Dialux microscope (1250×) fitted for epifluorescence (HBO 100 W mercury light source;. Osram, Winterthur, Switzerland; with an excitation filter for 270 and 450 ...
Supplementary information to:
Predator effects on a detritus-based food web are primarily mediated by nontrophic interactions Nabil Majdi, Anatole Boiché, Walter Traunspurger and Antoine Lecerf
Appendix S1 Assessment of microbial decomposer communities
Bacterial biomass The density of bacteria was determined from five leaf discs (Ø 13 mm) preserved in 10 ml of a 4% formaldehyde solution following a modified DAPI-staining method (Porter & Feig 1980). Bacteria were detached from leaf surface using an ultrasonic bath (Branson 1510, Branson Ultrasonics, Danbury, CT, USA) and sodium pyrophosphate (0.1% final concentration) was added to efficiently crumble and disperse bacterial aggregates (Buesing & Gessner 2002). Bacterial count was performed from at least 20 images captured under a Leitz Dialux microscope (1250×) fitted for epifluorescence (HBO 100 W mercury light source; Osram, Winterthur, Switzerland; with an excitation filter for 270 and 450 nm, a barrier filter of 410 nm and a 515 nm cut-off filter). Each image corresponded to a microscopic field of 13610 µm2. Digital images were analysed using CMEIAS Ver. 1.27 (Liu et al. 2001) operating in UTHSCSA ImageTool Ver. 1.27 (Wilcox et al. 1997) and bacterial cell biovolume (V) was determined from cell length (L) and width (W) using the following equation: V = (π/4) × W² × (L – W/3) (Bratbak 1993). Bacterial biomass was estimated assuming a biovolume-to-carbon ratio of 0.38 (Lee & Fuhrman 1987). 1
Fungal biomass Fungal mycelium biomass in oak leaves was quantified through ergosterol content (Gessner & Chauvet 1993). Five frozen (–20 °C) leaf discs (Ø 13 mm) were freeze-dried and weighed to the nearest 0.1 mg before hot alkaline methanol extraction of lipids (80 °C, 30 min). The lipid extract was purified using solid-phase extraction cartridges (Oasis HLB, 60 mg, 3 cc; Waters, Milford, MA, USA), and ergosterol content was quantified by high-performance liquid chromatography following Gessner (2005). Ergosterol content was then converted to mycelium dry biomass (DM), assuming a multiplicative factor of 182 (Gessner & Chauvet 1993). Mycelium dry mass was converted to carbon (C) mass, assuming a DM-to-C ratio of 0.5 (Sterner & Elser 2002).
Aquatic hyphomycete community Ten fresh leaf discs (Ø 13 mm) were placed in a 100 ml Erlenmeyer flask filled with 25 ml of filtered (using Whatman GF/F glass fibre filter) stream water. The production and release of spores by aquatic hyphomycetes was induced by gentle shaking (100 rpm, 25 mm orbital path) at 10 °C for 48 h. The spore suspension in the flask was transferred into a polyethylene tube and the volume was adjusted to 35 mL with distilled water used to rinse flask walls, and 2 mL of 37% formaldehyde. Leaf discs were oven-dried (60 °C, 72h) and weighed to the nearest 0.1 mg. Triton X-100 (0.5 mL of 0.5% solution) were added to spore suspensions prior identification and enumeration. Samples were gently stirred to homogenise spore distribution and 5-mL aliquots were filtered through membrane filters (SMWP, 5 µm poresize, Ø 25 mm; Millipore corp., Billerica, MA, USA). Spores retained on the filters were stained with 0.1% Trypan blue in 60% lactic acid (Iqbal & Webster 1973). Spores were counted and identified to species level under a microscope (×200), following Gulis et al. 2
(2004). A mean C biomass was attributed to each spore species using DM values reported in the literature for common temperate hyphomycete species and assuming a DM-to-C conversion factor of 0.5 (Gessner & Chauvet, 1994; Chauvet & Suberkropp 1998). Spore production was then expressed as µg C released per g leaf C per day. The composition of aquatic hyphomycete community is given in Table S1.
