Data extraction
For invertebrate exclusion and/or inclusion treatments of each article,
we recorded sample sizes (n), means of mass loss or decomposition rates,
and standard deviations (SD) from tables directly or extracted data from
figures by performing Web-PlotDigitizer (Burda et al. 2017).
Standard errors (SE) reported in the original articles were converted
into SD using the formula \(\mathbf{S}\mathbf{D=SE\times\sqrt{}n}\).
Means of mass loss were converted into annual decomposition rates using
the negative exponential decomposition equation described by Olson
(1963). Other information we recorded from the original articles include
latitude, longitude, biome, mean annual temperature (MAT, °C), mean
annual precipitation (MAP, mm yr-1), soil pH, litter
traits (carbon (C), nitrogen (N), C:N ratio, lignin:N ratio), duration
of decomposition, and the method to exclude invertebrates (physical vs.
chemical).
All sites were classified into geographic groups for testing regional
variations. First, we grouped sites into ‘the tropics’ and ‘the
non-tropics’ based on biomes as stated in the original articles which we
further checked by spatial coordinates. Specifically, tropical wet and
dry forests were grouped into ‘the tropics’ (96% belonged to tropical
wet forests); other forest biomes were grouped into ‘the non-tropics’.
Biomes are powerful biogeographic units for studying large-scale
patterns of carbon and energy fluxes (Yi et al. 2010; Mucina
2019). Our classification of biomes followed Dinerstein et al.(2017). Fig. 1 was plotted using ArcGIS (version 10.2, ESRI, 2020). We
also assigned sites into zoogeographic realms to explore potential
biogeographic effects (e.g. dispersal and evolutionary histories).
Zoogeographic information of each observation followed Holt et
al. (2013) which is based on vertebrates but is generally pertinent to
the assessment of invertebrate distributions (Liria et al. 2021).
To explore potential moderators of regional variation of the effects of
invertebrate son decomposition, we tested several potential explanatory
factors: termite diversity (a decomposer group the diversity of which is
different in the tropics and non-tropics), litter traits (C, N, C:N and
lignin:N ratios), climate and soil pH. Termite diversity values were
extracted from a corresponding prediction model. The diversity
predictions are estimated from a model which was ′trained′ using
alpha-diversity values from 700 sites (Woon et al., in preparation). We
acknowledge that species diversity and richness do not always confer
higher contribution to ecosystem services compared with functional
diversity, but, currently, this is the best proxy we have to identify
global patterns of species distribution of the group. Where data were
absent from focal studies we obtained missing litter quality data from
the TRY plant trait database
(Kattge et al. 2020), missing soil pH data from the Harmonized
World Soil Database
(https://www.fao.org/soils-portal/en/,
resolution = 5′), and missing climate data (mean annual temperature, MAT
and mean annual precipitation, MAP) from the Worldclim database
(http://www.worldclim.org/,
resolution = 5′).