1. The high biodiversity and biomass of soil communities is crucial for litter decomposition in terrestrial ecosystems such as tropical forests. However, the leaf litter that these communities consume is of particularly poor quality as indicated by elemental stoichiometry. The impact of resource quantity, quality, and other habitat parameters on species richness and biomass of consumer communities is often studied in isolation, although much can be learned from simultaneously studying both community characteristics.
2. Using a data set of 780 macro-invertebrate consumer species across 32 sites in tropical lowland rainforest and agricultural systems on Sumatra, Indonesia, we investigated the effects of basal resource stoichiometry (C:X ratios of N, P, K, Ca, Mg, Na, S in local leaf litter), litter mass (basal resource quantity and habitat space), plant species richness (surrogate for litter habitat heterogeneity), and soil pH (acidity) on consumer species richness and biomass across different consumer groups (i.e., three feeding guilds and ten selected taxonomic groups).
3. In order to distinguish the most important predictors of consumer species richness and biomass, we applied a standardised model averaging approach investigating the effects of basal resource stoichiometry, litter mass, plant species richness, and soil pH on both consumer community characteristics. This standardised approach enabled us to identify differences and similarities in the magnitude and importance of such effects on consumer species richness and biomass.
4. Across consumer groups, we found litter mass to be the most important predictor of both species richness and biomass. Resource stoichiometry had a more pronounced impact on consumer species richness than on their biomass. As expected, taxonomic groups differed in which resource and habitat parameters (basal resource stoichiometry, litter mass, plant species richness, and pH) were most important for modulating their community characteristics.
5. The importance of litter mass for both species richness and biomass indicates that these tropical consumers strongly depend on habitat space and resource availability. Our study supports previous theoretical work indicating that consumer species richness is jointly influenced by resource availability and the balanced supply of multiple chemical elements in their resources.
Predictor variables for model averaging procedure
Litter mass, plant species richness, soil pH and litter carbon to element ratios for N, P, K, Ca, Mg, Na, and S measured on the core sites of the EFForTS project (http://www.uni-goettingen.de/en/310995.html) in the Jambi Province on Sumatra, Indonesia in 2012. Sites are coded as landscape-land use system-replicate (two landscapes: Bukit Duabelas (B) and Harapan (H); four land-use systems: Forest (F), Jungle rubber (J), Rubber (R), Oil palm (O); four replicates). Litter_mass gives the dry litter mass per ground area in g cm-2 (Krashevska et al., Biol. Fertil. Soils, 2015). Plant_rich gives the species richness of all trees with a diameter equal to or larger than 10 cm at breast height as well as all vascular plants per site. Soil pH, analysed in a 1:4 soil-to-water ratio, was taken from Allen et al. (PLoS ONE, 2015). Elemental ratios (CX) were calculated based on mg per g dry weight of the analysed leaf litter material. To obtain these site-averaged ratios, ratios of single leaf samples were weighted according to their relative importance in local leaf litter at each site (Jochum et al., The American Naturalist, 2017). Note that prior to analyses, predictor variables were transformed and standardized (see main article).
Jochum_JAE2017_predictors.csv
Observed litter macro-invertebrate species richness per consumer group and site
Litter macro-invertebrate species richness of 14 consumer groups observed on the core sites of the EFForTS project (http://www.uni-goettingen.de/en/310995.html) in the Jambi Province on Sumatra, Indonesia in 2012. Sites are coded as landscape-land use system-replicate (two landscapes: Bukit Duabelas (B) and Harapan (H); four land-use systems: Forest (F), Jungle rubber (J), Rubber (R), Oil palm (O); four replicates). Values represent species richness observed on 3 square metres per site for ten selected taxonomic groups (ants, blattodea, chilopoda, coleoptera, diplopoda, isopoda, isoptera, opiliones, orthoptera, spiders), three functional feeding guilds (detritivores, predators, omnivores) and the complete consumer community (all). It should be noted that the values for the selected taxonomic consumer groups do not necessarily add up to the values of the functional feeding guilds or the complete community as these aggregated groups contained more individuals than just the selected groups. These values are given as reference points for the extrapolated consumer species richness values actually used in the model averaging procedure.
