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Global decomposition experiment shows soil animal
impacts on decomposition are climate-dependent
DIANA H. WALL
*
,MARKA.BRADFORDw,MARKG.ST.JOHNz,JOHNA.TROFYMOW§,
VALERIE BEHAN-PELLETIER}, DAVID E. BIGNELLk,J.MARKDANGERFIELD
**
,
WILLIAM J. PARTONww,JOSEFRUSEKzz, WINFRIED VOIGT§§, VOLKMAR WOLTERS}},
HOLLEY ZADEH GARDELww,FREDO.AYUKEkk, RICHARD BASHFORD
***
,
OLGA I. BELJAKOVAwww, PATRICK J. BOHLENzzz, ALAIN BRAUMAN§§§,
STEPHEN FLEMMING}}}, JOH R. HENSCHELkkk, DAN L. JOHNSON
****
,
T. HE FI N JO NE Swwww, MARCELA KOVAROVAzzzz, J. MARTY KRANABETTER§§§§,
LES KUTNY}}}}, KUO-CHUAN LINkkkk,MOHAMEDMARYATI
*****
,
DOMINIQUE MASSEwwwww, ANDREI POKARZHEVSKIIzzzzz
{
,
HOMATHEVI RAHMAN
*****
,MILLORG.SABARA
´§§§§§, JOERG-ALFRED SALAMON}},
MICHAEL J. SWIFT}}}}}, AMANDA VARELAkkkkk, HERALDO L. VASCONCELOS
******
,
DON WHITEwwwwww and XIAOMING ZOUzzzzzz§§§§§§
*
Natural Resource Ecology Laboratory and Department of Biology, Colorado State University, Fort Collins, CO 80523, USA,
wOdum School of Ecology, University of Georgia, Athens, GA 30602, USA, zLandcare Research, PO Box 40, Lincoln 7640, New
Zealand, §Canadian Forest Service, Pacific Forestry Centre, Victoria, BC, Canada V8Z 1M5, }Agriculture and Agri-food Canada,
Ottawa, ON, Canada K1A 0C6, kQueen Mary University of London, London E1 4NS, UK,
**
Macquarie University, Sydney, NSW
2109, Australia, wwNatural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA, zzInstitute of
Soil Biology, Academy of Sciences, C
ˇeske
´Bude˘jovice 370 05, Czech Republic, §§Institute of Ecology, University of Jena, Jena 07743,
Germany, }}Department of Animal Ecology, Justus-Liebig-University, D-35392 Giessen, Germany, kkKenya Methodist University,
Kaaga Campus, Meru, Kenya,
***
Forest Entomology, Forestry Tasmania, Hobart, TAS 7000, Australia, wwwCentralno-
Chernozemnyj Reserve, Zapovednoe, Russian Federation, zzzMacArthur Agro-Ecology Research Center, Lake Placid, FL 33852,
USA, §§§Laboratoire MOST Centre IRD, Institut de Recherche pour le De
´veloppement, UR SeqBio, SupAgro, Montpellier, France,
}}}Gros Morne National Park, Rocky Harbour, NL, Canada A0K 4N0, kkkGobabeb Training & Research Centre, Box 953, Walvis
Bay, Namibia,
****
Department of Geography, University of Lethbridge, Lethbridge, AB, Canada T1K 3M4, wwwwCardiff School of
Biosciences, Cardiff University, Cardiff CF10 3US, UK, zzzzInstitute of Botany, Academy of Sciences, Pruhonice 252 43, Czech
Republic, §§§§B.C. Ministry of Forests, Smithers, BC, Canada V0J 2N0, }}}}Inuvik Research Centre, Inuvik, NT, Canada X0E
0T0, kkkkTaiwan Forestry Research Institute, Taipei 100, Taiwan,
*****
Institute of Tropical Biology and Conservation, Universiti
Malaysia Sabah, Sabah, Malaysia, wwwwwInstitut de Recherche pour le De
´veloppement, Ouagadougou 01 BP182, Burkina Faso,
zzzzzInstitute of Ecology and Evolution of RAS, Moscow 119071, Russian Federation, §§§§§Centro Universita
´rio do Leste de Minas
Gerais, Coronel Fabriciano 35170-056, Brazil, }}}}}Tropical Soil Biology & Fertility Institute of CIAT, ICRAF, Nairobi, Kenya,
kkkkkPontificia Universidad Javeriana, Bogota
´, DC, Colombia,
******
Institute of Biology, Federal University of Uberla
ˆndia, CP 593,
38400-902 Uberla
ˆndia, Brazil, wwwwwwForest Resources, Department of Indian and Northern Affairs, Whitehorse, YT, Canada Y1A
2B5, zzzzzzXishuangbanna Tropical Botanical Garden, The Chinese Academy of Sciences, Kunming, Yunnan 650223, China,
§§§§§§Institute for Tropical Ecosystem Studies, University of Puerto Rico, San Juan 00931-1910, Puerto Rico
OnlineOpen: This article is available free online at www.blackwell-synergy.com
Abstract
Climate and litter quality are primary drivers of terrestrial decomposition and, based on
evidence from multisite experiments at regional and global scales, are universally
factored into global decomposition models. In contrast, soil animals are considered
key regulators of decomposition at local scales but their role at larger scales is
unresolved. Soil animals are consequently excluded from global models of organic
mineralization processes. Incomplete assessment of the roles of soil animals stems from
Correspondence: Diana H. Wall, tel. 11 970 491 2504, fax 11 970
491 1965, e-mail: Diana@nrel.colostate.edu
{
Deceased.
Global Change Biology (2008) 14, 2661–2677, doi: 10.1111/j.1365-2486.2008.01672.x
r2008 The Authors
Journal compilation r2008 Blackwell Publishing Ltd 2661
the difficulties of manipulating invertebrate animals experimentally across large geo-
graphic gradients. This is compounded by deficient or inconsistent taxonomy. We report
a global decomposition experiment to assess the importance of soil animals in C
mineralization, in which a common grass litter substrate was exposed to natural
decomposition in either control or reduced animal treatments across 30 sites distributed
from 431Sto681N on six continents. Animals in the mesofaunal size range were
recovered from the litter by Tullgren extraction and identified to common specifications,
mostly at the ordinal level. The design of the trials enabled faunal contribution to be
evaluated against abiotic parameters between sites. Soil animals increase decomposition
rates in temperate and wet tropical climates, but have neutral effects where temperature
or moisture constrain biological activity. Our findings highlight that faunal influences on
decomposition are dependent on prevailing climatic conditions. We conclude that (1)
inclusion of soil animals will improve the predictive capabilities of region- or biome-
scale decomposition models, (2) soil animal influences on decomposition are important at
the regional scale when attempting to predict global change scenarios, and (3) the
statistical relationship between decomposition rates and climate, at the global scale, is
robust against changes in soil faunal abundance and diversity.
Keywords: climate decomposition index, decomposition, litter, mesofauna, soil biodiversity, soil
carbon, soil fauna
Received 28 February 2008 and accepted 18 April 2008
Introduction
The annual global release of carbon to the atmosphere
through decomposition of organic carbon by soil biota is
approximately 50–75 Pg, nearly 10 times that of fossil fuel
emissions (Schimel et al., 1996). Climate and plant litter
quality (i.e. chemical composition) are considered the
primary regulators of litter decomposition, explaining as
much as 65–77% of the variation in decomposition rates
(Moorhead et al., 1999; Gholz et al., 2000; Trofymow et al.,
2002). The residual variation (ca. 25%) in global decom-
position rate models remains a substantial source of error
in estimates of contemporary and future global carbon
dynamics (Schimel et al., 1996; Del Grosso et al., 2005).
Putatively, the unallocated error in global decomposi-
tion models has a significant biological component, for
example organismal biomass, size distribution, taxo-
nomic richness, and/or functional group composition.
