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FIGURE 8. A in Microlicia delicata and Microlicia pumila (Melastomataceae), two new species from unprotected mountains with campo rupestr vegetation in eastern Brazil

FIGURE 8. A. Map of Brazil with Minas Gerais state highlighted in black. B. Minas Gerais with distributions of Microlicia karinae, M. nervosa, M. naudiniana, M. obovatifolia and M. pumila. C. Satellite image of Serra do Cipó with distributions of M. karinae, M. nervosa, M. naudiniana and M. obovatifolia. D. Satellite image of Serra da Formosa with the distribution record of M. pumila.

FIGURE 1. A in Microlicia delicata and Microlicia pumila (Melastomataceae), two new species from unprotected mountains with campo rupestr vegetation in eastern Brazil

FIGURE 1. A summary of the elliptic Fourier analysis of leaf shape. A. Two main PC axes associated with variation in leaf shape. Each row represents variation in shape explained by each of the first two PC axes, whereas the columns correspond to the mean shape ± 1.5 standard deviations on each side. B. PCA ordination of the elliptical Fourier analysis showing ranges of variation in leaf shape associated with each species. C–G. Photos of representatives leaves of species sampled in the elliptic Fourier analysis. C. Microlicia pumila (R. Pacifico 725 & V.E. Bressan). D. Microlicia obovatifolia (F. Almeda et al. 8899). E. Microlicia karinae (F. Almeda et al. 9705). F. Microlicia naudiniana (R. Pacifico 321). G. Microlicia nervosa (F. Almeda et al. 9124). H. Heatmap highlighting regions that differ in mean leaf shape between M. pumila (outlined in red) and M. karinae (outlined in light blue). Scale bars: 2 mm.

FIGURE 4. A in Microlicia delicata and Microlicia pumila (Melastomataceae), two new species from unprotected mountains with campo rupestr vegetation in eastern Brazil

FIGURE 4. A. Map of Brazil with Bahia state highlighted in black. B. Bahia with distributions of Microlicia coronata, M. delicata, and M. plumosa. C. Satellite image of southwestern Chapada Diamanatina with distributions of M. coronata, M. delicata, and M. plumosa.

FIGURE 6. Microlicia pumila. A. Habit. B in Microlicia delicata and Microlicia pumila (Melastomataceae), two new species from unprotected mountains with campo rupestr vegetation in eastern Brazil

FIGURE 6. Microlicia pumila. A. Habit. B. Detail of the verrucose internodes. C. Apical vegetative branch. D. Leaf (abaxial surface). E. Leaf (adaxial surface). F. Floral bud. G. Antesepalous stamen. H. Antepetalous stamen. I. Capsule enveloped by the hypanthium. J. Capsule. K. Seed in lateral view. Voucher: R. Pacifico 725 & V.E. Bressan. Scale bars: A, 2 cm; B, 1 mm; C, 4 mm; D–J, 2 mm; K, 0.4 mm.

FIGURE 5 in Microlicia delicata and Microlicia pumila (Melastomataceae), two new species from unprotected mountains with campo rupestr vegetation in eastern Brazil

FIGURE 5. Photos of selected species compared to new taxa described in this study. A–D Microlicia plumosa. A. Population on the Pico do Barbado, Abaíra, Bahia, Brazil. B. Habit. C. Branchlet in lateral view, showing the "feather-like" foliage typical of this species. D. Branchlet in upper view showing the decussate cruciate leaf arrangement as seen from above. E. Microlicia coronata, fruiting branches. F. Microlicia naudiniana, flower close up (R. Pacifico et al. 403). G. Microlicia obovatifolia, flower close up (F. Almeda et al. 8899). Photos A–F by R. Pacifico, and G by F. Almeda.

FIGURE 2. A–B in Microlicia delicata and Microlicia pumila (Melastomataceae), two new species from unprotected mountains with campo rupestr vegetation in eastern Brazil

FIGURE 2. A–B. Comparative boxplots of leaf and calyx lobe length between Microlicia plumosa and M. delicata. A. Leaf length boxplot. B. Calyx lobe length boxplot. C–D. Photos of branches of M. delicata and M. plumosa, showing the general aspect of the foliage of each species when dry. C. Microlicia delicata (H.P. Bautista 2897). D. Microlicia plumosa (W. Ganev 2265).

