Effects of selection for early dispersal on the ambrosia beetle Xyleborinus saxesenii and its fungal symbionts

Effects of selection for early dispersal on the ambrosia beetle Xyleborinus saxesenii and its fungal symbionts | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 10 April 2025 V1 Latest version Share on Effects of selection for early dispersal on the ambrosia beetle Xyleborinus saxesenii and its fungal symbionts Authors : Antoine Melet 0000-0002-5500-4274 [email protected] and Peter Biedermann 0000-0003-4234-5659 Authors Info & Affiliations https://doi.org/10.22541/au.174428385.56091333/v1 286 views 151 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Overlapping generations is a defining characteristic of advanced social life. In cooperative breeding societies, temporary groups of mature offspring are formed that assist in the rearing of additional brood before the offspring disperse and reproduce independently. It is hypothesized that the delayed dispersal period of helpers will determine the number of siblings that can be reared, thus resulting in an indirect fitness gain. The objective of this study, was to investigate the effect of artificial selection for early dispersal of mature offspring on the life history, behaviour and fungal symbionts in the ambrosia beetle Xyleborinus saxesenii. Two lineages were bred in the laboratory for five generations. In one group, dispersing females were selected at random to initiate the next generation, while in the other group, only early dispersers were selected. A number of life-history traits exhibited a pronounced response in the initial generation, subsequently recuperating to levels approximating those observed at the outset of the experiment. Furthermore, significant differences were observed in the fungal communities from the fourth generation onwards. The results suggest that X. saxesenii has limited potential to respond to this selection pressure, potentially due to low genetic variability. Effects of selection for early dispersal on the ambrosia beetle Xyleborinus saxesenii and its fungal symbionts Melet Antoine * 1 and Biedermann Peter H.W. * 1 1 Chair of Forest Entomology and Protection, Faculty of Environment and Natural Resources, Albert-Ludwigs-Universität, Freiburg, Germany Author contributions AM and PHWB designed the study. AM performed the experiments and collected the data. AM analysed the data. AM wrote a first draft of the manuscript, which was reviewed by PHWB Corresponding authors: [email protected] ; [email protected] Funding This work was funded by the German Research Foundation (DFG) (Emmy Noether grant number BI 1956/1-1 to P.H.W.B.). Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be a potential conflict of interest. Data, availability The data and scripts that support the findings of this study are openly available in our GitHub Repository at https://github.com/AntMelet/Artificial-selection-on-X.-saxesenii Introduction A wide variety of animal species live in groups, ranging from the relatively simple herds of bison to the highly advanced hives of honeybees. Advanced sociality is defined by a number of features, including a high reproductive bias, the frequent occurrence of alloparental care and a high degree of relatedness among helpers, reproducers and offspring (Wilson, 1971). The most derived social lifestyles are cooperative breeding and eusociality. In these societies, offspring typically do not disperse immediately after maturity. Instead, they take over cooperative tasks in the natal nest, either for a limited period (cooperative breeding) or permanently (eusociality). It is therefore not surprising that alloparental care has evolved alongside philopatry in such societies (Hochberg et al., 2008; Le Galliard et al., 2005; Mullon et al., 2018). This theoretical assumption is also supported by correlative evidence in a wide range of social organisms ranging from microbes to vertebrates (Choe & Crespi, 1997; Fisher et al., 2013; Hamilton, 1964; Hatchwell, 2009; Koenig & Dickinson, 2016; Rainey & Rainey, 2003; Taborsky, 1994). Despite this, there is currently little experimental evidence for the co-evolution of philopatry and alloparental care. Xyleborinus saxesenii Razteburg (Curculionidae: Scolytinae: Xyleborini) is a Eurasian ambrosia beetle that lives in cooperatively breeding societies with variable duration of female offspring philopatry and alloparental care (Biedermann & Taborsky, 2011). Adult female offspring typically delay dispersal after fertilisation by their brothers. While staying in their natal nest, daughters assist their mother with brood care, fungus cultivation and the tunnel enlargement (Biedermann & Taborsky, 2011; Peer & Taborsky, 2007). In Xyleborus affinis Eichhoff , a related species with a similar social lifestyle, a long philopatric period has been shown to be associated with a direct fitness cost later in life (Biedermann et al., 2011). Both species can be reared in an artificial medium (Biedermann et al., 2009), providing a unique opportunity to select for short philopatric periods in adult offspring, and thus test for correlated effects on alloparental care. Ambrosia beetles are typically associated with mutualistic fungi, which they cooperatively cultivate in monocultures within tunnel systems and which serve as food for both larvae and adults (Biedermann & Vega 2020). Xyleborinus saxesenii cultivates two food fungi, Dryadomyces sulphureus and Raffaelea canadensis (Biedermann et al., 2013; Diehl et al., 2022; Francke-Grosmann, 1975), which develop in succession. Other fungal species are also present in X. saxesenii nests , although they are not as closely associated with the beetle (Biedermann et al., 2013). However, the accumulation of less beneficial fungi within nests over time also affects the fitness of late-dispersing offspring, who vertically transmit all these fungi to newly established nests (Diehl et al., 2022). A strong correlation has been found between highly productive nests, their durability, long philopatric periods and alloparental care by adult daughters (Biedermann & Taborsky 2011, Nuotclá et al. 2021). Therefore, it has been hypothesised