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Fig. 6 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 6. – Province species richness in relation to province characteristics. Species richness per province is shown dependent on 4 factors.
Fig. 1 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 1. – The provinces of Burkina Faso and their assignment to the phytogeographic zones used in this study. The classification of provinces to the PGZs is modified after WHITE (1983) and GUINKO (1984a). [1: Les Balé; 2: Bam; 3: Banwa; 4: Bazègua. 5: Bougouriba; 6: Boulgou; 7: Boulkiemdé; 8: Ganzourgou; 9: Gnagna; 10: Gourma; 11: Houet; 12: Ioba; 13: Kadiogo; 14: Kénédougou; 15: Comoé; 16: Komandjari; 17: Kompienga; 18: Kossi; 19: Koulpélogo; 20: Kouritenga; 21: Kourwéogo; 22: Léraba; 23: Loroum; 24: Mouhoun; 25: Nahouri; 26: Namentenga; 27: Nayala; 28: Oubritenga; 29: Oudalan; 30: Passoré; 31: Sanguié; 32: Sanmatenga; 33: Séno; 34: Sissili; 35: Soum; 36: Sourou; 37: Tapoa; 38: Tuy; 39: Yagha; 40: Yatenga; 41: Ziro; 42: Zondoma; 43: Zoundwéogo; 44: Poni; 45: Noumbiel]
Genetic structure in patchy populations of a candidate foundation plant: a case study of Leymus chinensis using genetic and clonal diversity
<p><strong>PREMISE</strong>: The distribution of genetic diversity on the landscape has critical ecological and evolutionary implications. This may be especially the case on a local scale for foundation plant species since they create and define ecological communities, contributing disproportionately to ecosystem function.</p> <p><strong>METHODS</strong>: We examined the distribution of genetic diversity and clones, which we defined first as unique multilocus genotypes (MLG), and then by grouping similar MLGs into multilocus lineages (MLL). We used 186 markers from inter-simple sequence repeats (ISSR) across 358 ramets from 13 patches of the foundation grass <em>Leymus chinensis</em>. We examined the relationship between genetic and clonal diversities, their variation with patch-size, and the effect of the number of markers used to evaluate genetic diversity and structure in this species.</p> <p><strong>RESULTS</strong>: Every ramet had a unique MLG. Almost all patches consisted of individuals belonging to a single MLL. We confirmed this with a clustering algorithm to group related genotypes. The predominance of a single lineage within each patch could be the result of the accumulation of somatic mutations, limited dispersal, some sexual reproduction with partners mainly restricted to the same patch, or a combination of all three.</p> <p><strong>CONCLUSIONS</strong>: We found strong genetic structure among patches of <em>L. chinensis</em>. Consistent with previous work on the species, the clustering of similar genotypes within patches suggests that clonal reproduction combined with somatic mutation, limited dispersal, and some degree of sexual reproduction among neighbors causes individuals within a patch to be more closely related than among patches.</p>
Using soil eDNA for plant diversity assessments
<p>This data sets corresponds to a publication in Methods in Ecology and Evolution titled: <strong>Plant biodiversity assessment through soil eDNA reflects temporal and local diversity</strong></p> <p>In August 2018, a single soil eDNA sample was collected from the centre of each permanent plot (1m2) in the Solhomfjell Forest Reserve stablished by the Sommerfeltia program. The soil eDNA samples were stored in individual plastic bags for transportation to the lab and stored at -20 °C prior to freeze-drying under vacuum. Each soil eDNA sample was separately homogenized with ceramic beads and one gram was used for eDNA extraction. The latter was done in five rounds of two steps: (1) CTAB/chloroform pre-treatment to increase the separation of the organic phase and (2) aqueous phase and using the E.Z.N.A. soil DNA kit following the manufacturer’s protocol (Omega Bio-tek, Norcross, Georgia, USA). The chloroplast marker trnL (UAA) intron P6 loop was chosen as its short sequence can yield amplification of old DNA material degraded in eDNA samples. This marker was amplified for each sample with the g and h primers by PCR, using three technical replicates (Taberlet et al. 2007; 5'-GGGCAATCCTGAGCCAA-3', 5'-CCATTGAGTCTCTGCACCTATC-3'). Forward and reverse primers were tagged with a unique 12 bp oligonucleotide on the 5’ end (Fadrosh et al. 2014). Unique combinations of tagged primers were set