Biology and Fertility of Soils

, Volume 53, Issue 5, pp 491–499 | Cite as

Stimulation of methane oxidation by CH4-emitting rose chafer larvae in well-aerated grassland soil

  • Claudia Kammann
  • Stefan Ratering
  • Carolyn-Monika Görres
  • Cécile Guillet
  • Christoph Müller
Short Communication
  • 202 Downloads

Abstract

In this study, the impact of rose chafer (Cetonia aurata L.) larvae on net and gross methane (CH4) fluxes in soil from an old permanent grassland site (Giessen, Germany) was investigated. Previous studies at this site suggested the existence of Scarabaeidae larvae-induced “CH4-emitting hot spots” within the soil profile which may subsequently lead to increased CH4 oxidation. The net (soil + larvae) and gross (soil and larvae separated) CH4 fluxes were studied in a 3-month laboratory incubation. Addition of larvae changed the soil from a net sink (−330 ± 11 ng CH4 kg−1 h−1) to a net source (637 ± 205 ng CH4 kg−1 h−1). Supply of plant litter to the soil + larvae incubation jars tended to increase CH4 emissions which was not significant due to large variability. After 11–13 weeks of incubation, the net soil CH4 oxidation was significantly stimulated by 13–21% in the treatments containing larvae when these were taken out. Analysis of archaeal 16S rRNA genes revealed that the majority of the obtained clones were closely related to uncultured methanogens from guts of insects and other animals. Other sequences were relative to cultivated species of Methanobrevibacter, Methanoculleus, and Methanosarcina. Hence, Scarabaeidae larvae in soils (i) may represent an underestimated source of CH4 emissions in aerobic upland soils, (ii) may stimulate gross CH4 consumption in their direct soil environment, and, thus, (iii) contribute to the spatial heterogeneity often observed in the field with closed-chamber measurements. Long-term CH4-flux balances may be wrongly assessed when “exceptional” net CH4 flux rates (due to larvae hot spots) are excluded from data sets.

Keywords

Scarabaeidae larvae Cetonia aurata Methane production Stimulation of CH4 consumption Methanogenic Archaea Grassland soil 

Introduction

About 75% of the greenhouse gas methane (CH4) is produced via methanogenic Archaea (methanogens) in anoxic environments, including methanogenesis in animal guts (Ciais et al. 2013). Methanogens harboring Arthropoda, in particular wood- and humus-feeding termites are estimated to contribute approximately 1–10% to the global natural CH4 sources (Brune 2010; Ciais et al. 2013; Rasmussen and Khalil 1983) through the digestion of organic material, e.g., feeding on organic litter (e.g., larvae of dung beetle (Scarabaeus sacer L.) and rhinoceros beetle (Oryctes nasicornis L.)), or foraging on living or dead roots (e.g., May/June beetle larvae (Melolontha spp. and Amphimallon spp.)). The microbial diversity of the gut methanogens of termites (Cubitermes spp.) was studied in detail by Schmitt-Wagner and Brune (1999) and Paul et al. (2012). Intestinal archaeal CH4 production in other arthropods is also known but less well studied although Breznak (1975) first described CH4 production in a cockroach. A range of different arthropods including Scarabaeidae larvae have been monitored for CH4 emission (Hackstein and Stumm 1994; Hackstein et al. 2006; Lemke et al. 2003; Šustr et al. 2014) and for the phylogenetic diversity of the intestinal methanogens (Egert et al. 2003, 2005). These studies focused on the contribution of arthropods to the global CH4 budget, the physiology of the gastro-intestinal CH4 production or the diversity of the methanogens, and did not investigate in situ impacts of CH4-producing soil macro-fauna on soil CH4 dynamics. Impact of soil macrofauna in general on the CH4 production in soils in-situ were shown in sediments like flooded rice soil (Kajan and Frenzel 1999) or lakes (Gentzel et al. 2012; Leal et al. 2007). The impact of earthworms in landfill cover soil was described by Hery et al. (2007), in pasture soil by Bradley et al. (2012), or in a cattle impacted soil by Koubová et al. (2012). However, the ecological implications of the CH4-emitting presence of Scarabaeidae larvae in soils for microbial CH4 oxidation (methanotrophy), or knowledge on the net CH4 balance between production and consumption has poorly been investigated.

We found evidence that Scarabaeidae larvae may act as CH4-producing hot-spots, as observed in soil cores taken from a grassland ecosystem (Kammann et al. 2009). Besides that, soil air sampling in the same grassland site showed highly variable CH4 dynamics suggesting that significantly increased CH4 concentration hot spots can be present in aerobic grassland soil, and that such a (former) hot spot may quickly turn into a spot with significantly increased CH4 oxidation activity, as evidenced by significantly lowered CH4 concentrations. However, final proof that these hot spots might have been Scarab beetle larvae would have demanded excavation and hence destruction of the site. This was not possible because the site is part of the (now 18 years running) Giessen-FACE experiment (FACE = free air CO2 enrichment experiment; see e.g., Keidel et al. 2015).

The aim of the current study was to quantify the interactions of CH4-producing Scarabaeidae larvae on soil CH4 dynamics. Cetonia aurata L. larvae were chosen for this study because they are easy to obtain (from compost piles where the females lay their eggs) without site-destroying excavations, and because they can feed on a broad variety of organic substrates. The following questions were addressed: How large is the impact of one single CH4-producing larva on the net CH4 flux of a (comparably large) amount of soil, can it turn a net CH4 sink soil into a source? Does the CH4 production of the larvae vary over time? Does it increase with the application of easily degradable organic substances when the larvae feed on it instead of feeding on the soil only? And most important, is the larval CH4 production able to stimulate methanotrophic CH4 consumption in the grassland soil? And, to confirm assumptions derived from the literature, and made here: is the production of CH4 associated with the larvae indeed due to methanogenic Archaea in their guts?

