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88 results for “population regulation”
Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation
<p>Data accompanying the manuscript "Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation".</p> <p>Note: For ATAC fragment files, e,caQTL full cis scan summary files, clustering objects, please see the CMDGA portal (https://cmdga.org/search/?searchTerm=stephen-parker%3AVarshney2024)<br>For raw data including fastq files, please see dbGaP repo phs001048.v3.p1</p> <p>Data in this repository includes:</p> <p>Filename: Description</p> <p>1. list of 8,666 genes for which exon-only counts were considered. See methods section "Adjusting RNA counts for overlapping gene annotations" in the manuscript.</p> <p>2. nucleus_sample_cluster_map.tsv: nucleus-sample-cluster map with other QC info. <br># index: nucleus identified syntax <modality>.<batch>.NM.<10X channel>.<barcode> <br># UMAP_1, UMAP_2: UMAP coordinates for visualization<br># modality: rna or atac<br># batch: processing batch identifier<br># hqaa_umi: high quality autosomal alignments (HQAA) for atac nuclei, unique molecular identifier (UMI) for tna <br># fraction_mitochondrial: fraction of reads mapping to the mitochondrial genome<br># cohort: sample cohort<br># tss_enrichment: TSS enrichment for atac nuclei<br># coarse_cluster_name: cluster name</p> <p>3. peaks.tar.gz: snATAC peak features including:<br># consensus-summits.bed: consensus summits along with the cell type that the summits was highest in.<br># narrow peaks in clusters<br># consensus summit feature (summit +- 150bp) identified in each cluster - these were used in GWAS enrichments.</p> <p>4. snrna-cell-type-specific-genes.tsv: Normalized expression scores for genes in each cell-type cluster</p> <p>5. eqtl_permute.tar.gz: Permutation scan eQTL in each cell-type cluster. Columns: <br># variant: syntax <chrom>:<hg38 pos>:<ref>:<alt><br># effect_allele: effect allele (was the alt allele)<br># other_allele: non-effect allele<br># feature: gene name<br># featureCoordinates_tss: gene TSS<br># p-value: nominal p value<br># beta: slope/beta of the linear regression. Keyed on the alt allele<br># se: standard error of the slope<br># snp: SNP ID<br># strand: gene strand<br># n_variants_tested: number of variants tested for the gene<br># distance_var_pheno: distance of the variant with the gene TSS<br># n_effective_tests: number of effective tests<br># p_beta: beta distribution adjusted p value<br># qvalue: qvalue (Storey)</p> <p>6. caqtl_permute.tar.gz: # Permutation scan caQTL in each cell-type cluster. Columns: <br># variant: syntax <chrom>:<hg38 pos>:<ref>:<alt><br># effect_allele: effect allele (was the alt allele)<br># other_allele: non-effect allele<br># feature: peak feature coordinates<br># p-value: nominal p value<br># beta: slope/beta of the linear regression. Keyed on the alt allele<br># se: standard error of the slope<br># snp: SNP ID<br># n_variants_tested: number of variants tested for the gene<br># distance_var_pheno: distance of the variant with the gene TSS<br># n_effective_tests: number of effective tests<br># p_beta: beta distribution adjusted p value<br># qvalue: qvalue (Storey)</p> <p>7. eqtl_credible_sets.tar.gz: # eQTL credible set. The file name denotes the egene and the signal hit id. Bed file columns: <br># 1: snp chromosome<br># 2: snp start<br># 3: snp end<br># 4: snp chrom_pos_ref_alt<br># 5: Bayes Factor <br># 6: PIP<br># 7: SNP rsid</p> <p>8. caqtl_credible_sets.tar.gz: # caqtl credible set. The file name denotes the capeak and the signal hit id. Bed file columns: <br># 1: snp chromosome<br># 2: snp start<br># 3: snp end<br># 4: snp chrom_pos_ref_alt<br># 5: Bayes Factor <br># 6: PIP<br># 7: SNP rsid</p> <p>9. cicero_all.tar.gz # Cicero coaccessibility results. Columns<br># Peak 1: Macs2 narrowpeak coordinate for peak 1<br># Peak 2: Macs2 narrowpeak coordinate for peak 2<br># coaccess: Cicero coaccessibility score</p> <p>10. cicero_gene_tss.tar.gz: Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name. Columns<br># Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name.Columns<br># Peak 1: Macs2 narrowpeak coordinate for peak 1<br># gene_name: Assigned gene<br># Peak 2: Macs2 narrowpeak coordinate for peak 2<br># coaccess: Cicero coaccessibility score<br>## Peak1 is the narrowpeak in the TSS region, peak2 is the distal peak</p> <p>11. mash.tar.gz Mashr results for e/caQTL - lfsr, posterior means and posterior SD for each tested eSNP-eGene, caSNP-caPeak pair. </p> <p>12. cellregmap.tar.gz: Cellregmap results for endothelial nucleus-level eQTL scans.