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Alternative Approaches: Seeing the Forest for the Trees

Alternative Approaches: Seeing the Forest for the Trees

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Linking Soil Food Webs to Ecosystem Functioning and Environmental Change



323



the overall stability and productivity of the food web and (2) determine what properties of food webs make them resistant or resilient to

perturbations.



4.1. Nematode faunal analysis

4.1.1. Theory

It is typically difficult to quantify the condition of an ecosystem, which is

dependent on many factors (e.g., nutrient status, disturbance history). The

nematode faunal analysis concept attempts to gain information that surrogates

for ecosystem-level factors by estimating components of food web structure

from nematode communities. Nematodes are particularly suited as environmental indicators since they contain more trophic complexity than other

taxonomic groups of soil organisms (Fig. 1); nematodes represent multiple

trophic levels and occupy energy pathways based on all three resource-types

(roots, bacteria, fungi). Nematodes are also important as their trophic activities

influence nutrient cycling in natural and managed systems (Anderson et al.,

1983; Ingham et al., 1985). An analogous system for estimating food web

structure does not exist for any other group of soil organisms.

Various indices are used to interpret nematode community shifts at a

relatively high level of taxonomic resolution (family/genus); the most

frequently used are the maturity index (MI), channel index (CI), enrichment index (EI), and structure index (SI). The indices combine information

regarding the trophic guild (bacterivore, fungivore, herbivore, carnivore, or

omnivore) and life history of the sampled nematodes. Life history is scored

along a colonizer-persister scale; colonizer taxa have high population

growth rates and are typical of nematode communities following a recent

disturbance. Persister taxa are slower growing and typical of nematode

communities in environments with low frequency of disturbance. The

maturity index (MI; Bongers, 1990)



MI ¼



n

X

i¼1



n 

cÀp

kcÀp x

n



ð1Þ



accounts for the relative proportion (nc–p/n) of nematodes in a sample (excluding plant feeders) that fit into categories (c–p) along the colonizer-persister

scale, with k representing the weighting for any particular c–p category.

A sample with a low MI indicates that the sample is dominated by opportunist

taxa; as the MI approaches the maximum (5), the sample becomes increasingly

dominated by slower growing, disturbance-sensitive taxa. An analogous index

exists for plant-feeding nematodes, the plant-parasite index (Bongers, 1990),

and the weighted MI (Yeates, 1994) includes plant-feeding and free-living

taxa.



324



Jeff R. Powell



Nematologists proposed additional indices that incorporate life history

characteristics and trophic behavior of nematodes to a greater extent. The

channel index (CI; Ferris et al., 2001)







0:8Fu2

CI ẳ 100

3:2Ba1 ỵ 0:8Fu2





2ị



estimates the relative weighting of the bacterial and fungal pathways of the

soil food web by measuring the relative abundances of opportunitistic, freeliving nematodes in these guilds. A CI that approaches 0 indicates dominance by the bacterial energy pathway, while an index approaching 100

indicates dominance by the fungal pathway. The perceived benefit of

employing the CI, as opposed to estimating the ratio of bacterial- or

fungal-feeding nematodes to all microbivorous nematodes (the nematode

channel ratio), is that the CI focuses on the faster-growing, opportunistic

bacterial- and fungal-feeding species that respond rapidly to enrichment,

while attempting to correcting for differences in the rate at which energy

flows through the two pathways.

The EI (Ferris et al., 2001), which estimates responses associated with the

nutrient status of a system, is calculated





EI ¼ 100



Pn



e ne

iẳ1 kP

Pn

n

iẳ1 ke ne ỵ

iẳ1 kb nb





3ị



and the SI (Ferris et al., 2001), which estimates the degree to which trophic

interactions within food webs have developed, is calculated





SI ¼ 100



Pn



s ns

iẳ1 kP

Pn

n

iẳ1 ks ns ỵ

iẳ1 kb nb





4ị



where n represents abundance and k represents the weightings for feeding

guilds associated with enrichment (e), structure (s), and basal (b) components

of the food web. Both indices scale on a range from 0 to 100. A high EI

indicates greater availability of labile nutrients in the system, which stimulates the more rapidly cycling bacterial pathway. A high SI indicates the

greater abundance of carnivorous and omnivorous nematodes, presumably

due to a lack of disturbance in the system or greater resilience/resistance of

the food web as structured.

