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549 results for “population size”
Assessing size at sexual maturity and fine-scale population structure in a direct developing whelk (Buccinum undatum) in Southern Newfoundland, Canada
<p>R script file used to filter genotype data, estimate L50, and analyze patterns of population structure of <em>Buccinum undatum </em>in Southern Newfoundland, Canada. Also included are the following files required to run the script:</p> <p>populationsNWA.snps.vcf - Northwest Atlantic group output at the conclusion of the Stacks de novo pipeline<br>pop_map_NWA.txt - Population map for the Northwest Atlantic group<br>genlightNWAFullFilt.rds - Filtered genotype data for the Northwest Atlantic group<br>populations3Ps.snps.vcf - 3Ps group output at the conclusion of the Stacks de novo pipeline <br>pop_map_3Ps.txt - Population map for the 3Ps group<br>genlight3PsFullFilt.rds - Filtered genotype data for the 3Ps group<br>maturity_data.csv - Data set containing, shell length, sex, and maturity status for samples.<br>sample_site_coordinates_3Ps.csv - Data set containing coordinates of 3Ps sample sites</p> <p> </p>
How early does the selfing syndrome arise? Associations between selfing ability and flower size within populations of the mixed mater Collinsia verna
<p>Widespread associations between selfing rate and floral size within and among taxa suggest that these traits may evolve in concert. Does this association develop immediately, because of shared genetic/developmental control, or stepwise with selection shaping the evolution of one trait following the other? If the former, then association ought to appear within and across populations. We explore this fundamental question in three populations of the mixed-mater Collinsia verna where autonomous selfing (AS) ability has been shown to be under selection by the pollination environment. We grew clonal replicates of C. verna in a controlled environment to characterize broad-sense genetic correlations among traits within populations and to assess whether divergence in mating system and floral traits among these populations is consistent with their previously observed selection pressures. As predicted by their respective pollination environments, we demonstrate significant genetic divergence among populations in AS ability. However, patterns of divergence in floral traits (petal, stamen, and style size, stigmatic receptivity, and stigma-anther distance) were not as expected. Within populations, genetic variation in AS appeared largely independent from floral traits, except for a single weak negative association in one population between flower size and AS rate. Together, these results suggest that associations between selfing rate and floral traits across Collinsia species are not reflected at microevolutionary scales. If C. verna were to continue evolving toward the selfing syndrome, floral trait evolution would likely follow stepwise from mating system evolution.</p>
Fig. 3 in Differences In Skull Size Of Harbour Porpoises, Phocoena Phocoena (Cetacea), In The Sea Of Azov And The Black Sea: Evidence For Different Morphotypes And Populations
Fig. 3. The skull measurements of the harbour porpoises from the Sea of Azov and the Black Sea: 1 — zygomatic width vs rostrum width at the mid-point; 2 — parietal width vs rostrum width at the mid-point.
Fig. 4 in Differences In Skull Size Of Harbour Porpoises, Phocoena Phocoena (Cetacea), In The Sea Of Azov And The Black Sea: Evidence For Different Morphotypes And Populations
Fig. 4. Черепа морских свиней, Phocoena phocoena relicta, из Азовского и Чёрного морей, вид сверху: 1 — Азовское море, самец; 2 — Азовское море, самка; 3 — Чёрное море, самец; 4 — Чёрное море, самка. Фото М. П. Чоповди.
Fig. 2 in Differences In Skull Size Of Harbour Porpoises, Phocoena Phocoena (Cetacea), In The Sea Of Azov And The Black Sea: Evidence For Different Morphotypes And Populations
Fig. 2. Skull proportions of the harbour porpoises from the Sea of Azov and the Black Sea (mean ± standard deviation is presented as the box, upper and lower limits as the lines): 1 — zygomatic width as the CBL percentage; 2 — rostrum width at the mid-point as the CBL percentage.