Materials and methods

Incubation experiment with Cetonia aurata larvae

Field-fresh grassland top soil (0–15 cm depth; Ah horizon) was sampled in October 2008 at the Giessen-FACE site (Jäger et al. 2003; Kammann et al. 2001b, 2009). For seasonal variations of methanotrophic community composition and soil CH4 dynamics at the site, see Shrestha et al. (2012). Soil cores were directly transported to the laboratory. The top 2 cm of root and rhizome material was cut and removed, but other organic material was left in the soil as a feeding substrate for Cetonia aurata larvae. The soil of the sampled cores was hand-homogenized and filled into ten 2.5-l jars (Weck®, Germany; 1 kg of fresh soil, equal to 662 ± 11 g dry weight).

Cetonia aurata larvae were obtained in late-September 2008 from a compost (deposit) place where they were abundant in the deposited, mostly composted piles of saw dust, straw and horse manure; end-larval stages of equal size were selected. Before the incubation started, larvae were kept in a soil-compost mixture (with their original compost substrate) for 3 weeks, to acclimatize them to the new substrate.

Measurement of the CH4 flux rates and incubation study

Methane uptake rates were determined once before and repeatedly after one larva was added to each of six randomly chosen jars (with larvae, +L), while four jars did not receive larvae (control, −L). More +L than −L jars were prepared to compensate for possible deaths of larvae; however, this did not occur. Every 2–7 days, jars were weighed and re-wetted to the original moisture (0.51 g H2O g−1 dry soil); they lost on average 1–2 g of water over 1 week. The mean incubation temperature was 22.2 ± 1.5 °C.

For CH4 flux measurements, jars were closed with gas-tight glass lids fitted with a rubber septum, and 50-ml samples were taken by a disposable plastic syringe four times during a 3–4-h incubation period. The air was replaced with laboratory air which was analyzed separately to correct for the CH4-concentration dilution effect of room air. At each sampling, two closed, empty jars were sampled to check if other CH4 sources were present (e.g., rubber seals) which was not the case. Gas samples were analyzed within 24 h on a gas chromatograph (GC-14B, Shimadzu, Japan) equipped with a flame ionization detector (FID) for CH4, an electron capture detector (ECD) for N2O and CO2, and an automated sampling unit for syringes (Loftfield et al. 1997). Greenhouse gas fluxes were calculated by linear regression (all R2 were 0.98 or better) and expressed as CH4 flux per soil dry weight or per larval living weight.

Between measurements, jars were covered with perforated sealing film (Parafilm® M, Bemis Flexible Packaging, USA) to enable gas diffusion while minimizing soil water losses. Headspace atmosphere was occasionally sampled with 120-mm-long needles from under the parafilm to ensure that gases did not built up under the sealing film. On four dates (Fig. 2 b), gross CH4 fluxes in the +L treatment were assessed by removing the larvae gently with a spoon. The soil in the −L control jars was treated in the same manner to avoid biases due to the stirring with the spoon, and net CH4 fluxes were determined as described above. On three of the four dates (13., 22., and 27. Jan., see Fig. 2 b), gross CH4 production from the larvae was quantified by incubating them in 0.5-l jars on wetted paper, with a wet-paper only control to ensure that the jar setup did not contribute CH4 emissions (which was never the case).

To test the influence of the diet on net CH4 emissions, a plant litter mix was added two times during the incubation (arrows in Fig. 1). On 10 December 2008, 7 g of decaying leaf and fruit litter (mostly Rosa canina L. litter) was cut and added to the middle of each jar in both treatments (−L and +L), and covered with a soil layer of 1 cm thickness. On 20 January 2009, a 5-g mix of freshly cut peel of carrot, parsley root, and cauliflower was added to the jars in the same manner (all additions: fresh weight).
Fig. 1

Mean CH4 flux during incubation of grassland soil with one Cetonia aurata larva per jar (n = 6 jars) or without larvae (n = 4 jars), ± standard deviation. First measurement (29 October 2008): without larvae in the +L treatment. Symbols in the top line: significant differences between treatments by t test. Arrows mark the first and second feed material addition date

Note that one larva (jar 3) was pupated from 12. January 2009 onwards (resulting in near-zero CH4 fluxes), other larvae followed, as indicated by the increasingly larger error bars, until all larvae had pupated at the termination of the experiment; arrows indicate litter application for feeding.

In this study, the “gross consumption and production” is defined as the CH4 uptake of the pure soil (gross consumption, no larvae), or CH4 emissions of the larvae (gross production), respectively, although we are aware that a small contribution of CH4 emissions from anaerobic soil microsites cannot completely be ruled out under the chosen incubation conditions. However, we consider this source to have been negligible, compared to the much larger CH4 emissions by the larvae when quantified alone without soil (not shown).

