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495 results for “spatial scale”
Data from: Disentangling the drivers of ground-dwelling macro-arthropod metacommunity structure at two different spatial scales
<p>The goal of this study was to explore the community assembly rules at local and regional scales.</p> <p> </p> <p><strong><em>Site description </em></strong></p> <p>All sampling locations were selected within the black soil region (Fig. 1), which is predominantly located in the temperate continental monsoon climatic zone in North China. It is characterized by a dry and cold winter and warm and humid summer. The soil was classified as black soil following the Chinese Soil Classification System, which is equivalent to a Typic Hapludoll in the USDA Soil Taxonomy. More specific details for this soil (such as black soil coverage area, geographical and ecological resources, etc.) can be obtained from Wen and Liang (2001). Samples were collected from three municipal districts: Bei'an, Hulan and Dehui.</p> <p> </p> <p><strong><em>Sampling design and setup</em></strong></p> <p>We conducted field sampling of ground-dwelling macro-arthropods and measured a set of environmental and spatial variables across all sampling locations three times: in May, July and September 2015. In total, 15 plots (five plots in each of the three municipal districts) were selected and sampled. At each plot, we further selected five sampling sites (approximately 10 m away from each other).</p> <p>We collected additional samples for estimating soil abiotic parameters at each site. Soil samples (5 × 5 cm and 10 cm depth) were collected near each pitfall trap site. The exact geographic coordinates of each sampling site were obtained by GPS.</p> <p>Ground-dwelling macro-arthropods were sampled by a pitfall trapping method. For pitfall traps, we used plastic cups (7 cm in diameter and 12 cm deep), which were partially filled with saturated salt water. The traps were exposed for one week in each sampling month. All collected ground-dwelling macro-arthropods were removed from the pitfall traps, sorted and preserved in a 95% alcohol solution. All adult macroarthropods from pitfalls were identified at the species or genus level using appropriate keys (e.g., Simon (1879), Martens (1978) and Barrientos (2004) for Opiliones; Roberts (1993, 1995) for Lycosidae; and Forel and Leplat (2001) and Ortuño and Marcos (2003) for Carabidae) and then were counted. Juvenile ground-dwelling arthropods were excluded from all analyses due to difficulties with their identification (Gao et al., 2016).</p> <p> </p> <p><strong><em>Environmental and spatial variables</em></strong></p> <p>Environmental variables used in our analysis included soil organic matter, soil total nitrogen, water content, pH, temperature. Soil water content (SWC%) was measured in the laboratory after the fresh soil was loaded into an aluminium box. Prior to estimating soil total nitrogen (TN) (Kjeldahl's method described by Duchaufour (1975)), soil organic matter (SOM) (Anne's method described by Duchaufour (1975)) and pH (Pansu and Gautheyrou, 2003), the collected soil samples were air-dried at 25℃ for one week and sieved (1 mm mesh size). Local temperature values were obtained from the publicly available datasets (The Local Chronicles of Bei’an, Hulan and Dehui). Geographic coordinates were recorded for further spatial modelling analysis.</p> <p> </p> <p>We have seven data files:</p> <p>env BAHLDH may.csv</p> <p>env BAHLDH july.csv</p> <p>env BAHLDH september.csv</p> <p>sp BAHLDH may.csv</p> <p>sp BAHLDH july.csv</p> <p>sp BAHLDH september.csv</p> <p>Geospatial coordinates.csv</p> <p> </p> <p>Explanation of the variables in the datasets:</p> <p>Site: Bei’an, Hulan, Dehui represent sampling district; I-V represent sampling plot; 1-5 represent replicate</p> <p>SOM: soil organic matter</p> <p>pH: soil pH</p> <p>SWC: Soil water content</p> <p>TN: soil total nitrogen</p>
Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network
<p>Supporting information, datasets, and R and JavaScript code for the the National Ecological Observatory Network’s Airborne Observation Platform (AOP) sampling design and publication, <em>"Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network"</em></p>
Stability of rocky intertidal communities in response to species removal varies across spatial scales