Table S1. Aquatic hyphomycetes in leaf packs. The mean and relative contribution of each taxa to total spore production was calculated across all litter bags (N = 56)
Spore production (µgC gLeafC–1 day–1)
Aquatic hyphomycete species
Tricladium chaetocladium Ingold Anguillospora filiformis Greath. Clavariopsis aquatica De Wild. Tetracladium marchalianum De Wild. Tetrachaetum elegans Ingold Alatospora acuminata Ingold Flagellospora curvula Ingold Culicidospora aquatica Petersen Lunulospora curvula Ingold Lemonniera terrestris Tubaki Alatospora flagellata (Gönczöl) Marvanová Lemonniera aquatica De Wild. Tumularia aquatica (Ingold) Marvanová & Descals Heliscus lugdunensis Saccardo & Therry Stenocladiella neglecta Marvanová & Descals Clavatospora longibrachiata (Ingold) Marvanová & Nilsson Articulospora tetracladia Ingold Lemonniera cornuta Ranzoni Total
3
Mean
%
1086.3 570.0 387.7 181.5 93.8 15.5 13.2 12.7 11.2 5.8 4.1 3.9 2.0 0.6 0.5 0.3 0.2 0.1 2389.48
45.46 23.85 16.23 7.59 3.93 0.65 0.55 0.53 0.47 0.24 0.17 0.16 0.09 0.02 0.02 0.01 0.01 0.01
Appendix S2 Assessment of invertebrate communities
The largest invertebrates (including Polycelis felina) retained on the 50 µm sieve were handpicked and preserved in 70 % ethanol prior sorting and counting. Identification was performed to the lowest practicable taxonomic level. Each taxon was individually dried and weighed to the nearest 1 µg. Dry mass (DM) was converted to carbon (C), assuming a DM-toC ratio of 0.5 (Sterner & Elser 2002). Small-invertebrates remaining on the sieve were extracted from fine sediment bulk using a density-gradient centrifugation technique involving Ludox HS-40 after Pfannkuche & Thiel (1988). Samples were preserved in a 4 % formaldehyde solution and invertebrates were stained with 1 % Rose Bengal. At least 150 individuals were counted and identified in a sample aliquot spread in a Dolfuss cell (Elvetec services, Clermont-Ferrand, France) under a Leica MZ 9.5 stereomicroscope (9×–90×). Their biovolumes were estimated based on body-dimension measurements of >10 individuals per taxon, in each sample whenever possible. We converted biovolumes into C biomass using standard biometric conversions (Benke et al. 1999; Giere 2009). Invertebrates were assigned to either of the two size groups (see main text, and Table S4), macrofauna (mean body mass: 5–50 mgC) and meiofauna (mean body mass: 0.02–0.2 mgC). The composition of invertebrate communities is given in Table S2.
Table S2. Meiofauna and macrofauna communities in leaf packs. The mean and relative contribution of each taxon to community biomass was calculated across all samples (N = 56). Contribution of each community to total leaf-associated biota biomass (including microbial decomposers) was highlighted in bold. Asterisks show invertebrate groups entered in path models 4
Biomass (µgC gLeafC–1)
Taxon / Groups
Mean
%
336 163 141 21 7 3
0.5 48.6 42.0 6.2 2.2 1.0
12877.4
18.4
7957.4 2319.0 435.1 333.8 60.0 23.6
63.4 18.5 3.5 2.7 0.5 0.2
Hydrachnidia (water mites)*
390.9
3.1
Oligochaeta*
200.7
1.6
Predatory Diptera* Tanypodinae Atherix sp. Empididae Pedicini Hexatoma sp. Ceratopogoninae
807.8 61.6 54.5 30.5 22.2 3.4
6.4 0.49 0.43 0.24 0.18 0.03
81.5 22.7 4.1 1.5 0.2
0.65 0.18 0.03 0.01 0.00
16.5 11.0 9.8 4.4 1.1
0.13 0.09 0.08 0.04 0.01
14.1 7.1 3.1
0.11 0.06 0.02
Meiofauna Nematoda* Copepoda Harpacticoida*1 Rotifera* Tardigrada* Gastrotricha* Macrofauna Non-predatory Chironomidae* Early larval stages2 Orthocladinae Chironomini Nymphs Tanytarsini Imagos
Leaf-shredding Plecoptera (stoneflies)* Early larval stages3 Leuctra sp. Nemouridae Protonemura sp. Nemoura sp. Biofilm-grazing mayflies and beetles* Ephemeroptera (mayflies) Ephemerella sp. Habrophlebia sp. Habroleptoides sp. Leptopblebiidae Caenis sp. Coleoptera (beetles) Elmis sp. Limnius sp. Hydrocyphon sp. 1
Includes Nauplii and all copepodite stages.
