Soil animals and ecosystem processes: How much does nutrient cycling explain?

ARTICLE IN PRESS Pedobiologia 51 (2008) 367—373

www.elsevier.de/pedobi

Soil animals and ecosystem processes: How much does nutrient cycling explain? Jouni K. Nieminen Department of Biological and Environmental Science, University of Jyva ¨skyla ¨, P.O. Box 35 (YAC), FI-40014 Jyva ¨skyla ¨, Finland Received 3 April 2007; received in revised form 12 September 2007; accepted 19 September 2007

KEYWORDS Soil fauna; Trophic dynamics; Microcosms experiments; Nitrogen mineralization

Summary Trophic-dynamic hypotheses have been extensively tested by manipulating the presence of soil animals in experimental laboratory microcosms. Soil animals typically have pronounced effects on microbial populations, nutrient cycling and plant growth. However, because often only the total effect has been reported, the relative importance of feeding interactions versus non-trophic effects remains obscure. Using simple calculations based on mass conservation I argue that the observed faunal effect on microbes and system functioning is often larger than can be explained by trophic dynamics and nutrient cycling. Non-trophic effects may help to explain why microcosm experiments have failed to support trophic-dynamic predictions like trophic cascades. Since such effects are also likely independent of species identity and population density, they may facilitate the interpretation of experiments where decomposition processes have been found to be largely insensitive to soil fauna diversity. & 2007 Elsevier GmbH. All rights reserved.

The role of soil fauna in decomposition processes has been long recognized (Petersen and Luxton 1982; Fitter et al. 1985). The more recent concern over the functional consequences of declining biodiversity has brought about an increasing interest in linking below-ground food webs and ecosystem processes (Brussaard et al. 1997; Wall and Moore 1999; Wardle 2002). Tel.: +358 14 2602300; fax: +358 14 2602321.

E-mail address: [email protected].

Several mechanisms by which soil fauna may affect ecosystem processes such as nutrient cycling have been described. Every species contributes to nutrient cycling by consuming resources. According to the mass conservation law, the proportion of consumed nutrients not incorporated in consumer biomass is released in the resource–consumer interaction. Probably the most extensive account of nutrient cycling through a soil food web is that of Hunt et al. (1987) who calculated nutrient flux rates for a number of food-web components

0031-4056/$ - see front matter & 2007 Elsevier GmbH. All rights reserved. doi:10.1016/j.pedobi.2007.09.001

ARTICLE IN PRESS 368 assuming that the biomasses are at steady state. This modeling strategy, originally designed at field scale, has subsequently been applied to laboratory data on a finer temporal scale (Vreeken-Buijs et al. 1997). Steady-state models have, however, been criticized and the need for more dynamic and mechanistic models has been recognized (Verhoef and Brussaard 1990). Trophic dynamics and element cycling are only a part of the story, though it has been well established that some species, the so-called ecosystem engineers, affect ecosystem processes by creating or modifying the physical environment for other species (Lawton 1994; Anderson 1995; Lavelle et al. 1997). An understanding of the relative importance of various faunal mechanisms (e.g., temporal and spatial trophic dynamics, nutrient cycling, behavioral responses and ecosystem engineering) is needed. The purpose of this paper is to draw attention to other soil fauna such as microbialfeeding nematodes, which are not apparent ecosystem engineers but may have considerable nontrophic effects on nutrient cycling. To do so, I first reviewed two experiments from which the upper limits of N mineralized by fungal-feeding nematode were calculated using the observed population dynamics and independently measured consumption rates. For experiments with enchytraeids and microbial-feeding nematodes, which do not allow dynamical modeling, I derived simple formulas. Finally, I discussed the role of predators of microbial feeders in nutrient mineralization. Every consumer has, by definition, a negative effect on its resource population by feeding on it, but it is well known that all effects of soil animals cannot be explained by consumption alone. For example, microbial feeders do not always reduce their resource population on a large scale, but may actually have positive effects on microbial populations (e.g., Ingham et al. 1985; Mikola and Seta ¨la ¨ 1998a) and processes. An apparent explanation for these positive effects in nutrient-limited conditions is that animals make immobilized nutrients available to the same or other microbial species, or to plants by excreting nutrients not incorporated in biomass. Both mechanisms, consumption and excretion, are captured by dynamic models, if limiting nutrients are included in model equations. However, soil animals may have effects that are not and perhaps cannot be explicitly incorporated in such models, and can therefore be called indirect effects. Engineering soil, dispersing microbes and behavioral changes triggered by animals (e.g., switch from vegetative growth to sexual reproduction) are examples of such mechanisms. A manipulative microcosm experiment with or

J.K. Nieminen without a species of interest can determine the total effect of that species on nutrient mineralization, but it does not indicate what proportion of the total effect is due to direct nutrient cycling by the consumer and how much to other effects. In this paper, I have shown that even when excretion cannot be measured, crude approximations based on population dynamics and consumption rates may facilitate the interpretation of experimental results by taking into account indirect effects.