Detection of methanogenic archaea DNA in scarab beetle guts

DNA was isolated from the gut-system of 3 larvae (larvae length 2–3 cm). The gut was prepared from frozen larvae (−80 °C) sterilized outside by 3 time washing in 70% ETOH (−20 °C) and DNA- and RNA-free PCR-water (Invitrogen, Carlbad, USA). Thawing of the larvae was done under a clean bench in sterile petri dishes. Guts were prepared from the larvae with a sterile disposal scalpel und sterilized tweezers. From the gut, the DNA was isolated using the QiaAmpStool kit (Qiagen, Hilden, Germany) according the protocol of Grosskopf et al. (1998), Kampmann et al. (2012) and Stahl and Amann (1991)and amplified with the primer pair A112f (GCT CAG TAA CAC GTG G) and A934B (GTG CTC CCC CGC CAA TTC CT) using the protocol of Thummes et al. (2007). Briefly, the reaction mixture (25 μl) contained 2.5 μl 1× PCR buffer, 3 ml MgCl2 (25 mM), 2.5 ml dNTPs (2 mM), 0.5 μl of each primer (25 mM, Eurofins Genomics, Ebersberg, Germany), 16 μl RNAse- and DNAse-free water (Invitrogen, Carlsbad, CA), 0.08 μl BSA (20 mg ml−1), 0.1 μl Taq polymerase (5 U ml−1) (all MBI Fermentas, St. Leon Rot), and 1 μl of diluted DNA extract. PCR (MyCyclerTM, BioRad, München, Germany) was started with an initial denaturation step at 94 °C for 4 min, followed by 20 cycles with denaturation for 45 s at 94 °C, annealing for 45 s beginning at 65 °C, following every cycle a decrease of 0.5 °C up to 55 °C and extension for 2 min at 72 °C. The last 10 cycles were performed with denaturation at 94 °C for 45 s, annealing at 55 °C for 45 s and extension for 2 min at 72 °C. The PCR products were cloned in Escherichia coli and sequenced as described by Kampmann et al. (2012). From the clone library, 20 clones were chosen randomly. Quality checks and cutting of archaeal 16S RNA sequences were done with software package MEGA 5 (Tamura et al. 2011). One clone sequence was too short and was excluded from the analysis. Alignment was done with the online aligner tool SINA of the SILVA database (Pruesse et al. 2012) and chimera check with uchime (Edgar et al. 2011). The aligned sequences and the next uncultivated relatives retrieved from the SILVA database were merged with the pre-aligned 16S rRNA gene online database LTPs119 (Yarza et al. 2008). Construction of phylogenetic tree was performed with the software package ARB 6.01 (Ludwig et al. 2004) using the maximum likelihood algorithm (PHYML) for the full length sequences and ARB parsimony tool for adding the shorter clone sequences (<800 bp) to the maximum likelihood tree. Similarity values were calculated by using the neighbor-joining algorithm (PHYLIP, Felsenstein 1989) of the ARB software package. All sequences were deposited in the NCBI GenBank database under the following accession numbers: KU661854-KU661872.

Statistical tests

Methane flux differences between both treatments were tested for each measurement day by two-sided t-tests assuming (i) heterogeneous variances in −L versus +L comparisons, and (ii) homogenous variances when CH4 uptake was compared while the larvae were removed. For testing the effect of larval presence on net CH4 fluxes over the entire study, mean CH4 flux rates for both treatments were averaged for each jar and compared via t-test. For averaging, all measured fluxes shown in Fig. 1 were used, but only to the point when the first larvae started to pupate (at the end of the study); CH4 production was observed to cease quickly when larvae pupated (see results). We also linearly interpolated CH4 fluxes over time to calculate averages for +L and −L treatments, and to evaluate the impact of feeding materials. From the thereby linearly interpolated CH4 flux rates, we averaged 7 days before and after feeding occasions for each +L jar (n = 6 or n = 5, after pupation of one larva), and used a paired t test (before–after feeding). Mixed linear models could not be used due to the large variability caused by larval presence. Results are considered significantly different at P < 0.05. All results are reported as means ± standard deviation (stdev.) if not mentioned otherwise. Calculations and statistical tests were performed with Microsoft Excel Version 2003 (tests for linear mixed ANOVA models with R). Figures were created using SigmaPlot (Version 13.0.2).

Results

Incubation study

Before addition of the Cetonia larvae, net CH4 consumption rates between the (later) larvae (+L) and the −L control jars were not significantly different (overall mean −423.6 ± 19.7 ng CH4 kg−1 dry soil h−1; P = 0.735; Fig. 1). However, addition of the pre-conditioned larvae to the grassland soil caused a sudden significant switch from CH4 consumption to CH4 emission (paired t test +L treatment, P = 0.003; Fig. 1). Initially, jars with and without larvae showed CH4 flux rates of 1471 ± 894 (+L) and −423 ± 39 (−L) ng CH4 kg−1 h−1, respectively. During the following days, the CH4 consumption rates in the −L soils remained unchanged, compared to the initial values of the same jars (P = 0.913, paired t test). In contrast, CH4 emissions in the +L treatment declined to near-zero (Fig. 1), but still balanced gross CH4 consumption. CH4 emissions in the +L treatment increased again before the first feeding (litter application). Both litter applications visibly increased the net CH4 emissions in the jars with larvae compared to the time period before litter application (Fig. 2 a) The first litter application also reduced the standard deviation to some extent (+L treatment, first feeding, Fig. 1). However, averaged 7-day periods before and after feeding were not significantly different due to large variabilities between +L jars (Fig. 2 a). No CH4 emissions were observed in the −L jars that had received the same amount of litter. By mid-January and at the end of the study, no remainder of the first and second litter application was visible in the +L treatments, while litter remainders were still visible in the −L treatments.
Fig. 2

a Mean net CH4 emissions ± standard deviation (+Larva—treatment only) averaged over 7 (interpolated) days prior to and after the first and second feeding date, respectively (before feed. 1, after feed. 1: n = 6 jars; before feed. 2, after feed. 2: n = 5 jars). Due to large jar-to-jar variability, the tendencies of increased net CH4 emissions after feeding (Fig. 1) were not significant (paired t tests, P < 0.05). b CH4 uptake rates in +Larvae soil with the larvae temporarily removed for measuring gross CH4 uptake, compared to the −Larvae jars. Percentage values above bars: CH4 uptake in +L jars compared to −L jars. P values below bars: t test results