<br>## Persistent genetic effect beta_g was calculated in a simple association model. <br>## An interaction model was fit to test for GxC effect. columns:<br># rho1, g2, e1, and eps2 are variance component measures outputs from CellRegMap corresponding to interaction, genetic, environment and residual variance components. <br># p_nominal: nominal p from cellRegMap<br># kind: model kind in CellRegMap - simple association or interaction<br># beta_g: Persistent genetic effect<br># gene_name: gene name for eQTL or peak feature name for caQTL<br># context: context used either factors (continuous) or subclusters (discrete)<br># snp: index snp for which model is fit. This is the most significant identified snp from our standard e,caQTL scans. chrom-hg38pos-rsid</p> <p><br>13. coloc-eqtl-caqtl.tsv: # Summary of eQTL-caQTL coloc in each cluster. Columns:<br># nsnps: Number of SNPs in the region<br># eqtl_hit: SNP with the highest Bayes factor in the SuSiE eQTL credible set<br># caqtl_hit: SNP with the highest Bayes factor in the SuSiE caQTL credible set<br># PP.H0.abf: Coloc posterior probability for no signal<br># PP.H1.abf: Coloc posterior probability for signal in dataset 1<br># PP.H2.abf: Coloc posterior probability for signal in dataset 2<br># PP.H3.abf: Coloc posterior probability for different signals in datasets 1 and 2<br># PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br># idx1: Index of the SuSiE credible set for dataset 1<br># idx2: Index of the SuSiE credible set for dataset 2<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates</p> <p>14. cit-mrs-summary.tsv: Summary from CIT and MR Steiger directionality tests. Columns:<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates<br># eqhit: SNP with the highest Bayes factor in the SuSiE eQTL credible set<br># cahit: SNP with the highest Bayes factor in the SuSiE caQTL credible set<br># p.cit_c_c-e: P value for CIT causal cahit-ca-to-e model<br># q.cit_c_c-e: q value for CIT causal cahit-ca-to-e model<br># p.cit_rc_c-e: P value for CIT reverse-causal eqhit-ca-to-e model <br># q.cit_rc_c-e: value for CIT reverse-causal eqhit-ca-to-e model <br># p.cit_c_e-c: P value for CIT causal eqhit-e-to-ca model<br># q.cit_c_e-c: q value for CIT causal eqhit-e-to-ca model<br># p.cit_rc_e-c: P value for CIT reverse-causal cahit-e-to-ca model <br># q.cit_rc_e-c: q value for CIT reverse-causal cahit-e-to-ca model <br># cit_direction: Direction inferred from CIT <br># correct_causal_direction--ca-to-e: MR Steiger directionality test - is ca-to-e direction correct?<br># correct_causal_direction--e-to-ca: MR Steiger directionality test - is e-to-ca direction correct?<br># sensitivity_ratio--ca-to-e: MR Steiger Sensitivity ratio for ca-to-e model <br># sensitivity_ratio--e-to-ca: MR Steiger Sensitivity ratio for e-to-ca model<br># steiger_test--ca-to-e: MR Steiger directionality test P value for ca-to-e model<br># steiger_test--e-to-ca: MR Steiger directionality test P value for e-to-ca model<br># steiger_q--ca-to-e: MR Steiger directionality test q value for ca-to-e model<br># steiger_q--e-to-ca: MR Steiger directionality test q value for e-to-ca model<br># mrs_direction: Direction inferred from MR Steiger<br># direction: Direction inferred requiring consistent results between CIT and MR Steiger directionality test</p> <p>15. coloc-gwas-eqtl.tsv and<br>16. coloc-gwas-caqtl.tsv # Summary of e/caQTL coloc with GWAS in each cluster. Columns:<br># nsnps: Number of SNPs in the region<br># gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set<br># eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set<br># caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set<br># PP.H0.abf: Coloc posterior probability for no signal<br># PP.H1.abf: Coloc posterior probability for signal in dataset 1<br># PP.H2.abf: Coloc posterior probability for signal in dataset 2<br># PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2<br># PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br># idx1: Index of the SuSiE credible set for dataset 1<br># idx2: Index of the SuSiE credible set for dataset 2<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates<br># p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization<br># trait: GWAS trait name<br># gwas_locus: GWAS locus name for the coloc test - a 250kb left and right flanking genomic window on this SNP was considered for testing coloc between all pairs of GWAS/QTL signals identified in this region <br># traitname: Expanded GWAS trait name<br># variable_type: GWAS type <br># source: Source of GWAS - either UKBB or other study</p> <p>17. supplementary_tables.xlsx: Supplementary tables from the manuscript.