Divergent Selection on Dispersal Targets Chemosensory and Neuronal Genes in Tribolium castaneum - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Mol Ecol . 2026 Apr 15;35(8):e70350. doi: 10.1111/mec.70350 Search in PMC Search in PubMed View in NLM Catalog Add to search Divergent Selection on Dispersal Targets Chemosensory and Neuronal Genes in Tribolium castaneum Michael D Pointer Michael D Pointer 1 University of East Anglia, Norwich Research Park, Norwich, UK Find articles by Michael D Pointer 1, ✉ , Will J Nash Will J Nash 1 University of East Anglia, Norwich Research Park, Norwich, UK 2 Earlham Institute, Norwich Research Park, Norwich, UK Find articles by Will J Nash 1, 2 , Lewis G Spurgin Lewis G Spurgin 1 University of East Anglia, Norwich Research Park, Norwich, UK Find articles by Lewis G Spurgin 1 , Mark McMullan Mark McMullan 2 Earlham Institute, Norwich Research Park, Norwich, UK Find articles by Mark McMullan 2 , Simon Butler Simon Butler 1 University of East Anglia, Norwich Research Park, Norwich, UK Find articles by Simon Butler 1 , David S Richardson David S Richardson 1 University of East Anglia, Norwich Research Park, Norwich, UK Find articles by David S Richardson 1 Author information Article notes Copyright and License information 1 University of East Anglia, Norwich Research Park, Norwich, UK 2 Earlham Institute, Norwich Research Park, Norwich, UK * Correspondence: Michael D. Pointer ( [email protected] ) ✉ Corresponding author. Revised 2026 Apr 1; Received 2025 Dec 5; Accepted 2026 Apr 8; Issue date 2026 Apr. © 2026 The Author(s). Molecular Ecology published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMC Copyright notice PMCID: PMC13083851 PMID: 41987353 ABSTRACT Dispersal is key to the life history, ecology and evolution of many organisms and important in pest invasiveness. However, the genetic architecture underlying variation in dispersal behaviour remains poorly understood outside of a few model species. We investigated the genomic basis of dispersal using artificial selection on replicated lines of Tribolium castaneum , a flour beetle, an emergent model system and an economically important agricultural pest. Combining whole‐genome resequencing with population‐level genotype–phenotype association analysis, we identify nine genomic regions and 42 genes associated with selection on dispersal. Identified candidate genes were significantly enriched for functions related to neuronal structure and function, as well as chemosensory behaviour and mating, suggesting that variation in dispersal is mediated by neural and chemosensory pathways. Our results demonstrate that dispersal propensity has a polygenic basis and support an interaction between dispersal and mating ecology in this system. These findings contribute to a deeper understanding of the genetic mechanisms driving dispersal evolution and its role in shaping eco‐evolutionary dynamics. Keywords: dispersal, emigration, experimental evolution, flour beetle, Tribolium 1. Introduction Dispersal is a complex life history trait with a critical role in the ecology and evolution of many species (Ronce 2007 ). Individual movements influence population size, density and range (Kokko and López‐Sepulcre 2006 ), and thus the metapopulation dynamics that contribute to population persistence or expansion in fragmented landscapes (Clobert 2012 ; Legrand et al. 2017 ). Furthermore, dispersal determines gene flow and the patterns of genetic variation within and among populations, with implications for their evolutionary trajectories (Holsinger and Weir 2009 ). Dispersal is also an important aspect of evolutionary responses to anthropogenic climate change, habitat fragmentation, the dynamics of biological invasions and agricultural pest distributions (Travis et al. 2013 ; Legrand et al. 2017 ; Renault et al. 2018 ). Thus, knowledge of the genetic underpinnings of dispersal is essential to understand the causes and consequences of dispersal evolution (Ronce 2007 ). Across taxa, dispersal is commonly seen as a component of suites of coevolving phenotypes, referred to as behavioural syndromes (Clobert 2012 ). Individual variation in such syndromes may represent different life‐history strategies (Sih et al. 2004 ). Patterns of association between these traits are complex, highly context‐dependent and the mechanistic basis of such associations is poorly understood (Clobert 2012 ). Despite this, recent studies have revealed that, under conditions such as range expansion, dispersal itself can rapidly evolve over short timescales (Ochocki and Miller 2017 ; Weiss‐Lehman et al. 2017 ; Simcox et al. 2024 ). For example, dispersal evolution during the cane toad ( Bufo marinus ) invasion of Australia has accelerated the advance of the range front by fivefold in less than 100 years (Shine et al. 2021 ). Uncovering the genetic architecture of dispersal traits will clarify how variation underlying dispersal is maintained in populations, aid in elucidating the basis of behavioural syndromes, inform our ability to trace how molecular variation leads to phenotypic differences in movement patterns and enable predictions around the adaptive potential of dispersal (Saastamoinen et al. 2018 ). The genetic architecture of traits is key to how they respond to selection (Pritchard and Di Rienzo 2010 ; Le Corre and Kremer 2012 ). The genetic basis of dispersal‐related traits has been described in a range of species, revealing highly varying genetic architectures (Saastamoinen et al. 2018 ; Dochtermann et al. 2019 ). Dispersal is usually thought to be a polygenic trait (Merilä and Sheldon 1999 ; Pritchard and Di Rienzo 2010 ), an idea supported by work in both insects and vertebrates (Jordan et al. 2012 ; Saatoglu et al. 2024 ). For example, ~300 genes were associated with dispersal phenotype in Mountain Pine Beetles ( Dendroctonus ponderosae ; Shegelski et al. 2021 ). However, in some taxa large‐effect loci influence dispersal (Saastamoinen et al. 2018 ), which can be separated into those with metabolic (Niitepõld and Saastamoinen 2017 ) or neurophysiological (Sokolowski 1980 ; Trefilov et al. 2000 ; Fidler et al. 2007 ; Krackow and König 2008 ; Anreiter and Sokolowski 2019 ) effects on movement. Notable examples include the For gene ( Foraging ; and its homologues), a neuro‐signalling regulator linked to movement behaviour in taxa from C. elegans to humans (reviewed in Anreiter and Sokolowski 2019 ); and Pgi , which underlies phenotypic variation in flight metabolism and dispersal propensity in wild butterflies (reviewed in Niitepõld and Saastamoinen 2017 ). Simulation studies modelling the rate of dispersal evolution under different architectures have also provided conflicting results (Saastamoinen et al. 2018 ; Weiss‐Lehman and Shaw 