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Fig. 53 in Mating behavior and description of immature stages of Cyclocephala melanocephala (Fabricius, 1775) (Coleoptera: Scarabaeidae: Dynastinae), identification key and remarks on known immatures of Cyclocephalini species

Fig. 53. (a) Average temperature (◦C) and radiation (kJ/m2) obtained from meteorological station (INMET). (b) Adults of Cyclocephala melanocephala collected with a light trap. Means followed by the same letters do not differ by the Skott-Knott test √

Figs. 48–51 in Mating behavior and description of immature stages of Cyclocephala melanocephala (Fabricius, 1775) (Coleoptera: Scarabaeidae: Dynastinae), identification key and remarks on known immatures of Cyclocephalini species

Figs. 48–51. Cyclocephala melanocephala, pupa; 48, head, frontal; 49–50, prosternal posterior process (male, female); 51, male terminalia, ventral. Scale = 2 mm.

Figs. 17–22 in Mating behavior and description of immature stages of Cyclocephala melanocephala (Fabricius, 1775) (Coleoptera: Scarabaeidae: Dynastinae), identification key and remarks on known immatures of Cyclocephalini species

Figs. 17–22. Cyclocephala melanocephala, third instar larva; 17–19, right mandible (ventral, internal, dorsal); left mandible (dorsal, internal, ventral). Scale = 0.5 mm.

Figs. 15–16 in Mating behavior and description of immature stages of Cyclocephala melanocephala (Fabricius, 1775) (Coleoptera: Scarabaeidae: Dynastinae), identification key and remarks on known immatures of Cyclocephalini species

Figs. 15–16. Cyclocephala melanocephala, third instar larva; 15, epipharynx; 16, cibarium. aca, anterior most acanthoparia seta; acr, lateroposterior acroparia seta; crp, right part of crepis; lip, ligular tubercle-like process; ppa, posterior preoral area. Scale = 0.5 mm.

Figs. 5–6 in Mating behavior and description of immature stages of Cyclocephala melanocephala (Fabricius, 1775) (Coleoptera: Scarabaeidae: Dynastinae), identification key and remarks on known immatures of Cyclocephalini species

Figs. 5–6. Cyclocephala melanocephala, third instar larva; 5, lateral; 6, head, dorsal. t10, abdominal tergite X; usb, U-shaped sclerotized bar. Scale = 1 mm.

Figs. 1–4 in Mating behavior and description of immature stages of Cyclocephala melanocephala (Fabricius, 1775) (Coleoptera: Scarabaeidae: Dynastinae), identification key and remarks on known immatures of Cyclocephalini species

Figs. 1–4. Cyclocephala melanocephala, adult; 1, 2, habitus (male, female); 3, 4, protibia and tarsus (male, female). Scale: 1, 2 = 5 mm; 3, 4 = 1 mm.

Fig. 11 in Comparative morphology and identification key for females of nine Sarcophagidae species (Diptera) with forensic importance in Southern Brazil

Fig. 11. Female terminalia. (A) Oxysarcodexia paulistanensis (sternites 1–4 omitted); (B) Oxysarcodexia riograndensis (sternites 1–4 omitted); (C) Peckia (Pattonella) resona (sternites 1–4 omitted); (D) Peckia (Euboettcheria) florencioi (sternites 1–4 omitted); (E) Peckia (Sarcodexia) lambens (sternites 1–4 omitted); (F) Microcerella halli (sternites 1–4 omitted); (G) Peckia (Pattonella) intermutans (tergite 6 and sternite 1 omitted); (H) Peckia (Euboettcheria) australis; (I) Sarcophaga (Bercaea) africa (sternite 1 omitted). Scales: 1 mm.

Fig. 8 in Comparative morphology and identification key for females of nine Sarcophagidae species (Diptera) with forensic importance in Southern Brazil

Fig. 8. External female morphology of Peckia (Sarcodexia) lambens. (A) Habitus, lateral view; scale: 2 mm; (B) abdomen, dorsal view; scale: 1 mm; (C) abdominal terminal segments, ventral view; scale: 0.5 mm; (D) abdomen, ventral view; scale: 1 mm.

Fig. 3 in Comparative morphology and identification key for females of nine Sarcophagidae species (Diptera) with forensic importance in Southern Brazil

Fig. 3. External female morphology of Oxysarcodexia riograndensis. (A) Habitus, lateral view; scale: 2 mm; (B) abdomen, dorsal view; scale: 1 mm; (C) abdominal terminal segments, ventral view; scale: 0.5 mm; (D) abdomen, ventral view; scale: 1 mm.