<br>Information included in sheets:<br>1. "marker_genes": Marker genes known from literature used to annotate clusters<br>2. "n_nuclei": n pass-QC nuclei per modality-sample-cluster</p> <p>2. "snrna_GO_enrichment": GO term enrichment: matrix of cluster vs top 2 GO terms</p> <p>3. "qtl_scan_info": e/caQTL scan info<br>cluster: cluster<br>ntested_eqtl: N genes tested for eQTL<br>nsig_eqtl: N significant (5% FDR) eGenes<br>n_pheno_pcs_eqtl: N phenotype PCs considered for eQTL<br>ratio_eqtl: Ratio of N eGenes/N genes tested<br>nsig_caqtl: N peaks tested for caQTL<br>ntested_caqtl: N significant (5% FDR) caPeaks<br>n_pheno_pcs_caqtl: N phenotype PCs considered for caQTL<br>ratio_caqtl: Ratio of N caPeaks/N peaks tested<br>nsamples_eqtl: N samples for eQTL<br>nsamples_caqtl: N samples for caQTL</p> <p>4. "gwas_trait_list": GWAS trait info<br>trait: GWAS trait ID<br>traitname: GWAS trait description<br>variable_type: GWAS type. case/control (cc), continuous_irnt=continuous inverse-normal transformed<br>source: GWAS source<br>doi: GWAS study DOI</p> <p>5. "traits_in_ldsc_baseline" - list of annotations included in the baseline model for LDSC</p> <p>6. "gwas_enrichment_in_peaks" GWAS enrichment in cluster peaks (S-LDSC)</p> <p>7. "gwas_enrichment_in_qtl_peaks" GWAS enrichment in QTL peaks (fGWAS) # fGWAS results comparing GWAS enrichment in type 1 annotations<br>CI_lower_ln, estimate_ln, CI_upper_ln: natural log of lower confidence interval, estimate, and upper confidence interval<br>trait: trait id<br>traitname: trait name<br>annotation: annotation<br>sig: 1 if CIs don't overlap 0, otherwise 0</p> <p>8. t2d_gwas_caqtl_coloc and<br>9. t2d_gwas_eqtl_coloc:<br>Summary of e,caQTL coloc with T2D GWAS in each cluster, along with target gene nominations. Columns:<br>nsnps: Number of SNPs in the region<br>gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set<br>eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set<br>caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set<br>PP.H0.abf: Coloc posterior probability for no signal<br>PP.H1.abf: Coloc posterior probability for signal in dataset 1<br>PP.H2.abf: Coloc posterior probability for signal in dataset 2<br>PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2<br>PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br>idx1: Index of the SuSiE credible set for dataset 1<br>idx2: Index of the SuSiE credible set for dataset 2<br>cluster: cluster name<br>egene: eGene name<br>capeak: caPeak coordinates<br>p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization<br>trait: GWAS trait id<br>diamante_gwas_locus: GWAS signal from the DIAMANTE 2018 study. Some signals that our SuSiE runs identified were not present in the original study in which case this column is NA<br>traitname: Expanded GWAS trait name<br>capeak_in_tss: caPeak in TSS + 1kb upstream region of a gene<br>gene_target_standard_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from all samples for co-accessibility<br>gene_target_allelic_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from samples homozygous for the caSNP allele associated with increased accessibility<br>gwashit_nominal_egene: gwas_hit nominally associated with these genes nominated in the columns capeak_in_tss, gene_target_standard_cicero, and gene_target_allelic_cicero</p> <p>10. MPRA results for the C2CD4A locus</p>
Data from: Complex selection on a regulator of social cognition: evidence of balancing selection, regulatory interactions and population differentiation in the prairie vole Avpr1a locus