FIGURE 3. Microlicia delicata. A. Habit. B in Microlicia delicata and Microlicia pumila (Melastomataceae), two new species from unprotected mountains with campo rupestr vegetation in eastern Brazil

FIGURE 3. Microlicia delicata. A. Habit. B. Cluster of trichomes above a node. C. Leaf in lateral view (left), abaxial view (middle) and adaxial view (right). D. Close up of a leaf in lateral view showing cilia on the margin. E. Detail of leaf trichome. F. Branchlet terminating in a flower. G. Antesepalous stamen. H. Antepetalous stamen. I. Capsule enveloped by the hypanthium, after calyx lobes fell away. J. Capsule. Voucher: Bautista 2897. Scale bars. A, 2 cm; B, D and E, 0.5 mm; C, G, H, I and J, 2 mm; F, 4 mm.

opencc-by-4.0Jun 2023View details →

Monitoring programme on strict forest reserves in Flanders (Belgium) - site level stand structure, regeneration and vegetation data

This dataset contains comprehensive statistics on stand structure, rejuvenation, and vegetation for each forest reserve included in the monitoring program on strict forest reserves in Flanders (Belgium). The data collection and processing methodology used are described in <a href="https://purews.inbo.be/ws/portalfiles/portal/41050863/Vandekerkhove_etal_2021_MonitoringProgrammeOnStrictForestReservesFlanders.pdf">Vandekerkhove et al., 2021</a>. The dataset encompasses information on 15 distinct strict forest reserves and covers one to three consecutive forest inventories following a 10-year cycle.  The "site_info.xlsx" file provides details on the 15 sites, including central coordinates, surface area, forest type, and the year when they were set aside. For detailed descriptions of the information provided, please refer to the "_metadata_site_info.xlsx" file. The “statistics_per_reserve.zip” file contains 12 separate csv-files with the following data: <ul> <li> stat_dendro(_by)(_diam)(_species).csv : statistics on basic stand structure attributes (volume, basal area, number of trees per hectare, …) for living and dead standing trees (DBH-threshold 5cm): overall values and values per diameter class and/or species  </li> <li> stat_carbon.csv : statistics on biomass and carbon stock related to living trees </li> <li> stat_logs(_by)(_decay)(_species).csv : statistics on volume per hectare of lying deadwood: values per decay stage and/or species  </li> <li> stat_reg(_by)(_height)(_species).csv : statistics on rejuvenation (numbers per ha of young trees - seedlings up to trees with DBH< 5cm): overall values and values per heightclass and/or species  </li> <li> stat_veg.csv : statistics on vegetation characteristics, including number of species, moss, herb, shrub, tree and waterlayer cover, cumulated canopy cover and soil disturbance by game </li> <li> stat_herbs.csv : species-specific mean cover, percentage of plots where each species occurs </li> </ul> For descriptions of the common fields used in the above csv-files, please consult the "_metadata_statistics.xlsx" file.  For detailed and comprehensive information regarding the meaning and characteristics of the calculated variables, please refer to the "_metadata_variables.xlsx" file.  Plot-level results regarding dendrometry and regeneration are published separately<a href="https://zenodo.org/record/7588680"> here</a>, while information on vegetation can be found<a href="https://zenodo.org/record/7870740"> here</a>. For any inquiries or further information, please contact Kris.vandekerkhove@inbo.be or Anja.leyman@inbo.be. The provided csv-files will be updated as required to address any issues or include data from additional surveys. . Please check for updated versions periodically. We ask the users of the dataset to notify us of its use.

opencc-by-4.0Jun 2023View details →

Late-Quaternary megafauna extinctions have strongly reduced mammalian vegetation consumption

Scrips and data for recreating the results and figures in the paper Late-Quaternary megafauna extinctions have strongly reduced mammalian vegetation consumption DOI: 10.1111/geb.13723 Journal Global Ecology and Biogeography: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13723">https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13723</a> The three folders work as stand alone R-Studio projects, for estimating metabolic rates, densities, and the finally calculating total carbon consumption and creating summary statistics and figures. They require PHYLACINE v. 1.2.1 (https://zenodo.org/record/3690867) to be in another folder next to them.

opencc-by-4.0Jul 2023View details →

Data manuscript Gomez et al. Facilitation by pioneer trees and herbivore exclusion allow regeneration of woody species in the semiarid ecosystem of central Chile. Applied Vegetation Science

