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Towers Forestry Plot, Long-term Vegetation Monitoring in a 1-ha old-growth Rainforest, La Selva Research Station, OTS, Sarapiquí, Heredia, Costa Rica, 2010–2020
The Towers Plot is a 1-hectare permanent vegetation plot established in 2010 under the canopy towers at La Selva Research Station, Sarapiquí, Heredia, Costa Rica. The plot was created by the Organization for Tropical Studies (OTS) to monitor long-term changes in forest structure, composition, and dynamics in an old-growth tropical rainforest. All woody stems with a diameter at breast height (DBH) of 10 cm or greater—including trees, palms, and lianas—were tagged, mapped, and measured following standardized procedures. Censuses were conducted between 2010 and 2020 to document growth, mortality, and recruitment. The dataset includes taxonomic identifications, stem diameter measurements, spatial coordinates within the plot, and metadata describing field methods and species composition. The plot was established beneath three canopy towers that had been previously constructed through the NSF-funded Major Research Instrumentation (MRI) project, NSF 0722741, which provided key infrastructure for canopy and environmental research at La Selva. This proximity created a valuable opportunity to integrate vegetation monitoring with existing environmental instrumentation. Johana Hurtado, coordinator of the Tropical Ecology, Assessment and Monitoring (TEAM) project at La Selva, collaborated with OTS staff in the establishment of the plot, ensuring methodological consistency with other tropical forest monitoring sites. This dataset provides a comprehensive record of woody plant diversity and forest structure in a lowland old-growth Neotropical rainforest. It supports research on forest dynamics, carbon storage, and ecosystem change. The overall monitoring project is ongoing; this data package contains observations from 2010 through 2020.
Fruit, seed dispersal, and life history traits of tropical rainforest trees of the Anamalai Hills, Western Ghats, India
<p>This dataset contains compiled Fruit, seed dispersal, and life history traits of tropical rainforest trees of the Anamalai Hills, Western Ghats, India. The list of species included are mainly from the following two related publications:<br>- Muthuramkumar, S., Ayyappan, N., Parthasarathy, N., Mudappa, D., Raman, T.R.S., Selwyn, M.A. and Pragasan, L.A. (2006), <a href="https://doi.org/10.1111/j.1744-7429.2006.00118.x">Plant Community Structure in Tropical Rain Forest Fragments of the Western Ghats, India</a>. <em>Biotropica</em>, 38: 143-160. https://doi.org/10.1111/j.1744-7429.2006.00118.x<br>- Osuri, A., Chakravarthy, D., Mudappa, D., Raman, T., Ayyappan, N., Muthuramkumar, S., & Parthasarathy, N. (2017). <a href="http://httpd//doi.org/10.1017/S0266467417000219">Successional status, seed dispersal mode and overstorey species influence tree regeneration in tropical rain-forest fragments in Western Ghats, India</a>. <em>Journal of Tropical Ecology</em>, 33(4), 270-284. doi:10.1017/S0266467417000219<br>The present dataset is an expanded and updated version of the related dataset available at <a href="https://doi.org/10.5061/dryad.vd0nn">https://doi.org/10.5061/dryad.vd0nn</a><br> <br>Species traits information was collated from <a href="http://www.biotik.org/">BIOTIK (http://www.biotik.org/</a>), <a href="http://www.flowersofindia.net/">Flowers of India (http://www.flowersofindia.net/)</a>, India Biodiversity Portal (http://indiabiodiversity.org/), <a href="https://doi.org/10.5061/dryad.234/1">Global wood density database (https://doi.org/10.5061/dryad.234/1)</a> and <a href="https://doi.org/10.1017/S0266467417000219">Osuri et al. (2014): https://doi.org/10.1017/S0266467417000219</a>. We also referred to the following previous studies that provided information on the successional status of rain-forest species in the Western Ghats (Chetana 2013, Pascal 1988, Raman et al. 2009, Sreejith 2005).</p> <p><strong>References:</strong><br>CHETANA, H. C. 2013. Assessing the ecological processes in abandoned tea plantations and its implication for ecological restoration in the Western Ghats, India. PhD thesis, Manipal University.<br>OSURI, A. M., KUMAR, V. S. & SANKARAN, M. 2014. Altered stand structure and tree allometry reduce carbon storage in evergreen forest fragments in India’s Western Ghats. <em>Forest Ecology and Management </em>329: 375–383.<br>PASCAL, J. P. 1988. <em>Wet evergreen forests of the Western Ghats of India: Ecology, structure, floristic composition and succession</em>. Institut Français de Pondichéry, Pondicherry.<br>RAMAN, T. R. S., MUDAPPA, D. & KAPOOR, V. 2009. Restoring rainforest fragments: survival of mixed-native species seedlings under contrasting site conditions in the Western Ghats, India. <em>Restoration Ecology</em> 17:137–147.<br>SREEJITH, K. A. 2005. Ecological and ecophysiological studies on the successional status of tree seedlings in tropical wet evergreen and semi-evergreen forests of Kerala. PhD thesis, Forest Research Institute, Dehradun.</p> <p><strong>Geographic Coverage:</strong><br>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India<br>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><strong>Temporal Coverage:</strong><br>1. Begins: 2003-03-01 (Year, Month, Day)<br>2. Ends: 2024-02-10 (Year, Month, Day)</p> <p>Besides the <strong>README.txt</strong> file, the dataset includes the following comma-delimited text (csv) file with the data in columns as explained below:</p> <p><strong>Anamalai_tree_traits_2024.csv</strong></p> <p><strong>spec_name_ORIG:</strong> Scientific name of the species used during the data collection<br><strong>genus:</strong> Genus of the taxon<br><strong>specificEpithet:</strong> Specific epithet of the taxon in the Latin binomial name<br><strong>Accept_name_WFO:</strong> Updated scientific name of the species as in Plants of the World Online (POWO, https://powo.science.kew.org/)<br><strong>Habit:</strong> life form of the species(tree/shrub/cane/palm)<br><strong>Distribution:</strong> Distribution of the species in the study area (Native/Endemic/Introduced)<br><strong>IUCN_status:</strong> IUCN status of the species (CR-Critically Endangered,DD-Data deficient,EN-Endangered,LC-Least Concern,NT-Near Threatened,VU-Vulnerable,NA-Unknown)<br><strong>Wden_final:</strong> Wood density value assigned for the species (g cm^-3); NA - not available; sourced from Global wood density database (https://doi.org/10.5061/dryad.234/1)<br><strong>wd_level:</strong> Level in which the wood density value belongs (Species - wood density value is from species level; genus - wood density value assigned is the genus level average value)<br><strong>fruit_type:</strong> Morphological type of fruit<br><strong>fleshy_dry:</strong> Whether fruit is a dry fruit or fleshy, with aril or other parts <br><strong>seed_size:</strong> Species seed size: L = Large (>3 cm); M = Medium (1-3 cm); S = Small (<1 cm)<br><strong>disperser:</strong> Categories indicating seed dispersal mode: Bird, mammal, bird and mammal (Mammal_bird), gravity, wind, or unknown<br><strong>habitat:</strong> Habitat affinity category: EG_edg - evergreen forest edge; EG_for - evergreen forest; Dec_for - deciduous forest; Int – Introduced species; Unknown – Unknown<br><strong>habt_new:</strong> Habitat affinity new category: Mature – mature forest; Secondary – secondary forest, NA - unknown/Introduced species<br><strong>ad_ht:</strong> Species maximum adult height (m)</p>
Resilience estimates of Amazon and Congo rainforests based on mean annual precipitation and root zone storage capacity
<p>Resilience refers to the capacity of the ecosystem to absorb perturbations and remain in its native stable state. Here, we quantified forest resilience of South American and African ecosystems using mean annual precipitation and root zone storage capacity (2000-2019). We adopted Hirota et al. (2011) methodology for calculating resilience using logistic regression. This logistic regression predicts the probability of forest (tree cover > 50%) as a function of the independent variable. The predicted resilience estimates range between 0 to 1, where 1 represents the highest probability of finding forest – interpreted as highly resilient forest ecosystems.</p> <p>For more information, check: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16115">https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16115</a></p>
Data from: Structure and dynamics of secondary and mature rainforests: insights from South Asian long-term monitoring plots