Nutrient cycling by microbial feeders If the rate, n, at which an individual soil animal (or unit of animal biomass), j, mineralizes nutrients from its resource i, is known, the total amount mineralized during a time interval (0,T) is: Z T Nij ¼ nxðtÞ dt (1) 0

where x is the size or total biomass of the animal population. An observed effect of a soil animal exceeding predicted Nij cannot be explained by nutrients liberated in the feeding interaction. It should be noted that any function x(t) adequately explaining the data could be used in the numerical integration as long as the consumer population is sampled frequently enough to allow curve fitting. Unfortunately, even if that parameter is known, few studies report more than one to four sample dates. One of the most extensively investigated parts of the general soil food web is that consisting of bacteria, fungi, microbial-feeding nematodes and their predators. According to Ingham et al. (1985), an individual fungal-feeding nematode Aphelenchus avenae consumed 6 ng N d1. Chen and Ferris (1999) reported that the mineralization rate of another fungal-feeding nematode, Aphelenchoides composticola, was 3.3 ng N d1 per individual. I studied the dynamics of Aphelenchoides sp. (x2) in microcosms containing 30 g (initial fresh mass) of sterilized humus and 0.5 g of pine needle litter and a decomposer fungus (x1) (Nieminen 2002). Two replicates were destructively sampled at 1-week intervals for 16 weeks. Details of the experimental procedures have been described in conjunction with another experiment (Nieminen and Seta ¨la ¨ 2001a). The first phase (to3.5 weeks) was described using an exponential model: x2 ðtÞ ¼ 5:33e0:58t

(2)

which explained 98% of the variation in nematode biomass.

ARTICLE IN PRESS Soil animals and ecosystem processes Table 1.

List of symbols

Symbol

Typical unit 1

Nij

mg N (g dm)

n mij(gj)

mg N [t]1 [biomass]1 mg N [t]1

xi a

[m] –

e

–

P m N0 ci

[t]1 [t]1 mg N (g dm)1 –

369

Interpretation Total nutrient mineralization in interaction (i,j) Mean mineralization rate Mineralization rate (as a function of consumer j growth rate) Biomass of species i Availability (usable/total biomass) Inefficiency (consumption/production) Productivity Mortality Total amount of nutrient Nutrient concentration in the biomass of species i

The latter phase (t43.5) was best described with a solution of a food chain model: dx 2 a2 ¼ p 2 x 1  m2 x 2 dt e2

(3a)

dx 1 ¼ p1 C  ðm1 þ a2 p2 Þx1 dt

(3b)

dC ¼ e1 p1 C dt

(3c)

where x1 and C are fungal biomass and its carbon resource, respectively. A list of symbols is in Table 1. The food chain model accounted for 89% of the observed variation. When these population models were used with the highest reported consumption rate, n ¼ 6 ng N d1, the integral function (1) equals N12 ¼ 4.2 mg NH4–N, which is only about 3.5% of the total amount of ammonium nitrogen released in the course of the 16-week experiment (Nieminen 2002). At the same time, it has been calculated that soil animals can directly account for up to 35% of total mineralization in various systems, and the majority (up to ca. 80%) of this is due to their effect on microfauna (see e.g., Anderson 1988 and references therein; Persson 1983; Hunt et al. 1987; Verhoef and Brussaard 1990). The percentages reported here and in the literature cannot be readily compared, because most of the natural or agroecosystems studied (but see Persson 1983) were bacterial-dominated and consequently, bacterial-feeding protozoa and nematodes were the dominant microfaunal groups. However, given that

total nematode contribution has been reported to be 410% and that fungal-feeding nematodes are likely to be more important in a fungal-dominated compared to a bacterial-dominated systems, the above-calculated, absolute upper limit of 3.5% for the contribution of fungal-feeding nematodes seems to contradict the literature. Nieminen and Seta ¨la ¨ (2001b) also used Aphelenchoides sp. in a fungus-nematode system, which was kept severely nutrient limited by excluding all organic N sources and supplying glucose throughout the experiment. In such a system all the nitrogen mineralized after the initial immobilization phase would be due to nematode activity. When the above calculation using a logistic growth curve: x2 ðtÞ ¼

