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Microbial BiogeographyChina A Hanson, University of California, Irvine, CA, USAM Claire Horner-Devine, University of Washington, Seattle, WA, USAJennifer BH Martiny, University of California, Irvine, CA, USAJed A Fuhrman, University of Southern California, Los Angeles, CA, USA

r 2013 Elsevier Inc. All rights reserved.

GlossaryCommunity composition It is the identity and relative

abundances of all taxa in a community.

Cosmopolitan This refers to being present everywhere,

globally distributed.

Dispersal limitation It is the inability to move to and/or

colonize/establish at some location.

Drift It is the influence of stochastic demographic

variability (such as birth, death, and migration rates) on

biotic composition in an area.

Historical processes These are the evolutionary and

ecological processes that occur in the recent or distant

past including immigration, emigration, speciation,

extinction, drift, and/or past selection by the

environment.

cyclopedia of Biodiversity, Volume 5 http://dx.doi.org/10.1016/B978-0-12-3847

Microorganism or microbe These are bacteria, archaea,

protists, fungi, and small metazoans, typically smaller than

0.5 mm in equivalent diameter and with mass o10�5 g;

may also include viruses.

Spatial autocorrelation It is the tendency of

geographically close observations to be more similar than

geographically distant observations.

Stochastic It means a random effect due to chance events.

Taxon A group into which related organisms are classified

based on a designated relatedness cutoff, often using

sequence similarity of a marker gene; for example, species-

equivalent taxa may be defined as at least 99% similar in

their 16S rRNA gene sequence.

Taxonomic resolution The level of genetic variation of

the taxa considered.

Introduction

Biogeography is the study of the distribution of biodiversity

over space and time. It aims to reveal why organisms live

where they do, and at what abundance. The study of bio-

geography offers insights into the ecological and evolutionary

mechanisms that generate and maintain diversity, such as

speciation, extinction, dispersal, and species interactions. The

results of biogeographic studies also have direct application in

the modeling of ecosystem responses as they differ between

habitats and respond to global change. Since the eighteenth

century, biologists have investigated the geographic distri-

bution of plant and animal diversity, leading to important

advances in ecological theory. However, until recently,

microorganisms have rarely been considered in this regard.

The Baas-Becking Hypothesis

Although few early microbiologists performed biogeographic

studies, a central theme in microbial biogeography today is

the so-called ‘‘Baas-Becking hypothesis,’’ dating back to almost

a century. The hypothesis suggests that for microbial taxa,

‘‘everything is everywhere – the environment selects,’’ (Baas-

Becking, 1934). The claim ‘‘everything is everywhere’’ implies

that microorganisms are so abundant and have such enor-

mous dispersal capabilities that they can rapidly erase

any geographic patterns generated by past evolutionary and

ecological events. The claim that ‘‘the environment selects’’

implies that different contemporary environments allow for

selective growth of distinctive microbial assemblages. If true,

and dispersal and selection are effectively instantaneous,

the hypothesis implies two expectations: (1) microbial

communities will vary among habitats but similar habitats in

different locations will harbor similar active microbial com-

munities and (2) the lack of dispersal limitation means that

‘‘seed’’ populations of every kind of microbe can be found

everywhere, albeit possibly at very low abundance.

In the sections that follow, some of the recently observed

microbial distribution patterns are described. A framework is

reviewed for testing the Baas-Becking hypothesis as well as

three other alternative hypotheses. It is seen that the current

literature generally supports the second expectation of the

Baas-Becking hypothesis; the environment clearly has a strong

effect on microbial composition, as it does for the com-

position of larger organisms. However, there is also evidence

that the first expectation does not always hold; in other words,

that some microorganisms may be dispersal limited.

The Molecular Genetic Revolution

For many decades it was not feasible to evaluate the Baas-

Becking hypothesis because suitable methodologies for iden-

tifying the majority of microbial taxa in an environmental

sample were not available. Until the 1980s classical cultivation

techniques were necessary for proper identification of bacteria,

archaea, and the smallest protists, and such identification,

applied to whole communities, would have been onerous.

However, some of the first modern studies to describe the

large-scale biogeography of microbes did so by employing

morphological identification techniques to group eukaryotic

microbes (Protista) to the genus or near species levels. These

studies found that virtually all species were detected in a range

of widespread and varied habitats, suggesting that such

microbes were indeed globally distributed and therefore

19-5.00402-0 271

272 Microbial Biogeography

corroborating the first expectation of the Baas-Becking dictum

(Finlay, 2002; Fenchel and Finlay, 2004). In recent years these

claims have been challenged (Foissner, 2006), however, with

one argument being that morphologically defined microbial

species are ‘‘cryptic’’ in that they are not adequate represen-

tations of genetically differentiated species.

The application of newly developed molecular method-

ologies has now allowed microbial biogeography surveys to

characterize microbial taxa genetically, thereby avoiding the

caveats associated with cultivation-dependent or morphological-

based techniques. Such techniques obtain DNA sequences

directly from environmental samples without the need for

cultivation. Sequences are then classified into microbial taxa,

often referred to as operational taxonomic units (OTUs), which

are defined by the nucleotide sequence similarity of one or more

genomic regions, usually a marker gene such as 16S rRNA.

