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.

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from Mediterranean to boreal forests with mean annual temperatures ranging from -0.1 to 14.9°C, based on published and unpublished experiments available among co-authors.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 et al. 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 “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”. Following this definition, we used relevant literature and publications to classify beetle species (Koch 1989; Bouget et al. 2008, 2019; Reinheimer & Hassler 2013; Seibold et al. 2015; Eckelt et al. 2018). Furthermore, we classified saproxylic beetles into four major feeding guilds by the feeding type of their larvae, following Seibold et al. (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 raster package (Hijmans et al. 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 (Fagus sylvatica) sessile oak (Quercus petraea) and Scots pine (Pinus sylvestris). 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_b): 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_b) according to Williamson & Wiemann (2010).

ii) Lumen-to-sapwood area ratio (A_l:A_x, %), vessel density (VD, n mm^-2), average vessel diameter (D, µm), hydraulically-weighted vessel diameter (D_h, µm) and potential hydraulic conductivity (K_p, kg m^-1 MPa^-1 s^-1) 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_l, m²), vessel density (VD, n mm^-2) 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)³) / (a² + b²))^¼ and used to calculate the hydraulically-weighted diameter (D_h, µm) according to Sperry et al. (1994) as D_h = D⁵/D⁴. Lumen-tosapwood area ratio (A_l:A_x, %) was calculated by dividing cumulative vessel lumen area (A_l, m²) by the corresponding sapwood area (A_x, m²). Potential sapwood area-specific hydraulic conductivity (Kp, kg m^-1 MPa^-1 s^-1) was calculated according to the Hagen-Poiseuille equation as K_p = (((? × D⁴) / 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_x (m²) 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 et al. (2015) and Kotowska et al. (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 Hedera helix and Ulmus glabra. 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_DM = HHV_DM - (206.0 x H_DM) (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 et al. 2005).

LHV_(DM) = 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_(DM) = 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 HNO₃ 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 et al. (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 elevatr package based on raster data of Amazon Web Services Terrain Tiles (Hollister et al. 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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Data files can be accessed using MS Excel (or Open Offive Calc) and R.