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213 results for “species monitoring”
Supplementary material 1 from: Anđelković AA, Lawson Handley L, Marchante E, Adriaens T, Brown PMJ, Tricarico E, Verbrugge LNH (2022) A review of volunteers' motivations to monitor and control invasive alien species. NeoBiota 73: 153-175. https://doi.org/10.3897/neobiota.73.79636
List of the studies used in the analysis
Supplementary material 2 from: Anđelković AA, Lawson Handley L, Marchante E, Adriaens T, Brown PMJ, Tricarico E, Verbrugge LNH (2022) A review of volunteers' motivations to monitor and control invasive alien species. NeoBiota 73: 153-175. https://doi.org/10.3897/neobiota.73.79636
Overview of the study characteristics and methodological approaches of the selected papers
Supplementary material 2 from: Sildever S, Nishi N, Inaba N, Asakura T, Kikuchi J, Asano Y, Kobayashi T, Gojobori T, Nagai S (2022) Monitoring harmful microalgal species and their appearance in Tokyo Bay, Japan, using metabarcoding. Metabarcoding and Metagenomics 6: e79471. https://doi.org/10.3897/mbmg.6.79471
Tables S1–S13
Supplementary material 1 from: Sildever S, Nishi N, Inaba N, Asakura T, Kikuchi J, Asano Y, Kobayashi T, Gojobori T, Nagai S (2022) Monitoring harmful microalgal species and their appearance in Tokyo Bay, Japan, using metabarcoding. Metabarcoding and Metagenomics 6: e79471. https://doi.org/10.3897/mbmg.6.79471
Figures S1–S6
Figure 1 in The diversity of polychaetes (Annelida: Polychaeta) in a longterm pollution monitoring study from the Levantine coast of Turkey (Eastern Mediterranean), with the descriptions of four species new to science and two species new to the Mediterranean fauna
Figure 1. Map of the study area with the location of monitoring stations.
Supplementary material 2 from: Maurizi E, Campanaro A, Chiari S, Maura M, Mosconi F, Sabatelli S, Zauli A, Audisio P, Carpaneto GM (2017) Guidelines for the monitoring of Osmoderma eremita and closely related species. In: Carpaneto GM, Audisio P, Bologna MA, Roversi PF, Mason F (Eds) Guidelines for the Monitoring of the Saproxylic Beetles protected in Europe. Nature Conservation 20: 79-128. https://doi.org/10.3897/natureconservation.20.12658
Field sheet to fill during each survey and its legend :
Supplementary material 2 from: Hernández-Triana LM, Brugman VA, Nikolova NI, Ruiz-Arrondo I, Barrero E, Thorne T, de Marco MF, Krüger A, Lumley S, Johnson N, Fooks AR (2019) DNA barcoding of British mosquitoes (Diptera, Culicidae) to support species identification, discovery of cryptic genetic diversity and monitoring invasive species. ZooKeys 832: 57-76. https://doi.org/10.3897/zookeys.832.32257
: Data type: molecular data
Supplementary material 1 from: Hernández-Triana LM, Brugman VA, Nikolova NI, Ruiz-Arrondo I, Barrero E, Thorne T, de Marco MF, Krüger A, Lumley S, Johnson N, Fooks AR (2019) DNA barcoding of British mosquitoes (Diptera, Culicidae) to support species identification, discovery of cryptic genetic diversity and monitoring invasive species. ZooKeys 832: 57-76. https://doi.org/10.3897/zookeys.832.32257
: Data type: molecular data
Figure 2 from: Hernández-Triana LM, Brugman VA, Nikolova NI, Ruiz-Arrondo I, Barrero E, Thorne T, de Marco MF, Krüger A, Lumley S, Johnson N, Fooks AR (2019) DNA barcoding of British mosquitoes (Diptera, Culicidae) to support species identification, discovery of cryptic genetic diversity and monitoring invasive species. ZooKeys 832: 57-76. https://doi.org/10.3897/zookeys.832.32257
Figure 2 Neighbor joining tree of COI DNA barcodes (658 bp) for mosquito species. A divergence of > 2% may be indicative of separate operational taxonomic units. Only bootstrap values higher than 70% are shown.
