehoppers (plant-feeding insects) exemplifies how outcomes can vary with predator density. In a high-predator year or location, treehoppers are decimated by predators if not protected by ants. In contrast, at places and times where predators are few, the interaction is commensal or even parasitic: ant protection is not necessary, yet treehoppers still must pay the cost of providing food resources to the ants. Thus, variation in the magnitude of benefits of the mutualism to treehoppers generates a shift in the outcome of the interaction: it is conditional upon the abundance of predators.

The interaction between plants and root-associated mycorrhizal fungi represents an example of how the outcome of mutualistic interactions can be conditional upon nutrient availability. Mycorrhizal fungi increase the availability of soil phosphorus for the host plants; in turn, the plants provide mycorrhizae with carbon resources (root exudates). When plants are grown in phosphorus-rich habitats, the cost of providing mycorrhizae with carbon can exceed the benefits of the phosphorus obtained from mycorrhizae. Consequently, some plants can reduce their mycorrhizal infections under these conditions, even excluding mycorrhizae from their roots altogether.

In addition to spatiotemporal variation in environmental resources and predators, variation in benefits and costs associated with functional responses can lead to conditional outcomes of mutualism. As shown in the yucca/moth example above, irrespective of the particular species involved, the strength and outcome of a mutualism will vary with the densities of interacting partners. If mutualist densities occur at which costs equal or exceed benefits (Figure 2), then the outcome of an interaction will degrade into commensalism or predation (Figures 1 and 2). Thus, it is feasible for one ‘mutualistic’ species to have positive net effects on its partner at some population densities, and commensal or parasitic net effects at other densities. These examples demonstrate how complex mutualisms can be, and how dependent their outcomes are on the biotic and abiotic environment in which they occur.

Mutualism and Commensalism

Mutualistic relationships leading to positive interspecific interactions within physically stressful habitats like sandy beaches have been sparsely documented. Manning and Lindquist (2003) demonstrated that the clam Donax variabilis facilitated epibiotic occupation by providing a stable substrate for attachment of the hydroid Lovenella gracilis. The hydroid in turn defended the clam against fishes by means of its nematocysts. However, it facilitated predation by crabs because projection of the hydroid above the surface of the sand allowed the crabs to more readily detect clams. Depending on relative predation pressure, the occupation of D. variabilis by L. gracilis could be characterized as beneficial or detrimental to the host. Cases like this one, where the harm or benefit to the host cannot be clearly established, are classified as commensal or “host–guest” interactions (sensu Begon et al., 2006) and could be characterized as +0 (i.e., one partner gains, the other is neither harmed nor benefits).

There are several other cases of commensalism recorded in sandy-beach macrobenthos. Hydroids attach to the shells of some clams and snails and small commensals may live in the mantle cavities of mollusks. In sheltered shores, some species can construct permanent burrows that can host commensals. For example, burrows of the sand prawn or ghost shrimp Callianassa may be inhabited by gobies, polychaete worms, crabs, and even clams. Epibionts could potentially affect digging capacity and hinder the ability of clams to close their valves, thus making them more vulnerable to predators and climatic factors. Epibionts reduce the ability of sandy-beach macrofauna to escape from predators by increasing mass, reducing motility, and ultimately prolonging the burrowing time during the tidal migrations (Villegas et al., 2005; Firstater et al., 2009). Hidalgo et al. (2010) showed that macroalgal fouling on the mole crab E. analoga facilitates bird predation; fouled crabs being more conspicuous and their burrowing being retarded together made them more accessible to these predators. By modifying the body surface properties of the mole crab, epibionts can attract predators, modulating in this way predator–prey interactions.

These examples also suggest that the concurrent effects of several factors and processes acting simultaneously (here exemplified by mutualism and predation) could affect community organization and structure in sandy beaches. Therefore, indirect interactions may be an important source of variation in macrofaunal communities. These interactions could occur, as shown before (Hidalgo et al., 2010) because of (1) linked direct interactions between species pairs (i.e., interaction chain) and (2) a third species changing how a pair of other species interact (i.e., interaction modification). Climatic factors add another source of complexity and variability. For example, Ortega et al. (2016) identified heavy coverings of Acoela on yellow clam valves and mantle edges concurrently with systematic increases in sea surface temperature, causing long-term variations in body abnormalities and increasing mortality rates (see also Chapter 16).

