DOI: 10.5061/dryad.brv15dvch
title: Data for: Ambient and substrate energy influence decomposer diversity differentially across trophic levels

description: The species-energy hypothesis predicts increasing biodiversity with
 increasing energy in ecosystems. Proxies for energy availability are often
 grouped into ambient energy (i.e., solar radiation) and substrate energy
 (i.e., non-structural carbohydrates or nutritional content). The relative
 importance of substrate energy is thought to decrease with increasing
 trophic level from primary consumers to predators, with reciprocal effects
 of ambient energy. Yet, empirical tests are lacking. We compiled data on
 332,557 deadwood-inhabiting beetles of 901 species reared from wood of 49
 tree species across Europe. Using host-phylogeny-controlled models, we
 show that the relative importance of substrate energy versus ambient
 energy decreases with increasing trophic levels: the diversity of
 zoophagous and mycetophagous beetles was determined by ambient energy,
 while non-structural carbohydrate content in woody tissues determined that
 of xylophagous beetles. Our study thus overall supports the species-energy
 hypothesis and specifies that the relative importance of ambient
 temperature increases with increasing trophic level with opposite effects
 for substrate energy.
description: 1. Species data
 We
 compiled a dataset from experimental studies on saproxylic beetles across
 Europe, ranging from Mediterranean to boreal forests with mean annual
 temperatures ranging from -0.1 to 14.9°C, based on published and
 unpublished experiments available among co-authors. We only considered
 experimental setups that sampled long enough to include the complete adult
 activity spectrum of all saproxylic beetle species in the respective areas
 and allowed us to assign emerging species of saproxylic beetles to a
 specific deadwood object, e.g., stem-emergence traps or rearing of
 deadwood objects. Comparability of the sampling methods is ensured, as
 both trap types yield highly correlated measures of biodiversity
 (Hagge<em> et al</em>. 2019). Furthermore, we only included
 data without artificial manipulation and experimental treatments of the
 deadwood objects (e.g., bark-scratching, bark-removal, burning, or
 exposure in canopy). We classified species as obligatory saproxylic
 sensu Schmidl & Bussler (2004), who defined saproxylic beetles as
 species that reproduce and obligatorily spend most of their lifespan in
 any kind of dead or dying wood of any decay stage, including fungi living
 on wood. We expanded this specification by the definition of Alexander
 (2008), who includes “<em>species which are involved in or dependent
 on the process of fungal decay of wood, or on the products of that decay,
 and which are associated with living as well as dead trees</em>”.
 Following this definition, we used relevant literature and publications to
 classify beetle species (Koch 1989; Bouget <em>et
 al</em>. 2008, 2019; Reinheimer & Hassler 2013; Seibold
 <em>et al</em>. 2015; Eckelt<em> et al</em>.
 2018). Furthermore, we classified
 saproxylic beetles into four major feeding guilds by the feeding type of
 their larvae, following Seibold <em>et al</em>. (2015). These
 feeding types are xylophagous (feeding on wood and bark), mycetophagous
 (feeding solely on deadwood fungi or cultivating fungi within deadwood),
 zoophagous, and detritivorous. Xylo-mycetophagous beetles were classified
 according to their dominant feeding type. Detritivorous beetles were
 excluded from analyses due to their low abundance. We supplemented missing
 information on feeding-types for beetle genera with the proportion of
 feeding types within the same beetle family. 
 2. Ambient
 energy Data on climate was extracted on the object
 level using R v.4.0.4. Temperature, temperature seasonality, and solar
 radiation were extracted from the WorldClim2 database with a spatial
 resolution of ~1km² aggregated across a target temporal range of 1970–2000
 (Fick & Hijmans 2017), by using the <em>raster</em>
 package (Hijmans <em>et al</em>. 2022). The seasonality of
 solar radiation and temperature was calculated as the standard deviation
 of the monthly means. To correct temperature seasonality for influences of
 temperature, we used the residuals of a linear regression model of both
 variables. 3. Substrate Energy
 We measured 12 anatomical and
 24 chemical wood traits from 75 European tree species from branches with 2
 to 4 cm diameter. Branches were collected from natural stands in northern
 Bavaria, Germany (N 49°50´; E 10°29´) in a temperate climate with mean
 annual temperatures of 7-8°C and annual precipitation of 750-850 mm
 (BayFORKLIM 1996). Forest stands in the region consist mainly of European
 beech (<em>Fagus sylvatica</em>) sessile oak
 (<em>Quercus petraea</em>) and Scots pine (<em>Pinus
 sylvestris</em>). Branches were cut from lower parts of trees
 between October 2017 and February 2018. 
