# 16.4: Competitive exclusion - Biology

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Consider what will happen with two species using the same resource, such as light or space or nitrogen fertilizer. With (u_i) being the amount of resource tied up in each individual of species (i), the resource remaining at any time will be

(R,=,R_{max},-,u_1N_1,-,u_2N_2)

Or for many species

(R,=,R_{max},-,u_1N_1,-,u_2N_2,-u_3N_3,-cdots,-u_hN_h)

[R,=,R_{max},-sum_{i=1}^h u_iN_i]

Each species has its own growth equation, identical in form for all species, but different in the critical level of resource, (R_i^{ast}), and the growth coefficient, (m_i):

[frac{1}{N_i}frac{dN_i}{dt},=,m_i(R,-,R_i^{ast})]

What remains is to consider how the growth coefficient (m_i) relates to the minimal level of resource tolerated, (R_i^{ast}). It turns out to be a tradeoff between the two. Consider, for example, a plant species that is limited by the amount of nitrogen available, as plants are. And to have a large growth coefficient (m_i) the plant must produce abundant seed. To have superior nitrogen use, measured by a low value of (R^{ast}), it needs abundant roots. But it cannot do both. There is a limited amount of solar energy to exploit, so if the plant allocates more to roots there is less to allocate to seeds, and vice versa.

It therefore turns out that species which are good colonizers, producing abundant seed, are poorer competitors for resources, having a higher value of (R^{ast}). This idea is illustrated by measurements reported in Figure (PageIndex{1}).

In Figure (PageIndex{2}), tradeoffs are formulated for modeling. Species 2 grows more rapidly when resources are abundant. This is the case at time 0, marked with (t_0) on the top axis.

As the populations grow they reduce the amount of resource available in the environment. At time 1, marked with (t_1) on the upper axis, Species 2 can continue to grow faster than Species 1, though the margin is deteriorating. But there comes a point at which the resources become depleted enough that the characteristics of Species 2 do not let it gather enough resources to maintain its advantage. This is the crossing point of the blue and red lines in the figure. At time 2, both species are still growing, but Species 1 is growing faster. At time 3, with still lower resource levels—drawn down by Species 1—the resource falls below the minimal level for Species 2, (R_2^{ast}). The growth rate of Species 2 falls negative and Species 2 starts to die out.

Finally, at time 4, Species 1 depletes the resource to the level that it can just barely survive, and it stands alone, having wiped out its competitor. This process is called “competitive exclusion.”

How this plays out over time is illustrated in Figure (PageIndex{3}). At the top, Species 2 alone does just fine, rapidly rising to its carrying capacity of 50 and pulling the resource down to its (R^{ast}

) of 2. In the middle, Species 1 alone also does just fine, rapidly rising to its carrying capacity of 60 and pulling the resource down to its (R^{ast}) of 1.

But grown together, Species 2 makes an initial splash and then declines. This is due to the incessant growth of Species 1, which outcompetes it. Species 1 simply draws the resource down below the level at which Species 2 can survive.

Competitive exclusion, which assumed that no more species could exist than there were resources, was treated as an inviolable law of ecology for over fifty years. In the 1970s, however, this was shown not to be the case (Armstrong and McGehee 1980). More about that later in the chapter.

## Interspecific competition

Interspecific competition, in ecology, is a form of competition in which individuals of different species compete for the same resources in an ecosystem (e.g. food or living space). This can be contrasted with mutualism, a type of symbiosis. Competition between members of the same species is called intraspecific competition.

If a tree species in a dense forest grows taller than surrounding tree species, it is able to absorb more of the incoming sunlight. However, less sunlight is then available for the trees that are shaded by the taller tree, thus interspecific competition. Leopards and lions can also be in interspecific competition, since both species feed on the same prey, and can be negatively impacted by the presence of the other because they will have less food.

Competition is only one of many interacting biotic and abiotic factors that affect community structure. Moreover, competition is not always a straightforward, direct, interaction. Interspecific competition may occur when individuals of two separate species share a limiting resource in the same area. If the resource cannot support both populations, then lowered fecundity, growth, or survival may result in at least one species. Interspecific competition has the potential to alter populations, communities and the evolution of interacting species. On an individual organism level, competition can occur as interference or exploitative competition.