<p>Improving our understanding of stability across spatial scales is crucial in the current scenario of biodiversity loss. Still, most empirical studies of stability target small scales. Here we experimentally removed the local space-dominant species (macroalgae, barnacles, or mussels) at eight sites spanning more than 1000 km of coastline in north- and south-central Chile, and quantified the relationship between area (the number of aggregated sites) and stability in aggregate community variables (total cover) and taxonomic composition. Resistance, recovery, and invariability increased nonlinearly with area in both functional and compositional domains. Yet, the functioning of larger areas achieved a better, albeit still incomplete, recovery than composition. Compared with controls, smaller disturbed areas tended to overcompensate in terms of total cover. These effects were related to enhanced available space for recruitment (resulting from the removal of the dominant species), and to increasing beta diversity and decaying community-level spatial synchrony (resulting from increasing area). This study provides experimental evidence for the pivotal role of spatial scale in the ability of ecosystems to resist and recover from chronic disturbances. This knowledge can inform further ecosystem restoration and conservation policies.</p>
Spatial Scaling Challenge. COST Action CA17134 SENSECO. Working Group 1
<p>This dataset contains the data, documentation, and scripts that compose the <strong>SPATIAL SCALING CHALLENGE</strong> organized in the framework of the <strong>SENSECO COST Action CA17143</strong> “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (<a href="https://www.senseco.eu/">https://www.senseco.eu/</a>), by the <strong>Working Group 1</strong><strong>.</strong> “Closing the scaling gap: from leaf measurements to satellite images” (<a href="https://www.senseco.eu/working-groups/wg1-scaling-gap/">https://www.senseco.eu/working-groups/wg1-scaling-gap/</a>).</p> <p>The <strong>SPATIAL SCALING CHALLENGE</strong> is an open exercise where we challenge the remote sensing community to retrieve relevant vegetation biophysical and physiological variables such as leaf chlorophyll content (<em>C</em><sub>ab</sub>), leaf area index (<em>LAI</em>), maximal carboxylation rate (V<sub>c</sub><sub>max,25</sub>), and non-photochemical quenching (<em>NPQ</em>) from simulated (hyperspectral reflectance (<em>HDRF</em>), sun-induced chlorophyll fluorescence (<em>F</em>) and land surface temperature (<em>LST)</em>) imagery.</p> <p>The dataset contains the simulated remote sensing and field data, their description, and scripts in Matlab, Python, and R languages to facilitate importing and handling the data and producing the standardized outputs necessary to participate.</p> <p><strong>IMPORTANT:</strong> <strong>Additional data</strong> that can be used at the discretion of the participants have been released in <strong><a href="https://doi.org/10.5281/zenodo.6530187">https://doi.org/10.5281/zenodo.6530187</a></strong></p> <p>The <strong>SPATIAL SCALING CHALLENGE</strong> aims at gathering the community’s expertise and knowledge to tackle the scaling problems posed by variables of different nature. These experiences will be summarized in a journal article where all the participants are invited to contribute. The exercise is internationally open. Ph.D. students, early career and senior researchers, spin-offs, and companies working in the field of remote sensing of vegetation ecophysiology are welcome to participate.</p> <p><strong>STILL OPEN FOR PARTICIPATION! New deadline 31<sup>st</sup> of October 2022.</strong></p> <p>Follow all the <strong>communications and updates</strong> of the <strong>SPATIAL SCALING CHALLENGE</strong> in the RG site: <strong><a href="https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1">https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1</a></strong>.</p> <p> </p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data Revision
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Spatial Scaling Challenge - Additional Data. COST Action CA17134 SENSECO. Working Group 1
<p>This dataset contains the data additional data provided for the <strong>SPATIAL SCALING CHALLENGE</strong> organized in the framework of the <strong>SENSECO COST Action CA17143</strong> “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (<a href="https://www.senseco.eu/">https://www.senseco.eu/</a>), by the <strong>Working Group 1</strong><strong>.</strong> “Closing the scaling gap: from leaf measurements to satellite images” (<a href="https://www.senseco.eu/working-groups/wg1-scaling-gap/">https://www.senseco.eu/working-groups/wg1-scaling-gap/</a>).</p> <p> </p> <p>The main dataset, documentation and scripts of the <strong>SPATIAL SCALING CHALLENGE</strong> must be downloaded from<strong>: <a href="https://doi.org/10.5281/zenodo.6451335">https://doi.org/10.5281/zenodo.6451335</a></strong></p> <p>This additional dataset includes half-hourly time series of down-welling incoming spectral irradiance (W m<sup>-2</sup> µm<sup>-1</sup>) in the visible and near-infrared domains as measured by a field spectroradiometer operating in a nearby ecosystem station. The inclusion of these data in the Spatial Scaling Challenge is at the discretion of the participants. </p> <p> </p> <p>The <strong>SPATIAL SCALING CHALLENGE</strong> aims at gathering the community’s expertise and knowledge to tackle the scaling problems posed by variables of different nature. These experiences will be summarized in a journal article where all the participants are invited to contribute. The exercise is internationally open. Ph.D. students, early career and senior researchers, spin-offs, and companies working in the field of remote sensing of vegetation ecophysiology are welcome to participate.