2
Mostly Orthocladinae.
3
Mostly Nemouridae and Leuctridae.
Since nematodes contribute predominantly to meiofauna community (Traunspurger 2000), we determined the species composition in each sample based on 20–60 randomly picked individuals. Nematodes were mounted on slides following Seinhorst (1959) and were 5
identified under a Leitz Dialux microscope (×1250) with differential interference contrast. Nematode species were assigned to feeding groups (deposit-feeders, epistrate-feeders, suction-feeders and chewers) following Traunspurger (1997). This information was used to infer the main food sources used by nematodes. The composition of nematode assemblages is given in Table S3.
Table S3. Nematode assemblage in leaf packs. The main diet of each taxa was extrapolated from their feeding types. Abbreviations: Bacterial-feeders (B), algal-feeders (A), fungalfeeders (F), omnivores (O) and predators (P)
Nematode feeding types and taxa
Main diet
Deposit-feeders Eumonhystera vulgaris (De Man) Andrássy
B
%
Nematode feeding types and taxa
87.98
Suction-feeders
Main diet
% 2.35
31.06
Dorylaimoides sp.
O
0.79
Eumonhystera pseudobulbosa (Daday)
B
23.93
Aphelenchoides cf fluviatilis Andrássy
F
0.29
Eumonhystera dispar (Bastian)
B
19.93
Deladenus sp.
F
0.27
Eumonhystera barbata Andrássy
B
5.72
Filenchus vulgaris (Brzeski) Lownsbery
F
0.21
Eumonhystera longicaudatula (Gerlach & Riemann)
B
2.92
Eudorylaimus agilis (De Man)
O
0.13
Eumonhystera simplex (De Man)
B
1.26
Dorylaimus stagnalis Dujardin
O
0.13
Plectus aquatilis Andrássy
B
1.22
Tylenchus davanei Bastian
F
0.12
Cylindrolaimus communis De Man
B
0.34
Mesodorylaimus sp.
O
0.08
Plectus longicaudatus Bütschli
B
0.29
Aphelenchoides sp.
F
0.07
Monhystera sp.
B
0.27
Criconema loofi (De Grisse)
O
0.07
Teratocephalus sp.
B
0.26
Eudorylaimus carteri (Bastian) Andrássy
O
0.07
Monhystrella sp.
B
0.25
Tylenchus sp.
F
0.06
Euteratocephalus palustris (De Man)
B
0.12
Thornia cf hirschmannae Andrássy
O
0.05
Plectus acuminatus Bastian
B
0.11
Wilsonema otophorum (De Man)
B
0.09
Eumonhystera sp.
B
0.08
Fictor fictor (Bastian)
O
2.82
Plectus parvus Bastian
B
0.05
Tobrilus sp1
O
1.66
Chewers
7.79
Alaimus sp.
B
0.05
Tobrilus sp2
O
1.43
Rhabdolaimus terrestris De Man
B
0.05
Tobrilus gracilis (Bastian)
O
0.91
Mononchus truncatus Bastian
P
0.32
1.81
Tobrilus sp3
O
0.22
Epistrate-feeders Ethmolaimus pratensis De Man
A
0.76
Clarkus sp.