2022 ). Replicated experimental evolution, in combination with resequencing (Schlötterer et al. 2015 ) and powerful statistical methods enable genotype–phenotype associations to be resolved (Coop et al. 2010 ; Gautier 2015 ; Olazcuaga et al. 2020 ). The emergent genomic model Tribolium castaneum is highly suited to such experimental evolution studies (Pointer et al. 2021 ; Campbell et al. 2022 ). The species is a globally significant pest, responsible for large economic losses and impacts on food security (Phillips and Throne 2010 ), consequently, its dispersal ecology is of great applied interest. Previous work on T. castaneum has indicated that dispersal may be a component of a behavioural syndrome, covarying with key life‐history traits (Lavie and Ritte 1978 ; Zirkle et al. 1988 ; Pointer, Spurgin, McMullan, et al. 2024 ). In this species, individual‐level dispersal variation seems to be driven by activity levels and movement patterns (Pointer, Spurgin, Vasudeva, et al. 2024 ), suggesting that, as in other systems, the phenotype may stem from differences in neurophysiology and/or metabolism (Saastamoinen et al. 2018 ). Some genes whose expression covaries with walking motivation have been identified in T. castaneum (Matsumura et al. 2024 ), but as of yet no study has investigated the molecular genomic basis of dispersal. Here, we utilise whole genome re‐sequencing of individuals from replicated lines of T. castaneum ( n = 32), previously selected for divergent dispersal propensity, and displaying robustly repeatable behaviour (Pointer et al. 2023 ; Pointer, Spurgin, McMullan, et al. 2024 ; Pointer, Spurgin, Vasudeva, et al. 2024 ), to identify the genetic architecture of adaptation to selection on dispersal. We employ BayPass, leveraging the power of the study's population‐level replication, to highlight candidate SNPs significantly associated with dispersal selection regimes. Signatures of suppressed nucleotide diversity around a subset of identified candidates support the recent occurrence of selective sweeps in these regions. We then functionally enrich sets of candidate genes to explore how molecular variation might be linked to dispersal phenotypes. We find many genes associated with dispersal phenotype, supporting a polygenic trait architecture. The functions of the candidate genes suggest that neuronal structure and function affecting chemosensation are the principal mechanisms underpinning dispersal evolution in our experiment. 2. Methods 2.1. Beetles and Husbandry The Krakow super‐strain (KSS) of T. castaneum flour beetles was created by combining 14 laboratory populations from across the world, producing a highly outbred strain, the ideal substrate for selection to act upon (Laskowski et al. 2015 ). This strain has been maintained at a census size of 600 individuals for ~150 generations. All beetle populations were kept on a fodder medium of 90% organic flour and 10% brewers yeast, on a constant 12:12 light:dark cycle, with relative humidity of 60% and a regime of non‐overlapping generations. At 12 ± 3 days post eclosion, adults are sieved from the fodder and a randomly chosen subset is combined in fresh fodder to begin a 7‐day mating and oviposition period, after which adults are removed. Eggs remaining in the fodder then develop over a 35‐day development period. By preventing any interaction between sexually mature adults and offspring, this method reduces the risk of negative density‐dependent effects, removes the opportunity for intergenerational interactions, such as egg cannibalism, and allows accurate tracking of passing generations. This study complies with applicable UK legislation on sampling from natural populations and animal experimentation (SI 2012/3039). 2.2. Artificial Selection for Dispersal Thirty‐two experimental lines were founded from KSS stock and artificially selected for dispersal propensity as described by Pointer et al. ( 2023 ). Briefly, high ( n = 16) and low ( n = 16) dispersal lines were bred under divergent artificial selection over five generations, using a dispersal assay in which each individual, housed within groups of 200, was given three opportunities to ‘disperse’: that is leave a patch of suitable habitat, cross a short distance of unsuitable habitat and not return. Individuals that ‘dispersed’ three times out of the three opportunities were considered to display a dispersive phenotype. Individuals that ‘dispersed’ zero times were considered to display a non‐dispersive phenotype. Thirty individuals of each of these phenotypes were selected to found the subsequent generation of the relevant treatments, while individuals of intermediate phenotype were discarded. After a single generation of selection, the mean dispersal phenotype (mean number of dispersals per individual out of three opportunities) between the treatments was significantly different. After five generations of selection, the distribution of the dispersal phenotype between the two treatments was non‐overlapping (Pointer et al. 2023 ). Between generations six and 16, a less stringent selection regime was applied. In odd‐numbered generations, a reduced assay that allowed a single dispersal opportunity was used to phenotype the dispersers and non‐dispersers, of which 30 of the relevant type were selected to found the subsequent generation of each line. In odd‐numbered generations, 100 randomly chosen individuals founded the next generation. In generation 17, dispersal was quantified and found to be still strongly divergent and non‐overlapping between treatments (Pointer, Spurgin, Vasudeva, et al. 2024 ; dispersals per individual out of a maximum of three, low dispersal lines = 0.70 ± 0.06; high dispersal lines = 2.44 ± 0.04). 2.3. Sample Preparation and Sequencing In generation 17, samples were collected for sequencing from dispersal selection lines and from the ancestral KSS population. Adult females were sampled and flash‐frozen in liquid nitrogen. For each individual, DNA extraction was conducted using the DNeasy blood and tissue kit (insect tissue protocol, Qiagen), with the whole individual ground in liquid nitrogen. The extract was then purified using a 1× AMPure XP SPRI (Beckman Coulter) bead cleanup. Library preparation and sequencing were performed at the Earlham Institute (Norwich, UK) using the low‐input transposase‐enabled pipeline (LITE; see Methods S1 ). Libraries were sequenced on two S4 flowcells over two lanes on the Illumina Novaseq 6000 platform. Sequences were obtained from 210 individuals, six from each of 32 dispersal lines and 18 from the KSS stock. 