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Figure 5 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean

Figure 5. Weed biomass in each weed management treatment pooled across all site-years.Biomass was sampled in mid-August after all management tactics had been applied. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.

Figure 4 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean

Figure 4. Soybean density in August after all weed management treatments were applied. Data were pooled across all site-years. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.

Figure 8 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean

Figure 8. Soybean yield from each weed management treatment pooled across fields. Yield is dry weight corrected to 13% moisture. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.

Figure 1 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean

Figure 1. The interrow mower used in this experiment, attached to a John DeereṜ 5100R tractor with a three-point hitch. The mower is powered with a hydraulic system and was custom made by IRM X4, R-Tech Industries (Homewood, MB, Canada).

Figure 2. The model 6R30 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean

Figure 2. The model 6R30 Weed Zapper™ used in this experiment. The generator is attached to the back of a John DeereṜ 5100R tractor with a three-point hitch. The 4.6-m electric copper boom is attached to the front of the tractor with a three-point hitch. The Weed Zapper™ was purchased from Old School Manufacturing (Sedalia, MO, USA).

opencc-by-4.0Dec 2023View details →

Figure 2 in Nest refuse of Acromyrmex balzani (Hymenoptera: Formicidae) increases the plant vigor in Turnera subulata (Turneraceae)

Figure 2. Boxplot of attributes evaluated in T. subulata plants after thirty days between two treatments: (a) stem diameter (mm); (b) root length (cm); (c) plant height (cm), (d) the number of leaves, (e) dry aboveground biomass (g) and (f) fresh aboveground biomass (g). Horizontal lines inside each box indicate the median.

opencc-by-4.0Dec 2023View details →

Figure 1 in Nest refuse of Acromyrmex balzani (Hymenoptera: Formicidae) increases the plant vigor in Turnera subulata (Turneraceae)

Figure 1. Detail of the studied species. (a) Turnera subulata in the study area; (b) nest mound of Acromyrmex balzani with a nest refuse pile on the right and the colony entrance, characterized by three small straw holes (left).

opencc-by-4.0Jun 2024View details →

Improving the application of Important Plant Areas to conserve threatened habitats: a case study of Uganda

This data set relates to the publication: Richards, S. L., Kalema, J., Ojelel, S., Williams, J., & Darbyshire, I. (2024). Improving the application of Important Plant Areas to conserve threatened habitats: A case study of Uganda. Conservation Science and Practice, e13246. https://doi.org/10.1111/csp2.13246 Abstract: Important Plant Areas (IPAs) are a successful method of identifying priority areas for plant conservation. Assessment of IPAs, however, often relies on criteria related to species, while incorporation of habitats has been less consistent. Using Uganda as a case study, we test the application of the threatened habitat criterion – criterion C. We identified nationally threatened habitats using Red List of Ecosystems criteria and assess, for the first time, how differing application of thresholds under IPA criterion C can influence IPA network outcomes. Eleven threatened habitats were identified, with declines switching from predominantly forest to savanna after the mid-20th century. Significantly, we found current IPA guidance on use of criterion C needlessly limits the number of sites that qualify as IPAs. The “five best sites” IPA threshold is reserved for countries where quantitative data is unavailable, however, the application of the relevant numerical thresholds (site contains ≥10% of national resource or site is among the best quality examples required to collectively prioritisie up to 20% of the national resource) to quantitative data largely generated fewer than five IPAs, comparably limiting conservation opportunities identified. We recommend, therefore, that the “five best” threshold is available for application on both qualitative and quantitative data. This will bolster the value of IPAs in conserving and restoring threatened and ecologically important habitats under the Kunming-Montreal Global Biodiversity Framework. Dataset: Within this dataset is a shapefile of the estimated extent of threatened habitats in Uganda. Each polygon represents a single "site" for each threatened habitat, with methodology for site identification given in the manuscript. Feature area and percentage national resource are given for each site, enabling users to identify those that trigger the different IPA criterion C thresholds. In this study, we have preliminarily identified the threatened habitats and IPAs for Uganda. However, it is important to seek the expertise and views of stakeholders, consider other IPA criteria met and any complementarity between sites when identifying IPAs. In addition, ground-truthing or more localised data could validate the threat status of a vegetation type as well as identifying which sites are best to conserve these habitats.