The role of population size in folk tune complexity
<p>Demography, particularly population size, plays a key role in cultural complexity. However, the relationship between population size and complexity appears to vary across domains: while studies of technology typically find a positive correlation, the opposite is true for language, and the role of population size in complexity in the arts remains to be established. Here, we investigate the relationship between population size and complexity in music using Irish folk session tunes as a case study. Using analyses of a large online folk tune dataset, we show that popular tunes played by larger communities of musicians have diversified into a greater number of different versions which encompass more variation in melodic complexity compared with less popular tunes. However, popular tunes also tend to be intermediate in melodic complexity and variation in complexity is lower than expected given the increased number of tune versions. We also find that user preferences for individual tune versions are more skewed in popular tunes. Taken together, these results suggest that while larger populations create more frequent opportunities for musical innovation, they encourage convergence upon intermediate levels of melodic complexity due to a widespread inverse U-shaped relationship between complexity and aesthetic preference. We explore the assumptions underlying our empirical analyses further using simple simulations of tune diffusion through populations of different sizes, finding that a combination of biased copying and structured populations appears most consistent with our results. Our study demonstrates a unique relationship between population size and cultural complexity in the arts, confirming that the relationship between population size and cultural complexity is domain-dependent, rather than universal.</p>
Additional files for manuscript titled 'The double round-robin population unravels the genetic architecture of grain size in barley'
<p>Additional file 1: Parental allele for barley orthologs of genes controlling grain size in rice</p> <p>Additional file 2: Cross-validation of quantitative trait loci (QTLs) detected for grain size characters in rice</p> <p>Additional file 3: Adjusted entry means of recombinant inbred lines of 45 HvDRR sub-populations</p>
Рис. 2. Размерная структура G. lacustris в ΛитораΛьной зоне озера АрахΛей: 1 — июнь; 2 — август; 3 — октябрь Fig. 2. G. lacustris population size structure in the Lake Arakhley littoral zone: 1 — June, 2 — August, 3 — October in The life cycle of Gmelinoides fasciatus (Stebbing, 1899) and Gammarus lacustris (Sars, 1863) amphipods in the lake Arakhley littoral during the extreme low-water phase of the hydrological cycle
Рис. 2. Размерная структура G. lacustris в ΛитораΛьной зоне озера АрахΛей: 1 — июнь; 2 — август; 3 — октябрь Fig. 2. G. lacustris population size structure in the Lake Arakhley littoral zone: 1 — June, 2 — August, 3 — October
Рис. 1. Размерная структура Gm. fasciatus в ΛитораΛьной зоне озера АрахΛей: 1 — в июне; 2 — в августе; 3 — в октябре; 4 — в Αекабре 2017 г. и июне 2018 г. Fig. 1. Gm. fasciatus population size structure in the Lake Arakhley littoral zone: 1 — June; 2 — August; 3 — October; 4 — December, 2017 and June, 2018 in The life cycle of Gmelinoides fasciatus (Stebbing, 1899) and Gammarus lacustris (Sars, 1863) amphipods in the lake Arakhley littoral during the extreme low-water phase of the hydrological cycle
Рис. 1. Размерная структура Gm. fasciatus в ΛитораΛьной зоне озера АрахΛей: 1 — в июне; 2 — в августе; 3 — в октябре; 4 — в Αекабре 2017 г. и июне 2018 г. Fig. 1. Gm. fasciatus population size structure in the Lake Arakhley littoral zone: 1 — June; 2 — August; 3 — October; 4 — December, 2017 and June, 2018
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 8. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска M. catrusiana (А), фитомассы (В), твердости грунта на глубине 5–10 см (C) и доли агрегатных фракций 3–5 мм (D) на участке № 2 в 2011 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 8. 3D–diagrams of the abundance spatial distribution of the land snail M. catrusiana (A), phytomass (B), 0–10 cm layer soil penetration resistance (C), aggregate particle size 3–5 mm (D) at the site 1 in 2011 (axes X and Y presented in meters).
Рис. 2. Диаграммы распределениЯ обилиЯ наЗемного моллюска M. cartusiana: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков). Fig. 2. Diagram of the abundance distribution of the land snail M. cartusiana: A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Х and Y axes presented in meters; abundance proportional to sphere sizes). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 2. Диаграммы распределениЯ обилиЯ наЗемного моллюска M. cartusiana: A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.; D – участок № 5, 2012 г. (единицы иЗмерениЯ осей Х и Y даны в метрах; численность особей пропорциональна раЗмерам Шариков). Fig. 2. Diagram of the abundance distribution of the land snail M. cartusiana: A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Х and Y axes presented in meters; abundance proportional to sphere sizes).