 3.1 Wood anatomical
 properties: i) Dry density and basic wood specific gravity
 (G<sub>b</sub>): For this measurement, the branch was fully
 debarked, and the phloem extracted. We saturated a section of 2 cm with
 water for 2 days in vacuum and determined the volume by water
 displacement, whereby possible medullary cavities were removed before.
 Afterwards, we dried this section, the remaining part as well as the
 phloem in a compartment drier (T 5050, Heraeus, Hanau, Germany) by 100°C
 for 10 days. Following this procedure, we weighed the section of 2 cm to
 calculate basic wood specific gravity (G<sub>b</sub>)
 according to Williamson & Wiemann (2010). 
 ii) Lumen-to-sapwood area
 ratio (A<sub>l</sub>:A<sub>x</sub>, %), vessel
 density (VD, n mm<sup>-2</sup>), average vessel diameter (D,
 µm), hydraulically-weighted vessel diameter (D<sub>h</sub>,
 µm) and potential hydraulic conductivity (K<sub>p</sub>, kg
 m<sup>-1</sup> MPa<sup>-1</sup>
 s<sup>-1</sup>) of wood: For characterizing the wood
 anatomical properties, semi-thin transverse sections were cut with a
 sliding microscope (G.S.L.1, Schenkung Dapples, Zürich, Switzerland),
 stained with safranin-alcian blue and permanently embedded on glass slides
 using Euparal (Carl Roth, Karlsruhe, Germany). Subsequently, the complete
 cross-section was digitalized at 100-times magnification using a light
 microscope equipped with an automated table and a digital camera
 (Observer.Z1, Carl Zeiss MicroImaging GmbH, Jena, Germany; Software:
 AxioVision c4.8.2, Carl Zeiss MicroImaging GmbH). Image processing was
 done with the software GIMP version 2.10.6 (GIMP Development Team, 2018)
 and ImageJ (v1.52p, http://rsb.info.nih.gov/ij) using the particle
 analysis function to estimate single and cumulative vessel lumen area
 (A<sub>l</sub>, m<sup>2</sup>), vessel density
 (VD, n mm<sup>-2</sup>) and vessel diameters (D, µm) from
 major (a) and minor (b) vessel radii according to the equation given by
 White (2005) as D = ((32 × (a × b)<sup>3</sup>) /
 (a<sup>2</sup> +
 b<sup>2</sup>))<sup>¼</sup> and used to calculate
 the hydraulically-weighted diameter (D<sub>h</sub>, µm)
 according to Sperry <em>et al</em>. (1994) as
 D<sub>h</sub> =
 D<sup>5</sup>/D<sup>4</sup>. Lumen-tosapwood area
 ratio (A<sub>l</sub>:A<sub>x</sub>, %) was
 calculated by dividing cumulative vessel lumen area
 (A<sub>l</sub>, m<sup>2</sup>) by the
 corresponding sapwood area (A<sub>x</sub>,
 m<sup>2</sup>). Potential sapwood area-specific hydraulic
 conductivity (Kp, kg m<sup>-1</sup>
 MPa<sup>-1</sup> s<sup>-1</sup>) was calculated
 according to the Hagen-Poiseuille equation as K<sub>p</sub> =
 (((? × D<sup>4</sup>) / 128 ×
 ?) × ? / Axylem, where ? is the viscosity (1.002 10-9 MPa s) and
 ? the density of water (998.2 kg m-3), both at
 20°C, and A<sub>x</sub> (m<sup>2</sup>) the
 analysed sapwood area. iii) Anatomical fractions in percent for the
 fibre fraction, the conduit fraction, the ray parenchyma fraction, the
 axial parenchyma fraction, and the fraction of remaining compartments:
 To determine the different
 cell type fractions, we used a grid-based counting method as described by
 Ziemińska <em>et al</em>. (2015) and Kotowska<em> et
 al</em>. (2020). A 1-2 mm wide sample of a cross section from the
 pith to the bark digitalized at 200x magnification was overlaid with a
 regular grid. At each crossing point of the grid, the corresponding cell
 type was determined and marked. A minimum 300 grid points for each sample
 was analyzed using the Cell Counter Plugin for ImageJ (version 1.52a).
