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opencc-by-4.0Nov 2023View details →

Phylogenetic analysis, morphological studies, element profiling, and muscarine detection reveal a new toxic Inosperma (Inocybaceae, Agaricales) species from tropical China

Open the record for dataset details and reuse information.

dryad40/100

Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation

1. Patterns in, and the underlying dynamics of, species cooccurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterisations have been published that could be used with the same fundamental modelling framework that accounts for imperfect detection, although each parameterisation has different advantages and disadvantages. 2. We propose a parameterisation based on log-linear modelling that does not require a species hierarchy to be defined (in terms of dominance), and enables a numerically robust approach for estimating covariate effects. 3. Conceptually the parameterisation is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, 'symmetric', interpretations of parameter estimates. 4. The parameterisation can be applied to many species, in either a maximum-likelihood or Bayesian estimation framework. We illustrate the method using camera trapping data collected on three mesocarnivore species in South Texas.

opencc-zeroApr 2022View details →

dryad40/100

Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection

Fauna monitoring often relies on visual monitoring techniques such as camera trappings, which have biases leading to underestimates of vertebrate species diversity. Environmental DNA (eDNA) has emerged as a new source of biodiversity data that may improve biomonitoring; however, eDNA based assessments of species richness remain relatively untested in terrestrial environments. We investigated the suitability of fallen log hollow sediment as a source of vertebrate eDNA, across two sites in south-western Australia - one with a Mediterranean climate and the other semi-arid. We compared two different approaches (camera trapping and eDNA metabarcoding) for monitoring of vertebrate species, and investigated the effect of other factors (frequency of species, timing of visits, frequency of sampling, body size) on vertebrate species detectability. Metabarcoding of hollow sediments resulted in the detection of higher species richness in comparison Hollow sediment detected higher species richness (29 taxa: six birds, three reptiles and 20 mammals) to metabarcoding of soil at the entrance of the hollow (13 taxa: three birds, two reptiles and eight mammals). We detected 31 taxa in total with eDNA metabarcoding and 47 with camera traps, with 14 taxa detected by both (12 mammals and two birds). By comparing camera trap data with eDNA read abundance, we were able to detect vertebrates through eDNA metabarcoding that had visited the area up to two months prior to sample collection. Larger animals were more likely to be detected, and so were vertebrates that were identified multiple times in the camera traps. These findings demonstrate the importance of substrate selection, frequency of sampling, and animal size, on eDNA based monitoring. Future eDNA experimental design should consider all these factors as they affect detection of target taxa.

opencc-zeroMay 2022View details →

opencc-by-4.0Jun 2022View details →

CoFly-WeedDB: A UAV image dataset for weed detection and species identification

The CoFly-WeedDB contains 201 RGB images (~436MB) from the attached camera of DJI Phantom Pro 4 from a cotton field in Larissa, Greece during the first stages of plant growth. The RGB images were collected while the Unmanned Aerial Vehicle (UAV) was performing a coverage mission over the field's area. During the designed mission, the camera angle was adjusted to -87°, vertically with the field. The flight altitude and speed of the UAV were equal to 5m and 3m/s, respectively, aiming to provide a close and clear view of the weed instances.  All images have been annotated by expert agronomists using the LabelMe annotation tool, providing the exact boundaries of 3 types of common weeds in this type of crop, namely (i) Johnson grass, (ii) Field bindweed, and (iii) Purslane. The dataset can be used alone and in combination with other datasets to develop AI-based methodologies for automatic weed segmentation and classification purposes.

opencc-by-4.0Dec 2020View details →

Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer

Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand).

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>

opencc-by-4.0Dec 2022View details →

Figure 1 in Early detection of alien fern species through the consultation of horticultural catalogues

Figure 1. Prevalence of different (a, c) scales and (b, d) modes of trade expressed as proportions of the total number of traders and total number of species recorded per trade country. Treatment of species that were traded both on-ground and via e-commerce is detailed in the methods. Countries of trade: Canada (CA), United States of America (US), the United Kingdom and the Republic of Ireland (UKI), South Africa (ZA), Australia (AU), New Zealand (NZ).

Figure 2 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)

Figure 2. The Rapid Environmental (e)DNA Assessment and Deployment Initiative & Network (READI-Net) project components being developed to support molecular detection of invasive species. Molecular tools, like eDNA sampling, are sensitive and costeffective for early detection of invasive species and are one component of the National Early Detection Rapid Response framework.

Figure 1 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)

Figure 1. The National Early Detection Rapid Response framework developed by the U.S. Department of the Interior Invasive Species Task Force. Open circles represent the components to be enacted for effective early detection and rapid response of invasive species. The associated commentary reflects the primary questions, observations, and directives that the process from one component to the next. At the core of the process, are the informational inputs necessary for management decision-making.

opencc-by-4.0Dec 2021View details →

Map. Species diversity of Diplopoda within the Sikhote-Alin State Nature Biosphere Reserve. The numbers of the natural landmarks (1–18) are explained in the "Material and Methods section" Карта. ВиΑовое разнообразие Αвупарноногих многоножек в Сихотэ-АΛинском заповеΑнике. Номера урочищ (1–18) поясняются в разΑеΛе «МатериаΛ и метоΑы» in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)

Map. Species diversity of Diplopoda within the Sikhote-Alin State Nature Biosphere Reserve. The numbers of the natural landmarks (1–18) are explained in the "Material and Methods section" Карта. ВиΑовое разнообразие Αвупарноногих многоножек в Сихотэ-АΛинском заповеΑнике. Номера урочищ (1–18) поясняются в разΑеΛе «МатериаΛ и метоΑы»

Fig. 4 in Detection of cryptic species of Rugopharynx (Nematoda: Strongylida) from the stomachs of Australian macropodid marsupials

Fig. 4. Phylogenetic relationships of species of Rugopharynx and Rugonema labiatum based on a neighbor-joining analysis of the sequence data of the ITS+ nuclear ribosomal DNA. Values above and below branches represent the NJ and MP bootstrap values (respectively) that were greater than 70%.

