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Supplementary material 5 from: Bowman EA, Plowes RM, Gilbert LE (2023) Evidence of plant-soil feedback in South Texas grasslands associated with invasive Guinea grass. NeoBiota 81: 33-51. https://doi.org/10.3897/neobiota.81.86672

Results of t-test examining differences in soil characteristics between invaded and uninvaded sites. Electrical conductivity, phosphorus, and sulfur were log-transformed prior to analysis.

Supplementary material 4 from: Bowman EA, Plowes RM, Gilbert LE (2023) Evidence of plant-soil feedback in South Texas grasslands associated with invasive Guinea grass. NeoBiota 81: 33-51. https://doi.org/10.3897/neobiota.81.86672

Results of one-way ANOVA examining the effect of autoclave time on soil characteristics. Electrical conductivity, phosphorus, and sulfur were log-transformed prior to analysis.

Supplementary material 1 from: Bowman EA, Plowes RM, Gilbert LE (2023) Evidence of plant-soil feedback in South Texas grasslands associated with invasive Guinea grass. NeoBiota 81: 33-51. https://doi.org/10.3897/neobiota.81.86672

Soil sampling sites showing extent of Guinea grass patch (white boundary, I) and adjacent uninvaded grassland (N) with nearby mesquite tree mottes. Google Earth Imagery date 1/13/2014. Scale bar 70m.

Supplementary material 2 from: Bowman EA, Plowes RM, Gilbert LE (2023) Evidence of plant-soil feedback in South Texas grasslands associated with invasive Guinea grass. NeoBiota 81: 33-51. https://doi.org/10.3897/neobiota.81.86672

Initial germination of Guinea grass seed (a) and the seedbank (b) during week 1 was higher in soil from invaded sites than uninvaded sites. All data shown here are non-transformed.

Supplementary material 3 from: Bowman EA, Plowes RM, Gilbert LE (2023) Evidence of plant-soil feedback in South Texas grasslands associated with invasive Guinea grass. NeoBiota 81: 33-51. https://doi.org/10.3897/neobiota.81.86672

Effect of soil handling method on Guinea grass seedling count (a), native community plant abundance (b), and native community biomass (c). MSS: mixed soil sampling; ISS: individual soil sampling. All data shown are non-transformed.

opennotspecifiedMar 2023View details →

FIGURE 2. Bulbine capitata. A in Bulbine decastroi (Asphodelaceae subfam. Asphodeloideae), a new peatland species with grass tuft-like rosettes from Mpumalanga, South Africa

FIGURE 2. Bulbine capitata. A. Growing in a recently burned grassland. Note the arid substrate. B. The leaves are most often much broader than those of B. decastroi. C. Close-up of a raceme and flowers. Note the long pedicels. All photographs: Neil R. Crouch.

opennotspecifiedMar 2023View details →

FIGURE 1. Bulbine decastroi. A in Bulbine decastroi (Asphodelaceae subfam. Asphodeloideae), a new peatland species with grass tuft-like rosettes from Mpumalanga, South Africa

FIGURE 1. Bulbine decastroi. A. In its natural peatland habitat. The peatland burned a few months before the photograph was taken. Note the high water table in the bottom right of the image. B. This section of the peatland did not burn. The vegetation is very dense. C. Contractile roots. D. A dense cluster of 40 rosettes. Note the glaucous sheen to the leaves. E. Close-up of racemes and flowers. F. Tony de Castro (1970–) after whom B. decastroi is named. Bulbine decastroi grows in the foreground. All photographs: Gideon F. Smith.

opencc-by-4.0Apr 2023View details →

Mapping forests with different levels of naturalness using machine learning and landscape data mining - GRASS GIS DB

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled: "Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review) Abstract: To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration. This database was compiled from the following sources: 1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency. source: <a href="https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip">https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip</a> 2. NMD. National Land Cover Data. Swedish Environmental Protection Agency. source: <a href="https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/">https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/</a> 3. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance. source: <a href="https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/">https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/</a> 4. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland. source: <a href="https://glad.earthengine.app">https://glad.earthengine.app</a> 5. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN). source: <a href="https://doi.org/10.6084/m9.figshare.9828827.v2">https://doi.org/10.6084/m9.figshare.9828827.v2</a> 6. POPULATION. Total Population in Sweden. Statistics Sweden. source: <a href="https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/">https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/</a>   To learn more about the GRASS GIS database structure, see: <a href="https://grass.osgeo.org/grass82/manuals/grass_database.html">https://grass.osgeo.org/grass82/manuals/grass_database.html</a>

opennotspecifiedNov 2014View details →

Figure 6 in Aloe liliputana, a new grass aloe from Pondoland, Eastern Cape, Republic of South Africa

Figure 6. Aloe liliputana in its coastal habitat growing on shallow bedrock (Mkweni Gorge) near Lupathana, Pondoland. Photograph: Adam Harrower

opennotspecifiedNov 2014View details →

Figure 2 in Aloe liliputana, a new grass aloe from Pondoland, Eastern Cape, Republic of South Africa

Figure 2. Aloe liliputana in its natural habitat north of Lupathana Gorge. Photograph: Adam Harrower

opennotspecifiedNov 2014View details →

Figure 9 in Aloe liliputana, a new grass aloe from Pondoland, Eastern Cape, Republic of South Africa

Figure 9. Aloe liliputana in cultivation at Kirstenbosch National Botanical Garden Photograph: Adam Harrower

opennotspecifiedAug 2003View details →

Figure 2. Aloe craibii Gideon F in Aloe craibii Gideon F.Sm. (Asphodelaceae: Alooideae): a new species of grass aloe from the Barberton Centre of Endemism, Mpumalanga, South Africa

