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FIGURE 5 in A new species of Bryconamericus (Characidae: Stevardiinae) with breeding tubercles from the upper rio Paraná basin

FIGURE 5 | Geographic distribution of Bryconamericus misei (red marks). The star represents the type-locality. The red rectangle on detail indicates the position of the map in relation to Brazilian borders.

FIGURE 2 in A new species of Bryconamericus (Characidae: Stevardiinae) with breeding tubercles from the upper rio Paraná basin

FIGURE 2 | Left jaws of Bryconamericus misei, NUP 24155, paratype, 44.7 mm SL. A. Maxilla. B. Premaxilla in anterior view. C. Ventral view of premaxilla. D. Dentary in lateral view.

FIGURE 4 in A new species of Bryconamericus (Characidae: Stevardiinae) with breeding tubercles from the upper rio Paraná basin

FIGURE 4 | Sexual dimorphism in Bryconamericus misei. A. In males, the distal margin of the anal-fin is straight. B. In females, the distal margin of the anal-fin is slightly concave. C. In males, the pelvicfin reaches distinctly past the urogenital opening. D. In females, the pelvic-fin reached at most the urogenital opening. A, C. NUP 24150, holotype, 54.8 mm SL. B, D. NUP 24149, paratype, 54.9 mm SL.

opencc-by-4.0Jan 2016View details →

Fig. 1 in SHORT COMMUNICATION Monitoring a population of Cruziohyla craspedopus (Funkhouser, 1957) using an artificial breeding habitat

Fig. 1. Site map for ABHab points at LPS: dashed line is approximate separation of terra firma and flood plain forest.

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Data from: The scope and adaptive value of modulating aggression across breeding stages: Case study in a competitive female songbird

In seasonally breeding animals, costs and benefits of territorial aggression should vary over time; however, little work thus far has directly examined the scope and adaptive value of individual-level plasticity in aggression across breeding stages. We explore these issues using the tree swallow (Tachycineta bicolor), a bird species in which females compete for limited nesting sites before producing a single brood. We measured the aggressiveness of nearly 100 females within three different stages: (1) shortly after territory-establishment, (2) during early incubation, and (3) while caring for young chicks. We used k-means clustering to categorize females into four distinct plasticity 'types' based on the timing, direction, and magnitude of their changes in aggression between stages. We then tested whether plasticity type and stage-specific aggression vary with key performance metrics. Two of the four plasticity types became less aggressive across consecutive breeding stages, consistent with population-level patterns, though these plasticity types largely did not differ from one another in survival or reproductive success. A third type was characterized by high levels of among-stage plasticity; these females, had significantly lower body mass while parenting, tended to hatch fewer eggs, and had the lowest observed overwinter survival rates. A final type exhibited limited plasticity, with moderate to low levels of aggression in all stages; this low plasticity - low aggression phenotype was not associated with any negative effects to performance. These results reveal substantial among-individual variation in behavioral plasticity, which may reflect diverse solutions to trade-offs between current reproduction and future survival.

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Рис. 2. КоΛичество жиΛых гнезΑ ΑаΛьневосточного аиста, учтенных в 2018–2019 гг. по аΑминистративным районам Амурской обΛасти Fig. 2. The number of inhabited Oriental stork nests recorded in 2018–2019 in different administrative districts of the Amur region in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019

Рис. 2. КоΛичество жиΛых гнезΑ ΑаΛьневосточного аиста, учтенных в 2018–2019 гг. по аΑминистративным районам Амурской обΛасти Fig. 2. The number of inhabited Oriental stork nests recorded in 2018–2019 in different administrative districts of the Amur region

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Data from: Migratory singers dynamically overlap the signal space of a breeding warbler community