Sequence similarity cutoffs are often arbitrarily chosen to be

coarsely analogous to species or genus in larger organisms.

However, taxa can be defined at any level of taxonomic reso-

lution, from unique sequences to OTUs that lump together

genetically and phenotypically diverse microbes. Thus, as com-

pared to morphological techniques, sequence-based identifi-

cation generally allows a greater amount of microbial diversity to

be distinguished and taxa to be defined with greater resolution.

Indeed, some of the first studies utilizing culture-

independent molecular methods revealed that past culture-

based studies missed the vast majority of the microbial

diversity present in the environment (e.g., Ward et al., 1990).

Additionally because these techniques continue to evolve

and improve in coverage and depth of sampling, microbial

diversity has been sampled much more deeply and widely

than ever before (e.g., Sogin et al., 2006). Microbial bio-

geography has benefited tremendously from these advances,

and in fact most of the work presented in this article relies on

this approach.

General Patterns in the Distribution of Microbes

Micro- and macroorganisms are often involved in intimate

and specific associations that affect each other’s geographic

distributions. Studies have recognized for many years that

host-associated and pathogenic microorganisms exhibit pat-

terns of genetic, morphological, and functional differentiation

related to the distribution of their hosts (Hedlund and Staley,

2003). Thus, there is little debate that most host-associated

microbes display biogeographic patterns that parallel those of

animals and plants.

Debate remains about the extent to which nonhost-

associated microbes display biogeographic patterns. Studies

from a diversity of habitats, including soils, sediments, hot

springs, marine, and freshwaters, show clearly that community

composition differs across space and time, as reviewed ex-

tensively in recent years (e.g., Foissner, 2006; Martiny et al.,

2006; Lindstrom and Langenheder, 2011). Not only do the

distributions of microorganisms appear to be nonrandom, but

these distributions are also often similar to those observed for

animals and plants.

Certainly, not every study thus far has found evidence for

significant biogeographic patterns among the microbial taxon

of interest (e.g., Fenchel and Finlay, 2004). However, the

existence of microbial biogeography does not depend on

every microbial taxon being nonrandomly distributed. The

presence of cosmopolitan microorganisms – the ‘‘pigeons’’ of

the microbial world – does not exclude the possibility that

other microbial taxa have strikingly uneven geographic dis-

tributions. Similarly, microbial communities that contain a

large proportion of evenly distributed cosmopolitan taxa can

still exhibit biogeographic patterns driven by a minority frac-

tion of taxa that are not evenly abundant everywhere.

The evidence for microbial biogeography is reviewed below

by describing some general biogeographic patterns that have

been observed for free-living microbes.

Endemism

The existence of endemic microbial taxa would, in theory, be

the simplest demonstration that microorganisms are non-

randomly distributed. Endemics are, by definition, restricted

to a particular geographic range and therefore are not evenly

distributed across the Earth. In practice, however, character-

izing the geographic ranges of microbes is difficult. First, be-

cause a vast majority of bacterial taxa in a sample are rare

(Sogin et al., 2006), many taxa that are present will not be

detected. Thus, proving the absence of a microbial taxon

within highly diverse natural assemblages is nearly impossible

with currently available techniques. Second, it is equally dif-

ficult to determine whether observed taxa are actually active in

a given sample, and therefore perceiving the location as a

suitable habitat. Indeed, many microbes are capable of

dormancy stages, and recent evidence suggests that many

taxa detected in a location are dormant (Lennon and Jones,

2010), perhaps having arrived there as transient migrants.

Thus, without employing a strategy to identify active

versus dormant taxa, the actual active habitat range of some

microbes might not be distinguishable from a cosmopolitan

distribution.

Significant genetic divergence among taxa in different

locations is perhaps the strongest evidence for microbial

endemism. Many studies employ statistics such as Sewall

Wright’s FST or similar metrics, which compare the genetic

diversity of a microbial taxon within locations relative to that

among all locations combined. Some studies further find that

particular microbial clades are restricted to different regions of

the world (as demonstrated in Figure 1). For instance, Whi-

taker et al. (2003) showed clear genetic differentation among

hyperthermophilic archaea from geothermal hot springs on

different continents. Significant genetic divergence among

geographic locations is not just a demonstration that the

genetic diversity of microbes varies in space, but it is also

evidence that the sampled microbial communities are not

completely mixed by dispersal of individuals between them –

that is, the communities are isolated from each other due to

dispersal limitation.

Latitudinal Gradients

Since the eighteenth century, it has been widely recognized

that there is a broad tendency for animals and plants to

0.001

Figure 1 Phylogenetic tree depicting the genetic relatedness of hypothetical isolates (indicated by colored circles) collected from similar habitatsaround the world (inset). Isolates from geographically nearer locations are more closely related than they are to isolates from more distantlocations. Similar patterns have been observed with hot spring Archaea, for example, with isolates from locations in North America being moresimilar to each other than to those from Asia. Inset: Map showing sampled locations. Different colors correspond to the geographic origin ofisolates. Scale bar approximates 1 nucleotide substitution per 1000 bases.