2.4. Read Processing and Mapping Raw reads were processed to improve sequence quality prior to alignment, to minimise mis‐mapping and knock‐on effects on variant identification. Adapter sequences were trimmed using Trimmomatic v.039 (Bolger et al. 2014 ) and aligned to the T.cast5.2 reference genome ( https://doi.org/10.1186/s12864‐019‐6394‐6 ), using BWA‐MEM v0.7.17 (Li 2013 ). After alignment, several post‐processing steps were applied to ensure that only high‐confidence read pairs contributed to variant discovery. SAMtools v1.18 fixmate was used to ensure consistent mate‐pair formation and SAMtools sort to order reads by genomic location (Danecek et al. 2021 ). PCR duplicates were then removed using Picard v2.26.2 RemoveDuplicates (Broad Institute 2019 ). Finally, mappings were filtered for complete read pairs and those with a mapping quality (MAPQ) > 25 using SAMtools view. 2.5. Genotyping Joint genotyping was conducted using BCFtools v1.18.0 mpileup (Danecek et al. 2021 ). BCFtools call was then used to call all sites under the multi‐allelic model (‐m). This two‐step method combines base identity and base quality scores from all reads across all individuals to compute genotype likelihoods under a probabilistic model before calling individual genotypes using across‐individual likelihoods. In our sequencing depth range (~8×), this is important, allowing us to pool evidence across samples to further increase confidence in both variant detection and genotype assignment. 2.6. Variant Filtering Next, variants were filtered to retain a set of high‐quality, well‐represented sites, using BCFtools filter to remove: (1) variants within 3 bp of other variants, as clusters can be symptomatic of misalignment around indels or repetitive regions; (2) variants with quality score < 30, a common threshold indicating low confidence in a true variant; (3) variants at loci with sequencing depth less than 578 and greater than 5201 (±3 times total sequencing depth), because extremely low‐coverage positions have insufficient information for reliable genotype calls, while extremely high depth positions can indicate collapsed repeats, paralogous regions, or other mapping artefacts; (4) variants represented by data at that locus in less than 50% of individuals (‐g 3 ‐G 3 ‐e ‘DP < 578 || DP > 5201 || F_MISSING > 0.5 || QUAL < 30’). The resulting file is referred to hereafter as the allsites vcf. From the allsites vcf, single nucleotide polymorphisms (SNPs) were extracted using BCFtools view, and further filtered to remove sites with minor allele count < 3, to remove rare variants that are more likely to represent sequencing errors. This file is referred to hereafter as the SNP vcf. Some downstream analyses require information on all sites, not just those with variants. Therefore, to prepare an allsites vcf for these analyses, variant and invariant sites were handled separately. Invariant sites were extracted using VCFtools 0.16.0 (Danecek et al. 2011 ) and stored in a separate file. Variant sites were filtered using VCFtools to only contain biallelic SNPs that did not deviate significantly from Hardy–Weinberg Equilibrium (HWE p < 0.001). Following this, invariant and variant sites were concatenated and indexed using BCFtools concat and index. This file is referred to hereafter as the filtered allsites vcf. QC of the SNP vcf revealed three individual samples (10HT, 11HT3, 11HT4) exhibited low coverage, huge counts of singleton SNPs and indels compared to other samples. As this is likely a sign of sequencing error, we excluded reads from these samples from the raw sequence data and reran the entire pipeline to regenerate both the SNP vcf and the allsites vcf. Thus, these samples were totally removed from the analysis. 2.7. Linkage Disequilibrium The SNP vcf was down‐sampled to 1 SNP per 0.5 kb and used to calculate pairwise linkage disequilibrium (LD) between SNPs up to a maximum distance of 5 Mb, using VCFtools. As per‐line sample sizes were not sufficient to reliably estimate LD, estimates were obtained from ‘populations’ consisting of all individuals from within each treatment (high dispersal, low dispersal, KSS) and within all treatments combined. Using a custom R script (see Data Availability Statement for link to the Github repository), we calculated mean LD within distance bins of 1 kb and plotted this to visualise LD in the data (Figure S1 ). In each treatment, LD halved from the maximum at ~50 kb; we therefore took this as a window size with which to begin to examine patterns across the genome. 2.8. Population Structure As a measure of artifact detection and replicate verification, principal component analysis (PCA) was performed using plink (v1.9; Purcell et al. 2007 ). To ensure that sites represented independent signal, the SNP vcf was pruned for linkage with bcftools (+prune ‐m 0.3 ‐w 50 kb) before PCA was conducted with plink2 (Chang et al. 2015 ). 2.9. Identification of Candidate Loci We performed a genome‐wide scan for selection using BayPass v2.4 (Gautier 2015 ), implementing a Bayesian framework sensitive to demography. BayPass estimates a background allele frequency (omega) matrix across populations to account for the confounding effect of demography, which can frustrate the identification of selected variants (Günther and Coop 2013 ; Gautier 2015 ). This approach allowed us to control for unquantified differences in relatedness among individuals used to found each selection line. The BayPass model uses an omega matrix, calculated from neutral SNPs, to correct for population covariation when testing allele frequencies for population divergence or association with environmental or trait variables. Within BayPass, we utilised the statistic, C 2 , which contrasts allele frequencies between two groups of populations specified by a binary trait (Olazcuaga et al. 2020 ). This method outperforms others in identifying SNPs under selection (Olazcuaga et al. 2020 ). We computed C 2 across our 32 dispersal lines, with the dispersal selection treatment as the binary covariable. To avoid the impact of small, annotation‐sparse, unplaced scaffolds in the reference genome, we ran BayPass on the 10 linkage‐group‐level Tcas5.2 scaffolds. We computed the BayPass omega matrix using a curated subset of 12,232 independent, high confidence, non‐exonic SNPs at putatively neutral loci across the genome, affording the best opportunity to estimate the neutral covariance in allele frequencies (omega dataset; see Methods S1 ). The foreground dataset used for BayPass analysis contained a more permissive set of 3,240,899 SNPs derived from the SNP vcf. This set leveraged BayPass's robustness to missing data while maximising the number of high‐confidence SNPs in the analysis (see Methods S1 ). We performed two independent Baypass runs with different random seed initiators and computed correlations to test the consistency of model performance with our data (Dickson et al. 2020 ; Olazcuaga et al. 2020 ). The C 2 