These components are normally considered as either
the direct effects of physical conditions (through addi-
tion or removal of habitats and therefore species) or
indirect effects (delivered via multiple climate effects
defining plant communities and the area of distribution
available to major groups of soil organisms). Conse-
quently, biota (animals and microbes) are not explicitly
considered in global decomposition models (VEMAP,
1995; Moorhead et al., 1999; Gholz et al., 2000; Lavelle
et al., 2004), although conceptually considered to be key
drivers of litter decomposition rates (e.g. Wardle, 1995;
Lavelle, 1997; Coleman & Hendrix, 2000; Kibblewhite
et al., 2008). Instead, soil biota have been evaluated
(relative to nonbiotic agencies) for their contribution
to aggregated ecosystem functions (or services, sensu
Daily et al., 1997; Wall, 2004), including decomposition,
together with the related processes of carbon sequestra-
tion and greenhouse gas emission. To refine this con-
cept, ecosystem services have been apportioned to
functional assemblages of named organisms (for exam-
ple Lavelle, 1997; Swift et al., 2004; Kibblewhite et al.,
2008), such that the effects of specific disturbances on
the delivery of individual services can be elucidated
(Wardle, 2002). The modeling, however, is at best semi-
quantitative and, most crucially, it is ecosystem-specific
and cannot be deployed much beyond the landscape
scale or made responsive to the incremental changes in
physical environmental parameters inherent in climate
change scenarios. To factor soil biota into future global
decomposition modeling, it is necessary to assess de-
composition against a biotic assemblage character on a
supraregional scale and by an experimental procedure
that will adequately distinguish between the abiotic and
the biotic agencies responsible for the process.
Almost 30 years ago, Swift et al. (1979) hypothesized
that the relative contribution of soil fauna (vs. micro-
flora) to decomposition was dependent on the climatic
region, being greatest at midlatitudes and decreasing
towards the poles. In contrast to the many multisite
experiments on climate and litter quality (Moorhead
et al., 1999; Trofymow et al., 2002), relationships between
soil animals and litter decomposition have never been
experimentally tested at global or regional scales.
Furthermore, there are few data to judge the signifi-
cance of changes in diversity of soil fauna at these scales
(Swift et al., 1998), which reflects the scarcity of the
2662 D. H. WALL et al.
r2008 The Authors
Journal compilation r2008 Blackwell Publishing Ltd, Global Change Biology,14, 2661–2677
required taxonomic expertize and the prohibitive effort
needed to characterize biota in a sufficient number of
sites and with a resolution that reflects true biodiversity.
Inferences from local scale and the few cross-site ex-
periments that manipulated animals (Heneghan et al.,
1999; Gonzalez & Seastedt, 2001) are restricted by the
low number of sites, differences in litter types and
qualities between experiments, and the fact that due
to a vast and mostly unknown biodiversity in many
biomes, invertebrate analyses have been typically
restricted to one to three taxonomic groups.
The Global Litter Invertebrate Decomposition Experi-
ment (GLIDE) tests the hypothesis that soil animals
significantly influence litter decomposition rates, over
and above climate alone, at regional and global scales.
A second hypothesis, related to the first and following
Swift et al. (1979), is that the role of animals in terrestrial
decomposition changes from region to region rather
than having a single global character. The centerpiece
of GLIDE’s design was the use of a single plant litter
substrate, of known quality and from a single origin,
which was exposed in standard litterbags while manip-
ulating the abundance and richness of soil fauna with a
generalized suppressor (naphthalene). Decomposition
was monitored in both animal-suppressed bags and
untreated controls by gravimetric measurement and
chemical analysis at 30 sites distributed across broad
climatic regions from 431Sto681N (Table 1). Addition-
ally, the extraction and characterization of associated
(mainly mesofaunal) invertebrate animals was assessed.
The use of the faunal-suppressant separated climate
from animal effects on litter decomposition. Animal data,
with complementary information on local climate and
the weight and carbon content of litter at the start of the
decomposition process and after exposure in the field,
show that soil animals positively influence decomposi-
tion rates in the temperate and wet tropical biomes.
Material and methods
Site management and location
Forty-one sites were initially established in 2001–02, as
part of the DIVERSITAS IBOY (International Biodiver-
sity Year) Project network, and with ILTER (Interna-
tional Long Term Ecological Research) collaborators.
Sites were selected to achieve the fullest practical climatic
and geographic range, but partially reflected the mostly
volunteer participation by national science teams in IBOY.
Eleven sites were dropped from analyses due to destruc-
tion of bags in situ, excessive extraneous organic matter or
soil in the litterbags, or where export of specimens from
countries was prohibited or impractical. The 30 remaining
sites are described in Table 1.
Experimental design
Two thousand glass-fiber, 20 cm 20 cm, 2 mm mesh
litterbags (Harmon et al., 1999), were each filled with
10 0.5 g dried, gamma-sterilized grass hay [Agropyron
cristatum (L.) Gaertn.] foliar litter that had been air-
dried for 2 years at o20% rH. The litter, with all florets
removed, was preprocessed through a 1.0 mm screen (to
remove loose material before shipping) and then
shipped from Colorado State University (CSU) in the
constructed glass-fiber bags to sites. A single litter
quality was selected to facilitate site-to-site comparisons
and because the scale of resources required to analyze
all taxa from just one litter type made multilitter field
experiments unfeasible. Bags were weighed on-site to the
nearest 0.001 g before being secured to the soil surface,
and then progressively removed over time intervals. Of
24 bags at each site, six were placed randomly in each of
four experimental blocks. Within each block, three bags
were spaced along each of two paired 20 m transects,
with each transect being assigned to either the control or
animal-suppressed treatment. Transects ran parallel to
each other and were 10m apart. The three bags within
each transect were spaced at 0, 10, and 20m. All blocks
were at least 10 m apart, and positioned randomly. This
design was chosen to (a) ensure the suppression treat-
ment did not influence the control litterbags, (b) reduce
the impacts of disturbance by vertebrates, and (c) reduce
the effects of spatial autocorrelation.
Naphthalene treatment: animal suppression
The animal inhibitor naphthalene was added to half of
the litterbags; the other half was assigned to untreated
controls. Naphthalene was applied at the start of the
experiment in crystalline form (as ‘mothballs’ from a
single commercial source) and again at each sampling
occasion to litterbags that remained in the field for later
retrieval. For each treatment renewal, two mothballs
(33.17 g per mothball) were placed adjacent to treat-
ment bags. Naphthalene was chosen to displace soil
animals because it reportedly has less biocidal effects
than other pesticides (Blair et al., 1989). An additional
three ‘traveler bags’ were shipped to each site, placed in
the field, immediately retrieved, reweighed, and re-
shipped to CSU for analysis. Traveler bags controlled
for material loss due to handling (Harmon et al., 1999).
See also www.nrel.colostate.edu/projects/glide/study
design.html.
Litterbag retrieval and processing
One control and one treatment litterbag were retrieved
from each block on three sampling occasions. Time of
SOIL FAUNA IMPACTS ON GLOBAL DECOMPOSITION 2663
r2008 The Authors
Journal compilation r2008 Blackwell Publishing Ltd, Global Change Biology,14, 2661–2677
Table 1 GLIDE site descriptions, GLIDE climatic regions, and annual CDI values
Country
Site
code
Latitude
Longitude Soil type Vegetation type Holdridge Life Zone
GLIDE climate
region
Precipitation
(cm)
Temperature
(1C)
Annual
CDI
Australia TAS1 431040S
1461400E
Planosol Callidendrous fern rainforest Cool temperate moist forest Temperate 110.1 8.8 0.2711
Australia TAS2 431040S
1461400E
Acrisol Intermediate rainforest Cool temperate moist forest Temperate 110.1 8.8 0.2711
Brazil MAN 021250S
601000W
Ferralsol Tropical moist forest Tropical rain forest Wet tropical 211.1 26.3 0.8311
Brazil RUB 191300S
421300W
Ferralsol,
Acrisol
Tropical rainforest Subtropical rain forest Wet tropical 116.8 22.7 0.4881
Burkina Faso BON 111200N
41150W
Luvisol Shrub to wooded savanna Tropical dry forest Dry tropical 95.7 27.1 0.4150
Canada INU 681190N
1331320W
Gleysol Black spruce, paper birch forest Subpolar dry tundra Cold or dry 17.3 11.3 0.0443
Canada YUK 601510N
1351120W
Luvisol Pine, spruce, aspen forest Boreal moist forest Cold or dry 26.5 4.2 0.0583
Canada TOP 541360N
1261180W
Luvisol Pine, balsam, fir, spruce forest Cool temperate moist forest Cold or dry 48.8 2.1 0.1071
Canada LET 501110N
1131540W
Chernozem Fescue grassland Cool temperate steppe Cold or dry 44.3 4.4 0.1477
Canada VAN 481380N
1231420W
Cambisol Douglas fir forest Cool temperate wet forest Temperate 196.0 6.2 0.2287
Canada ROC 49132 0N
581490W
Podzol Balsam fir, white birch forest Boreal wet forest Temperate 115.2 1.6 0.2287
Canada ONE 49101 0N
1101230W
Chernozem Shortgrass Cool temperate steppe Cold or dry 36.0 3.9 0.1080
China XIS 211410N
1011250E
Acrisol Tropical seasonal rainforest Subtropical moist forest Wet tropical 132.2 20.5 0.5275
Colombia BOG 041370N
741180W
Andosol,
Cambisol
Montane cloud forest Tropical wet forest Dry tropical 117.5 14.7 0.4288
Czech Rep. BIO 481420N
161490E
Fluvisol Riparian oak forest Cool temperate moist forest Temperate 54.0 9.3 0.2462
Czech Rep. SUM 491970N
131450E
Cambisol Mountain climax spruce forest Cool temperate moist forest Temperate 64.8 7.6 0.2537
Czech Rep. KOM 481420N
161490E
Fluvisol Riparian oak forest Cool temperate moist forest Temperate 54.0 9.3 0.2462
Czech Rep. PAL 481420N
161490E
Rendzina Xerothermic grass/deciduous forest Cool temperate steppe Temperate 54.0 9.3 0.2462