Microbial Biogeography 273

exhibit a gradient of higher species richness (i.e., a-diversity)

in tropical regions and lower diversity in polar ones. There are

numerous nonexclusive explanations of such patterns, but

prominent among them is the suggestion that this relation-

ship is driven by latitudinal changes in temperature and/or

productivity (Rosenzweig, 1995; Hillebrand, 2004). Some

meta-analyses have indicated that the gradient tends to get

weaker as animals get smaller, which might be extrapolated to

suggest little or no latitudinal richness gradient for bacteria-

sized organisms (Hillebrand, 2004). Recent studies of bac-

terial diversity have begun to look for such patterns. In a study

of 98 soil samples from North and South Americas, Fierer and

Jackson (2006) reported no relationship between bacterial

richness and latitude or temperature, instead finding that soil

type, and especially pH (which varied greatly among samples)

was an overwhelmingly important factor, with lower diversity

in more acidic soils. In contrast, studies of marine planktonic

bacteria, living in the seawater environment that is much more

chemically and physically uniform than soils, found evidence

of a latitudinal gradient in bacterial richness (Pommier et al.,

2007; Fuhrman et al., 2008).

Elevational and Depth Gradients

Scientists have long-recognized elevational gradients in plant

and animal community composition. Over relatively short

geographic distances, dramatic changes in climate with ele-

vation are often associated with stark biotic turnover in a wide

range of taxonomic groups such as birds, trees, and insects. In

particular, species richness tends to exhibit either a hump-

shaped or monotonically decreasing relationship with ele-

vation. Like with latitudinal gradients, a number of non-

exclusive hypotheses have been proposed, but the cause of

elevational patterns remains poorly understood. Recent work

in the Rocky Mountains showed that bacterial richness de-

creased monotonically from low to high elevations; while

plants from the same system exhibited a unimodal pattern of

richness, with richness peaking at midelevations (Bryant et al.,

274 Microbial Biogeography

2008). In contrast, bacterial richness in organic soil, mineral

soil, and on leaf surfaces along an elevational gradient in the

eastern Andes of Peru showed no significant relationship be-

tween richness and elevation (Fierer et al., 2011), while bat,

bird, and tree richness decreased along the same gradient.

Finally, bacterial richness increased monotonically with

elevation in a stony stream in China, while diatom richness

decreased and macroinvertebrate richness showed a clear

unimodal pattern with elevation in the same system (Shen

et al., 2011). Overall, these studies suggest that bacteria also

vary in composition and richness along elevational gradients,

but so far, no consistent patterns have emerged across studies

and ecosystems. A similar analysis applies to depth in aquatic

and marine systems, which are often physically, chemically,

and biologically stratified (layered with depth). Indeed, mi-

crobial communities have been known for decades to vary

with depth and oceanographic conditions, and even have the

potential to be tracers for vertical water movement (e.g., Kriss,

1960). Correspondingly, several studies have demonstrated

clear variations of microbial composition with depth (e.g.,

reviewed by Martiny et al., 2006; Lindstrom and Langenheder,

2011).

Distance–Decay Relationships

Generally, the similarity between community composition

decreases as the distance between two locations increases

(Nekola and White, 1999). This so-called distance–decay re-

lationship (Figure 2) indicates not only that communities are

different in different locations, but also that this variation is

spatially autocorrelated. Multiple factors may act alone or in

combination to produce this pattern. First, environmental

variables tend to be spatially autocorrelated (as in a cline

or gradient) and organisms with differing environmental or

Geographic distance

Com

mun

ity s

imila

rity

Figure 2 An example of a distance–decay curve. Each pointrepresents a single pairwise comparison of the total number oflocations sampled. The solid line represents a negative relationshipbetween community similarity and geographic distance across allpoints. Using the same hypothetical microbial isolates from Figure 1,the pairwise comparisons from North America would be on the upperleft side of the figure (more similar to each other and closer togetherspatially), and comparisons between North America and Europe orAsia would be in the lower right side.

niche preferences are selected from the available pool of taxa.

Second, restricted dispersal of organisms over space can result

in geographic isolation or clustering of taxa.

Distance–decay curves have been consistently observed for

microorganisms at a range of taxonomic and spatial scales

(e.g., Cho and Tiedje, 2000; Green et al., 2004; Horner-Devine

et al., 2004; Martiny et al., 2011). For instance, Whitaker et al.

(2003) showed that not only did archaea diverge among the

hot spring locations, but also these differences were spatially

autocorrelated. The particular processes underlying these pat-

terns can also vary with taxonomic or spatial scale, as has been

shown for bacteria from salt-marsh sediments from around

the world (Martiny et al., 2011). Regardless of how the pattern

is created, the decline of similarity in communities across

space is considered to be powerful evidence of a nonrandom

geographic distribution and has been repeatedly observed

for microbes in an exhaustive range of habitats and spatial

scales.

