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Figure 2 in Characterization of insect galls from a vegetation area in Altinópolis, São Paulo State, Brazil
Figure 2. Gall morphotypes in host plants from Altinópolis, São Paulo State, Brazil, (A) Leaf gall of Tapirira guianensis (Anacardiaceae); (B) Bud leaf convolute in a complex gall of Moquiniastrum pulchrum (Asteraceae); (C) Leaf gall of Moquiniastrum pulchrum (Asteraceae); (D) Leaf gall of Amphilophium elongatum (Bignoniaceae), arrow on gall; (E) Leaf gall of Caryocar brasiliense (Caryocaraceae), galls in higher magnification in the upper right corner; (F) Leaf gall of Couepia grandiflora (Chrysobalanaceae), arrow on gall; (G) Cylindrical leaf gall with star trichomes of Croton floribundus (Euphorbiaceae), galls in higher magnification in the upper right corner; (H) Exuvia of Cecidomyiidae emerging from previous gall; (I) Adaxial surface of lenticular leaf gall of Croton floribundus (Euphorbiaceae), galls in abaxial surface in the upper right corner; (J) Hollow globoid leaf gall with long trichomes of Croton floribundus (Euphorbiaceae), gall opened, with a larva in higher magnification in the upper right corner; (K) Filled globoid leaf gall of Croton floribundus (Euphorbiaceae); gall opened, with fungus associated in higher magnification in the upper right corner; (L) Leaf vein gall of Croton floribundus (Euphorbiaceae), exuvia leaving the gall in the upper left corner. (Pictures: Urso-Guimarães, M.V.).
Figure 3 in Characterization of insect galls from a vegetation area in Altinópolis, São Paulo State, Brazil
Figure 3. Gall morphotypes in host plants from Altinópolis, São Paulo State, Brazil. (A) Globoid and fusiform stem galls of Croton floribundus (Euphorbiaceae), arrow on both galls; (B) Immature leaf gall of Bauhinia holophylla (Fabaceae), arrow on gall; (C) Mature leaf gall of Bauhinia holophylla (Fabaceae), arrow on gall; (D) Stem gall of Bauhinia holophylla (Fabaceae); (E) Convex leaf gall of Copaifera langsdorffii (Fabaceae); (F) Globoid glabrous leaf gall of Copaifera langsdorffii (Fabaceae); (G) Globoid leaf gall with red and orange trichomes of Copaifera langsdorffii (Fabaceae); (H) Lenticular leaf gall of Copaifera langsdorffii (Fabaceae); (I) Triangular leaf gall of Copaifera langsdorffii (Fabaceae); (J) Globoid red leaf gall of Copaifera langsdorffii (Fabaceae); (K) Cylindrical leaf gall of Copaifera langsdorffii (Fabaceae); (L) Globoid stem gall of Copaifera langsdorffii (Fabaceae). (Pictures: Urso-Guimarães, M.V.).
Figure 4 in Characterization of insect galls from a vegetation area in Altinópolis, São Paulo State, Brazil
Figure 4. Gall morphotypes in host plants from Altinópolis, São Paulo State, Brazil. (A) Leaf gall of Nectandra sp. (Lauraceae); (B) Leaf gall Ocotea sp. (Lauraceae); (C) Cylindrical and lenticular hairy leaf galls of Diplopterys pubipetala (Malpighiaceae); (D) Lenticular leaf gall of Diplopterys pubipetala (Malpighiaceae); (E) Bud leaf gall of Diplopterys pubipetala (Malpighiaceae); (F) Leaf gall of Miconia stenostachya (Melastomataceae); (G) Leaf hairy gall of Tibouchina sp. (Melastomataceae); (H) Leaf gall of Guarea guidonea (Meliaceae); (I) Leaf gall of Myrcia bella (Myrtaceae); (J) Leaf gall of Eugenia punicifolia (Myrtaceae), arrow on gall; (K) Leaf gall of Eugenia speciosa (Myrtaceae), arrow on gall; (L) Leaf gall of Myrtaceae sp., arrow on gall; (M) Leaf gall of Serjania reticulata (Sapindaceae); (N) Leaf gall of Smilax oblongifolia (Smilacaceae). (Pictures: Urso-Guimarães, M.V.).
Figure 1 in Characterization of insect galls from a vegetation area in Altinópolis, São Paulo State, Brazil
Figure 1. Aerial map of study area in Altinópolis, São Paulo State, Brazil. In the right corner, a map of Altinópolis in light green with the neighboring municipalities in grey, and in the upper right corner, Altinópolis' location in the State of São Paulo (Sources: IBGE and CNES/Airbus. Digital Globe/Google).
Figure 3 in Distribution of gall-inducing arthropods in areas of deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil): effects of anthropization, vegetation structure and seasonality
Figure 3. Morphological characterization of the galls induced by arthropods in areas of deciduous seasonal forest of Parque da Sapucaia, Montes Claros, MG, Brazil: (A, B, C) Fabaceae – Dalbergia sp.; (D) undetermined; (E, F, G) Loganiaceae – Sthrychnos sp.; (H) Opiliaceae – Agonandra brasiliensis; (I) Sapindaceae – Serjania sp.; (J) Vitaceae – Cissus sp.; (K) Undetermined family 1; and (L) Undetermined family 2.