K 1 þ ððK=x0 Þ  1Þert

(4)

is fitted to their data (x0 ¼ 0.001, T ¼ 16 w, r ¼ 1.670.06, K ¼ 5.570.2, model SS/total SS ¼ 0.989), it would suggest that nematodes could have recycled no more than 13% of the total nitrogen in the system (initially 100 mg NH4–N g1 dry soil). Yet, no increase in the N concentration was detected (Nieminen and Seta ¨la ¨ 2001b), which indicates that fungal immobilization counteracted the mineralization by the nematode. Still, one would expect that in a nutrient-limited system even a weak nutrient cycle might be important. Indeed, the presence of the nematode increased fungal biomass and enhanced carbon utilization (Nieminen and Seta ¨la ¨ 2001b). However, a more detailed analysis of the food chain shows that the effect of the nematode cannot be explained by nutrient cycling. In a closed nutrient-limited system the amount of a limiting nutrient is: X y ¼ N0  ci x i (5) i

where ci is the nutrient concentration of the biomass of species i and N0 is the total amount of the nutrient. In a nitrogen-limited situation, Eq. (3b) can be rewritten as follows: ! 2 X dx1 ¼ pN;1 N0  ci xi  ða2 p2 þ m1 Þx 1 (6) dt i¼1 where pN,1 is the nitrogen productivity of the fungus. At steady state (dx/dt ¼ 0) the equation is: pN;1 N0 c1 pN;1 þ m1 þ a2 p2 þ ðc2 p1 p2 =e2 m2 Þ pN;1 N0 ¼ x1 o 1 c1 pN;1 þ m1

x2 1 ¼

ð7Þ

ARTICLE IN PRESS 370

J.K. Nieminen

where the superscript denotes food chain length (1 ¼ fungus alone, 2 ¼ fungus+fungal-feeder). In other words, the trophic-dynamic model predicts that the addition of a fungal-feeding animal will reduce steady-state fungal biomass. What follows is a very coarse approximation of the faunal mineralization that can be applied to evaluate experiments for which calculation (1) is not possible. When gj is the growth rate of consumer j and cn the nutrient concentration in biomass (n ¼ i,j), then the mineralization rate related to the resource–consumer interaction (i,j) is: mij ¼ ðci ej  cj Þgj

(8)

where ej is the inefficiency (inverse of efficiency) of the consumer. The consumer population cannot have a fasterthan-exponential growth rate: gj ðx i ; xj Þppj xj

(9)

where pj is the productivity of the consumer population. Now the total amount of N mineralized in the interaction (i,j) can be approximated using Eqs. (8) and (9): Z T Nij ¼ mij dtppj ðci ej  cj Þ 0

Z

T

x j dt ¼ pj ðci ej  cj Þ¯ x j T,



ð10Þ

0

where x¯j is the mean consumer biomass, which can be empirically estimated, and T is the length of the growing period. In four of the eight studies compiled in Table 2, the observed increase in nitrogen concentration caused by consumer addition clearly exceeds the upper limit of N mineralization calculated using Eq. (10). In these experiments, the consumer addition has had a larger effect on N mineralization than can be explained by excretion. In addition, there is one study where the difference is insignificant due to high variation. In four of the studies, the consumer is a nematode (three bacterivores) and in the fifth it is an enchytraeid. In all five cases the decomposers apparently became carbon limited (no easily utilizable carbon was supplied by the investigator during the experiment). In the remaining three cases, the upper limit is too crude an overestimate to permit any reasonable conclusion. A look at the lower limit, however, might be useful: Z Z ej X1 Nij ¼ ðci ej  cj Þgj X ðci  cj Þ gj . (11) In particular, if the consumer population is increasing (gj4mj), as was clearly the case in

Seta ¨la ¨ et al. (1991), Z Nij Xðci  cj Þ ðgj  mj Þ ¼ ðci  cj Þðxj ðTÞ  xj ð0ÞÞ, (12) because dxj ¼ gj  m j dt