Fig. 6 in Comparative morphology and identification key for females of nine Sarcophagidae species (Diptera) with forensic importance in Southern Brazil

Fig. 6. External female morphology of Peckia (Euboettcheria) australis. (A) Habitus, lateral view; scales: 2 mm; (B) abdomen, dorsal view; scale: 2 mm; (C) abdominal terminal segments, ventral view; scale: 0.5 mm; (D) abdomen, ventral view; scale: 1 mm.

Fig. 1 in Comparative morphology and identification key for females of nine Sarcophagidae species (Diptera) with forensic importance in Southern Brazil

Fig. 1. General morphology of female terminalia. (A) Oxysarcodexia paulistanensis (pink = tergite 8; green = cercus; yellow = hypoproct; blue = vaginal plate; dark red = spiracle 6; dark green = spiracle 7. (B) Peckia (Euboettcheria) florencioi (orange = epiproct). (C) Microcerella halli (light yellow = sternite 5; light green = sternite 6; light pink = sternites 7 + 8).

Fig. 2 in Comparative morphology and identification key for females of nine Sarcophagidae species (Diptera) with forensic importance in Southern Brazil

Fig. 2. External female morphology of Oxysarcodexia paulistanensis. (A) Habitus, lateral view; scale: 2 mm; (B) abdomen, dorsal view; scale: 1 mm; (C) abdominal terminal segments, ventral view; scale: 0.5 mm; (D) abdomen, ventral view; scale: 1 mm.

opencc-by-4.0Dec 2020View details →

Fig. 2. A-B in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 2. A-B: (A) Derivative melt curves of T. copemani, T. noyesi G8, T. vegrandis G7; (B) Derivative melt curves of T. microti, T. cruzi and T. rangeli.

opencc-by-4.0Dec 2020View details →

Fig. 6. A-D in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 6. A-D: (A) Derivative melt curves of wildlife samples containing T. copemani and T. vegrandis G7 infections from woylie tissue and the (B) the respective normalised melt domains; (C) Derivative melt curves of wildlife samples containing mixed infections with T. copemani and T. noyesi G8 in woylie blood; (D) Derivative melt curves showing wildlife samples containing mixed infections of T. copemani, T. noyesi G8 and T. vegrandis G7 in woylie tissue.

opencc-by-4.0Apr 2020View details →

Fig. 2 in Molecular identification of Taenia hydatigena and Mesocestoides species based on copro-DNA analysis of wild carnivores in Mongolia

Fig. 2. Phylogenetic tree based on a partial sequence of tapeworms obtained by the neighbor joining method was conducted using the TN93 + I nucleotide substitution model. Numbers above branches are percent bootstrap values based on 1,000 replicates. Bootstrap value> 70% are shown. (a) Phylogenetic tree based on the cox1 sequences of T. hydatigenaand Mesocestoides sp. isolates available in the GenBankṜ database were included. Hymenolepis nana served as an out-group. (b) Phylogenetic analysis of the 12SrRNA partial sequence of T. hydatigena, Mesocestoides sp., and M. lineatus inferred using the sequence distance method and maximum likelihood. Hymenolepis nana was used as an out-group.

opennotspecifiedDec 2019View details →

Fig. 3 in The planthopper genus Sogana Matsumura, 1914 in Vietnam: Two new species, new records and identification key (Hemiptera: Fulgoromorpha: Tropiduchidae)

Fig. 3. Sogana spp. of Vietnam, distribution map.

opennotspecifiedAug 2024View details →

FIGURE 30 in Siberian mites of the genus Zerconopsis Hull, 1918 (Mesostigmata, Ascidae): description of a new species, re-description of Z. michaeli Evans & Hyatt, 1960, and key for species identification

FIGURE 30. Distribution records of Zerconopsis sibiricus sp. nov., original.

openagpl-3.0-or-laterAug 2024View details →

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>

opennotspecifiedDec 2017View details →

FIGURE 2 in Morphological and molecular identification of a new species of Atraporiella (Polyporales, Basidiomycota) in China

FIGURE 2. Basidiomata of Atraporiella yunnanensis (holotype). Scale bars: a—3 mm; b—2 mm.