Global contemporary effective population sizes across taxonomic groups
<p>Effective population size (<em>N<sub>e</sub></em>) is a particularly useful metric for conservation as it affects genetic drift, inbreeding and adaptive potential within populations. Current guidelines recommend a minimum <em>N<sub>e</sub> </em>of 50 and 500 to avoid short-term inbreeding and to preserve long-term adaptive potential, respectively. However, the extent to which wild populations reach these thresholds globally has not been investigated, nor has the relationship between <em>N<sub>e</sub></em><sub> </sub>and human activities. Through a quantitative review, we generated a dataset with 4610 georeferenced <em>N<sub>e</sub></em> estimates from 3829 unique populations, extracted from 723 articles. These data show that certain taxonomic groups are less likely to meet 50/500 thresholds and are disproportionately impacted by human activities; plant, mammal, and amphibian populations had a <54% probability of reaching = 50 and a <9% probability of reaching = 500. Populations listed as being of conservation concern according to the IUCN Red List had a smaller median than unlisted populations, and this was consistent across all taxonomic groups. was reduced in areas with a greater Global Human Footprint, especially for amphibians, birds, and mammals, however relationships varied between taxa. We also highlight several considerations for future works, including the role that gene flow and subpopulation structure plays in the estimation of in wild populations, and the need for finer-scale taxonomic analyses. Our findings provide guidance for more specific thresholds based on <em>N<sub>e</sub></em> and help prioritize assessment of populations from taxa most at risk of failing to meet conservation thresholds.</p>
Supplementary information for "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability"
<p>Reproducibility data for the manuscript "<em>Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability</em>", it contains data and script for :</p> <ol> <li> <p>The R script <code>01_HDSfreq_Calibration.R</code> of the developed approach to estimate national breeding bird population size using Hierarchical Distance Sampling (HDS) and the secondary candidate set model selection method (Morin et al., 2020)</p> </li> <li> <p>The R script <code>02_pglmm_figures.R</code> for the calibration of the Phylogenetic Generalised Mixed Model (PGLMM) used in the manuscript to compare previous population size estimates to ones modelled using <code>01_HDSfreq_Calibration.R</code>, while accounting for species phylogenetic relatedness</p> </li> <li> <p>The R script <code>03_results_tables.R</code>, used to generate supplementary tables S2.1-3 and S6.1-2.</p> </li> </ol> <ul> <li> <p>Column names are highlighted in italics.</p> </li> </ul> <h2>Data description</h2> <h5>A. BirdPhylo_Burleigh_et_al.tre</h5> <p>A phylogenetic tree from Burleigh et al., 2015. Phylogenetic distances are used as random effect for the PGLMM in script <code>02_pglmm_figures.R</code></p> <h5>B. Conservation_status.txt</h5> <p>A <code>.txt</code> file of the conservation status for France (<em>Statut_FR</em>) and Europe (<em>Statut_EU</em>) for the studied species retrieved from (UICN France et al., 2016). Only <em>Statut_FR</em> is used for the table S6.1.</p> <h5>C. FBBS_trends_20122023.txt</h5> <p>A <code>.txt</code> file containing species trend of the French Breeding Bird Survey data from 2012 to 2023.</p> <ul> <li> <p>Species names (English, French) associated with FBBS trend estimated using data collected from 2012 - 2023</p> </li> <li> <p><em>Hab_specialization</em>, determined from Julliard et al. 2006 approach</p> </li> <li> <p><em>infPrec, supPerc, estimate, se, pval</em> : Species trends over 2012-2023 period in % | lower and upper confidence intervals, mean, standard error and significance</p> </li> </ul> <h5>D. PrepData_HDS.RData</h5> <p>A file containing <code>.RData</code> environment required to run <code>01_HDSfreq_Calibration.R</code> script, it contains :</p> <ul> <li> <p><strong>ATLAS12</strong> : A dataframe with breeding status information from 2012 breeding bird atlas (used to restrict model prediction grid, in regard of 2012 known breeding locations)</p> </li> <li> <p><strong>ConcordTBL</strong> : A concordance table for species names (English, French and scientific notation)</p> </li> <li> <p><strong>EPOC_ODF</strong> : observation dataset, each line corresponds to detected individuals</p> <ul> <li> <p><em>UUID, Ref, ID_liste, ID, ID_place, Grid_10x10</em> : Columns used to identify observations, lists, sites, locations, 10x10 