Estimates from the enrichment and structure indices can be calculated from

the same sample and graphed together (Fig. 2); the placement of data points in

one of the four quadrats in the bivariate plot space suggests certain functional

properties of the ecosystem within which the food web resides (Table 1).



325



Linking Soil Food Webs to Ecosystem Functioning and Environmental Change



cto

ry



Enriched



Quadrat B



t in



de



x



Quadrat A



Structured



en

ric

hm



Fu2

(0.8)



Quadrat D



Quadrat C



En



En



ric

hm



en

t tr

aje



Ba1

(3.2)



Fu2

(0.8)

Basal

condition



Ba2

(0.8)



Basal



Structure index

Ca2 (0.8)

Om4 (3.2)

Om5 (5.0)

Ca4 (3.2)

Ca3 (1.8)

Ca5 (5.0)

Fu3 (1.8)

Fu5 (5.0)

Fu4 (3.2)

Ba3 (1.8)

Ba5 (5.0)

Ba4 (3.2)

Structure trajectory



Figure 2 Functional groups of soil nematodes characterized by trophic group and

life history characteristics. Groups belonging to basal, enriched, or structured food

webs are included and their weightings for calculation of structure and enrichment

indices indicated. Reprinted from Ferris et al. (2001), with permission from Elsevier.



4.1.2. Application

Several recent studies have employed this version of the nematode faunal

analysis concept. Most of these studies were conducted in agricultural systems,

estimating soil food web responses to soil and crop management practices. In a

series of papers, Wang et al. (2003, 2004, 2006b) evaluated the main effects of

amendments on nematode trophic structure and their interactive effects with

other management practices. Compost amendment (269 Mg haÀ1 yearÀ1,

derived from sticks, lawn clippings, and wood fragments) for 5 years increased

nutrient availability (higher EI: 31.8 vs 23.9 in the absence of compost) and the

relative contribution of the bacterial energy pathway (low CI: 18.5 vs 59.4);

the SI (38.4–52.2) indicated an intermediate level of trophic organization but

was not significantly affected by compost amendment (Wang et al., 2004).

Amending soil from compost-incorporated and control plots with sunn hemp

(Crotalaria juncea) hay (1 g per 100 g soil) resulted in a greater MI in one of two

greenhouse experiments (2.02–2.12 vs 1.97–2.00 in the C. juncea unamended

soil) but no effects on the structure, enrichment, or channel indices (Wang

et al., 2003). In a field experiment, amendment with C. juncea hay resulted in a

greater reduction in the maturity and channel indices, suggesting increased

abundance of opportunitistic, bacterial-feeding nematodes, and a greater

increase in the EI, indicating more rapid nutrient cycling, than ammonium

nitrate application (Wang et al., 2006b).



326



Jeff R. Powell



Table 1 Soil nutrient status and food web condition inferred from combined

calculation of nematode community structure and enrichment Indicesa



General

diagnosis



Quadrat A



Quadrat B



Quadrat C



Quadrat D



Disturbance



High



Low to

moderate

N-enriched

Balanced



Undisturbed



Stressed



Moderate

Fungal



Depleted

Fungal



Moderate to

high

Structured



High



Enrichment

N-enriched

Decomposition Bacterial

channels

C:N ratio

Low

Food web

condition

a



Disturbed



Low

Maturing



Degraded



Quadrats refer to those presented in Fig. 2. Reprinted from Ferris et al. (2001), with permission from

Elsevier.



In another study, Liang et al. (2005) observed reduction in the CI following

fertilization with urea, associated with increased NO3 and NH4 levels; however, the slow-release urea formulation resulted in a higher value for the SI,

indicating greater trophic diversity. In a comparison of long-term organic,

low-input, and conventional management systems, Berkelmans et al. (2003)

observed that the organic and low-input systems, relative to the conventional

system, were frequently associated with higher enrichment and SI, indicating

higher fertility and greater trophic structure, and lower basal and channel

indices, reflecting reduced abundance of opportunistic nematodes and rapid

nutrient cycling through the bacterial pathway of the soil food web. Ferris

et al. (2004) manipulated the trophic structure of nematode communities

(and presumably, other microbial feeders) through a combination of fall

irrigation and carbon input, following which they observed greater nitrogen

mineralization in the subsequent cropping season.