Adaptive variation in social behavior depends upon standing genetic variation, but we know little about how evolutionary forces shape genetic diversity relevant to brain and behavior. In prairie voles (Microtus ochrogaster), variants at the Avpr1a locus predict expression of the vasopressin 1a receptor in the retrosplenial cortex (RSC), a brain region that mediates spatial and contextual memory; cortical V1aR abundance in turn predicts diversity in space-use and sexual fidelity in the field. To examine the potential contributions of adaptive and neutral forces to variation at the Avpr1a locus, we explore sequence diversity at the Avpr1a locus and throughout the genome in two populations of wild prairie voles. First, we refine results demonstrating balancing selection at the locus by comparing the frequency spectrum of variants at the locus to a random sample of the genome. Next, we find that the four SNPs that predict high V1aR expression in the RSC are in stronger linkage disequilibrium than expected by chance despite high recombination among intervening variants, suggesting that epistatic selection maintains their association despite recombination. Analysis of population structure and a haplotype network for two populations revealed that this excessive LD was unlikely to be due to admixture alone. Furthermore, the two populations differed considerably in the region shown to be a regulator of V1aR expression despite the extremely low levels of genome-wide genetic differentiation. Together, our data suggest that complex selection on Avpr1a locus favors specific combinations of regulatory polymorphisms, maintains the resulting alleles at populations-specific frequencies, and may contribute to unique patterns of spatial cognition and sexual fidelity among populations.
Population density does not affect seasonal regulation of reproductive physiology in male water voles
<p>Most small rodent species display cyclic fluctuations in their population density. The mechanisms behind these cyclical variations are not yet clearly understood. Density-dependent effects on reproductive function could affect these population variations. The fossorial water vole ecotype, Arvicola terrestris, undergoes a multi-year cycle dynamic with outbreak peaks. Here, we monitored different water vole populations over three years, in spring and autumn, to evaluate whether population density be related to male reproductive physiology. Our results show an effect of season and inter-annual factors in testes mass, plasmatic testosterone level, and androgen-dependent seminal vesicles mass. By contrast, population density does not affect any of these parameters, thus suggesting a lack of modulation of population dynamics of population density.</p>
Population regulation and density‐dependent demography in the Trinidadian guppy
<p>Classic theory for density-dependent selection for delayed maturation requires that a population be regulated through some combination of adult fecundity and/or juvenile survival. We tested whether those demographic conditions were met in four experimental populations of Trinidadian guppies in which delayed maturation of males evolved when the densities of those populations became high. We used monthly mark-recapture data to examine population dynamics and demography in these populations. Three of the four populations displayed clear evidence of regulation. In all four populations, monthly adult survival rates were independent of biomass density or actually increased with increased biomass density. Juvenile recruitment, which is a combination of adult fecundity and juvenile survival, decreased as biomass density increased in all four populations. Demography showed marked seasonality, with greater survival and higher recruitment in the dry season than the wet season. Population regulation via juvenile recruitment supports the hypothesis that density-dependent selection was responsible for the evolution of delayed maturity in males. This body of work represents one of the few complete tests of density-dependent selection theory.</p>
Data from: What is regulating chironomid populations? The influence of food supply and interference competition on development and mortality in <em>Chironomus riparius</em>
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Data from: Complex selection on a regulator of social cognition: evidence of balancing selection, regulatory interactions and population differentiation in the prairie vole Avpr1a locus
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Population density does not affect seasonal regulation of reproductive physiology in male water voles
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Population regulation and density‐dependent demography in the Trinidadian guppy
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Data from: Role of multiple invasion mechanisms and their interaction in regulating the population dynamics of an exotic tree
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Detrimental impacts of climate change may be exacerbated by density dependent population regulation in blue mussels
