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ES43CH15-Sinsabaugh ARI 26 September 2012 11:56
Ecoenzymatic Stoichiometry
and Ecological Theory
Robert L. Sinsabaugh1and Jennifer J. Follstad Shah2
1Biology Department, University of New Mexico, Albuquerque, New Mexico 87131;
email: rlsinsab@unm.edu
2Watershed Sciences Department, Utah State University, Logan, Utah 84322
Annu. Rev. Ecol. Evol. Syst. 2012. 43:313–43
First published online as a Review in Advance on
September 4, 2012
The Annual Review of Ecology, Evolution, and
Systematics is online at ecolsys.annualreviews.org
This article’s doi:
10.1146/annurev-ecolsys-071112-124414
Copyright c
2012 by Annual Reviews.
All rights reserved
1543-592X/12/1201-0313$20.00
Keywords
ecoenzyme, ecological stoichiometry, biogeochemistry, decomposition,
microbial growth, resource allocation model
Abstract
The net primary production of the biosphere is consumed largely by
microorganisms, whose metabolism creates the trophic base for detrital
foodwebs, drives element cycles, and mediates atmospheric composition.
Biogeochemical constraints on microbial catabolism, relative to primary
production, create reserves of detrital organic carbon in soils and sediments
that exceed the carbon content of the atmosphere and biomass. The produc-
tion of organic matter is an intracellular process that generates thousands
of compounds from a small number of precursors drawn from intermediary
metabolism. Osmotrophs generate growth substrates from the products of
biosynthesis and diagenesis by enzyme-catalyzed reactions that occur largely
outside cells. These enzymes, which we define as ecoenzymes, enter the
environment by secretion and lysis. Enzyme expression is regulated by envi-
ronmental signals, but once released from the cell, ecoenzymatic activity is
determined by environmental interactions, represented as a kinetic cascade,
that lead to multiphasic kinetics and large spatiotemporal variation. At the
ecosystem level, these interactions can be viewed as an energy landscape
that directs the availability and flow of resources. Ecoenzymatic activity
and microbial metabolism are integrated on the basis of resource demand
relative to environmental availability. Macroecological studies show that the
most widely measured ecoenzymatic activities have a similar stoichiometry
for all microbial communities. Ecoenzymatic stoichiometry connects the
elemental stoichiometry of microbial biomass and detrital organic matter
to microbial nutrient assimilation and growth. We present a model that
combines the kinetics of enzyme activity and community growth under
conditions of multiple resource limitation with elements of metabolic and
ecological stoichiometry theory. This biogeochemical equilibrium model
provides a framework for comparative studies of microbial community
metabolism, the principal driver of biogeochemical cycles.
313
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1. INTRODUCTION
The net primary production (NPP) of the biosphere is consumed largely by microorganisms, whose
metabolism creates the trophic base for detrital foodwebs, drives global carbon (C) and nutrient
cycles, and mediates atmospheric composition. Biogeochemical constraints on these catabolic pro-
cesses, relative to primary production, create reserves of detrital organic C in soils and sediments
that exceed the C content of the atmosphere and biomass by a factor of two or more (Cole et al.
2007, Houghton 2007).
The production of organic molecules is an intracellular process, broadly similar across do-
mains, that generates thousands of compounds from a small number of precursors drawn from
intermediary metabolism, fueled by the consumption of C from the environment. For autotrophs,
the C source is CO2. For osmotrophic prokaryotes and fungi, the C sources are low–molecular
mass compounds, with individual taxa limited to a small number (∼1–20) of growth substrates.
These growth substrates are generated from the myriad products of biosynthesis and diagenesis
by enzyme-catalyzed reactions that occur largely outside cells (Burns 1978, Chr´
ost 1991, Burns &
Dick 2002, Shukla & Varma 2011, Trasar-Cepeda et al. 2011, Dick 2012). The production of these
catabolic enzymes is directed by environmental signals in relation to cellular resources, but once
released into the environment by secretion or lysis, their activity and turnover are determined by
complex physicochemical and biochemical interactions. The extracellular catabolism of organic
matter, i.e., decomposition, is considered a rate-controlling step in the global C cycle. For this
reason, considerable research has focused on environmental enzyme reactions across molecular
to biosphere scales.
Herein, we briefly review the history of environmental enzyme research with a focus on
concepts and models that link these enzyme activities to biogeochemical processes, microbial
metabolism, and ecological theory. We refer readers to previous reviews and texts for background
on the biochemistry of particular enzymes and the methodology for measuring reaction rates. We
conclude with a synthesis of empirical data and model consensus that describes the equilibrium re-
lationships among environment resource availability, microbial metabolism, and enzymatic indices
of catabolic potential, the three components of the successional loop that drives decomposition
(Sinsabaugh et al. 2002).
2. TERMINOLOGY AND ENZYMES OF INTEREST
Several terms have been used to describe the distribution or origin of enzymes found outside
of cells, including extracellular enzymes, ectoenzymes, exoenzymes, abiontic enzymes, and free
enzymes, with varying definitions. In recent papers, we use the term ecoenzyme to broadly
encompass all enzymes located outside the confines of intact cell membranes regardless of
whether such enzymes enter the environment by secretion or lysis (Sinsabaugh et al. 2009).
This definition provides the closest correspondence between environmental enzyme activity and
organic matter decomposition.
The most studied ecoenzymes catalyze the degradation of the largest environmental sources of
organic C, nitrogen (N), and phosphorus (P) (Figure 1). The largest organic C pool is structural
polysaccharides that form the cell walls and matric glycolates of plants and microorganisms, fol-
lowed by lignins and other secondary polyphenolic molecules, storage polysaccharides, and lipids.
Polysaccharide degradation is primarily hydrolytic; lipid and phenolic degradation is primarily
oxidative. The organic N pool includes polymers of amino acids and aminosaccharides, which are
sources of C and N. The organic P pool includes labile nucleic acids and phospholipids and more
recalcitrant P storage products, principally inositol phosphates.
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Microbial
biomass
CO2
Signal induction
Derepression
Repression
CELL
Catabolic repression
ENVIRONMENT
Induced expression
Constitutive expression
Endproduct
inhibition
Enzymes
ENZYMES
Organic
phosphorus
Nucleic acids
Phospholipids
Inositol phosphates
Polysaccharides
Cellulose
Hemicellulose
Pectin
Starch
Chitin
Peptidoglycan
Exopolysaccharides
Organic
nitrogen
Protein
Chitin
Peptidoglycan
Polyphenols
and
hydrocarbons
Lignin
Tannin
Cutin
Suberin
Fatty acids
Humic substances
EEA
processing Assimilation Production
Figure 1
Expanded successional loop that links microbial production, detrital organic matter, and ecoenzyme activity
(EEA). (Upper loop) Principal environmental sources of organic carbon, nitrogen, and phosphorus. (Lower
loop) Expression of extracellular enzymes is controlled by signal pathways linked to environmental cues.
Some enzymes are produced constitutively, usually at low levels. More generally, production is upregulated
by induction-derepression pathways and downregulated by catabolic repression pathways controlled by
environmental signals and cellular resources. Once released, the activity of extracellular enzymes is subject to
environmental controls such as end-product inhibition. Intracellular enzymes released through cell lysis are
subject to the same suite of controls. A broader description of environmental controls is presented in Figure 2.
Within these classes, the ecoenzymatic activities (EEAs) most commonly measured generate
low–molecular mass products that can be directly consumed by microorganisms. These include
α-andβ-1,4-glucosidase, which catalyze the terminal reactions in the hydrolysis of storage and
structural glucans; leucine and alanine aminopeptidase, which hydrolyze the two most abundant
amino acids from the N-terminus of polypeptides; β-1,4-N-acetylglucosaminidase, which
catalyzes the terminal reaction in the hydrolysis of chitin; and phosphatase, which hydrolyzes
phosphate from phosphoesters. Phenol oxidases and peroxidases, which use molecular oxygen
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and peroxide, respectively, as electron acceptors, catalyze the oxidative degradation of lignin and
the formation of humus. These choices are partially methodological; it is easier to assay activities
using soluble substrates that yield soluble products than to study the degradation of insoluble
polymers or humic complexes. This bias has value in that reactions that yield assimilable products
are the ones most directly linked to microbial metabolism (Meyer-Reil 1987, Hoppe et al. 1988,
M¨
unster 1991). In the molecular sieve model (Burns 1978), such enzymes are intermediate
agents in reaction pathways that connect the activities of polymer-degrading enzymes distributed
throughout the environmental matrix and cell membrane permeases that transport substrates into
the cell. In many cases, the enzymes that catalyze the terminal reactions in polymer degradation
are localized on cell surfaces and periplasmic spaces.
3. HISTORY OF ECOENZYMATIC RESEARCH
Skujin¸ ˇ
s (1978) summarized the history of soil enzyme research, beginning with the first report of
catalase activity in soil in 1899. Through the 1970s, the major research topics were the source of
soil enzymes (plant or microbe, intracellular or extracellular), the physicochemical interactions
of enzymes with the soil matrix, correlating EEA with soil properties, and using EEA to classify
soils. Phosphatase and urease (amidohydrolase) activities were of particular interest because of
their role in generating P and N for plant growth.
Progress was limited by the lack of sensitive methods for measuring EEA and microbial dynam-
ics. But there was a consensus that phosphatase activity increased in response to N fertilization,
an indication that microbial communities allocate resources in relation to environmental nutrient
availability (Skujin¸ˇ
s 1978). In the final synthesis chapter of Soil Enzymes, Burns (1978) described
the soil system as a molecular sieve of stabilized matric enzymes linked to cell surface enzymes,
periplasmic enzymes, and permeases.