estimates were calibrated using a pseudo‐observed dataset (POD; Gautier 2015 ; see Methods S1 ). The 0.999 quantile of C 2 values from the POD analysis was used as the outlier threshold for empirical C 2 values. C 2 candidate SNPs were those with C 2 above this threshold, and C 2 candidate regions were defined as those containing ≥ 2 outlier SNPs separated by < 50 kb (Gautier 2015 ). Nucleotide diversity (π) in each replicate population was computed in 10 kb non‐overlapping windows along the genome using pixy , from the filtered allsites VCF. Mean π per window was calculated across the 16 individual populations within each treatment. To identify regions of low π, we computed the mean across all windows for each linkage‐group level scaffold; outlier windows were those with mean π more than four standard deviations below the scaffold mean. Trends were visualised using ggplot in R v4.3.3 (see Data Availability Statement for link to the Github repository), with rolling mean π calculated over 5 windows. We thereby generated two sets of candidate genes, those within (1) C 2 candidate regions identified by BayPass and (2) a subset of C 2 candidate regions that overlapped a π outlier window. 2.10. Characterisation of Candidate Genes Genes associated with selection candidates were identified by intersecting their positions with the annotation (Tribolium_castaneum.T.cas5.2.59.gff3; bedtools intersect ). The two sets of candidate genes (1 and 2 described above) were used as input. Returned gene lists were used as input to g:Profiler (Reimand et al. 2007 ; Kolberg et al. 2023 ) to test for enrichment of functional terms derived from gene ontology (GO). All other settings were the G:profiler defaults and the background used was all genes in the Tcas5.2.59 annotation. The OSG3 annotation ( https://ibeetle‐base.uni‐goettingen.de/download/species/Tcas/OGS3.gff.gz ) and the iBeetle‐Base database (Dönitz et al. 2018 ) were used to manually identify gene functions, and orthologous genes in Drosophila were identified using Flybase (Öztürk‐Çolak et al. 2024 ). 3. Results 3.1. DNA Sequencing and Data Curation Following adapter trimming, 199,248–32,028,177 reads per sample were mapped to the Tcas5.2 reference. Following removal of PCR duplicates and quality filtering, 106,340–13,399,500 reads per sample remained, representing 0.09–11.83× mean coverage. After quality control, genotype calling and variant filtering, we retained 4,418,204 SNPs across 204 samples with a mean (±SE) coverage of 7.8× (±0.008×) and per‐sample missingness of 0.046 (±0.006). The filtered allsites vcf contained 37,840,898 sites. 3.2. Linkage Disequilibrium Linkage disequilibrium (LD) was similar in high dispersal, low dispersal, and all treatments combined, peaking at Ca. R 2 = 0.2, and halving from the maximum at 0.6–0.7 Mb (Figure S1A–C ). The KSS control samples had the highest LD, with a peak of 0.25 and halving at ~1.3 Mb (Figure S1D ), although this estimate is less reliable due to smaller sample size ( n = 18 vs. n ≥ 93). 3.3. Population Structure An LD pruned and MAF filtered set of 223,034 SNPs was used to perform principal component analysis. PCA showed no unexpected batch effects (Figures S2 and S3 ). 3.4. Identification of Candidate Loci BayPass analysis showed high repeatability, with the C 2 estimates associated with SNPs being highly correlated across two replicate runs with different starting seeds (Pearson's r = 0.96). The genome‐wide allele frequency matrix did not show any pattern of relationship between populations (Figure S4 ). Of the 3,240,899 sites in the dataset, we identified 267 SNPs as C 2 outliers, with representation across all 10 linkage groups (Figure 1A ). Linkage groups two, three and four contained the most outlier SNPs (44, 93 and 32, respectively), with 88 of those on LG3 being within a single peak. Grouping SNPs following Gautier ( 2015 ), we recovered 22 candidate regions associated with dispersal, representing 256 candidate SNPs (Figure S5 , Table 1 ). Nine of the 22 candidate regions overlapped a π outlier window (Figure 1 , Table 1 , Table S1 ), with the strongest signals of suppressed nucleotide diversity associated with BayPass outlier regions on LG3 (Figure 1Bi ) and LG7 (Figure 1Bii ). Candidate SNPs were generally aggregated within candidate genes; for example, the two genes within the large peak on LG3 contained 76 and 12 candidate SNPs, respectively. FIGURE 1. Open in a new tab Genomic variation associated with divergent artificial selection for dispersal propensity in 32 independently evolving lines of Tribolium castaneum . (A) Values of the C 2 statistic generated by BayPass analysis as a measure of SNP associations with the direction of selection. Red points are in excess of the 0.999 quantile of C 2 values from a BayPass run on a pseudo‐observed dataset of putatively neutral SNPs, shown by the dotted line. (B) Expanded view of regions on linkage groups three (i) and seven (ii), where BayPass peaks coincide with regions of low nucleotide diversity (π), computed in 10 kb windows within each population and averaged across populations from the same selection regime. Dotted red lines represent the mean π of both selection regimes combined on the focal chromosome minus four standard deviations (0.01% of windows are expected to fall beneath this threshold). Yellow shading links the region of interest across panels and indicates the extent of the BayPass outlier regions in B. TABLE 1. Candidate regions identified with BayPass and nucleotide diversity (π) analyses between two sets of Tribolium castaneum populations artificially selected for divergent dispersal propensity. Chrom Start pos End pos Extent # SNPs Supported by C 2 Supported by π LG2 9,994,244 9,994,716 472 3 ✓ ✓ LG2 10,202,157 10,219,438 17,281 20 ✓ — LG2 13,961,555 14,042,317 80,762 21 ✓ — LG3 4,347,867 4,370,105 22,238 88 ✓ ✓ LG3 5,470,109 5,471,688 1579 2 ✓ ✓ LG4 4,048,154 4,109,033 60,879 14 ✓ ✓ LG4 5,845,018 5,921,563 76,545 16 ✓ — LG5 1,467,648 1,540,584 72,936 15 ✓ — LG5 1,730,741 1,730,802 61 3 ✓ — LG5 3,795,349 3,810,931 15,582 7 ✓ — LG6 1,461,754 1,487,897 26,143 3 ✓ ✓ LG6 9,253,132 9,300,171 47,039 4 ✓ — LG7 2,901,546 2,901,843 297 5 ✓ — LG7 6,777,038 6,819,916 42,878 3 ✓ ✓ LG7 8,227,860 8,228,283 423 3 ✓ — LG8 9,610,013 9,636,502 26,489 35 ✓ — LG8 12,871,312 12,897,244 25,932 2 ✓ ✓ LG9 10,353,204 10,353,689 485 2 ✓ ✓ LG9 12,357,074 12,388,871 31,797 4 ✓ ✓ LG10 4,040,062 4,040,694 632 2 ✓ — LG10 6,637,835 6,637,917 82 2 ✓ — LGX 1,559,420 1,559,524 104 2 ✓ — Open in a new tab 3.5. Characterisation of Candidate Genes GO analysis was performed independently on two sets of genes. Genes with the strongest evidence of responding to dispersal selection (supported by C 2 and π; 42 genes) showed enrichment for functions