grids</p> </li> <li> <p><em>ID_species_Biolovision, Nom_espece, english_name, scientific_name</em> : Species ID and names</p> </li> <li> <p><em>Date, Day, Month, Year, Julian_date, Obs_hour, Hour_list, Complete_checklist, Commentary, Project_name, Scheme, Observer, List_time, List_diversity, List_abundance</em> : Lists and Observation related effort covariates and metadata</p> </li> <li> <p><em>X_Lambert93_m, Y_Lambert93_m</em> : Observation locations in <code>(crs = 2154)</code></p> </li> <li> <p><em>GPS_loc_observer</em> : Logical, TRUE : location of observers corresponds to true GPS information ; FALSE : observer's location approximated as the barycenter of observations</p> </li> <li> <p><em>X_barycentre_L93, Y_barycentre_L93</em> : Observers location in <code>(crs = 2154)</code></p> </li> <li> <p><em>Use_distance_sampling, Observation_distance_m, Distance_bin_logical, Distance_class_0_25, Distance_class_25_100, Distance_class_100_200, Distance_class_200_more</em> : Distance sampling related informations</p> </li> <li> <p><em>Abudance_brut, Estimate, Number, Nb_male_identified, Nb_female_identified, Nb_juvenile_identified, Nb_grounded, Nb_flying, Nb_auditory, Nb_NA</em> : Observation metadata, used in case of <em>a priori</em> filter over male detection.</p> </li> </ul> </li> <li> <p><strong>grid_pred_envvar</strong> : Prediction grid with environmental covariates, see appendix S3 of the manuscript, covering metropolitan France</p> </li> <li> <p><strong>grid_pred.sf</strong> : corresponding sf object</p> </li> <li> <p><strong>L93_10x10</strong> : sf object corresponding to 10x10 grid used in 2012 atlas</p> </li> <li> <p><strong>ObsVar_EPOCODF</strong> : dataframe specifying lists effort covariates</p> </li> <li> <p><strong>OCCU_EPOC_ODF</strong> : Environmental covariate agregated over lists</p> </li> <li> <p><strong>OCCU_EPOC_ODF_sites_envvar</strong> : Environmental covariate agregated over sites</p> </li> <li> <p><strong>table.pheno</strong> : species table specifying related phenology filter</p> </li> </ul> <h5>E. ReadOutput_HDSfreq_comparison.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over areas determined as breeding in the 2012 atlas. <strong>Predictions were restrained over location known as breeding in 2012 for the sake of comparison.</strong></p> <ul> <li> <p><em>HDS_estimUnfenced_XXX</em> : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript)</p> </li> <li> <p><em>HDS_estim_ExtrapolFence_XXX</em> : average pop. size estimated with confidence interval after prediction post-treatment</p> </li> <li> <p><em>NB_data_calib</em> : Number of observations (distance data, not sites) used for calibration</p> </li> <li> <p><em>MALE_FILTERING</em> : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for the comparison with the previous atlas (in pairs). (cf . line 88-90 in <code>02_pglmm_figures.R</code>)</p> </li> <li> <p><em>EcartDTF_filtrage_maleOnly</em> : If MALE_FILTERING == T, proportion of the remaining data used for calibration after removal of list with individual tagged as female/juvenile (in %)</p> </li> <li> <p><em>Max_dist_breaks</em> : Maximal distance for detection function, after right-side truncation of 5%</p> </li> <li> <p><em>Chat</em> : Coefficient of overdisperion of the best model in the second candidate set</p> </li> <li> <p><em>MED_MEAN_Prob_Detect</em> (.._SE) : weighted averaged median of intercept from the availability state from HDS models, weigthed AICc-wise</p> </li> <li> <p><em>MED_MEAN_Density</em> : weighted averaged median of intercept from the abundance state from HDS models, weigthed AICc-wise</p> </li> <li> <p><em>Significant_phi/lambda</em> : Categorial (Significant/Near/Not), are availability/abundance intercepts significatively different from 0 (significant : alpha = 0.05, near : alpha = 0.1)</p> </li> <li> <p><em>KeyFun_used</em> : Key function used for distance sampling</p> </li> <li> <p><em>Mixtured_used</em> : Mixture used in the abundance state for HDS</p> </li> </ul> <h5>F. ReadOutput_HDSfreq_comparison_20212022.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2022 EPOC-ODF data over areas determined as breeding in the 2012 atlas. Used for the robustness analysis of HDS estimated population size, see appendix S2 and table S2.2 of the manuscript.</p> <h5>G. ReadOutput_HDSfreq_EstimMetropole.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over <strong>metropolitan France</strong>.