2664 D. H. WALL et al.
r2008 The Authors
Journal compilation r2008 Blackwell Publishing Ltd, Global Change Biology,14, 2661–2677
Germany GIE 511200N
101220E
Luvisol Temperate beech forest Cool temperate moist forest Temperate 68.3 8.1 0.2618
Germany JEN 501570N
111350E
Rendzina Xerothermic grassland Cool temperate steppe Temperate 58.6 8.2 0.2432
Kenya KEN 01150S
341500E
Acrisol Rainforest Tropical wet forest Wet tropical 185.6 20.7 0.6406
Malaysia MAL 051470N
1161240E
Acrisol Tropical lower montane forest Subtropical wet forest Wet tropical 260.7 22.8 0.7587
Namibia NAM 231330S
151020E
Gleysol,
Histosol
Sparse dwarf shrub Subtropical desert Cold or dry 0.1 23.5 0.0264
Russia KUR 511340N
361050E
Chernozem Oak forest, meadow steppe Cool temperate steppe Temperate 65.2 5.5 0.2253
Senegal SEN 16125 0N
151300W
Arenosol Dry savanna, balanites Tropical desert scrub Dry tropical 26.1 27.1 0.1565
Taiwan TAI 241460N
1211350E
Acrisol,
Cambisol
Evergreen, hardwood forest Subtropical moist forest Wet tropical 208.2 20.6 0.7313
UK CAR 511500N
041100E
Cambisol Oak forest Cool temperate moist forest Temperate 82.6 9.8 0.2961
UK SIL 511240N
01400W
Cambisol Oak, oak-birch, alder forest Cool temperate moist forest Temperate 74.1 9.3 0.2414
USA COL 401490N
1041460W
Yermosol Shortgrass steppe Cool temperate steppe Cold or dry 38.5 8.7 0.1222
USA FLO 271090N
811120W
Luvisol,
Histosol
Native tallgrass wet prairie Subtropical moist forest Wet tropical 127.4 22.7 0.6060
GLIDE refers to the Global Litter Invertebrate Decomposition Experiment. Soil type is a UN FAO classification. Vegetation type is the description provided by the 30 GLIDE site
collaborators (see following names). Holdridge Life Zone classification (Leemans, 1990) is determined by latitude and longitude coordinates. Precipitation and temperature
values are long-term averages based on climate data gathered from weather stations nearest to each study site. CDI is the Climate Decomposition Index (Parton et al., 2007). We
categorized sites into four climatic regions based on site vegetation and abiotic data, with wet and dry tropical sites considered separately, based on precipitation and abiotic
models (Schimel et al., 1996). Site codes (see Table 1) location, and collaborators are as follows: TAS1 and TAS2, Tasmania, Warra Long Term Ecological Research (LTER),
R. Bashford; MAN, Manaus, H. Vasconcelos; RUB, Rubro Negra, M. Sabara
´; BON, Bondoukuy, D. Masse; INU, Inuvik, NT, Canadian Intersite Decomposition Experiment
(CIDET), L. Kutny; YUK,Whitehorse, YT, CIDET, D. White; TOP, Topley, BC, CIDET, M. Kranabetter; LET, Stavely, AB, D. Johnson; VAN, Shawnigan, Vancouver Isl., BC, CIDET,
J. Trofymow; ROC, Rocky Harbour, NL, CIDET, S. Flemming; ONE, Onefour, AB, D. Johnson; XIS, Xishuangbanna Tropical Botan. Grdn., X. Zou; BOG, Bogota
´, A. Varela; BIO,
Palava Biosphere Reserve South Moravia, J. Rusek; SUM, Sumava National Park, M. Kovarova; KOM, Palava National Park 2, J. Rusek; PAL, Palava National Park 1, J. Rusek;
GIE, Giessen, J. Salamon; JEN, Jena, W. Voigt; KEN, Kakamega Forest, M. Swift; F. Ayuke; MAL, Tambunan, M. Maryati and R. Homathevi; NAM, Gobabeb, J. Henschel; KUR,
Central Chernozem Reserve, A. Pokarzhevskii O. Beljakova; SEN, Souilene, A. Brauman; TAI, Fu-shan Taiwan Ecological Research Network, K. Lin; CAR, Cardiff, Wales,
T. Jones; SIL, Silwood Park, London, M. Bradford; COL, Colorado, Shortgrass Steppe LTER, D. Wall; FLO, Florida, MacArthur Agro-Ecolog. Res. Center, P. Bohlen. Contacts for 11
GLIDE sites not included in this analyses are as follows: Australia (one site), T. Adams; Brazil (one site), D. da Motta Marques; Beijing (one site), Y. Wang; Israel (one site),
Y. Steinberger; Mongolia (two sites), R. Baatar; Mongolia (three sites), Sh. Tsooj; Poland (one site), M. Sterzynska; Venezuela (one site), A. Torres Lezama.
SOIL FAUNA IMPACTS ON GLOBAL DECOMPOSITION 2665
r2008 The Authors
Journal compilation r2008 Blackwell Publishing Ltd, Global Change Biology,14, 2661–2677
field exposure was dependent on climate, longer where
litter decomposition rates were slower due to high
latitude (e.g. temperate sites) or high altitude (e.g.
BOG site; Table 1). Three Canadian sites were sampled
at 2 and 12 months, and another two were sampled at 2
and 4 months only (Table 2). Soil animals were extracted
from bags at sites by a standardized Tullgren dry heat
apparatus shipped from CSU. Animals were shipped in
70% ethanol to BioTrack
s
Australia Pty Ltd (Macquarie
University, Sydney) for centralized taxonomic identifi-
cation. Voucher specimens were stored until investiga-
tors requested return. After weighing, fauna-extracted
Table 2 GLIDE (Global Litter Invertebrate Decomposition Experiment) climatic region, sampling intervals, and analyses
performed for each site
Laboratory analyses Sampling times (months)
Climatic region
treatment
GLIDE
site code
Min.
FRC (%) k(day
1
)kr
2
Taxonomic
richness 1 2 3 4 12
Cold or dry INU X X X
Naphthalene 45.31 0.00077 0.76
No naph 42.51 0.00080 0.87
Cold or dry YUK X X X
Naphthalene 46.52 0.00077 0.95
No naph 49.96 0.00075 0.91
Cold or dry TOP X X X
Naphthalene 33.59 0.00105 0.88
No naph 39.61 0.00096 0.77
Cold or dry LET X X X
Naphthalene 56.39 0.00175 0.98
No naph 57.33 0.00185 0.99
Cold or dry ONE X X X
Naphthalene 48.54 0.00225 0.99
No naph 45.04 0.00257 0.95
Cold or dry NAM XXX
Naphthalene 69.58 0.00036 0.91
No naph 67.52 0.00045 0.95
Cold or dry COL X X X X
Naphthalene 58.74 0.00056 0.97
No naph 60.23 0.00055 0.95
Temperate TAS1 XXX
Naphthalene 43.76 0.00088 0.61
No naph 32.99 0.00130 0.57
Temperate TAS2 XXX
Naphthalene 42.13 0.00114 0.77
No naph 37.20 0.00115 0.71
Temperate VAN X X X X
Naphthalene 33.44 0.00127 0.83
No naph 28.59 0.00146 0.86
Temperate ROC X X X X
Naphthalene 38.02 0.00129 0.81
No naph 26.25 0.00139 0.82
Temperate BIO X X X X
Naphthalene 23.97 0.00188 0.85
No naph 13.36 0.00224 0.93
Temperate SUM X X X X
Naphthalene 37.48 0.00120 0.82
No naph 43.75 0.00114 0.78
Temperate KOM X X X X
Naphthalene 6.35 0.00290 0.95
No naph 5.45 0.00338 0.98
Continued
2666 D. H. WALL et al.
r2008 The Authors
Journal compilation r2008 Blackwell Publishing Ltd, Global Change Biology,14, 2661–2677
Table 2. (Contd.)