Data of the paper: Nicolás Gómez-Fernández, Cecilia Smith-Ramírez, Cristian A. Delpiano, Alejandro Miranda, Inao Vásquez, Pablo I. Becerra. Facilitation by pioneer trees and herbivore exclusion allow regeneration of woody species in the semiarid ecosystem of central Chile. Applied Vegetation Science

opencc-by-4.0Jul 2023View details →

Supplementary Material for "Automated detection of an insect-infested keystone vegetation phenotype using airborne LiDAR"

Ecologists, foresters and conservation practitioners need "biodiversity scanners" to effectively inventory biodiversity, audit conservation progress and track changes in ecosystem function. Quantifying biological diversity using airborne LiDAR remains challenging, especially for small invertebrates. However, insect aggregations can drastically alter landscapes and vegetation, and these "extended phenotypes" could serve as environmental landmarks of insect presence in LiDAR data. To test the feasibility of this approach, we studied the symbiotic ants that alter canopy shapes of whistling thorn acacia, a keystone tree species of the black cotton soils of east African savannas. We demonstrate a protocol of LiDAR data collection, training data preparation (including a customizable tree-segmentation algorithm) and convolutional neural network-based classification for the detection of ant-infested, within-species acacia tree phenotypic variations. Surveying ant occupancy of 402 hectares of 9,680 acacia trees took 1,000 work hours, while surveyed patterns of ant distribution was replicated by trained classifier based on an hour-long airborne LiDAR collection. We suggest that large scale surveys of insect occupancy (or insect-vectored disease) can be automated through a combination of airborne LiDAR and machine learning.

opencc-by-4.0Dec 2022View details →

Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part II: Main runs)

This is Part II of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to Limnology and Oceanography Letters. It contains all input and output files for every simulations used in the paper. Each zip file corresponds to a model run.  TIGER_XX.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 0-100. TIGER_XX_100.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 100-200. TIGER_XX_HYYY.zip: Scenario XX, hydro-morphodynamics only, year YYY. Main scenarios: - 01: Spartina (Figures 1-5, S3-S10) - 02: Salicornia (Figures 1-5, S3-S10) - 83: No vegetation (Figures 1-5, S3, S8-S10) Additional scenarios: - 146: Spartina, low bulk drag coefficient (Figure S3) - 147: Spartina, very low bulk drag coefficient (Figure S3) - 148: Salicornia, low bulk drag coefficient (Figure S3) - 149: Salicornia, very low bulk drag coefficient (Figure S3) - 122: Spartina, low settling velocity (Figure S8) - 123: Spartina, high settling velocity (Figure S8) - 124: Salicornia, low settling velocity (Figure S8) - 125: Salicornia, high settling velocity (Figure S8) - 126: No vegetation, low settling velocity (Figure S8) - 127: No vegetation, high settling velocity (Figure S8) - 128: Spartina, low critical bed erosion shear stress (Figure S8) - 129: Spartina, high critical bed erosion shear stress (Figure S8) - 130: Salicornia, low critical bed erosion shear stress (Figure S8) - 131: Salicornia, high critical bed erosion shear stress (Figure S8) - 132: No vegetation, low critical bed erosion shear stress (Figure S8) - 133: No vegetation, high critical bed erosion shear stress (Figure S8) - 134: Spartina, low Partheniades constant (Figure S8) - 143: Spartina, high Partheniades constant (Figure S8) - 136: Salicornia, low Partheniades constant (Figure S8) - 144: Salicornia, high Partheniades constant (Figure S8) - 138: No vegetation, low Partheniades constant (Figure S8) - 145: No vegetation, high Partheniades constant (Figure S8) - 150: Spartina, low sediment dry bulk density (Figure S8) - 151: Spartina, high sediment dry bulk density (Figure S8) - 152: Salicornia, low sediment dry bulk density (Figure S8) - 153: Salicornia, high sediment dry bulk density (Figure S8) - 154: No vegetation, low sediment dry bulk density (Figure S8) - 155: No vegetation, high sediment dry bulk density (Figure S8) - 76: Spartina, replicate #1 (Figures S9-S10) - 77: Spartina, replicate #2 (Figures S9-S10) - 78: Spartina, replicate #3 (Figures S9-S10) - 88: Spartina, replicate #4 (Figures S9-S10) - 80: Salicornia, replicate #1 (Figures S9-S10) - 81: Salicornia, replicate #2 (Figures S9-S10) - 82: Salicornia, replicate #3 (Figures S9-S10) - 89: Salicornia, replicate #4 (Figures S9-S10) - 85: No vegetation, replicate #1 (Figures S9-S10) - 86: No vegetation, replicate #2 (Figures S9-S10) - 87: No vegetation, replicate #3 (Figures S9-S10) - 90: No vegetation, replicate #4 (Figures S9-S10)

opencc-by-4.0Dec 2022View details →

Effects of submerged flexible vegetation on scalar transport in an open-channel flow

The data were used in the paper "Effects of submerged flexible vegetation on scalar transport in an open-channel flow" which was submitted to "Water Resources Research".In this paper, a 3D numerical model simulating scalar transfer in water flow with flexible submerged vegetation is established. The effects of vegetation on enhancing the scalar transfer and the selection of turbulent Schmidt number are investigated by numerical simulations.

