Find research datasets worth reusing

opencc-by-4.0Mar 2020View details →

Fig. 7. Gambrus homonae Sonan, 1930 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 7. Gambrus homonae Sonan, 1930 (A, KPM-NK 76572; B-D, 76568; E, KPM-NK 76573) ― A, E, lateral habitus; B, head, frontal view; C, propodeum, dorsal view; D, right fore wing.

opencc-by-4.0Mar 2020View details →

Fig. 3 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 3. Aritranis kuro sp. nov. (A, KPM-NK 75838), Buathra nipponica sp. nov. (B, KPM-NK 75746), Cryptus daidaigaster sp. nov. (C, NIAES), Gambrus homonae Sonan, 1930 (D, KPM-NK 76566); Glabridorsum japonicum sp. nov. (E, KPM-NK 75742), Gotra elegans sp. nov. (F, KPM-NK 75822); Hoplocryptus ashoroensis sp. nov. (G, NIAES), Ho. ezoensis sp. nov. (H, KPM-NK 75800), Ho. intermedius sp. nov. (I, KPM-NK 75804), Ho. japonicus sp. nov. (J, KPM-NK 75771), Ho. maculatus sp. nov. (K, KPM-NK 75781), Ho. pini (L, KPM-NK 76579), Ho. toshimensis sp. nov. (M, KPM-NK 75797), Picardiella melanoleuca (Gravenhorst, 1829) (N, KPM-NK 75744) and Trychosis breviterebratus sp. nov. (N, KPM-NK 75758), apex of ovipositor, lateral view.

opencc-by-4.0Mar 2020View details →

Fig. 8 in Expression of green fluorescent protein defines a specific population of lamina II excitatory interneurons in the GRP::eGFP mouse

Fig. 8. Glabridorsum japonicum sp. nov. (A, C, E, G, KPM-NK 75742; B, D, F, KPM-NK 75743) ― A, lateral habitus; B, head, mesosoma and metasoma, dorsal view; C, D, mesosoma, lateral view; E, head, frontal view; F, propodeum, dorsal view; G, apical part of right fore wing.

dryad40/100

Data from: Natural coral recovery despite negative population growth

Demographic processes that ensure the recovery and resilience of marine populations are critical as climate change sends an increasing proportion on a trajectory of decline. Yet for some populations, recovery potential remains high. We conducted annual monitoring over 9-years (2012–2020) to assess the recovery of coral populations belonging to genus Pocillopora. These populations experienced a catastrophic collapse following a severe typhoon in 2009. From the start of the monitoring period, high initial recruitment led to the establishment of a juvenile population that rapidly transitioned to sexually mature adults, which dominated the population within six years after the disturbance. As a result, coral cover increased from 1.1% to 20.2% during this time. To identify key demographic drivers of recovery and population growth rates (λ), we applied kernel resampled Integral Projection Models (IPMs), constructing eight successive models to examine annual change. IPMs were able to capture reproductive traits as key demographic drivers over the initial 3 years, whilst individual growth was a continuous key demographic driver throughout the entire monitoring period. IPMs further detected a pulse of reproductive output subsequent to two further Category 5 typhoon events during the monitoring period, exemplifying key mechanisms of resilience for coral populations impacted by disturbance. Despite rapid recovery, (i.e., increased coral cover, individual colony growth, low mortality), IPMs estimated predominantly negative values of λ, indicating a declining population. Indeed, whilst λ translates to a change in the number of individuals, the recovery of coral populations can also be driven by an increase in the size of coral surviving colonies. Our results illustrate that accumulating long-term data of historical dynamics and applying IPMs to extract demographic drivers are crucial for future predictions that are based on comprehensive and robust understandings of ecological change.

opencc-zeroMay 2024View details →

opencc-by-4.0May 2015View details →

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.

Fig. 2. A in First population assessment of two cryptic Tiger Geckos (Goniurosaurus) from northern Vietnam: Implications for conservation

Fig. 2. A: Macrohabitat and B: Microhabit of Goniurosaurus luii in Ha Lang District, Cao Bang Province, North Vietnam; specimens of G. luii from C: Cao Bang Province and D: Lang Son Province (new record). Photos Mona van Schingen, Marta Bernardes, and Tao Thien Nguyen.

Fig. 4. A in First population assessment of two cryptic Tiger Geckos (Goniurosaurus) from northern Vietnam: Implications for conservation

Fig. 4. A: Average population structure of Goniurosaurus catbaensis and a continental G. luii population from Cao Bang Province (May–August vs. June, respectively); B: Frequency histogram of Snout-vent length of G. catbaensis for the months May, July, and August.

Fig. 3. A in First population assessment of two cryptic Tiger Geckos (Goniurosaurus) from northern Vietnam: Implications for conservation

Fig. 3. A: Macrohabitat of Goniurosaurus catbaensis at the coast of Cat Ba Island; B: Limestone cliffs, the typical microhabitat of G. catbaensis; C: Adult male of G. catbaensis marked for population assessment; D: First evidence for the occurrence of G. catbaensis within limestone cave of small offshore Island in Ha Long Bay archipelago. Photos Hai Ngo, Tao Thien Nguyen, and Minh Le Pham.