Laboratory analyses Sampling times (months)
Climatic region
treatment
GLIDE
site code
Min.
FRC (%) k(day
1
)kr
2
Taxonomic
richness 1 2 3 4 12
Temperate PAL X X X X
Naphthalene 17.85 0.00204 0.89
No naph 7.81 0.00260 0.95
Temperate GIE X X X X
Naphthalene 24.48 0.00163 0.98
No naph 23.39 0.00182 0.94
Temperate JEN XXX
Naphthalene 20.39 0.00203 0.90
No naph 18.71 0.00189 0.89
Temperate KUR XXX
Naphthalene 27.60 0.00150 0.87
No naph 30.97 0.00151 0.85
Temperate CAR XXX
Naphthalene 32.55 0.00156 0.79
No naph 23.5 0.00196 0.73
Temperate SIL XXX
Naphthalene 23.83 0.00193 0.78
No naph 10.53 0.00245 0.90
Wet tropical RUB X X X
Naphthalene 23.21 0.00579 0.91
No naph 9.53 0.00737 0.90
Wet tropical MAN X X X
Naphthalene 22.69 0.00737 0.97
No naph 11.82 0.00853 0.79
Wet tropical XIS X X X X
Naphthalene 19.66 0.00838 0.93
No naph 19.77 0.00823 0.90
Wet tropical KEN X X X X
Naphthalene 61.33 0.00143 0.45
No naph 39.06 0.00376 0.78
Wet tropical MAL X X X X
Naphthalene 17.36 0.00752 0.98
No naph 24.60 0.00619 0.91
Wet tropical TAI X X X
Naphthalene 19.40 0.00653 0.75
No naph 9.25 0.00887 0.66
Wet tropical FLO X X X X
Naphthalene 30.24 0.00349 0.59
No naph 31.78 0.00514 0.91
Dry tropical BON X X X
Naphthalene 5.71 0.00891 0.78
No naph 20.45 0.00562 0.76
Dry tropical BOG X X X X
Naphthalene 0.54 0.00581 0.98
No naph 0.10 0.00679 0.96
Dry tropical SEN X X X
Naphthalene 3.99 0.00706 0.62
No naph 26.20 0.00501 0.96
Fraction remaining carbon of litter (FRC) reported is for the final sampling date – note that values are only comparable across sites
where the final sampling times are the same; r
2
is calculated for the exponential fit used to derive the k-value (litter decomposition
rate). Sites organized alphabetically by country (see Table 1 for site code explanation). Sampling dates and taxonomic richness
analyses performed for each site are designated by X.
SOIL FAUNA IMPACTS ON GLOBAL DECOMPOSITION 2667
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litterbags were returned to CSU where subsamples of
oven-dried, plant material were milled and analyzed
for C on a LECO 1000 CHN analyzer (LECO Corp., St
Joseph, MI, USA). Initial litter C concentration was
44.13%. The composition of the initial litter ‘lignin’
(Preston et al., 1997, 2006) was as follows: neutral
detergent fiber or NDF extract, 67.91% (SD 0.74); acid
hydrolyzable extract, 27.54% (SD 0.83); acid unhydro-
lyzable residue or AUR, 4.14% (SD 0.18); and ash,
0.41% (SD 0.44). ADF, acid detergent fiber, is the sum
of the last three measures.
Taxonomic characterization
Taxonomy was determined according to The Tree of
Life (http://www.tolweb.org/tree/) and Systema Nat-
urae 2000 (http://sn2000.taxonomy.nl/). BioTrack
s
(Harvey & Yen, 1990; Oliver et al., 2000) identified all
animals, except for Czech Republic sites BIO, KOM,
PAL, and SUM; Brazilian sites MAN and RUB; and
Colombian site BOG (Table 1) where taxonomy was
carried out by qualified experts. All adult animals
extracted from litterbags were identified to 38 inverte-
brate groupings (Table 3) with Pauropoda and Symphy-
la identified to Class, mites (Acari) identified to
Subclass, and other taxa identified to the Order level.
The number of Orders ranged from one each in Phyla
Annelida and Mollusca, two in Class Crustacea, four in
Subclass Arachnida (Acari considered one ‘Order’), 11
in Class Myriapoda (Pauropoda and Symphyla consid-
ered as ‘Orders’), to 19 in Class Hexapoda.
Statistical analyses
Biota (Colwell, 2004), a relational database application,
was used to manage data. Of four litterbags removed
per treatment (control and suppression treatment) per
sampling period at each site, two were for determina-
tion of C concentrations of the residual litter. Knowing
the initial- and post-field-exposure litter masses, and
the initial and retrieved litter C concentrations, the log
e
of the fraction remaining litter C (FRC) was regressed
against the number of field days of litter exposure and k
was determined from the slope as an estimate of litter
decomposition rate (Harmon et al., 1999). By using FRC
rather than fraction litter mass remaining per se,we
could correct for incorporation of mineral material into
litterbags, a procedure similar to the use of ash-free dry
mass (Harmon et al., 1999).
For each of the 30 sites, the k-value for the inhibitor
treatment bags was regressed against the k-value for
control bags (Fig. 1), deviations from the 1 : 1 line
being associated with potential treatment effects. Paired
(by site) t-tests were performed using untransformed
k-values and one-tailed tests, given that variance was
determined to be equal and the hypothesis that animal
inhibitors would only decrease decomposition rates
respectively. These tests were first performed for all
sites together and, second, to determine if there were
region-specific effects, for sites within each climatic
region.
For 18 of our 30 sites, we had complete animal
abundance and taxonomic richness data (Tables 2 and
3) for each of the eight litterbags removed on each
sampling occasion. The other 12 sites had missing
values for some sample times. For the 18 sites with
complete datasets, mean abundance and richness were
determined for control and animal-suppressed treat-
ments at each sampling period, and then mean abun-
dance and richness across all sampling times. This gave
single animal abundance and taxonomic richness va-
lues for both the control and animal-suppressed treat-
ments for each site. Effects of the animal inhibitor on
animal abundance and taxonomic richness were tested
using, as for the kdata, one-tailed paired t-tests (Fig. 2).
Unequal variance in abundance data was equalized by
natural log transformation.
To test whether the impacts of the animal suppressor
on litter decomposition rates were correlated with treat-
ment effects on animal abundance and/or taxonomic
richness, we used multiple linear regression and regres-
sion tree approaches (Crawley, 2002). The primary
reason for conducting these analyses was to evaluate
whether the expected statistical relationship between
decomposition rate and climate was altered by changes
in the abundance and/or diversity of fauna. In both
regression analyses, decomposition rate (k) was used as
the response variable, natural log-transformed to meet
assumptions of homogeneity of variance. There were
three explanatory variables: mean animal taxonomic
richness, mean animal abundance, and the function
‘climate decomposition index’ (CDI; Table 1). The influ-
ence of climate on decomposition is usually represented
in global decomposition models as a function of tem-
perature and water availability (Liski et al., 2003; Parton
et al., 2007). For analysis of our data, we used the
function CDI, a widely recognized index for predicting
climate effects on litter decomposition (Moorhead et al.,
1999; Gholz et al., 2000) and an integral part of the global
carbon model CENTURY (Parton, 1996). An annual CDI
value for each site was calculated from monthly values
for precipitation and temperature (Tables 1 and A1)
(Parton et al., 2007). The temperature function uses
average monthly maximum and minimum air tempera-
tures and has been validated on an extensive global soil
respiration dataset (Del Grosso et al., 2005). The water
stress term is calculated as a function of the ratio of
monthly rainfall to potential evapotranspiration rate
2668 D. H. WALL et al.
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(Parton et al., 2007). For GLIDE, average CDI values
were calculated for the period that corresponded to the
maximum sampling time achieved (e.g. 12 months for
temperate sites) (Table 1). Monthly values of maximum
and minimum air temperature and precipitation data
from weather stations close to sites were used (see
Appendix) because there were incomplete data at all
sites for these years.