related to neurons, chemosensory behaviour and mating (Table 2 ). The broader set (supported by C 2 alone; 104 genes) was enriched for functions related to protease activity, wounding response, reproduction, transport and DNA processing. TABLE 2. Gene ontology analysis using G:Profiler to identify functional enrichment among genes in candidate regions located by BayPass and nucleotide diversity analyses, between two sets of Tribolium castaneum populations artificially selected for divergent dispersal propensity. Gene set GO/KEGG category GO/KEGG term P adj Genes in C 2 candidate regions overlapping π outlier windows ( n = 42) GO:MF Primary active transmembrane transporter activity < 0.01 GO:BP Reproductive process < 0.05 GO:BP Flagellated sperm motility < 0.05 GO:BP Sperm motility < 0.05 GO:BP Courtship behaviour < 0.05 GO:BP Chemosensory behaviour < 0.05 GO:BP Male courtship behaviour < 0.05 GO:BP Reproductive behaviour < 0.05 GO:BP Male mating behaviour < 0.05 GO:BP Mating behaviour < 0.05 GO:CC Neuronal cell body < 0.05 GO:CC Cell body < 0.05 GO:CC Dendrite < 0.05 GO:CC Dendritic tree < 0.05 GO:CC Somatodendritic compartment < 0.05 GO:CC Axon < 0.05 Genes in C 2 candidate regions ( n = 104) GO:MF Serine‐type endopeptidase activity < 0.001 GO:MF Serine‐type peptidase activity < 0.001 GO:MF Serine hydrolase activity < 0.001 GO:MF Endopeptidase activity < 0.001 GO:MF Peptidase activity < 0.001 GO:MF DNA topoisomerase type I (single strand cut, ATP‐independent) activity < 0.05 GO:BP Blood coagulation < 0.01 GO:BP Haemostasis < 0.01 GO:BP Coagulation < 0.01 GO:BP Reproduction < 0.01 GO:BP Wound healing < 0.01 GO:BP Regulation of body fluid levels < 0.01 GO:BP Multicellular organism reproduction < 0.05 GO:BP Response to wounding < 0.05 KEGG ABC transporters < 0.05 Open in a new tab 4. Discussion Here, we use replicated lines of red flour beetles, artificially selected for differential dispersal behaviour, to undertake the first study of the genomic basis of this trait in Tribolium . Using whole genome resequencing with population‐level genotype–phenotype association (GPA) analysis, we identify signatures of selection at many regions across the genome, indicating that adaptation was polygenic. Genes within candidate regions were enriched for functions associated with neuronal function and chemosensation, suggesting a possible mechanism underlying dispersal variation. Previous work in T. castaneum suggests that dispersal behaviour may have a relatively simple genetic basis (Pointer et al. 2023 ), possibly a single locus of large effect (Ogden 1970a ; Ritte and Lavie 1977 ). Challenging this suggestion, here we find nine regions across the genome strongly associated with dispersal phenotype, with weaker support for a further 13 regions, indicating that dispersal behaviour is likely a polygenic trait. While a rapid response to selection, as seen in our selection lines, was suggested by Ritte and Lavie ( 1977 ) to be an indicator of a simple genetic basis, recent theory indicates that rapid phenotypic change can also result from relatively small shifts across many loci, especially under a strong novel selection pressure (Pritchard and Di Rienzo 2010 ; Jain and Stephan 2017 ). Indeed, a previous study imposing artificial selection on Tribolium dispersal and using line cross‐analysis to dissect the trait supported a polygenic basis (Ruckman and Balckmon 2020 ). Our result also aligns with others from insect and vertebrate systems, showing a complex genetic basis of dispersal‐related traits. For example, 192 genes were found to be associated with Drosophila locomotion (Jordan et al. 2012 ) and ~300 genes were differentially expressed between dispersal phenotypes of Mountain Pine Beetles ( Dendroctonus ponderosae ) (Shegelski et al. 2021 ). Similarly, dispersal in the House sparrow ( Passer domesticus ) is polygenic, with a complex basis involving gene × environment interactions (Saatoglu et al. 2024 ). The regions of the genome with strongest links to dispersal from our analyses were characterised as involved in neuron structure and functioning and affecting chemosensory behaviour, courtship and reproduction. Dispersal in Tribolium is well known to be influenced by the conspecific environment and chemical signals associated with population density (King and Dawson 1972 ; Pointer et al. 2021 ), with beetles dispersing more readily from high density populations (Ziegler 1978 ), and from environments with chemical signals of high density, even in the absence of other beetles (Ogden 1970b ). Hence, it seems possible that increased sensitivity to chemical cues could lead to greater dispersal propensity for a given population density. This suggests a potential mechanistic link between the candidate genes and dispersal variation, via altered perception or processing of environmental cues; however, confirming this would require considerable functional validation. Given that chemosensation is also key to finding mates in Tribolium and across insect systems (Krieger and Breer 1999 ; Fedina and Lewis 2008 ), it follows that changes in chemosensation may also affect the mating behaviour. Interestingly, a previous study examining reproductive behaviour in these same experimental lines indicated altered male investment in different reproductive strategies with dispersal phenotype, favouring either increased duration or increased frequency of mating (Pointer, Spurgin, McMullan, et al. 2024 ). In the present study, we identify divergence in genes related to sperm motility, supporting a link between dispersal strategy and post‐copulatory sexual selection. In particular, the gene TC033673 is a homologue of Drosophila's Lost Boys , which encodes a flagellar protein determining the likelihood of the ejaculate reaching the female's sperm storage receptacle (Yang et al. 2011 ). In addition, the association between genes involved in neural structure and dispersal phenotypes is intriguing, as key traits within dispersal and broader behavioural syndromes are thought to covary via shared neural mechanisms (Sih et al. 2004 ). While further investigation is needed to make robust mechanistic conclusions, it is already known that the focal beetle lines differ in traits such as boldness and movement pattern (Pointer, Spurgin, Vasudeva, et al. 2024 ). To conclude, we recover candidate loci across the genome showing associations with dispersal. Enrichment for functions related to neuron structure and function affecting chemosensation suggests these as likely mechanisms underpinning dispersal variation. In addition, we show that reproductive traits are also responding to dispersal selection, potentially via shared pathways and/or ecological interactions. Our findings highlight how selection acts on dispersal in this system, a