Migratory species inhabit many communities along their migratory routes. Across taxa, these species repeatedly move into and out of communities, interacting with each other and locally breeding species and competing for resources and niche space. However, their influence is rarely considered in analyses of ecological processes within the communities they temporarily occupy. Here, we explore the impact of migratory species on a breeding community using the framework of acoustic signal space, a limited resource in which sounds of species within communities co-exist. Migrating New World warblers (Parulidae, hereafter referred to as migrant species) often sing during refueling stops in areas and at times during which locally breeding warbler species (hereafter breeding species) are singing to establish territories and attract mates. We used eBird data to determine co-occurrence of 19 migrant and 11 breeding warbler species across spring migration in SW Michigan, generated a signal space from song recordings of these species, and examined patterns of signaling overlap experienced by breeding species as migrants moved through the community. Migrant species were present for two-thirds of the breeding season of local species, including periods when breeding species established territories and attracted mates. Signaling niche overlap experienced by individual breeding species was idiosyncratic and varied over time, yet niche overlap between migrant and breeding species occurred more commonly than between breeding species or between migrant species. Nevertheless, the proportion of niche overlap between migrant and breeding warblers was similar to overlap among breeding species. Our findings showed that singing by migrant species overlapped the signals of many breeding species, suggesting that migrants could have unexplored impacts on communication in breeding species, potentially affecting song detection and song evolution. Our study contributes to a growing body of research documenting impacts of migratory species on communities and ecosystems.

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The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty. in Fowler's Toad (Anaxyrus fowleri) occupancy in the southern mid-Atlantic, USA

The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty.

Supplementary information for "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability"

