Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
243
datasets available to search
ShareScore release 0.9.0
Dataset results
243 results for “subtropical forest”
Litterfall in tabonuco (subtropical wet) forest in the Luquillo Experimental Forest, Puerto Rico (MRCE Litterfall data)
Treatments common to both sites are quarterly fertilization (macro- and micronutrients) and unmanipulated. At El Verde, a third set of plots was subject to a one time removal of litter and woody debris generated by Hurricane Hugo (September 1989). Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Data_Schönauer et al. (2023)_Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest
<p>Data used in</p> <p>Schönauer, M., Hietz, P., Schuldt, B., and Rewald, B. (2023). Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest. Frontiers in plant science 14, 1127292. doi: 10.3389/fpls.2023.1127292</p>
Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil
<p>Title:</p> <p>Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170711</p> <p>Time of day (BRT = -3)</p> <p>10h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p>Breunig, Fábio Marcelo (2017, July 7). Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4327943</p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil
<p>Title:Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170707</p> <p>Time of day (BRT = -3)</p> <p>14h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High-speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p>Title:</p> <p> </p> <p>Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170707</p> <p>Time of day (BRT = -3)</p> <p>14h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p>
Data from: Proximate cues of flowering in a subtropical rain forest
<p><span>Plants have evolved mechanisms to track seasonal variation in environmental resources, enabling them to time key life-history events to appropriate seasons. While the proximate cues for flowering initiation are well documented in the temperate region, it is still unclear what the flowering cues are in the tropics, especially in the subtropics. Our study compared first flowering dates (FFDs) predicted by eight hypothesized proximate cues concerning photoperiod, mean and directional changes in solar irradiance and warm/cool temperature, and rainfall with flowering dates observed over 19 years of weekly monitoring for 16 species in a subtropical rainforest. We observed considerable inter-annual variation in the median FFDs for the study species, ranging from 21 to 101 days. The early-spring flowering species tended to have greater inter-annual variation in FFDs than the summer flowering species. For 13 study species, temperature cues best explained inter-annual variation in FFDs. Cool temperatures in the previous fall/winter and warm temperatures in the current spring (or previous summer) might trigger the onset of flowering in these 13 species. Cues associated with photoperiod and irradiance also predicted inter-annual variation in FFDs with small root mean square error (<1.5 census intervals) for 12 species but generally had higher prediction errors than temperature-related cues. Cues associated with seasonal variation in rainfall failed to predict flowering times in any species. Our results suggest that future changes in temperature may alter flowering times for most species in subtropical forests, leading to changes in ecosystem processes and biosphere feedback to the climate system.</span></p>
Enemy exclusion effects on biodiversity-productivity relationship in subtropical forest experiment
<p>This data set is described in full detail in Huang et al. (2022) Journal of Ecology XXX.</p> <p>In brief, we used a large tree biodiversity experiment (BEF-China), established in 2009-2010, to test whether the application of fungicide or insecticide changes observed tree species richness effects on tree growth. We used a subset of plots in which tree species numbers ranged from 1 to 8. To these plots, a factorial split-plot treatment was added in April 2014. The new treatments (I: insecticide; F: fungicide; C: untreated control) were applied to subplots located along one side of the main plots. Each subplot contained 4 × 4 = 16<br>trees. We further used the central 4 × 4 trees of the main plot for additional measurements (central control subplot).</p> <p>Insecticide and fungicide solutions (4 L per subplot) were sprayed over tree crowns every 4 weeks, but only on days with no or very little wind. During the rainy season, application<br>intervals were halved to 2 weeks to compensate for more rapid leaching. The insecticide solution contained 10 mL dimethoate (an organophosphate) and 10 mL deltamethrin (a<br>pyrethroid). The fungicide solution contained 8 g of mancozeb (a dithiocarbamate) and 25 mL of myclobutanil (a triazole). Control subplots were sprayed with 4 L of water.</p>