up in panels for each PCR reaction for a total of 309 samples (100 samples with 3 PCR replicates each, 5 extractions blanks and 4 PCR negatives). The PCR negatives had no DNA template and were placed on the 96th well position in each panel. Composition of PCR reactions, final volumes and number of cycles can be found in Supporting Information Data S1. The PCR products were run on a 2% agarose gel, and the amplicon concentrations were measured via band intensity using ImageLab software (Bio-Rad, California, USA). The lowest concentration (μM) available for all PCR products and its relative volume was identified and the relative concentrations of the PCR products were adjusted to this same concentration. Amplicons were pooled in one library using a Biomek 4000 automated liquid handler (Beckman Coulter Life Sciences, Indianapolis, Indiana, USA). The library was cleaned using AMPure XP reagent beads (Beckman Coulter Life Sciences, Indianapolis, Indiana, USA). The length for all amplicons in the library was determined using a Fragment Analyzer (Agilent Technologies, Santa Clara, California, USA). The library was sequenced on an Illumina MiSeq platform with 150 bp paired-end reads (Illumina Inc., San Diego, California, USA).</p> <p>Sequence data was analyzed and curated using OBITools 2 (Boyer et al. 2016) following the wolf tutorial with adaptations for demultiplexing dual indexes from QIIME2 (Caporaso et al. 2010). Sequences were retained with both indexes for dereplication for further analysis. Similar sequences were clustered with obiclean (Boyer et al. 2016) only when the read count of the less abundant sequence was below 5% of the most abundant sequence. To reduce multiple identifications of the same sequence, taxonomic assignment of dereplicated and denoised sequences was done by matching to three reference sequences databases containing: (i) only taxa registered in the local Solholmfjell reference library; (ii) the complete arctic boreal database for vascular plants and bryophytes (Sønstebø et al.2010; Willerslev et al. 2014; Soininen et al. 2015); and (iii) taxa available in the EMBL database (downloaded on 7/02/2020) filtered to sequences with trnL (UUA) intron g-h primers using ecoPCR tool from OBITools (Boyer et al. 2016). Resulting identifications from the three databases were merged by sequence and duplicates were eliminated giving priority to reference databases (i), (ii), and (iii) in that order. To minimize erroneous taxonomic assignments, only taxa with a 100% match to a reference sequence were retained. We observed that below this threshold, sequences remained without a taxonomic rank assigned. Further, assigned taxa names were changed to the lowest taxonomic rank possible with trnL (UUA) intron and thus are identical to those registered in vegetation surveys. When different sequences were identified with identical taxa names, a unique entry was retained and the read counts within plots and replicates were summed. Read counts were averaged across all samples and negative controls (extraction + PCR).</p> <p>All analyses are plot-based, and coded using R v 1.4.17 (R Core Team, 2019) and with packages listed in the code. Separate analyses are made for vascular plants and bryophytes, and/or for spruce and pine data subsets, or combinations thereof, when relevant.</p>
Dataset of Rainy years counteract negative effects of drought on taxonomic, functional, and phylogenetic diversity: resilience in annual plant communities
<p>Data used in the article: </p> <p><strong>Rainy years counteract negative effects of drought on taxonomic, functional, and phylogenetic diversity: resilience in annual plant communities</strong></p> <p><strong>Abstract</strong></p> <p>1- Climate models forecast changes in the amounts and distribution of rain, which may affect ecosystems worldwide, especially in drylands where water is already the limiting factor for plant life. Annual plant communities are common in drylands where they can complete their entire life cycle during the rainy period while avoiding the dry season. Moreover, seed dormancy allows them to disperse over time by remaining in the seed bank for long periods. However, the extent to which these communities will be able to tolerate increasing drought is uncertain.</p> <p>2- We performed a five-year rainfall reduction treatment under field conditions and determined its effects on annual plant communities in a Mediterranean gypsum ecosystem. We assessed the taxonomic, functional, and phylogenetic diversity of these communities each year for five years.