<p>1. The climate on our planet is changing and the range distributions of organisms are shifting in response. In aquatic environments, species might not be able to redistribute poleward or into deeper water when temperatures rise because of barriers, reduced light availability, altered water chemistry, or any combination of these. How species respond to climate change may depend on physiological adaptability, but also on the population dynamics of the species.</p> <p>2. Density dependence is a ubiquitous force that governs population dynamics and regulates population growth, yet its connections to the impacts of climate change remain little known, especially in marine studies. Reductions in density below an environmental carrying capacity may cause compensatory increases in demographic parameters and population growth rate, hence masking the impacts of climate change on populations. On the other hand, climate-driven deterioration of conditions may reduce environmental carrying capacities, making compensation less likely and populations more susceptible to the effects of stochastic processes.</p> <p>3. Here we investigate the effects of climate change on Baltic blue mussels using a 17-year data set on population density. Using a Bayesian modelling framework, we investigate the impacts of climate change, assess the magnitude and effects of density dependence, and project the likelihood of population decline by the year 2030.</p> <p>4. Our findings show negative impacts of warmer and less saline waters, both outcomes of climate change. We also show that density-dependence increases the likelihood of population decline by subjecting the population to the detrimental effects of stochastic processes (i.e., low densities where random bad years can cause local extinction, negating the possibility for random good years to offset bad years).</p> <p>5. We highlight the importance of understanding, and accounting for both density dependence and climate variation when predicting the impact of climate change on keystone species, such as the Baltic blue mussel. 08-Oct-2020</p>
Positive Emotion Dispositions and Emotion Regulation in the Italian Population
<p>Dataset of the Dispositional Positive Emotion Scale in the Italian population</p>
Data from: Herbivore control in connected seascapes: habitat determines when population regulation occurs in the life history of a key herbivore
Herbivore outbreaks often trigger catastrophic overgrazing events in marine macrophyte ecosystems. The sea urchin Paracentrotus lividus, the dominant herbivore of shallow Mediterranean seascapes, is capable of precipitating shifts to barrens when its populations explode. P. lividus is found ubiquitously in rocky macroalgal communities and in sandy seagrass meadows of Posidonia oceanica, two of the most important subtidal habitats in the Mediterranean. We explored if habitat-specific regulation across the principal stages of the urchin life cycle could help explain the persistence of these populations in connected mosaics. We measured each of three relevant ecological process (i.e. settlement, post-settlement survival and predation) across a wide stretch of the Mediterranean coast (ca. 600km). Our results show that habitat-specific regulation is critical in determining urchin populations: each habitat limited urchin sub-populations at different life stages. Settlement was never limiting; urchins settled at similar rates in both habitats across the coast. Post-settlement survival was a clear bottleneck, particularly in seagrass meadows where no juvenile urchins were recorded. Despite this bottleneck in seagrasses, adult urchin populations were very similar in both seagrass and macroalgal habitats indicating that other processes (potentially migration) could be key in determining adult distributions across the mosaic. The fact that population regulation is clearly habitat-specific suggests that sea urchin populations may be significantly buffered from bottlenecks in mixed seascapes where both habitats co-occur. Sea urchin populations can therefore persist across the seascape despite strong habitat-specific regulation either by maintaining reproductive output in one habitat or by migrating between them. By affording these regulatory escapes to habitat-modifying species, patchy mosaics may be much more prone to herbivore outbreaks and a host of cascading effects that come in their wake.