The type of amendment used will play a role in determining the overall

effect on nutrient availability. Ferris and Matute (2003) observed structural

and functional succession of the nematode community in response to substrates of differing C/N ratios. The EI declined over time at a rate regardless of

the substrate added. Progression toward fungal domination of energy flow was

faster for wheat straw (C/N ¼ 75.9) than for alfalfa (C/N ¼ 10.6), but

not faster than for compost (C/N ¼ 10.6), indicating that factors in addition

to C/N are also important. There was also a succession from enrichment

opportunist bacteriovores to general opportunist bacteriovores, but the rate

of succession did not differ among the types of amendments (Ferris and

Matute, 2003).

Other studies have incorporated the nematode faunal analysis concept

into estimates of soil biodiversity in grasslands and pastures, the advantage



Linking Soil Food Webs to Ecosystem Functioning and Environmental Change



327



being that functional components of the ecosystem are also measured with

potential implications for nutrient cycling and grassland productivity. For

example, Zolda (2006) studied the nematode fauna of grazed and ungrazed

grasslands in Austria, Stirling and Lodge (2005) estimated the relationships

among climatic and plant species factors and nematode communities in

Australian pastures, Bell et al. (2005) studied the nematode fauna of New

Zealand tussocks, and De Deyn et al. (2004) employed the nematode faunal

analysis concept to address the effects of plant diversity on nematode

taxonomic and functional diversity.

Hoeksema et al. (2000) and Sonnemann and Wolters (2005) used the MI

in their evaluations of the effects of elevated CO2 on nematode community

structure. Hoeksema observed an increase in the MI associated with elevated CO2 in a low-N soil, indicating greater abundance of slower-growing

nematode taxa; however, this result was not observed in the high-N soil,

nor in the study by Sonnemann and Wolters (2005).

Nematode faunal analyses suggest that nematode communities are quite

susceptible to disturbance. For example, Berkelmans et al. (2003) observed

that 1 year of a common crop and tillage undid the effects of several years of

divergent management practices (organic/low input/conventional). However, some analyses suggest that nematode communities are also resilient to

some disturbances. Wang et al. (2006a) observed only short-term effects of

solarization or cowpea cover cropping on the SI, disappearing by the end of

the experiment (5–6 months); methyl bromide fumigation, however, had

persistent effects. Wang et al. (2004) observed little difference in the trophic

structure of nematode fauna when comparing untilled plots versus plots

undergoing multiple roto-tilling events for 25 years; the tilled plots had

been left fallow for 1.5 years prior to sampling, leaving the possibility open

that the nematode community recovered quickly once frequent manual

disturbance was removed from the system. The time required to recover

from disturbance provides additional information regarding ecosystem

recovery and should be a focus of future research.

Further research should improve the utility and sensitivity of nematode

faunal analysis. Debate continues regarding the placement of taxa into c–p

groups (Bongers, 1990) and the generalities of genera and family-level

resolution of trophic groups (Yeates et al., 1993). Both are based largely

on observations of nematode behavior on agar media, which may not be

representative of behavior in nature. Tylenchid nematodes, classified as

plant-, algal-, and lichen feeders but possibly also fungal feeders (Yeates

et al., 1993), can constitute 30% or more of a sample (Ferris and Bongers,

2006). Furthermore, an evaluation of nematode community indices in three

different ecosystem types (wetland, forest, and agricultural) indicated that

the indices were differentially sensitive to disturbance in the different

ecosystems and that variance within community composition at the genus

level within families was more sensitive than the community indices

to ecosystem type and disturbance (Neher et al., 2005). Fiscus and Neher



328



Jeff R. Powell



(2002) used multivariate statistical techniques to evaluate the sensitivity of

nematode taxa to particular agricultural disturbances, suggesting that individual analyses could be tailored to have greater sensitivity by selecting

particular taxa relevant to the disturbance(s) under study.