opencc-by-4.0Jun 2016View details →

Figs. 9–14 in Taxonomic studies on the genus Caryopemon (Coleoptera: Chrysomelidae: Bruchinae) of China and Myanmar with some new host plants

Figs. 9–14. Caryopemon luteonotatus; 9, dorsal view; 10, ventral view; 11, hind femur; 12, median lobe, ventral view; 13, median lobe, lateral view; 14, lateral lobes, ventral view. Scale bars = 1 mm.

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Mechanisms of coexistence: Exploring species sorting and character displacement in woody plants to alleviate belowground competition

Rarely do we observe competitive exclusion within plant communities, even though plants compete for a limited pool of resources. Thus, our understanding of the mechanisms sustaining plant biodiversity might be limited. In this study, we explore two common ecological strategies, species sorting and character displacement, that promote coexistence by reducing competition. We assess the degree to which woody plants may implement these two strategies to lower belowground competition for nutrients which occurs via nutritional (mostly mycorrhizal) mutualisms. First, we compile data on plant traits and the mycorrhizal association state of woody angiosperms using a global inventory of indigenous flora. Our analysis reveals that species in locations with high mycorrhizal diversity exhibit distinct mean values in leaf area and wood density based on their mycorrhizal type, indicating species sorting. Second, we reanalyze a large dataset on leaf area to demonstrate that in areas with high mycorrhizal diversity, trees maintain divergent leaf area values, showcasing character displacement. Character displacement among plants is considered rare, making our observation significant. In summary, our study uncovers a rare occurrence of character displacement and identifies a common mechanism employed by plants to alleviate competition, shedding light on the complexities of plant coexistence in diverse ecosystems.

opencc-zeroJun 2024View details →

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Data from: Climatic conditions and landscape diversity predict plant-bee interactions and pollen deposition in bee-pollinated plants.

Climate change, landscape homogenization and the decline of beneficial insects threaten pollination services to wild plants and crops. Understanding how pollination potential (i.e. the capacity of ecosystems to support pollination of plants) is affected by climate change and landscape homogenization is fundamental for our ability to predict how such anthropogenic stressors affect plant biodiversity. Models of pollinator potential are improved when based on pairwise plant-pollinator interactions and pollinator´s plant preferences. However, whether the sum of predicted pairwise interactions with a plant within a habitat (a proxy for pollination potential) relates to pollen deposition on flowering plants has not yet been investigated. We sampled plant-bee interactions in 68 Scandinavian plant communities in landscapes of varying land-cover heterogeneity along a latitudinal temperature gradient of 4–8 C°, and estimated pollen deposition as the number of pollen grains on flowers of the bee-pollinated plants Lotus corniculatus, and Vicia cracca. We show that plant-bee interactions, and the pollination potential for these bee-pollinated plants increase with landscape diversity, annual mean temperature, plant abundance, and decrease with distances to sand-dominated soils. Furthermore, the pollen deposition in flowers increased with the predicted pollination potential, which was driven by landscape diversity and plant abundance. Our study illustrates that the pollination potential, and thus pollen deposition, for wild plants can be mapped based on spatial models of plant-bee interactions that incorporate pollinator-specific plant preferences. Maps of pollination potential can be used to guide conservation and restoration planning.

opencc-zeroJun 2024View details →

opencc-by-4.0May 2024View details →

TreeGOER 2024 Expansion: Expansion with additional tree and bamboo species identified via the World Checklist of Vascular Plants