‘Prey–Predator’ System

Predation is often defined as an interspecific interaction in which an individual of one animal species kills an individual of another species for dietary use. As a broader definition, predation can include an interaction between an animal and seeds or between a parasitoid and a host. However, predation rarely includes disease-causing organisms or herbivores that do not kill their food.

Predation is one of the most important interactions between species, ranking with parasitism, competition, and mutualism. Predation can affect changes in population sizes, traits, or phenotypes, and consequently promote the evolution of underlying genetic traits. These interactions are termed ‘prey–predator’ or ‘predator–prey’ systems. The existence of such interactions creates a link between the prey and predator species, termed a ‘trophic link’. The assembly of trophic links within a community forms a ‘food web’. Predation probably plays a major role in determining the life-history pattern of every species, and organismal complexity may increase due to predation. Here we consider the co-dynamics of prey and predator populations and coevolution of prey and predator species. We also consider the relationship between population dynamics and evolutionary change in trait values.

Hereafter we focus on a system involving one predator and one prey species. The following dynamic model describes temporal changes in the predator and prey populations:

[1]dN/dt =−r(N)N−f(N,P)P dP/dt=[−d(P)+g(N,P)]P

where P and N are the population sizes (or densities) of the predator and prey; t is time; d(P) and r(N) are the intrinsic death rate of the predator and the intrinsic growth rate of the prey; and f(N, P) and g(N, P) are the per capita rate of predation and the contribution of predation to the predator’s per capita growth rate, respectively. These factors are often considered independent of predator abundance P. Conditions where f and g depend on the ratio N/P are analyzed (this type of predation is called ‘ratio-dependent predation’).

Functions f and g are characterized by the predatory interaction; they are termed the functional response and the numerical response, respectively. Three variations of f(N) exist: (1) a linear relationship (f(N) = aN); (2) a convex curve (f(N) = aN/(1 + ahN)); and (3) a sigmoid curve (e.g., f(N) = aN2/(1 + ahN2)), where h is handling time and a is the predation coefficient. For the mathematically simplest model with a type 2 functional response:

[2]dN/dt=[r−kN−aP/(1+ahN)N]dP/dt=[−d+baN/(1+ahN)]P

where k is the magnitude of an intraspecific density effect from growth of the prey population, and b is the conversion efficiency of ingested prey into the predator. This system can produce either a stable or unstable equilibrium (Figures 1a and 1b). Note that increases in the predator population lag one-quarter of a period behind increases in the prey population (Figure 1c). This is fairly intuitive, since the predator population decreases to the left of the predator’s ‘null cline’ (the vertical line in Figures 1a and 1b) and increases on the right side of the line, whereas the prey population increases below the prey null cline (the parabolic curve in Figures 1a and 1b) and decreases above the curve. These null clines are obtained by the solution of the equations dP/dt = 0 and dN/dt = 0, respectively. The equilibrium of coexistence is obtained by the intersection of these null clines.

Especially in host–parasitoid dynamics, time-discrete models, such as the famous Nicholson–Bailey model, are more reasonable because the hosts reproduce seasonally, and the life cycle of a parasitoid often synchronizes with that of its host. Because of time discreteness, these models are less likely to produce a stable equilibrium.

Using the Nicholson–Bailey model, many studies have incorporated factors representing the spatial distributions and behavioral characteristics of both the host and parasitoid. If parasitoids focus on the center of the host distribution, the risk of parasitism is low for hosts that are far from the population center. This aggregative response has a stabilizing effect on the host–parasitoid system. If parasitoids that share an individual host interfere mutually, a type 3 functional response is again possible because the potential for interference increases as the host density decreases. Another important factor in prey–predator systems is stochasticity. Many stochastic factors have been studied. For example, demographic stochasticity and genetic drift usually destabilize the equilibrium of prey–predator systems, despite a few examples to the contrary.