## Interference competition and parasite virulence.

Within-host competition between parasites, a consequence of infection by multiple strains, is predicted to favour rapid host exploitation and greater damage to hosts (virulence). However, the inclusion of biological variables can drastically change this relationship. For example, if competing parasite strains produce toxins that kill each other (interference competition), their growth rates and virulence may be reduced relative to single-strain infections. Bacteriocins are antimicrobial toxins produced by bacteria that target closely related strains and species, and to which the producing strain is immune. We investigated competition between bacteriocin-producing, insect-killing bacteria (Photorhabdus and Xenorhabdus) and how this competition affected virulence in caterpillars. Where one strain could kill the other, and not vice versa, the non-killing strain was competitively excluded, and insect mortality was the same as that of the killing strain alone. However, when caterpillars were multiply infected by strains that could kill each other, we did not observe competitive exclusion and their virulence was less than single-strain infections. The ubiquity and diversity of bacteriocins among pathogenic bacteria suggest mixed infections will be, on average, less virulent than single infections.

## 1 INTRODUCTION

Many biological communities are structured by obligate, mutualistic symbioses consisting of a relatively long-lived macro-organism that provides habitat for diverse short-lived microbial symbionts. Genetic inquiries into the symbiont communities of yeast-termite, fig tree-fig wasp, plant-fungi, and coral-dinoflagellate symbioses have revealed the presence of dozens of symbiont species, whose influence on host fitness can range from mutualistic to parasitic across space and time (Baker, Freeman, Wong, Fogel, & Knowlton, 2018 Heath, Burke, & Stinchcombe, 2012 Lesser, Stat, & Gates, 2013 Livne-Luzon et al., 2017 Prillinger et al., 1996 ). Symbiont genetic diversity may be beneficial if symbiont types provide distinct and/or complementary resources to their host (sensu Palmer et al., 2010 Stachowicz & Whitlatch, 2005 Wagg, Jansa, Stadler, Schmid, & Heijden, 2011 ), especially if these resources vary by environment. However, competitive interactions among diverse symbionts for access to host-derived resources may also destabilize the symbiosis in the short term (Cushman & Addicott, 1989 Frank, 1996 ) or result in ecologically suboptimal holobionts in the long term (Afkhami, Rudgers, & Stachowicz, 2014 Miller, 2007 Palmer, Young, & Stanton, 2002 ). Thus, holobionts that can maintain diversity/flexibility in symbiotic associations in either space or time while minimizing antagonism and parasitism may achieve a wider and more dynamic niche space (Jandér & Steidinger, 2017 Livne-Luzon et al., 2017 Palmer, 2003 Palmer et al., 2010 Stachowicz & Whitlatch, 2005 ).

Despite the role of the coral-dinoflagellate symbiosis in supporting the most diverse marine ecosystems in the world, the ecological mechanisms that structure in-hospite Symbiodiniaceae communities are poorly understood. In adult corals, a single, predictable symbiont species generally dominates the in-hospite symbiont community (Goulet, 2006 Parkinson & Baums, 2014 ). However, the majority of coral juveniles take up symbionts from the environment anew each generation, initially establishing symbiosis with a genetically diverse subset of locally available Symbiodiniaceae, including types not typically found in adults (Coffroth, Lewis, Santos, & Weaver, 2006 Coffroth, Santos, & Goulet, 2001 Gómez-Cabrera, Ortiz, Loh, Ward, & Hoegh-Guldberg, 2008 Little, Oppen, & Willis, 2004 Poland et al., 2013 Reich, Robertson, & Goodbody-Gringley, 2017 Yamashita, Suzuki, Hayashibara, & Koike, 2013 ). Winnowing and/or restructuring of symbiont communities (i.e., symbiont switching/shuffling Baker, 2003 ) can occur during host ontogeny (Abrego, Oppen, & Willis, 2009 McIlroy & Coffroth, 2017 Poland & Coffroth, 2017 Poland et al., 2013 ), in response to environmental heterogeneity (Chen, Wang, Fang, & Yang, 2005 Rowan, 2004 Rowan & Knowlton, 1995 ), or through stress-induced loss and subsequent re-establishment of symbiont communities (i.e., coral “bleaching” and recovery Baker, 2001 Baker, Starger, McClanahan, & Glynn, 2004 Cunning, Silverstein, & Baker, 2015 Jones, Berkelmans, Oppen, Mieog, & Sinclair, 2008 Rowan, Knowlton, Baker, & Jara, 1997 Toller, Rowan, & Knowlton, 2001 ).