Over the 3 months of incubation, the overall mean net CH4 fluxes were 686 ± 197 and −330 ± 11 ng CH4 kg−1 h−1 in +L and −L treatments, respectively. In total, net CH4 fluxes in the +L jars ranged from −439 to 2616 ng CH4 kg−1 h−1 with large temporal variations. Every time a larva pupated, its CH4 production declined to near-zero. Visually, it was observed that the larvae became less active in the soil and when taken out. At the end of the study, high CH4 emissions (after the second feeding) declined gradually with more and more pupating larvae, until net CH4 consumption was measured (Fig. 1). Throughout the incubation, no significant differences in respiratory CO2 fluxes or N2O emissions due to the presence of the larvae could be quantified since the soil background production rates were much larger (not shown).

On four dates larvae were removed from the jars (8, 13, 22, and 27 January 2009) to determine gross CH4 uptake of both, +L and −L treatments. The pure +L soil without larvae consumed significantly higher amounts of CH4 (Fig. 2b). The stimulation of the CH4 uptake rates in the +L soil declined over time (Fig. 2 b).

We measured pure-larval CH4 emissions (i.e., gross CH4 production if the larvae are assumed to be the only CH4 source) on the 13th January. Production rates, based on the soil weight of the jars the larvae normally inhabited (but not during this measurement) were 1277.7 ± 164.3 ng CH4 kg−1 h−1 when the pupated larva was excluded (CH4 production pupated larva: 9.6 ng CH4 kg−1 h−1). The casts (3–4 per animal) left behind after the animals were put back into the soil were incubated for another 24 h, but CH4 flux rates were below the detection limit.

Phylogenetic analysis of the archaeal community of the gut

Archaeal 16S rRNA gene sequences retrieved from the extraction of the DNA of the gut of the Cetonia aurata larvae belonged to four clusters within the Archaea (Fig. 3). Clone sequences of L26, L6, L10, and L24 (KU661865, KU661870, KU661855, KU661863) were closely related to methanogens from guts of Termitidae and Scarabaeidae which were also close relatives to Methanobrevibacter arboriphilus (Fig. 3). Closest-relative sequences (97.6%) were sequences of methanogens from the hindgut of a Pachnoda ephippiata larva (AJ576172, AJ576153). A large part of the clones (L27, L15, L22, L12, L21, L13, L3, L30, L5, L8, L9, L2) and (KU661866, KU661858, KU661862, KU661856, KU661861, KU661857, KU661867, KU661868, KU661869, KU661871, KU661872, KU661860) belonged to a cluster of the Archaea without a cultivated member inside of the order Methanomassiliicoccales (Fig. 3). All close relatives of the clones were from sequences retrieved from gut systems of Scarabaidae or Blaberidae. Closest relatives (94.8–96.5%) were again sequences from the hindgut of a Pachnoda ephippiata larva (AJ576170, AJ576178). The sequences of clone L16 and L25 (KU661859, KU661864) both belonged to a cluster of sequences relative to Methonoculleus species from compost, digester environment, or humic soil (Fig. 3). Closest relative sequences (97.7–99.6%) were sequences from the food soil of Pachnoda ephippiata larva (AJ576194, AJ576197) and sequences from anaerobic digester sludge (GQ328817). Clone sequences L1 (KU661854) were relative to Methanosarcina species (Fig. 3) and closest relatives were from cattle manure compost (AB541801), from thermophilic solid waste bioreactor (DQ887953), from leachate sediment (HQ141852), or from the food soil of Pachnoda ephippiata larva (AJ576201).
Fig. 3

Phylogenetic tree of the archael 16S rRNA clone gene sequences derived from partial gene sequences from DNA of the gut of the Cetonia aurata larvae and 16S rRNA gene sequences of the next relative sequences derived from the SILVA databases. The tree was calculated based on the maximum likelihood algorithm (PHYML) with the nearly full length sequences (>1300 bp) and the partial sequences (< 800 bp) were added using the ARB-parsimony tool. Sulfolobus acidocaldarius (D14876) was used as outgroup. Bootstrap values lower than 80% are not shown. Bar, 0.1 substitutions per nucleotide position