Hereafter we focus on time-continuous models. The Lotka–Volterra predator–prey model is an extremely simplified model of a prey–predator system, involving the case where k = 0 and h = 0 in model [2]. Even in this simple model, a time-dependent analytical solution has not been obtained. The Lotka–Volterra model has an interior equilibrium, (N, P) = (d/ba, r/a). Because this equilibrium is neutrally stable, an interior equilibrium of variants of the Lotka–Volterra model can produce either stability or instability, as shown in Figures 1a and 1b. Type 3 functional responses and the density effects from both prey and predator growth rates produce stabilizing effects on the prey–predator dynamics. Type 2 functional responses and time lags between predation and population growth have destabilizing effects.

Increasing productivity of the prey usually has a destabilizing effect on the equilibrium. This is called the ‘paradox of enrichment’. This is an intuitive result because the null cline of the prey (parabolas in Figures 1a and 1b) shifts to the right; therefore, the equilibrium (the intersection of the parabola and the vertical line) point occurs to the left of the parabola’s peak, as in Figure 1b.

Changes in the predator population lag behind changes in the prey population by one-quarter of a period (Figure 1c); however, few examples of such prey–predator cycles have been observed in the field. The prey population often regulates the predator population, whereas the latter less frequently regulates the former. One of the best examples is presented in Figure 1d, showing a one-half period lag between prey and predator cycles, as discussed later.

I. Introduction

The application of formal mathematical theory to interspecific interactions has a well-known history, dating from the work of Lotka and Volterra in the 1920s (Kingsland, 1985). The usefulness of such classical mathematical theory for understanding the population dynamics of herbivorous insects, however, has at times been questioned (Onstad, 1991; Strong, 1986). This chapter is intended to demonstrate that theory can be useful both qualitatively and quantitatively. I consider first a variety of mathematical models that have been used to reach qualitative conclusions, and then I describe my own work using a mathematical model to make quantitative predictions.

Theoreticians using differential equation models usually focus on either stability or what is known in mathematics as complex dynamics (Drazin, 1992), which means oscillatory behavior that includes limit cycles and chaos. The question of biological interest is, typically, under what conditions will populations of the species in a model be stable, or alternatively show complex dynamics? Whether mathematical stability properties are ecologically meaningful has been controversial (Murdoch et al., 1985); in fact, stability (Connell and Sousa, 1983), limit cycles (Gilbert, 1984), and chaos (Hassell et al., 1976; Berryman and Millstein, 1989) have all been questioned for their relevance to real ecological systems. Work with long time series of a wide variety of different animals, however, has suggested that many animal populations, at least, experience complex dynamics (Turchin and Taylor, 1992), in turn reemphasizing a need for simple mechanistic models.

One of the root causes of criticism of mathematical models by field ecologists is model simplicity (Onstad, 1991). That is, the models often consider only a small fraction of the biological detail that field ecologists believe is important, and this is sometimes used as an argument in favor of complex simulation models (Logan, 1994). Simple models, however, have the advantage that they allow the relationship between biological mechanism and population dynamics to be much more easily understood. Moreover, the idea of simplifying a situation is a standard research strategy, and is just as often used when designing models as when designing experiments. In Section II, an attempt is made to make clear the value of even the simplest models by briefly reviewing nonintuitive qualitative results from a variety of models of interspecific interactions among insects. In Section III, the aim is to show that simplicity is not necessarily a barrier to quantitative accuracy.

Diseases and Parasites

Diseases and parasites are specific instances of interspecific interactions that will likely be affected by climate change. The impacts of climate change on terrestrial species (i.e., phenological changes, range shifts, and changes in population processes) also affect parasites, diseases, disease vectors, the susceptibility of hosts, and the interactions between all of these organisms. Therefore, climate change will both directly and indirectly affect the emergence and spread of parasites and disease (Canto et al., 2009). The impacts of climate change on parasites, diseases, vectors, and hosts are individualistic, and interactions between these impacts are complex (Moller, 2010; Luck et al., 2011; Lafferty, 2009; Garrett et al., 2011). However, the frequency of parasite and disease outbreaks will likely increase in a changing climate (Canto et al., 2009; Brooks and Hoberg, 2007). These outbreaks have the potential to negatively impact plants and wildlife, agriculture, and human health (Reid and Gamble, 2009; Luck et al., 2011; Patz et al., 2007; Fussel, 2008; Garrett et al., 2006).