In this study, we focused on the role of competition and succession in the initial establishment of symbiont communities in newly settled coral recruits. We adapted the framework and basic expectations of the de Wit replacement series design (De Wit, 1960 Harper, 1967 ) (Figure 1) to evaluate competition, coexistence, and species turnover within newly available host habitat. We offered three Symbiodiniaceae species as monocultures and as three cross-paired mixtures (0.5:0.5 ratio) to aposymbiotic octocoral recruits (Briareum asbestinum) and used quantitative genetic assays to determine the presence and abundance of each species within each coral recruit. We considered three models of competitive opportunistic niche exploitation: (a) competitive exclusion (one symbiont excludes another from entering into symbiosis at detectable levels) which would favor the first symbiont to enter symbiosis (b) competitive dominance (in which one symbiont reduces the abundance of a co-occurring symbiont) which would favor fast proliferation and (c) a null model (no competition), in which symbiont uptake would follow the availability of each type in the environment regardless of whether additional symbiont types were present (Figure 1). We also tested whether these interactions are modulated by light levels as this is a known environmental factor that influences Symbiodiniaceae distributions in nature (Kemp, Fitt, & Schmidt, 2008 Rowan et al., 1997 ).

## Disturbance, Mechanisms of

### Disturbance and Biodiversity

The “ intermediate disturbance hypothesis ” (IDH Connell, 1978 ) predicts that maximum levels of biodiversity should be observed under some intermediate disturbance frequency because few species are able to tolerate very intense disturbance regimes, and few are able to compete successfully in habitats that experience little or no disturbance. The IDH also implies that maximum diversity should be found at some intermediate span of time since the last disturbance. The IDH has been expanded to incorporate intermediate levels of disturbance intensity and extent, and it has been tested and supported in a wide variety of ecosystems.

The best experimental examples of the IDH come from the study of sessile species competing for space or some space-associated resource. Counter-examples come largely from the study of mobile consumers (e.g., freshwater invertebrates), for which rapid immigration may override local disturbance effects. Defining “intermediate” in the context of specific organisms and choosing the scale to measure diversity are also important issues in assessing the validity of the IDH, about which there is ongoing debate. In general, the IDH applies to small-scale disturbances and to plants and sessile filter feeders. The relationship between disturbance and diversity is more complicated at larger scales and may not apply when interactions among multiple trophic levels are considered.

At spatial scales, much larger than the characteristic size of single disturbance events, disturbance regimes generate disturbance mosaics that maintain beta diversity in landscapes or regions by promoting coexistence of dispersal-limited competing species or prey species and by maintaining environmental heterogeneity and multiple serai stages ( Pickett and White, 1985 ).

If they recur with sufficiently high frequency (e.g., on average at least once per generation), ecological disturbances can be a strong selective force operating on species’ morphology, physiology, and behavior. Not surprisingly, many organisms are adapted to or depend on specific kinds of disturbances and disturbance regimes. Grime (1979) proposed that herbaceous plant life histories can be ordered along three fundamental axes: stress tolerance, competition, and disturbance. Somewhat analogously, animal species are often called r-strategists or k-strategists depending on whether they have high intrinsic rates of reproduction and tend to be favored by disturbances or whether they have lower reproductive rates but exert competitive dominance in the absence of disturbance. Because local abundance of many species is increased or maintained by disturbance, the long absence of specific kinds of disturbances (e.g., fire or floods) may have large negative impacts on biodiversity.

The life history strategies of some organisms may promote specific disturbance regimes (e.g., some fire-adapted shrub species possess canopy structure and foliar chemistry that promote fire spread). Organisms that recover quickly after a disturbance are said to be resilient to that disturbance, as opposed to those that show little response to disturbance and are considered resistant. These concepts are also applied to ecological communities, and the relationship between community diversity and community stability and resilience has long preoccupied the ecologists ( Holling, 1973 ). The subject has received renewed attention due to increasing concern over human-caused species extinctions and community impoverishment associated with habitat fragmentation. To date, the hypothesis that community stability to disturbance increases with community richness has met with mixed results in modeling and empirical studies, in part owing to differences in spatial and temporal scale and in how stability is measured.