A total of 35 values were used for regression analyses,
from the subset of 18 sites for which complete animal
data were available. These comprised two sets of animal
variables (richness, abundance), one from each treat-
ment (control, animal-suppressed). Through model
checking, one point (Kenya, animal-suppressed litter-
bag) was omitted because it consistently violated as-
sumptions of homogeneity of variance irrespective of
the transformations used.
For regressions using CDI, richness, and abundance
values, the simplest possible model that included all
three explanatory variables was fitted first. Both kand
CDI were plotted as natural log-transformed variables.
Next, the curvature was tested by fitting the quadratic
Table 3 Taxa of invertebrates identified in GLIDE (Global Litter Invertebrate Decomposition Experiment) litterbags
Phylum Subphylum Class Subclass Order
Arthropoda Crustacea Malacostraca Peracarida Isopoda
Amphipoda
Hexapoda Entognatha Diplura
Entognatha Protura Eosentomata
Entognatha Collembola
Insecta Archaeognatha Microcoryphia
Pterygota Blattodea
Coleoptera
Dermaptera
Diptera
Embioptera
Hemiptera
Hymenoptera
Isoptera
Lepidoptera
Megaloptera
Neuroptera
Orthoptera
Psocoptera
Thysanoptera
Trichoptera
Myriapoda Diplopoda Helminthomorpha Spaerotheriida
Chordeumatida
Julida
Spirobolida
Spirostreptida
Polydesmida
Penicillata Polyxenida
Chilopoda Pleurostigmophora Geophilomorpha
Lithobiomorpha
Pauropoda
Symphyla
Chelicerata Arachnida Acari
Micrura Araneae
Dromopoda Opilionida
Pseudoscorpionida
Annelida Clitellata Oligochaeta Haplotaxida
Mollusca Gastropoda Orthogastropoda Pulmonata Stylommatophora
Tree of Life (http://www.tolweb.org/tree/phylogeny.html), accessed on February 11, 2008.
Systema Naturae 2000 (http://www.taxonomy.nl/Main/Classification/1.htm), accessed on February 11, 2008. Taxa listed under
Class and Subclass, but not under Order, were not identified to the Order level.
SOIL FAUNA IMPACTS ON GLOBAL DECOMPOSITION 2669
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terms for the variables; a square-root transformation of
the abundance was found necessary to correct for
curvature.
Results
Soil animal diversity
The 2 mm litterbag mesh prevented the access of soil
animals greater than 2 mm in diameter, excluding larger
animals. Some macrofauna (e.g. spiders, termites, bee-
tles, and other insects) could gain access as immatures,
but of the 80 606 individuals sorted to 38 higher taxo-
nomic groupings (i.e. Class, Subclass, Order), the
majority were soil mesofauna (defined as having max-
imum body widths ranging from 100 mm to 2 mm as
adults; Swift et al., 1979) (Table 3).
Latitudinal effects of animal suppressant naphthalene
The k-values from the 30 sites (Table 1) were used to test
whether the naphthalene reduced litter decomposition
rates. For each site, the k-value for the animal-
suppressed treatment bags was plotted against the
k-value for control bags (Fig. 1), deviations from the 1: 1
line being associated with potential treatment effects.
In some climate regions, the inhibitor had a negative
effect on k.
Across all 30 sites, treatment with naphthalene did
not affect litter decomposition rates (k-value, Fig. 1a).
When the same analyses were performed for each of our
four climatic regions (Table 1), decomposition rates
were significantly reduced by the animal suppressant
in temperate and wet tropical regions (temperate,
t
1,12
53.6, Po0.01; wet tropical, t
1,6
52.1, Po0.05), but
not in cold or dry regions, or the tropics as a whole
(P40.05 for all three datasets) (Fig. 1a and b). To
corroborate that these negative and neutral effects on
decomposition rates were associated with animal re-
sponses to the suppressant, we evaluated whether
naphthalene decreased the abundance and/or mean
taxonomic richness (Table 3) of animals extracted from
litterbags. When the suppressant was present, there
were significant reductions in both abundance
(t
1,17
51.8, Po0.05) and richness (t
1,17
52.8, Po0.01).
Log-transformed abundance and taxonomic richness
were found to be correlated but weakly, using Pearson’s
product-moment statistic (R
2
50.376, t
34
54.5, Po0.01,
n518 sites). Treatment values (means 1 SE) for each
(b)
6
5
4
3
2
1
0
Control Naphthalene
600
(a)
500
400
300
200
100
0
Control Naphthalene
Fig. 2 Impact of the animal-suppressor naphthalene on animal
diversity in litterbags. (a) Animal abundance in the control (open
bars) and suppressant (filled bars) treatment. (b) Animal taxo-
nomic richness.
k-value without faunal inhibitor (no naphthalene)
k-value with faunal inhibitor (naphthalene)
–0.008
–0.006
–0.004
–0.002
0.000
–0.008 –0.006 –0.004 –0.002 0.000
Tropical
Tropical
(wet only)
Temperate
Cold or dry
(a)
k-value with faunal inhibitor (naphthalene)
–0.004
–0.003
–0.002
–0.001
0.000
k-value without faunal inhibitor (no naphthalene)
–0.004 –0.003 –0.002 –0.001 0.000
Temperate
Cold or dry
(b)
Fig. 1 Impact of the animal-suppressant naphthalene on litter
decomposition rate (k) at climatic sites. (a) Departures (repre-
sented by broken lines for each climatic region) from the 1 : 1
solid line represents an impact of the animal suppressant on
decomposition rates: values above the line indicate lower de-
composition rates when the suppressant was present and vice
versa. Data for 30 sites and four climatic regions (Table 1) are
shown: cold or dry (circles), temperate (squares), wet tropical
(triangles), dry tropical (diamonds). (b) Data for the cold or dry
(circles) and temperate (squares) sites are replotted to clearly
show the departure from the 1 : 1 line for the temperate region.
2670 D. H. WALL et al.
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site were calculated as the mean of all three sample
periods (Fig. 2a and b).
Animal effects on the relationship between climate and
litter decomposition
Analysis of covariance was used to test whether the
global CDI–decomposition relationship was influenced
by the presence of naphthalene. The interaction be-
tween the CDI and suppressant terms was not signifi-
cant (P40.05), suggesting that the global relationship
between climate and litter decomposition rates is robust
to experimental reductions in soil animal abundance
and taxonomic richness. The animal-suppressed treat-
ment was then substituted for mean animal abundance
and mean animal richness data. These variables could
be used along with CDI annual values (Tables 1 and A1)
as continuous explanatory variables, providing 35 in-
dependent data points for regression analyses (see
‘Material and methods’). That is, the subset of 18 sites
(Table 2) for which complete animal abundance and
taxonomic richness data were available (Table 3) was
used for the regression. This confounds the animal and
climate effects and therefore requires cautious interpre-
tation (Loreau et al., 2002; Wardle, 2002); this limitation
is more fully evaluated in ‘Discussion’. The mini-
mal adequate model (log
e
[k-value] 50.1271[taxonomic
richness] 10.6299 log
e
[CDI]5.755) obtained using
step-wise multiple linear regression retained CDI and
taxonomic richness as explanatory variables and ac-
counted for 77% of the variation in decomposition rates
[Po0.001 (F
2
,
32
553; n535)] (Fig. 3).
Given that CDI alone explained 70% of the variation
in measured decomposition rates, as shown in other
multisite global decomposition experiments, the valid-
ity of designating fauna as an explanatory variable was
further tested using a regression tree approach (results
not shown). This confirmed that animal abundance
per se offered little explanatory power, but that taxo-
nomic richness of soil fauna, in addition to climate, was
correlated with decomposition rates across broad cli-
matic regions.
Discussion
GLIDE provides the first high-resolution taxonomic
database relating soil fauna to decomposition on a
global scale. It improves upon pairwise comparisons
of temperate vs. tropical regions (Heneghan et al., 1999;
Gonzalez & Seastedt, 2001) to include a gradient of
climatic conditions from 30 sites globally, and improves
taxon breadth from three or less to 38 taxonomic
groups. The findings of this experiment indicate that
soil mesofaunal assemblages, dominated by arthro-
pods, influence litter decay across broad climatic re-
gions, namely temperate and wet tropical, as predicted
by Swift et al. (1979). If this applies to all soil inverte-
brates, the original conceptual model of three primary
drivers of litter decomposition (climate, litter quality,
and biota; Swift et al., 1979) is validated.