Discussion

Scarabaeidae larvae may act as CH4-producing hot-spots (Kammann et al. 2009) in soils through their indigenous methanogenic archaea and therefore influence the methane flux rates. However, not only the production can be influenced but also the methane oxidation, first of all by the burrowing activity (soil aeration). Nothing is known about the quantitative impact of these larvae on the methane budget. Therefore, a laboratory study was conducted to quantify the interactions of CH4 producing Scarabaeidae larvae on soil CH4 dynamics. Cetonia aurata larvae were chosen as model organisms. From other Scarabaeidae larvae, e.g., Pachnoda ephippiata (Egert et al. 2003), it is already known that the gut of the larvae contain different groups of methanogenic archaea. Also in the gut of the Cetonia aurata larvae 16S rRNA sequences of different members of the methanogenenic archaea could be found (Fig. 3) indicating active methane emissions from the gut of the larvae. Whereas the sequences of the clones L1, L16, and L25 were most closely related to sequences found in compost, waste bioreactor, or food soil of the Pachnoda ephippiata larvae suggesting that these methanogens were ingested with the food soil, the other sequences were exclusive most closely related to sequences found in the gut of the Pachnoda ephippiata larvae (Fig. 3). Both beetle species belongs to the same subfamily the Cetoniinae of the Scarabaeidae and the larvae have similar food requirements. If the similar methanogenic archaea sequences are caused by the close relationship of the species, or by their similar food requirements is still open and an interesting question for future research. The majority of the clone sequences belonged to the gastro-intestinal tract clade (Söllinger et al. 2016) inside of the order Methanomassimiliicoccales (Fig. 3). From molecular and cultivation studies, it is known that members of the order Methanomassimiliicoccales are methanogens that can only use hydrogen and methyl-compounds as substrate (Borrel et al. 2014; Paul et al. 2012). Also, the sequences of the clones L26, L6, and L10 belonged to the Methanobacteriales, with the next close-related isolated species being Methanobrevibacter arboriphilus. All isolated members of the order (e.g., the order Methanomassiliicoccales) are methanogens, indicating that these archaea from the guts of Cetonia aurata can indeed produce methane within the animals’ hind guts.

Therefore, the significant change from CH4 consumption to CH4 emission with addition of larvae to the soil was indeed due to the production of CH4 by larval gut microbiome. It has the potential to significantly impact the net CH4 exchange between soils and the atmosphere. Taken together with the molecular biological results, this confirms earlier studies with other Scarabaeidae larvae species (Hackstein and Stumm 1994; Egert et al. 2003), and it underlines our assumption that larvae found in situ may be significant CH4 sources in soils (Kammann et al. 2009). CH4 emissions were higher when the larvae came fresh from the compost-soil mixture in which they were pre-incubated, i.e., were humus-fed, and CH4 emissions were lower when larvae had to feed on soil and (small amounts of) root litter. Moreover, larval CH4 emissions increased in response to feeding events. Variations in the CH4 emissions depending on the diet were also found in other insects like termites (Brauman et al. 2001) or cockroaches (Gijzen et al. 1994; Kane and Breznak 1991) where high-fiber diets caused the highest CH4 emissions. The initial decline in larval CH4 emissions probably reflects an adaptation of larvae to a new soil substrate with less soil organic matter.

The relationship between the ability for CH4 production and phylogenetic position within the animal kingdom provides guidance for identifying CH4 producers within the soil macro-fauna (Hackstein et al. 1996). For instance, Lemke et al. (2003) measured CH4 production rates of several Pachnoda ephippiata instars between 0.13 (± 0.04) and 0.36 (±0.11) μmol g−1 h−1. However, our results imply that the ability of soil macro-fauna for CH4 production may be underestimated or even missed when the animals are supplied with unsuitable substrate, when they are starved, or when they adapt to a new substrate.

The temporary removal of larvae from +L jars for gross CH4 consumption measurements revealed that CH4 oxidation had significantly increased in the +L than compared to the L treatment over time, respectively, which is reported here for the first time to our knowledge, with a relatively low CH4-concentraton increase provided by the larvae. Since the jars ware always aerated and not closed, the jar headspace CH4 concentrations in the +L treatment were only moderately increased in their CH4 concentration, compared to headspace CH4 concentrations that are often used in microbiological studies for the enrichment and culture of methanotrophic bacteria (e.g., Dunfield et al. 1999, >200 μl l−1 CH4; Mohanty et al. 2016, 5 ml pure CH4 into 130 ml vessels with 10 g, i.e., about 4.1% CH4). The highest headspace CH4 concentration recorded in +L jars was 2.74 ± 0.55, versus 1.78 ± 0.06 μl l−1 in −L jars, respectively. (However, CH4 concentrations in the soil air around the larvae might have been higher than the concentrations measured in the headspace.) Initially, larvae were actively foraging and moving in the soil volume so that they likely caused good soil mixture and aeration. The significant increase in the overall CH4 consumption on the soil that normally hosted the larvae (but measured when they were removed) indicates increasing abundance and/or activity of methanotrophic bacteria in response to the presence of the CH4-producing larvae. There may be a correlation to the activity of the larvae: The magnitude of the stimulation seemed to decline over time, as the larvae became more passive and started to pupate. The stimulation of CH4 oxidation through insect larvae, until now only observed by Chironomidae larvae (Kajan and Frenzel 1999), could probably have been related to (i) the increased supply of the CH4 substrate, (ii) a better soil aeration by the foraging and digging activity of the larvae (i.e., bioturbation, as observed with earthworms, Kernecker et al. 2015), or (iii) a better, more constant soil moisture supply due to metabolic water that the larvae transpire while foraging the soil, since methanotrophy can be sensitive to soil drying (Kammann et al. 2001a). However, there was no difference in the soil moisture at the end of the experiment, thus the last hypothesis was not supported by data. In addition, all jars (+L and −L) were treated identically each time the larvae were removed. Therefore, the most likely mechanism of stimulation of the gross CH4 consumption was the increase in the CH4 substrate for the enzymes involved in the CH4 consumption.