Overbeck (1991) summarized the history of aquatic enzyme research, beginning with a 1906
paper on proteolytic activity in surface water. The importance of ecoenzymes in aquatic systems
was highlighted by ZoBell (1943), who included ecoenzymes in his model of the organization
of attached microbial communities. The text Microbial Enzymes in Aquatic Environments (Chr´
ost
1991) contains several reviews and perspectives on the role of ecoenzymes in the organization
of aquatic microbial communities. In particular, Hoppe (1991) added ecoenzymes as an integral
component of the microbial loop model for the trophic organization of planktonic microbial
communities, and Wetzel (1991) described the functional organization of aquatic ecosystems as a
system of stored, immobilized enzymes.
The questions of interest to aquatic researchers differed from those of soil researchers. Many
studies correlated phosphatase, aminopeptidase and glucosidase activities to the composition
and turnover of dissolved organic and inorganic nutrient pools and rates of microbial produc-
tion (Chr´
ost 1991), establishing the fundamental relationships on which subsequent models are
based.
By the time Enzymes in the Environment was published (Burns & Dick 2002), the perspectives
of soil and aquatic researchers had largely converged through improvements in methodology
that facilitated comparisons across systems and through conceptual advances such as the biofilm
concept (Characklis & Marshall 1990) that established a common paradigm for the organization
of attached microbial communities. During the past decade, EEA studies have merged with the
biomics paradigm, which considers ecological communities as metagenomes and metaproteomes,
while simulation models with increased mechanistic resolution connect EEA to ecological pro-
cesses and theory.
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Km
kcat
Ea
Enzyme concentration (V
max
)
Competitive inhibition
Noncompetitive inhibition
Diusion (D)
Nonspecic sorption
Eective sorption
Temperature
Water potential (Ψ)
pH
Figure 2
Environmental control of ecoenzymatic activity: a kinetic cascade. The kinetics of enzyme reactions can be
characterized by the parameters kcat (the rate of substrate conversion), Km(the half-saturation constant), and
Ea(activation energy), which are determined by enzyme structure. A cascade of enzyme-environment
interactions affects the apparent values of these parameters. Enzyme interactions with reaction substrates and
products (competitive inhibition), other reactive species (noncompetitive inhibition), and particle surfaces
generally reduce kcat and increase Kmand Eavalues. Other rate-controlling matric effects include
sorption-desorption reactions and diffusion rates of enzymes and substrates. These effects, in turn, are
influenced by broader environmental variables such as pH, temperature, and water potential. As a result,
kinetic parameters show multiphasic distributions and high spatiotemporal variation.
4. ENZYME EXPRESSION AND ENVIRONMENTAL CONTROL
4.1. Kinetic Cascade
Approximately 1–4% of the production of heterotrophic microbial communities is used to make
enzymes for secretion into the environment; many more are released through lysis (Maire et al.
2012). Modeling studies suggest that production of extracellular enzymes has first priority on cell
metabolism above the level of maintenance respiration (Schimel & Weintraub 2003, Moorhead
& Sinsabaugh 2006, Moorhead et al. 2012, Wang et al. 2012a). Given the energy and material
cost of producing enzymes whose control is lost to the cell, expression is closely regulated at
the level of transcription. Specifics vary among enzymes and organisms, but the general model is
that transcription is ultimately linked to environmental signals (Figure 1). These signals may be
substrates consumed by the cell, indicators of toxicity, or quorum sensing molecules (DeAngelis
et al. 2008). However, once released from the cell, whether by direct secretion or cell lysis, EEA is
determined by a hierarchy of interactions that can be represented as a kinetic cascade (Figure 2).
In the kinetic cascade, ecoenzyme function, measured by the parameters kcat (the rate of
substrate conversion), Km(the half-saturation constant), and Ea(activation energy), is determined
by interactions with reaction substrates and products (competitive inhibition), other reactive
species (noncompetitive inhibition), and matric interactions that control the sorption-desorption
and diffusion of enzymes and substrates (Resat et al. 2012). These molecular processes are also
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0
10
20
30
40
50
60
70
80
90
100
S min P min N min CH2O CHO SOC ROC
Apparent Ea (kJ mol–1)
Figure 3
Mean apparent activation energy (Ea±standard deviation) for enzyme catalyzed processes in litter and soils
based on short-term temperature manipulations. Enzyme data for sulfur (S), phosphorus (P), and nitrogen
mineralization (N min), carbohydrate hydrolysis (CH2O), and polyphenol oxidation (CHO) come from
Table 1. Values for mineralization (respiration) of soil organic carbon (SOC) and recalcitrant organic
carbon (ROC) are taken from Gillooly et al. (2001) and Ramirez et al. (2012), respectively.
influenced by broader environmental variables such as pH, temperature, and water potential.
As a result, EEA exhibits multiphasic kinetics (Overbeck 1975, 1991; McLaren 1978; Azam &
Hodson 1981; Vrba et al. 2004) and high spatiotemporal variation (Sinsabaugh et al. 2008).
4.2. Ecoenzymatic Activities and Thermodynamics
In general, environmental interactions decrease the apparent Vmax and increase the apparent Km
and Eaof EEAs. At a macroecological scale, apparent Eareceives the most attention because of its
debatable utility for predicting changes in decomposition processes, microbial respiration, and C
storage as a consequence of warming climate (Davidson & Jannsens 2006, Sierra 2012). In general,
the activation energy of a process increases with its complexity, i.e., the number of reactions
(Sierra 2012). Short-term temperature manipulations of soils show that the apparent Eaof enzymes
mediating sulfur, P, and N mineralization are lower on average (30–34 kJ mol−1) than the apparent
Eaof enzymes involved in lignocellulose degradation (44–47 kJ mol−1)(Figure 3). An independent
compilation by Wang et al. (2012b) yielded mean Eaestimates of 54 kJ mol−1for phenol oxidase
and peroxidase, 43 kJ mol−1for β-glucosidase, 34 kJ mol−1for β-endoglucanase, and 32 kJ mol−1
for cellobiohydrolase. The apparent Eaof C mineralization (65 kJ mol−1), measured as respiration,
is greater than the apparent Eaof ecoenzymatic reactions, and values for the mineralization of
recalcitrant C range up to 85 kJ mol−1(Table 1,Figure 3).
The totality of reactions that direct resource flow to microorganisms, which include sorption-
desorption and diffusion as well as catalysis, can be represented as an activation energy landscape
(Gfeller et al. 2007). As system complexity increases, mean activation energies increase (Figure 4).
In laboratory trials, the temperature sensitivity of EEA is typically measured by adding substrate at a
saturating concentration, which simplifies the energy landscape by overwhelming potential kinetic
bottlenecks associated with sorption-desorption and diffusion processes. In contrast, respiration
responses are often measured in situ without substrate amendment and the more complex landscape
contributes to greater apparent Eavalues.
In an ecosystem context, changes in the energy landscape translate to changes in the availability
of substrates for EEA and microbial metabolism. A dynamic energy landscape complicates the
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Table 1 Apparent activation energies (Ea) for ecoenzymes that catalyze sulfur, P, and N mineralization; carbohydrate
hydrolysis; and polyphenol oxidation
Reference System Enzyme Ea(kJ mol−1)
Trasar-Cepeda et al. 2007 Soil Sulfatase 29.6
Elsgaard & Vinther 2004 Soil Sulfatase 42.2
Tabatabai & Bremner 1970 Soil Sulfatase 25.3
Oshrain & Wiebe 1976 Soil Sulfatase 29.0
Beil et al. 1995 Free Sulfatase 46.6
Ramirez-Martinez & McLaren 1966 Clay loam Phosphatase 63.2
Kaziev 1975 Soil Phosphatase 23.0
Menezes-Blackburn et al. 2011 Free Phytase 35.8
Stone et al. 2012 Soils N-acetylglucosamindase 44.1
Bremner & Mulvaney 1978 Soils Urease 52.2
Ambus 1993 Riparian soil Denitrification 64.9
Peterjohn 1991 Desert soil Denitrification 41.0
Frankenberger & Tabatabai 1991b Soils L-glutaminase 32.4
Frankenberger & Tabatabai 1991a Soils L-asparaginase 26.6
Trasar-Cepeda et al. 2007 Grassland soil Casein protease 38.0
Trasar-Cepeda et al. 2007 Grassland soil L-argininase 23.3
Trasar-Cepeda et al. 2007 Grassland soil Urease 29.5
McClaugherty & Linkins 1990 Chitinase 25.4
Trasar-Cepeda et al. 2007 Grassland soil Cellulase 48.6
Stone et al. 2012 Soil α-glucosidase 38.7
Stone et al. 2012 Soil β-glucosidase 41.5
Stone et al. 2012 Soil β-xylosidase 46.8
Stone et al. 2012 Soil Cellobiohydrolase 52.8
Trasar-Cepeda et al. 2007 Grassland soil β-glucosidase 28.6
McClaugherty & Linkins 1990 Forest soil Exocellulase 44.8
McClaugherty & Linkins 1990 Forest soil Endocellulase 50.4
Kahkonen et al. 2001 Pine soil β-glucosidase 56.3
Davidson et al. 2012 Forest soil β-glucosidase 61.8
Kocabas et al. 2008 Free Phenol oxidase 42.3
Zhang et al. 2008 Soils Laccase 44.8
Di Nardo et al. 2004 Oak litter Laccase 55.0
Di Nardo et al. 2004 Oak litter Peroxidase 60.0
McClaugherty & Linkins 1990 Forest soil Laccase 54.4
McClaugherty & Linkins 1990 Forest soil Peroxidase 39.6
Valtcheva et al. 2003 Wood pulp Laccase 22.3
Lo et al. 2001 Free Laccase 12.4
Aktas¸ et al. 2001 Free Laccase 57.0
Acevedo et al. 2010 Clay bound Manganese peroxidase 51.9
Acevedo et al. 2010 Free Manganese peroxidase 34.4
Davidson et al. 2012 Soil Phenol oxidase 32.5
Annuar et al. 2009 Bound Laccase 23.0
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System
complexity
Free energy
Reaction coordinate
Figure 4
The effect of enzyme-environment interactions on the availability of resources can be represented as an
activation energy landscape. In this conception, resource availability is related to free energy changes
associated with catalytic, sorption-desorption and diffusion reactions involving enzymes, substrates,
products, and inhibiters. As the matric and enzymatic complexity of the system increases, the energy
landscape becomes more complex and apparent activation energies trend greater. Colors indicate increasing
elevation on the vertical axis.
extrapolation of process rates to future conditions using Arrhenius models because altered resource
flows affect microbial metabolism and enzyme expression. Consequently, the equilibrium between
environmental resources, EEAs, and microbial metabolism is not easily linked to temperature.