<p><strong>1) DESCRIPTION </strong></p> <p>The dataset contains annual woody stems (shrubs and trees) census data collected from two long-term ecological monitoring plots spanning one hectare each in the Anamalai Hills of the Southern Western Ghats, India. These two plots represent one situated in a mature forest located within relatively undisturbed rainforest of the Anamalai Tiger Reserve (ATR) and one in secondary forest on the Valparai Plateau, respectively. Both plots have been censused and measured from 2017 to 2022 following the standardized protocol (RAINFOR-GEM, Marthews et al. 2014).</p> <p><br><strong>2) CONTACTS</strong></p> <p>CONTACT #1<br>1. Name: Akhil Murali<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 82812 97441<br>4. Email address: akhil@ncf-india.org<br>5. ORCID: 0000-0001-6149-6458</p> <p>CONTACT #2<br>1. Name: Srinivasan Kasinathan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: srini@ncf-india.org<br>5. ORCID: 0000-0001-7323-6653 </p> <p>CONTACT #3<br>1. Name: Kshama Bhat<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: kshama@ncf-india.org<br>5. ORCID: 000-0002-6190-2687</p> <p>CONTACT #4 <br>1. Name: Jayashree Ratnam <br>2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India<br>3. Work Phone: +91 80 23666001 <br>4. Email address: jratnam@ncbs.res.in <br>5. ORCID: 0000-0002-6568-8374</p> <p>CONTACT #5<br>1. Name: Mahesh Sankaran <br>2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India<br>3. Work Phone: +91 80 23666001<br>4. Email address: mahesh@ncbs.res.in <br>5. ORCID: 0000-0002-1661-6542</p> <p>CONTACT #6<br>1. Name: Divya Mudappa<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org<br>5. ORCID: 0000-0001-9708-4826</p> <p>CONTACT #7<br>1. Name: T. R. Shankar Raman<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org<br>5. ORCID: 0000-0002-1347-3953</p> <p>CONTACT #8<br>1. Name: Anand M Osuri <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: aosuri@ncf-india.org <br>5. ORCID: 0000-0001-9909-5633</p> <p><br><strong>3) GEOGRAPHIC COVERAGE and SITE DESCRIPTION</strong></p> <p>a) Site type: : Tropical Forest<br>b) Geography: : Anamalai Tiger Reserve, Southern Western Ghats.<br>c) Habit: : Mid elevation Wet evergreen Forest<br>d) Site History: : </p> <p>i) MANAMBOLI- The Mature Forest plot (10.357748° N, 76.889747° E; 825 m asl) is situated within a relatively undisturbed 200-hectare mid-elevation tropical wet evergreen rainforest tract at the core of the Anamalai Tiger Reserve (ATR). This area has been protected from logging and other significant disturbances since its establishment as a protected area in 1979.</p> <p>ii) CANDURA- The Secondary Forest plot (10.30855411° N, 76.83391853° E; 875 m asl) is situated within a 124-hectare rainforest remnant on the Valparai Plateau: the Candura rainforest remnant. The Candura site experienced episodic selective logging in the 1990s and early 2000s, with the last logging episode occurring in 2004. In the early 2000s, the understorey of the remnant was cleared for Vanilla (Vanilla planifolia) cultivation in the central and southern parts (abandoned in 2007), robusta coffee (Coffea canephora) in the northwestern corner (abandoned in the early 2000s), and pepper in 21 hectares in the northeastern part (established in 2015, abandoned in 2021).</p> <p>Climate: Humid tropical with about 2400 mm rainfall annually, falling mainly during the southwest monsoon.</p> <p><br><strong>4) TEMPORAL COVERAGE</strong></p> <p>a) Begins: 2017-11-30 (Year, Month, Day)<br>b) Ends: 2022-11-12 (Year, Month, Day)</p> <p><br>5) SAMPLING DESIGN AND METHODS </p> <p>a) Plot Design: Each 1 ha plot of 100 m × 100 m, sub-divided into 100 continuous sub-plots of 10 m × 10 m, was surveyed and mapped to maximum accuracy using a theodolite in the field, with grid corners permanently staked. <br>b) Data collection period and frequency: After the plot establishment in NOvember -- December 2017, the plots were recensused each year (around November). <br>c) Research Methods: All woody plant individuals with girth at breast height (GBH, at 1.3 m) ≥10 cm were tagged with numbered aluminum tags and spatially mapped. Plant species were identified using standard floral keys. Stem GBH was measured for all single stemmed individuals. For trees with buttresses, the GBH point of measurement (POM) was taken at 50 cm above the buttresses or at the height where the stem is regular. New saplings that recruited into the ≥10 cm GBH class were identified, mapped, tagged, and added to the monitoring. Stems that appeared to be dead were recorded at each monitoring and those that showed no signs of recovery in subsequent visits were recorded as mortality.</p> <p><br><strong>6) FILES INCLUDED</strong></p> <p>The dataset includes the following 9 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)</p> <p>01_README.txt<br>Metadata (this file) including information on the dataset explaining associated files and their contents.</p> <p>02_Candura_annual_census.csv <br>This contains the Annual census data with the following column headings: <br>site: Site name (Can = Candura)<br>cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022)<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ht1: Point of measurement at 1.3 m above the ground or 50 cm above the top of the highest buttress or stilt root (POM1)<br>c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97)<br>g1: Stem girth at POM1 (in centimetre)<br>ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached<br>c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97)<br>g2: Girth at POM2 (in centimetre)<br>dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No)<br>da: alive status of stem (d-dead,a-alive)<br>remarks: remarks or notes</p> <p>03_Manamboly_annual_census.csv<br>This contains Annual census data with the following column headings: <br>site: Site name (Man = Manamboli)<br>cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022)<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ht1: Point of measurement at 1.3 m above the ground or 50 cm above the top of the highest buttress or stilt root (POM1)<br>c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97)<br>g1: Stem girth at POM1 (in centimetre)<br>ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached<br>c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97)<br>g2: Girth at POM2 (in centimetre)<br>dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No)<br>da: alive status of stem (d-dead,a-alive)<br>remarks: remarks or notes</p> <p>04_Candura_vernier.csv<br>This file has the girth measurement of trees with lianas where digital vernier calipers were used to measure stem diameter since it was not possible to measure stem girth using measuring tape.<br>site: Site name (Can = Candura)<br>cno: Census Number<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>vern1_d1 First measure of diameter at POM1 (in millimetre) <br>vern2_d1 Second measure of diameter at POM1 (in millimetre) <br>vern3_d1 Third measure of diameter at POM1 (in millimetre) <br>calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>vern1_d2 First measure of diameter at POM2 (in millimetre) <br>vern2_d2 Second measure of diameter at POM2 (in millimetre) <br>vern3_d2 Third measure of diameter at POM2 (in millimetre) <br>calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>Remarks Remarks and notes</p> <p>05_Manamboli_vernier.csv<br>This file has the girth measurement of trees with lianas where digital vernier calipers were used to measure stem diameter since it was not possible to measure stem girth using measuring tape.<br>site: Site name (Man = Manamboli)<br>cno: Census Number<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>vern1_d1 First measure of diameter at POM1 (in millimetre) <br>vern2_d1 Second measure of diameter at POM1 (in millimetre) <br>vern3_d1 Third measure of diameter at POM1 (in millimetre) <br>calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>vern1_d2 First measure of diameter at POM2 (in millimetre) <br>vern2_d2 Second measure of diameter at POM2 (in millimetre) <br>vern3_d2 Third measure of diameter at POM2 (in millimetre) <br>calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>Remarks Remarks and notes</p> <p>06_Candura_Height_data.csv<br>This contains data on the heights of individual trees in plot as measured in 2018.