The database provides globally observed environmental ranges for an additional list of species not included in the <a href="https://zenodo.org/records/7922927">TreeGOER database</a>. Candidate species were identified via the <a href="https://powo.science.kew.org/about-wcvp">World Checklist of Vascular Plants (WCVP) version 11</a>. Many of the additional species were hybrids or bamboo species that were excluded from GlobalTreeSearch. Taxonomical details given in a separate file correspond to information provided by the WCVP, as well as information on the life form of each species. Field n in the taxonomical data sets shows the number of records used to provide range information for the expansion of TreeGOER. Tree species were filtered from the WCVP by selecting species records with an empty acceptedNameUsageID field (a field that refers to a current name if not empty) and afterwards filtering for records where the lifeform_description field contained one from the categories of tree (3580 candidate species for the TreeGOER 2024 expansion), shrub or tree (3129), scrambling shrub or tree (138), climbing shrub or tree (58), succulent shrub or tree (40), succulent tree (37), scrambling tree (32), liana or tree (6), , tuberous tree (3), tuberous shrub or tree (3), semisucculent tree (2), or semisucculent shrub or tree (3) Species that could not be matched with the <a href="https://tools.bgci.org/global_tree_search.php">GlobalTreeSearch database (version 1.7)</a> were candidates for the expansion of species documented in TreeGOER. Standardization to the WCVP was achieved via the <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">WorldFlora R package</a>, using the same scripts available in this Rpub: . <a href="https://rpubs.com/Roeland-KINDT/1134151">https://rpubs.com/Roeland-KINDT/1134151</a>. Occurrence data were obtained from the Global Biodiversity Information Facility via the following downloads. Downloads were facilitated by prior identification of the GBIF usageKey via the <a href="https://docs.ropensci.org/rgbif/reference/name_backbone.html">rgbif::name_backbone</a> function, afterwards filtering records that matched with a current species name in the GBIF backbone taxonomy, using package <a href="https://cran.r-project.org/package=rgbif">rgbif</a> version 3.7-9. <ul> <li>batch 1:  GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.upqqve">https://doi.org/10.15468/dl.upqqve</a></li> <li>batch 2:  GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.fhqdpg">https://doi.org/10.15468/dl.fhqdpg</a></li> <li>batch 3:  GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.w3k3n7">https://doi.org/10.15468/dl.w3k3n7</a></li> <li>batch 4:  GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.xqp7qg">https://doi.org/10.15468/dl.xqp7qg</a></li> <li>batch 5:  GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.t92qsg">https://doi.org/10.15468/dl.t92qsg</a></li> <li>batch 6:  GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.7ug5z5">https://doi.org/10.15468/dl.7ug5z5</a></li> <li>batch 7:  GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.8tp9p8">https://doi.org/10.15468/dl.8tp9p8</a></li> <li>batch 8:  GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.ewdj7m">https://doi.org/10.15468/dl.ewdj7m</a></li> <li>batch 9:  GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.gccja8">https://doi.org/10.15468/dl.gccja8</a></li> <li>batch 10: GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.jg7gzs">https://doi.org/10.15468/dl.jg7gzs</a></li> <li>batch 11: GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.6gww7b">https://doi.org/10.15468/dl.6gww7b</a></li> <li>batch 12: GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.9h8axh">https://doi.org/10.15468/dl.9h8axh</a></li> <li>batch 13: GBIF.org (27 March 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.mswf23">https://doi.org/10.15468/dl.mswf23</a></li> </ul>   For bamboo species, identified by a similar process as documented above but now filtering in the WCVP for the lifeform of bamboo, occurrence data were obtained from the Global Biodiversity Information Facility via the following downloads: <ul> <li>batch 1:  GBIF.org (08 April 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.6xt5qu">https://doi.org/10.15468/dl.6xt5qu</a></li> <li>batch 2:  GBIF.org (08 April 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.tdmva4">https://doi.org/10.15468/dl.tdmva4</a></li> <li>batch 3:  GBIF.org (08 April 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.u2gpxv">https://doi.org/10.15468/dl.u2gpxv</a></li> </ul>   After downloading the GBIF occurrence data sets, the same procedures were used to calculate the globally observed environmental ranges as in the TreeGOER database which have been described by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.    A new check for the availability of species observations was made also for species listed in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees database</a>, but not in TreeGOER. Taxonomical details for these species are given as a 'set 2' in the database. Downloads from the Global Biodiversity Information Facility were: <ul> <li>batch 1: GBIF.org (04 April 2024) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.ajwegx">https://doi.org/10.15468/dl.ajwegx</a></li> <li>batch 2: GBIF.org (04 April 2024) GBIF Occurrence Download  <a href="https://doi.org/10.15468/dl.hxk5be">https://doi.org/10.15468/dl.hxk5be</a></li> </ul>   Version 2024.06 included a new field in the Tmo10 zones files of 'A18' that flags 717 species that occur in zones where all months have a mininum temperature of 18 degrees or above, using similar methods as the 2024.06 version of TreeGOER.   The development of the TreeGOER 2024 Expansion was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project and through the Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso, by the Bezos Earth Fund to the Bezos Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa. When using TreeGOER 2024 Expansion in your work, cite the publication (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>) as well as this repository using the DOI (<a href="../doi/10.5281/zenodo.11208040">https://zenodo.org/doi/10.5281/zenodo.11208040</a>).