Taxa–Area Relationships

Closely related to distance–decay patterns are species– (or

taxa–) area relationships (Figure 3). Here species are used to

refer to any microbial taxon defined by relatedness. A positive

relationship between the number of species observed and the

size of the area sampled has been observed repeatedly in

plants and animals in a wide range of ecosystems for over a

hundred years, often ascribed in part to the fact that as area

increases, more types of habitats and microenvironments

are included (Rosenzweig, 1995). This empirical species–area

relationship usually fits a power-law function, S¼cAz, where S

is the number of species, A is the area sampled, and c is the

intercept in log–log space. The species–area exponent, z, is a

measure of the rate of change of the slope with increasing area,

that is, the rate of turnover of species across space.

It is rarely, if ever, possible to exhaustively sample the

microbial diversity in even a small area, let alone in areas of

Log (Area)

Log

(Tax

on r

ichn

ess)

S = cAz

Figure 3 A taxa–area relationship, showing a linear increase in thenumber of taxa observed with increasing area sampled, on a log–logscale. S is the taxon richness, A is area, and c and z are constantsthat differ between taxa and habitats, with c the richness at thesmallest scales and z the slope of the line reflecting the rate ofincreasing richness with increasing area or time. Note that on alog–log scale, the taxa–area relationship is equivalent tolog(S)¼ log(c)þ zlog(A).

Microbial Biogeography 275

increasing size. However, researchers have used distance–decay

curves to determine the taxa–area exponent, z, and have

shown that taxa–area relationships also hold in microbial

communities (e.g., Green et al., 2004; Horner-Devine et al.,

2004). Currently, much microbial biogeographic research

focuses on understanding why ‘‘z,’’ or the rate of microbial

turnover, varies among different taxa, ecosystems, and spatial

scales. Such an approach offers an insight into the turnover of

community composition across space and how this turnover

may vary with the spatial scales sampled. In addition, it is

important to consider the spatial scales on which the under-

lying processes operate and how the relevant scales compare

across taxa.

Seasonality and Taxa–Time Relationships

Although most microbial biogeography studies focus on spa-

tial variation, community composition and diversity can also

vary nonrandomly over time. For instance, many studies ob-

serve very strong temporal and seasonal dynamics in bacterial

community composition. Fuhrman et al. (2006) showed ro-

bust, repeatable temporal patterns in the community com-

position for bacterioplankton off the coast of Southern

California. Similarly, near-surface ocean bacterioplankton in

the English Channel exhibited strong repeatable seasonal

patterns with peaks in richness during the winter (Gilbert

et al., 2009).

As for area, the number of species observed also increases

with the time span over which the community is observed

(i.e., a positive species–time relationship) due to immigration

and also inclusion of more varied conditions. Although less

well studied than the taxa–area relationship, organisms from a

range of taxonomic groups and ecosystems types exhibit sig-

nificant and similar taxa–time relationships within and across

years (White et al., 2006). Recent work in the Mid-Atlantic

Bight showed that time rather than space or measured en-

vironmental variables was the primary driver of microbial

community composition (Nelson et al., 2008). Bacterial

taxa–time relationships have also been observed in terrestrial

environments (e.g., Redford and Fierer, 2009).

Interpreting Microbial Biogeographic Patterns

The patterns discussed above (see General Patterns in

Microbial Distribution) indicate that not all microbial taxa are

randomly distributed and show that at least some, if not most,

microbes exhibit biogeographic patterns. Indeed, recent

advances have been made to describe the existence of such

nonrandom patterns in a wide variety of microbial taxa from

a broad range of habitat types, as has been reviewed previously

(Foissner, 2006; Martiny et al., 2006; Lindstrom and Langen-

heder, 2011). However, it is considerably more challenging

to interpret these patterns in terms of the ecological and

evolutionary processes that may generate them. A community

assembly perspective that divides these processes into the

effects of current environmental factors and the effects of

historical processes is a common framework for evaluating

the processes responsible for generating spatial biodiversity

patterns in macroorganisms (Ricklefs, 2007) and has been

recently applied to microbes (Martiny et al., 2006). Here

‘‘history’’ includes events from the very recent to the geological

past that might have influenced present-day distributions,

such as past environmental conditions or dispersal limitation.

This framework draws many similarities to the metacommu-

nity theory of community assembly, which has also been re-

cently applied toward interpreting observed patterns in the

biogeography of microbes (Lindstrom and Langenheder,

2011).

Following from traditional biogeography, one can consider

a framework comparing the importance of the contemporary

environment versus the legacies of historical processes for

microbial biogeographic patterns. The contemporary en-

vironment includes abiotic variables (e.g., temperature,

pH, and nutrient availability) as well as biotic factors such as

the abundance and composition of other organisms. Histor-

ical events include anything from the effects of the past

environment on the community, as well as evolutionary or

ecological drift and speciation and extinction, which

would cause differences in composition among localities. To

observe evidence of historical processes, however, there must

be dispersal limitation of some microbial taxa in the

community, otherwise the effects would be long-since hom-

ogenized, and microbial composition would only depend on

the current environment. This dispersal limitation allows

for the effects of events in the recent or distance past to

have a current signature on the microbial taxa present in a

location.