</p> <ul> <li> <p><em>HDS_estimUnfenced_XXX</em> : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript)</p> </li> <li> <p><em>HDS_estim_ExtrapolFence_XXX</em> : average pop. size estimated with confidence interval after prediction post-treatment</p> </li> <li> <p><em>MALE_FILTERING</em> : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for conversion to pop. size in breeding pairs</p> </li> <li> <p><em>Chat</em> : Coefficient of overdisperion of the best model in the second candidate set</p> </li> <li> <p><em>KeyFun_used</em> : Key function used for distance sampling</p> </li> <li> <p><em>Mixtured_used</em> : Mixture used in the abundance state for HDS</p> </li> </ul> <h5>H. TABLE_SpeciesFilters_and_2012Estimates.txt</h5> <p>A <code>.txt </code>table containing species names (English, French and scientific notation), filters and 2012 French atlas pop. size estimates</p> <ul> <li> <p><em>debut_jour</em> : starting day of the month for phenology filter</p> </li> <li> <p><em>debut_mois</em> : starting month for phenology filter</p> </li> <li> <p><em>fin_jour</em> : ending day of the month for phenology filter</p> </li> <li> <p><em>fin_mois</em> : ending month for phenology filter</p> </li> <li> <p><em>Estim_low/up_Atlas2012</em> : Lower and Upper interval of estimated pop. size in 2012 (number in breeding pairs)</p> </li> <li> <p><em>gregarious</em> : logical (0,1) specifying if the species is considered gregarious during its breeding season</p> </li> </ul> <h5>I. sessionInfo_script_XX</h5> <p>User R session information, obtained from <code>sessionInfo()</code> R function, used for running R script.</p> <h2>Code</h2> <h5>A. <code>01_HDSfreq_Calibration.R</code></h5> <p>R script showcasing data formatting and model calibration of the HDS based upon frequentist aproach from <code>unmarked</code> R package. For more details of the model calibration approach, see appendix S4 of the manuscript.</p> <h5>B. <code>02_pglmm_figures.R</code></h5> <p>Script for the calibration of the PGLMM and generation of figure 5 of the manuscript.</p> <h5>C. <code>03_results_tables.R</code></h5> <p>Script to generate tables depicted in Appendices S2 (S2.1-3) and S6 (S6.1-2)</p> <h5>D. <code>HDS_functions.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>, contains 2 functions:</p> <ul> <li> <p><code>Try_HDS()</code> : Function implementing a try-catch permitting calibration of multiple species in a loop.</p> <ul> <li> <p>Species with non convergent models are skipped sending a notification to the user R interface.</p> </li> <li> <p>Used in all sub-candidate sets (i.e. "null", "p", "phi", "lambda")</p> </li> <li> <p>When phase="ALL" corresponding to the second candidate set (i.e. ensemble of best model candidates, with delta_AIC <= 10, from previous sub-candidate sets), it permits the use of previous sub-candidates set coefficients as starting values, with <code>StartValues </code>argument</p> </li> <li> <p>Later part of the function hack the call of the unmarkedFit class, in order to accommodate from calibrating a gdistsamp using characters formulas</p> </li> </ul> </li> <li> <p><code>fitstats()</code> : Function from unmarked::parboot(), available with <code>help(parboot)</code>. Allow estimation of multiple goodness-of-git statistic (Freeman-Tukey, Chi-squared and Sum of Squared Estimate of errors) through parametric bootstrap. In the manuscript, only chi-squared metric is used.</p> </li> </ul> <h5>E. <code>dsmextra_modif_function.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>. Miscellaneous adjustment of core function from <code>dsmextra </code>package (main change being the integration of tolerance argument (<code>tol</code>) in the chain of function.</p> <h5>F. <code>misc_unmarked.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>. modify Setmethods for unmarked function, in particular for <code>unmarked::parboot</code>, allowing parallelization of parametric bootstrap with prior unmarked version (<code>unmarked < 1.3.0</code>).</p> <h2>References</h2> <p>Data was derived from the following sources:</p> <ul> <li> <p>Burleigh, J.G., Kimball, R.T., Braun, E.L., 2015. Building the avian tree of life using a large-scale, sparse supermatrix. Molecular Phylogenetics and Evolution 84, 53–63. <a href="https://doi.org/10.1016/j.ympev.2014.12.003">https://doi.org/10.1016/j.ympev.2014.12.003</a></p> </li> </ul> <p>Other sources :</p> <ul> <li> <p>Julliard, R., Clavel, J., Devictor, V., Jiguet, F., Couvet, D., 2006. Spatial segregation of specialists and generalists in bird communities. Ecology Letters 9, 1237–1244. <a href="https://doi.org/10.1111/j.1461-0248.2006.00977.x">https://doi.org/10.1111/j.1461-0248.2006.00977.x</a></p> </li> <li> <p>Morin, D.J., Yackulic, C.B., Diffendorfer, J.E., Lesmeister, D.B., Nielsen, C.K., Reid, J., Schauber, E.M., 2020. Is your ad hoc model selection strategy affecting your multimodel inference? Ecosphere 11, e02997. <a href="https://doi.org/10.1002/ecs2.2997">https://doi.org/10.1002/ecs2.2997</a></p> </li> <li> <p>UICN France, MNHN, LPO, SEOF, ONCFS, 2016. La Liste rouge des espèces menacées en France - Chapitre Oiseaux de France métropolitaine. Paris, France.</p> </li> </ul>