Figure 3 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean

Figure 3. Monthly temperature and precipitation in Aurora, NY, USA, in 2021 and 2022. Pink lines indicate 30-yr average.

Figure 10. a in Ramie Moth, Arcte coerula (Lepidoptera: Noctuidae): A New Invasive Pest in Hawaii on Endemic Plants

Figure 10. a. Feeding damage by 1st instar Arcte coerula larvae; b. Feeding damage by 3rd instar larvae.

Figure 5. a in Ramie Moth, Arcte coerula (Lepidoptera: Noctuidae): A New Invasive Pest in Hawaii on Endemic Plants

Figure 5. a. Sixth instar larva of Udea stellata; b. Larvae typically found in a silken protective structure (arrow) on the underside of leaves with brown speckling from feeding damage.

Figure 6 in Ramie Moth, Arcte coerula (Lepidoptera: Noctuidae): A New Invasive Pest in Hawaii on Endemic Plants

Figure 6. Late instar larvae of tomato looper, Chrysodeixis chalcites. Although this caterpillar also has black markings on its side like A. coerula, their head capsule is green rather than tan or black and has a black line across the side (arrow).

Figure 2. a in Ramie Moth, Arcte coerula (Lepidoptera: Noctuidae): A New Invasive Pest in Hawaii on Endemic Plants

Figure 2. a. Unhatched Arcte coerula egg (arrow), 1 mm in diameter. Laid on the underside of host plant leaves; b. Arcte coerula egg mass with 150 hatched eggs.

Figure 11. a in Ramie Moth, Arcte coerula (Lepidoptera: Noctuidae): A New Invasive Pest in Hawaii on Endemic Plants

Figure 11. a. Egg parasitoid of Arcte coerula emerging; b. Trichogramma sp. parasitizing the egg of Arcte coerula; c. Gregarious larval Eulophidae ectoparasitoid of Arcte coerula.

Figure 1. a in Ramie Moth, Arcte coerula (Lepidoptera: Noctuidae): A New Invasive Pest in Hawaii on Endemic Plants

Figure 1. a. Distribution (red dots) of Arcte coerula on the island of Maui: 1 Kahakuloa, 2 ʻĪao Valley, 3 Waikapū, 4 Olowalu, 5 Olinda, 6 Kēōkea; b. Distribution (red dots) of A. coerula on the island of Hawaii: 1 Honokaʻa, 2 Pāpaʻikou, 3 Hilo, 4 Mountain View, 5 Pāhoa, 6 Kalapana, 7 Volcano, 8 Pāhala.

Figure 3 in Ramie Moth, Arcte coerula (Lepidoptera: Noctuidae): A New Invasive Pest in Hawaii on Endemic Plants

Figure 3. First instar larva of Arcte coerula. Black markings (arrow) on its side distinguish these larvae from other Lepidoptera that also use māmaki as a food source.