that the management of beneficial fungal communities is an important factor for nest durability and consequently for the philopatry in ambrosia beetles (Biedermann & Rohlfs 2017). Artificial selection experiments are a valuable research tool for studying evolutionary processes. Phenotypic responses can be measured as a direct response to selective pressure (Brakefield, 2003; Conner, 2003). Under natural conditions, adaptation is typically too slow to be observed within the time frame of a scientific project or even a lifetime. By applying strong selection pressure under controlled conditions, researchers can detect adaptive change within a few generations (Conner, 2003). Combined with the relatively short generation time of certain insects, it is possible to observe adaptation over a period of just a few months. The use of artificial selection experiments allows for direct experimental testing of hypotheses that can be used to complement the results of correlative studies (Lewis & Morran, 2022). Such experiments are also used to study communities of two or more species (Blouin et al., 2015; Swenson et al., 2000). The aim of this study was to examine the relationship between short philopatric periods in adult female offspring and the expression of other life history traits in the subsequent generation of offspring, with a particular focus on alloparental care and nest founding success. It was hypothesised that beetles selected for early dispersal would show a tendency to disperse earlier and earlier over generations, produce less durable nests with fewer offspring, and show a reduction in alloparental care. We also sought to determine the effects of offspring philopatry has on fungal communities. It was hypothesised that these communities would be less beneficial in nests of females with long philopatric periods and that they would contain higher relative abundances of R. canadensis than D. sulphureus fungal cultivars (Diehl et al., 2022). Material and methods Model species Xyleborinus saxesenii females emerge from their native nest already mated and proceed to initiate new nests by excavating into suitable pieces of wood. They tunnel deep into the xylem, and inoculate the wood with mutualistic fungi that they have transported from their native nest in mycetangia (specialized organs for transporting fungal spores) and guts (Batra, 1966; Francke-Grosmann, 1975; Mayers et al., 2022). Once the fungi are established, the foundress begins to lay eggs. The mutualistic fungi serve as the food source for the adults and to a large extent also for the larvae (i.e., larvae feed on fungus-infested xylem; De Fine Licht & Biedermann, 2012). After pupation, adults mate with their siblings and exhibit a philopatric period, during which they show cooperative behaviours (Biedermann, Peer, et al., 2011). Males only rarely leave the nest to mate with unrelated individuals, so inbreeding is likely to be very high. This is a common phenomenon in Xyleborini and is tolerated by the species, as deleterious mutations are fully expressed in the haploid males and thus eliminated from the population (Biedermann, 2010). Artificial rearing Beetles were captured in a forest near Würzbürg, Germany, and brought to the lab for rearing in artificial medium. All captured females were briefly immersed in 70% ethanol, and rinsed in distilled water to remove the ethanol. The primary fungal mutualists are not adversely affected by this sterilization protocol (Biedermann et al. 2009) as they are protected within the mycetangia. The beetles were then briefly dried on sterile tissue paper and immediately transferred individually to rearing tubes. The resulting nests were designated as “nests of wild-caught females”. The rearing tubes were transparent plastic tubes, filled to two-thirds capacity with artificial rearing media composed of beech sawdust, agar, and additional nutrients (see standard media in Biedermann et al. 2009). The tubes were sealed with “drosophila plugs”, which are gas-permeable sponge stoppers made of high-density foam. All nests were individually labeled with unique identifiers and their date of founding was recorded. Nests were observed three times per week from the time of foundation until all beetles had emerged. All nests in this experiment were maintained at a constant temperature of 25°C, 70% humidity, and darkness. Mature and sib-mated females left the nest after a variable philopatric period. Because the tubes were closed, females attempting to disperse could not exit the tube, and were thus trapped between the artificial medium and the plug. We collected these females every second day, surface sterilized them as described above, and put them individually in fresh rearing tubes to initiate a new generation. Pilot studies indicated that periods of diapause are essential for the successful long-term rearing of X. saxesenii . Therefore, during the second generation (F2), the breeding temperature was lowered to 8°C for ten weeks to simulate the overwintering period. Egg-laying ceased during this period. Only larvae and adults were observed in the diapausing galleries, along with dead pupae that were rapidly cannibalized at the end of diapause. No dispersal was observed during diapause. Directional artificial selection All daughter females that dispersed from two initial nests of wild-caught females were collected and used to initiate the F0 generation of the experiment. From F0, 5 nests were randomly assigned to the treatment lineage and 5 nests were randomly assigned to the control lineage (Fig. 1). In nests assigned to the treatment lineage, we always selected the first seven dispersing females as foundresses for the next generation, while in the control lineage we randomly selected seven dispersing females. This process was repeated for five generations, resulting in the groups ‘treatment F1’ to ‘treatment F5’ and ‘control F1’ to ‘control F5’ (Fig. 1). Using this method, up to 15% of the nests successfully developed and contributed to the next generation. Nests that failed were excluded from our analysis. Nest failure typically occurs in the first few days after initiation, as the mutualistic fungi fail