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Data from: Studying long-term, large-scale grassland restoration outcomes to improve seeding methods and reveal knowledge gaps
Studies are increasingly investigating effects of large-scale management activities on grassland restoration outcomes. These studies are providing useful comparisons among currently used management strategies, but not the novel strategies needed to rapidly improve restoration efforts. Here we illustrate how managing restoration projects adaptively can allow promising management innovations to be identified and tested. We studied 327 Great Plains fields seeded after coal mining. We modelled plant responses to management strategies to identify the most effective previously used strategies for constraining weeds and establishing desired plants. Then, we used the model to predict responses to new strategies our analysis identified as potentially more effective. Where established, the weed crested wheatgrass (Agropyron cristatum L.) increased through time, indicating a need to manage establishment of this grass. Seeding particular grasses reduced annual weed cover, and because these grasses appeared to become similarly abundant whether sown at low or high rates, low rates could likely be safely used to reduce seeding costs. More importantly, lower than average grass seed rates increased cover of shrubs, the plants most difficult to restore to many grassland ecosystems. After identifying grass seed rates as a driver, we formulated model predictions for rates below the range managers typically use. These predictions require testing but indicated atypically low grass seed rates would further increase shrubs without hindering long-term grass stand development. Synthesis and applications. Designing management around empirically based predictions is a logical next step towards improving ecological restoration efforts. Our predictions are that reducing grass seed rates to atypically low levels will boost shrubs without compromising grasses. Because these predictions derive from the fitted model, they represent quantitative hypotheses based on current understanding of the system. Generating data needed to test and update these hypotheses will require monitoring responses to shifts in management, specifically shifts to lower grass seed rates. A paucity of data for confronting hypotheses has been a major sticking point hindering adaptive management of most natural resources, but this need not be the case with degraded grasslands, because ongoing restoration efforts around the globe are providing continuous opportunities to monitor and manage processes regulating grassland restoration outcomes.
GAP_workshop_2_materials
<p>Materials for ASBS 2021 conference workshop 2: Yang and Smith paralogy resolution.</p>
FIGURE 3. Gap data matrix for 28S D2 in Molecular relationships of the Australian Ennominae (Lepidoptera: Geometridae) and implications for the phylogeny of the Geometridae from molecular and morphological data
FIGURE 3. Gap data matrix for 28S D2 sequences of geometrid species used to create a phylogeny for the Australian Geometridae. (0) gap absent; (1) gap present; () inapplicable.
FIGURE 1 in Patterns of diversity and gaps in vascular (hemi-)epiphyte flora of Southwestern Amazonia
FIGURE 1. Geographic distribution of epiphytes in Acre, Southwestern Amazonia, by political-geographic division of Brazil.
FIGURE 2 in Psilochilus dressleri (Orchidaceae), a new species from the Darién Gap, Panama
FIGURE 2. Comparison of the lip shape of Psilochilus dressleri (A. Drawn from the holotype) and P. macrophyllus (B, redrawn by A. Król from Ames & Correll 1952).
FIGURE 1. Psilochilus dressleri. Dissected perianth. A in Psilochilus dressleri (Orchidaceae), a new species from the Darién Gap, Panama
FIGURE 1. Psilochilus dressleri. Dissected perianth. A. habit. Scale bar = 5 cm. B. Dorsal sepal. C. petal. D. Lateral sepal. E. Lip. Scale bars = 5 mm. Drawn from the holotype.
Supplementary material 4 from: Haubrock PJ, Cuthbert RN, Yeo DCJ, Banerjee AK, Liu C, Diagne C, Courchamp F (2021) Biological invasions in Singapore and Southeast Asia: data gaps fail to mask potentially massive economic costs. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 131-152. https://doi.org/10.3897/neobiota.67.64560
Extrapolated annual average costs for those invasive species known to be in Singapore with recorded costs in InvaCost
Supplementary material 1 from: Haubrock PJ, Cuthbert RN, Yeo DCJ, Banerjee AK, Liu C, Diagne C, Courchamp F (2021) Biological invasions in Singapore and Southeast Asia: data gaps fail to mask potentially massive economic costs. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 131-152. https://doi.org/10.3897/neobiota.67.64560
Description of the procedure used for collecting and describing cost data in the InvaCost database (adapted from Diagne et al. 2020)
Supplementary material 5 from: Haubrock PJ, Cuthbert RN, Yeo DCJ, Banerjee AK, Liu C, Diagne C, Courchamp F (2021) Biological invasions in Singapore and Southeast Asia: data gaps fail to mask potentially massive economic costs. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 131-152. https://doi.org/10.3897/neobiota.67.64560
Relationships between trade value and recorded cost entries per country in InvaCost, as well as land area and human population with total cost