<br>site: Site name (Can = Candura)<br>ymd: Date in YYYY/MM/DD format (Year Month Day)<br>gno: Grid Number: <br>tno: unique tag number: <br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes: <br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>height: Height of tree in metres<br>remarks: Remarks: and notes</p> <p>07_Manamboli_Height_data.csv<br>This contains data on the heights of individual trees in plot as measured in 2018.<br>site: Site name (Man = Manamboli)<br>ymd: Date in YYYY-MM-DD format (Year Month Day)<br>gno: Grid Number: <br>tno: unique tag number: <br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes: <br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>height: Height of tree in metres<br>remarks: Remarks: and notes</p> <p>08_Species_name_match.csv<br>This file provides the combined list of species codes updated taxonomy and successional guild. Scientific names were updated to current taxonomy using the species name matching tool of the Global Biodiversity Information Facility, GBIF (www.gbif.org).<br>sps: Species codes<br>query : Scientific name of the plant at the time of data collection: <br>scientificName: : with auther citation: <br>key: GBIF key<br>rank: Taxonomic rank or level of identification (GENUS, SPECIES)<br>kingdom: Taxonomic Kingdom (plants) provided by GBIF name matching tool: <br>phylum: Taxonomic Phylum provided by GBIF name matching tool<br>class: Taxonomic Class provided by GBIF name matching tool<br>order: Taxonomic Order provided by GBIF name matching tool<br>family: Taxonomic Family provided by GBIF name matching tool<br>genus: Taxonomic Genus provided by GBIF name matching tool<br>botanical_name: Updated scientific name of the species provided by GBIF name matching tool<br>habt_new: Successional guild of the species (Mature = mature forest species; Secondary = secondary successional species; Int - Introduced species)</p> <p>09_R_scrpt_for_manuscript.R<br>Text file with code in the R statistical and programming environment (www.r-project.org).</p> <p><br><strong>Reference</strong><br>Marthews TR, Riutta T, Oliveras Menor I, Urrutia R, Moore S, Metcalfe D, Malhi Y, Phillips O, Huaraca Huasco W, Ruiz Jaén M, Girardin C, Butt N, Cain R and colleagues from the RAINFOR and GEM networks (2014). Measuring Tropical Forest Carbon Allocation and Cycling: A RAINFOR-GEM Field Manual for Intensive Census Plots (v3.0). Manual, Global Ecosystems Monitoring network, http: //gem.tropicalforests.ox.ac.uk/.</p> <p> </p>
Dataset for assessing amazon rainforest regrowth with GEDI and ICESat-2 data
<p>This dataset includes GEDI data, ICESat-2 data, auxiliary data, and intermediate results necessary to reproduce results in <a href="https://doi.org/10.1016/j.srs.2022.100051">Milenkovic et al. 2022</a>. The code required to process the data is on: <a href="https://github.com/MilutinMM/SecFor-Regrowth.git">https://github.com/MilutinMM/SecFor-Regrowth.git</a>.</p> <p>Short descriptions of files:</p> <ul> <li><strong>ATL08_gdf.json</strong> - ICESat-2 ATL08 segments in Rondonia </li> <li><strong>ATL08_gdf_Para_MG.json</strong> - ICESat-2 ATL08 segments intersecting the two calibration sites</li> <li><strong>ATL08_h5_fileNames_Rondonia.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting Rondonia (primary input)</li> <li><strong>calibartionModels.zip</strong> - GEDI and ICESat-2 calibration models and statistics (xlsx files)</li> <li><strong>deforested_poligons_2018_2019.zip</strong> - SPH file of a deforested polygon in the calibration site</li> <li><strong>gedi_L2A_allTime_gdf_Para_MG.json</strong> - GEDI shots intersecting the two calibration sites</li> <li><strong>gedi_L2A_allTime_MG_all.csv</strong> - GEDI shots within the FN calibration site</li> <li><strong>gedi_L2A_allTime_Para_all.csv</strong> - GEDI shots within the TNF calibration site</li> <li><strong>GEDI_L2A_fileNames_Rondonia.txt </strong> - A list of GEDI orbits (L2A h5 files) intersecting Rondonia (primary input)</li> <li><strong>gedi_L2A_gdf_Para_MG_sens_a2.json</strong> - GEDI shots intersecting the two calibration sites with sensitivities derived from the algorithm setting group 2</li> <li><strong>gedi_L2A_gdf_sens_a2.json</strong> - GEDI shots in Rondonia</li> <li><strong>gedi_L2A_MG_all_sens_a2.csv </strong>- GEDI shots within the FN calibration site (sensitivity from the alg. set. group 2)</li> <li><strong>gedi_L2A_Para_all_sens_a2.csv</strong> - GEDI shots within the TFN calibration site (sensitivity from the alg. set. group 2)</li> <li><strong>gedi_L2A_Rondonia_all_sens_a2.csv</strong> - GEDI shots in Rondonia </li> <li><strong>MG_ATL08_h5_fileNames.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the FN calibration site</li> <li><strong>Para_ATL08_h5_fileNames.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the TFN calibration site</li> <li><strong>svbr-rondonia-2018.tif</strong> - Forest age map for Rondonia (Silva Junior et al. 2020)</li> <li><strong>svbr-rondonia-2018_bw_eroded.tif</strong> - a secondary forest extent mask with removed border pixels</li> </ul> <p>References:</p> <p>Milenković, M., Reiche, J., Armston, J., Neuenschwander, A., De Keersmaecker, W., Herold, M., Verbesselt, J., Assessing amazon rainforest regrowth with GEDI and ICESat-2 data, <em>Science of Remote Sensing</em>, 2022, 100051, ISSN 2666-0172, <a href="https://doi.org/10.1016/j.srs.2022.100051">https://doi.org/10.1016/j.srs.2022.100051</a>.</p> <p>Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G. <em>et al.</em> Benchmark maps of 33 years of secondary forest age for Brazil. <em>Sci Data</em> <strong>7, </strong>269 (2020). <a href="https://doi.org/10.1038/s41597-020-00600-4">https://doi.org/10.1038/s41597-020-00600-4</a></p>
Global restoration opportunities in tropical rainforest landscapes - Supplementary Materials - Spatial Data Layers
<p><strong>Global restoration opportunities in tropical rainforest landscapes</strong></p> <p><strong>Sci Adv 5 (7), eaav3223</strong></p> <p><strong>DOI: 10.1126/sciadv.aav3223</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/5/7/eaav3223">https://advances.sciencemag.org/content/5/7/eaav3223</a></strong></p> <p><strong>Supplementary Materials</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1">https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1</a></strong></p> <p><strong>Spatial Data layers:</strong></p> <p><strong><a href="https://doi.org/10.5281/zenodo.3233495">https://doi.org/10.5281/zenodo.3233495</a></strong></p> <p><strong>_OutR10:</strong></p> <p><strong>r_10.img → Global restoration opportunity score (ROS)</strong></p> <p><strong>r_10_sc.img → Global restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_aa_sc.img → Australiasia restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_at_sc.img → Afro Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_im_sc.img → Indo Malay restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p> </p> <p><strong>_OutBasics:</strong></p> <p><strong>r_1.img → Study Area</strong></p> <p><strong>r_2.img → Restorable Area</strong></p> <p><strong>r_3.img → Restoration Benefits</strong></p> <p><strong>r_4.img → Restoration feasibility</strong></p> <p><br> <strong>_OutCountry:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for country XXX – rescaled 0-1</strong></p> <p><br> <strong>_OutHotspots:</strong></p> <p><strong>r_10_hotspot_XXX_hotspot_area_sc.tif → restoration opportunity score (ROS) for conservation hotspot area XXX – rescaled 0-1</strong></p> <p><strong>r_10_hotspots_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in conservation hotspots</strong></p> <p><br> <strong>_OutKBA:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for Key Biodiversity Area XXX – rescaled 0-1</strong></p> <p><strong>r_10_kba_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Key Biodiversity Areas</strong></p> <p><br> <strong>_OutAichi:</strong></p> <p><strong>r_10_aichi_XXX.tif → Top 15% area of with highest restoration opportunity score (ROS) in country XXX</strong></p> <p><strong>r_10_aichi.img → Top 15% area of with highest restoration opportunity score (ROS) global</strong></p> <p><br> <strong>_OutBonn:</strong></p> <p><strong>r_10_XXX_Bonn.img → Area with highest restoration opportunity score (ROS) in country XXX according to their Bonn Challenge commitments</strong></p> <p> </p> <p><strong>_OutParis:</strong></p> <p><strong>r_10_at_paris.img → Area with highest restoration opportunity score (ROS) in Afro Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_im_paris.img → Area with highest restoration opportunity score (ROS) in Indo Malay Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_nt_paris.img → Area with highest restoration opportunity score (ROS) in Neo Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><br> <strong>_OutTEOW:</strong></p> <p><strong>r_10_ECOREGION_XXX_sc.tif → restoration opportunity score (ROS) for Ecoregion XXX – rescaled 0-1</strong></p> <p><strong>r_10_ECOREGION_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Ecoregions</strong></p> <p> </p> <p><strong>_OutAll</strong></p> <p><strong>alltargets.img → Area with highest restoration opportunity score (ROS) according to all targets (excluded from the paper)</strong></p> <p> </p>