(13)

where mj is the mortality rate of the consumer. For the enchytraeid Cognettia sphagnetorum used in Seta ¨la ¨ et al. (1991), the lower limit given by Eq. (12) is slightly higher than the observed increase in N concentration (Table 2), which indicates that in their study, the N cycled by C. sphagnetorum sufficiently explains the enhanced nitrogen mineralization. This seems to contradict Seta ¨la ¨ (2000) in which the observed increase in N uptake by pine seedlings was about three times the maximal mineralization by C. sphagnetorum. The experiment conducted by Seta ¨la ¨ et al. (1991), in which the direct effect of the enchytraeid on N proved relatively large, was heterotrophic, while the other two experiments included plants (Laakso et al. 2000; Seta ¨la ¨ 2000). It is possible that enchytraeids – which are undoubtedly ecosystem engineers – directly enhanced plant nutrient uptake and growth in those studies, for example, by improving aeration of the soil (Didden 1993). In contrast to nematodes, the enchytraeid worm C. sphagnetorum (Oligochaeta) seems to be a quantitatively efficient N mineralizer. Its role in decomposition processes in coniferous forest soil may thus be indispensable (Huhta et al. 1998), because it has both large direct (N cycling) and mediating (modifies microbial performance, engineers soil) effects on nutrient cycling. The direct contribution of the consumer in the last two experiments (Mikola and Seta ¨la ¨ 1998a; Laakso et al. 2000) remains indeterminate. The effect of C. sphagnetorum on N uptake of birch seedlings observed by Laakso et al. (2000) is between the calculated lower and upper limits. The experiment conducted by Mikola and Seta ¨la ¨ (1998a) is problematic in that glucose was added to all microcosms initially so that N mineralization only began towards the end of the experiment. Overall, it seems that the effect of microbial-feeding fauna on nutrient mineralization cannot always be explained by their microbial feeding. It appears that even though microbialfeeding nematodes release a relatively large proportion of the nitrogen they consume, their direct contribution to N mineralization is small. This is in contradiction with some earlier studies which indicate that up to one-third of N mineralization

ARTICLE IN PRESS Soil animals and ecosystem processes

371

Table 2. Upper and lower limits for faunal N mineralization calculated using Eq. (10) with parameter values taken from literature Consumer

p2

c1

e2

c2

x¯2

T (weeks)

Min(N12)

Max(N12)

Observed net mineralization (mg N g1)

S.D.

Data from

Enchytraeid

0.167a

0.2b

17c

0.1d

187

19.6

35

2029

32

20

Ffe nematode Bfh nematode Bf nematode

1f

0.125

7g

0.11

0.096

3

0.22

3.3

1

0.2

9

0.11

0.15

2

0.52

14

1

0.2

9i

0.11

0.17

9.6

2.87

420

Bf nematode Enchytraeid

1

0.2

9

0.11

0.66

9.3

10

15.23

0.167

0.14

17

0.14

18322

37

734

24656

4000

0.167 1

0.2 0.2

17 7

0.1 0.11

90 5

60 22

95

3006 141

9118 41

Seta ¨la ¨ et al. (1991) Chen and Ferris (1999) Brussaard et al. (1995) Allen-Morley and Coleman (1989) Anderson et al. (1981) Laakso et al. (2000) Seta ¨la ¨ (2000) Mikola and Seta ¨la ¨ (1998a)

Enchytraeid Bf+ff nematodes

16.5

1998

In controversial cases parameter values were chosen so as to maximize N12 a Haimi et al. (2006). b Biomasses are reported as biomass C, and nutrient concentrations as N/C ratios. Microbial N/C, if not reported by the authors, has been assumed to be 0.2 (an upper limit, see Lavelle and Spain 2001). c Petersen and Luxton (1982). d Didden (1993), Lundkvist (1981). e Ff ¼ fungal-feeding. f Nieminen (2002). g Hunt et al. (1987). h Bf ¼ bacterial-feeding. i Sohlenius (1980).

can be due to microbial-feeding animals (Persson 1983; Verhoef and Brussaard 1990). These high estimates were based on models which assumed that biomass was at steady state and that the faunal contribution was based on excretion. Uncertainties also exist in the parameter estimates, especially when values estimated from one system are applied to another system. The main purpose of the present paper is to demonstrate how basic calculations may point to the limits of trophic dynamics and nutrient cycling.