Figure 1 from: Hernández-Triana LM, Brugman VA, Nikolova NI, Ruiz-Arrondo I, Barrero E, Thorne T, de Marco MF, Krüger A, Lumley S, Johnson N, Fooks AR (2019) DNA barcoding of British mosquitoes (Diptera, Culicidae) to support species identification, discovery of cryptic genetic diversity and monitoring invasive species. ZooKeys 832: 57-76. https://doi.org/10.3897/zookeys.832.32257
Figure 1 Location of study sites in the United Kingdom. Key: 1 ADAS Arthur Rickwood; 2 Church Farm; 3 Coombelands Farms; 4 Elmley Nature Reserve; 5 Glendell Livery, Mill Lane; 6 Frimley; 7 Mudchute Farm; 8 Northney Farm, Hayling Island; 9 White Lodge, Bisley; 10 Bartley Heath; 11 Dee Marsh.
Towards Enhancing Field-Based Vegetation Monitoring: A Deep Learning Approach for Species Identification and Coverage Estimation from Ground-level Imagery
<h1>🌿Species Identification and Coverage Estimation from Ground-level Imagery for Vegetation Monitoring 📷</h1> <p>This repository contains the data and code used in <a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.70024"><strong>Müller, Puliti, and Breidenbach (2025)</strong></a> to train and apply deep learning models for <strong>species coverage estimation</strong> using ground-level imagery.</p> <p>It includes:<br>✅ A <strong>YOLOv8 object detection </strong>model for detecting frames in images and one <strong>species instance segmentation</strong> model for species identification and identifying and segmenting species.</p> <p>✅ Method to parse instance segmentation masks to <strong>species-specific coverage estimates</strong> in images.</p> <p>✅ The <strong>data </strong>used to train and evaluate the models</p> <p> </p> <h2>🚀 Workflow Overview</h2> <p>This demo provides a step-by-step approach for training and applying the models:</p> <p>1️⃣ <strong>Training</strong>:</p> <ul> <li>Train two models using labeled images: <ul> <li><strong>Frame Object Detection</strong> (dataset: <code>Frame_data</code>)</li> <li><strong>Species Instance Segmentation</strong> (dataset: <code>Species_segmentation_data</code>)</li> </ul> </li> </ul> <p>2️⃣ <strong>Confidence Optimization</strong>:</p> <ul> <li>Optimize the confidence threshold based on downstream <strong>cover estimation</strong> performance.</li> </ul> <p>3️⃣ <strong>Inference</strong>:</p> <ul> <li>Predict on test images (<code>Species_cover_data_test</code>).</li> </ul> <p>4️⃣ <strong>Evaluation</strong>:</p> <ul> <li>Compare predictions with <strong>field estimates</strong> (<code>Field_data_NFI</code>).</li> </ul> <p>📌 The code has been tested on <strong>Windows</strong> with <strong>Python 3.10</strong>.</p> <p> </p> <h2>🛠 How to Run the Demo</h2> <p>Follow these steps to set up and run <code>demo.ipynb</code>:</p> <div> <div> <div> </div> </div> <div> <blockquote> <p># Create a new environment<br>conda create -n VegCover python=3.10</p> <p># Activate the environment<br>conda activate VegCover</p> <p># Install dependencies<br>pip install -r requirements.txt</p> <p># Install Jupyter Lab<br>pip install jupyterlab</p> <p># Open the demo notebook<br>jupyter-lab</p> </blockquote> </div> </div> <h2> </h2> <h2>📖 How to Cite</h2> <p>If you use this work, please cite:</p> <p><strong>Müller, P., Puliti, S., & Breidenbach, J. (2025).</strong> Towards Enhancing Field-Based Vegetation Monitoring: A Deep Learning Approach for Species Coverage Estimation from Ground-Level Imagery. <em>Methods in Ecology and Evolution.</em></p> <h2> </h2> <h2>📜 License</h2> <p>This project is licensed under the <strong>GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later)</strong>.</p> <p>🔹 <strong>Key points of this license:</strong></p> <ul> <li>You are free to <strong>use, modify, and distribute</strong> the software.</li> <li>If you modify and deploy this software (even as a web service), you <strong>must share your modifications</strong> under the same AGPL-3.0-or-later license.</li> <li>This ensures that improvements remain open-source and benefit the community.</li> </ul> <p>📖 Full license text: <a href="https://www.gnu.org/licenses/agpl-3.0.en.html">GNU AGPL v3.0</a></p> <div> <pre> </pre> </div>
Data from: Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring
1. Global concern about human impact on biological diversity has triggered an intense research agenda on drivers and consequences of biodiversity change in parallel with international policy seeking to conserve biodiversity and associated ecosystem functions. Quantifying the trends in biodiversity is far from trivial, however, as recently documented by meta-analyses which report little if any net change of local species richness through time. 2. Here, we summarize several limitations of species richness as a metric of biodiversity change and show that the expectation of directional species richness trends under changing conditions is invalid. Instead, we illustrate how a set of species turnover indices provide more information content regarding temporal trends in biodiversity, as they reflect how dominance and identity shift in communities over time. 3. We apply these metrics to three monitoring data sets representing different ecosystem types. In all data sets, nearly complete species turnover occurred, but this was disconnected from any species richness trends. Instead, turnover was strongly influenced by changes in species presence (identities) and dominance (abundances). We further show that these metrics can detect phases of strong compositional shifts in monitoring data and thus identify a different aspect of biodiversity change decoupled from species richness. 4. Synthesis and application: Temporal trends in species richness are insufficient to capture key changes in biodiversity in changing environments. In fact, reductions in environmental quality can lead to transient increases in species richness if immigration or extinction have different temporal dynamics. Thus, biodiversity monitoring programs need to go beyond analyses of trends in richness in favour of more meaningful assessments of biodiversity change.01-Jun-2017
Figure 8 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 8 - Anthropogenic disturbance within Ankarafa Forest: A Area of savannah, dividing Ankarafa Forest into many smaller fragments (14 December 2011; 14°22.77'S, 47°45.58'E) B Recent forest clearance (11 November 2011; 14°23.09'S, 47°44.92'E) C A fire lit to clear forest for agriculture (16 November 2011; 14°23.20'S, 47°44.80'E) D a Tavy field with intact forest in the background and the river acting as the boundary line (30 October 2011; 14°22.82'S, 47°45.28'E).
Figure 7 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 7 - Habitat of Boophis ankarafensis sp. n. in Ankarafa Forest. A 21 November 2011; 14°23.39'S, 47°46.37'E; B 3 January 2012; 14°22.83'S, 47°45.57'E.
Figure 6 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 6 - Representative call of Boophis ankarafensis sp. n. and comparative call of Boophis bottae from Betampona (Rosa et al. 2011, track #08): A Boophis ankarafensis sp. n. sonagrams (top) and oscillograms (bottom) referring to a section of trill note (type 1) constructed of alternating broad- and narrow-band pulses and click notes (type 2) (recorded at 25.2 °C, 13 October 2011) B Boophis bottae oscillograms of types 1 and 2 notes (recorded at 23.0 °C, 17 November 2007). Spectrogram parameters: FFT length 512, Hamming window.
Figure 5 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 5 - Nocturnal variation in calling activity of Boophis ankarafensis sp. n.; activity shown as a proportion of total calling activity and time shown as a proportion of night length from dusk [0%] until dawn [100%].
Figure 4 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 4 - Detection of Boophis ankarafensis sp. n. Upper panel: Location of acoustic recording sites indicating presence (green) or absence (black) of vocal activity and transect encounters (red). Lower left: The Sahamalaza Peninsula: A–B Ankarafa Forest C Antafiabe Village D–F Anabohazo Forest. Lower right: location of the Sahamalaza Peninsula, in northwestern Madagascar.
Figure 1 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 1 - The Sahamalaza Peninsula in northwest Madagascar, indicating the study sites of (A) Ankarafa Forest, (B) Antafiabe Village, (C) Anabohazo Forest and (D) Betsimpoaka village. Source: Madagascar National Parks (MNP).
Figure 3 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 3 - Breeding activity of Boophis ankarafensis sp. n.: A Paratype MRSN A6976 B–C Vocalising males sitting on leaves and on a branch (specimens not collected) D Male holotype MRSN A6973 and female A6974 E Couple in axillary amplexus.
Figure 2 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383
Figure 2 - Life colouration of Boophis ankarafensis sp. n.: A Rostral view of a male paratype (MRSN A6975) B Dorsal view of the same male C Female specimen in resting position on a leaf (specimen not collected) D Dorso-lateral view of the holotype with day-time colouration (MRSN A6973).
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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.
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DANDI Archive for NWB datasets
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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.