Figure 2. Aloe craibii Gideon F.Sm. A. Inflorescence (length = 230 mm). B. Leaf (length = 240 mm). C. Upper half of post-flowering inflorescence in fruit (length = 170 mm). D. Habit (total height of plant illustrated, including inflorescence = 900 mm ). Artist: Gillian Condy.

opennotspecifiedAug 2003View details →

Figure 2. Aloe craibii Gideon F in Aloe craibii Gideon F.Sm. (Asphodelaceae: Alooideae): a new species of grass aloe from the Barberton Centre of Endemism, Mpumalanga, South Africa

opencc-by-4.0May 2023View details →

Table S1: Projective cover of dwarf shrub, grass, moss and lichens layers at the studied bog massif (%)

Projective cover of dwarf shrub, grass, moss layers and lichens at the ombrotrophic Ilasskoe bog, Arkhangelsk regoin russia

Fig. 4 in The grass root endophytic fungus Flavomyces fulophazii: An abundant source of tetramic acid and chlorinated azaphilone derivatives

Fig. 4. Characteristic MS fragmentation of azaphilone compounds 6a, 7–11 (A) and 6b flavochlorine F (B) along with their backbone specific fragment ion structure (C). Corresponding fragment ions generated from protonated molecular ions of these azaphilones by various collision induced dissociation energies, are detailed in the Supplementary Tables S2 and S3.

Fig. 3 in The grass root endophytic fungus Flavomyces fulophazii: An abundant source of tetramic acid and chlorinated azaphilone derivatives

Fig. 3. Extracted ion chromatograms (A–F) for m/z 340.1 (A), m/z 296.1 (B), m/z 252.1 (C), m/z 310.1 (D), m/z 352.1 (E), and m/z 253.1 (F) corresponding to azaphilones, and HR-MS spectra (A′–F′) of azaphilones 6a (flavochlorine E), 7 (flavochlorine A), 8 (flavochlorine B), 9 (flavochlorine C), 10 (flavochlorine G) and 11 (flavochlorine D), respectively, along with their chemical structures (note: HR-MS spectrum of 6b (flavochlorine F) comparable with that of compound 6a, was not depicted). Chromatograms and spectra were obtained from a HPLC separation of the extract prepared from Flavomyces fulophazii culture sample HF-3A.

Fig. 2. A in The grass root endophytic fungus Flavomyces fulophazii: An abundant source of tetramic acid and chlorinated azaphilone derivatives

Fig. 2. A HPLC separation of the extract prepared from Flavomyces fulophazii culture sample HF-3A [full chromatogram A was recorded using UV detection (λ = 280 nm), and trace chromatograms (B, C, D, E, F) were obtained by MS detection, monitoring the extracted ion current for m/z 252.1 (B), m/z 236.1 (C), m/z 234.1 (D), m/z 250.1 (E) and m/z 218.1 (F), corresponding to tetramic acids] and HR-MS spectra (B′, C′, D′, E′, F′) of tetramic acids 1 (dihydroxyvermelhotin), 2 (hydroxyvermelhotin), 3 (oxovermelhotin), 4 (methoxyvermelhotin) and 5 (vermelhotin), respectively, along with their chemical structures.

Fig. 1 in The grass root endophytic fungus Flavomyces fulophazii: An abundant source of tetramic acid and chlorinated azaphilone derivatives

Fig. 1. Maximum Likelihood (ML) phylogenetic tree of ITS sequences of representative species in Periconiaceae and Massarinaceae in the suborder Massarineae (Pleosporales). Highlighted sections indicate affiliations to families. Flavomyces fulophazii isolates and the vermelhotin producing CRI247-01 strain (see Kasettrathat et al., 2008) are shown in bold. ML bootstrap support values (≥70) are shown at branches. GenBank accession numbers of the sequences and strain numbers are shown before and after the species names, respectively. Three representative species of the family Lentitheciaceae served as multiple outgroups (highlighted with blue). The scale bar indicates 0.05 expected changes per site per branch.. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

opencc-zeroAug 2023View details →

dryad32/100

Meta-model for Patagonian grass steppe dynamics - library of transition matrices for steppe dynamics under sheep grazing

One of the central problems in ecology is how to scale from small-scale observations and experiments to large-scale patterns and processes. One approach to such upscaling is to use dynamic simulation models, but their application to large scales relevant for management is limited by computational costs, and their outputs are difficult to analyse without a systematic strategy. Our general objective is to propose such a strategy. The idea is to approximate the dynamics of detailed simulation models through a set of states, external drivers, and transition matrices, and then use Markov chain and network analysis of the resulting transition matrices to gain insights into the dynamics of the underlying detailed model. We used the individual-based model COIRON, which simulates the dynamics of semiarid grass steppes in Patagonia (Argentina) under alternative grazing management, as example. Our specific objectives are to identify pathways of degradation and rehabilitation, as well as critical grazing thresholds and early-warning vegetation states to guide sustainable grazing management in these steppes. Our results indicate nonlinear effects of stocking rate and grazing season on steppe dynamics. Markov chain analysis suggests benefits of seasonal over continuous grazing at intermediate stocking rates, and network analysis of recovery and degradation trajectories shows that intermediate stocking rates maximize differences between grazing seasons. Finally, our analysis identified specific vegetation states as early warning signals that indicate a high risk of irreversible vegetation changes. Patagonian grass steppes should ideally be managed with multi-paddock grazing at moderate stocking rates around 0.5 sheep·ha-1. The transition matrices summarize the relevant key features of the detailed model for larger scales, and applying Markov and network theory provides a systematic strategy to analyse its dynamics to respond to biological questions, both are often difficult to obtain by direct analysis of the detailed model.