Reproducibility data for the manuscript "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability", it contains data and script for : <ol> <li> 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) </li> <li> 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 </li> <li> The R script <code>03_results_tables.R</code>, used to generate supplementary tables S2.1-3 and S6.1-2. </li> </ol> <ul> <li> Column names are highlighted in italics. </li> </ul> <h2>Data description</h2> <h5>A. BirdPhylo_Burleigh_et_al.tre</h5> 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> <h5>B. Conservation_status.txt</h5> A <code>.txt</code> file of the conservation status for France (Statut_FR) and Europe (Statut_EU) for the studied species retrieved from (UICN France et al., 2016). Only Statut_FR is used for the table S6.1. <h5>C. FBBS_trends_20122023.txt</h5> A <code>.txt</code> file containing species trend of the French Breeding Bird Survey data from 2012 to 2023. <ul> <li> Species names (English, French) associated with FBBS trend estimated using data collected from 2012 - 2023 </li> <li> Hab_specialization, determined from Julliard et al. 2006 approach </li> <li> infPrec, supPerc, estimate, se, pval : Species trends over 2012-2023 period in % | lower and upper confidence intervals, mean, standard error and significance </li> </ul> <h5>D. PrepData_HDS.RData</h5> A file containing <code>.RData</code> environment required to run <code>01_HDSfreq_Calibration.R</code> script, it contains : <ul> <li> ATLAS12 : A dataframe with breeding status information from 2012 breeding bird atlas (used to restrict model prediction grid, in regard of 2012 known breeding locations) </li> <li> ConcordTBL : A concordance table for species names (English, French and scientific notation) </li> <li> EPOC_ODF : observation dataset, each line corresponds to detected individuals <ul> <li> UUID, Ref, ID_liste, ID, ID_place, Grid_10x10 : Columns used to identify observations, lists, sites, locations, 10x10 grids </li> <li> ID_species_Biolovision, Nom_espece, english_name, scientific_name : Species ID and names </li> <li> Date, Day, Month, Year, Julian_date, Obs_hour, Hour_list, Complete_checklist, Commentary, Project_name, Scheme, Observer, List_time, List_diversity, List_abundance : Lists and Observation related effort covariates and metadata </li> <li> X_Lambert93_m, Y_Lambert93_m : Observation locations in <code>(crs = 2154)</code> </li> <li> GPS_loc_observer : Logical, TRUE : location of observers corresponds to true GPS information ; FALSE : observer's location approximated as the barycenter of observations </li> <li> X_barycentre_L93, Y_barycentre_L93 : Observers location in <code>(crs = 2154)</code> </li> <li> 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 : Distance sampling related informations </li> <li> Abudance_brut, Estimate, Number, Nb_male_identified, Nb_female_identified, Nb_juvenile_identified, Nb_grounded, Nb_flying, Nb_auditory, Nb_NA : Observation metadata, used in case of a priori filter over male detection. </li> </ul> </li> <li> grid_pred_envvar : Prediction grid with environmental covariates, see appendix S3 of the manuscript, covering metropolitan France </li> <li> grid_pred.sf : corresponding sf object </li> <li> L93_10x10 : sf object corresponding to 10x10 grid used in 2012 atlas </li> <li> ObsVar_EPOCODF : dataframe specifying lists effort covariates </li> <li> OCCU_EPOC_ODF : Environmental covariate agregated over lists </li> <li> OCCU_EPOC_ODF_sites_envvar : Environmental covariate agregated over sites </li> <li> table.pheno : species table specifying related phenology filter </li> </ul> <h5>E. ReadOutput_HDSfreq_comparison.csv</h5> 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. Predictions were restrained over location known as breeding in 2012 for the sake of comparison. <ul> <li> HDS_estimUnfenced_XXX : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript) </li> <li> HDS_estim_ExtrapolFence_XXX : average pop. size estimated with confidence interval after prediction post-treatment </li> <li> NB_data_calib : Number of observations (distance data, not sites) used for calibration </li> <li> MALE_FILTERING : (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>) </li> <li> EcartDTF_filtrage_maleOnly : If MALE_FILTERING == T, proportion of the remaining data used for calibration after removal of list with individual tagged as female/juvenile (in %) </li> <li> Max_dist_breaks : Maximal distance for detection function, after right-side truncation of 5% </li> <li> Chat : Coefficient of overdisperion of the best model in the second candidate set </li> <li> MED_MEAN_Prob_Detect (.._SE) : weighted averaged median of intercept from the availability state from HDS models, weigthed AICc-wise </li> <li> MED_MEAN_Density : weighted averaged median of intercept from the abundance state from HDS models, weigthed AICc-wise </li> <li> Significant_phi/lambda : Categorial (Significant/Near/Not), are availability/abundance intercepts significatively different from 0 (significant : alpha = 0.05, near : alpha = 0.1) </li> <li> KeyFun_used : Key function used for distance sampling </li> <li> Mixtured_used : Mixture used in the abundance state for HDS </li> </ul> <h5>F. ReadOutput_HDSfreq_comparison_20212022.csv</h5> 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. <h5>G. ReadOutput_HDSfreq_EstimMetropole.csv</h5> A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over metropolitan France. <ul> <li> HDS_estimUnfenced_XXX : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript) </li> <li> HDS_estim_ExtrapolFence_XXX : average pop. size estimated with confidence interval after prediction post-treatment </li> <li> MALE_FILTERING : (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 </li> <li> Chat : Coefficient of overdisperion of the best model in the second candidate set </li> <li> KeyFun_used : Key function used for distance sampling </li> <li> Mixtured_used : Mixture used in the abundance state for HDS </li> </ul> <h5>H. TABLE_SpeciesFilters_and_2012Estimates.txt</h5> A <code>.txt </code>table containing species names (English, French and scientific notation), filters and 2012 French atlas pop. size estimates <ul> <li> debut_jour : starting day of the month for phenology filter </li> <li> debut_mois : starting month for phenology filter </li> <li> fin_jour : ending day of the month for phenology filter </li> <li> fin_mois : ending month for phenology filter </li> <li> Estim_low/up_Atlas2012 : Lower and Upper interval of estimated pop. size in 2012 (number in breeding pairs) </li> <li> gregarious : logical (0,1) specifying if the species is considered gregarious during its breeding season </li> </ul> <h5>I. sessionInfo_script_XX</h5> User R session information, obtained from <code>sessionInfo()</code> R function, used for running R script. <h2>Code</h2> <h5>A. <code>01_HDSfreq_Calibration.R</code></h5> 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. <h5>B. <code>02_pglmm_figures.R</code></h5> Script for the calibration of the PGLMM and generation of figure 5 of the manuscript. <h5>C. <code>03_results_tables.R</code></h5> Script to generate tables depicted in Appendices S2 (S2.1-3) and S6 (S6.1-2) <h5>D. <code>HDS_functions.R</code></h5> R script called in <code>01_HDSfreq_Calibration.R</code>, contains 2 functions: <ul> <li> <code>Try_HDS()</code> : Function implementing a try-catch permitting calibration of multiple species in a loop. <ul> <li> Species with non convergent models are skipped sending a notification to the user R interface. </li> <li> Used in all sub-candidate sets (i.e. "null", "p", "phi", "lambda") </li> <li> 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 </li> <li> Later part of the function hack the call of the unmarkedFit class, in order to accommodate from calibrating a gdistsamp using characters formulas </li> </ul> </li> <li> <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. </li> </ul> <h5>E. <code>dsmextra_modif_function.R</code></h5> 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. <h5>F. <code>misc_unmarked.R</code></h5> 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>). <h2>References</h2> Data was derived from the following sources: <ul> <li> 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> </li> </ul> Other sources : <ul> <li> 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> </li> <li> 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> </li> <li> UICN France, MNHN, LPO, SEOF, ONCFS, 2016. La Liste rouge des espèces menacées en France - Chapitre Oiseaux de France métropolitaine. Paris, France. </li> </ul>