representative of the most species‐rich order of organisms and an economically important pest. These findings add to our understanding of the evolution of dispersal, a trait at the heart of many key issues in contemporary biology. Author Contributions Michael D. Pointer: investigation (lead); methodology (lead); formal analysis (lead); software (lead); writing – original draft (lead); writing – review and editing (equal). Will J. Nash: investigation (supporting); methodology (supporting); software (supporting); writing – review and editing (equal). Lewis G. Spurgin: conceptualisation (supporting); funding acquisition (equal); supervision (supporting). Mark McMullan: conceptualisation (supporting); supervision (supporting). Simon Butler: project administration (supporting); supervision (supporting). David S. Richardson: supervision (lead); project administration (lead); funding acquisition (equal); writing – review and editing (equal). Funding This work was supported by Biotechnology and Biological Sciences Research Council, BB/M011216/1. Conflicts of Interest The authors declare no conflicts of interest. Supporting information Methods S1 Detailed library preparation and BayPass methods. Figure S1: Linkage decay estimated individually for each treatment and for all treatments combined. Figure S2: Main genomic principle component plots for principle components one and two. Figure S3: Matrix of plots of genomic principle components one to eight. Figure S4: Visualisation of the BayPass omega matrix representing background allele frequency relationships between populations. Figure S5: Each genomic region identified by BayPass outlier analysis, visualised with Integrative Genomics Viewer. Table S1: Details of Genes identified as associated with dispersal behaviour. MEC-35-e70350-s001.docx (5.4MB, docx) Acknowledgements This work was funded by a Biotechnology and Biological Sciences Research Council (BBSRC) studentship to M.D.P. (BB/M011216/1). We are grateful to past and present members of the Tribolium lab at the University of East Anglia, without whose dedicated efforts the study of long‐term selection lines would not be possible. Sequencing was performed at the Earlham Institute, provided via the Core Capability Grant BB/CCG2220/1 and its constituent work packages (BBS/E/T/000PR9818 and BBS/E/T/000PR9819), and the Core Capability Grant BB/CCG1720/1 and the National Capability at the Earlham Institute BBS/E/T/000PR9816 (NC1—Supporting EI's ISPs and the UK Community with Genomics and Single Cell Analysis), BBS/E/T/000PR9811 (NC4—Enabling and Advancing Life Scientists in data‐driven research through Advanced Genomics and Computational Training), and BBS/E/T/000PR9814 (NC 3—Development and deployment of versatile digital platforms for ‘omics‐based data sharing and analysis’). Authors also acknowledge support from BBSRC Core Capability Grant BB/CCG1720/1 and the work delivered via the Scientific Computing group, as well as support for the physical HPC infrastructure and data centre delivered via the NBI Computing infrastructure for Science (CiS) group. Part of this work was delivered via the BBSRC funded National Bioscience Research Infrastructure (BBS/E/ER/23NB0006) at Earlham Institute by members of the Technical Genomics and Core Bioinformatics Groups. We acknowledge the use of ChatGPT (Open AI) to improve language clarity, particularly in drafting of technical methods. The authors critically reviewed and edited all machine‐generated output and take full responsibility for the final content. Data Availability Statement Sequence data for this project are archived under European Nucleotide Archive (PRJEB90247). Scripts are available on Github ( https://github.com/mdpointer/Tribolium_dispersal_genomics ), and on Dryad ( https://doi.org/10.5061/dryad.v6wwpzh8b ). This research provides benefits via the sharing of data and results on public databases as described above. References Anreiter, I. , and Sokolowski M. B.. 2019. “The Foraging Gene and Its Behavioural Effects: Pleiotropy and Plasticity.” Annual Review of Genetics 53: 373–392. [ DOI ] [ PubMed ] [ Google Scholar ] Bolger, A. M. , Lohse M., and Usadel B.. 2014. “Trimmomatic: A Flexible Trimmer for Illumina Sequence Data.” Bioinformatics 30: 2114–2120. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Broad Institute . 2019. “Picard Toolkit.” GitHub Repository. https://broadinstitute.github.io/picard/ . Campbell, J. F. , Athanassiou C. G., Hagstrum D. W., and Zhu K. Y.. 2022. “ Tribolium castaneum : A Model Insect for Fundamental and Applied Research.” Annual Review of Entomology 67: 347–365. [ DOI ] [ PubMed ] [ Google Scholar ] Chang, C. C. , Chow C. C., Tellier L. C., Vattikuti S., Purcell S. M., and Lee J. J.. 2015. “Second‐Generation PLINK: Rising to the Challenge of Larger and Richer Datasets.” GigaScience 4: 7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Clobert, J.
2012. Dispersal Ecology and Evolution. Oxford University Press. [ Google Scholar ] Coop, G. , Witonsky D., Di Rienzo A., and Pritchard J. K.. 2010. “Using Environmental Correlations to Identify Loci Underlying Local Adaptation.” Genetics 185: 1411–1423. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Danecek, P. , Auton A., Abecasis G., et al. 2011. “The Variant Call Format and VCFtools.” Bioinformatics 27: 2156–2158. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Danecek, P. , Bonfield J. K., Liddle J., et al. 2021. “Twelve Years of SAMtools and BCFtools.” GigaScience 10: giab008. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Dickson, L. B. , Merkling S. H., Gautier M., et al. 2020. “Exome‐Wide Association Study Reveals Largely Distinct Gene Sets Underlying Specific Resistance to Dengue Virus Types 1 and 3 in Aedes aegypti .” PLoS Genetics 16: e1008794. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Dochtermann, N. A. , Schwab T., Anderson Berdal M., Dalos J., and Royauté R.. 2019. “The Heritability of Behavior: A Meta‐Analysis.” Journal of Heredity 110: 403–410. [ DOI ] [ PubMed ] [ Google Scholar ] Dönitz, J. , Gerischer L., Hahnke S., Pfeiffer S., and Bucher G.. 2018. “Expanded and Updated Data and a Query Pipeline for iBeetle‐Base.” Nucleic Acids Research 46: D831–D835. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Fedina, T. Y. , and Lewis S. M.. 2008. “An Integrative View of Sexual Selection in Tribolium Flour Beetles.” Biological Reviews of the Cambridge Philosophical Society 83: 151–171. [ DOI ] [ PubMed ] [ Google Scholar ] Fidler, A. E. , van Oers K., Drent P. J., Kuhn S., Mueller J. C., and Kempenaers B.. 2007. “Drd4 Gene Polymorphisms Are Associated With Personality Variation in a Passerine Bird.” Proceedings of the Biological Sciences 274: 1685–1691. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Gautier, M.