 Anatomical definitions followed ‘IAWA list of microscopic features for
 hardwood identification’ (IAWA Committee 2007). Cell types were grouped in
 five categories based on staining, cell wall characteristics and cellular
 content: (1) Conduit fraction: vessel and tracheid lumen; (2) Fibre
 fraction: fibre and conduit wall together with fibre lumen; (3) Axial
 parenchyma fraction; (4) Ray parenchyma fraction, (5) Other fraction:
 other cell types including raisin channels or mucilage cells. A literature
 search was conducted for all studied species to consult tangential wood
 sections, the occurrence of different axial parenchyma types and the
 presence of septate fibres (Wheeler 2011). Septate fibres have been
 described for two of the studied species, namely <em>Hedera
 helix</em> and <em>Ulmus glabra</em>. For this study, we
 included septate fibres under category (2), as their relative abundance is
 difficult to quantify accurately, and the determination of their metabolic
 activity requires molecular techniques, radioactive stains or
 high-resolution microscopy. We excluded category (5) from our analyses to
 prevent violating our model assumption, as this fraction was only present
 in a small number of the analysed tree species.
 Physicochemical tree
 traits: i) Gross calorific value: The HHV (High Heating
 Value) of the raw materials was determined with approx. 1 g of DM per
 sample at a constant volume in minimum triplicate for each variant with an
 automated isoperibol calorimeter (C6000, IKA-Werke, Stauffen, Germany).
 The LHV at a constant volume can be calculated with the H content of the
 elemental analysis, according to Eq. 1.
 LHV<sub>DM</sub> =
 HHV<sub>DM</sub> - (206.0 x
 H<sub>DM</sub>) (Eq. 1) Before the experiment, 5 mL of water were added
 to the calorimeter bomb for subsequent chloride determination with the
 same repetition number as the HHV experiments. The residual solution after
 the HHV measurement was analyzed with an ion chromatograph (833 Basic IC
 plus, Methrom, Filderstadt, Germany) that was equipped with a separation
 column (Metrosep A Supp – 250/4.0, Methrom, Filderstadt, Germany). Based
 on the results for chloride, Cl contents were calculated and converted to
 mg/kg DM subsequently. Both HHV and Cl determination were executed
 according to DIN EN ISO 18125 and DIN EN ISO 16994.
 The
 LHV (Low Heating Value) can be calculated based on the elemental
 composition. For this purpose, the approximation formula according to Boie
 (Eq. 2) was used (Friedl <em>et al</em>.
 2005). LHV<sub>(DM) </sub>= 34.8 C + 93.9H
 + 10.5S +6.3N -10.8O (Eq.
 2) A
 corresponding correlation for the HHV calculation (Eq. 3) was used
 according to Friedl et al. (2005).
 HHV<sub>(DM)</sub> = 1.87C² - 144C -2820H -63.8CH +
 129N + 20147 (Eq. 3) We used the LHV as gross calorific value in our
 analyses. ii) Micronutrients: The total content of Al, Ca, Cu, Fe, K, Mg, Mn,
 Na, P, Zn, and S in the wood samples was determined at the University of
 Göttingen by ICP-OES analysis (Thermo Scientific iCAP 7000 ICP-OES, Thermo
 Fisher Scientific, Germany) after HNO<sub>3</sub> digestion of the ground
 material. iii) Non-structural carbohydrates: From dried
 debarked branch, we drilled wood chips that were subsequently ground by a
 planetary ball mill (4 ± 2 min, 29.8 m/s, MM400, Retsch GmbH, Haan,
 Germany), separately for phloem and xylem fractions, for analysis of
 starch, sucrose, fructose and glucose. Extraction and quantification were
 based on the standard protocol for wood samples from Landhäusser et al.