Under this framework, one can consider four alternative

hypotheses. The null hypothesis (Hypothesis 1 in Figure 4) is

that microorganisms are not affected by the current environ-

ment or historical events. In this case, microbial taxa would be

randomly distributed over space. Because there is no selection

in the community by any contemporary environmental factor,

there is no relationship between community similarity and

environmental similarity (dotted line in Figure 4). Similarly,

because there is no dispersal limitation of the microbial taxa,

there are no lingering effects of historical events. Thus, com-

munity composition would not be correlated with geographic

distance (solid line).

Hypothesis 2 is that microbial biogeography reflects the

influence of the current environment, but no historical effects.

This is the Baas-Becking hypothesis discussed above. None of

the taxa is dispersal limited, thus historical events do not in-

fluence present-day assemblages. As a result, community

similarity is correlated with environmental similarity, but once

this relationship is accounted for, there is no correlation be-

tween community similarity and geographic distance. A key

point here is that the effect of the current environment must

be first statistically removed before testing for a remaining

correlation with geographic distance, as environmental vari-

ables are usually spatially autocorrelated.

A third alternative (Hypothesis 3) is that all spatial vari-

ation in microbial composition is due to dispersal limitation

and resulting historical effects. Finally, Hypothesis 4 proposes

that, like macroorganisms, microorganisms (or at least

some taxa within a community) are both dispersal limited

and under current environmental selection. As a result, the

distance–decay curve can be partitioned into a correlation

No dispersal limitation Dispersal limitation

No currentenvironmental

selection

Currentenvironmental

selection

Geographic distanceEnvironmental similarity

Geographic distanceEnvironmental similarity

Geographic distanceEnvironmental similarity

Geographic distanceEnvironmental similarity

Com

mun

ity s

imila

rity

Com

mun

ity s

imila

rity

Com

mun

ity s

imila

rity

Com

mun

ity s

imila

rity

Hypothesis 1

Hypothesis 2 Hypothesis 4

Hypothesis 3

Figure 4 The relationship between community similarity and geographic distance (solid line), or environmental similarity (dotted line) for fourhypotheses of microbial biogeography. As described in the text, the relationships depend on the degree of dispersal and the effectiveness ofenvironmental selection.

276 Microbial Biogeography

with the environment as well as a remaining effect of geo-

graphic distance.

Evidence for the Importance of Contemporary EnvironmentalFactors

To assess the importance of selection by the current environ-

ment on microbial composition (hypotheses 2 and 4;

Figure 4), biogeography studies examine the correlation be-

tween community composition with measured environmental

variables. In fact, almost every microbial biogeography study

observes some such relationship between similarity in com-

munity composition and similarity in environmental charac-

teristics of those samples. For example, numerous studies have

linked spatial variation in microbial communities to variation

in pH or salinity (e.g., Fierer and Jackson, 2006). Other studies

have demonstrated that broadly defined habitat types,

which are a proxy measure for a range of associated environ-

mental variables, harbor significantly different microbial

communities (e.g., Nemergut et al., 2011). Additionally, sea-

sonal variation in microbial communities tends to be signifi-

cantly correlated with variation in environmental variables

(Fuhrman et al., 2006; Gilbert et al., 2009). Together these

studies provide support for the importance of environmental

variation in explaining why microbes are nonrandomly dis-

tributed in time and space.

Evidence for the Importance of Historical Factors

In contrast, variation in microbial community composition in

space may arise because not all taxa can disperse to or

establish in all locations equally (hypotheses 2 and 4,

Figure 4). Assessing the importance of historical factors in

shaping biogeographic patterns is not straightforward, how-

ever. Because the movement and establishment of microbes is

difficult to observe, the degree of dispersal limitation for mi-

crobes is often interpreted from the observed correlation be-

tween microbial community composition and geographic

distance, after removing the influence of contemporary en-

vironmental factors. Since environmental variation is often

spatially structured, as in a cline or gradient, controlling for

this variation, either experimentally or statistically, is key to

observing the effect of historical factors.

When microbial biogeography studies use this approach,

some find a correlation between microbial composition and

geographic distance after removing the effect of the con-

temporary environment (e.g., Cho and Tiedje, 2000; Whitaker

et al., 2003; Martiny et al., 2011). Such studies provide evi-

dence that historical factors, manifested by some dispersal

limitation, can indeed be responsible for creating bio-

geographic patterns in microbes. Moreover, it suggests that not

every microbial taxon is cosmopolitan; in other words, not

every microbe is everywhere.

At the same time, many other studies find no evidence for

dispersal limitation in microorganisms (e.g., Finlay, 2002;

Fierer and Jackson, 2006; Van der Gucht et al., 2007; Cermeno

and Falkowski, 2009; Zinger et al., 2011). Thus, a current

question in the field is to uncover when and where dispersal

limitation in microorganisms is more likely. In addition, if

there is dispersal limitation, it remains to be investigated what

historical processes are creating the signal (e.g., drift, speci-

ation, extinction, or past environmental legacies).