2015. “Genome‐Wide Scan for Adaptive Divergence and Association With Population‐Specific Covariates.” Genetics 201: 1555–1579. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Günther, T. , and Coop G.. 2013. “Robust Identification of Local Adaptation From Allele Frequencies.” Genetics 195: 205–220. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Holsinger, K. E. , and Weir B. S.. 2009. “Genetics in Geographically Structured Populations: Defining, Estimating and Interpreting F(ST).” Nature Reviews. Genetics 10: 639–650. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jain, K. , and Stephan W.. 2017. “Rapid Adaptation of a Polygenic Trait After a Sudden Environmental Shift.” Genetics 206: 389–406. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jordan, K. W. , Craver K. L., Magwire M. M., Cubilla C. E., Mackay T. F. C., and Anholt R. R. H.. 2012. “Genome‐Wide Association for Sensitivity to Chronic Oxidative Stress in Drosophila melanogaster .” PLoS One 7: e38722. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] King, C. E. , and Dawson P. S.. 1972. “Population Biology and the Tribolium Model.” Evolutionary Biology 5: 133–227. [ Google Scholar ] Kokko, H. , and López‐Sepulcre A.. 2006. “From Individual Dispersal to Species Ranges: Perspectives for a Changing World.” Science 313: 789–791. [ DOI ] [ PubMed ] [ Google Scholar ] Kolberg, L. , Raudvere U., Kuzmin I., Adler P., Vilo J., and Peterson H.. 2023. “G:Profiler‐Interoperable Web Service for Functional Enrichment Analysis and Gene Identifier Mapping (2023 Update).” Nucleic Acids Research 51: W207–W212. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Krackow, S. , and König B.. 2008. “Microsatellite Length Polymorphisms Associated With Dispersal‐Related Agonistic Onset in Male Wild House Mice ( Mus musculus domesticus ).” Behavioral Ecology and Sociobiology 62: 813–820. [ Google Scholar ] Krieger, J. , and Breer H.. 1999. “Olfactory Reception in Invertebrates.” Science 286: 720–723. [ DOI ] [ PubMed ] [ Google Scholar ] Laskowski, R. , Radwan J., Kuduk K., Mendrok M., and Kramarz P.. 2015. “Population Growth Rate and Genetic Variability of Small and Large Populations of Red Flour Beetle ( Tribolium castaneum ) Following Multigenerational Exposure to Copper.” Ecotoxicology 24: 1162–1170. [ DOI ] [ PubMed ] [ Google Scholar ] Lavie, B. , and Ritte U.. 1978. “The Relation Between Dispersal Behavior and Reproductive Fitness in the Flour Beetle Tribolium castaneum .” Canadian Journal of Genetics and Cytology 20: 589–595. [ Google Scholar ] Le Corre, V. , and Kremer A.. 2012. “The Genetic Differentiation at Quantitative Trait Loci Under Local Adaptation.” Molecular Ecology 21: 1548–1566. [ DOI ] [ PubMed ] [ Google Scholar ] Legrand, D. , Cote J., Fronhofer E. A., et al. 2017. “Eco‐Evolutionary Dynamics in Fragmented Landscapes.” Ecography 40: 9–25. [ Google Scholar ] Li, H.
2013. “Aligning Sequence Reads, Clone Sequences and Assembly Contigs With BWA‐MEM.” arXiv [q‐bioGN]. Matsumura, K. , Onuma T., Kondo S., et al. 2024. “Transcriptomic Comparison Between Populations Selected for Higher and Lower Mobility in the Red Flour Beetle Tribolium castaneum .” Scientific Reports 14: 67. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Merilä, J. , and Sheldon B. C.. 1999. “Genetic Architecture of Fitness and Nonfitness Traits: Empirical Patterns and Development of Ideas.” Heredity 83, no. Pt 2: 103–109. [ DOI ] [ PubMed ] [ Google Scholar ] Niitepõld, K. , and Saastamoinen M.. 2017. “A Candidate Gene in an Ecological Model Species: Phosphoglucose Isomerase (Pgi) in the Glanville Fritillary Butterfly (Melitaea Cinxia).” Anzf 54: 259–273. [ Google Scholar ] Ochocki, B. M. , and Miller T. E. X.. 2017. “Rapid Evolution of Dispersal Ability Makes Biological Invasions Faster and More Variable.” Nature Communications 8: 1–8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ogden, J. C.
1970a. “Artificial Selection for Dispersal in Flour Beetles (Tenebrionidae: Tribolium).” Ecology 51: 130–133. [ Google Scholar ] Ogden, J. C.
1970b. “Aspects of Dispersal in Tribolium Flour Beetles.” Physiological Zoology 43: 124–131. [ Google Scholar ] Olazcuaga, L. , Loiseau A., Parrinello H., et al. 2020. “A Whole‐Genome Scan for Association With Invasion Success in the Fruit Fly Drosophila Suzukii Using Contrasts of Allele Frequencies Corrected for Population Structure.” Molecular Biology and Evolution 37: 2369–2385. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Öztürk‐Çolak, A. , Marygold S. J., Antonazzo G., et al. 2024. “FlyBase: Updates to the Drosophila Genes and Genomes Database.” Genetics 227: iyad211. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Phillips, T. W. , and Throne J. E.. 2010. “Biorational Approaches to Managing Stored‐Product Insects.” Annual Review of Entomology 55: 375–397. [ DOI ] [ PubMed ] [ Google Scholar ] Pointer, M. D. , Gage M. J. G., and Spurgin L. G.. 2021. “Tribolium Beetles as a Model System in Evolution and Ecology.” Heredity 126: 869–883. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pointer, M. D. , Spurgin L. G., Gage M. J. G., McMullan M., and Richardson D. S.. 2023. “Genetic Architecture of Dispersal Behaviour in the Post‐Harvest Pest and Model Organism Tribolium castaneum .” Heredity 131: 253–262. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pointer, M. D. , Spurgin L. G., McMullan M., Butler S., and Richardson D. S.. 2024. “Life History Correlations and Trade‐Offs Resulting From Selection for Dispersal in Tribolium castaneum .” Journal of Evolutionary Biology 37: voae041. [ DOI ] [ PubMed ] [ Google Scholar ] Pointer, M. D. , Spurgin L. G., Vasudeva R., McMullan M., Butler S., and Richardson D. S.. 2024. “Traits Underlying Experimentally Evolved Dispersal Behavior in Tribolium castaneum .” Journal of Insect Behavior 37: 220–232. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pritchard, J. K. , and Di Rienzo A.. 2010. “Adaptation—Not by Sweeps Alone.” Nature Reviews. Genetics 11: 665–667. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Purcell, S. , Neale B., Todd‐Brown K., et al. 2007. “PLINK: A Tool Set for Whole‐Genome Association and Population‐Based Linkage Analyses.” American Journal of Human Genetics 81: 559–575. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Reimand, J. , Kull M., Peterson H., Hansen J., and Vilo J.. 2007. “G:Profiler—A Web‐Based Toolset for Functional Profiling of Gene Lists From Large‐Scale Experiments.” Nucleic Acids Research 35: W193–W200. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Renault, D. , Laparie M., McCauley S. J., and Bonte D.. 2018. “Environmental Adaptations, Ecological Filtering, and Dispersal Central to Insect Invasions.” Annual Review of Entomology 63: 345–368. [ DOI ] [ PubMed ] [ Google Scholar ] Ritte, U. , and Lavie B.. 1977. “The Genetic Basis of Dispersal Behavior in the Flour Beetle Tribolium castaneum .” Canadian Journal of Genetics and Cytology 19: 717–722. [ Google Scholar ] Ronce, O.