The observation of stimulated soil CH4 consumption in the presence of larvae is consistent with earlier observations at the site where Amphimallon solstitiale (June beetle) larvae had been found in overall CH4-emitting oxic soil cores (Kammann et al. 2009). Furthermore, in the former study, soil air samplers in 5 cm depth usually showed sub-atmospheric CH4 concentrations, with one super-ambient (~4 ppm) exception over several weeks. In the high CH4 spot, CH4 concentrations changed from above-ambient suddenly to strongly sub-ambient, with the CH4 concentration suddenly significantly lower than in all other samplers at the same depth (>2σ of this depths’ mean; Kammann et al. 2009). This pattern suggested that the CH4 uptake around this particular sampler may have been stimulated by a CH4 emitting source beforehand. One possible option for a CH4 hot spot may have been the initial presence of a larva that later moved away, leaving behind a spot of higher CH4 consumption (Kammann et al. 2009). Mohanty et al. (2016) also observed stimulated CH4 oxidation and increased methanotroph abundances with deliberate CH4 feeding under oxic conditions (although at higher CH4 concentrations).

It is evident that the CH4 production of one single larva has the potential to alter the net CH4 flux balance of a fairly large amount of soil. For example, under the experimental settings used in this study, about 4-kg soil (dry soil equivalent, with a mean CH4 consumption of the −L control soil) would have been necessary to compensate the average CH4 emission of a single larva (1–1.5 g live weight). In a back-of-the-envelope calculation, assuming a bulk density of 1 g cm−3 and 15 cm depth, and the larva-only CH4 production rate reported above, about 5 larvae per m2 would reduce the average net CH4 consumption rate of 27 μg CH4 m−2 h−1 by 50% (mean CH4 uptake as determined earlier at the grassland site, Kammann et al. 2001a). Moreover, feeding the larvae with fresh, easily degradable substrate may further increase the CH4 emissions; this will likely also be the case for European June beetle larvae feeding on root material at the grassland site, i.e., the CH4 emissions measured here with Cetonia (having to live on root debris and mineral soil mostly instead of compost, its preferred substrate) may have been at the lower end of the spectrum.

Hence, in agricultural, natural or forest ecosystems, the presence and activity of these larvae may have much larger impact on net CH4 exchange rates between soils and the atmosphere (i.e., on the annual net CH4 flux balance) than previously thought. The animal impact may be hard to detect. In the field, CH4 fluxes are usually measured with the closed-chamber method. It is likely that CH4 production by Scarabaeidae larvae may contribute to the spatial heterogeneity often observed with chamber CH4 flux measurements in upland soils. Due to the spatial heterogeneity of larval occurrence in soils, CH4 emission findings may remain undetected particularly with small chamber sizes. Moreover, it is not unlikely that the headspace CH4 concentration increase in a chamber with soil underneath with one or more larval hot spots in it will show a discontinuous or maybe “saturation-curve” CH4 concentration increase, because CH4 back diffusion and also CH4 consumption rates will increase when the CH4 concentration in the headspace rises. Such non-linear concentration increases in chambers, resulting in low R2 values in the quality check, will likely be excluded by researchers from their datasets, so that larval CH4 flux contributions may be underestimated.

In any case, field conditions will differ from the laboratory conditions in this study (i.e., bulk density and hence gas diffusivity), and in situ larval occurrences, activities, and contribution to the CH4 balance remain elusive. Relationships between larval species, body size, developmental stage, substrate quality and CH4 emission rate, and relationships between the two simultaneously occurring processes “gross CH4 production” and “gross CH4 oxidation” and the resulting net CH4 fluxes need to be studied in more detail, in particular in the field. This will likely demand the development of new methodical approaches.