 (2018). Briefly, ~ 30 mg of dry plant material was extracted in 90C hot
 80% ethanol for 10 min, centrifuged at 13000 g for 1 min and the
 supernatants were analysed with High-Performance Liquid Chromatography
 with Pulsed Amperometric Detection (HPLC-PAD). From the remaining pellet,
 starch was hydrolyzed using in a first step α-amylase for the conversion of starch to soluble
 glucans and in a second step amyloglucosidase for the conversion of
 soluble glucans to glucose. Glucose hydrolysate was measured by HPLC-PAD
 as equivalent to starch, after applying the mass correction factor 0.9
 (Sullivan 1935). iv) Total organic carbon, total nitrogen content
 and C:N ratio: Total
 organic carbon (TOC) and total nitrogen were separately measured in milled
 wood samples with a CN analyzer (Vario Max CN, Elementar, Germany). A
 specific wood standard was used for determination of low N concentrations
 in wood samples. v) For the measurement of pH-value, we followed
 the procedure described by Arnstadt <em>et al</em>. (2016). We
 extracted 3g of milled wood samples (xylem and phloem) with 30 ml
 distilled water by shaking on a rotary shaker (11°C, 120 rpm, 120 min, KS
 15A, Edmund Bühler GmbH, Bodelshausen, Germany). The aqueous extracts were
 filtered through a Steriflip filter unit (nylonnet, 100 μm, Merck
 Millipore, Darmstadt, Germany) and 1600 µl were centrifuged for 10 min at
 16 000 g (centrifuge MicroStar 17, VWR, Darmstadt, Germany). The pH-value
 was measured in the aqueous extracts (FiveEasy Plus™ FP20-TRIS, electrode
 LE420, Mettler Toledo, Greifensee, Switzerland).
 Co-Variables
 To obtain precise data on the
 deadwood, we compiled potentially important co-variables. Those included:
 i) the geographic coordinates of the plot, ii) tree species, iii) object
 position (ground, elevated (without contact to soil), snag), iv) volume
 calculated as a cylinder using the length of the object for deadwood which
 was completely enclosed by the trap (rearing) and the length of the trap
 for objects which were not fully enclosed (emergence traps). Furthermore,
 we included: v) decay stage, and vi) the exposure time in years after the
 experiment was started. We used the exposure time for each deadwood object
 as a proxy for decay stage. For studies that were not set up
 experimentally, but included deadwood of different decay stages on sites,
 we predicted the exposure time by a linear model of decay stage and
 exposure. We also gathered the environmental co-variables (vii) canopy
 closure, which was measured between 0 and 100 percent with either
 different laser-, photography-, lidar-, or radar techniques or was
 visually estimated by the data contributors in 5% steps. Furthermore, we
 mined data for viii) precipitation, ix) precipitation seasonality, and x)
 elevation from the <em>elevatr</em> package based on raster
 data of Amazon Web Services Terrain Tiles (Hollister <em>et
 al</em>. 2021). The seasonality of precipitation was calculated as
 the coefficient of variance of the monthly values. We extracted the xi)
 minimum genus age for each tree species based on the phylogenetic tree
 provided by Durka & Michalski (2012) as a proxy of phylogenetic
 isolation to account for differences in the evolutionary history of the
 tree species. Data preparation
 All analyses were carried out
 using R v.4.0.4. (R Core Team 2020). Prior to statistical analyses,
 abundances of beetles were aggregated to the object level within each
 year. The phylogenetic relationship of species violates the statistical
 requirement of independent observations regarding tree physiological
 traits (Felsenstein 1985). Hence, we corrected tree species traits by
 their respective phylogenetic relationship among each other. For this, we
 used the phylogenetic tree of European flora provided by Durka and
 Michalski (2012). We decomposed each trait into its phylogenetic component
 (ancestral contribution to the trait, P-component) and the residual
 deviation (species-specific variance of the trait, S-component) using
 Lynch's comparative method (Lynch, 1991). As results of
 this process are subject to random variation, we replicated this step 999
 times and used the mean values of the species-specific variance of each
 trait in our analyses. Tree species for which we were not
 able to measure traits (12% of our tree species) were complemented by the
 mean of each trait from all tree species within the same genus.
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description: Data files can be accessed using MS Excel (or Open Offive Calc)
 and R.