</p> <p>3-The taxonomic and functional diversity decreased under the rainfall reduction treatment whereas the phylogenetic diversity increased. Moreover, the relative importance of species with drought-resistant functional designs increased in the community assemblages. However, after a rainy season with above average rainfall, all of the diversity values recovered completely even under the rainfall reduction treatment.</p> <p>4- Our results provide important insights into the responses of these plant communities under a climate change scenario, where they indicate high losses of diversity during drought events but rapid recovery in milder years.</p> <p><em>Synthesis</em> Our findings highlight the great resilience of annual plant communities in drylands, which may allow them to tolerate increased drought under the present climate change scenario.</p>
Data from: Diversity among rare and common congeneric plant species from the Garry oak and Okanagan shrub-steppe ecosystems in British Columbia: implications for conservation
<p>Using universal non-coding chloroplast DNA markers (cpDNA), we investigated genetic diversity and genetic structure in four rare and common plant species pairs inhabiting threatened ecosystems (Garry Oak and Okanagan shrub-steppe) in British Columbia. <span>The species found in the Garry oak ecosystem are:</span><span> </span><em>Sanicula bipinnatifida </em><span>(purple sanicle; Apiaceae; rare),</span><span> </span><em>Sanicula crassicaulis </em><span>(Pacific sanicle; Apiaceae; common), and</span><span> </span><em>Balsamorhiza deltoidea </em><span>(deltoid balsamroot; Asteraceae; rare). The species found in the Okanagan shrub-steppe ecosystem are:</span><span> </span><em>Balsamorhiza sagittata </em><span>(arrowleaf balsamroot; Asteraceae; common),</span><span> </span><em>Orthocarpus barbatus </em><span>(Grand Coulee owl-clover; Orobanchaceae; rare),</span><span> </span><em><u>Orthocarpus </u>luteus </em><span>(yellow owl-clover; Orobanchaceae; common),</span><span> </span><em>Phacelia ramosissima </em><span>(branching phacelia; Hydrophyllaceae; rare), and</span><span> </span><em>Phacelia linearis </em><span>(thread-leaved phacelia; Hydrophyllaceae; common). </span>Eight cpDNA regions were sequenced for each study species. Sequences were aligned and concatenated within each species, and single nucleotide polymorphisms (SNPs) were used to analyze patterns of regional genetic diversity and phylogeographic structure within genera and species. Results include: total gene diversity (Ht), nucleotide diversity (π), number of private alleles, haplotype networks, isolation by distance, and analysis of molecular variance. </p> <p> </p>
Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns
<p>Data for paper "Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns" in Scientific Reports.</p> <p>Code for analysis and plots <a href="https://github.com/vojtechabraham/SpatialScalingPollenDiversity">https://github.com/vojtechabraham/SpatialScalingPollenDiversity</a>. Download original pollen and resample them to the same pollen sum by function spectra_to_target_sum in <a href="https://github.com/vojtechabraham/pollen">https://github.com/vojtechabraham/pollen</a> or work with resampled datasets below.</p> <p>Original pollen data stored in <a href="https://www.neotomadb.org/">https://www.neotomadb.org/</a>:</p> <table> <tbody> <tr> <td><strong>species-poor region Bohemian-Moravian Highland (Vrchovina)</strong></td> </tr> <tr> <td><strong>forested</strong></td> <td> </td> <td> </td> <td><strong>open</strong></td> </tr> <tr> <td><strong>SiteName</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>SiteName</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> </tr> <tr> <td>Račín</td> <td>V06</td> <td><a href="https://data.neotomadb.org/54872">54872</a></td> <td>Plíčky</td> <td>V03</td> <td><a href="https://data.neotomadb.org/54870">54870</a></td> </tr> <tr> <td>Vepřová-Žlábek</td> <td>V07</td> <td><a href="https://data.neotomadb.org/54873">54873</a></td> <td>Louky u Černého lesa</td> <td>V04</td> <td><a href="https://data.neotomadb.org/54871">54871</a></td> </tr> <tr> <td>Stropnická cesta</td> <td>V18</td> <td><a href="https://data.neotomadb.org/54882">54882</a></td> <td>Suché Kopce</td> <td>V10</td> <td><a