Microbial Biogeography 277

Caveats to Interpreting Biogeographic Patterns

A balance between both contemporary environmental factors

and historical processes likely shapes natural microbial

communities. However, unraveling the importance of en-

vironmental selection, dispersal, and historical factors from

observations of biogeographic patterns is not straightforward.

First, these processes are likely to be highly intertwined over

multiple scales of time and space (Rosenzweig, 1995; Ricklefs,

2007), and pinpointing the singular influence of any par-

ticular process within natural communities will be difficult.

Second, multiple processes can result in identical patterns, yet

many of the underlying processes cannot be directly measured

or observed. Therefore, the observation of the existence of a

particular biogeographic pattern does not prove the import-

ance of any process.

Additionally, identifying underlying processes from bio-

geographic patterns is particularly difficult for microbes,

as there is a limited understanding of microbial diversity.

The physiological requirements and ecological interactions

of the majority of microbial taxa in nature are unknown, so

it is quite likely that many important selective factors of

microbial habitats go unmeasured. For example, species

interactions, such as predation, competition, facilitation, or

mutualism, are generally under-considered in biogeographic

studies of free-living microbes. Nonetheless, these intera-

ctions can alter taxonomic occurrences in space and may be

strong forces in determining the habitable ranges of

some microbes. For instance, Zinger et al. (2011) found

that the composition of bacteria, archaea, and fungi in alpine

soils were significantly correlated with plant species com-

position, with other abiotic environmental and spatial factors

being less important. In general, if any unmeasured, yet se-

lective, habitat features are spatially structured, they can pro-

duce a spurious correlation between microbial composition

and geographic distance. This would lead to a potentially false

conclusion that the biogeographic pattern is shaped by his-

torical factors.

Dispersal is also particularly difficult to quantify for

microscopic organisms, and thus geographic distance is used

as a proxy indicator for the degree of dispersal between

locations. However, this does not adequately represent

the nature of the processes involved because successful dis-

persal is dependent on both the movement of a viable pro-

pagule in space and its successful establishment and

colonization of a location. The lack of either movement or

establishment, therefore, can produce a pattern consistent

with dispersal limitation. The difference between these two,

while seemingly subtle, could be the result of different eco-

logical processes. For instance, lack of establishment may be

caused by unfavorable local habitat conditions or biotic

interactions (known as priority effects), whereas degree of

movement may be a primarily stochastic process, especially for

microbes, which are dispersed mostly by passive means.

Therefore, the interpretation of microbial biogeographic pat-

terns would be facilitated by future work on the importance of

movement versus establishment. This insight could be at-

tained by use of experimental manipulations, or by measuring

the presence of microbial taxa in dispersal vectors such as air

and rain.

Future Considerations

Evidence suggests that many microbes exhibit biogeographic

patterns, and that these patterns are likely shaped by a com-

bination of ecological and evolutionary processes, similar to

animals and plants. However, while at least some microbes are

dispersal limited, the importance of the contemporary en-

vironment is often more prominent than that of historical

effects. Thus the future of microbial biogeography lies in

identifying the conditions under which the relative import-

ance of these underlying processes may vary. For example,

response to environmental conditions and dispersal abilities

may differ by taxonomic groups, taxonomic resolutions,

habitat types, and spatial scales. If so, then the ability to detect

biogeographic patterns and the importance of particular

underlying processes may vary by scope, scale, and focus of the

study. Here, some of the conditions under which bio-

geographic patterns may vary for microbes are highlighted, a

consideration of which may facilitate detection of particular

underlying biogeographic processes.

Taxonomic Focus and Resolution

Microbial taxa likely vary in traits that affect their dispersal

and colonization abilities or their response to the environ-

ment, thereby influencing their distributions. Such traits in-

clude body/cell size, spore formation, and other dormancy life

stages, or the ability to associate with other biological entities

that could serve as dispersal vectors. Body size is thought to be

negatively correlated with dispersal ability, whereby smaller

organisms disperse farther than larger ones (Fenchel and

Finlay, 2004). Additionally, spore formation and dormancy

stages can increase the likelihood that a dispersing microbe

will survive migration, allowing it to potentially travel greater

distances until reaching a suitable habitat (Lennon and Jones,

2010). Microbial taxa also vary in traits that could affect

the ability of migrants to colonize and establish in new

locations through their response to the external environment.

Some of these traits could include differential growth rates,

‘‘specialist’’ versus ‘‘generalist’’ physiologies, and susceptibility

to pathogens or predators. Generalist taxa, for example, might

be more resilient to environmental variation, and thus are able

to colonize a larger range. Thus the relative importance of

environmental variation and dispersal limitation may vary

depending on the microbial taxa examined.