Fig. 9 in Natural variation in specialised metabolites production in the leafy vegetable spider plant (Gynandropsis gynandra L. (Briq.)) in Africa and Asia

Fig. 9. Relative levels of glucosinolates and isothiocyanates in 43 accessions of Gynandropsis gynandra from Asia (red), East/Southern Africa (black) and West Africa (blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 8 in Natural variation in specialised metabolites production in the leafy vegetable spider plant (Gynandropsis gynandra L. (Briq.)) in Africa and Asia

Fig. 8. Sparse partial least square discriminant analysis on the 48 accessions of Gynandropsis gynandra based on 130 volatile metabolites: (a) Score plot showing the projection of the 48 accessions Asia (red), East/Southern Africa (black) and West Africa (blue) on the two dimensions; (b) Selected variables representation on two dimensions on the correlation circles (0.5 and 1 correlation values). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 7 in Natural variation in specialised metabolites production in the leafy vegetable spider plant (Gynandropsis gynandra L. (Briq.)) in Africa and Asia

Fig. 7. Heatmap of the 130 volatile metabolites detected in the leaves of 46 accessions of Gynandropsis gynandra from Asia (red), East/Southern Africa (black) and West Africa (blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in Natural variation in specialised metabolites production in the leafy vegetable spider plant (Gynandropsis gynandra L. (Briq.)) in Africa and Asia

Fig. 4. Sparse partial least square discriminant analysis on the 48 accessions of Gynandropsis gynandra based on 936 semi-polar metabolites: (a) Score plot showing the projection of the 48 accessions from Asia (red), East/Southern Africa (black) and West Africa (blue) on the first two dimensions; (b) Selected variables representation on two dimensions on the correlation circles (0.5 and 1 correlation values). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5 in Natural variation in specialised metabolites production in the leafy vegetable spider plant (Gynandropsis gynandra L. (Briq.)) in Africa and Asia

Fig. 5. Box plots showing the variation in relative levels of 14 annotated semi-polar metabolites in the leaves of 48 accessions of Gynandropsis gynandra. Lower and upper box boundaries represent 25th and 75th percentiles, respectively, the line inside the box is the median, lower, and upper error lines are 10th and 90th percentiles, respectively. Filled circles represent outliers. Putative identities: (a) LC2540: caffeoyl-oxalosuccinate; (b) LC3607: caffeoyl-hydroxycitric acid; (c) LC3341: dihydroxy-eudesmenolide-hexoside; (d) LC2765: Icariside B8; (e) LC3830: rhamnazin-hexoside-deoxyhexoside; (f) LC3890 quercetin-3-O-rutinoside; (g) LC880: glucocapparin; (h) LC 2021: caffeoyl-citric acid; (i) LC2468: coumaroyl-glucaric acid; (j) LC2400: glucaric acid-C26H26O14 conjugate; (k) LC2749: feruloylglucaric acid; (l) LC5323: dihydroxy-eudesmanolide-hexoside.

Fig. 6 in Natural variation in specialised metabolites production in the leafy vegetable spider plant (Gynandropsis gynandra L. (Briq.)) in Africa and Asia

Fig. 6. Principal component analysis score plot of relative levels of 130 volatile metabolites detected in the leaves of 46 accessions of Gynandropsis gynandra from Asia (red), East/Southern Africa (black) and West Africa (blue). The first two dimensions explaining 52.9% of the total variation are shown. 95% confidence ellipses are presented for the three regions. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in Natural variation in specialised metabolites production in the leafy vegetable spider plant (Gynandropsis gynandra L. (Briq.)) in Africa and Asia

Fig. 3. Heatmap of 107 significant semi-polar metabolites with high PCA loadings (>|0.7|) in 48 accessions of Gynandropsis gynandra from Asia (red), East/Southern Africa (black) and West Africa (blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)