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221 results for “species detection”
Data from: Species-habitat networks reveal conservation implications that other community analyses do not detect
<p><span>Grassland restoration is an important conservation intervention supporting declining insect pollinators, particularly in threatened calcareous grassland landscapes. While restoration is often assessed using simple diversity or the similarity to a target community metrics, this can fail to represent key aspects of community reconstruction. Here, we compare a new method, species-habitat networks, with techniques previously relied upon to understand the process of pollinator community restoration. The species-habitat network approach reveals details relevant to insect conservation that are not visible using standard measures of species richness, abundance, community similarity or network metrics. For instance, a shared set of butterflies and bumblebees found in ancient extensively managed grassland, the target community for restoration, were more likely to inhabit previously disturbed grassland than recently disturbed or reverting grasslands. We propose that species-habitat networks should be part of the standard analytical toolkit assessing the effectiveness of restoration, particularly for mobile species such as insects.</span></p>
Data from: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Detector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation
<p>Automatic patient-level recognition of four <em>Plasmodium</em> species on thin blood smear by a Real Time Dectector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation</p> <p>Emilie Guemas, Baptiste Routier, Théo Ghelfenstein-Ferreira, Camille Cordier, Sophie Hartuis, Bénédicte Marion, Sébastien Bertout, Emmanuelle Varlet-Marie, Damien Costa, Grégoire Pasquier</p> <p><strong>Abstract:</strong></p> <p>Malaria remains a global health problem with 247 million cases and 619,000 deaths in 2021. Diagnostic of <em>Plasmodium</em> species is important for administering the appropriate treatment. The gold-standard diagnosis from accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a RT-DETR (Real-Time Detection Transformer)object detection algorithm to discriminate <em>Plasmodium</em> species on thin blood smears images. The algorithm was trained and validated on a dataset consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test dataset of 4,508 images from 170 smears corresponding to 358,825labels coming from six French university hospital. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among <em>Plasmodium </em>positive smears, the global sensitivity was 82.7% (91/110) with a sensitivity of 90% (38/42), 81.8% (18/22) and 76.1% (35/46) for <em>P. falciparum</em>, <em>P. malariae </em>and <em>P. ovale/vivax,</em> respectively. The YOLOv5 model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas where microscopy experts are lacking.</p> <p><strong>Data collection:</strong></p> <p>The training and validation dataset included 24,720 pictures taken from 475 manually May Grunwald-Giemsa (MGG)-stained thin blood smears from the Montpellier University Hospital collection and for a smaller part from the Toulouse University Hospital collection. In Montpellier, the pictures were taken with a Flexcam C1 microscope camera (Leica) attached to a Leica DM 2000 microscope and Leica DF450C microscope camera adapted with a Leica DM2500 microscope at X1000 magnification. Labelling of pictures was performed manually, and then automatically with manual correction with a Computer Visual Annotation Tools (CVAT) free software. Nine categories of labels were used: white blood cells (n=3,338), red blood cells (n=1,887,781), platelets (n=48,520), <em>Trypanosoma brucei </em>(n=2,773), and red blood cells infected by <em>P. falciparum </em>(n=43,545), <em>P. ovale </em>(n=4,651), <em>P. vivax </em>(n=4,115), <em>P. malariae </em>(n=2,849) and <em>Babesia divergens</em> (n=5,142).</p> <p>The test dataset included 4,508 pictures taken from 170 thin blood smears from the same number of patients from the Parasitology laboratories of University Hospitals of Montpellier, Toulouse, Rouen, Lille, Nantes and Saint-Louis in Paris (Table 1). Among these 170 patients, 54 were not infected, including two patients with Howell-Jolly bodies, and 116 were infected with hematozoa. For each patient, between 20 and 30 photos were taken from one thin blood smear with at least one hematozoan parasite per picture for infected patients.</p> <p>Accurate species diagnostic was made by a senior parasitologist, and for recent smears, it was confirmed by specific PCR, either performed locally (Toulouse) or at the Malaria French National Reference Center (Montpellier, Saint Louis, Rouen, Lille, Nantes).</p>
Data for: Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes
<p>DNA metabarcoding is used to enumerate and identify taxa in both environmental samples and tissue mixtures, but the effectiveness of particular markers depends on their sensitivity to the taxa involved. Using multiple primer sets that amplify different genes can mitigate biases in amplification efficiency, sequence resolution, and reference data availability, but few empirical studies have evaluated markers for complementary performance. Here, we assess the individual and joint performance of 22 markers for detecting species in a DNA pool of 98 species of marine and freshwater bony fishes from geographically and phylogenetically diverse origins. We find that a portfolio of four markers targeting 12S, 16S, and two regions of COI identifies 100% of reference taxa to family and nearly 60% to species. We then use these four markers to evaluate metabarcoding of heterogeneous tissue mixtures, using experimental fishmeal to test: 1) the tissue input threshold to ensure detection; 2) how read depth scales with tissue abundance; and 3) the effect of non-target material in the mixture on recovery of target taxa. We consistently detect taxa that make up >1% of fishmeal mixtures and can detect taxa at the lowest input level of 0.01%, but rare taxa (<1%) were detected inconsistently across markers and replicates. Read counts showed only a weak correlation with tissue input, suggesting they are not a reliable quantitative proxy for relative abundance. Despite the limitations arising from primer specificity and reference data availability, our results demonstrate that a modest portfolio of markers can perform well in detecting and identifying aquatic species in complex mixtures despite heterogeneity in tissue representation, phylogenetic affinities, and from a broad geographic range.</p>
Figure 1 in On the occurrence of the Synodontis eupterus (Mochokidae) in the Adriatic drainage system of Croatia: a case of an introduced aquarium species and suggestions for alien species detection measures
Figure 1. – Synodontis eupterus (from Mala Neretva River) (TL = 193 mm) (catalogue number SE-IOR 8112017).