Figure 2 in Distribution of gall-inducing arthropods in areas of deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil): effects of anthropization, vegetation structure and seasonality
Figure 2. Morphological characterization of the galls induced by arthropods in areas of deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil): (A) Anacardiaceae – Myracrodruon urundeuva; (B) Schinopsis brasiliensis; (C, D) Asteraceae – Vernonanthura brasiliana; (E) Bignoniaceae undetermined; (F, G) Cannabaceae – Celtis brasiliensis; (H) Combretaceae – Combretum leprosum; (I) Combretum duarteanum; (J) Terminalia phaeocarpa; (K) Cucurbitaceae undetermined; (L) Fabaceae – Anadenanthera colubrine; (M, N) Apuleia leiocarpa; (O) Bauhinia pulchela; and (P) Bauhinia rufa.
Figure 1 in Distribution of gall-inducing arthropods in areas of deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil): effects of anthropization, vegetation structure and seasonality
Figure 1. Location and characterization of the study area. (A) Location of Parque da Sapucaia (Montes Claros, MG, Brazil), between the urban area of Montes Claros and Parque Estadual da Lapa Grande. Source: Google Earth.(B) Characterization of the vegetation in the rainy season.(C) Characterization of the vegetation in the dry season.
Figure 6 in Distribution of gall-inducing arthropods in areas of deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil): effects of anthropization, vegetation structure and seasonality
Figure 6. Comparison of the richness of gall morphotypes between different sampling seasons in the deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil). (A) Comparison of the richness of gall morphotypes between the rainy and dry seasons. (B) Richness of gall morphotypes in the preserved and anthropized plots during the rainy and dry seasons.
Deep learning object detection to estimate the nectar sugar mass of flowering vegetation
<p>Floral resources are a key driver of pollinator abundance and diversity, yet their quantification in the field and laboratory is laborious and requires specialist skills.</p> <p>Using a dataset of 25000 labelled tags of fieldwork-realistic quality, a Convolutional Neural Network (Faster R-CNN) was trained to detect the nectar-producing floral units of 25 taxa in surveyors' quadrat images of native, weed-rich grassland in the UK.</p> <p>Floral unit detection on a test set of 50 model-unseen images of comparable vegetation returned a precision of 90%, recall of 86% and F1 score (the harmonic mean of precision and recall) of 88%. Model performance was consistent across the range of floral abundance in this habitat. </p> <p>Comparison of the nectar sugar mass estimates made by the CNN and three human surveyors returned similar means and standard deviations. Over half of the nectar sugar mass estimates made by the model fell within the absolute range of those of the human surveyors.</p> <p>The optimal number of quadrat image samples was determined to be the same for the CNN as for the average human surveyor. For a standard quadrat sampling protocol of 10–15 replicates, this application of deep learning could cut pollinator-plant survey time per stand of vegetation from hours to minutes.</p> <p>The CNN is restricted to a single view of a quadrat, with no scope for manual examination or specimen collection, though in contrast to human surveyors its object detection is deterministic and floral unit definition is standardised.</p> <p>As agri-environment schemes move from prescriptive to results-based, this approach provides an independent barometer for grassland management which is usable by both landowner and scheme administrator. The model can be adapted to visual estimations of other ecological resources such as winter bird food, floral pollen volume, insect infestation and tree flowering/fruiting, and by adjustment of classification threshold may show acceptable taxonomic differentiation for presence-absence surveys.</p>
Distribution of vegetation sampling plots in four ecosystem types
<p>The table contains a number of bear cuscus presence points based on direct and indirect encounters (representative information from the local guide), as well as a number of plots constructed in each bear cuscus presence point in various ecosystem types in Bantimurung Bulusaraung National Park and Hasanuddin University Educational Forest, South Sulawesi.</p> <p>Three plots of 20 x 20 m were constructed in each bear cuscus presence point, with fifteen plots were established in each ecosystem type.</p>
Distribution of vegetation sampling plots in various ecosystem types
<p>The table contains no. of bear cuscus presence points based on direct and indirect encounters (representative information from the local guide), as well as no. of plots constructed in each bear cuscus presence point in various ecosystem types in Bantimurung Bulusaraung National Park and Hasanuddin University Educational Forest, South Sulawesi.</p> <p>Three plots of 20 x 20 m were constructed in each bear cuscus presence point, with fifteen plots were established in each ecosystem type.</p>
Long-term fire and vegetation change in northwestern Amazonia
<p>Amazonian forest plots are used to quantify biodiversity and carbon sequestration, and provide the foundation for much of what is known about tropical ecology. Many plots are assumed to be undisturbed, but recent work suggests that past fire, forest openings, and cultivation created vegetation changes that have persisted for decades to centuries (ecological legacies). The Yasuní Forest Dynamics plot is one of the most biodiverse places on earth, yet its human history remains unknown. Here, we use charcoal and phytolith analysis to investigate the fire and vegetation history of the Yasuní forest plot, and compare results with nearby forest plots in Colombia (Amacayacu) and Peru (Medio Putumayo-Algodón (MPA)) to explore the spatial variability of past disturbances and ecological legacies in northwestern Amazonia. Three 14C dated charcoal fragments provided evidence for a modern (1956 CE) and a past fire event ca. 750 years ago at Yasuní, compared with fire ages of 1000-1600 years ago documented at Amacayacu and MPA. Small-scale disturbances and localized canopy-openings also occurred in the Yasuní plot. Phytolith assemblages from Yasuní and Amacayacu showed more variability in past vegetation change than MPA. Low-intensity, non-continuous disturbances occurred at all three plots in the past, and our results highlight the variability of past human activities both in space and time in northwestern Amazonia. Our data also suggest that post-Columbian human disturbances from the Rubber Boom (AD 1850-1920) and subsequent oil exploration have likely left stronger ecological legacies than those left by pre-Columbian peoples in our studied regions.</p>
