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104 results for “Animal Ecology”
Figure 4 from: Peart RA (2018) Ampeliscidae (Crustacea, Amphipoda) from the IceAGE expeditions. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 145–173. https://doi.org/10.3897/zookeys.731.19948
Figure 4 Byblisoides bellansantiniae sp. n. Holotype, female, 14 mm, ZMH K-47035, Irminger Basin, Iceland, 2537.3–2538.1 m. Scales represent 0.5 mm
Figure 10 from: Peart RA (2018) Ampeliscidae (Crustacea, Amphipoda) from the IceAGE expeditions. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 145–173. https://doi.org/10.3897/zookeys.731.19948
Figure 10 Haploops kaimmalkai sp. n. holotype, female, 6 mm, ZMH K-47057, Iceland Basin, 1384.8–1389 m. Pereopods 3–7 scales represent 0.2 mm. Uropods 1–3 and Telson scales represent 0.1 mm.
Figure 2 from: Lörz A-N, Tandberg AHS, Willassen E, Driskell A (2018) Rhachotropis (Eusiroidea, Amphipoda) from the North East Atlantic. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 75–101. https://doi.org/10.3897/zookeys.731.19814
Figure 2 16S gene tree calculated as in Fig. 1- Rhachotropis samples collected during IceAGE (details Supplementary Table 1). Clades are coloured for depth strata for sampling: 0–200 m light grey, 201–500 m light green, 501–1000 m turquoise, 1000+ m blue.
Figure 3 from: Peart RA (2018) Ampeliscidae (Crustacea, Amphipoda) from the IceAGE expeditions. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 145–173. https://doi.org/10.3897/zookeys.731.19948
Figure 3 Byblisoides bellansantiniae sp. n. Holotype, female, 14 mm, ZMH K-47035, Irminger Basin, Iceland, 2537.3–2538.1 m. Antennae 1–2, gnathopods 1–2 scales represent 0.5 mm. Mouthparts scales represent 0.2 mm.
Figure 1 from: Peart RA (2018) Ampeliscidae (Crustacea, Amphipoda) from the IceAGE expeditions. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 145–173. https://doi.org/10.3897/zookeys.731.19948
Figure 1 Map to the distribution of species from Icelandic waters in the genera of Ampeliscidae, highlighting the new species documented.
Figure 1 from: Krapp-Schickel T (2018) Leucothoe vaderotti, a new Atlantic Leucothoe (Crustacea, Amphipoda) belonging to the "spinicarpa-clade" (Crustacea, Amphipoda). ). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 135-144. https://doi.org/10.3897/zookeys.731.19813
Figure 1 Leucothoe vaderotti sp. n. A 1, A 2 antennae Mx 1, Mx 2 maxillae Gn 1, Gn 2 gnathopods Gn 1', Gn 2' gnathopods enlarged.
Data and R Code from "PAT-GEOM: A Software Package for the Analysis of Animal Patterns" (published in Methods in Ecology and Evolution)
<p>Datasets for Figures 2 and 4 in the article "PAT-GEOM: A Software Package for the Analysis of Animal Patterns" (published in Methods in Ecology and Evolution) and the R code used to perform the analysis described in the article.</p>
Peer-reviewed papers included in topic model of old animal ecology and conservation
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Data from: Calculating the ecological impacts of animal-borne instruments on aquatic organisms
Open the record for dataset details and reuse information.
Data from: Evolutionary ecology of beta-lactam gene clusters in animals
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Data from: A computer vision for animal ecology
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Data from: Cryptic ecology among host generalist Campylobacter jejuni in domestic animals
Open the record for dataset details and reuse information.
Data from: Prey fractionation in the Archaeocyatha and its implication for the ecology of the first animal reef systems
Archaeocyaths are the most abundant sponges from the Cambrian period, forming the first animal reef communities over 500 million years ago. The Archaeocyatha are index fossils for correlating rocks of similar ages globally, because of their abundance, extensive geographic distribution, their detailed anatomy and well established taxonomy. Their ecological significance remains incompletely explored yet they are known to strongly competitively interact unlike modern sponges. This study examines the feeding ecology of the fossil remains of Siberian archaeocyath assemblages. As suspension feeders, archaeocyaths filtered plankton from the water column through pores in their outer wall. Here we outline a new method to estimate the limit on the upper size of plankton that could be consumed by an archaeocyath during life. The archaeocyaths examined were predominantly feeding on nanoplankton and microplankton such as phytoplankton and protozooplankton. Size-frequency distributions of pore sizes from six different Siberian archaeocyath assemblages, ranging from Tommotian to Botoman in age, reveal significantly different upper limits to the prey consumed at each locality. Some of the assemblages contain specimens that could have fed on larger organisms extending in to the mesoplankton including micro-invertebrates as a possible food resource. These results show that during the establishment of the first animal reef systems, prey partitioning was established as a way of reducing competition. This method has applicability for understanding the construction and the functioning of the first reef systems, as well as helping to understanding modern reef systems and their development though time and space.
Data from: Machine learning to classify animal species in camera trap images: applications in ecology
Motion‐activated cameras ("camera traps") are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses. We trained machine learning models using convolutional neural networks with the ResNet‐18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out‐of‐sample (or "out‐of‐distribution" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out‐of‐sample dataset from Tanzania, containing a faunal community that was novel to the model. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out‐of‐sample validation from Canada achieved 82% accuracy and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an r package (Machine Learning for Wildlife Image Classification) that allows the users to (a) use the trained model presented here and (b) train their own model using classified images of wildlife from their studies. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non‐invasive sampling designs in ecological studies by reducing the burden of manually analysing images. Our r package makes these methods accessible to ecologists.
Figure 10 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 10 Amphilochus hamatus. Mouthparts. IINH37894. Scale bars: 0.1 mm.
Figure 6 from: Brix S, Lorz A-N, Jazdzewska AM, Hughes L, Tandberg AHS, Pabis K, Stransky B, Krapp-Schickel T, Sorbe JC, Hendrycks E, Vader W, Frutos I, Horton T, Jazdzewski K, Peart R, Beermann J, Coleman CO, Buhl-Mortensen L, Corbari L, Havermans C, Tato R, Campean AJ (2018) Amphipod family distributions around Iceland. In: Brix S, Lorz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 1-53. https://doi.org/10.3897/zookeys.731.19854
Figure 6 - Distribution map for a Synopiidae b Urothoidae, in sorted IceAGE EBS samples.
Figure 7 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 7 Amphilochus manudens. Pereopods. IINH37885. Scale bars: 0.1 mm.
Figure 6 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 6 Amphilochus manudens. Mouthparts. IINH37887. Scale bars: 0.1 mm.
Figure 2 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 2 Amphilochus anoculus sp. n. Head and mouthparts. ZMBN121952. Scale bars: 0.1 mm.
Figure 9 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 9 Amphilochus hamatus. Habitus. IINH37900. Scale bar: 0.5 mm.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.