Population genomics and haplotype analysis in spelt and bread wheat identifies a gene regulating glume color
<p>The cloning of agriculturally important genes is often complicated by haplotype variation across crop cultivars. Access to pan-genome information greatly facilitates the assessment of structural variations and rapid candidate gene identification. Here, we identified the <i>red glume 1</i> (<i>Rg-B1</i>) gene using association genetics and haplotype analyses in ten reference-grade wheat genomes. Glume color is an important trait to characterize wheat cultivars. Red glumes are frequent among Central European spelt, a dominant wheat subspecies in Europe before the 20<sup>th</sup> century. We used genotyping-by-sequencing to characterize a global diversity panel of 267 spelt accessions, which provided evidence for two independent introductions of spelt into Europe. A single region at the <i>Rg-B1</i> locus on chromosome 1BS was associated with glume color in the diversity panel. Haplotype comparisons across ten high-quality wheat genomes revealed a <i>MYB</i> transcription factor as candidate gene. We found extensive haplotype variation across the ten cultivars, with a particular group of <i>MYB</i> alleles that was conserved in red glume wheat cultivars. Genetic mapping and transient infiltration experiments allowed us to validate this particular <i>MYB</i> transcription factor variants. Our study demonstrates the value of multiple high-quality genomes to rapidly resolve copy number and haplotype variations in regions controlling agriculturally important traits.</p>
Data from: Density dependence and population regulation in marine fish: a large-scale, long-term field manipulation
Do small-scale experiments showing spatial density dependence in marine fishes scale-up to temporal density dependence and regulation of relatively large local populations? If so, what are the causative mechanisms and their implications? We conducted an 8-year multi-generation study of population dynamics of bicolor damselfish (Stegastes partitus) inhabiting four large coral reefs in the Bahamas. After a 4-year baseline period, it was clear that two populations naturally received very few settlement-stage larvae, so recruitment of recently settled fish was artificially enhanced at one low-settlement reef and reduced at one high-settlement reef to ensure a broad range of population sizes over which to test for regulation. Over all 8 years, populations on the two naturally high-settlement reefs experienced temporal density dependence in multiple per capita demographic rates mortality, survival to adulthood, and fecundity. These local populations also displayed components of regulation persistence, boundedness, and return tendency. Reefs supporting regulated populations were structurally complex, providing sufficient prey refuges that ensured high survival at low densities. In contrast, populations at low-settlement reefs experienced either density-independent or slightly inversely density-dependent demographic rates, even though recruitment was artificially augmented to high levels at one reef. There was no evidence of regulation at these reefs, and indeed, one local population suffered temporary extirpation. Here, habitat complexity was relatively low, increasing the risk of predation, especially at low population densities when fish would have to travel longer distances when finding mates or home sites inhabited by conspecifics. Among all demographic parameters, density dependence in individual growth (an indicator of within-species competition for food) was least correlated with the presence or absence of local population regulation. We conclude that, for systems like these, the environmental context of a local population -- especially predation risk and the distribution and abundance of spatial refuges -- is more important than the magnitude of larval supply alone in determining the existence of regulating density dependence. At the broader metapopulation scale, density dependence in both survival and fecundity exogenously caused by predation may provide regulation for the entire stock, even when endogenous within-species competition (assumed to be important in most fisheries models) is weak.
Data from: Territory size decreases minimally with increasing food abundance in stream salmonids: implications for population regulation
How the local density of territorial animals responds to changes in food abundance will depend on the flexibility of territory size. Quantitative estimates of territory size over a broad range of food abundance are relatively rare because of the difficulty of measuring food abundance in the wild. Stream salmonids are an ideal model system for investigating flexibility in territory size, because food abundance can be quantified in the field and manipulated in the laboratory. We conducted a meta-analysis to test whether territory size decreases with increasing food abundance, and a mixed model analysis to test among three competing predictions: with increasing food abundance, territory size will be (1) fixed – the slope of a regression of log territory size vs. log food abundance = 0; (2) flexible and decreasing, as if individuals are defending a fixed amount of food – a slope = -1; and (3) initially compressible, but with an asymptotic minimum size – a slope between 0 and -1. We collected data from 16 studies that manipulated or measured food abundance while monitoring changes in territory size of young-of-the-year salmonids; 10 were experimental laboratory studies, whereas six were observational field studies. Overall, territory size decreased significantly with increasing food abundance; the weighted average correlation coefficient was -0.31. However, the estimated slope of the relationship between log territory size and log food abundance was only -0.23, significantly different from 0, but also significantly shallower than -1. Our estimated slope suggests that attempts to increase the density of territorial salmonids by increasing food abundance and reducing territory size will be inefficient; a 20-fold increase in food abundance would be required to double population density. Our analysis may also have implications for other species with a territorial mosaic social system – i.e. contiguous territories. In these social systems, social inertia will dampen any effects of changes in food abundance on the local density of settlers, compared to non-territorial species or those with non-contiguous territories.