Jochum_JAE2017_observed_consumer_richness.csv
Extrapolated litter macro-invertebrate species richness per consumer group and site
Extrapolated litter macro-invertebrate species richness of 14 consumer groups for the core sites of the EFForTS project (http://www.uni-goettingen.de/en/310995.html) in the Jambi Province on Sumatra, Indonesia in 2012. Sites are coded as landscape-land use system-replicate (two landscapes: Bukit Duabelas (B) and Harapan (H); four land-use systems: Forest (F), Jungle rubber (J), Rubber (R), Oil palm (O); four replicates). Values represent extrapolated species richness per site for ten selected taxonomic groups (ants, blattodea, chilopoda, coleoptera, diplopoda, isopoda, isoptera, opiliones, orthoptera, spiders), three functional feeding guilds (detritivores, predators, omnivores) and the complete consumer community (all). For each consumer group, extrapolated species richness was calculated based on the three subplot samples, using the nonparametric “jacknife 2” estimator (Brose et al., Ecology, 2003).
Jochum_JAE2017_extrapolated_consumer_richness.csv
Litter macro-invertebrate biomass per consumer group and site
Fresh biomass of 14 litter macro-invertebrate groups from the core sites of the EFForTS project (http://www.uni-goettingen.de/en/310995.html) in the Jambi Province on Sumatra, Indonesia in 2012. Sites are coded as landscape-land use system-replicate (two landscapes: Bukit Duabelas (B) and Harapan (H); four land-use systems: Forest (F), Jungle rubber (J), Rubber (R), Oil palm (O); four replicates). Values represent fresh biomass per site for ten selected taxonomic groups (ants, blattodea, chilopoda, coleoptera, diplopoda, isopoda, isoptera, opiliones, orthoptera, spiders), three functional feeding guilds (detritivores, predators, omnivores) and the complete consumer community (all). Fresh biomass in mg m-2 was calculated as the sum of individual body masses of all sampled individuals that were, in turn, calculated using length-mass regressions from the literature (Barnes et al., Nat. Commun., 2014). It should be noted that the values for the selected taxonomic consumer groups do not necessarily add up to the values of the functional feeding guilds or the complete community as these aggregated groups contained more individuals than just the selected groups.
Jochum_JAE2017_consumer_biomass.csv
Coefficients and 95% confidence intervals for averaged consumer species richness models (Fig1)
Standardized coefficients and 95% confidence intervals for each of the ten predictors from each of 14 averaged consumer species richness models presented in Figure 1. Consumer groups are the ten selected taxonomic groups (ants, blattodea, chilopoda, coleoptera, diplopoda, isopoda, isoptera, opiliones, orthoptera, spiders), three functional feeding guilds (detritivores, predators, omnivores) and the complete consumer community (all). Predictor variables are litter mass (LM), plant species richness (prich), soil pH (pH), and litter carbon to element ratios for N, P, K, Ca, Mg, Na, and S. Coef gives the standardized coefficient of each predictor for each averaged model. Confint_lower_boundary and confint_upper_boundary give the 95% confidence interval for the coefficients. It should be noted that for all stoichiometric parameters, negative coefficients indicate a positive effect of the respective elemental availability as the models were run on C:element ratios. As such, a positive effect of e.g. C:N corresponds to a negative effect of N in main text Fig. 1.
Jochum_JAE2017_Fig1_richness_coefs_confint.csv
Coefficients and 95% confidence intervals for averaged consumer biomass models (Fig2)
Standardized coefficients and 95% confidence intervals for each of the ten predictors from each of 14 averaged consumer biomass models presented in Figure 2. Consumer groups are the ten selected taxonomic groups (ants, blattodea, chilopoda, coleoptera, diplopoda, isopoda, isoptera, opiliones, orthoptera, spiders), three functional feeding guilds (detritivores, predators, omnivores) and the complete consumer community (all). Predictor variables are litter mass (LM), plant species richness (prich), soil pH (pH), and litter carbon to element ratios for N, P, K, Ca, Mg, Na, and S. Coef gives the standardized coefficient of each predictor for each averaged model. Confint_lower_boundary and confint_upper_boundary give the 95% confidence interval for the coefficients. It should be noted that for all stoichiometric parameters, negative coefficients indicate a positive effect of the respective elemental availability as the models were run on C:element ratios. As such, a positive effect of e.g. C:N corresponds to a negative effect of N in main text Fig. 2.
Jochum_JAE2017_Fig2_biomass_coefs_confint.csv