to establish. For each female that initiated a new nest, the nest of origin was recorded, allowing us to establish the ‘genealogy’ of each nest. All the nests were traced back to two nests of wild-caught females (W5 and W13, see Fig. 1 and Supplementary tables 1 and 2). This lineage origin was included as a random factor in subsequent analyses to account for possible effects due to the nest of origin. In our analyses, we examined (i) the effect of the selection at each step, by comparing successive generations within each lineage (control F1 vs. F2, F2 vs. F3, F3 vs. F4, F4 vs. F5 and treatment F1 vs. F2, F2 vs. F3, F3 vs. F4, F4 vs. F5), (ii) the ultimate effect of the selection, by comparing the first and last generations within each lineage (control F1 vs. F5 and treatment F1 vs. F5), and (iii) the divergence between the two lineages, by comparing the two groups within each generation (control F1 vs. treatment F1, control F2 vs. treatment F2, control F3 vs. treatment F3, control F4 vs. treatment F4, control F5 vs. treatment F5). Special attention was paid to the first generation in order to identify changes that occur early in the selection process. Nest development The number of eggs, larvae, pupae, adult females in the nest and dispersing females was recorded for each observation. The three larval instars were not distinguished. A nest was considered to have reached the end of its lifespan when no adults or dispersers were observed for seven consecutive days. Last observations, that did not include a record of adults or dispersers, were excluded from the dataset to avoid artificially inflating the lifespan of nests. Two key variables were extracted from the census data: (i) total nest lifespan, defined as the number of days between the start day and the day the last disperser was observed, and (ii) timing of first dispersal, defined as the number of days between the start day and the day the first disperser was observed. Developmental times were compared using multi-level generalized linear models, followed by multiple a comparison using Tukey contrasts. Dispersal patterns were compared using pairwise survival analysis, which was based on a Cox proportional hazards model, and followed by a multiple comparison of means using Tukey contrasts. Productivity The total number of dispersers collected from a nest was used as the productivity of that nest. Productivities were compared using a multi-level generalized linear model, followed by a multiple comparison using Tukey contrasts. The effect of the timing of first dispersal on the productivity was analysed using a linear model. Behavioural observations After each count of a nest, a scan observation was made, to record the behaviour of each larva and adult encountered, following the method described by Biedermann & Taborsky (2011). The behaviour of larvae and adults was recorded separately. The duration of the scan observations was kept to a minimum in order to obtain a snapshot of the behaviour of the entire nest. No behavioural data were recorded between the date of 11.07.2022 and 18.07.2022, and between 08.08.2022 and 15.08.2022. From the behavioural recordings, two categories of behaviours were analysed: (i) activity, defined as all the behaviours except resting behaviour, and (ii) social behaviour, defined as the behaviours directed towards nest excavation and hygiene (i.e. digging, balling, grooming and cannibalism for the larvae and digging, shuffling, grooming and cannibalism for the adults). Activity and social behaviour of larvae and adult females were analysed separately using multi-level generalized linear models with binomial errors, followed by a multiple comparison using Tukey contrasts. Fungal community sampling and DNA extraction Beginning with the second generation, nests were randomly selected from each group. The first seven dispersing females from the selected nests were collected and frozen at -20° C until DNA extraction and fungal community sequencing. Some nests did not produce dispersers, resulting in the following final sample sizes for the different groups: control F2 (N = 8), F3 (N = 8), F4 (N = 8), F5 (N = 8), and treatment F2 (N = 7), F3 (N = 6), F4 (N = 7), F5 (N = 7). The first generation was not included due to a lack of available nests. Nests selected for the fungal community analysis were excluded from the life history and behavioural analyses. To extract fungal DNA, the seven females that dispersed from a nest were pooled into a single sample. The samples were subjected to mechanical grinding at 2700 rpm for 20 minutes in a ZR BashingBead Lysis Tube 2.0 mm with 750 µL of lysis solution (Zymo Research, Germany) on a Vortex Genie 2 (Scientific Industries). After centrifugation at 18,000 g for 1 min, the supernatant was collected and transferred to a ZR BashingBead Lysis Tube 0.1 and 0.5 mm. Thereafter, 300 µL of lysis solution was added, and the mixture was vortexed at 2700 rpm for 20 minutes on a Vortex Genie 2. DNA was then extracted using ZymoBIOMICS DNA Miniprep kits (Zymo Research, Germany) according to the manufacturer’s instructions. The isolated DNA was stored at -20° C until further processing. Amplicon sequencing of fungal communities The amplification of fungal DNA was achieved by using LSU (28S) rRNA primers, since the primers typically used for the analysis of fungal communities, targeting the ITS region, proved ineffective for the amplification of Ophiostomataceae species, including Raffaelea and Dryadomyces (Kostovcik et al., 2015). The dual-index primers of LIC15R and nu-LSU-355-30 are described in the electronic supplementary material of the first study in which they were used (Nuotclà et al., 2021). The PCR conditions were as follows: an initial denaturation at 98°C for 3 minutes, 35 cycles of denaturation at 98°C for 10 seconds, annealing at 54°C for 30 seconds and elongation at 72°C for 20 seconds; followed by a final extension at 72°C for 10 minutes. After amplification, DNA samples were sequenced by StarSeq (Mainz, Germany) using the described primers on the Illumina MiSeq v3 2 x 300-bp platform according with the Illumina protocol. To ensure the quality of our fungal community