Fig. 3 in Detection of cryptic species of Rugopharynx (Nematoda: Strongylida) from the stomachs of Australian macropodid marsupials

Fig. 3. Phylogenetic relationships of species of Rugopharynx and Rugonema labiatum based on a Bayesian analysis of the sequence data of the ITS+ nuclear ribosomal DNA. Values above branches indicate posterior probabilities that were greater than 0.8. Abbreviations of Australian state names are provided in Table 1.

Fig. 2 in Detection of cryptic species of Rugopharynx (Nematoda: Strongylida) from the stomachs of Australian macropodid marsupials

Fig. 2. Morphological buccal capsule types in the genus Rugopharynx. I, simple cylindrical buccal capsule, R. macropodis; IIA, bilobed buccal capsule with subequal divisions, R. epsilon; IIB, bilobed buccal capsule with anterior lobe shorter, R. rufogrisea; III, trilobed buccal capsule, R. longibursaris.

Fig. 1 in Detection of cryptic species of Rugopharynx (Nematoda: Strongylida) from the stomachs of Australian macropodid marsupials

Fig. 1. Localities within Australia at which specimens of Rugopharynx used in this study were collected. Coordinates for each locality are provided in Table 1. 1, Lake Clifton; 2, Waroona; 3, Collie, Wellington Dam; 4, Perup River; 5, Kalgoorlie; 6, Wallerberdina Station; 7, Port Augusta; 8, Ashbourne; 9, Kangaroo Island; 10, Naracoorte; 11, Hattah Lakes National Park; 12, Yan Yean; 13, The Gurdies; 14, Launceston; 15, Emu Flat, Bondo State Forest; 16, Trangie; 17, Grafton; 18, Lamington National Park; 19, Miles; 20, Dawes; 21, Mt Sebastopol; 22, Rockhampton; 23, Winton; 24, Proserpine; 25, Bowen; 26, Magnetic Island; 27, Lake Barrine.

Fig. 5 in Detection of cryptic species of Rugopharynx (Nematoda: Strongylida) from the stomachs of Australian macropodid marsupials

Fig. 5. Molecular phylogeny of species of Rugopharynx and Rugonema labiatum based on a consensus of the BI, NJ and MP trees (Figs. 3 and 4), and the relationships of their hosts. This figure includes species of Macropus (M. agilis, M. antilopinus, M. bernardus) which are not hosts to species of Rugopharynx, as well as M. parma, from which no material could be obtained for genetic studies. Only those species of Petrogale included in this study are shown on the host tree. The morphology of the buccal capsule for each nematode taxon is also shown.

opencc-by-4.0Aug 2018View details →

Fig. 3. Seasonal detection rate for E. uekii, type B in Surveillance of Eimeria species in wild Japanese rock ptarmigans, Lagopus muta japonica, and insight into parasitic seasonal life cycle at timberline regions of the Japanese Alps

Fig. 3. Seasonal detection rate for E. uekii, type B, and mixed Eimeria spp. oocyst infection in both adults and chicks in 2006 and 2007. Numbers in parentheses below months indicate the total number of fecal samples analyzed. Data for the number of chicks (23 in 2006 and 11 in 2007) were only available for August.

Fig. 1 in Alien hitchhiker insect species detected from the international vessels entering into Korea in 2021

Fig. 1. Body photos on detected individuals of the alien hitchhiking insect species from the international vessels. A. Chondracris rosea (Acrididae, Orthoptera); B. Mecopoda elongata (Tettigoniidae, ditto); C. Epidaus famulus (Reduviidae, Hemiptera); D. Poecilocoris druraei (Scutelleridae, ditto); E, F. Neochauliodes meridionalis (Corydalidae, Megaloptera); G. Calathus fuscipes (Carabidae, Coleoptera); H. Odontolabis cuvera (Lucanidae, ditto); I. Eriopis chilensis (Coccinellidae, ditto); J. Sagra femorata (Chrysomelidae, ditto); K. Anoplophora horsfieldii (Cerambycidae, ditto); L. Cryptocheilus australis (Pompilidae, Hymenoptera); M. Atta colombica (Formicidae, ditto); N. Eucera nigrescens (Apidae, ditto); O. Palpita quadristigmalis (Crambidae, Lepidoptera).

Fig. 2 in Alien hitchhiker insect species detected from the international vessels entering into Korea in 2021

Fig. 2. Body photos on detected individuals of the alien hitchhiking insect species from the international vessels. A. Arippara disticha (Pyralidae, Lepidoptera); B, C. Blenina donans (Nolidae, ditto); D. Gadirtha fusca (Nolidae, ditto); E. Chloroclystis pyrrholopha (Geomet- ridae, ditto); F. Achaea serva (Erebidae, ditto); G. Anomis combinans (Erebidae, ditto); H. Eudocima procus (Erebidae, ditto); I. Laspeyria subrosea (Erebidae, ditto); J. Mimophisma delunaris (Erebidae, ditto); K, L. Euhampsonia serratifera (Notodontidae, ditto); M. Dendrolimus punctatus (Lasiocampidae, ditto); N. Clanis stenosema (Sphingidae, ditto).