4.2. Modeling food web dynamics

4.2.1. Theory

The modeling approach to studying food webs highlights properties of the

system emerging from the individual interactions occurring within. Early

models focused on connectivity food webs, in which linkages between two

interacting groups indicate where trophic interactions occur but all linkages

are assigned equal weight. Models by May (1972, 1973) arrived at the

conclusion that complex food webs (i.e., those containing many interacting

species) are less likely to be stable than simple webs; increases in species

richness (S) must be accompanied by a decrease in either connectance,



 

L

Cẳ

S2



5ị



where L is the proportion of all possible linkages that are realized, or the

average strength of the interactions (per capita effect of one species on another)

occurring in the system. May observed, however, that the presence of compartments in food webs, within which species interact readily with each other

but very little with species in other compartments, increased the feasibility of

constructing large food webs (May, 1972, 1973). Lower richness within

individual compartments allowed for more and stronger interactions among

species without risking instability.

In the 1980s and early 1990s, soil ecologists conducted surveys of soil

food webs whereby they represented interactions as quantifiable flows of

material cascading through the web. These surveys, and subsequent modeling exercises, are built upon available descriptions of connectivity webs

(Fig. 1) by assigning weights to these linkages. Weights represented either

the amount of energy present within and moving between pools (energy

webs), or the per capita effects of one functional group on another (functional or interaction strength webs). From these models it became clear that,

in determining the stability of food webs, the number of interactions within

a food web is less important than how those interactions are structured. As a

result, these researchers were capable of addressing questions related to the

emergent properties of the food web, emphasizing properties associated

with trophic diversity and structure (how many trophic levels/linkages

are supported at any particular level of productivity?) and ecosystem stability (how resistant/resilient is food web structure to environmental

perturbation?).



Linking Soil Food Webs to Ecosystem Functioning and Environmental Change



329



Hunt et al. (1987) derived equations for modeling the flux of energy, as

carbon and nitrogen, through food webs. For the consumption rate, F, of a

consumer, j





Fj ¼





ðdj Bj ỵ Pj ị

aj pj



6ị



where P and d represent the predatory and nonpredatory death rates,

respectively, B represents biomass, and a and p represent the assimilation

(ingested and not lost in feces) and production (retained as biomass) efficiencies, respectively, of the consumer. For consumers that feed on more

than one prey, the consumption rate of prey, i, is a function of the

preference, w, and biomass of i relative to that of all prey, k, so that





Fij ẳ





wij Bi

Pn

Fj

kẳ1 wkj Bk



7ị



Energy webs are particularly useful for estimating how sensitive the

length and reticulation of the food web is to the amount of energy entering

and moving within the web. Moore and Hunt (1988) demonstrated that

energy channeled through a soil food web largely via compartments (roots,

bacteria, and fungi as basal resources in each pathway) with little movement

of energy between pathways at intermediate trophic levels. The authors’

analysis of published connectivity webs of trophic relationships showed that

the number of energy pathways (resource richness) in a food web correlated

positively with the richness of consumers and negatively with connectance.

This result supports resource compartmentation, reducing the proportion of

species that directly interact, as a mechanism allowing stable species rich

food webs to persist (May, 1972, 1973; Moore and Hunt, 1988).

