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137 results for “conifer species”
FIG. 1 in Two fossil conifer species from the Neogene of Alonissos Island (Iliodroma, Greece)
FIG. 1. — Geological sketch – map of Alonissos Island, by Jacobshagen & Matarangas (2004) (based on the work of Kelepertsis [1975] for the Institute of Geology and Mineral Exploration [I.G.M.E.]), modified. The Neogene formations are included by the red circles.
Variations in tree growth provide limited evidence of species mixture effects in Interior West U.S.A. mixed-conifer forests
<p>1. In mixed stands, species complementarity (e.g., facilitation and competition reduction) may enhance forest tree productivity. Although positive mixture effects have been identified in forests worldwide, the majority of studies have focused on two-species interactions in managed systems with high functional diversity. We extended this line of research to examine mixture effects on tree productivity across landscape-scale compositional and environmental gradients in the low functional diversity, fire-suppressed, mixed-conifer forests of the U.S. Interior West.</p> <p>2. We investigated mixture effects on the productivity of <i>Pinus ponderosa</i>, <i>Pseudotsuga menziesii</i>, and <i>Abies concolor</i>. Using region-wide forest inventory data, we created individual-tree generalized linear mixed models and examined the growth of these species across community gradients. We compared the relative influences of stand structure, age, competition, and environmental stress on mixture effects using multi-model inference. We analyzed growth of neighboring tree species to infer whether a mixture effect in a single species translated to the stand-level.</p> <p>3. We found support for a positive mixture effect in <i>P. menziesii</i>, although our results were equivocal in light of a weaker but still plausible alternative model. Growth of <i>P. menziesii</i> neighboring species in mixed stands declined or held constant depending on aridity, suggesting that a positive mixture effect in <i>P. menziesii</i> does not necessarily extend to the stand level. We found no evidence for mixture effects in <i>P. ponderosa</i>, <i>A. concolor</i> or their neighboring species.</p> <p>4. Complementarity appears to have a limited influence on tree growth in the mixed-conifer systems of the U.S. Interior West, reflecting limited functional diversity. Historical changes in stand structure following fire exclusion, particularly high stand densities, may limit the potential for positive species mixture effects. The limited species pool of Interior West forests increases the risk that, without careful management, what functional diversity exists could be lost to compositional changes resulting from stand dynamics or disturbance.</p>
A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery
<p>Published online: <a href="https://www.mdpi.com/2072-4292/11/19/2326">https://www.mdpi.com/2072-4292/11/19/2326</a></p> <p>DOI: 10.3390/rs11192326</p> <p><strong>Abstract:</strong></p> <p>In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data</p> <p>Digital Publication of the training data polygons and hyperspectral imagery used in the manuscript "A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery".</p> <p>Code is available in a Jupyter Notebook and can be found here: <a href="https://github.com/jonathanventura/canopy">https://github.com/jonathanventura/canopy</a></p> <p>National Ecological Observatory Network. 2018. Provisional data downloaded from <a href="http://data.neonscience.org/">http://data.neonscience.org</a> on 22 June 2018. Battelle, Boulder, CO, USA</p>
FIG. 72 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 72. Male genitalic morphology of Erysivena sydneyensis. A. Pygophore, ventral. B. Pygophore, right lateral. C. Aedeagus, left lateral. D. Aedeagus, ventral. E. Aedeagus, right dorsolateral. Scale bars = 30 µm.
FIG. 58 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 58. Male genitalic morphology of Erysivena kalbarri. A. Pygophore, left lateral. B. Pygophore, ventral. C. Aedeagus, left lateral. D. Aedeagus, ventral. E. Aedeagus, right lateral. Arrow indicates membrane join between PES and DES2 endosomal spicules. Scale bars = 30 µm.
FIG. 55 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 55. Female genitalia of Erysivena emeraldensis. A. Bursa copulatrix, ventral. B. Posterior wall, dorsal. Scale bar = 0.1 mm.
FIG. 65 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 65. Male genitalia of Erysivena mareeba. A. Pygophore, dorsal. B. Left paramere, ventral. C. Right paramere, right lateral. D. Phallotheca, right lateral. E. Aedeagus, right lateral. Scale bars = 0.1 mm.