results, we included negative and positive controls along with our samples. The negative controls were prepared and sterilized as previously described, but lacked the presence of the beetles. Two negative controls were prepared, alongside the samples from the control F5 and treatment F5 groups. In addition, the sequencing company utilised two samples of pure water, which were devoid of any DNA, and which underwent all library preparation steps. The positive control was a mock community that had been prepared for another experiment and sent for sequencing within the same batch. The mock community consisted of equal amounts of Dryadomyces sulphureus, Raffaelea canadensis, Beauveria bassiana and the yeasts Pichia sp. and Candida sp. DNA was extracted as previously described. The positive control provided by the company consisted of a defined amount of DNA from eight bacterial species ( Pseudomonas aeruginosa, Escherichia coli, Salmonella enterica, Lactobacillus fermentum, Enterococcus faecalis, Staphylococcus aureus, Listeria monocytogenes, Bacillus subtilis ) and two yeast species ( Saccharomyces cerevisiae, Cryptococcus neoformans ) (ZymoBIOMICS Microbial community DNA standard, Zymo Research, Germany). The mock community and the positive control provided by the company were replicated twice, resulting in a total of four positive controls. Bioinformatics processing The raw, demultiplexed reads were processed using Usearch v11 (Edgar, 2010). The forward and reverse reads were merged using the -fastq_mergepairs command, with a minimum of 200 base pairs for the merged sequence and a maximum of 20 mismatches in the alignment as a preliminary quality filtering step. The primers were truncated from the reads using the -fastx_truncate command, and the overall quality was then assessed using the -fastq_filter command, with an expected total error threshold of 1. Unique sequences were identified using the -fastx_uniques command, and the singletons were excluded using the - ­sortbysize command with a minimum size of 2. The amplicon reads were denoised using the ­- unoise3 command. This command does not cluster similar sequences; rather, it identifies and corrects reads with sequencing errors and removes chimeras, resulting in amplicon sequence variants (ASVs), a higher resolution analog of the traditional OTU (Edgar, 2016b). The ASVs were taxonomically classified in two steps using the -usearch_global command. The command is based on the USEARCH algorithm, which searches for high identity hits to a database sequence (Edgar, 2010). In the first step, a database of LSU sequences from stock cultures of fungi isolated from X. saxesenii was used. The database contained eighteen reference sequences from twelve fungal species (Diehl et al., 2022). The identity threshold was set at 97% due to the fact that the database includes sequences of known fungal symbionts of X. saxesenii. The unclassified ASVs were used as the input for the second step, which used a custom reference database constructed from NCBI data using BCdatabaser (Keller et al., 2020). The second database contained 82,250 sequences (Diehl et al., 2022). To ensure accurate classification, the identity threshold was set at 99%. In cases where the taxonomic outputs differed between the two steps, the output from the first step was retained. The remaining unclassified ASVs were then used as input for the final step, using the -sintax command with a sintax cutoff of 0.8. The SINTAX algorithm is comparable to a naive Bayesian classifier algorithm but it does not require training (Edgar, 2016a). Statistical analysis of metabarcode data To improve the quality of our dataset, we applied a contaminant removal method according to the R package ‘decontam’, taking into account the negative controls. This process reduces the complexity of the microbiome data in downstream analyses while preserving its integrity (Davis et al., 2018). The positive and negative controls were visualized but excluded from the sample set. After decontamination and the exclusion of the controls, the samples were rarefied, accounting for unequal numbers of reads between samples. The sample with the fewest reads had 9,838 reads; this threshold was used to generate rarefied samples for subsequent analysis. The alpha diversity of the fungal communities was compared between the different groups by comparing the observed ASVs richness and Shannon’s diversity index using multi-level generalized linear models, followed by a multiple comparison using Tukey contrasts. The beta diversity was analysed by calculating dissimilarity matrices using the Bray-Curtis method, taking into account both the presence/absence and relative abundances of ASVs. A pairwise PERMANOVA was used to analyze the matrices. Composition barplots were constructed for visualization, aggregated to the genus level and faceted by group. Statistical analysis All statistical tests were performed using R version 4.0.2 (R Core Team 2020), with the RStudio interface version 1.3.1073. The additional packages car (Fox & Weisberg, 2019), decontam (Davis et al., 2018) , lme4 (Bates et al., 2015), multcomp (Hothorn et al., 2008), pairwiseAdonis (Martinez Arbizu, 2020), phyloseq (McMurdie & Holmes, 2013), rstatix (Kassambara, 2023), survival (Therneau, Terry M. & Grambsch, Patrica M., 2000) and vegan (Oksanen et al., 2022) were used. Results Although the goal of directional artificial selection was to accelerate female dispersal over five generations, no significant difference in dispersal time was observed between the two lineages (control F5 vs. treatment F5, GLM, z = 0.842, p = 1). Thus, the artificial selection pressure did not produce the expected result. However, the data were duly reported and analysed to determine the reasons for the failure of artificial selection to produce the expected results. Lifespan of nests In the control lineage, nest lifespan was 57.43 days in F1, longer than in F5 (control F1 vs. F5, GLM, z = -4.21, p 0.05). In the treatment lineage, nest lifespan was 47 days in F1, not significantly shorter than in F5 (treatment F1 vs. F5, GLM, z = 0.07, p = 1), and no two successive generations were found to