Data from: Effects of age, breeding strategy, population density, and number of neighbors on territory size and shape in Savannah Sparrows
<p>The size and shape of an animal's breeding territory are dynamic features influenced by multiple intrinsic and extrinsic factors and can have important implications for survival and reproduction. Quantitative studies of variation in these territory features can generate deeper insights into animal ecology and behavior. We explored the effect of age, breeding strategy, population density, and number of neighbors on the size and shape of breeding territories in an island population of Savannah Sparrows (<em>Passerculus sandwichensis</em>). Our dataset consisted of 407 breeding territories belonging to 225 males sampled over 11 years. We compared territory sizes to the age of the male territorial holder, the male's reproductive strategy (monogamy vs. polygyny), the number of birds in the study population (population density), and the number of immediate territorial neighbors (local density). We found substantial variation in territory size, with territories ranging over two orders of magnitude from 57 to 5727 m2 (0.0057 to 0.57 ha). Older males had larger territories, polygynous males had larger territories, territories were smaller in years with higher population density, and larger territories were associated with more immediate territorial neighbors. We also found substantial variation in territory shape, from near-circular to irregularly-shaped territories. Males with more neighbors had irregularly shaped territories, but the shape did not vary with male age, breeding strategy, or population density. For males that lived two years or longer, we found strong consistent individual differences in territory size across years, but weaker individual differences in territory shape, suggesting that size has high repeatability whereas shape has low repeatability. Our work provides evidence that songbird territories are highly dynamic and that their size and shape reflect both intrinsic factors (age and number of breeding partners) and extrinsic factors (population density and number of territorial neighbors).</p>
Fig 2 in Sexual Size Dimorphism In Free-Living Populations Of Mus Musculus: Are Male House Mice Bigger?
Fig 2. Variation in SSD during the first five weeks of postnatal development in five mice populations. SSD is expressed as Lowich-Gibbons ratios of mean body weight (see under Material and Methods)
Fig. 1 in Sexual Size Dimorphism In Free-Living Populations Of Mus Musculus: Are Male House Mice Bigger?
Fig. 1. Map of the studied localities: 1 = Czech Republic, 2 = The Balkans, 3 = Iran, 4 = Jordan, 5 = hybrids. See Material and Methods for coordinates of the localities
Fig. 3 in Altitudinal Variation In Population Density, Body Size And Morphometric Structure In C A R A B U S O D O R At U S S H I L, 1996 (C O L E O P T E R A: Carabidae)
Fig. 3. Illustration of measurements: 1-2 – elytra length (hereafter "A", 3-4 – elytra width ("B"), 5-6 – pronotum length "C"), 7-8 – pronotum width (D), 9-10 – head length (E), 11–12 – distance between the eyes (signed as "head width" or "F" in the figures).
Fig. 7 in Altitudinal Variation In Population Density, Body Size And Morphometric Structure In C A R A B U S O D O R At U S S H I L, 1996 (C O L E O P T E R A: Carabidae)
Fig. 7. Descriptive statistics of elytra length means in C. odoratus at the plots on different altitudes.
Figure 3. The effective population size through recent time for 3 in Comparative analyses of past population dynamics between two subterranean zokor species and the response to climate changes
Figure 3. The effective population size through recent time for 3 clades of Gansu zokor (Eospalax cansus).
Figure 2 in Body size and age in three populations of the northern banded newt Ommatotriton ophryticus (Berthold, 1846) from Turkey
Figure 2. Cross-sections through phalanges of O. ophryticus. A) An 8-year-old female from Bahçesultan. Each LAG corresponds to a year. B) An 8-year-old female from Tosya. The first LAG was destroyed by endosteal resorption. Periphery was not regarded as a LAG. Arrowheads show the LAGs and red arrow shows the false LAG (e.b. = endosteal bone, e.r. = endosteal resorption, k.l. = Kastschenko line, m.c. = marrow cavity, p. = periphery).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.