Indicative distribution map for Ecosystem Functional Group T1.1 Tropical/Subtropical lowland rainforests
<p>This archive contains indicative distribution maps and profiles for <strong>T1.1 Tropical/Subtropical lowland rainforests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group T2.3 Oceanic cool temperate rainforests
<p>This archive contains indicative distribution maps and profiles for <strong>T2.3 Oceanic cool temperate rainforests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Survey of Odonata Abundance in Two Streams at El Yunque Rainforest (2018-2019)
The focus of this research was to study the natural history of odonate species in headwater montane streams following major hurricane impacts in Puerto Rico. We monitored assemblages from August 2018 to July 2019 in two headwater streams within El Yunque National Forest, Puerto Rico. Data contains information on abundance of adult and larvae of different species of Odonata, as well as head width and body length of larvae. Data a product of: Ashley Mariani-Ríos, Norman Maldonado-Benítez & Alonso Ramírez (2022) Natural history of Odonata assemblages in tropical streams in Puerto Rico, Neotropical Biodiversity, 8:1, 112-123, DOI: 10.1080/23766808.2022.2043699
Locations of rainforest transformation plots on Palmyra Atoll
Locations of rainforest transformation plots on Palmyra Atoll. Delineation of areas where non-native Cocos nucifera will be removed and replaced with native vegetation. Polygons were defined in 2019. Boundaries delineated in ArcGIS Desktop
Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India
<p><strong>TITLE</strong></p><p><strong>Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India</strong><br> </p><p><strong>DESCRIPTION</strong></p><p>This dataset contains point-centred quarter (PCQ) data on trees and habitat structure measurements data from rainforest fragments and some coffee plantations in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. The data were gathered to quantity habitat parameters for bird and small carnivorous mamm community studies. Data were gathered mainly by T. R. Shankar Raman and Divya Mudappa (2000 to 2003), Hari Sridhar (2005), and Akshay Surendra (2019).</p><p><strong>Publications</strong></p><p>Specific portions of the dataset have been used in the following publications:</p><ul><li>Mudappa, D. 2001. <a href="https://hdl.handle.net/10603/101890">Ecology of the brown palm civet <i>Paradoxurus jerdoni</i> in the tropical rainforests of the Western Ghats, India</a>. Ph. D. thesis, Bharathiar University, Coimbatore. https://hdl.handle.net/10603/101890</li><li>Raman, T. R. S. 2001. <a href="https://archive.org/details/raman-2001-ph-d-thesis-iisc">Community ecology and conservation of mid-elevation tropical rainforest bird communities in the southern Western Ghats, India</a>. PhD thesis, Indian Institute of Science, Bangalore. https://archive.org/details/raman-2001-ph-d-thesis-iisc</li><li>Raman, T.R.S. 2006. <a href="https://doi.org/10.1007/s10531-005-2352-5">Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India</a>. <i>Biodiversity and Conservation</i> 15: 1577–1607. https://doi.org/10.1007/s10531-005-2352-5</li><li>Sridhar, H., & Sankar, K. 2008. <a href="https://doi.org/10.1017/S0266467408004823">Effects of habitat degradation on mixed-species bird flocks in Indian rain forests</a>. <i>Journal of Tropical Ecology</i> 24: 135-147. https://doi.org/10.1017/S0266467408004823</li><li>Surendra, A. & Raman, T. R. S. 2022. <a href="https://doi.org/10.1101/2022.10.22.513365">Forest bird decline and community change over 19 years in long-isolated South Asian tropical rainforest fragments</a>. Preprint. <i>BioRxiv</i> 2022.10.22.513365. https://doi.org/10.1101/2022.10.22.513365<br> </li></ul><p>A related dataset is the following:<br>Raman, T. R. S. (2020). Data from: Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India. <i>Dryad Dataset.</i> https://doi.org/10.5061/dryad.4mw6m907q<br> </p><p><strong>Curation and corrections</strong></p><p>Data were collated, curated, and corrected before this upload. Besides addition of new columns, explanations of metadata, and other corrections included few related to canopy measurements, effective girth of multi-stem trees, and species identification.</p><p><strong>Acknowledgements</strong></p><p>We are grateful to P. Jeganathan and P. R. Shankar for assistance with data collection in 2000. Others who assisted with field research, and funding agencies related to the specific studies, are acknowledged in the above publications. The data compilation and publication was carried out as part of a grant from Fondation Franklinia to NCF.</p><p><br><strong>CONTACTS</strong><br> </p><p>CONTACT #1<br>1. Name: T. R. Shankar Raman<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0002-1347-3953</p><p>CONTACT #2<br>1. Name: Divya Mudappa<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0001-9708-4826</p><p>CONTACT #3<br>1. Name: Hari Sridhar<br>2. Work Address: Wildlife Institute of India, Post Bag #18, Chandrabani, Dehradun – 248001, Uttarakhand, India; Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: harisridhar1982@gmail.com<br>5. ORCID: https://orcid.org/0000-0003-3286-0120</p><p>CONTACT #4<br>1. Name: Akshay Surendra<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India; School of the Environment, Yale University, New Haven, CT – 06511, USA; New York Botanical Garden, 2900 Southern Blvd, Bronx, NY 10458<br>3. Work Phone: +91 821 2515601<br>4. Email address: akshaysurendra1@gmail.com<br>5. ORCID: https://orcid.org/0000-0003-2719-7432<br> </p><p><br><strong>GEOGRAPHIC COVERAGE</strong></p><p>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India</p><p>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p><p><br><strong>TEMPORAL COVERAGE</strong></p><p>1. Begins: 2000-01-01 (Year, Month, Day)<br>2. Ends: 2019-12-31 (Year, Month, Day)</p><p><br><strong>METHODS</strong></p><p>Methods involved are described in the publications listed above. The vegetation sampling methods are briefly described below.</p><p>PCQ data: Trees ≥30cm girth at breast height (gbh, at 1.3 m) were sampled in replicate point-centred quarter (PCQ) points in each of the sites (fragments or coffee plantations).</p><p>All trees in the PCQ plots were identified to species, or in a few cases to genus, using available field guides. Using a tape measure, distance from plot centre to the middle of the bole and GBH were recorded for each tree. At each of the PCQ plots, circular plots were laid to enumerate shrubs and cut trees and record presence or absence of lianas, cane, Lantana etc as described in the metadata. Canopy and leaf litter variables were measured at replicate points, spaced 25 to 50 m apart, in each site. Elevation readings were also taken at these points using an altimeter or handheld GPS. Canopy height was measured using a rangefinder. Percentage canopy cover was measured using a spherical densiometer at each of the 25 points in each site. Vertical stratification was assessed by noting presence or absence of foliage in the following height intervals (in metres): 0–1, 1–2, 2–4, 4–8, 8–16, 16–24, 24–32, and > 32, directly above and in a 0.5 m radius around each point. Leaf litter depth on the forest floor was measured using a calibrated wooden probe at each point. Where ground vegetation and litter were disturbed along trails, the samples were taken away from trails in the forest floor.