5. ECOENZYMATIC ACTIVITY AND ECOLOGICAL STOICHIOMETRY
The metabolic basis of EEA stoichiometry is organismal control over enzyme expression based
on environmental signals (Figure 1). The kinetic cascade (Figure 2) that controls the activity and
turnover of ecoenzymes provides feedback by generating assimilable substrates and other signals of
nutrient availability in relation to growth requirements. Studies of EEA stoichiometry, although
not described by that term, have proceeded largely independently of ecological stoichiometry
theory (Sterner & Elser 2002), which originated from comparisons of the elemental ratios of
biomass composition.
5.1. Phosphorus
The most studied case of stoichiometric control of EEA is the generally inverse relationship
between phosphatase activity and environmental P availability (Reichardt et al. 1967, Berman
1970, Jones 1972, Speir & Ross 1978, Wetzel 1981, Chr ´
ost & Overbeck 1987). Correlations
and relative response magnitudes vary widely across aquatic and terrestrial systems in relation to
measures of organic and inorganic P concentration or availability. But overall, the generalization
is well supported by both small-scale and large-scale studies (e.g., Olander & Vitousek 2000;
Sinsabaugh et al. 2008; Hill et al. 2010a,b, 2012; Marklein & Houlton 2012; Williams et al.
2012).
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5.2. Nitrogen
Relating the activities of proteases, aminopeptidases, and amidohydrolases to measures of N avail-
ability is tenuous because N sources are more varied, and amino acids and amino sugars can be
important sources of C as well as N. Proteins are the largest source of organic N. In soils, Abe &
Watanabe (2004) found that peptide N accounted for 66–90% of the N content of humic acids.
In planktonic systems, amino acids support a large fraction of bacterial production, ranging up
to 100% with a median value of about 40% (Kirchman 2003). As a result, relationships between
proteolytic activities and inorganic or organic N concentrations are variable. High-resolution
planktonic studies (e.g., Hollibaugh & Azam 1983, Someville & Billen 1983, Meyer-Reil 1987,
Cunningham & Wetzel 1989, Billen 1991, Hoppe 1991, M¨
unster 1991) generally show a close
linkage between the amino acid hydrolysis, amino acid uptake, and microbial metabolism with
turnover rates for free amino acids ranging from minutes to a few hours. Amendment studies show
that aminopeptidase activity can be depressed by additions of nitrate, ammonium, and amino acids
and induced by protein addition (Chr´
ost 1991, Foreman et al. 1998).
Jones et al. (2009) found that turnover rates for amino acids across a broad range of soils were
similar to those in aquatic systems, i.e., 1–4 h, and suggested that enzymatic hydrolysis was the
rate-limiting step for microbial metabolism. A study of 84 soils by Hofmockel et al. (2010) found
that potential rates of proteolysis, measured as amino acid production, were similar to rates of
N mineralization (amidohydrolase activity) in all ecosystems except semiarid grasslands, where
proteolysis rates were six times greater than amidohydrolase. In addition, proteolytic activity was
inversely related to extractable ammonium. In a study of leaf litter decomposition, Wanek et al.
(2010) used 15N isotopic dilution of labeled amino acids to show that rates of proteolysis exceeded
N mineralization by eight fold.
After protein, the aminopolysaccharides of chitin and peptidoglycan are the most abundant
sources of N. About 16% of the dry mass of filamentous fungi is chitin (Dahiya et al. 2006).
Zhang & Amelung (1996) reported that hydrolysable muramic acid, glucosamine, mannosamine,
and galactosamine concentration in soils was 6.2–9.5% of soil C. In a study of the Delaware
estuary, Kirchman & White (1999) found that potential chitinase activity usually exceeded 14C-
chitin degradation rates, but both measures were of the same order of magnitude. From these
data and other studies, they estimated that ∼10% of bacterial production in marine systems is
supported by chitin. Zeglin et al. (2012) found that N-acetylglucosamine and chitin addition
to coniferous forest soils increased β-N-acetylglucosaminidase activity, N mineralization, and
microbial respiration. Olander & Vitousek (2000) found that β-N-acetylglucosaminidase activity
decreased as soil N increased along a Hawaiian chronosequence, but N addition depressed β-N-
acetylglucosaminidase activity only in the youngest N-limited soil.
Experimental N amendment studies have been conducted in nearly every ecosystem type with
no clear trend for either proteolytic or chitinolytic enzyme activities. Reported responses, if sig-
nificant, are generally small. Enowashu et al. (2009) filtered N from rainwater in a spruce forest
and measured the response of 15 N-acquiring enzyme activities. Some activities increased, e.g.,
urease (amidohydrolase), whereas, others decreased, including leucine aminopeptidase and β-
N-acetylglucosaminidase. Zeglin et al. (2007) also found complementary responses in grassland
ecosystems: where the leucine aminopeptidase to β-N-acetylglucosaminidase ratio was high, N
amendment reduced leucine aminopeptidase activity and increased β-N-acetylglucosaminidase;
where the ratio was low, N deposition depressed β-N-acetylglucosaminidase and increased leucine
aminopeptidase. These examples highlight that there is no simple relationship between bulk mea-
sures of N availability and microbial N-acquiring EEAs at the ecosystem scale. A further com-
plication is that N addition to soils depresses microbial biomass and respiration (Treseder 2008,
Ramirez et al. 2012).
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Because amidohydrolases are the proximate agents of N mineralization, analogous to phos-
phatase and P mineralization, these activities may be more directly linked to N availability and
uptake than proteases or chitinases. But these activities are not commonly measured in ecological
studies, with the exception of urease (Bremner & Mulvaney 1978) whose activity can be induced
by urea fertilization and repressed by ammonium (Mobley et al. 1995, Hasan 2000). Blank (2002)
found that the activities of four amidohydrolases were positively correlated with N mineralization
and plant N uptake in riparian habitats colonized by an invasive crucifer. In a broad survey of soils,
Hofmockel et al. (2010) found a 1:1 ratio of protease to amidohydrolase activities.
Cellular N metabolism is mediated by the activities of glutamate dehydrogenase (GDH) and
glutamine synthetase (GS) (Marzluf 1997). GDH catalyzes a low-affinity NH+
4assimilation re-
action, the interconversion of glutamate and α-ketoglutarate; GS catalyzes a high-affinity NH+
4
assimilation reaction, the interconversion of glutamate and glutamine (Hoch et al. 2006). Jorgensen
et al. (1999) found that dissolved free amino acids were the dominant N sources for estuarine and
oceanic bacterioplankton and that greater cell-specific amino acid assimilation was associated with
greater cell-specific leucine aminopeptidase activity, a higher ratio of GDH:GS activities, and a
lower cell-specific respiration rate. Hoch & Bronk (2007) found that amendments of amino acids
and ammonium to marine bacterioplankton increased growth and repressed GS and assimilatory
nitrate reductase activities. Similarly, Geisseler et al. (2009) found that the GDH:GS activity ratio
for soil microbial communities was inversely related to the C:N availability.
5.3. Carbon
P and N mineralization are predominantly hydrolytic reactions. The ecoenzymatic degradation
of carbon molecules requires a large number of both hydrolytic and oxidative reactions, whose
stoichiometry is linked to the composition and oxidation state of available organic matter.
5.3.1. Polysaccharides. Cellulose production accounts for about half of terrestrial NPP
(Ericksson et al. 1990). Hemicelluloses, polymers of xylose, mannose, galactose, arabinose, and
glucose, compose 20–30% of the mass of plant cell walls (Ericksson et al. 1990). In aquatic sys-
tems, the capsules and glycocalyces of microorganisms and biofilms are composed of complex
polysaccharides whose mass can far exceed that of the microorganisms themselves (Decho 1990,
Leppard 1995, Pereira et al. 2009, Bellinger et al. 2010). Given the range of monomers, linkages,
and crystallinity, polysaccharide degradation requires the interaction of diverse enzymes (Warren
1996). In aquatic ecosystems, β-andα-glucosidase activities are correlated with the abundance of
dissolved polysaccharides and the turnover and uptake of neutral monosaccharides (Hoppe 1983,
Hoppe et al. 1988, M¨
unster 1991, Arnosti 2011). Polysaccharides are estimated to support ∼20%
of bacterial production in marine systems (Kirchman 2003, Piontek et al. 2011). In terrestrial
ecosystems, the activities of β-glucosidase and other cellulolytic enzymes are correlated with mi-
crobial metabolism and rates of mass loss from plant litter (Sinsabaugh et al. 1992, 1994; Jackson
et al. 1995; Allison & Vitousek 2004; Snajdr et al. 2011).