Physiological tolerances to climatic conditions often determine disease and parasite distribution and abundance. Therefore, climate change will directly impact diseases with free-living life stages and diseases that require ectothermic vectors or hosts (Mas-Coma et al., 2009; Patz et al., 2008; Polley and Thompson, 2009). For example, the ability for parasites or disease vectors to overwinter requires a specific range of climatic conditions (Garrett et al., 2006). Also, incubation time and the number of generations per year for some vectors and parasites are sensitive to temperature and humidity, and therefore, outbreaks of diseases and parasites will be impacted by climate change (Patz et al., 2008; Jaramillo et al., 2009). In general, higher precipitation and temperatures correspond with a higher disease transmission rates and higher diversity of diseases (Lafferty, 2009; Froeschke et al., 2010). However, the responses of parasites and diseases to climatic changes are species specific, and so the resultant impact on hosts may be positive, negative, or neutral (Garrett et al., 2006). For example, for a single host species, multiple parasites responded differently to changes in different climatic variables, resulting in no change to the fitness of the host species (Moller, 2010).

Because of the individualistic responses of parasites, diseases, vectors, and hosts to climate change and the complexity of the interactions of these responses, forecasting the impact of parasites and disease is difficult. Despite these complexities, projections are important to identify regions that are most susceptible to disease emergence or parasite outbreaks to facilitate proactive responses. Process-based models are often used to forecast the response of diseases to climatic changes by modeling climatic tolerances for survival, transmission, and reproduction (Rosenthal, 2009). For example, plague levels in black-tailed prairie dogs are forecasted to decrease due to inhibited transmission from higher temperatures (Snall et al., 2009). Likewise, a simulation of host–parasite dynamics forecasts reduced transmission rates from stochastic events in regions of host expansion (Phillips et al., 2010).

Phenological changes will also impact disease and parasite transmission and abundance. For example, increases in the length of flying seasons of disease vectors and parasites may increase disease transmission and the spread of the disease (Canto et al., 2009). Conversely, phenological changes may also reduce the impact of parasites and disease by causing mismatches with hosts. Process-based models can also forecast phenological changes and the effects of those changes on population dynamics and pathogen–host dynamics (Ogden et al., 2008a).

As climates change, new regions may become climatically suitable for a parasite, disease, or disease vector. Diseases and parasites may expand into these previously unsuitable, uninhabited regions. Species distribution models have been used to project range shifts for diseases, parasites, vectors, and hosts. For example, species distribution models for a tick, Rhipicephalus appendiculatus, and several host species forecasted overall range reductions for the tick and hosts, but an increase in tick–host assemblages in certain regions (Olwoch et al., 2009). As for all species, nonclimatic factors such as dispersal limitations, land use, and interspecific interactions may limit climate-induced range expansions (Lafferty, 2009). However, disease and parasite distributions may be even more sensitive to nonclimatic distributional determinants because of their complex interactions with vectors and hosts. Therefore, forecasts from species distribution models may not be as effective as process-based models for anticipating the impacts of climate change on parasites and disease. For parasites and diseases, in particular, host availability may influence range expansion (Lafferty, 2009; Rosenthal, 2009). For example, if parasite or disease distributions are limited by host availability, distributional shifts of host species may correspondingly cause shifts in disease and parasite distributions. Also, climate change increases the potential for host switching, which may cause disease outbreaks in previously unaffected species that may be difficult to anticipate (Brooks and Hoberg, 2007). Pest and disease control may also have a large influence on the distribution of a disease (Rosenthal, 2009). Diseases that affect humans in particular are sensitive to nonclimatic distributional determinants due to public health programs that are often influenced by socioeconomic distributions (Rosenthal, 2009). The extensive influence of nonclimatic factors on the distributions of diseases and parasites may overwhelm the impact of climate change, making impacts somewhat difficult to forecast.