Text-fig. 7. Stereomicroscope microphotographs of plant remains sieved out of a sediment bulk sample (C3X) from bed GLA10 of Govone. a: Tetraclinis salicornioides (UNGER) KVAČEK, shoot fragment, MGPT-PU141083). b: Toddalia latisiliquata (R.LUDW.) H.-J. GREGOR, seed, MGPT-PU141084. c: Toddalia rhenana H.-J.GREGOR, seed, MGPT-PU141085. d: Eurya stigmosa (R.LUDW.) MAI, small seed with piths filled by organic remains and sediment, MGPT-PU141086. e: Eurya stigmosa (R.LUDW.) MAI, fragmentary seed, MGPT-PU141087. f: Visnea germanica MENZEL, fruit from two opposite sides, MGPT-PU141088. g: Symplocos casparyi R.LUDW., endocarp in lateral view from two opposite sides, MGPT-PU141089. Scale bar 1 mm. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 7. Stereomicroscope microphotographs of plant remains sieved out of a sediment bulk sample (C3X) from bed GLA10 of Govone. a: Tetraclinis salicornioides (UNGER) KVAČEK, shoot fragment, MGPT-PU141083). b: Toddalia latisiliquata (R.LUDW.) H.-J. GREGOR, seed, MGPT-PU141084. c: Toddalia rhenana H.-J.GREGOR, seed, MGPT-PU141085. d: Eurya stigmosa (R.LUDW.) MAI, small seed with piths filled by organic remains and sediment, MGPT-PU141086. e: Eurya stigmosa (R.LUDW.) MAI, fragmentary seed, MGPT-PU141087. f: Visnea germanica MENZEL, fruit from two opposite sides, MGPT-PU141088. g: Symplocos casparyi R.LUDW., endocarp in lateral view from two opposite sides, MGPT-PU141089. Scale bar 1 mm.
Text-fig. 5. Plant fragments from Govone with evidence of preserved cuticle. a: Decussate pair of leaves of "Thuja" saviana (C.T.GAUDIN) C.T.GAUDIN with a window (arrow) opened in the brownish cuticle, showing the yellowish mesophyll cells and some possible resin canals (dark), MGPT-PU141094. b: Angiosperm leaf fragment (from sample MGPT-PU141017) under the stereomicroscope, showing the blackish compressed mesophyll on the right and patches of cleaned, yellowish cuticle at the top (arrow). Scale bar 1 mm. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 5. Plant fragments from Govone with evidence of preserved cuticle. a: Decussate pair of leaves of "Thuja" saviana (C.T.GAUDIN) C.T.GAUDIN with a window (arrow) opened in the brownish cuticle, showing the yellowish mesophyll cells and some possible resin canals (dark), MGPT-PU141094. b: Angiosperm leaf fragment (from sample MGPT-PU141017) under the stereomicroscope, showing the blackish compressed mesophyll on the right and patches of cleaned, yellowish cuticle at the top (arrow). Scale bar 1 mm.
Text-fig. 4. Laminated silts with almost complete leaves at the top of bed GLA20: Dicotylophyllum sp. 3 (left: MGPTPU141032) and Laurophyllum sp. 2 (right: MGPT-PU141082). Scale bar 10 mm. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 4. Laminated silts with almost complete leaves at the top of bed GLA20: Dicotylophyllum sp. 3 (left: MGPTPU141032) and Laurophyllum sp. 2 (right: MGPT-PU141082). Scale bar 10 mm.
Text-fig. 6. Transmitted light microphotographs of permineralized wood from Govone. a, b: cf. Cupressinoxylon sp., radial section, MGPT-PU141105, a – nodular end of ray parenchyma (arrow), b – thick and pitted horizontal walls of ray parenchyma (arrow). c–f: Pinaceae gen. et sp. indet., MGPT-PU141107, c – abnormal discoloration due to ecological disruptions (radial section), d – rays up to 10 cells high, uniseriate, partly biseriate (black arrow), intercellular spaces observed (white arrows) (tangential section), e – large, thick-walled axial resin canal with more than 9 epithelial cells observed, axial resin canal diameter>60 Μm (transverse section), f – spiral thickenings due to compression (white arrow) (radial section). in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 6. Transmitted light microphotographs of permineralized wood from Govone. a, b: cf. Cupressinoxylon sp., radial section, MGPT-PU141105, a – nodular end of ray parenchyma (arrow), b – thick and pitted horizontal walls of ray parenchyma (arrow). c–f: Pinaceae gen. et sp. indet., MGPT-PU141107, c – abnormal discoloration due to ecological disruptions (radial section), d – rays up to 10 cells high, uniseriate, partly biseriate (black arrow), intercellular spaces observed (white arrows) (tangential section), e – large, thick-walled axial resin canal with more than 9 epithelial cells observed, axial resin canal diameter>60 Μm (transverse section), f – spiral thickenings due to compression (white arrow) (radial section).