5.3.2. Lignin and humus. Lignin, the most recalcitrant component of plant litter, accounts for
about one-quarter of terrestrial NPP. The degradation of lignin and humus is mediated by an
array of oxidases, peroxidases, dehydrogenases, and supporting enzymes that vary widely among
taxa (Rabinovich et al. 2004, Baldrian 2006, Sinsabaugh 2010, Theuerl & Buscot 2010, Bugg
et al. 2011, Strong & Claus 2011). Many of these enzymes are produced for purposes other than
C acquisition including morphogenesis, response to oxidative stress, antimicrobial defense, and
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detoxification of reactive phenols. Many activities are nonspecific and may involve the generation
of secondary organic and inorganic redox mediators.
Fungi, particularly Basidiomycota, are the most efficient lignin degraders in terrestrial sys-
tems, but bacteria are the ultimate consumers of most lignin-derived C. Lignin degradation is a
rate-limiting process in litter decomposition and a lignocellulose index (ratio of lignin to lignin
plus cellulose) of 0.7 is considered the limit of decomposition, i.e., the transition between plant
litter and soil organic matter (Berg & McClaugherty 2010). In soils, the mining of humus for
polysaccharide and polypeptide C and N may lead to a positive correlation between oxidative and
hydrolytic enzyme activities, but more commonly these activities are uncorrelated (Sinsabaugh
2010, Sinsabaugh & Follstad Shah 2011, Sinsabaugh et al. 2011). Phenol oxidase and peroxidase
activities in soils and sediments generally increase with the concentration of polyphenols, but
other factors, including pH and oxygen levels and the availability of manganese and iron medi-
ators, are also important. In soils, N saturation reduces phenol oxidase activity, decomposition
rates, microbial respiration, and microbial biomass (Knorr et al. 2005, Treseder 2008, Sinsabaugh
2010, Ramirez et al. 2012). Oxidative activities are thought to play a major role in mediating C
sequestration (Freeman et al. 2001, 2004; Collins et al. 2008; Sinsabaugh & Follstad Shah 2011).
6. MICROBIAL RESOURCE ALLOCATION
6.1. Conceptual Model
Correlating EEAs with the availability of target substrates provides empirical relationships
for decomposition models based on the successional loop and corroborates the utility of EEA
measurements for inferring microbial nutrient needs in relation to supply. At the community and
ecosystem scale, the research focus shifts from the biochemistry of specific enzymes to microbial
metabolism as the driver of decomposition processes. For terrestrial systems, early research
involved correlating EEA with soil respiration. Correlating EEA to rates of litter decomposition
began in the 1980s (Sinsabaugh et al. 1981), but it was problematic to correlate snapshots of
highly dynamic activities with cumulative changes in litter mass or composition. Integrating EEA
over time provided the first statistical models for mass-loss rates as a function of potential EEAs
(Sinsabaugh & Linkins 1993). In aquatic systems, it is much easier to measure the turnover of
dissolved organic matter pools and its relationship to EEA and microbial metabolism (Chr ´
ost
1991). The 3H-thymidine and 3H-leucine assays for bacterial productivity, which came into
widespread use in the 1980s, facilitated empirical analyses of the microbial loop model (Fuhrman
& Azam 1982).
The development of resource allocation models in the 1990s provided a conceptual paradigm
for integrating EEA and microbial metabolism (Sinsabaugh & Moorhead 1994). This effort par-
alleled the development of the biofilm concept, which extended a common model of microbial
community organization across disciplines (Characklis & Marshall 1990). The view of microbial
communities as interacting consortia with 102–104populations and multiple resource require-
ments moved microbial ecology beyond the single limiting-nutrient approach of the Monod (1949)
and Droop (1977) models and moved EEA studies closer to the field of ecological stoichiometry
(Zinn et al. 2004, Cherif & Loreau 2007, Danger et al. 2008).
6.2. Empirical Studies of Resource Allocation
In his history of soil enzyme research, Skujin¸ˇ
s (1978) noted that N fertilization of soils generally
increased phosphatase activity. A meta-analysis of soil N and P fertilization studies by Marklein &
Houlton (2012) confirms this effect for a wide variety of soils. In aquatic systems, several studies
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showed that the ratio of glucosidase:aminopeptidase activities shifts in response to nutrient
availability (e.g., Hoppe 1991, Christian & Karl 1995).
Sinsabaugh et al. (1992, 1993) showed that differences in decomposition rate of birch sticks
placed at six sites were directly related to cellulolytic enzyme activities and inversely related to the
activities of enzymes involved in P and N acquisition. These studies provided the empirical basis
for a model that linked EEA to mass-loss rates using ratios of C-, N-, and P-acquiring EEAs as
indicators of microbial resource allocation (Sinsabaugh & Moorhead 1994).
Foreman et al. (1998) evaluated the resource allocation model by adding eight C and N amend-
ments to bacterioplankton communities from a eutrophic river sampled on eight dates over an
annual cycle. The responses of five enzymatic activities varied widely over the year with a mix
of induction-depression, feedback inhibition, and resource allocation effects. On average, ammo-
nium and leucine amendments increased bacterial production and growth efficiency and reduced
enzymatic activity (feedback inhibition). Albumin had similar effects on metabolism, but increased
peptidase and phosphatase activities (resource allocation). Glucose, cellobiose, and starch increased
production, respiration, and glycosidase activities (induction-derepression). Vanillin and tannin
increased respiration, but reduced growth efficiency and enzymatic activities. Across treatments,
EEAs were closely correlated with productivity and weakly correlated with respiration. Response
ratios were greatest for α-andβ-glucosidase followed by phosphatase and leucine aminopepti-
dase. These ratios were inversely related to ambient Vmax and apparent Km. The results supported
the resource allocation model, but illustrated that multiple mechanisms underlie shifts in ratios of
EEAs.
Allison & Vitousek (2005) tested the resource allocation model in a low-nutrient tropical
soil amended with simple and complex (insoluble) C, N, and P substrates, measuring respiration
and the activities of β-glucosidase, glycine aminopeptidase, and acid phosphatase. The activi-
ties of enzymes directed at complex nutrients generally increased in response to the addition of
complementary simple nutrients. However, ecoenzymatic responses were tempered by the ac-
tivity of enzymes stabilized on soil particles, and therefore uncoupled from microbial responses.
Hernandez & Hobbie (2010) conducted a similar experiment adding nine substrates singly or
in varying combinations and quantities to a low-nutrient grassland soil. Ecoenzymatic responses
included complementation consistent with resource allocation as well as substrate induction-
derepression. Respiration rates were directly related to the sum of β-glucosidase, α-glucosidase,
phosphatase, and leucine aminopeptidase activities.
Sinsabaugh & Follstad Shah (2010) compared the apparent activation energy of bacterioplank-
ton production in two rivers sampled over an annual cycle to EEA kinetics. Bacterial consumption
of carbohydrates and proteins varied by season due to changes in substrate concentration and
the EEAs that mediated substrate turnover. Production was closely related to the ecoenzyme-
mediated generation of assimilable substrates from dissolved carbohydrate, protein, and organic
phosphate pools. The analyses demonstrated that EEA kinetics and stoichiometry can be used to
resolve nutrient and temperature constraints on microbial community metabolism.
Collectively, these studies, and others, support resource allocation and multiple resource lim-
itation models for the functional organization of microbial communities. Changes in substrate
availability caused by altered inputs or shifts in the activation energy landscape affect the stoi-
chiometry of EEA and nutrient supply, altering microbial metabolism (Allison et al. 2007).
6.3. Macroecological Patterns of Resource Allocation
Recently, EEA data sets extending to continental and global scales make it possible to compare EEA
patterns to large-scale biogeochemical trends and evaluate models that link EEA stoichiometry to
metabolic and stoichiometric theories of ecology.
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Sinsabaugh et al. (2008) described the distribution of β-1,4-glucosidase (BG), leucine
aminopeptidase (LAP), β-1,4-N-acetylglucosaminidase (NAG), acid (alkaline) phosphatase (AP),
phenol oxidase and peroxidase activities in soils in relation to edaphic (soil pH, soil organic mat-
ter) and climatic (mean annual temperature, mean annual precipitation) variables. All activities
correlated with soil pH. Ratios of C:N and C:P acquiring hydrolytic activities reflected latitudinal
trends in P and N availability, with a global mean BG:(NAG+LAP):AP ratio of approximately
1:1:1. Sinsabaugh et al. (2009) extended these analyses, showing that terrestrial soils and fresh-
water sediments have similar stoichiometry with slopes near 1.0 for regressions of ln(BG) versus
ln(AP) and ln(BG) versus ln(NAG+LAP). They proposed that EEAs connect the stoichiometric
and metabolic theories of ecology by reflecting the equilibrium between the elemental composi-
tion of microbial biomass and detrital organic matter and the efficiencies of nutrient assimilation
and growth. Sinsabaugh et al. (2010) evaluated one of these predictions by normalizing EEA to
microbial productivity rates and showing that the regression slopes of ln(BG) versus ln(AP) and
ln(BG) versus ln(NAG+LAP) for plankton and biofilm communities differ in proportion to the
elemental C:P and C:N ratios of biomass.
Sinsabaugh & Follstad Shah (2011) analyzed the relationships between hydrolytic and oxidative
activities in soils and presented a conceptual model that links organic matter recalcitrance and EEA
stoichiometry. As N becomes increasingly concentrated in humus, N acquisition becomes linked
to phenol oxidase activity, and N availability to microbial communities is lower than elemental C:N
ratios suggest. The growth rate hypothesis (GRH) proposes that microbial growth rates are related
to the cellular P quota (Frost et al. 2006, Allen & Gillooly 2009). As microbial growth slows with
increasing organic matter recalcitrance, P demand should decrease relative to N. Normalizing
hydrolytic activities to phenol oxidase activity reduces the slope of the ln(BG) versus ln(AP)
regression and increases the slope of ln(BG) versus ln(NAG+LAP), reflecting the shift toward N
acquisition expected as microbial growth rates decline with increasing organic matter recalcitrance.