be significantly different (GLMs, all p > 0.05). The two lineages were not significantly different in any of the generations (GLMs, all p > 0.05) (Fig. 2). Time before the start of dispersal In the control lineage, the duration before the start of dispersal was 42.14 days in F1, not significantly longer than in F5 (GLM, z = -1.68, p = 0.78). In the control lineage, no two successive generations were found to be significantly different (GLMs, all p > 0.05). In the treatment lineage, the duration before the start of dispersal was 39.43 days in F1, not significantly longer than in F5 (GLM, z = -0.24, p = 1), and no two successive generations were found to be significantly different (GLMs, all p > 0.05). The two lineages were not significantly different in any of the generations (GLMs, all p > 0.05) (Fig. 2). Timing of dispersal In the control lineage, dispersal occurred later in F1 than in F5 (control F1 vs. F5, Cox model, z = 4.264, p 0.05). In the treatment lineage, no two successive generations were found to be significantly different (Cox models, all p > 0.05). A comparison of the two lineages showed that dispersal occurred later in control F1 than in treatment F1 (control F1 vs. treatment F1, Cox model, z = 3.577, p = 0.012). The two lineages were not significantly different in any of the other generations (Cox models, all p > 0.05) (Fig. 3). Productivity In the control lineage, productivity was 30.86 dispersing females per nest in F1, higher than in F5 (GLM, z = -15.27, p = < 0.001), and higher than in F2 (GLM, z = -9.38, p < 0.001). Productivity was lower in F2 than in F3 (GLM, z = 5.04, p < 0.001), and higher in F4 than in F5 (GLM, z = -9, p < 0.001). In the control lineage, productivity was not significantly different between F3 and F4 (GLM, p = 1). In the treatment lineage, productivity was 12.14 dispersing females per nest in F1, not significantly lower than in F5 (GLM, z = 0.29, p = 1). Productivity was lower in F2 than in F3 (GLM, z = 4.49, p < 0.001) and higher in F4 than in F5 (GLM, z = -8.17, p 0.05). Productivity was higher in control F1 than in treatment F1 (GLM, z = -5.97, p < 0.001), lower in control F4 than in treatment F4 (GLM, z = 3.24, p = 0.034) and lower in control F5 than in treatment F5 (GLM, z = 9.03, p 0.05) (Fig. 4). In all groups, productivity was strongly correlated with the timing of dispersal (linear model, z = 5.55, p < 0.001). Behavioural observations In the control lineage, the proportion of larval social behaviours was higher in F1 than in F5 (GLM, z = -3.42, p = 0.18). In the control lineage, no two successive generations were found to be significantly different (GLMs, all p > 0.05). In the treatment lineage, the proportion of larval social behaviours was not significantly higher in F1 than in F5 (GLM, z = -1.49, p = 0.87). In the treatment lineage, no two successive generations were found to be significantly different (GLMs, all p > 0.05). The two lineages were not significantly different in any of the generations (GLMs, all p > 0.05) (Fig. 5A). Similarly, in the control lineage, the proportion of adult social behaviours was not significantly lower in F1 than in F5 (GLM, z = -2.45, p = 0.26). In the control lineage, no two successive generations were found to be significantly different (GLMs, all p > 0.05). In the treatment lineage, the proportion of adult social behaviours was not significantly higher in F1 than in F5 (GLM, z = -0.21, p = 1). In the treatment lineage, no two successive generations were found to be significantly different (GLMs, all p > 0.05). The two lineages were not significantly different in any of the generations (GLMs, all p > 0.05) (Fig. 5B). In the control lineage, the proportion of larval active behaviours was higher in F1 than in F5 (GLM, z = 4.74, p < 0.001), and higher in F1 than in F2 (GLM, z = 4.24, p 0.05). In the treatment lineage, the proportion of larval active behaviours was not higher in F1 than in F5 (GLM, z = 2.66, p = 0.16). In the treatment lineage, the proportion of larval active behaviours was lower in F3 than in F4 (GLM, z = -3.12, p = 0.045), and higher in F4 than in F5 (GLM, z = 5.75, p 0.05). The proportion of larval active behaviours was lower in control F4 than in treatment F4 (GLM, z = -3.53, p = 0.012), and the two lineages were not significantly different in any of the other generations (GLMs, all p > 0.05) (Fig. 6A). In the control lineage, the proportion of adult active behaviours was higher in F1 than in F5 (GLM, z = 3.95, p < 0.001), and higher in F1 than in F2 (GLM, z = 3.8, p 0.05). In the treatment lineage, the proportion of adult active behaviours was not significantly lower in F1 than in F5 (GLM, z = 0.03, p = 1). In the treatment lineage, no two successive generations were found to be significantly different (GLMs, all p > 0.05). The two lineages were not significantly different in any of the generations (GLMs, all p > 0.05) (Fig. 6B). Fungal community The sequencing run of the 65 samples yielded 1203,328 raw reads, with a minimum of 145 reads per sample and a maximum of 34,470. The average number of reads per sample was 18,802. After alignment and bioinformatic processing, the resulting dataset contained 59 samples from eight treatment groups, with a total of 1128,177 raw reads. The number of reads per sample ranged from 9,838 to 28,168 (mean = 19,121.64). A total of 70 ASV were identified at the genus level. The water controls yielded a relatively low number of fungal reads, with a mean of less than 218 reads per sample. In contrast, the artificial medium controls yielded a higher number of reads, reaching levels comparable to those observed in the biological samples. The ‘decontam’ package was used to improve the quality of the dataset and identified a Sordariomycetes and a Lipomyces ASV as contaminants. Accumulation curves of the final dataset, excluding contaminants and control samples, showed that samples approached saturation after approximately 17,000 reads. Visualisation of the relative abundance of the genus showed that the groups appeared to be homogeneous (Fig. 7). The Shannon diversity index and the observed richness were found to be equal across all generations for each