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The microbial contribution to macroecology.

Barberán A, Casamayor EO, Fierer N - Front Microbiol (2014)

Bottom Line: Second, high speciation rates potentially lead to the convergence of ecological and evolutionary time scales.Then we demonstrate how two general theories of biodiversity (i.e., the recently developed theory of stochastic geometry and the neutral theory) can be adapted to microorganisms.We demonstrate how conceptual models that integrate evolutionary and ecological mechanisms can contribute to the unification of microbial ecology and macroecology.

View Article: PubMed Central - PubMed

Affiliation: Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, CO, USA.

ABSTRACT
There has been a recent explosion of research within the field of microbial ecology that has been fueled, in part, by methodological improvements that make it feasible to characterize microbial communities to an extent that was inconceivable only a few years ago. Furthermore, there is increasing recognition within the field of ecology that microorganisms play a critical role in the health of organisms and ecosystems. Despite these developments, an important gap still persists between the theoretical framework of macroecology and microbial ecology. We highlight two idiosyncrasies of microorganisms that are fundamental to understanding macroecological patterns and their mechanistic drivers. First, high dispersal rates provide novel opportunities to test the relative importance of niche, stochastic, and historical processes in structuring biological communities. Second, high speciation rates potentially lead to the convergence of ecological and evolutionary time scales. After reviewing these unique aspects, we discuss strategies for improving the conceptual integration of microbes into macroecology. As examples, we discuss the use of phylogenetic ecology as an integrative approach to explore patterns across the tree of life. Then we demonstrate how two general theories of biodiversity (i.e., the recently developed theory of stochastic geometry and the neutral theory) can be adapted to microorganisms. We demonstrate how conceptual models that integrate evolutionary and ecological mechanisms can contribute to the unification of microbial ecology and macroecology.

No MeSH data available.


Related in: MedlinePlus

Model simulation results of the stochastic geometry theory (McGill, 2010) as applied to either macroorganisms (top left) or microorganisms (bottom left). The only difference between both simulations is the “dispersal” parameter (i.e., larger spread of the spatial distributions for microbial species). For simplicity, the number of species (represented as different colors) has been set to fifteen for both macroorganisms and microorganisms. Axes represent the two spatial dimensions, while color intensity indicates relative abundance. As explained in McGill (2010), species abundance distributions (top right) are generated by sampling at one point in the spatial grid, species-area relationships (mid right) are created by sampling increasingly large areas, while the decrease of community similarity with spatial distance (bottom right) is derived by sampling areas of the same size at different distances. The tendency of microorganisms to be better dispersers (larger spatial distributions) is sufficient to reproduce the observed qualitative differences of macroecological patterns between macroorganisms and microorganisms. For microbes, the key differences observed for microorganisms versus macroorganisms include: richer species abundance distributions with longer tails of rare taxa (Curtis et al., 2006), species-area relationships with a higher total number of species and with lower slopes (Lennon and Jones, 2011), and a more moderate decay of community similarity with distance (Soininen, 2012). That is, microbial communities would tend to have a higher number of species (richness, or alpha-diversity) but lower turnover (beta-diversity).
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Figure 2: Model simulation results of the stochastic geometry theory (McGill, 2010) as applied to either macroorganisms (top left) or microorganisms (bottom left). The only difference between both simulations is the “dispersal” parameter (i.e., larger spread of the spatial distributions for microbial species). For simplicity, the number of species (represented as different colors) has been set to fifteen for both macroorganisms and microorganisms. Axes represent the two spatial dimensions, while color intensity indicates relative abundance. As explained in McGill (2010), species abundance distributions (top right) are generated by sampling at one point in the spatial grid, species-area relationships (mid right) are created by sampling increasingly large areas, while the decrease of community similarity with spatial distance (bottom right) is derived by sampling areas of the same size at different distances. The tendency of microorganisms to be better dispersers (larger spatial distributions) is sufficient to reproduce the observed qualitative differences of macroecological patterns between macroorganisms and microorganisms. For microbes, the key differences observed for microorganisms versus macroorganisms include: richer species abundance distributions with longer tails of rare taxa (Curtis et al., 2006), species-area relationships with a higher total number of species and with lower slopes (Lennon and Jones, 2011), and a more moderate decay of community similarity with distance (Soininen, 2012). That is, microbial communities would tend to have a higher number of species (richness, or alpha-diversity) but lower turnover (beta-diversity).

Mentions: Recently, McGill (2010) showed that most predictions about macroecological patterns can be generated by three simple rules regarding the random placement of organisms in space (i.e., stochastic geometry): (i) individuals within a species tend to be spatially clustered, (ii) abundance between species varies (many species are rare and a few are common), and (iii) the spatial distributions of individuals from one species are independent from the distributions of other species (i.e., species interactions are non-existent). Although the first two assumptions appear more reasonable than the third, interspecific spatial independence may indeed be a good statistical approximation in species-rich communities (Wiegand et al., 2012). Figure 2 shows simulation results from the stochastic geometry model (McGill, 2010) as applied to macroorganisms and microorganisms. All else being equal, the tendency of microbes to have greater dispersal capabilities compared to macroorganisms (represented as larger spatial distributions in Figure 2 bottom left) is sufficient to reproduce the abovementioned differences reported for the shape of the species abundance distribution, species-area relationship and the decrease of community similarity with distance (see Figure 2 for details). This simple modeling exercise demonstrates that incorporating the aforementioned microbial idiosyncrasies (in this case, high dispersibility) to existing macroecological models can generate some of the differences in community patterns between micro and macroorganisms observed in the environment.