Kelley et al. (2011) conducted a meta-analysis of 34 studies that measured soil enzyme responses
to elevated atmospheric CO2treatments. β-N-acetylglucosaminidase activity increased signifi-
cantly with CO2enrichment across studies, suggesting increased N demand. Activities directed
toward lignocellulose degradation and P mineralization increased on average, but the responses
were not statistically significant across all studies. Consequently, shifts in EEA stoichiometry
varied with ecosystem type and duration of treatment.
Williams et al. (2012) sampled 50 streams, measuring planktonic EEA and bacterial abundance
and production as well as taking several measures of land use. Abundance, productivity, and
EEA were positively related to nutrient availability and anthropogenic land use. The ratio of β-
glucosidase to alkaline phosphatase activity approached 1:1 with increasing anthropogenic land
use and total dissolved N. The ratio of leucine aminopeptidase to alkaline phosphatase activity
approached 1:1 with increased dissolved organic C and N. Ecoenzymatic C:N:P ratios moved
closer to 1:1:1 as bacterial turnover increased.
Sinsabaugh et al. (2011) analyzed EEA data collected from 2,200 stream sites nationwide by the
US Environmental Protection Agency. The data included nine hydrolytic activities; phenol oxidase
and peroxidase activities; elemental analyses of sediment C, N, and P content; and dehydrogenase
activity as a measure of microbial respiratory potential. On average, EEAs in stream sediments are
two to five times greater per gram C than those of terrestrial soils. The mean ratios of BG:AP and
BG:(NAG+LAP) were 1.64 ±0.18 [95% CI (confidence interval)] and 1.83 ±0.04, respectively,
compared to 0.62 ±0.04 and 1.43 ±0.22 for soils, reflecting differences in the mean elemental
C:P and C:N ratios of sediment and soil (57 versus 186 and 18.2 versus 14.3, respectively).
Here, we extend these stoichiometric analyses of soils and sediments by pooling data from
studies of limnetic and marine plankton (Table 2). The slopes for the standardized major axis
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Table 2 α-glucosidase (AG), β-glucosidase (BG), alkaline phosphatase (AP), leucine aminopeptidase (LAP), and
β-N-acetylglucosaminidase (NAG) activities (in nanomoles per hour per liter) of planktonic microbial communities
Reference System NAG BG AP LAP NAG BG/AP BG/LAP
Hendel & Marxsen 1997 Streams 84.11 37.9 54.1 0.108 0.076
Williams et al. 2012 Streams 37 12.9 71.0 138 0.182 0.093
Sinsabaugh et al. 1997 River 51 23.5 32.0 148 759 0.216 0.042
Findlay et al. 1998 River 18 10.0 19.4 179 179 14.1 0.108 0.108
Sinsabaugh & Foreman 2001 River 10 37.7 99.2 227 243 0.437 0.408
Boucher & Debroas 2009 Lake 25 3.64 3.71 3.74 69.6 0.992 0.053
Pamer et al. 2011 Lakes 10 61.0 852 0.072
Vrba et al. 2004 Lakes 10 3.25 8.97 12.9
M¨
unster et al. 1992 Humic lake 32 53.9 134 14.1 0.402 3.823
Mudryk & Sk´
orczewski 2004 Estuarine lake 5 96.4 166 1646 3223 56.8 0.101 0.052
Caruso et al. 2005 Littoral ponds 76.49 177 209 0.037 0.031
Taylor et al. 2003 Estuary 47 10.4 26.1 8.62 0.398
Celussi et al. 2009a Ross Sea 14 1.67 2.20 1.54 0.759
Celussi et al. 2009b Ross Sea 23 0.61 1.54 22.3 0.60 0.396 0.027
Monticelli et al. 2003 Ross Sea 80.26 0.64 79.1 0.406 0.003
Hoppe 1983 Baltic Sea 54.38 3.63 15.3 45.7 4.32 0.237 0.079
Caruso 2010 Mediterranean Sea 10 0.70 34.8 16.0 0.020 0.044
Karner & Rassoulzadegan 1995 Mediterranean Sea 50.31 0.84 24.8 0.034
Grossart & Simon 2002 Red Sea 221.0 26.5 0.792
Baltar et al. 2009 Atlantic Ocean 20.04 0.04 0.75 5.5 0.053 0.007
Christian & Karl 1995 Pacific Ocean 60.06 8.0 0.008
Hoppe & Ullrich 1999 Indian Ocean 31 1.46 2.91 9.93 0.502 0.147
Mean 366 9.90 7.60 31.3 71.0 5.47 0.243 0.107
regressions of ln(BG) versus ln(AP) and ln(BG) versus ln(NAG+LAP) are similar to those for
soils and sediments, showing that the ecoenzymatic scaling of planktonic communities does
not differ substantially from that of attached microbial communities (Figure 5). However, the
normalization constants and mean EEA ratios for plankton are much lower than those of soils
and sediments [BG:AP and BG:(NAG+LAP) ratios are 0.26 and 0.12, respectively], reflecting
−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−→
Figure 5
Global regressions for β-glucosidase (BG), acid (alkaline) phosphatase (AP), leucine aminopeptidase (LAP),
and β-N-acetylglucosaminidase (NAG). For sediments and soils, activity units are in nanomoles per hour
per gram of organic matter; for plankton, activity units are in nanomoles per hour per liter. (a) Standardized
major axis (SMA) regressions for ln(BG) versus ln(AP). Freshwater sediments (blue): b=0.946 ±0.030
(95% CI), a=1.02 ±0.29, R2=0.43, n=2,208, geomean BG:AP ratio is 1.636 ±0.179. Soils (orange):
b=1.162 ±0.056, a=–1.715 ±0.481, R2=0.40, n=929, geomean BG:AP ratio is 0.617 ±0.045.
Plankton ( gray): b=0.855 ±0.506, a=–0.836 ±0.219, R2=0.72, n=292, geomean BG:AP ratio is
0.243 ±0.086. The slope of the soil regression is significantly greater than that of the sediment regression
(p<0.05). (b) SMA regressions for ln BG versus ln (NAG+LAP). Freshwater sediments (blue): b=1.098 ±
0.028, a=–0.42 ±0.28, R2=0.62, n=2,208, geomean BG:(NAG+LAP) ratio is 1.832 ±0.036. Soils
(orange): b=1.091 ±0.063, a=–0.59 ±0.503, R2=0.16, n=929, geomean BG:(NAG+LAP) ratio is
1.434 ±0.220. Plankton ( gray): b=1.276 ±0.176, a=−3.300 ±0.808, R2=0.50, n=106, geomean
BG:(NAG+LAP) ratio is 0.123 ±0.139. The slopes of the sediment, soil and plankton regressions are not
significantly different. The BG versus LAP regression for plankton: b=1.086 ±0.104, a=–2.509 ±
0.521, R2=0.23, n=292, geomean BG/LAP ratio is 0.091 ±0.068. Trend lines highlight the range of
values and do not correspond with the SMA regressions. Freshwater sediment data from Sinsabaugh et al.
(2011). Soil data from Sinsabaugh et al. (2009). Plankton data from Table 2
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ES43CH15-Sinsabaugh ARI 26 September 2012 11:56
P limitation on growth and the importance of dissolved proteins and aminopolysaccharides as
sources of both C and N.
Some of the residual variance associated with the global regressions for soils, sediments, and
plankton (Figure 5) can be attributed to differences in nutrient availability, pH, and other param-
eters that differ at the ecoregional scale. For example, within the soil data, which have the weakest
regressions, the alkaline soils of arid lands have high aminopeptidase and phenol oxidase activities
(relative to β-glucosidase) compared to acidic soils; and highly weathered tropical soils have high
phosphatase activities (relative to β-glucosidase) compared to higher latitude soils (Sinsabaugh
et al. 2008). Further data collection may lead to specific stoichiometries for individual soil orders.
–4
–2
0
2
4
6
8
10
12
14
–2 0 2 4 6 8 10121416
ln (β-glucosidase activity)
ln (phosphatase activity)
–4
–2
0
2
4
6
8
10
12
14
2 4 6 8 10 12 14 16
ln (β-glucosidase)
ln (β-N-acetylglucosamindase
+ leucine aminopeptidase)
a
b
Freshwater sediments
Freshwater sediments
Soils
Soils
Plankton
Plankton
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0
0.10
0.20
0.30
0.40
0.50
0.60
0.70
NAG bXylo aGluc CBH bGal bGlcur aGal aArab
Activity: β-glucosidase ratio
River plankton
Arid soil
All soils
Freshwater sediments
Freshwater biolm
Lake plankton
0
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
Ala Gly Ser Arg Tyr Pro Asp Glu Asn
Activity: Leu aminopeptidase ratio
a
b
Figure 6
Mean glycosidase and aminopeptidase activities relative to β-glucosidase from representative studies. River
bacterioplankton data from Sinsabaugh & Foreman (2001; n=10); arid soil data from Stursova et al. (2006;
n=24–48); all soils data from Sinsabaugh et al. (2008; n=948–1012); freshwater sediments from Hill
et al. (2012; n=2,200); freshwater biofilm data from L.L.P. Lehto and B.H. Hill (unpublished data; n=
72); lake plankton data from Vrba et al. (2004; n=360). Abbreviations: NAG, β-N-acetylglucosaminidase;
bXylo, β-xylosidase; aGluc, α-glucosidase; CBH, cellobiohydrolase; bGal, β-galactosidase; bGlcur,
β-glucuronidase; aGal, α-galactosidase; aArab, α-arabinosidase; Ala, alanine; Gly, glycine; Ser, serine;
Arg, arginine; Tyr, tyrosine; Pro, proline; Asp, aspartate; Glu, glutamate; Asn, asparagine aminopeptidase.