href="https://data.neotomadb.org/54874">54874</a></td> </tr> <tr> <td>Žižkov</td> <td>V19</td> <td><a href="https://data.neotomadb.org/54883">54883</a></td> <td>Pihoviny</td> <td>V11</td> <td><a href="https://data.neotomadb.org/54875">54875</a></td> </tr> <tr> <td>Chlum</td> <td>V21</td> <td><a href="https://data.neotomadb.org/54885">54885</a></td> <td>Kocanda</td> <td>V12</td> <td><a href="https://data.neotomadb.org/54876">54876</a></td> </tr> <tr> <td>Míšek</td> <td>V22</td> <td><a href="https://data.neotomadb.org/54886">54886</a></td> <td>Porostliny</td> <td>V13</td> <td><a href="https://data.neotomadb.org/54877">54877</a></td> </tr> <tr> <td>Knížecí studánka</td> <td>V23</td> <td><a href="http://data.neotomadb.org/54887">54887</a></td> <td>Bahna</td> <td>V14</td> <td><a href="https://data.neotomadb.org/54878">54878</a></td> </tr> <tr> <td>Pod Šindelným vrchem</td> <td>V24</td> <td><a href="https://data.neotomadb.org/54888">54888</a></td> <td>Ratajské rybníky</td> <td>V15</td> <td><a href="https://data.neotomadb.org/54879">54879</a></td> </tr> <tr> <td>Rampoltův mlýn</td> <td>V25</td> <td><a href="https://data.neotomadb.org/54889">54889</a></td> <td>Zubří</td> <td>V16</td> <td><a href="https://data.neotomadb.org/54880">54880</a></td> </tr> <tr> <td>Brožova skála</td> <td>V26</td> <td><a href="https://data.neotomadb.org/54890">54890</a></td> <td>Nový Rybník</td> <td>V17</td> <td><a href="https://data.neotomadb.org/54881">54881</a></td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>Samotín</td> <td>V20</td> <td><a href="https://data.neotomadb.org/54884">54884</a></td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>species-rich region White-Carpathians Mountains (Bílé Karpaty)</strong></td> </tr> <tr> <td><strong>forested</strong></td> <td> </td> <td><strong>open</strong></td> </tr> <tr> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> </tr> <tr> <td>BK1</td> <td><a href="https://data.neotomadb.org/54770">54770</a></td> <td>BK2</td> <td><a href="https://data.neotomadb.org/54771">54771</a></td> </tr> <tr> <td>BK3</td> <td><a href="https://data.neotomadb.org/54772">54772</a></td> <td>BK4</td> <td><a href="https://data.neotomadb.org/54773">54773</a></td> </tr> <tr> <td>BK5</td> <td><a href="https://data.neotomadb.org/54774">54774</a></td> <td>BK6</td> <td><a href="https://data.neotomadb.org/54775">54775</a></td> </tr> <tr> <td>BK9</td> <td><a href="https://data.neotomadb.org/54777">54777</a></td> <td>BK8</td> <td><a href="https://data.neotomadb.org/54776">54776</a></td> </tr> <tr> <td>BK11</td> <td><a href="https://data.neotomadb.org/54779">54779</a></td> <td>BK10</td> <td><a href="https://data.neotomadb.org/54778">54778</a></td> </tr> <tr> <td>BK13</td> <td><a href="https://data.neotomadb.org/54781">54781</a></td> <td>BK12</td> <td><a href="https://data.neotomadb.org/54780">54780</a></td> </tr> <tr> <td>BK15</td> <td><a href="https://data.neotomadb.org/54783">54783</a></td> <td>BK14</td> <td><a href="https://data.neotomadb.org/54782">54782</a></td> </tr> <tr> <td>BK16</td> <td><a href="https://data.neotomadb.org/54784">54784</a></td> <td>BK20</td> <td><a href="https://data.neotomadb.org/54788">54788</a></td> </tr> <tr> <td>BK17</td> <td><a href="https://data.neotomadb.org/54785">54785</a></td> <td>BK23</td> <td><a href="https://data.neotomadb.org/54791">54791</a></td> </tr> <tr> <td>BK18</td> <td><a href="https://data.neotomadb.org/54786">54786</a></td> <td>BK25</td> <td><a href="https://data.neotomadb.org/54793">54793</a></td> </tr> <tr> <td>BK19</td> <td><a href="https://data.neotomadb.org/54787">54787</a></td> <td>BK27</td> <td><a href="https://data.neotomadb.org/54795">54795</a></td> </tr> <tr> <td>BK21</td> <td><a href="https://data.neotomadb.org/54789">54789</a></td> <td>BK29</td> <td><a href="https://data.neotomadb.org/54797">54797</a></td> </tr> <tr> <td>BK22</td> <td><a href="https://data.neotomadb.org/54790">54790</a></td> <td>BK31</td> <td><a href="https://data.neotomadb.org/54799">54799</a></td> </tr> <tr> <td>BK24</td> <td><a href="https://data.neotomadb.org/54792">54792</a></td> <td>BK33</td> <td><a href="https://data.neotomadb.org/54801">54801</a></td> </tr> <tr> <td>BK26</td> <td><a href="https://data.neotomadb.org/54794">54794</a></td> <td>BK35</td> <td><a href="https://data.neotomadb.org/54803">54803</a></td> </tr> <tr> <td>BK28</td> <td><a href="https://data.neotomadb.org/54796">54796</a></td> <td>BK36</td> <td><a href="https://data.neotomadb.org/54804">54804</a></td> </tr> <tr> <td>BK30</td> <td><a href="https://data.neotomadb.org/54798">54798</a></td> <td>BK38</td> <td><a href="https://data.neotomadb.org/54805">54805</a></td> </tr> <tr> <td>BK32</td> <td><a href="https://data.neotomadb.org/54800">54800</a></td> <td>BK39</td> <td><a href="https://data.neotomadb.org/54806">54806</a></td> </tr> <tr> <td>BK34</td> <td><a href="https://data.neotomadb.org/54802">54802</a></td> <td>BK40</td> <td><a href="https://data.neotomadb.org/54807">54807</a></td> </tr> <tr> <td> </td> <td> </td> <td>BK41</td> <td><a href="https://data.neotomadb.org/54808">54808</a></td> </tr> </tbody> </table> <p> </p>