Fig. 1 in First population assessment of two cryptic Tiger Geckos (Goniurosaurus) from northern Vietnam: Implications for conservation

Fig. 1. Distribution of the cryptic species Goniurosaurus catbaensis (gray circles) and G. luii (black circles): Filled circles represent study areas; dashed circle represents new record of G. luii; questions marks indicate former localities or sites where the current presence is unclear. Occurrence records were represented as big circles to prevent showing exact locality data.

opencc-zeroMay 2024View details →

dryad40/100

Data from: Female-biased population sex ratios caused by genetic rather than ecological mechanisms in dwarf willow (Salix herbacea L.)

Biased sex ratios among reproductive individuals are common in plants, but the underlying mechanisms, as well as the evolutionary consequences, are not well understood. The classical theory of Düsing and Fisher predicts an equal primary sex ratio at seed production, based on the selective advantage of the rare sex. Biased sex ratios among reproductive plants can arise from sexual dimorphism in survival and flowering. Sex ratio biases can also be present from the seed stage; in these cases, assumptions of Düsing's and Fisher's theory, for example, random mating or demographic equilibrium, are thought to be violated. We investigated mechanisms leading to female-biased sex ratios in the arctic-alpine dwarf willow Salix herbacea L. We studied sex ratios in three natural populations over three years as well as in 29 crosses (full-sib families) under controlled conditions over four growth periods. We tested whether sex ratio was associated with habitat parameters (elevation and snowmelt time), or with germination, survival or flowering, and whether females and males differed in size or flowering that may cause observation bias. We detected a strong and consistent female bias, both in natural populations (sex ratio [proportion of females]: 0.71-0.82) and in our controlled experiment (overall sex ratio: 0.70-0-72). Female bias became more pronounced with increasing elevation. Our data did not support sexual dimorphism in size or flowering. Family sex ratios varied largely (from 0.25 to 1), including many female-biased families, unbiased families and two male-biased families. Families with lower germination, seedling establishment, survival or flowering did not have stronger female bias, indicating that intrinsically higher survival or flowering in females does not explain overall female bias. Synthesis: Our results suggest that sex ratio bias in S. herbacea is already present in seeds and does not arise through intrinsic differences between sexes. Candidate mechanisms that can lead to both overall female bias and variation in sex ratio among families are meiotic drive or cyto-nuclear interactions. The pioneer habit of Salix may lead to non-equilibrium population dynamics that allow for the long-term persistence of variable genetic sex ratio distortion systems that arise from genetic conflict.

opencc-by-4.0May 2024View details →

SSP1 and SSP3 population projections with demographic categories

Demographic projections for the Shared Socioeconomic Pathways SSP1 and SSP3 scenarios with demographic breakdown for "young", "adult" and "old" populations, defined as <15 years old, 15-65 years old, and >65 years old, at 10 year intervals. Projections are calculated combining SSP population projections, UN WPP country wide demographic trends, and SEDAC demographic spatial distributions.   The "interp" version of the files includes linear interpolationed values for each grid cell for every year.

opencc-by-4.0Jul 2019View details →

23E. Rhinella margaritifera. Male.Triunfo, Bolivar.This population could represent R in Catalogue of the amphibians of Venezuela: Illustrated and annotated species list, distribution, and conservation

23E. Rhinella margaritifera. Male.Triunfo, Bolivar.This population could represent R. martyi. Photo: César Barrio-Amorós.

opencc-by-4.0Feb 2024View details →

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>

dryad40/100

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).

opencc-zeroJun 2024View details →

opencc-by-4.0Nov 2020View details →

Fig. 1 in On the Critically Endangered Cofre de Perote Salamander (Isthmura naucampatepetl): discovery of a new population in Puebla, Mexico, and update of its known distribution

Fig. 1. Distribution maps of Isthmura naucampatepetl. Black circles represent published records, star represents the new population in the Municipality of Chignautla, Puebla, Mexico. The photograph shows the habitat at the new population. Photo by L. Fernández-Badillo.

opencc-by-4.0Nov 2020View details →

Fig. 2 in On the Critically Endangered Cofre de Perote Salamander (Isthmura naucampatepetl): discovery of a new population in Puebla, Mexico, and update of its known distribution

Fig. 2. Color patterns of each captured Isthmura naucampatepetl individual. Photo numbers correspond to the specimen numbers given in Table 1. Photo 27 is a ventral view of the chins of an adult female (left) and an adult male (right), showing the male mentonian gland. Photos by L. Fernández-Badillo.

Figure 8 in The introduction of Physa acuta (Gastropoda: Physidae) on Ilha Grande, Southeast Brazil, from initial stages to an established population

Figure 8. Variation of environmental variables on the impacted stream of Vila do Abrãao. For rainfall was used the data from the city of Rio de Janeiro. A – water temperature, B – pH, C – conductivity, D – water deep, and E – rainfall.

Figure 7 in The introduction of Physa acuta (Gastropoda: Physidae) on Ilha Grande, Southeast Brazil, from initial stages to an established population

Figure 7. Population variation of Physa acuta, divided in size class, through the time in Ilha Grande. Feb = February, Apr = April, Jun = June, Sep = September, Nov = November.

Figure 3 in The introduction of Physa acuta (Gastropoda: Physidae) on Ilha Grande, Southeast Brazil, from initial stages to an established population

Figure 3. Variation of the abundance of Physa acuta in the eight collecting stations (CS) that was established on an impacted stream of Ilha Grande, along the monitoring time.

Figure 6 in The introduction of Physa acuta (Gastropoda: Physidae) on Ilha Grande, Southeast Brazil, from initial stages to an established population

Figure 6. The overall size class of Physa acuta representation found on the population of Ilha Grande.