P
0.16
Prismatolaimus intermedius (Bütschli) De Man
A
0.66
Mononchus aquaticus Coetzee
P
0.12
Chromadorina bioculata (Schultze in Carus) Wieser
A
0.26
Tripyla glomerans Bastian
O
0.08
Achromadora terricola (De Man)
A
0.08
Tobrilus cf pellucidus (Bastian)
O
0.06
Achromadora ruricola (De Man) Micoletzky
A
0.05
6
Appendix S3 Structural equation modelling: model building and validation
Method and procedure Partial Least Squares Path Modeling (PLSPM) was used to assess direct and indirect predator effects on the detrital food web in enclosures. This method is a robust form of Structural Equation Modeling (SEM) with latent variables estimated through PLS approach (EspositoVinzi, Tinchera & Amato 2010). Latent variables are multivariate constructs used to condense information encapsulated into several measured variables (= indicators) among which relationships are not fully understood a priori or are of lesser interest than the notion they intend to represent. Contrary to common SEM approach based on covariance structure analysis, PLSPM does not rely on strong assumptions such as normal distribution and data independency and it does not require large dataset to perform optimally (Chin 2010). The main reason for this is that PLS approach is based on iterative processes and nonparametric hypothesis testing (Esposito-Vinzi et al. 2010). PLSPM was therefore particularly well suited to our study in regards to the relatively high complexity of our path models and the modest size of dataset analysed here (N = 48). PLSPM can be viewed as a generalisation to multiple table problems of the PLS regression, a powerful explanatory tool for ecologists (Carrascal, Galván & Gordo 2009). PLSPM is thus more appropriate than traditional SEM approaches, when patterns and processes are not fully understood (Chin 2010), as this is often the case in community ecology. However, PLSPM does not come with a complete solution of statistical tools to assess the adequacy of path models and to compare amongst them as in traditional SEM
7
approach (e.g., Chi-square test, AIC). Rather, PLSPM relies on variance-based indices such as R-square values of endogenous (latent and manifest) variables and a goodness-to-fit index (i.e., GOF) reflecting the performance of the structural model (R2) and the ‘quality’ of the latent variables (Esposito-Vinzi et al. 2010). Models were fitted using the library PLSPM in R (Sanchez & Trinchera 2013). The ‘scale option’ was turned on to standardise variables expressed in different units. Latent variables were constructed in a reflective way (mode A) based on the path weighting scheme. Path coefficients were estimated through PLS regression to take full advantage of the PLSPM approach (Esposito-Vinzi et al. 2010). Path coefficients and loadings were used to assess the strength and direction of expected causal relationships and the contribution of observed variables to the definition of latent variables, respectively. Significance of these model parameters was assessed using 95% percentile confidence intervals calculated on 200 bootstrap samples. The ‘quality’ of latent variables has to be assessed prior interpreting the results of the structural model (i.e. paths). As information encapsulated in a set of indicators is condensed into a single axis (i.e. the latent variable), it is important to check that the variance captured by the latent variable override the remaining variance. This uni-dimensionality condition is met when the Dillon-Goldstein’s (DG) rho exceeds 0.7 (Esposito-Vinzi et al. 2010). Another prerequisite to PLS model interpretation is that latent variables are explained well by their own indicators. Cross-loadings table was used to verify that all indicators had greater correlations with their own latent variable than the latent variables they did not intend to represent. In addition, the AVE index was used to quantify the average variance extracted by a latent variable from its indicators (Chin 2010). The quality of ‘poor’ latent variable can be improved by removing non-significant indicators.
8
Variables incorporated into models and definition of model compartments Causal diagrams were constructed based on a pool of 16 continuous variables including predator density as exogenous variable, the biomass of leaf-colonizing biota, the amount of inorganic sediment deposited on leaf surface, and leaf litter mass remaining (see Table S4). Detrital mass was expressed as the percentage of litter mass remaining relative to initial mass introduced in bags, whereas other response variables were expressed per unit of detrital mass. Detrital mass and biomass values were converted into C mass (see main text and Appendix S1 and S2). The number of flatworm predators was square-root transformed to achieve linear relationship with measured variables. To remove temporal variability from the dataset, variables were centered by sampling date (14 and 24 days). Prior centering, we verified that predator effect on the response variables was not time dependent using a robust MANOVA approach (50-50 MANOVA: Predator-by-time interaction: Ppermutation = 0.18; Langsrud 2002). As functionally distinct groups of decomposers (Moore et al. 2004; Romaní et al. 2006), bacteria and fungi were represented by two distinct model compartments. Leafcolonizing invertebrates were assigned to meiofauna or macrofauna depending on body-size (see main text), each community was represented using a latent variable in path models. Enclosures trapped substantial amounts of drifting fine sediments, which might have influence substantially leaf colonization by microbial decomposers and invertebrates and ultimately litter decomposition (e.g., Sponseller & Benfield 2001; Schofield, Pringle & Meyer 2004). In turn, biota can influence sediment deposition and retention (Sanpera-Calbet, Chauvet & Richardson 2012; Statzner 2012). To assess such effects, we used the dry mass (g sediment per g Leaf C) of 50–500 µm inorganic particles as a proxy of total amount of sediment entrapped on leaf surface assuming constant inorganic/organic matter ratio.