## Determining the outcome of field-based competition between two Rhizopogon species using real-time PCR

Interest in the ecology of ectomycorrhizal (ECM) fungi has increased considerably, but little is known about interspecific interactions among ECM species. We examined competitive interactions between Rhizopogon occidentalis and R. salebrosus at Point Reyes National Seashore, California, USA. At three field sites, species abundances were compared in single- and two-species treatments on Pinus muricata seedlings inoculated with spores. Competition for root tips was assessed using real-time polymerase chain reaction (PCR) of internal transcribed spacer rDNA. In general, we found strong competitive exclusion of R. salebrosus by R. occidentalis, with >or= 75% of the seedlings in the two-species treatment colonized exclusively by R. occidentalis after 5 and 10 months. However, on the seedlings that were co-colonized, we observed no significant difference in the abundances of R. salebrosus and R. occidentalis, suggesting that once R. salebrosus was established, it was no longer competitively inferior. There were no significant differences in survival, growth, or percentage leaf nitrogen of seedlings colonized with either Rhizopogon species, but both growth and percentage leaf nitrogen were significantly higher for ECM than non-ECM seedlings. We also observed strong positive correlations between actual ECM root tip weight and that inferred from real-time PCR for both species, indicating that this method provided an accurate assessment of root tip occupation and hence ECM competitive dynamics. In conjunction with a previous experiment, our results indicate that competition between these two Rhizopogon species occurs similarly in both field and laboratory settings and that when colonizing from spore, timing largely determines the outcome of initial competitive interactions.

Brencic A, Winans SC (2005) Detection of and response to signals involved in host-microbe interactions by plant-associated bacteria. Microbiol Mol Biol Rev 69:155–194

Celani A, Vergassola M (2010) Bacterial strategies for chemotaxis response. Proc Natl Acad Sci 107(4):1391–1396

Dung L (2000) Coexistence with chemotaxis. SIAM J Math Anal 32:504–521

Dung L, Smith HL (1999) Steady states of models of microbial growth and competition with chemotaxis. J Math Anal Appl 229:295–318

Espejo EE, Stevens A, Velázquez JJL (2010) A note on non-simultaneous blow-up for a drift-diffusion model. Differ Integral Equ 23(5–6):451–462

Hawkins JB, Jones MT, Plassmann PE, Thorley-Lorson DE (2011) Chemotaxis in densely populated tissue determines germinal center anatomy and cell motility: a new paradigm for the development of complex tissues. PLoS ONE 6(12):e27650

Hibbing ME, Fuqua C, Parsek MR, Peterson SB (2010) Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol 8(1):15–25

Kelly FX, Dapsis KJ, Lauffenburger DA (1988) Effect of bacterial chemotaxis on dynamics of microbial competition. Microb Ecol 16:115–131

Kuiper HJ (2001) A priori bounds and global existence for a strongly coupled parabolic system modeling chemotaxis. Electron J Differ Equ 2001(52):1–18

Lauffenburger DA (1991) Quantitative studies of bacterial chemotaxis and microbial population dynamics. Microb Ecol 22:175–185

Murray JD (1993) Mathematical Biology, 2nd edn. Biomathematics series, vol. 19. Springer, Berlin

Painter KJ, Hillen T (2011) Spatio-temporal chaos in a chemotaxis model. Physica D 240:363–375

Painter KJ, Sherratt JA (2003) Modelling the movement of interacting cell populations. J Theor Biol 225:327–339

Quittner P, Souplet P (2007) Superlinear parabolic problems: blow-up, global existence and steady states. Birkhäuser advanced texts. Birkhäuser Verlag, Basel

Tello JI, Winkler M (2007) A chemotaxis system with logistic source. Commun Partial Differ Equ 32(6):849–877

Tello JI, Winkler M (2012) Stabilization in a two-species chemotaxis system with a logistic source. Nonlinearity 25:1413–1425

Tindall MJ, Maini PK, Porter SL, Armitage JP (2008) Overview of mathematical approaches used to model bacterial chemotaxis II: bacterial populations. Bull Math Biol 70:1570–1607