</p><p><br><strong>FILES INCLUDED</strong><br>Besides the 00_README.txt file that contains this metadata, the dataset includes the following 7 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)<br> </p><p><strong>01) sites.csv -- Details of study sites</strong><br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>decimalLongitude: Longitude in decimal degrees North (WGS 84 datum)<br>decimalLatitude: Latitude in decimal degrees East (WGS 84 datum)<br>habitat: Habitat type as mature tropical rainforest, tropical rainforest fragment, or coffee plantation<br>Description: Description of the place<br> </p><p><strong>02) allpcqdata.csv -- Tree data from point-centred quarter (PCQ) surveys</strong><br>Year: Year of survey for bird and vegetation study<br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>Point_name: Name ID of point-centred quarter (PCQ) point as used within a survey year<br>pointID: Unique ID of point-centred quarter (PCQ) point including year of survey<br>Tree_no: Tree number ID given to the four trees in each PCQ plot (T1 to T4)<br>verbatimIdentification: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name as currently identified under updated taxonomy<br>nativeAlien: Category indicating whether species is native or alien to the region/country<br>kingdom: Taxonomic Kingdom<br>phylum: Taxonomic Phylum<br>Distance_eff: Distance in metres from centre of PCQ plot to centre of tree trunk<br>Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals<br>locationRemarks: Code for site name as originally used<br>SpCode: Species code as originally used during data entry<br>TreeHeight: Tree height in metres (only available in 2019 survey)<br>identificationRemarks: Notes related to identification if available<br>occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and note on one possibly errorneous girth<br> </p><p><strong>03) pcqlocations.csv -- Locations of sample PCQ points</strong><br>pointID: Unique ID of point-centred quarter (PCQ) point including year of survey<br>note: Site name code<br>decimalLatitude: Latitude in decimal degrees East (WGS 84 datum)<br>decimalLongitude: Longitude in decimal degrees North (WGS 84 datum)<br>coordinateUncertaintyInMeters: Approximate uncertainty of the location in metres<br> </p><p><strong>04) allhabitat.csv -- Data on habitat structure variables</strong><br>Year: Year of survey for bird and vegetation study<br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>Point: ID of replicate survey point within the Fragment<br>0-1m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 0-1 m above ground<br>1-2m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 1-2 m above ground<br>2-4m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 2-4 m above ground<br>4-8m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 4-8 m above ground<br>8-16m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 8-16 m above ground<br>16-24m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 16-24 m above ground<br>24-32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 24-32 m above ground<br>over32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band greater than 32 m above ground<br>VertStrata: Number of vertical strata with foliage (sum of preceding 8 columns)<br>CanopyHeight: Canopy height in metres<br>CanopyOpenness: Canopy openness in percentage as measured using a spherical densiometer<br>CanopyCover: Canopy cover (closure) in percentage as measured using a spherical densiometer<br>CanopyOverlap: Canopy overlap rank: 0-open sky above; 1-branches above barely touching; 2-overlapping branches above, sky visible; 3-overlapping branches, sky not visible<br>UC: Canopy overlap rank as above, for understorey vegetation only<br>MC: Canopy overlap rank as above, for the midstorey only<br>CC: Canopy overlap rank as above, for the upper canopy only<br>Altitude: Altitude above sea leavel in metres, measued from hand-held altimeter or GPS device<br>RfShrub: Number of shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Coffee: Number of coffee bushes (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Maesopsis: Number of alien Maesopsis eminii stems (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Strobilanthes: Number of Strobilanthes shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>TotalShrub: Total number of shrubs within 2 m radius of point<br>Liana: Presence (1) or absence (0) of woody lianas within 5 m radius of point<br>Cane: Presence (1) or absence (0) of cane (Calamus sp.) within 2 m radius of point<br>Lantana: Presence (1) or absence (0) of Lantana camara shrubs within 2 m radius of point<br>Bamboo: Presence (1) or absence (0) of bamboo culms within 2 m radius of point<br>LeafLitter: Depth of leaf litter in cm (to 0.5 cm accuracy) measured using a calibrated wooden probe<br>CutTrees: Number of cut trees within 5 m radius of point<br> </p><p><strong>05) gbifnames.csv -- Results of GBIF name matching tool</strong><br>sno: Serial number<br>verbatimScientificName: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name after matching with Global Biodiversity Information Facility (GBIF) database to lowest taxonomic level<br>sciNameWithAuthor: Scientific name with author as provided by GBIF name matching tool<br>key: GBIF key as provided by GBIF name matching tool<br>matchType: Type of match as provided by GBIF name matching tool<br>confidence: Confidence as provided by GBIF name matching tool<br>status: Status as accepted name or synonym as provided by GBIF name matching tool<br>rank: Taxonomic rank as provided by GBIF name matching tool<br>kingdom: Kingdom as provided by GBIF name matching tool<br>phylum: Phylum as provided by GBIF name matching tool<br>class: Class as provided by GBIF name matching tool<br>order: Order as provided by GBIF name matching tool<br>family: Family as provided by GBIF name matching tool<br>genus: Genus as provided by GBIF name matching tool<br>species: Species as provided by GBIF name matching tool<br>canonicalName: Canonical name as provided by GBIF name matching tool<br>authorship: Author of name as provided by GBIF name matching tool<br> </p><p><strong>06) plots2000.csv -- Data from 5 m radius circular plots in select sites</strong><br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>PlotID: ID of 5 m radius plot<br>Treeno: Serial number of tree in the plot<br>verbatimIdentification: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name as currently identified under updated taxonomy<br>Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals<br>nativeAlien: Category indicating whether species is native or alien to the region/country<br>kingdom: Kingdom as provided by GBIF name matching tool<br>phylum: Phylum as provided by GBIF name matching tool<br>occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and identification</p><p> </p><p><strong>07) anampcqs4gbif.rmd -- Text file with code in the R statistical and programming language</strong> </p><p>This R code was used for converting data in this Zenodo dataset into Darwin Core occurrence dataset for upload to the Global Biodiversity Information Facility (GBIF, https://www.gbif.org). The published dataset can now be accessed at: https://doi.org/10.15468/cmsveh</p><p> </p><p><strong>Changes in Version 2</strong></p><p>In sites.csv, changed habitat from "Rainforest" to "Tropical rainforest fragment" for Puthuthottam</p><p>Added the anampcqs4gbif.rmd file with R code</p>
Factors determining distributions of rainforest Drosophila shift from interspecific competition to high temperature with decreasing elevation (original datasets)
<p>This repository provides the data for the manuscript "Factors determining distributions of rainforest Drosophila shift from interspecific competition to high temperature with decreasing elevation"</p> <p>We investigated thermal tolerances and interspecific competition as causes of species turnover in the nine most abundant species of <em>Drosophila</em> along elevational gradients in the Australian Wet Tropics. Specifically, we 1) analyzed the distribution patterns of the studies <em>Drosophila</em> species; 2) fitted thermal performance curves; 3) tested the correlation between multiple thermal traits and distribution patterns; 4) fitted the Beverton-Holt model to describe the single-generation intra- and inter-specific competition effect; 5) examined the long-term effect of competition and temperature on the population size of a pair of Drosophila species.</p> <p>More details are provided in the README file.</p>
Data from: Seasonal variation in wildlife roadkills in plantations and tropical rainforest in the Anamalai Hills, Western Ghats, India