Investigations using biogeographical comparisons to
explore linkages between ecosystem processes and or-
ganism diversity are typically frustrated by the positive
correlation between diversity and the product of tem-
perature and water availability (Cou
ˆteaux et al., 1995;
De Deyn & Van der Putten, 2005), though Maraun et al.
(2007) have offered oribatid mites, one of the most
abundant and species-rich groups of soil mesofauna,
as an exception to this rule. In our study, we overcame
this limitation by experimentally reducing animal
richness and abundance at each site independently
of climate, an approach already used effectively in
smaller scale, cross-site experiments (Heneghan et al.,
1999; Gonzalez & Seastedt, 2001). By using an analysis
of covariance (as opposed to regression approaches) in
this initial analysis, we ensured that our statistical tests
did not confound the animal and climate effects (Loreau
et al., 2002; Wardle, 2002). One potential limitation of the
–4.5
–5.0
–5.5
–6.0
–6.5
Observed k (log–normal of absolute k-value)
Predicted k (log–normal of absolute k-value)
–7.0
–7.5
–8.0
–4.
5
–5.0–5.5–6.0–6.5–7.0–7.5–8.0
Fig. 3 Fit of the minimal adequate model of decomposition rate
(k). In both these approaches, determined by step-wise multiple
linear regression to observed values, and using a regression tree
analysis, climate, as modeled using the temperature and water
availability climate function climate decomposition index (CDI)
(see ‘Material and methods’), and soil animal taxonomic richness
were retained as significant explanatory variables for decom-
position rate (k). Symbols for each climatic region (Table 1) are as
follows: cold or dry (circles), temperate (squares), wet tropical
(triangles), dry tropical (diamonds). Filled symbols represent the
inhibitor treatment while open symbols represent control.
SOIL FAUNA IMPACTS ON GLOBAL DECOMPOSITION 2671
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approach, however, is that naphthalene may negatively
affect other components of soil assemblages. Under
field conditions, however, naphthalene tends to have
negligible effects on soil bacterial and fungal growth
(Blair et al., 1989) and is relatively persistent. Using this
approach, we demonstrate that soil fauna positively
influence decomposition rates in the temperate and
wet tropical biomes and are neutral in regions where
biological activity is more constrained by temperature
and/or moisture, which was hypothesized by Swift
et al. (1979) but untested until now.
Our regression analyses, where the categorical factor
naphthalene was substituted for the continuous vari-
ables of abundance and richness, requires cautious
interpretation because of the confounding influence of
climate once this statistical approach is used. That is, in
contrast to the categorical factor naphthalene, abun-
dance and richness may be correlated with temperature
and moisture (i.e. climate), and so relationships be-
tween abundance and richness with decomposition
rates may be artefacts of this correlation. It is, however,
noteworthy that taxonomic richness of soil fauna, and
not abundance, was positively related to litter decom-
position rates. In contrast, experimental evidence from
microcosm and local-scale field experiments suggests
that it is animal abundance or biomass, and not rich-
ness, which operate as the driver (Cole et al., 2004). Our
experiment did not allow for the determination of
animal biomass per se, so we cannot discount the
possibility that the effect of taxonomic richness was
driven by biomass. The key difference between our
experiment and previous investigations into relation-
ships between soil animal diversity and litter decom-
position rates, aside from geographic scale, is that we
identified the entire extracted animal assemblage in our
litterbags to Class, Subclass or Order, rather than iden-
tifying only a few select groups to species (Table 3). The
high species richness of soil animals is assumed to be
associated with high functional redundancy (Andre
´n&
Balandreau, 1999), whereas animal richness at the
Order level or higher is concluded to be associated with
higher functional dissimilarity (Naeem & Wright, 2003;
Heemsbergen et al., 2004; St. John et al., 2006); that is, the
greater the value of taxonomic richness at this level, the
more functionally diverse the animal assemblage. How-
ever, this concept is difficult to test in the field because
no single soil or litter decomposition study has had the
resources to analyze all species of soil fauna present at
one time, owing to the expertize required for the
accurate identification of each group and the high
percentage (85%) of soil fauna yet to be described
(Eggleton & Bignell, 1995; Lawton et al., 1998; St. John
et al., 2006). The present study, utilizing the BioTrack
s
facilities, takes advantage of recent developments in
semiautomated methods of rapid biodiversity assess-
ment (Oliver et al., 2000) and suggests that functional
richness of soil fauna may be an important driver of
decomposition across large geographic regions. Further
work will be required to test this conclusion definitively.
We recommend that additional studies should address
fewer representative sites, but with greater replication
per site, higher taxonomic resolution, biomass determi-
nation, and the use of robust litterbags excluding me-
sofauna and macrofauna as an additional suppression
treatment augmenting naphthalene. The use of multiple
litter species of differing qualities should also be con-
sidered, given that faunal effects may be quality depen-
dent. A more recalcitrant litter than used in our study,
decomposing for a longer time, would likely involve a
different biodiversity and abundance and perhaps
show other effects on decomposition rates. However,
the effort required for this and the other improvements
to the field procedure is very large.
It is unlikely that potential nontarget effects of the
suppressant on microbial activity explain the results in
the field, because inhibitor effects were restricted to
specific climatic regions. If nontarget effects had oc-
curred, then litter decomposition should have been
retarded in all climatic regions because the climate
driver in decomposition models (CDI) is a surrogate
for bacterial and fungal activity per se. Therefore, soil
mesofauna, likely through their functional or taxonomic
richness, are an important driver of litter decomposition
rates in some climatic regions. When these regions
are viewed at a global scale under current climatic
conditions (Fig. 4), they account for 27.2% of the Earth’s
land area.
These results indicate that explicit inclusion of soil
animals in decomposition models may reduce the un-
explained variation in relationships between litter de-
composition and climate across regional scales. We
found evidence that inclusion of soil animals (specifi-
cally, their diversity) in models of global-scale decom-
position rates may provide modest improvements in
predictability [from 70% to 77% variance explained over
abiotic factors (CDI) alone] but the underlying mechan-
ism for this remains unclear and warrants further study.
Notably, however, our finding that animals have im-
portant influences on decomposition in certain climatic
regions has potential implications for global carbon
dynamics under changing climate, and may help ex-
plain regional variability in soil respiration (Davidson
et al., 2006). For example, regions predicted to have
warmer and wetter climates may see positive feedbacks
to soil communities, resulting in accelerated decompo-
sition rates and release of carbon. In higher latitude
systems, this feedback could substantially increase
respiration of C to the atmosphere given the greater
2672 D. H. WALL et al.
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proportions of relatively undecomposed, organic car-
bon that has accumulated due to historically cold and
dry conditions (Lal, 2005). Alternatively, global changes
such as land use conversion (Swift et al., 1998; Kaiser,
2004) may alter soil animal diversity and/or biomass
with consequences for decomposition rates in warm,
wet climatic regions. Thus, the specific inclusion of
regional impacts of soil animals on decomposition is
necessary to adequately model the impacts of global
change scenarios on decomposition and atmospheric C
dynamics. Additionally, because litter quality may
modify animal effects at a single site, future global
experiments will need to test simultaneously all three
primary drivers of decomposition.
Acknowledgements
We thank H. A. Mooney, D. A. Wardle, and R. D. Bardgett, and
the following for their assistance and advice throughout the
project: G. Adams, B. J. Adams, A. I. Breymeyer, E. J. Broos, V. K.
Brown, M. Chauvat, D. C. Coleman, R. K. Colwell, N. M.
DeCrappeo, A. N. Gillison, J. R. Gosz, J. Green, D. Gustine,
M. E. Harmon, S. W. James, M. Lane, A. N. Parsons, K. L. Prior,
S. D. Salvo, P. Smilauer, G. W. Yeates, X. Yang, and A. S. Zaitsev.
GLIDE research was a project of the DIVERSITAS International
Biodiversity Observation Year and supported by funding to
D. H. W. from the Winslow Foundation, the National Science
Foundation DEB 980637 and 0344834, and the Soil Science
Society of America Outreach Program. Funding to W. J. P. from
DOE 4000060456 contributed to publication cost.
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Appendix
Monthly values for mean precipitation (Precip, cm), maximum
(T
max
)andminimum(T
min
)temperatures(1C), and monthly
climate decomposition index (CDI) calculation for each site.
Italicised values indicate actual months in the field (see Table 2)
for sites where litterbags were in the field for o12 months. For
these sites only the italicised values were used to calculate the
mean CDI. See Table 1 for explanation of site codes.