Supplementary material 8 from: Renault D, Manfrini E, Leroy B, Diagne C, Ballesteros-Mejia L, Angulo E, Courchamp F (2021) Biological invasions in France: Alarming costs and even more alarming knowledge gaps. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 191-224. https://doi.org/10.3897/neobiota.67.59134
List of the 68 invasive alien species in metropolitan France for which no economic cost was documented in our database
Supplementary material 6 from: Renault D, Manfrini E, Leroy B, Diagne C, Ballesteros-Mejia L, Angulo E, Courchamp F (2021) Biological invasions in France: Alarming costs and even more alarming knowledge gaps. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 191-224. https://doi.org/10.3897/neobiota.67.59134
Categorical representation of the cumulated costs caused by invasive alien species in metropolitan France and French overseas over the period 1993–2018
Supplementary material 7 from: Renault D, Manfrini E, Leroy B, Diagne C, Ballesteros-Mejia L, Angulo E, Courchamp F (2021) Biological invasions in France: Alarming costs and even more alarming knowledge gaps. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 191-224. https://doi.org/10.3897/neobiota.67.59134
For each French region, listing of the taxa for which we had cost information in the InvaCost database over the time range 1993–2018
Supplementary material 1 from: Renault D, Manfrini E, Leroy B, Diagne C, Ballesteros-Mejia L, Angulo E, Courchamp F (2021) Biological invasions in France: Alarming costs and even more alarming knowledge gaps. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 191-224. https://doi.org/10.3897/neobiota.67.59134
Datasets of the economic costs of invasive alien species in France and descriptive variables (from Diagne et al. 2020b)
FIGURE 9 in Discovery of a new duiker species (Bovidae: Cephalophinae) from the Dahomey Gap, West Africa
FIGURE 9. Comparison of the skulls (with field numbers) of (top) males and (bottom) females P. maxwelli (580), P. walteri (17) and P. monticola (965), and (bottom) P. maxwelli (675), P. walteri (70) and P. monticola (1144).
FIGURE 6 in Discovery of a new duiker species (Bovidae: Cephalophinae) from the Dahomey Gap, West Africa
FIGURE 6. Bayesian tree based on the concatenated COI and cytb data set. Values on the branches represent bootstrap supports from the parsimony/maximum likelihood analyses, and the posterior probabilities (x100 for the latter values for easy comparison) from the Bayesian analysis. SNPs indicated above the branches represent the detected synapomorphies provided by the three nuclear markers. Colors, field numbers and locality identifiers are as in Figure 1 and Appendix 2.
FIGURE 8 in Discovery of a new duiker species (Bovidae: Cephalophinae) from the Dahomey Gap, West Africa
FIGURE 8. Photograph of the holotype and one of the paratypes of the new duiker species, Philantomba walteri. (TOP) Paratype, RMCA A7. 027M0002; field number BE 119. (BOTTOM) Holotype, RMCA A7. 027M0001; field number BE 118. Close-up photo gives detailed view of the head of the holotype.
FIGURE 4. Factor structure graph for Philantomba monticola, P. maxwelli and P in Discovery of a new duiker species (Bovidae: Cephalophinae) from the Dahomey Gap, West Africa
FIGURE 4. Factor structure graph for Philantomba monticola, P. maxwelli and P. walteri sp. n. (OTU30 [Benin+Togo]).
FIGURE 3 in Discovery of a new duiker species (Bovidae: Cephalophinae) from the Dahomey Gap, West Africa
FIGURE 3. Canonical analysis of the unidentified Philantomba population (OTU 30 [Benin+Togo]) with two groups of P. monticola [Cameroon, Gabon, Bioko] and P. maxwelli [Guinea].
FIGURE 1 in Discovery of a new duiker species (Bovidae: Cephalophinae) from the Dahomey Gap, West Africa
FIGURE 1. (A) Traditional distribution ranges of P. maxwelli (red) and P. monticola (blue). (B) Geographic sampling for P. maxwelli, P. monticola and the undescribed Philantomba species (green). Numbers correspond to localities given in Appendix 2. (Number 11 on map corresponds to a subadult specimen from Togo that was not used (neither for morphology nor for genetic analysis))
FIGURE 2 in Discovery of a new duiker species (Bovidae: Cephalophinae) from the Dahomey Gap, West Africa
FIGURE 2. Schematic view of the craniometric measurements used in this study: (1) Greatest skull length; (2) distance between basion and incisivion; (3) distance between basion and first premolars (PM1); (4) distance between basion and choanae; (5) length of premolar toothrow; (6) length of molar toothrow; (7) distance between incisivion and premolar toothrows (PM1); (8) distance between incisivion and nasion; (9) distance between nasion and bregma; (10) distance between bregma and inion; (11) horn length; (12) interorbital width; (13) nasal width (bi-lacrimal and nasofrontal suture); (14) nasal constriction; (15) greatest incisive width; (16) greatest bi-zygomatic width; (17) greatest braincase width; (18) largest width of the horn process of the frontal bone; (19) least width of the horn process of the frontal bone; (20) alveolar width at PM1-PM1; (21) width of the upper molar toothrows; (22) width of molar M2; (23) internal choanal width at M3; (24) orbital width (between lacrimo-frontal and fronto-zygomatic suture); (25) height of the vertical ramus; (26) height of the horizontal ramus between M1 and M2; (27) length between PM1 and the posterior part of the vertical ramus; (28) distance between basion and inion; (29) distance between basion and bregma; (30) distance between nasofrontal suture and palatine bone (31) lacrimal constriction.
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.