Supplementary material 1 from: Spafford R, Lortie C, Butterfield B (2013) A systematic review of arthropod community diversity in association with invasive plants. NeoBiota 16: 81-102. https://doi.org/10.3897/neobiota.16.4190
Supplementary material 1 from: Spafford R, Lortie C, Butterfield B (2013) A systematic review of arthropod community diversity in association with invasive plants. NeoBiota 16: 81-102. https://doi.org/10.3897/neobiota.16.4190
Supplementary material 2 from: Spafford R, Lortie C, Butterfield B (2013) A systematic review of arthropod community diversity in association with invasive plants. NeoBiota 16: 81-102. https://doi.org/10.3897/neobiota.16.4190
Supplementary material 2 from: Spafford R, Lortie C, Butterfield B (2013) A systematic review of arthropod community diversity in association with invasive plants. NeoBiota 16: 81-102. https://doi.org/10.3897/neobiota.16.4190
Native plant diversity creates microbial legacies that either promote or suppress non-natives, depending on drought history
<p>High-diverse native plant communities resist non-native plants more strongly than low-diverse communities, in part through resource competition. Yet, the role of soil biota is largely unknown, although non-native plants interact with soil biota. Here, we tested the responses of non-native plants to soil conditioned by different native plant diversities. We applied well-watered and dry treatments in the conditioning and response phases to explore the effects of historical and contemporary environmental stresses. Historical water conditions determined the effects of native diversity via soil biota on responding non-natives grown in well-watered environments. Non-native growth decreased with native species richness for well-watered soil inocula but increased for dry soil inocula. However, non-native growth in dry environments did not depend on conditioning native species richness of soil inocula. We provide a new understanding of mechanisms behind diversity-invasibility relationships and demonstrate that temporal variation in environmental stress shapes relationships among native plant diversity, soil biota, and non-native plants.</p>
Data from: Mycorrhizal symbiosis increases plant phylogenetic diversity and regulate community assembly
<p>The intricate mechanisms shaping plant diversity and community composition are the cornerstone of ecological understanding. Yet, the role of mycorrhizal symbiosis, the fundamental partnership between fungi and plant roots, in influencing community composition has often been underestimated. Here, we use extensive species survey data from 1,315 terrestrial ecosystem sites to elucidate the influence of mycorrhizal symbiosis on plant phylogenetic diversity and its implications for community assembly processes. Our findings demonstrate that increasing mycorrhizal symbiotic potential leads to greater phylogenetic dispersion within plant communities. Furthermore, we unveil a distinct dichotomy in the assembly processes governed by mycorrhizal status. Mycorrhizal species predominantly influence deterministic processes, suggesting a role in niche-based community assembly. Conversely, non-mycorrhizal species exert a stronger influence on stochastic processes, highlighting the importance of random events in shaping community structure. These results underscore the crucial but often hidden role of mycorrhizal symbiosis in driving plant community diversity and assembly. This study provides valuable insights into the complex mechanisms shaping ecological communities and the way for more informed conservation and management practices that acknowledge the complex interplay between symbiosis and ecological community dynamics.</p>
Data from: Climatic conditions and landscape diversity predict plant-bee interactions and pollen deposition in bee-pollinated plants.