From the freshwater sediment data, Hill et al. (2012) were able to resolve EEA stoichiometries
for nine ecoregions.
β-glucosidase and leucine aminopeptidase are the most widely measured glycosidase and
aminopeptidase activities because they generally yield the greatest potential rates and hydrolyze
the most abundant substrates. Some studies include other measures. The expectation is that
activities will scale in proportion to the relative abundance of substrates; that appears to be the
case (Figure 6), but there are few studies that combine high-resolution analyses of substrate
composition with corresponding EEAs. We note that leucine and alanine, the two most abundant
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protein amino acids, are hydrolyzed by the same enzymes, but hydrolysis of coumarin-linked
alanine is generally greater than coumarin-linked leucine even though the latter is more
commonly used (and costs more).
7. MODELS
7.1. Ecological Theory
The metabolic and stoichiometric theories of ecological function are founded on the kinetics
and structure of cellular components that mediate production and respiration (Sterner & Elser
2002, Brown et al. 2004, Gillooly et al. 2005). Each theory is based on invariance rules and
allometric relationships (Allen & Gillooly 2009, Doi et al. 2010). The metabolic theory extrapolates
the functions of terminal metabolic units involved in C fixation, respiration, and translation to
organismal and ecological levels of organization based on thermodynamics and the limitations of
material distribution, which differ for prokaryotes, protists, and metazoans (DeLong et al. 2010).
The stoichiometric theory extends the elemental composition of cellular components to ecological
processes and organization using cellular growth models and ratios of nutrient availability. These
theories are interrelated through the threshold element ratio (TER) and the GRH.
The TER is the elemental C:N or C:P ratio of available growth substrates at which control
of cellular or community metabolism switches from energy supply (C) to nutrient supply (N, P)
(Sterner & Elser 2002, Elser et al. 2003, Frost et al. 2006, Doi et al. 2010). Frost et al. (2006)
defined the TER as
TERC:P =(AP/GE) ·BC:P and TERC:N =(AN/GE) ·BC:N,1.
where APand ANare assimilation efficiencies for P and N, GE is microbial growth efficiency
with respect to C, and BC:P and BC:N are the elemental C:P and C:N ratios of microbial biomass.
Doi et al. (2010) presented a TER definition that replaces the AP/GE and AN/GE terms with
GEmax
P/GEmax
Cand GEmax
N/GEmax
C, which are maximum growth efficiencies with respect to C, N,
and P. This definition emphasizes that the TER is the condition for optimal growth. For microbial
communities and invertebrates, the ratio of GEmax
C/GEmax
Nis approximately 0.77; the GEmax
C/GEmax
P
is more variable with a mean of about 0.3 (Herron et al. 2009, Jones et al. 2009, Doi et al. 2010,
Zeglin et al. 2012). Moorhead et al. (2012) developed a simulation model for microbial community
growth on C and C+N substrates in which TER is defined as BC/N/CUE (carbon use efficiency).
Because TER links biomass composition and growth efficiency, values vary by the extent to which
elemental homeostasis is maintained in relation to external resource supply (Gusewell & Gessner
2009, Hladyz et al. 2009, Franklin et al. 2011, Hall et al. 2011).
Elemental homeostasis is itself dependent on growth rate. The GRH predicts that the cellular P
quota increases with growth rate because growth rate is directly related to ribosome density (Elser
et al. 2003, Doi et al. 2010), and ribosomes are the largest P stocks in many cells. Franklin et al.
(2011) show that the GRH does not apply under conditions of N limitation, because cells have
little capacity to alter their C:N stoichiometry by altering the relative abundance of biomolecules.
Sinsabaugh & Follstad Shah (2011) showed that increased N limitation associated with the hu-
mification of organic matter reverses the GRH by slowing growth and resource allocation to
P-acquiring enzymes.
7.2. Substrate Availability, Ecoenzymatic Activity, and Growth
The generation of an assimilable substrate from a single extracellular enzymatic reaction can be
represented by the Michaelis-Menten function (Michaelis & Menten 1913):
V=Vmax ·S/[Km+S],2.
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where V is the reaction rate; Vmax is the maximum reaction rate when available enzyme capacity
is saturated; S is environmental substrate concentration; and Kmis the half-saturation constant
for the enzyme, i.e., S at Vmax/2. The Monod model uses the same formulation to represent the
growth of single cell organisms as a function of the environmental concentration of a limiting
substrate (Monod 1949):
μ=μmax ·S/[Ks+S],3.
where μis growth rate, μmax is the maximum growth rate, and KSis the half-saturation constant.
The presumption is that μis controlled by the substrate affinity and active transport capacity of
integral membrane proteins (Button 1993). In simulation models, growth is more complex because
the energy and materials represented by substrate consumption must be allocated to a variety of
maintenance processes, only resource consumption beyond these requirements contributes to
growth (Moorhead et al. 2012, Wang et al. 2012a).
The Monod model is not broadly applicable to microbial communities because single sub-
strate limitation is not the typical condition (Chen & Christensen 1985, Zinn et al. 2004, Cherif
& Loreau 2007, Danger et al. 2008). Within microbial communities the thresholds for sub-
strate limitation of growth vary across populations. At any given state, there are many resources
that are limiting the growth of one or more of the 102–104extant populations. In addition,
(a) populations have some physiological capacity to adapt to changing nutrient availabilities
and (b) the acquisition of various substrates may not be independent. The latter occurs because
(a) the organic matter available for degradation by heterotrophic microorganisms has a limited
range of biochemical and stoichiometric composition and (b) the acquisition of multiple resources
is integrated through optimal resource allocation strategies selected to maximize growth. If envi-
ronmental conditions remain stable for an extended period, community adaptation also includes
succession of populations. As a result, the relationship between microbial community produc-
tion rate and resource availability is less subject to discrete thresholds and sequential limitations
than single populations. It is possible to posit growth models for specific types of resource in-
teractions (Danger et al. 2008). But for microbial communities as a whole, we propose that the
relationship among multiple resources and community production may be generally described
as
μ=μmax ·{(S1·S2·...Sn)/[(KS1 +S1)·(KS2 +S2)·(...KSn +Sn)]}1/n.4.
This community growth model relates production to the geometric mean of multiple resource
availabilities (Figure 7). In this formulation, the acquisition of multiple resources is neither wholly
independent nor fully integrated. In microbial communities, the assimilation of a limiting nutri-
ent into the community is mediated by populations that have the greatest affinity for the sub-
strate (lowest KS). But once acquired by the community, nutrients can be internally recycled.
Within biofilms, nutrient concentrations can be one or more orders of magnitude greater than
the environmental concentration (Hall-Stoodley et al. 2004, Van Horn et al. 2011). As a re-
sult, community growth rates should exceed those predicted by the Monod model for a single
population.
In the metabolic theory of ecology and ecological stoichiometry theory, microbial growth
efficiency, rather than growth rate, is linked to the availability of limiting nutrients, commonly N
and P relative to C, at both the organismal and community levels (Equation 1). The Michaelis-
Menten and Monod models, and the metabolic theory of ecology and ecological stoichiometry
theory models derived from them, can be related to the stoichiometry of the EEAs that mediate
microbial nutrient acquisition from environmental organic matter (Sinsabaugh et al. 2009, 2010,
330 Sinsabaugh ·Follstad Shah
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0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Substrate 2
Substrate 1
0.8–1.0
0.6–0.8
0.4–0.6
0.2–0.4
0–0.2
µ/µmax
Figure 7
Microbial community growth rate as a fraction of maximal growth rate (μ/μmax; see Equation 4 in text)
based on availability of two substrates.
2011; Sinsabaugh & Follstad Shah 2011):
EEAC/N∝BC/N/LC/N∝TERC/N/BC/N=AN/GEC,5.
EEAC/P∝BC/P/LC/P∝TERC/P/BC/P=AP/GEC,6.
where EEAC/Nand EEAC/Pare ratios of ecoenzymatic C acquisition activity to ecoenzymatic N
and P acquisition activity, and LC/Nand LC/Pare the C:N and C:P ratios of labile organic matter
in the environment. Using the most widely measured activities, we define
EEAC/N=BG/(LAP +NAG),7.
EEAC/P=BG/AP,8.
where BG =β-1,4-glucosidase activity; LAP =leucine aminopeptidase activity; NAG =β-
1,4-N-acetylglucosaminidase activity; and AP =acid (alkaline) phosphatase activity.
Following Equations 5 and 6, the generation of assimilable N and P substrates from environ-
mental sources via EEA, expressed as a fraction relative to C, can be represented as
SC/N=BC/N/LC/N·1/EEAC/N,9.
SC/P=BC/P/LC/P·1/EEAC/P.10.
Sc/nand SC/Pare scalars for the relative availability of hydrolyzed N and P in relation to micro-
bial community composition. Microbial growth efficiency as a function of the stoichiometry of
nutrients generated by EEA can be represented as
GEC=GEmax
C·SC/N/[KC/N+SC/N]=AN·BC/N/TERC/N,11.
GEC=GEmax
C·SC/P/[KC/P+SC/P]=AP·BC/P/TERC/P,12.
where KC/Nand KC/Pare half-saturation constants for GECbased on the stoichiometry of C:N
and C:P availabilities. GECapproaches GEmax
C, approximately 0.60, when SC/NKC/N,S
C/P
KC/P, and the ratios BC/N/TERC/Nand BC/P/TERC/Pare near 0.6 (assuming ANand AP=1).