The ability to detect variation in microbial community

composition, a necessity of the microbial biogeography study,

is highly dependent on the level of taxonomic resolution used

to define taxa. Taxonomic resolution is primarily constrained

by the sensitivity of the identification method, and can also be

arbitrarily defined in sequencing studies via sequence simi-

larity cutoff values. Communities defined by low-resolution

definitions or techniques may ‘‘lump’’ together taxa that vary

in traits, obscuring differences between the relative importance

of environmental selection and dispersal limitation among

taxa. As for animals and plants, such broad-level taxonomic

groups, akin to genus or family levels, are expected to have

larger distributions than a particular species within a genus

(Martiny et al., 2006). Thus, biogeographic patterns may be

278 Microbial Biogeography

generally weaker (e.g., Horner-Devine et al., 2004), and

the underlying processes more intertwined, when microbial

taxonomic definitions are broader.

Habitat Type

Habitat continuity could influence dispersal and therefore its

relative importance in shaping biogeographic patterns. Highly

connected habitats should be more linked via dispersal,

thus harboring more similar communities (Lindstrom and

Langenheder, 2011). Moreover, habitat types vary in degree of

connectivity and the mechanisms of dispersal utilized by or-

ganisms in those habitats. Aquatic systems, for example, likely

facilitate passive dispersal due to the fluidity of the medium.

In contrast, movement of microbes may be more restricted

within a more solid matrix such as soil. Therefore, microbial

communities associated with solid substrates or ‘‘patchy’’

habitat types are expected to be more limited by dispersal than

those in highly connected systems, such as the open ocean.

Moreover, a biogeographic study across environmental gradi-

ents or across habitat types results in large amounts of

environmental variation, and thus emphasizes the strength of

environmental selection. In this situation, the influence

of historical factors could be relatively weak, and possibly

undetectable. Thus to target the potential effects of historical

processes, habitat variability should be controlled for both via

sampling design and statistical methods.

Spatial Scale

The ability to disperse from one location to another should

decrease as the distance between those locations increases

(Nekola and White, 1999). Accordingly, dispersal limitation

should become more apparent as the spatial scale increases.

This could result in a greater relative importance of historical

factors at larger spatial scales. At the same time, clumping of

animal and plant taxa due to restricted dispersal also happens

on very short spatial scales. This small-scale clumping could

very well be important in some microbial habitats such as

biofilms and sediments (Martiny et al., 2011). Thus, the spatial

scale at which biogeographic patterns emerge for microbes

may provide some insight into the degree of microbial dis-

persal that leads to certain biogeographic patterns.

Conclusions

Microorganisms exhibit patterns in their distributions across

space at multiple scales, but given the immense diversity of

microbes and their ecological roles, microbes are probably not

uniformly shaped by the same ecological and evolutionary

processes. The distributions of free-living microbes do appear

to be influenced by both environment and history, with the

relative importance of these depending on spatial scale, taxo-

nomic resolution, habitat continuity, and life history traits.

While at least some microbes appear to be dispersal limited, it

is often secondary to the influence of the contemporary en-

vironment. Thus far, these conclusions are broadly similar to

macroorganisms, although the relative strength of the under-

lying processes and particular environmental factors may vary.

The motivation for understanding microbial biogeography

extends beyond drawing and interpreting a map of microbial

diversity. Understanding microbial biogeographic patterns

aids in investigating a range of microbial ecology questions –

for example, how much and on what scales to sample

microbial diversity, the nature and rate of microbial diversifi-

cation or speciation, and the role of microbes in ecosystems.

In fact, microbial composition drives a number of ecosystem

processes, including decomposition, autotrophic and hetero-

trophic production, and nitrogen cycling. Therefore, even

under similar environmental conditions, microbial com-

munities in different regions might function differently. A

better understanding of microbial biogeography is essential to

predict the effects of microbial diversity on ecosystem

functioning.

Appendix

List of Courses

1. Biodiversity

2. Biogeography

3. Environmental Microbiology

4. Microbial Ecology and Evolution

See also: Biogeography, Overview. Dispersal Biogeography.Diversity, Community/Regional Level. Elevational Trends inBiodiversity. Metapopulations. Microbial Biodiversity. PopulationGenetics

References

Baas-Becking LGM (1934) Geobiologie of Inleiding Tot de Milieukunde. The Hague:Van Stockkum & Zoon.

Bryant JA, Lamanna C, Morlon H, Kerkhoff AJ, Enquist BJ, and Green JL (2008)Microbes on mountainsides: Contrasting elevational patterns of bacterial andplant diversity. Proceedings of the National Academy of Sciences of the UnitedStates of America 105: 11505–11511.

Cermeno P and Falkowski PG (2009) Controls on diatom biogeography in theocean. Science 325: 1539–1541.

Cho JC and Tiedje JM (2000) Biogeography and degree of endemicity offluorescent Pseudomonas strains in soil. Applied and EnvironmentalMicrobiology 66: 5448–5456.

Fenchel T and Finlay BJ (2004) The ubiquity of small species: Patterns of local andglobal diversity. Bioscience 54: 777–784.

Fierer N and Jackson RB (2006) The diversity and biogeography of soil bacterialcommunities. Proceedings of the National Academy of Sciences of the UnitedStates of America 103: 626–631.

Fierer N, McCain CM, Meir P, et al. (2011) Microbes do not follow the elevationaldiversity patterns of plants and animals. Ecology 92: 797–804.