9
Table S4. Model compartments and variables considered into path models. Habitat and main trophic traits were inferred from Rundle et al. (2002) and Tachet et al. (2010). Abbreviations: fine particulate organic matter (FPOM), dissolved organic matter (DOM), carbon (C), dry mass (DM).
Model compartment
Variables and abbreviation
Flatworm
Numerically dominant taxa/morphotypes
Unit
Flatworm number
Macrofauna
Polycelis felina
Biomass of leaf-shredding Plecoptera (Plecosh) Biomass of biofilm-grazing Ephemeroptera and Coleoptera (ECgraz) Biomass of predatory Diptera (Diptpred) Biomass of non-predatory Chironomidae (Npchiro)
gC gLeafC–1
gC gLeafC–1
Orthocladinae
Oligochaeta biomass (Oligo)
gC gLeafC–1
Naididae
Hydrachnidia biomass (Mites)
gC gLeafC–1
Nematoda biomass (Nema)
gC gLeafC–1
gC gLeafC–1 gC gLeafC
–1
gC gLeafC
–1
gC gLeafC
–1
Harpacticoida biomass (Harp)
gC gLeafC
–1
Gastrotricha biomass (Gastro)
gC gLeafC–1
Fungi
Fungal biomass
gC gLeafC–1
Bacteria
Bacterial biomass
gC gLeafC–1
Sediment
Inorganic sediment
gDM gleafC–1
Percent leaf mass remaining
%
Rotifera biomass (Roti) Meiofauna
Tardigrada biomass (Tardi)
Leaf mass 1
Mean body mass of Nauplii larvae
2
Nemouridae, Leuctra spp. Ephemerella spp., Elmis spp. Tanypodinae
Habitat and trophic traits
Mean body mass (µgC)
Benthic, fluid feeders of invertebrates Benthic, shredders of leaf tissues Benthic, grazers of biofilm algae and FPOM Benthic/interstitial, predators of small invertebrates Benthic/interstitial, generalist biofilm and FPOM feeders Interstitial, Deposit-feeders (bacteria and FPOM) Benthic/interstitial, Predators/parasites of insect larvae
593.0 15.6 38.0 44.7 36.8 5.1 21.3
Eumonhystera spp.
Interstitial, bacterial-feeders
0.029
Monogononta
Benthic, filter-feeders
0.017
Interstitial, microphagous/predators Benthic, microphagous Interstitial, microphagous Clavariopsis aquatica, Anguillospora filiformis, Tricladium chaetocladium
Endophytic hyphae, exploit exclusively leaf C Interstitial and epiphytic biofilm, exploit FPOM, DOM and leaf C
Mean body mass of copepodite stages and adults
A priori hypothetical causal model CONSTRUCTION A first model was constructed to assess trophic and nontrophic predatory effects propagating exclusively downwards through the food web (Fig. S1). P. felina was assumed to interact directly with macrofauna through consumption (Armitage & Young 1990). Both compartments were also assumed to affect the amount of fine sediments on leaf surface directly through bioturbation, for instance (Jennings 1957; Zanetell & Peckarsky 1996; 10
0.054 1
0.053 / 0.1772 0.033 Not determined 1.34 ×10–10
Sanpera-Calbet et al., 2012; Statzner, 2012). In turn, sediments were expected to influence meiofauna and bacteria directly (Claret, Marmonier & Bravard 1998; Swan & Palmer 2000). Fungal biomass was considered to be insensitive to sedimentation (Sanpera-Calbet et al. 2012), which was consistent with the fact that most fungal mycelium is not located on leaf surface (Barlöcher 1992). A path from fungi to bacteria was initially included into models to represent possible competitive exclusion of bacteria by fungi (Moore et al. 2004; Romaní et al. 2006). However, as the path coefficient was always nearly null, it was not retained in the structure of our models. Macrofauna was assumed to exert a top-down control on meiofauna, bacteria and fungi through direct predation and/or, eventually, biotic disturbance (Arsuffi & Suberkropp 1989; Berg 1995; Schmid & Schmid-Araya 2002). Leaf litter decomposition was assumed to be largely mediated by bacteria, fungi, and macrofauna (Gessner, Chauvet & Dobson 1999). In contrast, no direct effect of meiofauna on leaf litter was included as meiofauna are not capable to breakdown leaf litter and consume coarse particulate organic matter (Rundle et al. 2002).