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Borrel G, Parisot N, Harris HMB, Peyretaillade E, Gaci N, Tottey W, Bardot O, Raymann K, Gribaldo S, Peyret P, O'Toole PW, Brugere J-F (2014) Comparative genomics highlights the unique biology of Methanomassiliicoccales, a Thermoplasmatales-related seventh order of methanogenic archaea that encodes pyrrolysine. BMC Genomics 15:679. doi:10.1186/1471-2164-15-679 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Bradley RL, Chronáková A, Elhottová D, Simek M (2012) Interactions between land-use history and earthworms control gross rates of soil methane production in an overwintering pasture. Soil Biol Biochem 53:64–71. doi:10.1016/j.soilbio.2012.04.025 CrossRefGoogle Scholar
  3. Brauman A, Doré J, Eggleton P, Bignell D, Breznak JA, Kane MD (2001) Molecular phylogenetic profiling of prokaryotic communities in guts of termites with different feeding habits. FEMS Microbiol Ecol 35:27–36. doi:10.1111/j.1574-6941.2001.tb00785.x CrossRefPubMedGoogle Scholar
  4. Breznak JA (1975) Symbiotic relationships between termites and their intestinal microbiota. Sym Soc Exp Biol 29:559–580Google Scholar
  5. Brune A (2010) Methanogenesis in the digestive tracts of insects. In: Timmis KN (ed) Handbook of hydrocarbon and lipid microbiology. Springer, Berlin Heidelberg, pp 707–728CrossRefGoogle Scholar
  6. Ciais P, Sabine C, Bala G, Bopp L, Brovkin V, Canadell J, Chhabra A, De Fries R, Galloway J, Heimann M, Jones C, Le Quéré C, Myneni RB, Piao S, Thornton P (2013) Carbon and other biogeochemical cycles. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge and New York, pp 465–570Google Scholar
  7. Dunfield PF, Liesack W, Henckel T, Knowles R, Conrad R (1999) High-affinity methane oxidation by a soil enrichment culture containing a type II methanotroph. Appl Environ Microb 65:1009–1014Google Scholar
  8. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200. doi:10.1093/bioinformatics/btr38 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Egert M, Wagner B, Lemke T, Brune A, Friedrich MW (2003) Microbial community structure in midgut and hindgut of the humus-feeding larva of Pachnoda ephippiata (Coleoptera: Scarabaeidae). Appl Environ Microb 69:6659–6668. doi:10.1128/AEM.69.11.6659-6668.2003 CrossRefGoogle Scholar
  10. Egert M, Stingl U, Dyhrberg Bruun L, Pommerenke B, Brune A, Friedrich MW (2005) Structure and topology of microbial communities in the major gut compartments of Melolontha melolontha larvae (Coleoptera: Scarabaeidae). Appl Environ Microb 71:4556–4566. doi:10.1128/AEM.71.8.4556-4566.2005 CrossRefGoogle Scholar
  11. Felsenstein J (1989) PHYLIP—phylogeny inference package (version 3.2). Cladistics 5:164–166Google Scholar
  12. Gentzel T, Hershey AE, Rublee PA, Whalen SC (2012) Net sediment production of methane, distribution of methanogens and methane-oxidizing bacteria, and utilization of methane-derived carbon in an arctic lake. Inland Waters 2:77–88. doi:10.5268/IW-2.2.416 CrossRefGoogle Scholar
  13. Gijzen HJ, Van Der Drift C, Barugahare M, Op Den Camp HJM (1994) Effect of host diet and hindgut microbial composition on cellulolytic activity in the hindgut of the American cockroach, Periplaneta americana. Appl Environ Microb 60:1822–1826Google Scholar
  14. Großkopf R, Janssen PH, Liesack W (1998) Diversity and structure of the methanogenic community in anoxic rice paddy soil microcosms as examined by cultivation and direct 16S rRNA gene sequence retrieval. Appl Environ Microb 64:960–969Google Scholar
  15. Hackstein JHP, Stumm CK (1994) Methane production in terrestrial arthropods. Proc Natl Acad Sci U S A 91:5441–5445CrossRefPubMedPubMedCentralGoogle Scholar
  16. Hackstein JHP, Langer P, Rosenberg J (1996) Genetic and evolutionary constraints for the symbiosis between animals and methanogenic bacteria. Environ Monit and Assess 42:39–56CrossRefGoogle Scholar
  17. Hackstein JHP, van Alen TA, Rosenberg J (2006) Methane production by terrestrial arthropods. In: König H, Varma A (eds) Soil biology—manual for soil analysis. Springer, Berlin Heidelberg, pp 155–180Google Scholar
  18. Hery M, Singer AC, Kumaresan D, Bodrossy L, Stralis-Pavese N, Prosser JI, Thompson IP, Murrell JC (2007) Effect of earthworms on the community structure of active methanotrophic bacteria in a landfill cover soil. ISME J 2:92–104. doi:10.1038/ismej.2007.66 CrossRefPubMedGoogle Scholar
  19. Jäger H-J, Schmidt SW, Kammann C, Grünhage L, Müller C, Hanewald K (2003) The University of Giessen Free-Air Carbon Dioxide Enrichment study: description of the experimental site and of a new enrichment system. J Appl Bot 77:117–127Google Scholar
  20. Kajan R, Frenzel P (1999) The effect of chironomid larvae on production, oxidation and fluxes of methane in a flooded rice soil. FEMS Microbiol Ecol 28:121–129. doi:10.1016/S0168-6496(98)00097-X CrossRefGoogle Scholar
  21. Kammann C, Grünhage L, Jäger H-J, Wachinger G (2001a) Methane fluxes from differentially managed grassland study plots: the important role of CH4 oxidation in grassland with a high potential for CH4 production. Environ Pollut 115:261–273. doi:10.1016/S0269-7491(01)00103-8 CrossRefPubMedGoogle Scholar
  22. Kammann C, Grünhage L, Jäger H-J (2001b) A new sampling technique to monitor concentrations of CH4, N2O and CO2 in air at well-defined depths in soils with varied water potential. Eur J Soil Sci 52:297–303. doi:10.1046/j.1365-2389.2001.00380.x CrossRefGoogle Scholar
  23. Kammann C, Hepp S, Lenhart K, Müller C (2009) Stimulation of methane consumption by endogenous CH4 production in aerobic grassland soil. Soil Biol Biochem 41:622–629. doi:10.1016/j.soilbio.2008.12.025 CrossRefGoogle Scholar