Finlay BJ (2002) Global dispersal of free-living microbial eukaryote species.Science 296: 1061–1063.

Foissner W (2006) Biogeography and dispersal of micro-organisms: A reviewemphasizing protists. Acta Protozoologica 45: 111–136.

Fuhrman JA, Hewson I, Schwalbach MS, Steele JA, Brown MV, and Naeem S(2006) Annually reoccurring bacterial communities are predictable from oceanconditions. Proceedings of the National Academy of Sciences of the UnitedStates of America 103: 13104–13109.

Fuhrman JA, Steele JA, Hewson I, et al. (2008) A latitudinal diversity gradient inplanktonic marine bacteria. Proceedings of the National Academy of Sciences ofthe United States of America 105: 7774–7778.

Microbial Biogeography 279

Gilbert JA, Field D, Swift P, et al. (2009) The seasonal structure of microbialcommunities in the Western English Channel. Environmental Microbiology 11:3132–3139.

Green JL, Holmes AJ, Westoby M, et al. (2004) Spatial scaling of microbialeukaryote diversity. Nature 432: 747–750.

Hedlund BP and Staley JT (2003) Microbial endemism and biogeography. In: BullAT (ed.) Microbial Diversity and Bioprospecting, pp. 225–231. Washington, DC:ASM Press.

Hillebrand H (2004) On the generality of the latitudinal diversity gradient. AmericanNaturalist 163: 192–211.

Horner-Devine MC, Lage M, Hughes JB, and Bohannan BJM (2004) A taxa-arearelationship for bacteria. Nature 432: 750–753.

Kriss AE (1960) Micro-organisms as indicators of hydrological phenomena in seasand oceans. 1. Methods. Deep-Sea Research 6: 88–94.

Lennon JT and Jones SE (2010) Dormancy contributes to the maintenance ofmicrobial diversity. Proceedings of the National Academy of Sciences of theUnited States of America 107: 5881–5886.

Lindstrom ES and Langenheder S (2011) Local and regional factors influencingbacterial community assembly. Environmental Microbiology Reports 4: 1–9.

Martiny JBH, Bohannan BJM, Brown JH, et al. (2006) Microbial biogeography:Putting microorganisms on the map. Nature Reviews Microbiology 4: 102–112.

Martiny JBH, Eisen JA, Penn K, Allison SD, and Horner-Devine MC (2011) Driversof bacterial beta-diversity depend on spatial scale. Proceedings of the NationalAcademy of Sciences of the United States of America 108: 7850–7854.

Nekola JC and White PS (1999) The distance decay of similarity in biogeographyand ecology. Journal of Biogeography 26: 867–878.

Nelson JD, Boehme SE, Reimers CE, Sherrell RM, and Kerkhof LJ (2008) Temporalpatterns of microbial community structure in the Mid-Atlantic Bight. FEMSMicrobiology Ecology 65: 484–493.

Nemergut DR, Costello EK, Hamady M, et al. (2011) Global patterns in thebiogeography of bacterial taxa. Environmental Microbiology 13: 135–144.

Pommier T, Canback B, Riemann L, et al. (2007) Global patterns of diversity andcommunity structure in marine bacterioplankton. Molecular Ecology 16:867–880.

Redford AJ and Fierer N (2009) Bacterial succession on the leaf surface: A novelsystem for studying successional dynamics. Microbial Ecology 58: 189–198.

Ricklefs RE (2007) History and diversity: Explorations at the intersection of ecologyand evolution. American Naturalist 170: S56–S70.

Rosenzweig ML (1995) Species Diversity in Space and Time. Cambridge, UK:Cambridge University Press.

Shen J, Wang JJ, Soininen J, Zhang Y, Wang BX, and Yang XD (2011) Contrastingpatterns in elevational diversity between microorganisms and macroorganisms.Journal of Biogeography 38: 595–603.

Sogin ML, Morrison HG, Huber JA, et al. (2006) Microbial diversity in the deepsea and the underexplored ‘‘rare biosphere’’. Proceedings of the NationalAcademy of Sciences of the United States of America 103: 12115–12120.

Van der Gucht K, Cottenie K, Muylaert K, et al. (2007) The power of speciessorting: Local factors drive bacterial community composition over a wide rangeof spatial scales. Proceedings of the National Academy of Sciences of the UnitedStates of America 104: 20404–20409.

Ward DM, Weller R, and Bateson MM (1990) 16S ribosomal-RNA sequences revealnumerous uncultured microorganisms in a natural community. Nature 345:63–65.

Whitaker RJ, Grogan DW, and Taylor JW (2003) Geographic barriers isolateendemic populations of hyperthermophilic archaea. Science 301: 976–978.

White EP, Adler PB, Lauenroth WK, et al. (2006) A comparison of the species–timerelationship across ecosystems and taxonomic groups. Oikos 112: 185–195.

Zinger L, Lejon DPH, Baptist F, et al. (2011) Contrasting diversity patterns ofcrenarchaeal, bacterial and fungal soil communities in an alpine landscape. PloSOne 6.