lineage, as well as between lineages for each generation (GLMs, p > 0.05). (Fig. 8). In the control lineage, there was a difference in the beta diversity between F2 and F5 (control F2 vs. F5, PERMANOVA, F = 4.04, p = 0.035), but no two successive generations were found to be significantly different (PERMANOVA, all p > 0.05). In the treatment lineage, there was no difference in beta diversity across generations (PERMANOVA, all p > 0.05). A comparison of the two lineages showed that beta diversities differed in F4 and F5 (control F4 vs. treatment F4, PERMANOVA, F = 3.11, p = 0.003; control F5 vs. treatment F5, PERMANOVA, F = 3.94, p = 0.02). Discussion The selection experiment was based on pilot results showing variability in the timing of dispersal and correlated effects on the success rate, productivity and social behaviour of X. saxesenii (Biedermann, 2012). It was hypothesised that this variability could be the result of either inter-individual plasticity or to heritability over generations. The results of the study do not indicate a heritable element; the observed variability in the philopatric period can be explained by inter-individual plasticity alone. After five generations of selection, no significant responses were observed in the timing of dispersal or the correlated variables, such as social behaviour, productivity and nest longevity between the control and the treatment lineages (Fig. 2, 3, 4 and 5). The proportion of social behaviour exhibited by larvae and adults was found to be similar across all groups (Fig. 5), indicating that this aspect of X. saxesenii behaviour is highly conserved. The inbreeding habits of X. saxesenii may result in a lack of variability for an evolutionary response to occur, especially if few nests are used as a starting point for the selection process. Previous research has demonstrated that inbreeding and genetic bottlenecks lead to a reduction in adaptive potential (Auld & Relyea, 2010; Dierks et al., 2012; Swindell & Bouzat, 2005) and a general decrease in phenotypic variance (Fowler & Whitlock, 1999; Reed et al., 2003). Interestingly, the majority of the observed changes in the control lineage occurred between the first and second generations. These findings are consistent with other studies that have demonstrated the significant impact of strong selective pressure on the first generations (Irwin & Carter, 2014; Tejeda et al., 2016). This suggests that the genetic variability present at the onset of the selection experiment, if present, was depleted after the first generation of selection. At the end of the experiment, the beetles in the control lineage dispersed earlier than in the first generation (Fig. 2 and 3), demonstrating the variability in this trait. However, this variability may have been selected against by our laboratory conditions. These conditions are favorable for X. saxesenii , because the breeding system is maintained at a constant temperature and the artificial medium is rich in nutrients and free of competing microbes. The benign conditions of the laboratory breeding may have reduced the pressure for cooperative management of beneficial microbes and brood care, resulting in earlier dispersal. In addition, nest excavation and expansion is much easier in the artificial sawdust media (held together by agar) than in solid wood, which may also have selected against cooperation. Overall, these assumptions are consistent with the predictions of theoretical models that have shown a correlation between harsh environments and increased levels of cooperation (Emlen, 1982). Notably, a change in the fungal community between beetle lineages was observed over time, with significant differences in beta diversity appearing from the fourth generation onwards (Fig. 7). The fungal community associated with X. saxesenii is complex, and microbial management is a critical factor in the evolution of cooperative behaviour (Biedermann & Rohlfs, 2017; Nuotclà et al., 2019). Mutualist fungi are conserved and transmitted across generations in the mycetangia (Biedermann et al., 2013; Diehl et al., 2022; Francke-Grosmann, 1975), and are significantly associated with the productivity of X. saxesenii (Biedermann et al., 2013; Nuotclà et al., 2021). The results show that the fungal communities can diverge without significant effects on X. saxesenii , suggesting that the observed difference in beta diversity is not due to the nutritional fungi and important antagonists, but rather to functionally unimportant fungi that do not exert a fitness difference in the beetles. The divergence of fungal communities may be due to either selection, with females dispersing at different times and transmitting different fungal communities, or to random drift, due to the repeated bottlenecks exposed to fungal communities in each generation, as fungal spores are taken up and selectively transmitted in female mycetangia (Mayers et al., 2022). In several ambrosia beetle species, adult females delay dispersal for variable periods of time and not all reproductive adults leave their natal nest at the same time (Biedermann et al., 2011; Nuotclà et al., 2021; Peer & Taborsky, 2007). Our results confirm that a reduction in the delay before the first dispersal of adult females from X. saxesenii nests is associated with a reduction in nest longevity and productivity (Figures 2 and 4). This provides experimental evidence that delayed dispersal is an important mechanism in the evolution of the social system of X. saxesenii . Furthermore, the results show that the first generations are the most responsive to the selection protocol. X. saxesenii is not a conventional model organism and is interesting because of its potential for laboratory rearing, manipulation and selection; there is still much to learn about this system, which offers a unique perspective on the evolution of sociality. To gain insight into the evolutionary potential of this species, future research should focus on investigating the genetic variability that exists within and between populations of X. saxesenii . Of course, in this experiment, we cannot rule out the possibility that selection on the philopatric period may eventually