Functional webs represent the dynamic effects of trophic interactions,

with a change in abundance at one trophic level eliciting a quantifiable

change at another. DeAngelis (1992) and Moore et al. (1993) derived the

dynamics of producer, consumer, and detritus density. Biomass density, X,

of producer i changes over time in relation to growth (at both the individual

and population levels combined) and consumption, such that







dXi

dt





¼ ri X i À



n

X



cij Xi Xj



8ị



jẳ1



where r represents the specific growth rate of the producer and c represents the

consumption coefficient for consumer j. Biomass density of detritus, d,



330



Jeff R. Powell



changes over time in relation to the amount of detritus entering the system

from allochthonous inputs, autochthonous inputs due to unassimilated and

unconsumed prey, and autochthonous inputs due to nonpredatory death of

consumers, and the consumption of detritus, such that







dXd

dt





ẳ Rd ỵ



n X

n

n

n

X

X

X

1 ai ịcji Xj Xi ị ỵ

d i Xi

cdj Xd Xj

iẳ1 jẳ1



iẳ1



jẳ1



9ị

where Rd represents the rate of allochthonous input. Biomass density of

consumer, j, changes over time in relation to decline due to nonpredatory

death, decline due to being consumed by n consumers, l, and growth

associated with consumption, such that

n

n

X

X

dXj

cjl Xi Xl ỵ

aj pj cij Xi Xj

ẳ dj Xj

dt

iẳ1

lẳ1



10ị



functional webs are particularly useful for estimating how perturbations of

the web, such as the removal of one or more trophic groups, will affect the

abundance of other trophic groups.

de Ruiter et al. (1995) linked the functional and energy web models by

assuming that feeding rates (Fij) and biomass (Bi,Bj) in the energy model

equal consumption rates (cijXiXj) and biomass density (Xi,Xj) in the functional model, respectively, in order to estimate the consumption coefficient





cij ẳ



Fij

Bi Bj





11ị



from nutrient flux data and estimate interaction strengths, a, as the per

capita effects of consumer j on prey i,



 

Fij

aij ẳ

Bj



12ị



and vice versa,







aj pj Fij

aji ¼ À

Bi





ð13Þ



Linking Soil Food Webs to Ecosystem Functioning and Environmental Change



331



in soil food webs. Strong interactions occur when per capita effects of

consumers on prey or vice versa are large. In an analysis of several soil

food webs by de Ruiter et al. (1995), complex interactions, both strong and

weak, had strong effects on stability. Varying the interaction strengths of

most pathways in the root-pathway and at intermediate and higher trophic

levels (secondary consumer and up) had strong impacts on food web

stability, while varying the strengths of interactions among fungi or bacteria

and their consumers had very little impact on stability.

Rooney et al. (2006) further linked the models by relating interaction

strength to the speed of energy flow, v, represented by the rate that

consumer biomass is turned over, such that



 Pn

vj ẳ



iẳ1 aij







Bj



14ị



for energy flux into consumer j and



 Pn



lẳ1 alj

vj ẳ

ỵ dj

Bj



15ị



for energy flux out of consumer j, suggesting that fast energy flux webs are

composed of strong interactions and slow energy flux webs contain weak

interactions. Rooney et al. (2006) observed similar asymmetrical partitioning of energy to pathways in six marine (pelagic vs benthic) and terrestrial

(bacterial vs fungal) food webs, with higher-order consumers deriving

energy from both pathways and coupling the pathways. By varying the

energy flowing through one pathway relative to a second constant pathway,

they observed that stability (associated with both resilience and resistance)

was lowest when the two were equal and increased with increasing difference between the variable and constant pathways. Temporal asynchrony in

the flux of energy through different pathways means that consumers at

higher trophic levels, where the soil food web is much more reticulate,

may be less likely to encounter highly variable resource availability

(McCann et al., 2005).

Moore et al. (2005) modeled the stability of a two-channel food web,

containing a single resource base, two primary consumers, two secondary

consumers, and a single top predator and using parameters from the Colorado

shortgrass steppe food web (Hunt et al., 1987), and varied the proportion of

energy partitioned to each pathway; they found that the system demonstrated

stability when $20–60% of energy was partitioned to the fast (bacterial)

pathway, the optimum being $40%. Simulated patterns of allocation outside

of this range result in unstable dynamics in food web structure. Stability is



332



Jeff R. Powell



thought to correspond to the nature of resource inputs into the system. Roots

respond dynamically to herbivory, so availability is subject to negative feedback dynamics between resource inputs and consumer activity; detritus,

however, is donor controlled, so consumer activity has no direct effect on

future resource inputs (Moore et al., 1993). In addition, the greater resistance

and/or resilience of the bacterial energy pathway also facilitates compartmentalization and overall system stability (Moore and de Ruiter, 1997; Whitford,

1989).