2674 D. H. WALL et al.
r2008 The Authors
Journal compilation r2008 Blackwell Publishing Ltd, Global Change Biology,14, 2661–2677
Table A1
Site
code Variable January February March April May June July August September October November December
TAS
1&2
Precip 8.04 6.30 4.71 8.28 8.17 9.55 12.18 12.22 9.62 11.78 8.94 10.32
T
max
17.00 17.10 15.60 13.30 10.20 8.20 8.00 8.60 9.70 11.80 13.70 15.20
T
min
8.20 8.50 7.40 6.10 4.20 2.40 2.20 2.60 3.30 4.40 5.90 7.40
CDI 0.3055 0.3223 0.2673 0.3177 0.2588 0.213 0.2077 0.2192 0.2411 0.2817 0.3048 0.3139
MAN Precip 19.82 21.85 25.92 25.07 27.04 19.87 17.69 10.50 9.36 8.58 11.94 13.46
T
max
29.95 30.05 30.25 30.00 29.90 30.00 29.40 30.45 31.70 31.90 30.95 30.15
T
min
21.95 22.05 22.25 22.00 22.10 22.00 21.40 22.45 21.90 22.20 22.95 22.15
CDI 0.8796 0.9023 0.9322 0.9283 0.9286 0.9277 0.9054 0.7639 0.6977 0.5399 0.6748 0.8926
RUB Precip 19.14 10.56 13.41 6.01 3.61 1.45 0.93 1.98 6.16 9.83 19.48 24.20
T
max
29.93 30.15 29.85 28.78 27.13 25.75 25.13 26.15 26.88 28.08 28.55 28.68
T
min
19.93 20.15 19.85 18.78 16.13 13.75 13.13 14.15 15.88 18.13 19.15 19.63
CDI 0.7624 0.6058 0.6773 0.4945 0.3025 0.1041 0.0529 0.1518 0.3905 0.5827 0.8606 0.8725
BON Precip 0.03 0.03 1.48 3.88 9.14 13.60 19.63 26.02 15.23 5.61 0.88 0.21
T
max
32.30 35.60 36.60 36.40 34.60 31.60 29.50 28.00 29.80 32.30 33.70 32.70
T
min
17.30 20.00 23.60 25.40 24.60 22.20 21.50 21.20 21.40 21.50 19.70 18.10
CDI 0.0296 0.0315 0.0831 0.1934 0.6064 0.8505 0.9154 0.8955 0.8907 0.3882 0.0619 0.0338
INU Precip 0.86 0.83 1.00 0.78 1.28 1.35 2.22 2.71 2.34 1.94 1.07 0.96
T
max
25.70 24.80 20.50 12.80 3.00 7.30 13.00 11.10 3.70 4.70 17.90 23.30
T
min
31.90 31.60 28.30 21.40 9.20 1.50 5.60 5.30 0.30 8.90 23.90 29.50
CDI 0.0004 0.0005 0.0005 0.0014 0.0224 0.0354 0.0852 0.194 0.1414 0.0488 0.0005 0.0005
YUK Precip 1.68 1.58 1.10 0.51 1.48 3.49 5.21 2.83 2.83 1.67 2.23 1.91
T
max
14.30 12.00 4.80 2.60 9.00 13.80 16.00 14.80 8.70 0.30 10.20 11.90
T
min
23.90 22.80 16.80 8.20 2.20 1.40 4.40 3.00 1.50 6.70 17.80 20.90
CDI 0.004 0.004 0.0143 0.0111 0.0272 0.0839 0.1846 0.1256 0.148 0.0786 0.0121 0.0067
TOP Precip 5.34 2.49 2.54 1.74 3.38 5.61 4.32 3.43 3.78 4.22 6.36 5.58
T
max
5.90 3.10 2.80 7.70 12.50 16.00 18.60 18.60 14.60 7.10 0.50 4.60
T
min
13.30 13.50 8.20 3.10 1.10 4.60 7.00 6.80 2.80 1.10 6.10 11.00
CDI 0.0321 0.0393 0.0683 0.0438 0.0941 0.188 0.1764 0.1732 0.1809 0.1666 0.0803 0.0422
LET Precip 1.63 1.79 2.21 3.02 5.38 7.80 6.85 5.86 3.86 2.34 2.03 1.57
T
max
0.70 0.40 5.50 11.20 16.30 19.90 21.80 21.40 18.10 12.10 3.00 0.80
T
min
11.90 12.60 6.50 1.40 3.10 7.30 8.80 7.80 3.90 1.30 7.40 11.60
CDI 0.0441 0.0405 0.0633 0.0861 0.1854 0.307 0.2853 0.3087 0.1788 0.1432 0.0821 0.0476
VAN Precip 27.20 16.86 18.12 13.91 11.75 11.95 7.15 7.22 6.14 18.62 31.07 26.02
T
max
1.80 2.60 5.10 8.40 12.40 15.40 18.50 19.20 16.50 9.80 3.80 1.30
T
min
2.80 3.20 1.30 1.60 4.20 7.00 9.90 10.00 7.30 3.60 0.40 2.70
CDI 0.1139 0.1154 0.1475 0.2058 0.25 0.3764 0.391 0.3617 0.2831 0.2459 0.144 0.1101
ROC Precip 10.85 9.27 7.60 8.35 8.18 9.90 8.44 10.66 10.04 11.26 10.90 9.74
T
max
6.50 8.40 2.90 3.00 8.90 14.90 18.70 18.70 14.10 8.20 1.90 3.40
T
min
14.90 17.00 11.70 3.60 0.50 5.10 8.90 10.30 5.70 1.40 4.10 9.80
CDI 0.0221 0.0125 0.0452 0.1133 0.174 0.3148 0.3839 0.5064 0.3432 0.2024 0.1051 0.0502
ONE Precip 1.53 1.58 2.33 2.37 3.84 7.16 5.41 3.16 3.40 1.62 1.88 1.68
T
max
4.90 3.10 4.60 11.30 17.30 22.40 24.50 24.50 19.20 11.30 1.10 4.00
T
min
15.50 15.50 7.40 1.50 4.10 9.00 10.30 9.50 4.00 3.10 10.30 15.40
CDI 0.0269 0.0242 0.0627 0.0661 0.1236 0.3062 0.254 0.1412 0.127 0.0807 0.0589 0.0248
XIS Precip 1.56 2.90 2.60 6.59 14.84 18.07 25.44 26.50 16.57 9.29 4.73 3.11
T
max
21.40 23.70 26.00 28.90 28.50 27.40 26.40 26.90 26.90 25.00 22.50 20.30
T
min
9.80 10.10 12.00 15.70 18.70 21.00 20.40 20.10 19.10 17.00 13.90 9.90
CDI 0.1066 0.1932 0.1456 0.3517 0.7411 0.8761 0.8652 0.8607 0.8512 0.7282 0.399 0.2113
BOG Precip 4.56 9.16 11.62 13.87 11.56 5.81 6.23 6.31 13.49 13.13 14.30 7.43
T
max
19.60 19.30 19.30 19.20 19.60 18.80 18.30 18.40 18.90 18.30 18.50 18.80
T
min
9.60 10.10 10.50 11.20 12.20 11.20 10.70 10.40 10.50 10.10 10.30 9.60
CDI 0.261 0.4723 0.4294 0.5169 0.5202 0.3416 0.3739 0.3617 0.5269 0.4776 0.5048 0.3588
Continued
SOIL FAUNA IMPACTS ON GLOBAL DECOMPOSITION 2675
r2008 The Authors
Journal compilation r2008 Blackwell Publishing Ltd, Global Change Biology,14, 2661–2677
Table A1. (Contd.)