VALIDATION The latent variable representing ‘macrofauna’ did not meet the assumption of unidimensionality in its indicators (DG rho < 0.7; Table S5). This issue was alleviated (DG rho = 0.75) after removing three taxa (non-predatory Chironomidae; biofilm-grazing Ephemeroptera and Coleoptera; and Hydrachnidia) that did not contribute significantly to the latent variable definition (loadings non significantly different from 0; Table S5). This simplification had little effect on loadings but resulted in two-fold increase in the variance explained by the latent variable (AVE = 0.5). No improvement was needed for the latent variable representing ‘meiofauna’ which largely met quality criteria (DG rho > 0.7 and AVE > 0.5; Table S5).
11
Examination of cross-loadings table revealed that, consistent with assumption, all indicators had greater loading on their own latent variable than on the latent variable it did not intend to represent (Table S6). The initial and final models yielded to similar R-square values (Table S7) although the latter have slightly higher GOF, suggesting that macrofaunal taxa removed from the model were not involved in the causal scheme.
Flatworm
Sediment
Macrofauna
Meiofauna
Bacteria
Fungi
Leaf litter
Fig. S1. Hypothetical (a priori) causal model in which predatory Polycelis felina (Flatworm) affects lower trophic levels through top-down cascading effects (trophic and potential nontrophic pathway: solid arrows) and through habitat (sediment) alterations (nontrophic pathway: dashed arrows). Rectangles denote manifest variables and ellipses denote latent variables.
12
Table S5. Quality assessment of the latent variables in the a priori causal model. Bootstrapped 95% percentile confidence intervals were used to assess the significance of loadings. Abbreviations are in Table S4
Latent variable
Indicator
Macrofauna
Plecosh ECgraz Diptpred Npchiro Oligo Mite
DG rho AVE
Meiofauna
Index
Initial model Perc. Loadings .025 0.75 0.30 0.22 -0.21 0.68 0.43 -0.11 -0.58 0.56 0.33 0.43 -0.06
Perc. .975 0.90 0.72 0.84 0.46 0.78 0.71
Index
0.62 0.26 Nema Roti Tardi Harp Gastro
DG rho AVE
Final model Perc Loadings .025 0.77 0.38 removed 0.72 0.51 removed 0.62 0.39 removed
Perc .975 0.87 0.88 0.83
0.75 0.50 0.76 0.60 0.77 0.89 0.87
0.71 0.42 0.56 0.78 0.78
0.89 0.79 0.87 0.95 0.93
0.76 0.61 0.77 0.89 0.87
0.89 0.62
0.70 0.39 0.56 0.76 0.76
0.89 0.62
Table S6. Correlation between latent variables and loadings in the a priori model. Abbreviations are in Table S4
Macrofauna
Plecosh ECgraz Diptpred Npchiro Oligo Mite
Initial model Macrofauna Meiofauna 0.75 0.57 0.22 0.00 0.68 0.33 -0.11 -0.04 0.56 0.36 0.43 0.36
Final model Macrofauna Meiofauna 0.77 0.57 Removed 0.72 0.33 Removed 0.62 0.36 Removed
Meiofauna
Nema Roti Tardi Harp Gastro
0.53 0.47 0.50 0.60 0.43
0.47 0.43 0.49 0.56 0.42
0.76 0.60 0.77 0.89 0.87
Table S7. A priori model fit quality index R2/GOF Macrofauna Sediment Meiofauna Fungi Bacteria Litter
Initial 0.01 0.54 0.51 0.10 0.04 0.34
Final