4.2.2. Application

An environmental stressor may have an effect on one functional group or

individual species within a number of functional groups. However, if the

strengths of interactions with that functional group or those species, or if

energy flow through the food web is sufficiently structured that the web is

stable in the face of environmental stressors, these impacts may be less

ecologically significant. For example, a simulated disturbance to an empirically based, two-compartment food web suggested that compartments

improve total food web stability by retaining the effects of disturbance to

the affected compartment, thus protecting other compartments (Krause

et al., 2003). On the other hand, environmental perturbations that alter

abundance within one or more components of the food web may affect

overall food web structure over a timescale greater than that of the experiment. Thus, modeling responses in soil food webs might be useful to

(1) predict how such perturbations may affect ecosystem function or (2)

estimate the degree to which one or more functional groups must be

affected to show a reduction in ecosystem stability.

To utilize this modeling approach, parameter estimates should be appropriate for the system under study. Moore et al. (1996) described the roles of

laboratory and microcosm experimentation required to parameterize these

models. Researchers estimated predation and death rates, consumption

coefficients, and assimilation and production rates of the organisms involved

in the food web (Table 2). They based estimates on laboratory experiments

(for lifespan and feeding behavior) and field measurements (for tissue digestibility, C:N ratios, and biomass C or N present within each of the trophic

groups). It is feasible to use many of these parameter estimates for studies

conducted in similar ecosystem types. However, the distribution of biomass

and energy flow in soil food webs varies in a number of ecosystem types and

assembled communities and, therefore, caution is necessary when employing parameter estimates derived from other studies. For example, meadows

typically have higher levels of available nitrogen, higher denitrification rates,

contain litter with lower C/N ratios, and retain less mineralized nitrogen

than do forests (Griffiths et al., 2005; Ingham et al., 1989).



Table 2



Estimates of parameter values used in food web models

Consumption coefficient cij [(g m^2)^1 year^1]



Functional group

Herbivores

Phytophagous

nematodes

Microbes

Bacteria

Fungi

Microbivores

Mycophagous

collembola

Mycophagous oribatida

Mycophagous

prostigmata

Mycophagous

nematodes

Protozoa

Bacterivorous

nematodes

Omnivorous

nematodes

Predators

Predatory nematodes

Nematophagous mites

Predatory mites

a



Horseshoe Bend



Lovinkhoeve



Kjettslinge



ai



pi



di (year )



CPER

native



0.25



0.37



1.08



0.010



0.013



0.018



0.166



0.133



0.026



0.026



1.00

1.00



0.4À0.5

0.4À0.5



0.50À1.20

0.50À1.20



<0.001

<0.001



<0.001

<0.001



<0.001

<0.001



<0.001

<0.001



<0.001

<0.001



<0.001

<0.001



<0.001

<0.001



0.50



0.35



1.84



0.016



0.008



0.009



0.026



0.045



0.002a



0.002a



0.50

0.50



0.40

0.40



1.20

1.84



0.011

0.016



0.005

0.008



0.006

0.009



0.018

0.026



0.033

0.045



0.38



0.37



1.92



0.032



0.010



0.011



0.596



0.733



0.004



0.002



0.95

0.60



0.40

0.37



1.00À6.00

2.68



0.005

0.006



0.001

0.002



0.001

0.003



0.002

0.022



0.003

0.023



<0.001

0.004



<0.001

0.004



0.60



0.37



4.36



0.008



NA



NA



NA



NA



NA



NA



0.50

0.90

0.30



0.37

0.35

0.35



1.69

1.84

1.84



0.003

0.058

0.060



NA

NA

0.327



NA

NA

0.294



0.013

0.554

0.485



0.017

0.865

0.545



1.081

0.155b



1.016

0.178b



^1



ct



nt



if



cf



B0



B120



Mycophagous arthropods were treated as a single group.

Predatory arthropods were treated as a single group.

Data were obtained from a native shortgrass prairie at the Central Plain Experimental Range (CPER) in Colorado, Horseshoe Bend in Georgia (ct, conventional tillage; nt, no

tillage), Lovinkhoeve in the Netherlands (if, integrated farming; cf, conventional farming), and Kjettslinge in Sweden (B0: barley low nitrogen; B120: barley high nitrogen).

ai, assimilation efficiency; pi, production efficiency; di, nonpredatory death rate; cij, consumption coefficient; NA, group was not present at the site or was included with

another functional group in the description. Reprinted from Moore et al. (1993), with permission from AAAS.

b



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