<p>This dataset contains animal roadkill data (2011-13) from the Valparai Plateau and Anamalai Tiger Reserve, Western Ghats, India. Occurrence records were gathered in the field by researchers of the <a href="https://www.ncf-india.org">Nature Conservation Foundation, India</a>. The dataset corresponds to the following publication:</p> <p>Jeganathan, P., Mudappa, D., Kumar, M. A., and Raman, T. R. S. 2018. <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Seasonal variation in wildlife roadkills in plantations and tropical rainforest in the Anamalai Hills, Western Ghats, India</a>. <em>Current Science</em> 114(3): 619-626. DOI: 10.18520/cs/v114/i03/619-626</p> <p>CONTACT #1<br> 1. Name: P. Jeganathan<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: jegan@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0002-0238-0655</p> <p>CONTACT #2<br> 1. Name: Divya Mudappa<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: divya@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0001-9708-4826</p> <p>CONTACT #3<br> 1. Name: M. Ananda Kumar<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: anand@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0001-7094-1314</p> <p>CONTACT #4<br> 1. Name: T. R. Shankar Raman<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: trsr@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0002-1347-3953</p> <p><strong>Keywords: </strong>tropical rainforest, plantations, Anamalai Hills, animal roadkill, linear infrastructure intrusions, highways, road ecology, animal-vehicle collisions </p> <p><strong>Geographic Coverage:</strong><br> 1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India<br> 2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><strong>Temporal Coverage:</strong><br> 1. Begins: 2011-06-01 (Year, Month, Day)<br> 2. Ends: 2013-05-31 (Year, Month, Day)</p> <p><strong>Methods:</strong></p> <p>Methods involved repeated surveys along the road routes searching for roadkills and habitat sampling as described in <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Jeganathan et al. (2018),<em> Current Science</em> 114(3): 619-626</a>, DOI: 10.18520/cs/v114/i03/619-626</p> <p><strong>Files included:</strong></p> <p>Besides this 00_README.txt file, the dataset includes the following six files as explained below:<br> 1) 01_habitat_length.csv -- details of road routes surveyed as line transects<br> 2) 02_sampling_events.csv -- details of individual line transect sample surveys along road routes<br> 3) 03_roadkill_data_final.csv -- roadkill occurrence data from sample surveys along road routes<br> 4) 04_canopy_and_habitat.csv -- canopy and habitat readings along road routes (transects) surveyed<br> 5) 05_roadkill_transects_all.kml -- KML file containing geographic tracks of 11 road routes surveyed as roadkill transects<br> 6) 06_road_transects_map.jpg -- Map of surveyed routes corresponding to Figure 1 in Jeganathan et al. (2018)</p> <p><strong>01_habitat_length.csv</strong><br> transect: name of road route surveyed as a line transect<br> route_description: description of road route<br> tlength_km: transect length along road in kilometres (km)<br> tlength_m: transect length along road in metres (m)<br> forest: extent of the road in metres (m) with forest on both sides<br> forest_tea: extent of the road in metres (m) with forest on one side, tea on the other<br> coffee_forest: extent of the road in metres (m) with forest on one side, coffee plantation on the other<br> tea: extent of the road in metres (m) with tea plantation on both sides<br> coffee: extent of the road in metres (m) with coffee plantation on both sides<br> eucalyptus: extent of the road in metres (m) with eucalyptus plantation on both sides<br> eucalyptus_tea: extent of the road in metres (m) with eucalyptus on one side, tea plantation on the other</p> <p><strong>02_sampling_events.csv</strong><br> season: monsoon (June to December 2011) or summer (March to June 2012 prior to the onset of 2012 monsoon)<br> transect: name of road route surveyed as a line transect<br> transect: name of road route surveyed as a line transect<br> tcode: unique code for each individual survey of a road route (transect) coevered on a specific date<br> eventDate: date of road survey<br> tlength: transect length along road in kilometres (km)</p> <p><strong>03_roadkilldata_final.csv</strong><br> sno: serial number of observation<br> season: monsoon (June to December 2011) or summer (March to June 2012 prior to the onset of 2012 monsoon)<br> transect: name of road route surveyed as a line transect<br> tcode: unique code for each individual survey of a road route (transect) coevered on a specific date<br> eventDate: date of road survey<br> fielddate: date of road survey as initially noted (for two surveys completed over two successive days, the initial date was recorded as eventDate for 2011-06-17 = 2011-06-16 and eventDate for 2011-07-06 = 2011-07-05<br> tlength: transect length along road in kilometres (km)<br> verbatimIdentification: original identification of roadkilled taxon<br> vernacularName: common name of taxon<br> scientificName: scientific name of taxon for corresponding taxonomic level of identification<br> taxonRank: rank of taxon indicating for corresponding taxonomic level of identification<br> taxonRemarks: category of taxon as noted for analysis<br> verbatimCoordinateSystem: coordinate system used for initial data collection<br> verbatimSRS: SRS of the location data collected (EPSG:32643/WGS84)<br> georeferenceRemarks: note indicating locations were converted from UTM (zone 43 N) to latitude longitude using QGIS software<br> verbatimLongitude: UTM longitude (Easting) as originally recorded<br> verbatimLatitude: UTM latitude (Northing) as originally recorded<br> decimalLongitude: longitude in decimal degree East<br> decimalLatitude: latitude in decimal degrees North<br> habitat: habitat on either side of the road (forest - forest on both sides; forest_tea - forest on one side, tea on the other; human - human settlements; coffee - coffee plantation on both sides; coffee_forest - coffee on one side, forest on the other; eucalyptus - eucalyptus plantation on both sides; eucalyptus_tea - eucalyptus on one side, tea on the other; tea - tea plantation)<br> individualCount: number of individuals recorded as roadkill (0 if no roadkills in that survey)<br> occurrenceStatus: indicated as 'present' for roadkills, or 'absent' if no roadkills recorded<br> occurrenceRemarks: notes and remarks if any</p> <p><strong>04_canopy_and_habitat.csv</strong><br> transect: name of road route surveyed as a line transect<br> verbatimroute: route name as originally noted<br> sno: serial number<br> canopycover: 0 if tree canopy absent, 1 if tree canopy present above point of observation<br> canopyoverlap: horizontal overlap of tree canopy above point of observation ranked as 0 - no canopy above; 1 canopy present but barely touching or overrlapping; 2 - canopy overlapping with sky still visible through leaves; 3 - canopy overlaps overhead densely with sky scarcely visible<br> verticaloverlap: vertical gap between canopy or branches of trees above point of observation ranked as 0 - very wide; 1 - barely touching, 2 - significant vertical overlap, 3 - substantial and dense vertical overlap<br> habcode: two letter alphabetical code with each letter indicating habitat on one side of the road at the point of observation with f - forest, t - tea, c - coffee, e - eucalyptus, v - village or human habitation, m - dam or reservoir<br> habno: numeric category for habitat on either side coded as 1 for monocultures (ee, tt); 2 for mixed forest and plantation (ef, ft, etc.); 3 for forest (ff), and 4 for coffee plantation (cc)<br> longitude: longitude in decimal degrees east<br> latitude: latitude in decimal degrees north</p> <p><strong>05_roadkill_transects_all.kml</strong><br> This KML file contains all 11 road routes surveyed as roadkill transects.</p> <p><strong>06_road_transects_map.jpg</strong><br> This map illustrating the surveyed road routes corresponds to Figure 1 in <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Jeganathan et al. (2018), <em>Current Science</em> 114(3): 619-626</a>, DOI: 10.18520/cs/v114/i03/619-626</p>
Data and code for 'Connectivity over a disease gradient enables recovery of rainforest frogs'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Bell, S.C., Heard, G.W., Berger, L. and Skerratt, L.F. (in review). Connectivity over a disease risk gradient enables recovery of rainforest frogs. </p> <p>The data are provided as a series of .csv files. R code is provided separately for each of the following components:</p> <p>1. A script to collate nightly air temperature data from stream-side temperature loggers, plus daily maximum air temperature data from the nearest Australian Bureau of Meteorology recording station to each site.</p> <p>2. A script to fit a hierarchical linear model to the stream-side air temperature, with the aid of the R2OpenBUGS package.</p> <p>3. A script to fit a hierarchical logistic regression model to the chytrid infection data, with the aid of the R2OpenBUGS package.</p> <p>4. A script to fit logistic regression models to the frog occupancy data, with the aid of the R2OpenBUGS package. </p> <p>These files allow all analyses from the paper to be reproduced. </p>