<p>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 <em>Lotus corniculatus</em>, and <em>Vicia cracca</em>. 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.</p>
Figure 5 in Hundred years of Botany at the NWU: contributions towards understanding plant and algae function, diversity and restoration in a changing environment
Figure 5: Mr Sello D. Phalatse, first Botanist at Mahikeng Campus (1983–2008), founder and curator of the University of NorthWest Herbarium (Source: NWU Records, Archives and Museum) (Source: NWU Corporate Relations and Marketing).
Figure 4 in Hundred years of Botany at the NWU: contributions towards understanding plant and algae function, diversity and restoration in a changing environment
Figure 4. Botanical Garden – Waterfall on Prof. Daan Botha's koppie. (Photo: Chris van Niekerk from NWU Botanical Garden collection).
Figure 3. Prof. A.P in Hundred years of Botany at the NWU: contributions towards understanding plant and algae function, diversity and restoration in a changing environment
Figure 3. Prof. A.P Goossens, first Professor and Head of Department of Botany (Source: NWU Records, Archives and Museum).
Figure 1 in Plant diversity in Sabkha ecosystems of arid region: spatial and environmental drivers
Figure 1. The first two axes of canonical analysis of principal coordinates based on discriminant analysis (CAP) of plant species composition in the three Sabkha ecosystems. Ash: Al-Oshaziyah; Qas: Qasab; Shaq: Shaqa. F-value= 2.879 and P-value=0.003 of the CAP model.
Fig. 4 in Vascular plant diversity of the Gogunsan Archipelago in the Korean Peninsula
Fig. 4. Dendrograms showing the degree of Sørensen similarity based on vascular flora data of the Gogunsan Archipelago.
Fig. 5 in Vascular plant diversity of the Gogunsan Archipelago in the Korean Peninsula
Fig. 5. Dendrograms showing the degree of Sørensen similarity based on native flora data (except invasive alien plants, ruderal plants) of the Gogunsan Archipelago.
Figure 5 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 5 Canonical correlation analysis (CCA) ordering diagram between environmental factors and Anopheles species in the pre (a) and post-construction (b) phases of the Jirau hydroelectric plant: Relative Humidity of the air (R. H%); Temp (Temperature ° C); Subtitle: Anopheles albit – An. albitarsis; Anopheles argyrit – An. argyritarsis; Anopheles benar – An. benarrochi; Anopheles braz – An. braziliensis; Anopheles darl – An. darlingi; Anopheles evan – An.evansae; Anopheles mattog – An. mattogrossensis; Anopheles mediop – An. mediopunctatus; Anopheles osw – An. oswaldoi; Anopheles per – An. peryassui; Anopheles rang – An. rangeli; Anopheles trian – An. triannulatus.
Figure 3 in Diversity and spatio-temporal variation of Anopheles (Diptera: Culicidae) before and after the construction of the Jirau hydroelectric plant, state of Rondônia, Brazil
Figure 3 Density of Anopheles species (x) in the sampled months (January to August) before (a) and after (March to October) the construction (b) of the Jirau hydroelectric w plant, in Rondônia, Brazil.
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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.