Vande Broek A, Vanderleyden J (1995) The role of bacterial motility, chemotaxis, and attachment in bacteria–plant interactions. Mol Plant Microbe Interact 8:800–810

Wang XF, Wu YP (2002) Qualitative analysis on a chemotactic diffusion model for two species competing for a limited resource. Q Appl Math 60:505–531

Winkler M (2010) Boundedness in the higher-dimensional parabolic-parabolic chemotaxis system with logistic source. Commun Partial Differ Equ 35:1516–1537

Winkler M (2011) Blow-up in a higher-dimensional chemotaxis system despite logistic growth restriction. J Math Anal Appl 384:261–272

Yao J, Allen C (2006) Chemotaxis is required for virulence and competitive fitness of the bacterial wilt pathogen Ralstonia solanacearum. J Bacteriol 188:3697–3708

Zeeman ML (1995) Extinction in competitive Lotka–Volterra systems. Proc AMS 123:87–96

Zhang Z (2006) Existence of global solution and nontrivial steady states for a system modeling chemotaxis. Abstr Appl Anal 2006:1–23. Article ID 81265

## MODEL STRUCTURE

A simple model to represent the problem of mutualist invasion is depicted in Fig. 1. A small region of habitat is occupied initially by a resident population of plants and soil bacteria that have no mutualistic interaction. This region is then invaded by a pair of species that interact mutualistically. Invading plants compete with resident plants, and invading mutualistic bacteria (rhizobia) compete with resident bacteria, but there are no direct interactions among any other pairs of species. Invasion is first considered only at the level of a single habitat, with mutualist success defined as the ability to establish a stable population size greater than zero in a site initially occupied by the residents (which may or may not become extinct as a result). Invasion at the level of a metapopulation will then be considered briefly, by using a coupled map lattice simulation model with dynamics at each site described in Fig. 1.

Model structure. Pi and Bi represent population sizes of plants and bacteria, respectively. + indicates a mutualistic interaction and − indicates a competitive interaction.

In the absence of mutualist partners, the two plant types and the two bacterial types are assumed to compete according to a discrete-time Lotka–Volterra competition model with no overlap of generations. A discrete-time formulation was chosen for simplicity and is suitable for populations of annual plants, but more complex models with age structure would be necessary to extend this analysis to perennial host species. The time increment for modelling change in rhizobial population size was identical to that of plants, which at first sight may seem inappropriate given that bacterial cells may replicate in < 1 day under ideal conditions in the laboratory. However, in natural environments, nodule formation and the release of progeny bacteria upon nodule senescence are often highly seasonal processes driven by host phenology ( Jimenez & Casadesus, 1989 ). Bacterial population growth may thus be strongly discontinuous in time, and synchronized with host life-cycle events, which justifies the use of a common time scale for both organisms. The equations for bacteria therefore represent their net change in abundance across an entire annual cycle of interaction with host plants. In future work, it will be important to explore the consequences of alternative assumptions regarding the time scale for bacterial population dynamics. Recursion equations for population sizes of the two plants are:

The parameter ri is the intrinsic rate of growth and cij is the reduction in growth of plant j caused by competition with plant i (1/cii is thus the carrying capacity of plant i in the absence of all competitors and mutualists). The term b(B1) represents the increment to population growth caused by the presence of B1 mutualist bacteria the specific functional form of b is discussed below. Bacterial population dynamics follow a similar set of recursion equations:

Here si is the intrinsic rate of bacterial growth and dij is the reduction in growth of bacterial type j caused by competition with type i (bacterial carrying capacity in the absence of interspecific competition or mutualism is 1/dii). For rhizobia, the term in square brackets in equation 2a represents the demography of cells in the soil that do not interact with legume hosts. If legumes are present (P1 > 0), a subset of the rhizobial population multiplies at a rate determined by the current number of partner plants, P1, multiplied by a function b*(B1), that represents how the yield of bacterial cells per host varies with bacterial population size.