lead to lineages that differ in their social behaviour. We obviously did not have enough genetic variability to start with (and our original plan was not to start with such a small population). However, we did see some changes between F1 and F2, suggesting that there was some genetic variability in the population that may have been lost later. As this may have been a result of rearing in the laboratory, it may be worthwhile to adjust the rearing system so that it does not select for shorter philopatric periods over time (e.g. by adding less nutrients) . Future studies should further investigate the relationship between productivity and philopatric period of X. saxesenii . Auld, J. R., & Relyea, R. A. (2010). Inbreeding depression in adaptive plasticity under predation risk in a freshwater snail. Biology Letters , 6 (2), 222–224. https://doi.org/10.1098/rsbl.2009.0726Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software , 67 (1), 1–48. https://doi.org/10.18637/jss.v067.i01Batra, L. R. (1966). Ambrosia Fungi: Extent of Specificity to Ambrosia Beetles. Science , 153 (3732), 193–195. https://doi.org/10.1126/science.153.3732.193Biedermann, P. H., & Rohlfs, M. (2017). Evolutionary feedbacks between insect sociality and microbial management. Current Opinion in Insect Science , 22 , 92–100. https://doi.org/10.1016/j.cois.2017.06.003Biedermann, P. H. W. (2010). Observations on sex ratio and behavior of males in Xyleborinus saxesenii Ratzeburg (Scolytinae, Coleoptera). ZooKeys , 56 , 253–267. https://doi.org/10.3897/zookeys.56.530Biedermann, P. H. W. (2012). Evolution of cooperation in ambrosia beetles .Biedermann, P. H. W., Klepzig, K. D., & Taborsky, M. (2009). Fungus Cultivation by Ambrosia Beetles: Behavior and Laboratory Breeding Success in Three Xyleborine Species. Environmental Entomology , 38 (4), 1096–1105. https://doi.org/10.1603/022.038.0417Biedermann, P. H. W., Klepzig, K. D., & Taborsky, M. (2011). Costs of delayed dispersal and alloparental care in the fungus-cultivating ambrosia beetle Xyleborus affinis Eichhoff (Scolytinae: Curculionidae). Behavioral Ecology and Sociobiology , 65 (9), 1753–1761. https://doi.org/10.1007/s00265-011-1183-5Biedermann, P. H. W., Klepzig, K. D., Taborsky, M., & Six, D. L. (2013). Abundance and dynamics of filamentous fungi in the complex ambrosia gardens of the primitively eusocial beetle Xyleborinus saxesenii Ratzeburg (Coleoptera: Curculionidae, Scolytinae). FEMS Microbiology Ecology , 83 (3), 711–723. https://doi.org/10.1111/1574-6941.12026Biedermann, P. H. W., Peer, K., & Taborsky, M. (2011). Female dispersal and reproduction in the ambrosia beetle Xyleborinus saxesenii Ratzeburg (Coleoptera; Scolytinae) . 6.Biedermann, P. H. W., & Taborsky, M. (2011). Larval helpers and age polyethism in ambrosia beetles. Proceedings of the National Academy of Sciences , 108 (41), 17064–17069. https://doi.org/10.1073/pnas.1107758108Blouin, M., Karimi, B., Mathieu, J., & Lerch, T. Z. (2015). Levels and limits in artificial selection of communities. Ecology Letters , 18 (10), 1040–1048. https://doi.org/10.1111/ele.12486Brakefield, P. M. (2003). Artificial Selection and the Development of Ecologically Relevant Phenotypes. Ecology , 84 (7), 1661–1671. https://doi.org/10.1890/0012-9658(2003)084[1661:ASATDO]2.0.CO;2Choe, J. C., & Crespi, B. J. (Hrsg.). (1997). The Evolution of Mating Systems in Insects and Arachnids . Cambridge University Press; Cambridge Core. https://doi.org/10.1017/CBO9780511721946Conner, J. K. (2003). Artificial Selection: A Powerful Tool for Ecologists. Ecology , 84 (7), 1650–1660. https://doi.org/10.1890/0012-9658(2003)084[1650:ASAPTF]2.0.CO;2Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A., & Callahan, B. J. (2018). Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome , 6 (1), 226. https://doi.org/10.1186/s40168-018-0605-2De Fine Licht, H. H., & Biedermann, P. H. W. (2012). Patterns of functional enzyme activity in fungus farming ambrosia beetles. Frontiers in Zoology , 9 (1), 13. https://doi.org/10.1186/1742-9994-9-13Diehl, J. M. C., Kowallik, V., Keller, A., & Biedermann, P. H. W. (2022). First experimental evidence for active farming in ambrosia beetles and strong heredity of garden microbiomes .Dierks, A., Baumann, B., & Fischer, K. (2012). Response to selection on cold tolerance is constrained by inbreeding. Evolution , 66 (8), 2384–2398. https://doi.org/10.1111/j.1558-5646.2012.01604.xEdgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics , 26 (19), 2460–2461. https://doi.org/10.1093/bioinformatics/btq461Edgar, R. C. (2016a). SINTAX: A simple non-Bayesian taxonomy classifier for 16S and ITS sequences [Preprint]. Bioinformatics. https://doi.org/10.1101/074161Edgar, R. C. (2016b). UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing [Preprint]. Bioinformatics. https://doi.org/10.1101/081257Emlen, S. T. (1982). The Evolution of Helping. I. An Ecological Constraints Model. The American Naturalist , 119 (1), 29–39. https://doi.org/10.1086/283888Fisher, R. M., Cornwallis, C. K., & West, S. A. (2013). Group Formation, Relatedness, and the Evolution of Multicellularity. Current Biology , 23 (12), 1120–1125. https://doi.org/10.1016/j.cub.2013.05.004Fowler, K., & Whitlock, M. C. (1999). The distribution of phenotypic variance with inbreeding. Evolution , 53 (4), 1143–1156. https://doi.org/10.1111/j.1558-5646.1999.tb04528.xFox, J., & Weisberg, S. (2019). An R Companion to Applied Regression (Third). Sage. https://socialsciences.mcmaster.ca/jfox/Books/Companion/Francke-Grosmann, H. (1975). Zur epizoischen und endozoischen Übertragung der symbiotischen Pilze des Ambrosiakäfers Xyleborus saxeseni . Entomologica Germanica , 1 , 279–292.Hamilton, W. D. (1964). The genetical evolution of social behaviour. I. Journal of Theoretical Biology , 7 (1), 1–16. https://doi.org/10.1016/0022-5193(64)90038-4Hatchwell, B. J. (2009). The evolution of cooperative breeding in birds: Kinship, dispersal and life history. Philosophical Transactions of the Royal Society B: Biological Sciences , 364 (1533), 3217–3227. https://doi.org/10.1098/rstb.2009.0109Hochberg, M. E., Rankin, D. J., & Taborsky, M. (2008). The coevolution of cooperation and