FIG. 71 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 71. External morphology of Erysivena sydneyensis. A. Head, dorsal, scale bar = 30 µm. B. Head, lateral, scale bar = 30 µm. C. Head and pronotum, dorsal, scale bar = 100 µm. D. Head, and pronotum, lateral, scale bar = 100 µm. E. Meso- and metathorax, scale bar = 30 µm. F. Metathoracic scent gland, scale bar = 10 µm. G. Tarsal claw, scale bar = 10 µm. H. Pygophore, dorsal, scale bar = 30 µm.
FIG. 77 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 77. Male genitalia of Ngullamiris whadjuk. A. Pygophore, dorsal. B. Left paramere, dorsal. C. Left paramere, ventral. D. Right paramere, dorsal. E. Phallotheca, dorsal. F. Aedeagus, ventral. Scale bars = 0.1 mm.
FIG. 78 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 78. Female genitalia of Ngullamiris whadjuk. A. Bursa copulatrix, ventral. C. Posterior wall, dorsal. Scale bar = 0.1 mm.
FIG. 68 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 68. Male genitalia of Erysivena paluma. A. Pygophore, dorsal. B. Left paramere, ventral. C. Right paramere, ventral. D. Phallotheca, dorsal. E. Phallotheca, right lateral. E. Aedeagus, left lateral. Scale bars = 0.1 mm.
FIG. 57 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 57. Scanning electron micrograph images of Erysivena kalbarri, male. A. Head, dorsal, scale bar = 30 µm. B. Head, lateral, scale bar = 30 µm. C. Head and pronotum, dorsal, scale bar = 100 µm. D. Head and pronotum, lateral, scale bar = 30 µm. E. Meso- and metathorax, scale bar = 30 µm. F. Metathoracic scent gland, scale bar = 10 µm. G. Tarsal claw, scale bar = 10 µm. H. Pygophore, dorsal, scale bar = 30 µm.
FIG. 70 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 70. Male genitalia of Erysivena schwartzi. A. Pygophore, dorsal. B. Left paramere, ventral. C. Right paramere, dorsal. D. Phallotheca, dorsal. E. Aedeagus, right dorsolateral. Scale bars = 0.1 mm.
FIG. 62 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 62. Male genitalia of Erysivena majori. A. Pygophore, dorsal. B. Left paramere, ventral. C. Right paramere, left lateral. E. Phallotheca, right lateral. D. Aedeagus, right lateral. Scale bars = 0.1 mm.
FIG. 50 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 50. Male genitalia of Erysivena bundjalung. A. Pygophore, dorsal. B. Left paramere, ventral. C. Right paramere, left lateral. D. Phallotheca, dorsal. E. Aedeagus, right lateral. F. Aedeagus, ventral. Scale bars = 0.1 mm.
FIG. 49 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 49. Male genitalia of Erysivena apta. A. Pygophore, dorsal, detail of left tergal process in lateral view. B. Left paramere, dorsal. C. Right paramere, left dorsolateral. D. Phallotheca, dorsal. E. Aedeagus, left lateral. F. Aedeagus, right lateral. Scale bars = 0.1 mm.
FIG. 64 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 64. Male genitalic morphology of Erysivena mareeba: A. Pygophore, left lateral. B. Pygophore, ventral C. Aedeagus, left lateral. D. Aedeagus, ventral. E. Aedeagus, dorsal. Scale bars = 30 µm.
FIG. 48 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 48. Callitricola female genitalia: right interramal lobes of species, posterior wall of C. graciliphila, dorsal. Scale bar = 0.1 mm.
FIG. 53 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 53. Male genitalia of Erysivena drepanomorpha. A. Pygophore, dorsal, detail of left tergal process, lateral. B. Left paramere, ventral. C. Right paramere, dorsal. D. Phallotheca, right lateral. E. Phallotheca, ventral. F. Left paramere, dorsal. G. Aedeagus, right lateral. H. Aedeagus, left lateral. Scale bars = 0.1 mm.
FIG. 61 in Systematics And Analysis Of The Radiation Of Orthotylini Plant Bugs Associated With Callitroid Conifers In Australia: Description Of Five New Genera And 32 New Species (Heteroptera: Miridae: Orthotylinae)
FIG. 61. Female genitalia of Erysivena kalbarri. A. Ventral labiate plate, dorsal. B. Bursa copulatrix, ventral. C. Posterior wall, dorsal. Scale bar = 0.1 mm.
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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)
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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.