Symbiosis

Symbiosis is a well-known phenomenon of interspecific interactions across all kingdoms. It was the German scientist Anton de Bary who firstly introduced the concept of symbiosis as “the permanent association between two or more specifically distinct organisms, at least during a part of the lifecycle” (de Bary, 1879). An endosymbiotic association is a special form of a symbiotic alliance in which the host ingests its symbiont and keeps it within its body. This kind of symbiosis often originates from predator-prey relations where the predator didn’t digest the symbiont but kept it in its tissue and used it as additional source of nutrients, for example due to the photosynthesis of the symbiont within its body (Munk, 2009).

There are several examples of marine animals which keep photosynthetic active algae within their tissues, such as cnidarians (Schoenberg and Trench, 1976), foraminifera (Hallock, 2003) and the sea slug of the genus Elysia (Hinde and Smith, 1975). For example the giant clam (the largest living bivalves) is able to filter food and host algae (Symbiodinium spp.) within their body for photosynthesis (Neo et al., 2017). The green worm, Symsagittifera roscoffensis, is an especially fascinating example for endosymbiotic association, as it lives solely from its photosynthetic active symbiont and requires no further food.

Symsagittifera roscoffensis is a small, green worm living with a photosynthetic active endosymbiont, the micro algae Tetraselmis convolutae. This fascinating animal, formerly called Convoluta schultzii, has sparked the interest of many scientists for over a century. The British scientist Patrick Geddes described the evolution of oxygen by captured worms and wondered about the origin of the green compound of the green worm and described it as “chlorophyll containing cells” (Geddes, 1879). The French biologist Yves Delange also worked intensively with the worm and first raised the question of associated micro algae within the animal in 1886. It was Ludwig von Graf who hinted at differences in taxonomic description by Geddes and Delange and found that animals from the Adriatic coast and from Roscoff are not the same and thus named the worms in Roscoff Convoluta roscoffensis. Further studies on the worms and the establishment of the symbiosis were conducted by Keeble (1911), Provasoli et al. (1968), as well as Douglas (1983). The taxonomy of these worms, and their relatives were revisited in 1991 and due to new techniques in the field of molecular biology Convoluta roscoffensis was renamed Symsagittifera roscoffensis.

Symsagittifera roscoffensis (v. Graff, 1891 Kostenko & Mamkaev, 1990) lives on sandy substrate in the intertidal zone along the Atlantic coast of Europe, from northern France, south England down to southern Portugal. The worms belong to the Class Acoela (no cavities) which are divided into the Families Symsagittiferidae and Convolutidae, with Symsagittifera roscoffensis belonging to the latter (Tyler et al., 2006–2021) The worms accumulate in groups of thousands of individuals forming dark green patches on the beach, covered with one to two centimeters of water (Fig. 1).

Mechanisms of Coexistence

The previous discussion emphasized constraints on species coexistence arising from interspecific interactions. Rules of dominance are important conceptual tools that quantify these constraints and help identify biological traits leading to dominance. However, even in simple microcosms, coexistence can occur, and most natural communities are rich in species. Species in principle may coexist when any of the assumptions leading to the competitive exclusion principle are violated. This suggests three classes of mechanisms promoting species coexistence of potentially competing species in a local community:

Species may coexist in a closed, temporally constant world if they experience different limiting factors; this includes classical niche partitioning of resources, as well as mechanisms involving predation and parasitism, and direct interference.

Species may coexist, even though they experience the same limiting factor, if the environment is temporally variable and species respond differently to this temporal variation (temporal niche partitioning).

Species may coexist if the environment is spatially open or interactions are localized; the implications of space for coexistence can include spatial niche partitioning at scales broader than the local community, mechanisms such as colonization–competition tradeoffs in metapopulations, and microscale habitat partitioning.

From the 1950s to the mid-1970s, stimulated largely by G.E. Hutchinson and his brilliant student Robert MacArthur, most community ecologists emphasized classical niche partitioning in studies of species coexistence. In recent years, the balance of attention has shifted markedly to a broader range of coexistence mechanisms. Ecologists now believe that maintenance of diversity – coexistence writ large – often depends on spatial dynamics in open communities, food web interactions (including predation and parasitism), and nonequilibrial dynamics reflecting either extrinsic temporal variation or the endogenous instability of complex ecological systems. Moreover, as noted above, some ecologists have concluded that in natural communities, many species co-occur, without necessarily permanently coexisting, in the sense of tending to increase when rare.