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Data from: Effects of age, breeding strategy, population density, and number of neighbors on territory size and shape in Savannah Sparrows

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 (Passerculus sandwichensis). 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).

Fig. 2 in Temperature Dependence Of The Breeding Parametres Of The Collared Flycatcher (Passeriformes, Muscicapidae) In The National Park Homilshanski Lisy (Ne Ukraine)

Fig. 2. First spring records and first-egg day of Collared Flycatcher at investigation plots in 2006–2017.

Fig. 5 in Temperature Dependence Of The Breeding Parametres Of The Collared Flycatcher (Passeriformes, Muscicapidae) In The National Park Homilshanski Lisy (Ne Ukraine)

Fig. 5. Model-averaged coefficients of the predictor variables from the subset of best-fitting models for the clutch size (eggs).

Fig. 1 in Temperature Dependence Of The Breeding Parametres Of The Collared Flycatcher (Passeriformes, Muscicapidae) In The National Park Homilshanski Lisy (Ne Ukraine)

Fig. 1. Map of boundaries of the Collared Flycatcher subpopulation study area (Keller et al., 2020).

Fig. 3 in Temperature Dependence Of The Breeding Parametres Of The Collared Flycatcher (Passeriformes, Muscicapidae) In The National Park Homilshanski Lisy (Ne Ukraine)

Fig. 3. Model-averaged importance of the predictor variables from the subset of best-fitting models for the first egg date (FED).

Figure 4 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus

Figure 4. Seasonality in breeding records of White-rumped Monjita Xolmis velatus in Brazil based on citizen science data, the literature and this study.

Figure 3 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus

Figure 3. Food delivered to nestlings of White-rumped Monjita Xolmis velatus, Rio Claro, São Paulo, Brazil. A: larva, B: Oligochaeta, C: Erythemis vesiculosa, D: Zammara tympanum, E: Lepidoptera (moth), F: Blattodea, G: Myriapoda, H: Kentropyx aff. paulensis (Luiz Carlos Ramassotti)

Figure 2 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus

Figure 2. Food delivered to nestlings of White-rumped Monjita Xolmis velatus, Rio Claro, São Paulo, Brazil. A‒C = Scarabaeidae; D = Grylloidea; E = Ensifera; F = Tettigoniidae, Conocephalinae, Copiphorini; G‒H = Lycosidae (Luiz Carlos Ramassotti)

Figure 1 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus

Figure 1. Nests of White-rumped Monjita Xolmis velatus. A: nest 1 with nestlings, Analândia, São Paulo, Brazil, 15 November 2008 (Rogério Carlos Machado); B: nest 2 with eggs, Marília, São Paulo, Brazil, 23 October 2010 (Manuel Gonzales); C‒F: nest 3, Rio Claro, São Paulo, Brazil; C: adult at entrance to PVC pipe, 20 October 2021 (Luiz Ramassotti); D: nest, 29 October 2021 (Carlos Otávio Araujo Gussoni); E: nest with nestlings, 20 October 2021 (Carlos Otávio Araujo Gussoni); F: nestling, 22 October 2021 (Carlos Otávio Araujo Gussoni)

Figure 5 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus

Figure 5. Begging calls of a fledgling (A) and calls of an adult (B) White-rumped Monjita Xolmis velatus. Sonogram made using software Raven Pro 1.6.1 (Center for Conservation Bioacoustics 2019).