2007. “How Does It Feel To Be Like a Rolling Stone? Ten Questions About Dispersal Evolution.” Annual Review of Ecology, Evolution, and Systematics 38: 231–253. [ Google Scholar ] Ruckman, S. N. , and Balckmon H.. 2020. “The March of the Beetles: Epistatic Components Dominate Divergence in Dispersal Tendency in Tribolium castaneum .” Journal of Heredity 111, no. 5: 498–505. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Saastamoinen, M. , Bocedi G., Cote J., et al. 2018. “Genetics of Dispersal.” Biological Reviews of the Cambridge Philosophical Society 93: 574–599. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Saatoglu, D. , Lundregan S. L., Fetterplace E., et al. 2024. “The Genetic Basis of Dispersal in a Vertebrate Metapopulation.” Molecular Ecology 33: e17295. [ DOI ] [ PubMed ] [ Google Scholar ] Schlötterer, C. , Kofler R., Versace E., Tobler R., and Franssen S. U.. 2015. “Combining Experimental Evolution With Next‐Generation Sequencing: A Powerful Tool to Study Adaptation From Standing Genetic Variation.” Heredity 114: 431–440. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Shegelski, V. A. , Evenden M. L., Huber D. P. W., and Sperling F. A. H.. 2021. “Identification of Genes and Gene Expression Associated With Dispersal Capacity in the Mountain Pine Beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae).” PeerJ 9: e12382. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Shine, R. , Alford R. A., Blennerhasset R., et al. 2021. “Increased Rates of Dispersal of Free‐Ranging Cane Toads ( Rhinella marina ) During Their Global Invasion.” Scientific Reports 11: 23574. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Sih, A. , Bell A., and Johnson J. C.. 2004. “Behavioral Syndromes: An Ecological and Evolutionary Overview.” Trends in Ecology & Evolution 19: 372–378. [ DOI ] [ PubMed ] [ Google Scholar ] Simcox, D. J. , Meredith S. A., and Thomas J. A.. 2024. Rapid Selection for Increased Dispersal Rates by the Endangered Butterfly Phengaris (Maculinea) Arion Across Restored Landscapes. Insect Conserv Divers. [ Google Scholar ] Sokolowski, M. B.
1980. “Foraging Strategies Of drosophila melanogaster : A Chromosomal Analysis.” Behavior Genetics 10: 291–302. [ DOI ] [ PubMed ] [ Google Scholar ] Travis, J. M. J. , Delgado M., Bocedi G., et al. 2013. “Dispersal and Species' Responses to Climate Change.” Oikos 122: 1532–1540. [ Google Scholar ] Trefilov, A. , Berard J., Krawczak M., and Schmidtke J.. 2000. “Natal Dispersal in Rhesus Macaques Is Related to Serotonin Transporter Gene Promoter Variation.” Behavior Genetics 30: 295–301. [ DOI ] [ PubMed ] [ Google Scholar ] Weiss‐Lehman, C. , Hufbauer R. A., and Melbourne B. A.. 2017. “Rapid Trait Evolution Drives Increased Speed and Variance in Experimental Range Expansions.” Nature Communications 8: 14303. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Weiss‐Lehman, C. , and Shaw A. K.. 2022. “Understanding the Drivers of Dispersal Evolution in Range Expansions and Their Ecological Consequences.” Evolutionary Ecology 36: 181–197. [ Google Scholar ] Yang, Y. , Cochran D. A., Gargano M. D., et al. 2011. “Regulation of Flagellar Motility by the Conserved Flagellar Protein CG34110/Ccdc135/FAP50.” Molecular Biology of the Cell 22: 976–987. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ziegler, J. R.
1978. “Dispersal and Reproduction in Tribolium: The Influence of Initial Density.” Environmental Entomology 7: 149–156. [ Google Scholar ] Zirkle, D. F. , Dawson P. S., and Lavie B.. 1988. “An Experimental Analysis of the Genetic Relationships Among Life‐History Traits and Emigration Behavior in Tribolium castaneum .” Oikos 53: 391–397. [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Methods S1 Detailed library preparation and BayPass methods. Figure S1: Linkage decay estimated individually for each treatment and for all treatments combined. Figure S2: Main genomic principle component plots for principle components one and two. Figure S3: Matrix of plots of genomic principle components one to eight. Figure S4: Visualisation of the BayPass omega matrix representing background allele frequency relationships between populations. Figure S5: Each genomic region identified by BayPass outlier analysis, visualised with Integrative Genomics Viewer. Table S1: Details of Genes identified as associated with dispersal behaviour. MEC-35-e70350-s001.docx (5.4MB, docx) Data Availability Statement Sequence data for this project are archived under European Nucleotide Archive (PRJEB90247). Scripts are available on Github ( https://github.com/mdpointer/Tribolium_dispersal_genomics ), and on Dryad ( https://doi.org/10.5061/dryad.v6wwpzh8b ). This research provides benefits via the sharing of data and results on public databases as described above. Articles from Molecular Ecology are provided here courtesy of Wiley ACTIONS View on publisher site PDF (818.1 KB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top