Exploring the use of the South African Nest Record Scheme to detect changes in phenology: A case study using four well represented species
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Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
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Data from: Species-habitat networks reveal conservation implications that other community analyses do not detect
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How, what, and where you sample environmental DNA affects diversity estimates and species detection
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Power of Bayesian and heuristic tests to detect cross-species introgression with reference to gene flow in the Tamias quadrivittatus group of North American chipmunks
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Data from: Are modern cryptic species detectable in the fossil record? A case study on agamid lizards
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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
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Hourly detections of echolocation clicks in Hawaiian Island HARP data from Hawai`i, Kaua`i, and Manawai with species labels
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Data for: Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes
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Data from: Weighting effective number of species measures by abundance weakens detection of diversity responses
1. The effective number of species (ENS) has been proposed as a robust measure of species diversity that overcomes several shortcomings of both diversity indices and species richness measures. However, it is not yet clear if ENS improves interpretation and comparison of biodiversity monitoring data, and ultimately resource management decisions. 2. We used simulations of five stream macroinvertebrate assemblages and spatially extensive field data of stream fishes and mussels to show (1) how different ENS formulations respond to stress and (2) how diversity-environment relationships change with values of q, which weight ENS measures by species abundances. 3. Values of ENS derived from whole simulated assemblages with all species weighted equally (true species richness) steadily decreased as stress increased, and ENS-stress relationships became weaker and more different among assemblages with increased weighting. 4. The amount of variation in ENS across the fish and mussel assemblages that was associated with environmental gradients decreased with increasing q. 5. Synthesis and applications: ENS does not improve interpretability of how diversity responds to stress or natural environmental gradients, and incorporating relative abundance into species diversity measures as implemented in ENS can actually weaken detection of diversity responses. Ecologists need to be cautious about use and interpretation of diversity measures whose values are jointly influenced by richness and evenness, including ENS, and instead separately assess species richness, species evenness, and compositional change in ecological communities.
Data from: Effectiveness of joint species distribution models in the presence of imperfect detection
<p>Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise.</p> <p>A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of "collapsed data". A case study of owls and gliders in Victoria Australia is also illustrated.</p> <p>Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to disentangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications.</p> <p>To avoid biased estimates of inter-species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter-dependencies and occupancy.</p>
Accounting for imperfect detection in data from museums and herbaria when modeling species distributions: Combining and contrasting data-level versus model-level bias correction
The digitization of museum collections as well as an explosion in citizen science initiatives has resulted in a wealth of data that can be useful for understanding the global distribution of biodiversity, provided that the well-documented biases inherent in unstructured opportunistic data are accounted for. While traditionally used to model imperfect detection using structured data from systematic surveys of wildlife, occupancy models provide a framework for modelling the imperfect collection process that results in digital specimen data. In this study, we explore methods for adapting occupancy models for use with biased opportunistic occurrence data from museum specimens and citizen science platforms using 7 species of Anacardiaceae in Florida as a case study. We explored two methods of incorporating information about collection effort to inform our uncertainty around species presence: (1) filtering the data to exclude collectors unlikely to collect the focal species and (2) incorporating collection covariates (collection type, time of collection, and history of previous detections) into a model of collection probability. We found that the best models incorporated both the background data filtration step as well as collector covariates. Month, method of collection and whether a collector had previously collected the focal species were important predictors of collection probability. Efforts to standardize meta-data associated with data collection will improve efforts for modeling the spatial distribution of a variety of species.