</p> <p> </p> <p><strong>STILL OPEN FOR PARTICIPATION! New deadline 31<sup>st</sup> of October 2022.</strong></p> <p> </p> <p>Follow all the <strong>communications and updates</strong> of the <strong>SPATIAL SCALING CHALLENGE</strong> in the RG site: <strong><a href="https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1">https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1</a></strong>.</p> <p> </p> <p> </p>
Rare and declining bee species are key to consistent pollination of wildflowers and crops across large spatial scales
<p>Biodiversity promotes ecosystem function in experiments, but it remains uncertain how biodiversity loss affects function in larger-scale natural ecosystems, where rare and declining species which are likely to be lost and function needs to be maintained across space and time. Here we explore the importance of rare and declining bee species to the pollination of three wildflowers and three crops using large-scale (72 sites across 5,000 km2), multi-year datasets. Half (82/164) bee species were rare or declining, but these species provided ~15% of overall pollination. To determine the number of species important to ecosystem function, we used two methods of 'scaling up', both of which have previously been used for biodiversity-function analysis. First, we summed bee species' contributions to pollination across space and time and then found the minimum set of species needed to provide a threshold level of function across all sites; according to this method, effectively no rare and declining bee species were important to pollination. Second, we account for the "insurance value" of biodiversity by finding the minimum set of bee species needed to simultaneously provide a threshold level of function at each site in each year. The second method leads to the conclusion that 25 rare and eight declining bee species (36% and 53% of all rare and declining bee species, respectively) are important. Our findings provide some of the strongest evidence yet for the importance of rare and declining species, thereby providing a more direct link between real-world biodiversity loss and ecosystem function.</p>
Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood: Additional data
<p>Contains data used in the following publication:</p> <p>Felix Neff, Jonas Hagge, Rafael Achury, Didem Ambarlı, Christian Ammer, Peter Schall, Sebastian Seibold, Michael Staab, Wolfgang W. Weisser, Martin M. Gossner (2022). <em>Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood. </em>Functional Ecology. <a href="https://doi.org/10.1111/1365-2435.14186">https://doi.org/10.1111/1365-2435.14186</a></p> <p>These are complementary data, which are needed to reproduce the analyses. Most data are archived in the Biodiversity Exploratories Information System (<a href="https://doi.org/10.17616/R32P9Q">https://doi.org/10.17616/R32P9Q</a>).</p> <p>The following data are included:</p> <ul> <li><strong>BELongDead_Subplots_Normal.csv</strong>: List of <em>normal</em> subplots within the BELongDead projects (only these were included in the analyses)</li> <li><strong>Body_length.csv</strong>: Body length data for study species assembled from Freude et al. (1965-1998)</li> <li><strong>Lightness_completion.csv</strong>: Colour lightness recorded from measured individuals and photos from coleonet.de (Lompe, 2002)</li> <li><strong>Name_standardisation.csv</strong>: Dataset used to standardise taxonomic names from different sources</li> <li><strong>Saproxylic_species_sub.csv</strong>: List of saproxylic species (according to Schmidl & Bussler (2004)), which were recorded in the project</li> <li><strong>Similar_Species.csv</strong>: List of similar species for species with missing traits. Based on these, missing traits were estimated</li> </ul> <p><strong>References</strong></p> <p>Freude, H., Harde, K. W., & Lohse, G. A. (1965–1998). <em>Die Käfer Mitteleuropas Band 1-15</em>. Goecke und Evers.</p> <p>Lompe, A. (2002). <em>Käfer Europas</em>. <a href="http://coleonet.de/">http://coleonet.de/</a></p> <p>Schmidl, J., & Bussler, H. (2004). Ökologische Gilden xylobionter Käfer Deutschlands. <em>Naturschutz und Landschaftsplanung</em>, <em>36</em>(7), 202–218.</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>
Fig. 2 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 2: Flowchart of steps and methods followed (AHP: Analytic Hierarchy Process, FM: Fuzzy Membership).
Fig. 7 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 7: Spatial representation of the Fishing pressure index from the small scale coastal fishery (FPc).
Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range
<p>Studies have found that biotic information can play an important role in shaping the distribution of species even at large scales. However, results from species distribution models are not always consistent among studies, and the underlying factors that influence the importance of biotic information to distribution models, are unclear. 2. We studied wild bees and plants, and cleptoparasite bees and their hosts in the Netherlands to evaluate how the inclusion of their biotic interactions affects the performance of species distribution models. We assessed model performance through spatial block cross-validation and by comparing models with interactions to models where the interacting species were randomized. Finally, we evaluated how, (i) spatial resolution, (ii) taxonomic rank (genus or species), (iii) degree of specialization, (iv) distribution of the biotic factor, (v) bee body size and (vi) type of biotic interaction, affect the importance of biotic interactions in shaping the distribution of wild bee species using generalized linear models. 3. We found that the models of wild bees improved when the biotic factor was included. The model performance improved the most for parasitic bees. Spatial resolution, taxonomic rank, distribution range of the biotic factor, and degree of specialization of the modelled species all influenced the importance of the biotic interaction to the models. 4. We encourage researchers to include biotic interactions in species distribution models, especially for specialized species and when the biotic factor has a limited distribution range. However, before adding the biotic factor we suggest considering different spatial resolutions and taxonomic ranks of the biotic factor. We recommend using single species or genus data as a biotic factor in the models of specialist species and for the generalist species, we recommend using an approximate measure of interactions, such as flower richness.</p>
Fig. 2 in The abundance of specialist and generalist lepidopteran larvae on a single host plant species: Does spatial scale matter?
Fig. 2. Specialist lepidopteran species on Roupala montana. (A–C) Chlamydastis platyspora: (A) larva, (B) larva inside the shelter, (C) adult; (E–G) Stenoma cathosiota: (E) larva, (F) shelter, (G) adult; (H–J) species of new genus of Depressariidae: (H) larva,(I) shelter, (J) adult; (K–M) Idalus lineosus: (K–L) 6th instar showing variation in color, (M) adult; (N–O) Symmachia hippodice: (N) larva, (O) adult female, (P) adult male; (Q–S) Eomichla sp.: (Q–R) larva inside the shelter, (S) adult.
Fig. 1 in The abundance of specialist and generalist lepidopteran larvae on a single host plant species: Does spatial scale matter?
Fig. 1. Locations of the 5 study areas, as follows: A) a map of Brazil, with the coverage area of the Cerrado Biome shaded; B) a map of Goiás State, showing the locations of Parque Estadual dos Pireneus (PEP) and Parque Nacional Chapada dos Veadeiros (PNCV); and C) a map of Distrito Federal (DF), showing the locations of Fazenda Água Limpa (FAL), Parque Nacional de Brasília (PNB), and Jardim Botânico de Brasília (JBB).
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
<p>Result Files for the Paper "A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations" to be published at IEEE Vis 2024</p>
Fig. 2 in The importance of considering small-scale variability in macrobenthic distribution: spatial segregation between two fiddler crab species (genus Leptuca) (Decapoda, Ocypodidae)
Fig. 2. NMDS ordination (stress = 0.16) of sites based on similarity of group composition. Leptuca leptodactyla (Rathbun in Rankin, 1898): JLM (juvenile males), JLF (juvenile females), ALM (adult males) and ALF (adult females). Leptuca uruguayensis (Nobili, 1901): JUM (juvenile males), JUF (juvenile females), AUM (adult males) and AUF (adult females).
Fig. 1 in The importance of considering small-scale variability in macrobenthic distribution: spatial segregation between two fiddler crab species (genus Leptuca) (Decapoda, Ocypodidae)
Fig. 1. Schematic representation of the sampling design, with subarea separation and the six random replicates. (Area=10 m²).
Fig. 3 in Influence of environmental variables on stream fish fauna at multiple spatial scales
Fig. 3. Venn diagrams representing the results of the variance partitioning with partial CCA (canonical correspondence analysis): percentage of variation in fish abundance (a) and incidence (b) explained by land use and land cover, site, and spatial variables, as well as that shared between the three sets of variables in the Upper Araguari River basin, Minas Gerais. See Table 4 for a list of all explanatory variables.
Fig. 1 in Influence of environmental variables on stream fish fauna at multiple spatial scales
Fig. 1. Locations of the 38 randomly selected sites sampled in the Upper Araguari River basin, State of Minas Gerais, Brazil.
Fig. 2 in Influence of environmental variables on stream fish fauna at multiple spatial scales
Fig. 2. Detrended correspondence analysis (DCA) of fish abundance (a) and incidence (b) along the sampling sites. The species are shown in triangle and sampling sites in X-mark.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.