Why are trophic cascades difficult to observe? Experimental systems are usually not at steady state. They are not even closed in terms of the limiting nutrient, because nutrients are bound in dead microbial biomass. A simulation model (Appendix, Figure 1, Table 3) illustrates such a case. The baseline nutrient dynamics in Figure 1 has been modeled using experimentally derived parameters (data from Nieminen and Seta ¨la ¨ 2001b),

Table 3. Initial values of the simulation model (Appendix, Figure 1) Variable

t ¼ 2.5

t ¼ 8a

t ¼ 8b

N(t) x1(t) x2(t) x3(t)

27.2 1820 0.027 0.001

20.8 1955 6.46

19.44 2013 0.029 0.056

a b

Predator absent. Predator present.

and the dashed curve predicts the impact of predator addition. The amount of the available nutrient declines because the system is not closed, but the predator slows down the transformation of lost nutrients into resistant detritus. Since the effect of the predator on nutrient dynamics varies temporally, it may be difficult to detect them empirically. I have shown here how relatively simple, dynamic models, based on mass conservation, can explain instances where soil animals contribute more to nutrient mineralization (and consequently to other ecosystem processes) than they do to direct

ARTICLE IN PRESS 372

Acknowledgment The study was supported by Finnish Cultural Foundation.

Appendix A two-phase food chain model

28 26 24 Available N

mineralization from their resource populations. Separating faunal mineralization and other effects helps us to understand the insensitivity of decomposition processes, in particular N cycling, to changes in predatory interactions in small scale experimental systems (Huhta et al. 1998; Wardle 1999). Predators have a significant numerical effect on microbial feeders (Seta ¨la ¨ et al. 1991; Mikola and Seta ¨la ¨ 1998a, b; Laakso and Seta ¨la ¨ 1999), but the effect of microbial feeders on their resource population may be largely density independent. Thus, the higher trophic levels have mainly indirect or ‘‘modulating’’ effects on decomposition processes with no ‘‘trophic cascade’’ arising as long as the predator does not deplete its resource. Predators do affect nutrient cycling, but the effect is relatively small and the direction of that effect depends on the phase of dynamics, (i.e., whether the microbial feeder is resource limited or not (Figure 1)). Hence, the average observed response is likely zero. Because most studies are highly specialized, data is lacking for an assessment of the relative importance of temporal and spatial, trophic and non-trophic, evolutionary and ecological effects of soil fauna on microbes and subsequently, on ecosystem processes. In addition, there is an understandable bias towards small spatial and temporal scales (Kampichler et al. 2001). Because of these limitations a synthesis, such as this one, can only scratch the surface. In studies designed to model animal dynamics explicitly, nutrients cycled by microbial-feeding animals play a minor role, and a number of other studies (Table 2) could be reinterpreted similarly. Therefore, we can anticipate that models directly linking a given food-web structure to an ecosystem process – especially if they assume steady state – provide only partial insight. The knowledge that soil fauna affect ecosystem processes in ways largely independent of species identity and population density facilitates an understanding of biodiversity experiments and shows that the soil system is not a house of cards, but a system with considerable buffering capacity at the species level (Liiri et al. 2002).

J.K. Nieminen

22 20 18 16 14 5

10

15

20

Time

Figure 1. Simulated effect of predatory nematode on available N (mg NH4–N g1 dm) in microcosms with a fungus–nematode food chain. Solid curve: predator absent, dashed curve: predator present. Parameter values: m1 ¼ 0, p1 ¼ 1.55, r1 ¼ 0.04, a2 ¼ 0.0093, e2 ¼ 7.1, m2 ¼ 0.3, p2 ¼ 1.01, r2 ¼ 0.11, e3 ¼ 5, m3 ¼ 0.17, p3 ¼ 0.9, r3 ¼ 0.11.

Figure 1 shows the simulated nutrient concentration with and without a predator (x3). ( p3 x 3  m3 x3 tp8 dx3 ¼ p3 x 2  m3 x3 t48 dt dx2 ¼ dt dx1 ¼ dt

(

(

p2 x 2  m2 x2  e3 p3 x3

tp8

a2 e2

t48

p2 x1  m2 x2  e3 p3 x 3

p1 N  m1 x1  e2 p2 x2 p1 N  m1 x1  a2 p2 x1

tp8 t48

8 > r 1 p1 N þ p2 ðr 1 e2  r 2 Þx2 þ p3 ðr 2 e3  r 3 Þx3   dN < r2 ¼ x1 þ p3 ðr 2 e3  r 3 Þx3 p N þ a p r  r dt > 1 2 2 : 1 1 e2

tp8 t48

where ri is the N/C ratio of biomass of species i. Note that the system is not closed: nutrients in dead mass are not returned to the available pool. This is often the case in species poor experimental systems.

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