Explainable few-shot learning workflow for detecting invasive and exotic tree species
<p>This is the supporting dataset of research work: <a href="Link"><strong>Explainable few-shot learning workflow for detecting</strong></a> <a href="Link"><strong>invasive and exotic tree species</strong></a>. (Link to be added after the publication) In this research, we presents a workflow that tackles both challenges by proposing an explainable few-shot learning workflow for detecting invasive and exotic tree species in the Atlantic Forest of Brazil using Unmanned Aerial Vehicle (UAV) images. By integrating a Siamese network with explainable AI (XAI), the workflow enables the classification of tree species with minimal labeled data while providing visual, case-based explanations for the predictions.</p> <p>The workflow is accessible in <a href="Link">this GitHub repository</a> (Link to be added after the publication). The required dataset of this workflow in provided in this Zenodo repository.</p> <p>This dataset repository has the following contents</p> <ul> <li> <p>uav_img.zip: the UAV orthomosaic image (.tif) of the study area used in this research, with related metadata</p> </li> <li>tree_labels.zip: the labels of trees created by expert, available in .shp and .gpkg</li> <li> <p>cutouts.zip: tree cutouts used in this study. They are two sub-directories:</p> <ul> <li>all_cutouts: all the candidated cutouts from three sources. See the README.md file insisde this folder for more information</li> <li>selected cutout: the manually selected cutouts from all cutouts used for training.</li> </ul> </li> <li> <p>training_pairs_20000.zarr.zip: training data created for base network traning. It is created by pairing the selected cutouts.</p> </li> <li>netflora.zip: Netflora workflow prediction results</li> <li>optimized_models.zip: Optimized base models (shallow and deep) and refined models with different shots/fold setup.</li> <li>n_fold_x_validation.zip: data pairs for refinement traing, with n fold and x valiation setup.</li> </ul>
Supplementary data for: simultaneous species detection and discovery with environmental DNA metabarcoding: a freshwater mollusk case study
<p>Environmental DNA (eDNA) sampling is a powerful tool for rapidly characterizing biodiversity patterns for specious, cryptic taxa with incomplete taxonomies. One such group that are also of high conservation concern are North American freshwater gastropods. In particular, springsnails of the genus <em>Pyrgulopsis</em> (Family: Hydrobiidae) are prevalent throughout the western United States where >140 species have been described. Many of the described species are narrow endemics known from a single spring or locality and it is believed that there are likely many additional species which have yet to be described. The distribution of these species across the landscape is of interest because habitat loss and degradation, climate change, groundwater mining, and pollution have resulted in springsnail imperilment rates as high as 92%. Determining distributions with conventional sampling methods is limited by the fact that these snails are often <5 mm in length with few distinguishing morphological characters, making them both difficult to detect and to identify. In order to facilitate detection of <em>Pyrgulopsis</em> we developed an eDNA metabarcoding protocol that is both inexpensive and capable of rapid, accurate detection of all known <em>Pyrgulopsis</em> species. When compared with conventional collection techniques, our pipeline consistently resulted in detection at sites previously known to contain <em>Pyrgulopsis </em>springsnails and at a cost per site that is likely to be substantially less than the conventional sampling and individual barcoding that has been done historically. Additionally, because our method uses eDNA extracted from filtered water it is non-destructive and suitable for the detection of endangered species where "no take" restrictions may be in effect. This effort represents both a tool which is immediately applicable to a group of high conservation concern across western North America and a case study in the broader application of eDNA sampling for landscape assessments of cryptic taxa of conservation concern.</p>
Leech-derived iDNA complements traditional surveying methods, enhancing species detections for rapid biodiversity sampling in the tropics
<p>Deforestation, exploitation, and other drivers of biodiversity loss in Madagascar leave its highly endangered and predominantly endemic wildlife at risk of extinction. Decreasing biodiversity threatens to compromise ecosystem functions and vital services provided to people. New, economical, and diverse methods of biodiversity monitoring can help to establish reliable baseline and long-term records of species richness. Metabarcoding with invertebrate-derived DNA (iDNA) has emerged as a promising new biosurveillance tool. An unexpected wet forest fragment tucked in the dry cliffs of Madagascar's southcentral plateau, the Ivohibory Protected Area (IPA), hosts a unique mosaic of species diversity, featuring both dry and wet forest species. Recently elevated to protected status, the IPA has been surveyed for flora and fauna with a range of inventory methods over three years and six expeditions (2016, 2017, & 2019). We collected 1,451 leeches over 12 days from the IPA to supplement known species richness and to compare results against current records. With iDNA, we pooled tissues, and isolated, and amplified bloodmeal DNA with five sets of primers. We detected 20 species of which four are species of frogs previously undetected and three of which are previously unknown to exist in this region. iDNA surveys can provide complementary data to traditional surveying methods like camera traps, line transects, and bioacoustic methods.</p>
Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference
<p>1. The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities.</p> <p>2. Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species-specific estimates by leveraging detection information among species. Here we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability.</p> <p>3. We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N-mixture models. Our approach substantially increases precision in estimates of abundance compared to models that do not share detection information among species. We then expand this model, and apply it to repeated detection/non-detection data collected on six species of tits (Paridae) breeding at 119 1 km<sup>2</sup> sampling sites across a <em>P. montanus</em> hybrid zone in northern Switzerland (2004-2020). We find strong impacts of forest cover and elevation on population persistence and colonisation in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonisation probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies.</p> <p>4. While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper, and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.</p>
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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.