  24. Kampmann K, Ratering S, Kramer I, Schmidt M, Zerr W, Schnell S (2012) Unexpected stability of Bacteroidetes and Firmicutes communities in laboratory biogas reactors fed with different defined substrates. Appl Environ Microb 78:2106–2119. doi:10.1128/AEM.06394-11 CrossRefGoogle Scholar
  25. Kane MD, Breznak JA (1991) Effect of host diet on production of organic acids and methane by cockroach gut bacteria. Appl Environ Microb 57:2628–2634Google Scholar
  26. Keidel L, Kammann C, Grünhage L, Moser G, Müller C (2015) Positive feedback of elevated CO2 on soil respiration rate in late autumn and winter. Biogeosciences 12:1257–1269. doi:10.5194/bg-12-1257-2015 CrossRefGoogle Scholar
  27. Kernecker M, Whalen JK, Bradleyet RL (2015) Endogeic earthworms lower net methane production in saturated riparian soils. Biol Fertil Soils 51:271–274. doi:10.1007/s00374-014-0965-0 CrossRefGoogle Scholar
  28. Koubová A, Goberna M, Šimek M, Chroňáková A, Pižl V, Insam H, Elhottová D (2012) Effects of the earthworm Eisenia andrei on methanogens in a cattle-impacted soil: a microcosm study. Eur J Soil Biol 48:32–40. doi:10.1016/j.ejsobi.2011.09.007 CrossRefGoogle Scholar
  29. Leal JJF, dos Santos Furtado AL, de Assis EF, Bozelli RL, Figueiredo-Barros M (2007) The role of Campsurus notatus (Ephemeroptera: Polymitarcytidae) bioturbation and sediment quality on potential gas fluxes in a tropical lake. Hydrobiologia 586:143–154. doi:10.1007/s10750-006-0570-9 CrossRefGoogle Scholar
  30. Lemke T, Stingl U, Egert M, Friedrich MW, Brune A (2003) Physicochemical conditions and microbial activities in the highly alkaline gut of the humus-feeding larva of Pachnoda ephippiata (Coleoptera: Scarabaeidae). Appl Environ Microb 69:6650–6658. doi:10.1128/AEM.69.11.6650-6658.2003 CrossRefGoogle Scholar
  31. Loftfield N, Flessa H, Augustin J, Beese F (1997) Automated gas chromatographic system for rapid analysis of the atmospheric trace gases methane, carbon dioxide, and nitrous oxide. J Environ Qual 26:560–564. doi:10.2134/jeq1997.00472425002600020030x CrossRefGoogle Scholar
  32. Ludwig W, Strunk O, Westram R, Richter L, Meier H et al (2004) ARB: a software environment for sequence data. Nucleic Acids Res 32:1363–1371. doi:10.1093/nar/gkh293 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Mohanty SR, Tiwari S, Dubey G, Ahirwar U, Kollah B (2016) How methane feedback response influence redox processes in a tropical vertisol. Biol Fertil Soils 52:479–490. doi:10.1007/s00374-016-1090-z CrossRefGoogle Scholar
  34. Paul K, Nonoh JO, Mikulski L, Brune A (2012) “Methanoplasmatales,” thermoplasmatales-related archaea in termite guts and other environments, are the seventh order of methanogens. Appl Environ Microb 78:8245–8253. doi:10.1128/AEM.02193-12 CrossRefGoogle Scholar
  35. Pruesse E, Peplies J, Glöckner FO (2012) SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28:1823–1829. doi:10.1093/bioinformatics/bts252 CrossRefPubMedPubMedCentralGoogle Scholar
  36. Rasmussen RA, Khalil MAK (1983) Global production of methane by termites. Nature 301:700–702CrossRefGoogle Scholar
  37. Schmitt-Wagner D, Brune A (1999) Hydrogen profiles and localization of methanogenic activities in the highly compartmentalized hindgut of soil-feeding higher termites (Cubitermes spp.) Appl Environ Microb 65:4490–4496Google Scholar
  38. Shrestha PM, Kammann C, Lenhart K, Dam B, Liesack W (2012) Linking activity, composition and seasonal dynamics of atmospheric methane oxidizers in a meadow soil. ISME J 6:1115–1126. doi:10.1038/ismej.2011.179 CrossRefPubMedGoogle Scholar
  39. Söllinger A, Schwab C, Weinmaier T, Loy A, Tveit AT, Schleper C, Urich T, King G (2016) Phylogenetic and genomic analysis of Methanomassiliicoccales in wetlands and animal intestinal tracts reveals clade-specific habitat preferences. FEMS Microbiol Ecol 92. doi:10.1093/femsec/fiv149
  40. Stahl DA, Amann A (1991) Development and application of nucleic acid probes in bacterial systematics. In: Stackebrandt E, Goodfellow M (eds) Nucleic acid techniques in bacterial systematics. Wiley, Chichester, pp 205–248Google Scholar
  41. Šustr V, Chroňáková A, Semanová S, Tajovský K, Šimek M, Oliveira PL (2014) Methane production and methanogenic archaea in the digestive tracts of millipedes (Diplopoda). PLoS One 9:e102659. doi:10.1371/journal.pone.0102659 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28:2731–2739. doi:10.1093/molbev/msr121 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Thummes K, Schäfer J, Kämpfer P, Jäckel U (2007) Thermophilic methanogenic archaea in compost material: occurrence, persistence and possible mechanisms for their distribution to other environments. Syst Appl Microbiol 30:634–643. doi:10.1016/j.syapm.2007.08.001 CrossRefPubMedGoogle Scholar
  44. Yarza P, Richter M, Peplies J, Euzeby J, Amann R, Schleifer K-H, Ludwig W, Glöckner FO, Rosselló-Móra R (2008) The all-species living tree project: a 16S rRNA-based phylogenetic tree of all sequenced type strains. Syst Appl Microbiol 31:241–250. doi:10.1016/j.syapm.2008.07.001 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Soil Science and Plant Nutrition, WG Climate Change Research for Special CropsHochschule Geisenheim UniversityGeisenheimGermany
  2. 2.Department of Plant EcologyUniversity GießenGiessenGermany
  3. 3.Department of Applied MicrobiologyUniversity GießenGiessenGermany
  4. 4.School of Biology and Environmental Science and Earth Science InstituteUniversity College DublinDublin 4Ireland

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