Data from: Dynamic expressions of hypothalamic genes regulate seasonal breeding in a natural rodent population
Seasonal breeding is a universal reproductive strategy in many animals. Hypothalamic genes, especially type 2 and 3 iodothyronine deiodinases (Dio2/3), RFamide-related peptide 3 (Rfrp-3), kisspeptin (Kiss-1), and gonadotropin-releasing hormone (GnRH), are involved in a photoperiodic pathway that encodes seasonal signals from day length in many vertebrate species. However, the seasonal expression patterns of these genes in wild mammals are less studied. Here, we present a four-year field investigation to reveal seasonal rhythm and age-dependent reproductive activity in male Brandt's voles (Lasiopodomys brandtii) and to detect relationships among seasonal expression profiles of hypothalamic genes, testicular activity, age, and annual day length. From breeding season (April) to non-breeding season (October), adult male voles displayed a synchronous peak in gonadal activity with annual day length around summer solstice, which was jointly caused by age structure shifts and age-dependent gonadal development patterns. Overwintered males maintained reproductive activity until late in the breeding season, whereas most newborn males terminated gonadal development completely, except for a minority of males born early in spring. Consistently, the synchronous and opposite expression profiles of Dio2/3 suggest their central function to decode photoperiodic signals and to predict the onset of the non-breeding season. Moreover, changes in Dio2/3 signals may guide the actions of Kiss-1 and Rfrp-3 to regulate the age-dependent divergence of reproductive strategy in wild Brandt's vole. Our results provide evidence on how hypothalamic photoperiod genes regulate seasonal breeding in a natural rodent population.
Data from: Parasitic versus nutritional regulation of natural fish populations
1. Although parasites are expected to affect their host's fitness, quantitative proof for impacts of parasitism on wild populations is hampered by confounding environmental factors, including dietary resource. 2. Herein, we evaluate whether the physiological conditions of European perch (Perca fluviatilis) in three large peri-alpine lakes (Geneva, Annecy, and Bourget) depend on (a) the nutritional status of the juvenile fish, as revealed by stable isotope and fatty acid compositions, (b) the prevalence of the tapeworm Triaenophorus nodulosus, a parasite transmitted to perch through copepod preys, or (c) interactive effects of both factors. 3. At the scale of lake populations, the deficit in growth and fat storage of juvenile perch during their first summer coincides with a high parasite prevalence and also a low quality of dietary resource. 4. Yet, at the individual level, parasites had no evident effect on the growth of the juvenile perch, while impacts on fat storage appeared only at the highest prevalence of the most infected lake. Fatty acid and stable isotope analyses of fish tissue do not reveal any impact of T. nodulosus on diet, physiology, and feeding behaviour of fish within lakes. 5. Overall, we found a low impact of parasitism on the physiological condition and trophic status of juvenile perch at the end of their first summer. We find instead that juvenile perch growth and fat storage, both factors tied to their winter survival, are under strong nutritional constraints. 6. However, the coinciding nutritional constraints and parasite prevalence of perch juveniles in these three lakes may result from the indirect effect of lake nutrient concentrations, which, as a major control of zooplankton communities, simultaneously regulate both the dietary quality of fish prey and the host–parasite encounter rates.
Fig. 2. Wood slice cut from a in Decade-Old Satellite Infestation ofAnoplophora glabripennisMotschulsky (Coleoptera: Cerambycidae) Found in Ontario, Canada Outside Regulated Area of Founder Population.
Fig. 2. Wood slice cut from a live section of a bole of Acer negundo felled in March 2014 and believed to be at the epicenter of a satellite infestation of Anoplophora glabripennis discovered in Mississauga, Ontario in August 2013. Note the presence of an exit hole created in 2008 (blue arrow) and an oviposition pit created in 2004 (red arrow).
Population-level amplification of gene regulation by programmable gene transfer
<p>Source data for Son et al, "Population-level amplification of gene regulation by programmable gene transfer", Nature Chemical Biology.</p>
Geometry of obstructed pathway regulates upstream navigational pattern of sperm population
<p>Raw data for figures in a manuscript.</p>
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