Putting green clipping yield, canopy reflectance, and vegetative indices by time from colorant and spray oil combination product application
<p>Multispectral radiometry resolutely quantifies canopy attributes of similarly managed monocultures over wide and varied temporal arrays. Likewise, liquid phthalocyanine-containing products are commonly applied to turfgrass as a spray pattern indicator, dormancy colorant, and/or product synergist. While perturbed multispectral radiometric characterization of putting greens within 24 h of treatment by synthetic phthalocyanine colorant has been reported, explicit guidance on subsequent use is absent from the literature. Our objective was to assess creeping bentgrass (Agrostis stolonifera L. 'Penn G2') putting green reflectance and growth one to 14 d following semi-monthly treatment by synthetic Cu II phthalocyanine colorant (Col) and petroleum-derived spray oil (PDSO) combination product at a 27 L ha–1 rate and/or 7.32 hg ha–1 soluble N treatment by one of two commercial liquid fertilizers. As observed in a bentgrass fairway companion study, mean daily shoot growth and canopy dark green color index (DGCI) increased with Col+PDSO complimented N treatment. Yet contrary to the fairway study results, deflated mean normalized differential red edge (NDRE) or vegetative index (NDVI) resulted from an associated Col+PDSO artifact that severely impeded near infrared (810-nm) putting green canopy reflectance. Regardless of time from Col+PDSO combination product treatment, the authors strongly discourage turfgrass scientists from employing vegetative indices that rely on 760- or 810-nm canopy reflectance when evaluating such putting green systems.</p>
The role of vegetation optimality in the Budyko framework
<p>In this study, we test how vegetation optimality explains convergence on the Budyko-curve. This is done for five study sites along the North Australian Tropical Transect, five catchments in Australia and a selection of catchments of the CAMELS-dataset.</p> <p>We test the following hypotheses:</p> <p>- Model simulations based on vegetation optimality lead to a better reproduction of the empirical Budyko curve than model simulations without self-optimized vegetation.<br> - The empirical parameter <em>n</em> stays constant as climate changes, as long as vegetation cover and rooting depths stay constant.<br> - Changes in <em>n</em> values are a result of slowly varying, long-term vegetation properties.</p> <p><strong>How to use this project</strong></p> <p>This project contains all pre- and post-processing scripts for the model runs to test our hypotheses. All final figures are in the notebooks, as well as the supplementary analyses. These scripts can be re-used for other applications.</p> <p>This is also a Renku project. Hence, to use this repository for your own analysis, create a free login for renku, then fork the project and modify it to your liking. The simplest way to start this project is right from the Renku<br> platform - just click on the `Environments` tab and start a new session. This will start an interactive environment right in your browser.</p> <p>To work with the project anywhere outside the Renku platform,<br> click the `Settings` tab where you will find the<br> git repo URLs - use `git` to clone the project on whichever machine you want. Python and conda packages are in `requirements.txt` and `environment.yml`.</p> <p>Please consult the various documentation sources for renkulab(https://renkulab.io/).</p>
Annual maps of swidden agriculture landscape derived from MODIS vegetation index in northern Laos during 2001-2020
<p>This document (Word) is a brief introduction about the resultant maps of swidden agricultural landscape in northern Laos during 2001-2020 derived from the MODIS13Q1 Normalized Difference Vegetation Index (NDVI) time-series products using a threshold method. For more information about the dataset, one can refer to the paper entitled “Swidden agriculture landscape mapping using MODIS vegetation index time series and its spatio-temporal dynamics in northern Laos” published in Remote Sensing. The format of this dataset (swidden agriculture landscape) is raster (.tif) with an attribute value of 1. It has a spatial resolution of 250m×250m and covers eleven provinces (including Bokeo, Borikhamxay, Huaphanh, Luangnamtha, Luangprabang, Oudomxay, Phongsaly, Vientiane, Xayaboury, Xaysomboon and Xieng-khuang) and one prefecture (Vientiane, Figure 1). The geographic projection is WGS_1984_UTM_Zone_48N.</p>
Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 13 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 13 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 08 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 8 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 06 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 6 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 04 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 4 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 10 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 10 after drought stress application. </p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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