For plants, mutualist benefits will depend on the number of nodules formed multiplied by the increment to plant reproductive success per nodule formed. Nodule formation is a highly non-linear function of rhizobial density (see Fig. 2 for an example). Typically, there is a threshold density of rhizobia below which few or no nodules are formed, because bacterial cells are distributed in the soil too sparsely to have much chance of encountering growing root tips where infection occurs. Nodule numbers then increase with rhizobial density over some intermediate range of bacterial abundance, but eventually reach an asymptotic value, which then remains unchanged with further increase in bacterial numbers (Fig. 2). Limitation of nodule formation by plants at high bacterial abundance is clearly demonstrated by the existence of so-called ‘supernodulation’ mutants, in which the normal regulatory process is disrupted by specific genetic defects that interfere with the plant’s ability to control nodule numbers ( Caetano-Anolles & Gresshoff, 1991 ). This regulation of nodule numbers suggests that benefits to plants reach a fixed level as rhizobial population size becomes large. A suitable functional form to represent how mutualist benefits per plant vary with rhizobial density is:

Legume nodule formation in relation to rhizobial population size. Dots depict the mean number of nodules formed (± 1 SD) by soybean seedlings exposed to different numbers of bacterial cells (Bradyrhizobium japonicum strain USDA 123), and then censused after 21 days. The curve represents predicted values from the function in equation 3 fitted to these data by the SAS NLIN procedure, which yielded the following parameter estimates: θ = 48, u = 0.00022 and Bo = 12 800.

The term enclosed by square brackets is the number of nodules formed per plant and mP is the increment to plant reproductive success per nodule formed. Figure 2 illustrates that this model can provide a reasonable fit regarding how nodule formation varies in relation to rhizobial abundance. Three parameters determine the nodule formation curve (Fig. 2): θ is the asymptotic number of nodules per plant at high rhizobial density, u controls the rate of increase of nodule numbers with rhizobial population size and Bo determines the displacement of the curve along the x-axis (to be precise, Bo is the density at which nodule number reaches θ/2). Thus, high values of Bo mean that numerous rhizobia must be present before any nodules are formed.

For rhizobial bacteria, the increment to population growth from each host plant is simply the number of nodules formed per plant (term enclosed by square brackets in equation 3) multiplied by the yield of bacteria per nodule (denoted mB):

This approaches a constant (mBθ) as rhizobial population size become large, consistent with the expectation that each host plant is a finite resource that can yield only a fixed output of progeny bacteria at high rhizobial density.

## Community Equilibria and Stability, and an Extension of the Competitive Exclusion Principle

It is shown in this paper that no stable equilibrium can be attained in an ecological community in which some r of the components are limited by less than r limiting factors. The limiting factors are thus put forward as those aspects of the niche crucial in the determination of whether species can coexist. For example, consider the following simple food web: Despite the similar positions occupied by the two prey species in this web, it is possible for them to coexist if each is limited by an independent combination of predation and resource limitation, since then two independent factors are serving to limit two species. On the other hand, if two species feed on distinct but superabundant food sources, but are limited by the same single predator, they cannot continue to coexist indefinitely. Thus these two species, although apparently filling distinct ecological niches, cannot survive together. In general, each species will increase if the predator becomes scarce, will decrease where it is abundant, and will have a characteristic threshold predator level at which it stabilizes. That species with the higher threshold level will be on the increase when the other is not, and will tend to replace the other in the community. If the two have comparable threshold values, which is certainly possible, any equilibrium reached between the two will be highly variable, and no stable equilibrium situation will result. This is not the same as dismissing this situation as "infinitely unlikely," which is not an acceptable argument in this case. Hutchinson's point of the preceding section vividly illustrates this. The results of this paper improve on existing results in three ways. First, they eliminate the restriction that all species are resource-limited, a restriction persistent in the literature. Second, the results relate in general to periodic equilibria rather than to constant equilibria. Third, the nature of the proof relates to the crucial question of the behavior of trajectories near the proposed equilibrium, and provides insight into the behavior of the system when there is an insufficient number of limiting factors.

## Contents

An ecotype is a variant in which the phenotypic differences are too few or too subtle to warrant being classified as a subspecies. These different variants can occur in the same geographic region where distinct habitats such as meadow, forest, swamp, and sand dunes provide ecological niches. Where similar ecological conditions occur in widely separated places, it is possible for a similar ecotype to occur in the separated locations. An ecotype is different than a subspecies, which may exist across a number of different habitats. In animals, ecotypes owe their differing characteristics to the effects of a very local environment. [6] Therefore, ecotypes have no taxonomic rank.