Data and code for 'Connectivity over a disease risk gradient enables recovery of rainforest frogs'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Bell, S.C., Heard, G.W., Berger, L. and Skerratt, L.F. (in review). Connectivity over a disease risk gradient enables recovery of rainforest frogs. </p> <p>The data are provided as a series of .csv files. R code is provided separately for each of the following components:</p> <p>1. A script to collate nightly air temperature data from stream-side temperature loggers, plus daily maximum air temperature data from the nearest Australian Bureau of Meteorology recording station to each site.</p> <p>2. A script to fit a hierarchical linear model to the stream-side air temperature, with the aid of the R2OpenBUGS package.</p> <p>3. A script to fit a hierarchical logistic regression model to the chytrid infection data, with the aid of the R2OpenBUGS package.</p> <p>4. A script to fit logistic regression models to the frog occupancy data, with the aid of the R2OpenBUGS package. </p> <p>These files allow all analyses from the paper to be reproduced. </p>
Data from: Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India
<p>This dataset contains data from the following publication:</p> <p>Sridhar, H., Raman, T. R. S. & Mudappa, D. 2008. <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. <em>Current Science</em> 94: 748-757.<br> URL: <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">https://www.currentscience.ac.in/Volumes/94/06/0748.pdf</a><br> URL2: <a href="https://www.jstor.org/stable/24100628">https://www.jstor.org/stable/24100628</a></p> <p><em>Corrigendum:</em></p> <p>Sridhar, H., Raman, T. R. S. & Mudappa, D. 2009. <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">Corrigendum: mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. <em>Current Science</em> 97: 612-613.<br> URL: <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">https://www.currentscience.ac.in/Volumes/97/05/0612.pdf</a></p> <p><strong>Description of dataset:</strong></p> <p>The data contains detections of mammals and hornbills (and few incidental records of other species) made along line transect surveys and opportunistic surveys in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. Further details of the Study Area and methods are available in Sridhar et al. (2008), but methods are briefly described below.</p> <p>Five rainforest patches were chosen within IGWLS and four privately-owned rainforest fragments in the Valparai plateau. Fifteen line transects, ranging in length from 1 to 3 km were laid across the nine sites, with the three largest sites having 2–4 transects each. The total distance covered by all transects was 32.02 km. Each transect was walked five times between September 2005 and April 2006 following standard distance sampling protocol. Two observers walked each transect at 0.75–1 km/h. For each detection, we recorded species, group size and perpendicular distance (measured using a rangefinder) from the transect. For animals which occurred in groups, perpendicular distances were measured to group centres. Apart from detections on transects, attempts were made to obtain group sizes of mammal species whenever incidentally detected. All transects were walked between 0630 and 1000 h. Indirect evidence (scat, tracks) on transects and incidental sightings (direct and indirect) of mammals were also recorded.</p> <p><strong>AUTHOR #1</strong></p> <p>1. Name: Hari Sridhar<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Current Work Address: Konrad Lorenz Institute for Evolution and Cognition Research, A-3400 Klosterneuburg, Austria<br> 4. Email address: harisridhar1982@gmail.com<br> 5. ORCID: https://orcid.org/0000-0003-3286-0120</p> <p><strong>AUTHOR #2</strong></p> <p>1. Name: T. R. Shankar Raman<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: trsr@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0002-1347-3953</p> <p><strong>AUTHOR #3</strong></p> <p>1. Name: Divya Mudappa<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: divya@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0001-9708-4826</p> <p><br> <strong>Keywords:</strong> tropical rainforest, tea plantation, coffee plantation, line transect, population density, distance sampling, Anamalai Tiger Reserve, Valparai Plateau, Anamalai Hills, Western Ghats, mammals, hornbills</p> <p><br> <strong>Geographic Coverage:</strong></p> <p>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India</p> <p>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><br> <strong>Temporal Coverage:</strong></p> <p>1. Begins: 2005-09-01 (Year, Month, Day)</p> <p>2. Ends: 2006-10-31 (Year, Month, Day)</p> <p> </p> <p><strong>Dataset files:</strong></p> <p>Besides the <strong>00_README.txt</strong> file, the dataset includes 4 comma-delimited text (csv) files with the data in columns as explained below:</p> <p><strong>01_Transect_locations.csv</strong> — contains transect location details and descriptions</p> <p><strong>02_Transects_and_opportunistic_surveys.csv</strong> — contains main dataset of observations on line transect and opportunistic surveys</p> <p><strong>03_Opportunistic_observations_locations.csv</strong> — contains location details of opportunistic surveys</p> <p><strong>04_Lion-tailed_macaque_counts.csv</strong> — contains counts of lion-tailed macaque (<em>Macaca silenus</em>) troops</p> <p><strong>05_allmammals_raw.xls</strong> — raw data NOT for use, for reference only in original Microsoft Excel format</p> <p> </p> <p><strong>Data variables and descriptions:</strong></p> <p><strong>01_Transect_locations.csv</strong><br> TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations<br> TransectLength_m: Length of line transect in metres<br> decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum<br> decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum<br> StartLat: transect starting point latitude in decimal degrees (N), WGS84 datum<br> StartLon: transect starting point longitude in decimal degrees (E), WGS84 datum<br> EndLat: transect ending point latitude in decimal degrees (N), WGS84 datum<br> EndLon: transect ending longitude in decimal degrees (E), WGS84 datum<br> MidLat: approximate mid-way location latitude in decimal degrees (N), WGS84 datum<br> MidLon: approximate mid-way longitude in decimal degrees (E), WGS84 datum<br> ExtraLatLon: additional pairs of latitude and longitude points along transect in decimal degrees (E, N), WGS84 datum<br> RouteDescription: description of transect route</p> <p><br> <strong>02_Transects_and_opportunistic_surveys.csv</strong><br> eventID: unique ID of sampling event corresponding to a single on-foot survey of a line transect, with elements separated by colons and last two elements referring to TransectCode and replicate survey number<br> occurrenceID: unique ID assigned to each occurrence (detection) along line transect<br> Sno: serial number<br> TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations<br> locality: name of transect<br> Transectno: unique number assigned to each transect survey or resurvey<br> Replicate: number indicating repeat survey of same transect<br> SiteCategory: Category indicating whether transcet was in Protected Area or Rainforest Fragment<br> Date: date of transect survey or opportunistic observation<br> Weather: Weather at time of survey<br> Habitat: Habitat where observation was made<br> Time: time in 24 h HH:MM format<br> verbatimIdentification: Identification as originally entered<br> scientificName: Scientific name of species or taxon observed<br> vernacularName: Common English name of species or taxon observed<br> Perpdist: Perpendicular distance in metres<br> Freshness: rating of freshness of faeces found (d=day, wk=week, mt=month)<br> individualCount: number of individuals counted, taken as minimum 1 if not noted in field<br> rawNumber: number as originally entered<br> DetectionType: type of observation classified as Call, Faeces, Indirect, Sighting, Track<br> verbatimDetection: raw entry corresponding to previous column<br> Height: height of observed animal above ground in metres<br> occurrenceRemarks: remarks on occurrence</p> <p><br> <strong>03_Opportunistic_observations_locations.csv</strong><br> locality: name of place or transect where opportunistic