The microbial contribution to macroecology.

Barberán A, Casamayor EO, Fierer N - Front Microbiol (2014)

Model simulation results of the stochastic geometry theory (McGill, 2010) as applied to either macroorganisms (top left) or microorganisms (bottom left). The only difference between both simulations is the “dispersal” parameter (i.e., larger spread of the spatial distributions for microbial species). For simplicity, the number of species (represented as different colors) has been set to fifteen for both macroorganisms and microorganisms. Axes represent the two spatial dimensions, while color intensity indicates relative abundance. As explained in McGill (2010), species abundance distributions (top right) are generated by sampling at one point in the spatial grid, species-area relationships (mid right) are created by sampling increasingly large areas, while the decrease of community similarity with spatial distance (bottom right) is derived by sampling areas of the same size at different distances. The tendency of microorganisms to be better dispersers (larger spatial distributions) is sufficient to reproduce the observed qualitative differences of macroecological patterns between macroorganisms and microorganisms. For microbes, the key differences observed for microorganisms versus macroorganisms include: richer species abundance distributions with longer tails of rare taxa (Curtis et al., 2006), species-area relationships with a higher total number of species and with lower slopes (Lennon and Jones, 2011), and a more moderate decay of community similarity with distance (Soininen, 2012). That is, microbial communities would tend to have a higher number of species (richness, or alpha-diversity) but lower turnover (beta-diversity).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4017162&req=5

Figure 2: Model simulation results of the stochastic geometry theory (McGill, 2010) as applied to either macroorganisms (top left) or microorganisms (bottom left). The only difference between both simulations is the “dispersal” parameter (i.e., larger spread of the spatial distributions for microbial species). For simplicity, the number of species (represented as different colors) has been set to fifteen for both macroorganisms and microorganisms. Axes represent the two spatial dimensions, while color intensity indicates relative abundance. As explained in McGill (2010), species abundance distributions (top right) are generated by sampling at one point in the spatial grid, species-area relationships (mid right) are created by sampling increasingly large areas, while the decrease of community similarity with spatial distance (bottom right) is derived by sampling areas of the same size at different distances. The tendency of microorganisms to be better dispersers (larger spatial distributions) is sufficient to reproduce the observed qualitative differences of macroecological patterns between macroorganisms and microorganisms. For microbes, the key differences observed for microorganisms versus macroorganisms include: richer species abundance distributions with longer tails of rare taxa (Curtis et al., 2006), species-area relationships with a higher total number of species and with lower slopes (Lennon and Jones, 2011), and a more moderate decay of community similarity with distance (Soininen, 2012). That is, microbial communities would tend to have a higher number of species (richness, or alpha-diversity) but lower turnover (beta-diversity).
Mentions: Recently, McGill (2010) showed that most predictions about macroecological patterns can be generated by three simple rules regarding the random placement of organisms in space (i.e., stochastic geometry): (i) individuals within a species tend to be spatially clustered, (ii) abundance between species varies (many species are rare and a few are common), and (iii) the spatial distributions of individuals from one species are independent from the distributions of other species (i.e., species interactions are non-existent). Although the first two assumptions appear more reasonable than the third, interspecific spatial independence may indeed be a good statistical approximation in species-rich communities (Wiegand et al., 2012). Figure 2 shows simulation results from the stochastic geometry model (McGill, 2010) as applied to macroorganisms and microorganisms. All else being equal, the tendency of microbes to have greater dispersal capabilities compared to macroorganisms (represented as larger spatial distributions in Figure 2 bottom left) is sufficient to reproduce the abovementioned differences reported for the shape of the species abundance distribution, species-area relationship and the decrease of community similarity with distance (see Figure 2 for details). This simple modeling exercise demonstrates that incorporating the aforementioned microbial idiosyncrasies (in this case, high dispersibility) to existing macroecological models can generate some of the differences in community patterns between micro and macroorganisms observed in the environment.

Bottom Line: Second, high speciation rates potentially lead to the convergence of ecological and evolutionary time scales.Then we demonstrate how two general theories of biodiversity (i.e., the recently developed theory of stochastic geometry and the neutral theory) can be adapted to microorganisms.We demonstrate how conceptual models that integrate evolutionary and ecological mechanisms can contribute to the unification of microbial ecology and macroecology.

View Article: PubMed Central - PubMed

Affiliation: Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, CO, USA.

ABSTRACT
There has been a recent explosion of research within the field of microbial ecology that has been fueled, in part, by methodological improvements that make it feasible to characterize microbial communities to an extent that was inconceivable only a few years ago. Furthermore, there is increasing recognition within the field of ecology that microorganisms play a critical role in the health of organisms and ecosystems. Despite these developments, an important gap still persists between the theoretical framework of macroecology and microbial ecology. We highlight two idiosyncrasies of microorganisms that are fundamental to understanding macroecological patterns and their mechanistic drivers. First, high dispersal rates provide novel opportunities to test the relative importance of niche, stochastic, and historical processes in structuring biological communities. Second, high speciation rates potentially lead to the convergence of ecological and evolutionary time scales. After reviewing these unique aspects, we discuss strategies for improving the conceptual integration of microbes into macroecology. As examples, we discuss the use of phylogenetic ecology as an integrative approach to explore patterns across the tree of life. Then we demonstrate how two general theories of biodiversity (i.e., the recently developed theory of stochastic geometry and the neutral theory) can be adapted to microorganisms. We demonstrate how conceptual models that integrate evolutionary and ecological mechanisms can contribute to the unification of microbial ecology and macroecology.

No MeSH data available.


Related in: MedlinePlus