Ecotypes are closely related to morphs. In the context of evolutionary biology, genetic polymorphism is the occurrence in the equilibrium of two or more distinctly different phenotypes within a population of a species, in other words, the occurrence of more than one form or morph. The frequency of these discontinuous forms (even that of the rarest) is too high to be explained by mutation. In order to be classified as such, morphs must occupy the same habitat at the same time and belong to a panmictic population (whose all members can potentially interbreed). Polymorphism is actively and steadily maintained in populations of species by natural selection (most famously sexual dimorphism in humans) in contrast to transient polymorphisms where conditions in a habitat change in such a way that a "form" is being replaced completely by another.

In fact, Begon, Townsend, and Harper assert that

There is not always clear distinction between local ecotypes and genetic polymorphisms.

The notions "form" and "ecotype" may appear to correspond to a static phenomenon, however this is not always the case. Evolution occurs continuously both in time and space, so that two ecotypes or forms may qualify as distinct species in only a few generations. Begon, Townsend, and Harper use an illuminating analogy on this:

… the origin of a species, whether allopatric or sympatric, is a process, not an event. For the formation of a new species, like the boiling of an egg, there is some freedom to argue about when it is completed.

Thus ecotypes and morphs can be thought of as precursory steps of potential speciation. [7]

Experiments indicate that sometimes ecotypes manifest only when separated by great spatial distances (of the order of 1,000 km). This is due to hybridization whereby different but adjacent varieties of the same species (or generally of the same taxonomic rank) interbreed, thus overcoming local selection. However other studies reveal that the opposite may happen, i.e., ecotypes revealing at very small scales (of the order of 10 m), within populations, and despite hybridization. [1]

In ecotypes, it is common for continuous, gradual geographic variation to impose analogous phenotypic and genetic variation. [1] This situation is called cline. A well-known example of a cline is the skin color gradation in indigenous human populations worldwide, which is related to latitude and amounts of sunlight. [8] But often the distribution of ecotypes is bimodal or multimodal. This means that ecotypes may display two or more distinct and discontinuous phenotypes even within the same population. Such phenomenon may lead to speciation and can occur if conditions in a local environment change dramatically through space or time. [1]

## 100 articles every ecologist should read

Reading scientific articles is a valuable and major part of the activity of scientists. Yet, with the upsurge of currently available articles and the increasing specialization of scientists, it becomes difficult to identify, let alone read, important papers covering topics not directly related to one’s own specific field of research, or that are older than a few years. Our objective was to propose a list of seminal papers deemed to be of major importance in ecology, thus providing a general ‘must-read’ list for any new ecologist, regardless of particular topic or expertise. We generated a list of 544 papers proposed by 147 ecology experts (journal editorial members) and subsequently ranked via random-sample voting by 368 of 665 contacted ecology experts, covering 6 article types, 6 approaches and 17 fields. Most of the recommended papers were not published in the highest-ranking journals, nor did they have the highest number of mean annual citations. The articles proposed through the collective recommendation of several hundred experienced researchers probably do not represent an ‘ultimate’, invariant list, but they certainly contain many high-quality articles that are undoubtedly worth reading—regardless of the specific field of interest in ecology—to foster the understanding, knowledge and inspiration of early-career scientists.

The progress of science is built on the foundations of previous research—we take the flame of our predecessors and pass it faithfully to the next generation of scientists, and so it has always been. But this implies knowing the state of the art of our field, as well as being aware as much as possible about progress in other relevant fields. Hence, science can be represented as an ever-growing brick wall of published evidence, which subsequent research bricks can add to—and sometimes challenge, erode or even smash. Scientific articles have more recently also started playing another role: as metrics of the progress of projects and of the ‘quality’ of researchers and institutions 1 . Regardless of the pros and cons of this additional function, boosted by a parallel increase in the number of researchers 2 , this has produced an enormous increase in the number of peer-reviewed scientific articles. There are now well over 50 million peer-reviewed scientific articles in existence 3 , with an increase of 8–9% each year over the past several decades 4 . This means that over 1.5 million new articles are published each year across all scientific disciplines 3 .

## Watch the video: Community ecology 2 competitive exclusion principle (July 2022).

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