observation was made<br> decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum<br> decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum<br> coordinateUncertaintyInMeters: approximate/estimated uncertainty in location coordinates (in metres)</p> <p><br> <strong>04_Lion-tailed_macaque_counts.csv</strong><br> Sno: Serial number of entry<br> Place_or_Transect: Transect (TransectCode) or place where lion-tailed macaques were counted<br> Date: Date of observation<br> Time: Time of observation in 24h HH:MM format<br> Weather: Weather<br> Groupid: ID of Lion-tailed macaque troop, if known<br> Total: Total number of individuals recorded<br> AM: number of adult males<br> AF: number of adult females<br> A: number of adults (unsexed)<br> SA: number of sub-adults (unsexed)<br> SAM: number of sub-adult males<br> SAF: number of sub-adult females<br> JUV: number of juveniles<br> INF: number of infants<br> CARINF: number of infants carried by mother<br> UNID: number of unclassified<br> Remarks: other notes</p> <p> </p> <p><strong>05_allmammals_raw.xls</strong></p> <p>Raw data file in Microsoft Excel format -- for reference only (not advised for use)</p> <p><br> <strong>ADDITIONAL NOTES</strong><br> General notes taken about survey:<br> Pannimade transect 3/11/05 - Most giant squirrel detections were made after squirrel alarm called on seeing a soaring raptor.<br> 36TH hpb transect - very poor visibility on one side as it is very steep<br> Giant squirrels present within LTM troops might go undetected. Need to look carefully and check every movement<br> KO transect 20/01/06 - 1 GS which wasn’t detected when walking transect detected when measuring at less than 20 metres<br> SHK transect 23/01/06 - Abandoned 100 metres from end because of elephants<br> BAN - Ignore detections after 2.05 KM for first two replicates<br> KSPV 26/01/2006 - 1 GS not detected on transect detected while returning at < 40 m<br> Anaigundi - Transect in december strayed slightly from correct path<br> Var 30/01/2006 transect Do not include for indirect signs encounter rate since replicates were done on consecutive days<br> Is there a difference in visibility between wet and dry months; atleast in the more deciduous forests like varagaliar that is the case<br> should I consider only january and afterwards for MGH numbers since vocal activity is much higher then?<br> TF transect 12/02/06 4 GS heard calling from coffee estate adjoining TF; could fewer detections on last transect be because they are moving into coffee, maybe because some tree is fruiting<br> Do NL individuals move solitarily; what average group size to use<br> visibility in BAN and VAR is much better than other sanctuary sites such as IYAK, AN, MA<br> rained on 1st & 2nd of March after a long dry spell<br> KSPV 31/03/06 - Could have missed some calls because of cicada noise<br> KSWT 01/04/06 - Could have missed some calls because of cicada noise<br> KSPV 02/04/06 - Could have missed some calls because of cicada noise<br> KSPV 02/04/06 Do not include for indirect signs encounter rate since replicates were done on consecutive days<br> Great hornbills seem to be more vocal during april. To do with end of nesting??<br> malabar grey hornbills more vocal from february onwards</p>
Data on specialist and generalist herbivory, environmental variation, and phytochemical similarity from the Atlantic Rainforest of Brazil: 2013-2014
What: These are data on specialist and generalist herbivory from naturally occurring Piper plants as well as environmental data collected in 10 m diameter plots across sites in the Atlantic Rainforest of Brazil. Data also include chemical similarity calculated as the Morisita similarity index for species in a given plot and chemical modules that demonstrate classes of compounds that group together and influence herbivory. Why: Data were collected to understand factors that influence herbivory and tropical forest richness with a particular focus on secondary chemistry metabolomics. Where: Field sites include São Bento de Sapucaí (-22.8758, -45.8581), Parque Nacional de Itatiaia (-22.3698, -44.6285), Parque Estadual de Intervales (-24.3088, -48.2736), and Parque Estadual de Serra do Mar - Núcleo Pincinguaba (-23.6200, -46.7222). When: Data were collected in the field from Dec 2013 – May 2014. How: Field data were collected in 10 m diameter plots centered on randomly located Piper plants. Chemical data come from 1H-NMR metabolomics.
Rainforest phenology: flower, fruit and seed production from biweekly collections of 200 traps in the Yasuní Forest Dynamics Plot, Ecuador, 2000-2018
We provide data on flowering and fruiting phenology from an equatorial, ever-wet rainforest in eastern Ecuador, in Yasuni National Park. This is the first long-term study (18 years) of phenology in a diverse equatorial neotropical forest. Although the site is ever-wet, there is some seasonal variation in rainfall and irradiance. One major question was to determine whether the seasonal variation in climate was sufficient to drive seasonality in reproduction in this hyper-diverse forest. The study began in 2000 with various funding, and became an LTREB-funded project in 2006. We used twice monthly censuses of 200 traps to document phenology. Parts of >1000 species were identified in the traps in the 18 year period (ending early in 2018), including trees, shrubs, lianas and epiphytes. Parts identified included buds, flowers, mature fruits and mature seeds, and aborted, damaged and immature fruits and seeds. The project is on-going, and additional data will be added as it is processed.
Small, coastal temperate rainforest watersheds dominate organic carbon transport to the northeast Pacific Ocean
The northeast Pacific Coastal Temperate Rainforest (NPCTR) extending from southeast Alaska to northern California is characterized by high precipitation and large stores of recently fixed biological carbon. We show that 3.4 Tg-C yr-1 as DOC is exported from the NPCTR drainage basin to the coastal ocean. More than 56% of this riverine DOC flux originates from thousands of small (mean = 118 km2), coastal watersheds that comprise 22% of the NPCTR drainage basin. The average DOC yield from NPCTR coastal watersheds (6.20 g-C m-2 yr-1) exceeds that from Earth’s tropical regions by roughly a factor of three. The highest yields occur in small, coastal watersheds in the central NPCTR due to the balance of moderate temperature, high precipitation, and high soil organic carbon stocks. These findings indicate that DOC export from NPCTR watersheds may play an important role in regional-scale heterotrophy within near-shore marine ecosystems in the northeast Pacific. These are the datasets used in this analysis
canopy herbivory and leaf traits of tree communities in tropical montane rainforests of southern Ecuador
This dataset contains herbivory data estimated as leaf area loss [cm²] and [%] and several leaf traits measured either conventionally or via spectral sensing-based techniques from canopies of tree communities in tropical montane rainforests of the Andes in southern Ecuador between February and March in 2019. The data were used by Schön et al. (in prep) to evaluate whether leaf traits are valuable indicators of canopy herbivory mainly caused by arthropods and further, whether chemical leaf traits estimated via spectral sensing-based techniques have similar strong relations to herbivory as leaf traits measured conventionally. Herbivory was estimated with the software WinFOLIA ™ 2019a from scanned mature and sun-exposed leaves of tree canopies. Spectral sensing-based leaf traits comprising secondary plant metabolites and both structural and nutritional cell components were estimated from leaves with an OceanOptics spectrometer HDX. Conventionally measured leaf traits comprising morphological and nutritional traits were derived by applying both elemental and morphometrical analyses (e.g., ICP analysis, a digital micrometer and penetrometer). For detailed descriptions of the methodology see Schön et al. (in prep), Homeier et al. (2021), and Limberger et al. (2021). Research was conducted by the subprojects A1, B1, and B4 within the framework of the RESPECT project (Environmental changes in biodiversity hotspot ecosystems of South Ecuador: RESPonse and feedback effECTs) funded by the DFG with the grant numbers: BE1780/51-1, BE1780/51-2, Ho3296/6-1, FA 925/11-1, FA 925/11-2, FA 925/16-1, BR1293/17-1.
ScienceDex guides
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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.