Site
code Variable January February March April May June July August September October November December
BIO Precip 3.11 3.33 4.58 4.77 6.50 8.97 8.34 7.65 4.87 3.09 4.45 5.13
T
max
0.80 1.70 6.40 12.10 17.30 19.20 22.40 22.20 17.10 12.10 3.80 0.70
T
min
3.60 3.50 0.60 2.30 7.10 10.00 12.40 12.00 8.50 3.70 0.20 2.50
CDI 0.1024 0.1127 0.151 0.1733 0.2555 0.4353 0.4473 0.5156 0.3649 0.2313 0.1474 0.1079
SUM Precip 3.11 3.33 4.58 4.77 6.50 8.97 8.34 7.65 4.87 3.09 4.45 5.13
T
max
0.80 1.70 6.40 12.10 17.30 19.20 22.40 22.20 17.10 12.10 3.80 0.70
T
min
3.60 3.50 0.60 2.30 7.10 10.00 12.40 12.00 8.50 3.70 0.20 2.50
CDI 0.1024 0.1127 0.151 0.1733 0.2555 0.4353 0.4473 0.5156 0.3649 0.2313 0.1474 0.1079
KOM Precip 2.28 2.89 3.40 3.96 6.28 7.51 5.31 5.72 5.03 3.27 4.27 4.09
T
max
1.20 3.10 7.90 14.70 18.80 21.60 25.30 24.70 19.10 13.90 5.10 1.40
T
min
2.80 2.30 1.10 4.50 9.60 12.20 15.10 14.30 10.90 5.30 1.10 1.80
CDI 0.0987 0.1266 0.1519 0.1572 0.3186 0.4368 0.2992 0.4443 0.3772 0.2573 0.1698 0.1166
PAL Precip 2.28 2.89 3.40 3.96 6.28 7.51 5.31 5.72 5.03 3.27 4.27 4.09
T
max
1.20 3.10 7.90 14.70 18.80 21.60 25.30 24.70 19.10 13.90 5.10 1.40
T
min
2.80 2.30 1.10 4.50 9.60 12.20 15.10 14.30 10.90 5.30 1.10 1.80
CDI 0.0987 0.1266 0.1519 0.1572 0.3186 0.4368 0.2992 0.4443 0.3772 0.2573 0.1698 0.1166
GIE Precip 5.41 3.94 6.02 4.70 5.28 7.61 7.54 5.95 6.05 4.15 5.09 6.59
T
max
1.90 2.20 6.80 11.20 16.60 18.20 21.40 21.10 15.80 11.40 5.60 2.40
T
min
1.30 2.20 0.60 3.20 7.60 10.40 13.40 12.30 9.00 5.60 1.40 0.60
CDI 0.1262 0.1258 0.1681 0.1857 0.2253 0.4134 0.4676 0.4392 0.4065 0.2731 0.1776 0.1329
JEN Precip 3.86 3.80 4.58 4.18 4.44 7.44 7.23 5.37 4.78 2.92 4.74 5.29
T
max
1.70 2.30 7.30 12.10 17.70 19.60 22.50 22.10 17.20 12.20 5.20 2.20
T
min
2.10 2.70 0.10 3.30 7.30 10.20 13.10 12.50 9.00 5.00 0.80 1.00
CDI 0.1194 0.123 0.1489 0.1677 0.2148 0.3764 0.4405 0.3962 0.3823 0.2524 0.1683 0.1283
KEN Precip 5.86 11.23 14.43 27.27 21.95 15.29 11.67 17.35 15.51 20.92 13.21 10.89
T
max
28.40 28.90 29.10 27.70 26.40 26.10 26.00 26.70 28.00 28.20 28.20 28.10
T
min
12.80 13.10 14.10 14.50 14.40 14.10 13.60 13.30 13.00 14.20 14.20 12.90
CDI 0.3105 0.5301 0.6646 0.7941 0.7647 0.7106 0.6566 0.7255 0.6663 0.7021 0.7107 0.4515
MAL Precip 7.29 9.96 11.03 12.12 30.39 23.80 23.18 32.12 27.61 29.60 30.51 23.10
T
max
25.70 25.50 26.60 27.20 27.20 26.30 26.00 26.00 26.20 26.80 26.40 26.30
T
min
19.50 18.50 19.00 19.20 19.40 19.10 19.20 18.80 19.20 19.20 19.00 19.70
CDI 0.6476 0.5711 0.6202 0.6266 0.8543 0.8334 0.8419 0.8357 0.8452 0.8543 0.8453 0.7291
NAM Precip 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
T
max
19.60 22.90 27.80 32.90 37.00 40.10 39.20 38.30 36.60 32.00 26.10 20.40
T
min
5.60 8.30 12.00 16.90 20.80 23.50 23.60 23.70 21.40 16.80 11.10 7.20
CDI 0.0144 0.0185 0.0242 0.0304 0.0317 0.033 0.0328 0.0327 0.0318 0.0287 0.0226 0.016
KUR Precip 4.38 3.28 3.81 5.47 5.90 8.82 6.24 5.91 7.03 5.10 4.75 4.48
T
max
4.40 4.10 1.50 10.90 18.10 22.20 22.60 22.30 15.90 8.30 0.80 4.60
T
min
9.60 9.30 4.30 3.10 8.30 12.80 14.20 12.70 8.30 2.30 4.60 8.80
CDI 0.0515 0.0533 0.1045 0.2302 0.274 0.5028 0.4013 0.4099 0.3469 0.1913 0.0887 0.0496
SEN Precip 0.05 0.05 0.01 0.03 0.07 0.83 3.77 9.68 10.05 1.51 0.00 0.06
T
max
30.55 32.30 34.60 35.75 37.20 37.05 34.20 33.55 33.85 35.65 33.75 30.90
T
min
14.05 16.40 17.90 19.05 20.70 22.45 23.40 24.35 24.15 23.15 19.55 16.10
CDI 0.0277 0.0295 0.0302 0.0312 0.0325 0.0481 0.2005 0.6514 0.6796 0.0878 0.0304 0.0291
TAI Precip 12.43 16.25 21.71 16.03 22.45 24.85 14.98 22.23 25.08 11.91 12.14 8.15
T
max
17.50 16.90 19.20 22.70 25.20 27.90 29.70 29.30 27.60 24.40 21.50 19.50
T
min
12.30 11.50 13.40 16.70 19.60 22.10 23.70 23.50 21.80 18.80 16.10 13.70
CDI 0.5385 0.5123 0.6039 0.7267 0.8193 0.8577 0.8653 0.9358 0.8976 0.7332 0.6855 0.5995
CAR Precip 7.67 4.77 6.75 4.05 4.33 6.74 7.80 8.62 8.75 8.11 7.44 7.53
T
max
5.70 5.20 9.30 11.70 16.70 18.00 21.40 21.00 16.90 13.50 8.90 6.20
T
min
1.70 0.40 2.70 4.10 7.90 10.80 13.80 12.80 10.50 7.70 4.50 2.80
CDI 0.1827 0.1629 0.2007 0.1931 0.2128 0.3687 0.4696 0.4692 0.4874 0.3594 0.249 0.1972
Continued
2676 D. H. WALL et al.
r2008 The Authors
Journal compilation r2008 Blackwell Publishing Ltd, Global Change Biology,14, 2661–2677
Table A1. (Contd.)
Site
code Variable January February March April May June July August September October November December
SIL Precip 9.09 6.01 5.35 5.97 4.15 4.72 4.89 4.88 6.27 8.44 6.38 7.96
T
max
6.50 6.10 9.30 11.60 15.90 18.50 21.00 20.80 17.20 13.20 9.00 6.60
T
min
1.50 0.70 2.50 3.60 6.70 9.30 12.00 11.20 8.80 6.20 3.20 1.60
CDI 0.1869 0.1654 0.2032 0.2279 0.1708 0.2172 0.2865 0.2965 0.3861 0.3361 0.2328 0.1876
COL Precip 0.85 1.33 3.58 3.54 6.30 6.23 4.20 4.06 3.38 2.31 1.88 0.88
T
max
4.90 5.90 10.80 15.40 19.80 25.90 28.60 27.70 23.30 17.10 9.30 4.30
T
min
8.90 7.70 3.40 0.20 5.40 10.70 12.80 12.30 7.10 0.90 4.70 9.10
CDI 0.032 0.0418 0.0804 0.0996 0.1988 0.2558 0.1512 0.1806 0.1857 0.1125 0.0918 0.0361
FLO Precip 6.81 5.47 8.36 4.79 7.77 18.94 17.67 19.10 17.74 10.62 6.45 3.67
T
max
22.90 23.80 25.70 27.40 30.80 31.80 32.50 32.10 31.50 29.20 26.50 23.20
T
min
11.50 11.80 13.70 15.20 18.60 21.60 22.50 22.50 22.50 19.20 16.30 12.00
CDI 0.4673 0.4399 0.4604 0.2806 0.4586 0.8612 0.9186 0.9172 0.9443 0.6969 0.5374 0.2893
SOIL FAUNA IMPACTS ON GLOBAL DECOMPOSITION 2677
r2008 The Authors
Journal compilation r2008 Blackwell Publishing Ltd, Global Change Biology,14, 2661–2677