Text-fig. 3. a: Panoramic reconstruction of the portion of the Govone outcrop from intervals GLA10 to GLA20 in condition of low river level. b: Transported leaf assemblage in the bottom part of bed GLA20. c: Detail of the outcrop of the leaf-bearing bed GLA20 and the underlying wood-rich layer GLA19. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 3. a: Panoramic reconstruction of the portion of the Govone outcrop from intervals GLA10 to GLA20 in condition of low river level. b: Transported leaf assemblage in the bottom part of bed GLA20. c: Detail of the outcrop of the leaf-bearing bed GLA20 and the underlying wood-rich layer GLA19.
Text-fig. 2. Stratigraphic column of the Govone section with a detail of the sampled intervals on the right. CCS – Cassano Spinola Conglomerates; Gm and Gg – sedimentary cycles, respectively marl-dominated or gypsum-dominated; MES – Messinian Erosional Surface; MSC – Messinian Salinity Crisis; PLG – Primary Lower Gypsum; RLG – Resedimented Lower Gypsum. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 2. Stratigraphic column of the Govone section with a detail of the sampled intervals on the right. CCS – Cassano Spinola Conglomerates; Gm and Gg – sedimentary cycles, respectively marl-dominated or gypsum-dominated; MES – Messinian Erosional Surface; MSC – Messinian Salinity Crisis; PLG – Primary Lower Gypsum; RLG – Resedimented Lower Gypsum.
Text-fig. 1. Context and location of the Govone outcrop. a: Location of the Piedmont Basin at the northern margin of the Mediterranean Basin and distribution of Messinian evaporites. b: Simplified geological map of the Piedmont Basin showing the location of the Govone outcrop close to the town of Alba. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 1. Context and location of the Govone outcrop. a: Location of the Piedmont Basin at the northern margin of the Mediterranean Basin and distribution of Messinian evaporites. b: Simplified geological map of the Piedmont Basin showing the location of the Govone outcrop close to the town of Alba.
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.
Physical seed damage, not rodent's saliva, accelerates seed germination of trees in a subtropical forest
<p>Many tree species adopt fast seed germination to escape the predation risk by rodents. Physical seed damage and the saliva of rodents on partially consumed seeds may also act as cues for the seed to accelerate the germination process. However, the impacts of these factors on seed germination rate and speed remain unclear. In this study, we investigated such impacts on the germination rate and speed (reversal of germination time) of four tree species (<em>Quercus variabilis</em>, <em>Q. serrata</em>, <em>Q. acutissima</em>, and <em>Q. glauca</em>) after partial consumption by four rodent species, through a series of experiments. We also examined how seed traits may affect the damage degree by rodents by analyzing the relationship between the germination rate and time of rodent-damaged seeds and the traits. We found that artificially and rodent-damaged seeds exhibited a significantly higher seed germination rate and speed, compared to intact seeds. Also, the rodent saliva on seeds showed no significant effect on seed germination rate and speed. Furthermore, We observed significant positive correlations between several seed traits (including seed mass, coat thickness, and protein content) and seed germination rate, but these seed traits had a positive correlation with the germination rate and speed. These correlations are likely due to the beneficial traits countering seed damage by rodents. Overall, our results highlight the significant role of physical seed damage by rodents (rather than their saliva) in facilitating seed germination of tree species and potential mutualism between rodents and trees. Additionally, our results may have some implications in forest restoration, such that intentionally sowing or dispersing slightly damaged seeds by humans or drones may increase the likelihood of successful seed regeneration.</p>
Figure 1 in Herbivory and leaf expansion of Cyathea phalerata Mart. (Cyatheaceae) in subtropical Atlantic Forest, southern Brazil
Figure 1. Accumulated monthly rainfall (columns) and monthly mean temperature (line) during monitoring of leaf expansion and herbivory on Cyathea phalerata from October 2014 to September 2015.