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ES43CH15-Sinsabaugh ARI 26 September 2012 11:56
Equations 11 and 12 propose that community growth efficiency is a saturating function of N and P
availability relative to C, linking substrate generation via EEA to microbial substrate assimilation
and biomass composition.
If community metabolism is controlled by the resource in least supply, following the Liebig
assumption, Equations 11 and 12 will generate independent estimates of GEC. In that case, com-
munity growth efficiency is equal to the lower estimate. At the other extreme, the equations
should yield identical estimates of GECif the generation and assimilation of C, N, and P are
perfectly integrated. For reasons presented above, neither condition is likely to apply. Following
the community growth model (Equation 4), we merge Equations 11 and 12 as
GEC=GEmax
C·{(SC/N·SC/P)/[(KC/N+SC/N)·(KC/P+SC/P)]}0.5
={[AN·BC/N/TERC/N]·[AP·BC/P/TERC/P]}0.5.13.
This biogeochemical equilibrium model expresses growth efficiency at the community level as the
geometric mean of the N and P supply, relative to C. The models for biogeochemical equilibrium
(Equation 13) and community growth (Equation 4) can be merged using GEC=μ/(μ+R),
where R is the community respiration rate. Equation 13 can be simplified to
GEC=GEmax
C·(Nsat ·Psat)0.5=(Ncon ·Pcon )0.5.14.
by defining Nsat and Psat as stoichiometric indices of environmental N and P saturation relative to
C,
Nsat =SC/N/(KC/N+SC/N)andP
sat =SC/P/(KC/P+SC/P),15.
and Ncon and Pcon as indices of microbial N and P consumption relative to C,
Ncon =AN·BC/N/TERC/Nand Pcon =AP·BC/P/TERC/P.16.
7.3. Model Prediction
For most ecosystems, the mean GECfor heterotrophic microbial communities is near GECmax/2
or 0.30 (Manzoni et al. 2012). In aquatic ecosystems, bacterial respiration (BR) generally increases
sublinearly with production (BR =3.42BP0.61; del Giorgio & Cole 1998). Consequently, GEC
also generally increases with production, approaching an asymptote of approximately 0.50, with
a global average of 0.26 (del Giorgio & Cole 1998). For decomposing plant litter in terrestrial
ecosystems, microbial C use efficiency, an indirect measure of growth efficiency, averages about
0.30 based on C:N stoichiometry (Manzoni & Porporato 2009, Manzoni et al. 2010). There are
fewer data for terrestrial soils, and most measurements involve calculating growth yield using
labile substrate (usually glucose) amendments. In some cases, N and P additions are also included.
Most of these estimates are in the range of 0.50–0.60 (Frey et al. 2001, Manzoni et al. 2012).
Herron et al. (2009) reported a microbial growth efficiency of 0.46 for semiarid soils using 13C-
labeled acetic acid vapor. These growth efficiency estimates for labile substrates approach maximal
values observed in culture. Community growth efficiency under ambient substrate and nutrient
conditions is probably lower and comparable to values for other ecosystems.
In theory, at GEC=0.5·GECmax balanced flows of C, N, and P would set
SC/N=KC/N=SC/P=KC/P=0.5 (Equations 11 and 12). The data set for terrestrial soils
(Figure 5) lacks estimates of LC/N,L
C/P,B
C/N,andB
C/Pfor individual samples. Using mean
values for LC/N(14.3), LC/P(186), BC/N(8.6), and BC/P(60) presented by Cleveland & Liptzin
(2007), the estimated mean values of SC/Nand SC/Pare 0.42 and 0.52, respectively. Using these
values and assuming KC/N=KC/P=0.5, the estimated mean growth efficiency for soil micro-
bial communities is 0.29 (Equation 13; Figure 8). The stream sediment data (Figure 5) include
332 Sinsabaugh ·Follstad Shah
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0
0.10
0.20
0.30
0.40
0.50
0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Growth eciency
SC/N × SC/P
Soils
Plankton
Sediments
0
0.10
0.20
0.30
0.40
0.50
0.60
0 1.0 2.0 3.0 4.0
Growth eciency
SC/N × SC/P
Figure 8
Biogeochemical equilibrium model (Equation 13) that predicts microbial community growth efficiency from
the elemental C:N and C:P ratios of biomass (BC/N,B
C/P) and environmental organic matter (LC/N,L
C/P),
and the ratios of ecoenzymatic activities (EEAs) that mediate C, N, and P acquisition (EEAC/N, EEAC/P).
SC/N=BC/N/LC/N·1/ EEAC/Nand SC/P=BC/P/LC/P·1/ EEAC/P. Circles are mean values for the soil,
sediment, and plankton data sets shown in Figure 5.(Inset) Predicted growth efficiencies for freshwater
sediments; this data set includes LC/Nand LC/Pmeasures for each sample (n=2,096; Hill et al. 2012).
LC/Nand LC/Pestimates for each sample, but not values for BC/Nand BC/P. Using mean BC/N
and BC/Pvalues of 8.6 and 60, respectively, for all samples, the mean values of SC/Nand SC/Pfor
stream sediments are 0.26 (95% CI =0.02, n=2192) and 0.64 (95% CI =0.15, n=2192),
respectively. The differences in SC/Nand SC/P, relative to soils is driven by the low C:P ratio of
sediments (57), which approximates BC/P.UsingK
C/N=KC/P=0.5, the mean GECfor stream
sediments is 0.27 (SD =0.10, 95% CI =0.004) (Figure 8), which is similar to the mean GEC
of 0.26 estimated by del Giorgio & Cole (1998).
Compared to soils and sediments, the mean BG/(LAP+NAG) ratio for plankton (0.123) is low
(Table 2;Figure 5) and yields a SC/Nvalue of 3.1 using a mean BC/Nof 6.6 (Sinsabaugh & Follstad
Shah 2010) and a mean LC/Nof 17.3 (Seitzinger et al. 2005), suggesting that BG/(LAP+NAG) is
not a good indicator of EEAC/Nfor bacterioplankton. Analyses of dissolved organic matter compo-
sition and bacterial metabolism show that carbohydrates and proteins make roughly equal contri-
butions to bacterioplankton production on a global basis (e.g., Kirchman & White 1999, Kirchman
2003, Rosenstock & Simon 2003, Simon & Rosenstock 2007). If proteins and aminopolysaccha-
rides supply about half the C for production (Kirchman & White 1999, Kirchman 2003), a better
indicator of EEAC/Nfor bacterioplankton is (BG+LAP+NAG)/(LAP+NAG), which has a mean
value of 1.123 and yields a SC/Nvalue of 0.34.
Using a mean BC/Pof 106 (Sinsabaugh & Follstad Shah 2010), a mean LC/Pof 656 (Seitzinger
et al. 2005), and a mean EEAC/Pratio of 0.257 (Table 2;Figure 5), the SC/Pestimate for plankton
is 0.63. This estimate is similar to that for stream sediments, but in this case SC/Pis driven by
high phosphatase activity in relation to environmental P availability, whereas sediments have high
environmental P availability and low phosphatase activities. For plankton, phosphatase expression
is presumably facilitated by the tight coupling of C and N acquisition that comes from reliance
on protein and chitin for growth. Using KC/N=KC/P=0.5 and values of 0.34 and 0.63 for
SC/Nand SC/P, respectively, the mean GECfor plankton is 0.28 (Figure 8), which approximates
the mean GECof 0.26 estimated by del Giorgio & Cole (1998). Thus, Equation 13 produces
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ES43CH15-Sinsabaugh ARI 26 September 2012 11:56
similar estimates for the mean growth efficiency of microbial communities in soils, sediments,
and plankton (0.29, 0.27, 0.28, respectively) despite large differences in resource availability and
biomass composition.
7.4. Model Synthesis
The biogeochemical equilibrium model (Equation 13) yields microbial community GEC
values that converge on GECmax/2 because resource allocation to substrate acquisition, measured
as potential EEA, generally scales with environmental substrate concentration such that enzymes
operate at about one-half their maximum catalytic capacity, i.e., the Michaelis-Menten parame-
ters Vmax, S, and apparent Km(Equation 2) are positively correlated (Williams 1973, Sinsabaugh
& Follstad Shah 2010). The Monod parameters KSand μmax (Equation 3) are also positively
correlated (Lobry et al. 1992). This condition sustains high substrate turnover while optimizing
responsiveness to fluctuations in substrate availability. Expending resources to produce additional
enzyme has marginal utility because additional enzyme depresses soluble substrate concentra-
tion, reducing the reaction velocity per enzyme. In addition, excess enzyme production depresses
growth efficiency by increasing maintenance costs (Wang et al. 2012a).
An analogous condition applies to insoluble substrates because the optimal substrate:enzyme
ratio is determined by the density of effective enzyme binding sites (B¨
unemann 2008, Lynd et al.
2002). This condition is modeled using adsorption isotherms or “reverse” Michaelis-Menten
kinetics (Schimel & Weintraub 2003). In this case, expressing more enzyme has marginal value
because substrate accessibility limits their effective use. This constraint can lead to C and nutrient
limitation even in environments with high organic matter concentrations.
The ecoenzymatic controls on microbial growth become less important as environmental nu-
trient availability increases to the point of saturating microbial demand (SC/Nand SC/P>1). In
Equations 5 and 6 the terms EEAC/Nand BC/N/LC/Nare linearly related to AN·BC/N/TER
C/N
and the terms EEAC/Pand BC/P/LC/Pare linearly related to AP·BC/P/TERC/P. In Equations 11
and 12 these linearities are limited to values of SC/Nand SC/P<1, corresponding to GEC<0.30
(Figure 8). As SC/Nand SC/Pincrease beyond the half-saturation constants KC/Nand KC/P, control
of growth efficiency progressively shifts from environmental resource availability to constraints
imposed by thermodynamics and cellular organization as predicted by metabolic theory.