dispersal in social groups and its implications for the emergence of multicellularity. BMC Evolutionary Biology , 8 (1), 238. https://doi.org/10.1186/1471-2148-8-238Hothorn, T., Bretz, F., & Westfall, P. (2008). Simultaneous Inference in General Parametric Models. Biometrical Journal , 50 (3), 346–363.Irwin, K. K., & Carter, P. A. (2014). Artificial selection on larval growth curves in Tribolium : Correlated responses and constraints. Journal of Evolutionary Biology , 27 (10), 2069–2079. https://doi.org/10.1111/jeb.12457Kassambara, A. (2023). rstatix: Pipe-Friendly Framework for Basic Statistical Tests . https://CRAN.R-project.org/package=rstatixKeller, A., Hohlfeld, S., Kolter, A., Schultz, J., Gemeinholzer, B., & Ankenbrand, M. J. (2020). BCdatabaser: On-the-fly reference database creation for (meta-)barcoding. Bioinformatics , 36 (8), 2630–2631. https://doi.org/10.1093/bioinformatics/btz960Koenig, W. D., & Dickinson, J. L. (Hrsg.). (2016). Cooperative Breeding in Vertebrates: Studies of Ecology, Evolution, and Behavior . Cambridge University Press; Cambridge Core. https://doi.org/10.1017/CBO9781107338357Kostovcik, M., Bateman, C. C., Kolarik, M., Stelinski, L. L., Jordal, B. H., & Hulcr, J. (2015). The ambrosia symbiosis is specific in some species and promiscuous in others: Evidence from community pyrosequencing. The ISME Journal , 9 (1), 126–138. https://doi.org/10.1038/ismej.2014.115Le Galliard, J.-F., Ferriere, R., & Dieckmann, U. (2005). Adaptive Evolution of Social Traits: Origin, Trajectories, and Correlations of Altruism and Mobility. The American Naturalist , 165 (2), 19.Lewis, J. A., & Morran, L. T. (2022). Advantages of laboratory natural selection in the applied sciences. Journal of Evolutionary Biology , 35 (1), 5–22. https://doi.org/10.1111/jeb.13964Martinez Arbizu, P. (2020). PairwiseAdonis: Pairwise multilevel comparison using adonis. R package version 0.4 [R].Mayers, C. G., Harrington, T. C., & Biedermann, P. H. W. (2022). Mycangia Define the Diverse Ambrosia Beetle-F­ungus Symbioses. In The Convergent Evolution of Agriculture in Humans and Insects (S. 38). The MIT Press.McMurdie, P. J., & Holmes, S. (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE , 8 (4), e61217. https://doi.org/10.1371/journal.pone.0061217Mullon, C., Keller, L., & Lehmann, L. (2018). Social polymorphism is favoured by the co-evolution of dispersal with social behaviour. Nature Ecology & Evolution , 2 (1), 132–140. https://doi.org/10.1038/s41559-017-0397-yNuotclà, J. A., Biedermann, P. H. W., & Taborsky, M. (2019). Pathogen defence is a potential driver of social evolution in ambrosia beetles. Proceedings of the Royal Society B: Biological Sciences , 286 (1917), 20192332. https://doi.org/10.1098/rspb.2019.2332Nuotclà, J. A., Diehl, J. M. C., & Taborsky, M. (2021). Habitat Quality Determines Dispersal Decisions and Fitness in a Beetle-Fungus Mutualism. Frontiers in Ecology and Evolution , 9 , 602672. https://doi.org/10.3389/fevo.2021.602672Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O’Hara, R. B., Solymos, P., Stevens, M. H. H., Szoecs, E., Wagner, H., Barbour, M., Bedward, M., Bolker, B., Borcard, D., Carvalho, G., Chirico, M., Caceres, M. D., Durand, S., … Weedon, J. (2022). vegan: Community Ecology Package . https://CRAN.R-project.org/package=veganPeer, K., & Taborsky, M. (2007). Delayed dispersal as a potential route to cooperative breeding in ambrosia beetles. Behavioral Ecology and Sociobiology , 61 (5), 729–739. https://doi.org/10.1007/s00265-006-0303-0Rainey, P. B., & Rainey, K. (2003). Evolution of cooperation and conflict in experimental bacterial populations. Nature , 425 (6953), 72–74. https://doi.org/10.1038/nature01906Reed, D. H., Lowe, E. H., Briscoe, D. A., & Frankham, R. (2003). Fitness and Adaptation in a Novel Environment: Effect of Inbreeding, Prior Environment, and Lineage. Evolution , 57 (8), 1822–1828.Swenson, W., Wilson, D. S., & Elias, R. (2000). Artificial ecosystem selection. Proceedings of the National Academy of Sciences , 97 (16), 9110–9114. https://doi.org/10.1073/pnas.150237597Swindell, W. R., & Bouzat, J. L. (2005). Modeling the Adaptive Potential of Isolated Populations: Experimental Simulations using Drosophila. Evolution , 59 (10), 2159–2169. https://doi.org/10.1111/j.0014-3820.2005.tb00925.xTaborsky, M. (1994). Sneakers, Satellites, and Helpers—Parasitic and Cooperative Behavior in Fish Reproduction. In P. Slater, J. Rosenblatt, C. Snowdon, & M. Milinski (Hrsg.), ADVANCES IN THE STUDY OF BEHAVIOR, VOL 23 (WOS:A1994BD99E00001; Bd. 23, S. 1–100). https://doi.org/10.1016/S0065-3454(08)60351-4Tejeda, M. T., Arredondo, J., Liedo, P., Pérez-Staples, D., Ramos-Morales, P., & Díaz-Fleischer, F. (2016). Reasons for success: Rapid evolution for desiccation resistance and life-history changes in the polyphagous fly Anastrepha ludens . Evolution , 70 (11), 2583–2594. https://doi.org/10.1111/evo.13070Therneau, Terry M. & Grambsch, Patrica M. (2000). Modeling Survival Data: Extending the Cox Model . Springer.Wilson, E. O. (1971). The Insect Societies . Belknap Press of Harvard University Press. https://books.google.de/books?id=K_uyQgAACAAJ Information & Authors Information Version history V1 Version 1 10 April 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecology and Evolution Keywords evolutionary ecology experimental evolution invertebrate terrestrial Authors Affiliations Antoine Melet 0000-0002-5500-4274 [email protected] University of Freiburg View all articles by this author Peter Biedermann 0000-0003-4234-5659 University of Freiburg Faculty of Environment and Natural Resources View all articles by this author Metrics & Citations Metrics Article Usage 286 views 151 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Antoine Melet, Peter Biedermann. Effects of selection for early dispersal on the ambrosia beetle Xyleborinus saxesenii and its fungal symbionts. Authorea . 10 April 2025. DOI: https://doi.org/10.22541/au.174428385.56091333/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.174428385.56091333/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffdd89bc9f8df94',t:'MTc3OTQ3NDE0NA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();