Figure 2 in Herbivory and leaf expansion of Cyathea phalerata Mart. (Cyatheaceae) in subtropical Atlantic Forest, southern Brazil
Figure 2. Monitoring of herbivory on Cyathea phalerata from October 2014 to September 2015: monthly damaged leaves (A), cumulative leaf blade consumption (B), leaves in each consumption class (C), monthly leaf blade consumption (D). Bars: standard deviation.
Data from: Temporal changes in taxonomic and functional alpha and beta diversity across tree communities in subtropical Atlantic forests
<h2><strong>The study is published in Oikos and available at: <a href="https://doi.org/10.1111/oik.10961">https://doi.org/10.1111/oik.10961</a></strong></h2> <p>Here we aim to assess temporal taxonomic and functional alpha and beta diversity of adult and juvenile tree communities across 11 sites in the subtropical Brazilian Atlantic Forest to infer about trends and drivers of biodiversity change. The tree communities were evaluated for temporal changes in: (1) taxonomic and functional alpha diversity, (2) taxonomic and functional composition (beta diversity), and (3) identifying potential abiotic and biotic drivers of these changes, considering three censuses across a period of 10 years.</p> <p> </p> <h2>Files description:</h2> <p><strong>traits-adults.csv</strong> - adult tree species and their functional traits values.</p> <p><strong>traits-juveniles.csv</strong> - juvenile tree species and their functional trait values.</p> <p><strong>abundance-adults_synthesis.csv</strong> - adult tree species abundance over the three time periods of forest surveys (T1, T2, and T3). Raw data on tree individual level is available at ForestPlots.net database (<a href="https://forestplots.net/">https://forestplots.net/</a>) under request.</p> <p><strong>abundance-juveniles_synthesis.csv</strong> - juvenile tree species abundance over the three time periods of forest surveys (T1, T2, and T3). Raw data on tree individual level is available at ForestPlots.net database (<a href="https://forestplots.net/">https://forestplots.net/</a>) under request.</p> <p>Functional traits abbreviations are defined as follows: LA = leaf area; SLA = specific leaf area; WD = wood density; SM = seed mass; range_temp = range of mean annual temperature; range_CWD = range of climatological water deficit; and biomes_distrib = number of Brazilian biomes that the species occur according to Flora and Funga do Brazil.</p> <p> </p> <h2><strong>Acknowledgments</strong></h2> <p>This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) – Finance Code 001, through Portal de Periódicos and scholarships granted to JMFK, JK and RCP. The fieldwork was supported by Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS grant numbers 2218 – 2551/12-2 and 19/2551-0001698-0), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq/FAPERGS/PELD number 441590/2020-9), and the Instituto Nacional de Ciência e Tecnologia (INCT) in Ecology, Evolution and Biodiversity Conservation, supported by MCTIC/CNPq (grant number 465610/2014-5). KMB gratefully acknowledge the financial support by the National Institute of Science and Technology in Low Carbon Emission Agriculture (INCT-ABC) sponsored by Brazil’s National Council for Scientific and Technological Development (CNPq, grant no. 406635/2022-6), the Foundation for Research Support of the State of Rio Grande do Sul (Fapergs, grant no. 22/2551-0000392-3), and the Ministry of Agriculture (MAPA). SCM is supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; grant number 309659/2019-1.</p> <p> </p> <h3><strong>Please find below the data used in the study.</strong></h3>
Figure 1 in Effects of forest conversion on tce assemblages' structure of aquatic insects in subtropical regions
Figure 1. Location of tce micro-basin and sampled streams in forested area (F1, F2, and F3) and converted area (C1, C2, and C3) at Parque Estadual do Turvo and adjacent areas, in soutcern Brazil.
Figure 3 in Effects of forest conversion on tce assemblages' structure of aquatic insects in subtropical regions
Figure 3. Ordination diagram of NMDS of Epcemeroptera, Plecoptera, and Triccoptera assemblages at streams in forested area (F) and converted area (C). Numbers 1-3 refer to tce stream; R refers to rocky bottom substrate, and L refers to leaf litter substrate.
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.