Equation 13 defines a biogeochemical equilibrium or attractor for environmental resource
availability, EEA stoichiometry, biomass stoichiometry, and microbial community metabolism
(Figure 8). At low resource availability, microbial growth is controlled by substrate generation
via EEA. At high resource availability, microbial growth is controlled by membrane transport
capacity and metabolic constraints. Throughout this range, growth efficiency is modulated by the
stoichiometry of C, N, and P availability in relation to microbial growth requirements. This equi-
librium attractor provides a framework for comparative studies of organic matter decomposition
and microbial community metabolism.
8. FUTURE DIRECTIONS
Several simulation models that include ecoenzyme pools have been recently published (e.g.,
Davidson et al. 2012, Moorhead et al. 2012, Resat et al. 2012, Wang et al. 2012a). These models
highlight the uncertainties in our understanding of biogeochemical processes at various scales of
resolution. They also show that resource flows and microbial community metabolism depend on
(a) rates and priorities of ecoenzyme expression and (b) the effective activity and turnover rate of
ecoenzymes in the environment. Better estimates of these parameters, and their range of variation,
334 Sinsabaugh ·Follstad Shah
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ES43CH15-Sinsabaugh ARI 26 September 2012 11:56
are needed to translate these models into applications that predict process rates, e.g., respiration
or production, from EEA monitoring data. The existence of a common EEA stoichiometry in
dynamic equilibrium with resource availability, biomass composition and community metabolism
suggests that such applications are feasible. One advantage of process models based on EEA stoi-
chiometry is the potential to include multiple resource drivers. Microbial metabolism in soils, for
example, is generally considered to be C limited. Some models include C and N, but EEA stoi-
chiometry suggests that P acquisition, rather than N, is often a constraint on microbial metabolism,
particularly for highly weathered soils.
Models that entrain EEA stoichiometry also facilitate monitoring of ecosystem responses to
environmental changes because EEA is easily measured at high throughput and relatively low cost
compared to most of the resource and metabolic variables to which it is dynamically linked. How-
ever, almost all empirical research is based on assays of potential activities most often conducted
under substrate saturating conditions at ∼20 C with pH buffering. Unfortunately, there are no
commercially available systems for continuous monitoring of in situ EEA at present, due largely
to lack of a market rather than technological impediments. Such systems could resolve a variety
of mechanistic questions and provide data to parameterize process models. Questions of interest
include the decomposition/nutrient mining activities of mycorrhizal fungi, signal transduction
between plants and rhizosphere microbial communities, pulse-response dynamics to precipitation
in arid land ecosystems or flooding in riparian ecosystems, and controls on soil organic C stores
in relation to fluctuating nutrient availability and temperature regimes.
In the biomics paradigm, ecoenzymes link microbial community structure to biogeochemi-
cal process. As the technology for extracting and sequencing these enzymes advances, it will be
increasingly possible to directly match enzymes to their producers. Eventually, comparisons of
metatransciptomes to metaproteomes will also show the relative turnover time of ecoenzymes
produced by various populations within the community, resolving legacy effects. These analyses
are the final link in the genes to ecosystems integration of biological organization.
DISCLOSURE STATEMENT
The authors are not aware of any affiliations, memberships, funding, or financial holdings that
might be perceived as affecting the objectivity of this review.
ACKNOWLEDGMENTS
This work was supported by the NSF EaGER (DEB-0946288) and Ecosystem Studies programs
(DEB-0918718) and the Sevilleta LTER. We thank Steve Allison, Christine Hawkes, Brian Hill,
Kirsten Hofmockel, Rob Jackson, Daryl Moorhead, Eldor Paul, Kathleen Treseder, Bonnie War-
ing, and Michael Weintraub for providing comments on various drafts.
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ES43-FrontMatter ARI 1 October 2012 13:46
Annual Review of
Ecology, Evolution,
and Systematics
Volume 43, 2012
Contents
Scalingy Up in Ecology: Mechanistic Approaches
Mark Denny and Lisandro Benedetti-Cecchi ppppppppppppppppppppppppppppppppppppppppppppppppp1
Adaptive Genetic Variation on the Landscape: Methods and Cases
Sean D. Schoville, Aur´elie Bonin, Olivier Fran¸cois, St´ephane Lobreaux,
Christelle Melodelima, and St´ephanie Manel pppppppppppppppppppppppppppppppppppppppppppppp23
Endogenous Plant Cell Wall Digestion: A Key Mechanism
in Insect Evolution
Nancy Calder´on-Cort´es, Mauricio Quesada, Hirofumi Watanabe,
Horacio Cano-Camacho, and Ken Oyama ppppppppppppppppppppppppppppppppppppppppppppppppp45
New Insights into Pelagic Migrations: Implications for Ecology
and Conservation
Daniel P. Costa, Greg A. Breed, and Patrick W. Robinson ppppppppppppppppppppppppppppppppp73
The Biogeography of Marine Invertebrate Life Histories
Dustin J. Marshall, Patrick J. Krug, Elena K. Kupriyanova, Maria Byrne,
and Richard B. Emlet ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp97
Mutation Load: The Fitness of Individuals in Populations Where
Deleterious Alleles Are Abunduant
Aneil F. Agrawal and Michael C. Whitlock pppppppppppppppppppppppppppppppppppppppppppppp115
From Animalcules to an Ecosystem: Application of Ecological Concepts
to the Human Microbiome
Noah Fierer, Scott Ferrenberg, Gilberto E. Flores, Antonio Gonz´alez,
Jordan Kueneman, Teresa Legg, Ryan C. Lynch, Daniel McDonald,
Joseph R. Mihaljevic, Sean P. O’Neill, Matthew E. Rhodes, Se Jin Song,
and William A. Walters ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp137
Effects of Host Diversity on Infectious Disease
Richard S. Ostfeld and Felicia Keesing ppppppppppppppppppppppppppppppppppppppppppppppppppppp157
Coextinction and Persistence of Dependent Species in a Changing World
Robert K. Colwell, Robert R. Dunn, and Nyeema C. Harris ppppppppppppppppppppppppppppp183
Functional and Phylogenetic Approaches to Forecasting Species’ Responses
to Climate Change
Lauren B. Buckley and Joel G. Kingsolver pppppppppppppppppppppppppppppppppppppppppppppppp205
v
Annu. Rev. Ecol. Evol. Syst. 2012.43:313-343. Downloaded from www.annualreviews.org
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ES43-FrontMatter ARI 1 October 2012 13:46
Rethinking Community Assembly through the Lens of Coexistence Theory
J. HilleRisLambers, P.B. Adler, W.S. Harpole, J.M. Levine, and M.M. Mayfield ppppp227
The Role of Mountain Ranges in the Diversification of Birds
Jon Fjelds˚a, Rauri C.K. Bowie, and Carsten Rahbek ppppppppppppppppppppppppppppppppppppp249
Evolutionary Inferences from Phylogenies: A Review of Methods
Brian C. O’Meara pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp267
A Guide to Sexual Selection Theory
Bram Kuijper, Ido Pen, and Franz J. Weissing pppppppppppppppppppppppppppppppppppppppppp287
Ecoenzymatic Stoichiometry and Ecological Theory
Robert L. Sinsabaugh and Jennifer J. Follstad Shah ppppppppppppppppppppppppppppppppppppp313
Origins of New Genes and Evolution of Their Novel Functions
Yun Ding, Qi Zhou, and Wen Wang ppppppppppppppppppppppppppppppppppppppppppppppppppppp345
Climate Change, Aboveground-Belowground Interactions,
and Species’ Range Shifts
Wim H. Van der Putten pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp365
Inflammation: Mechanisms, Costs, and Natural Variation
Noah T. Ashley, Zachary M. Weil, and Randy J. Nelson pppppppppppppppppppppppppppppppp385
New Pathways and Processes in the Global Nitrogen Cycle
Bo Thamdrup pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp407
Beyond the Plankton Ecology Groug (PEG) Model: Mechanisms Driving
Plankton Succession
Ulrich Sommer, Rita Adrian, Lisette De Senerpont Domis, James J. Elser,
Ursula Gaedke, Bas Ibelings, Erik Jeppesen, Miquel L¨urling, Juan Carlos Molinero,
Wolf M. Mooij, Ellen van Donk, and Monika Winder ppppppppppppppppppppppppppppppppp429
Global Introductions of Crayfishes: Evaluating the Impact of Species
Invasions on Ecosystem Services
David M. Lodge, Andrew Deines, Francesca Gherardi, Darren C.J. Yeo,
Tracy Arcella, Ashley K. Baldridge, Matthew A. Barnes, W. Lindsay Chadderton,
Jeffrey L. Feder, Crysta A. Gantz, Geoffrey W. Howard, Christopher L. Jerde,
Brett W. Peters, Jody A. Peters, Lindsey W. Sargent, Cameron R. Turner,
Marion E. Wittmann, and Yiwen Zeng pppppppppppppppppppppppppppppppppppppppppppppppp449
Indexes
Cumulative Index of Contributing Authors, Volumes 39–43 ppppppppppppppppppppppppppp473
Cumulative Index of Chapter Titles, Volumes 39–43 ppppppppppppppppppppppppppppppppppp477
Errata
An online log of corrections to Annual Review of Ecology, Evolution, and Systematics
articles may be found at http://ecolsys.annualreviews.org/errata.shtml
vi Contents
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