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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>
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>
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 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>
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.
What do we know about the missing millions of Earth's insect species: evidence from Australian tropical rainforest bark beetles?
<p><span>Only 20% of the estimated five million species of insects on Earth are named despite over 240 years of taxonomy. Yet insects are poorly represented in protected area assessments, and insect declines are of concern globally. Here we explore how to increase the discovery of new species and understanding of this group through analysis of 10,097 tropical rainforest bark beetles (Scolytinae) from eight different ecological studies using beetles between 2000 and 2018 in the Australian Wet Tropics. Of the 107 species identified, 58 are undescribed: an increase of 35% on the 166 species known from Australia. As hypothesised, new species are significantly smaller, less abundant and less widespread than described species making them more extinction prone than named species. Rarefaction indicates doubling sampling would increase the number of species by 17. Flight Interception Traps (FIT) collected 84% of individuals and 98% of species confirming the effectiveness of a single sampling method for some beetles. Increased locations and collection from the canopy may sample further species rather than additional collecting methods.<span> </span>Scolytines are relatively well studied with a cadre of taxonomists at the forefront of using modern methods to resolve formerly intractable groups. These new species are more likely to be named than others in many other beetle groups where taxonomy has largely stalled. To increase species description rates and to avoid most species becoming extinct before being named, we call on taxonomists to use new character systems provided by DNA methods and to look at working with Artificial Intelligence tools.<span> </span></span></p>
Data and scripts for: Island geography drives evolution of rattan palms in tropical Asian rainforests
<p>This repository contains data and scripts for the research paper "<strong>Island geography drives evolution of rattan palms in tropical Asian rainforests</strong>".</p> <p><strong>Authors</strong>: Benedikt G. Kuhnhäuser, Christopher D. Bates, John Dransfield, Connie Geri, Andrew Henderson, Sang Julia, Jun Ying Lim, Robert J. Morley, Himmah Rustiami, Rowan J. Schley, Sidonie Bellot, Guillaume Chomicki, Wolf L. Eiserhardt, Simon J. Hiscock, William J. Baker</p> <p>Corresponding authors: <a href="mailto:b.kuhnhaeuser@kew.org">b.kuhnhaeuser@kew.org</a>, <a href="mailto:w.baker@kew.org">w.baker@kew.org</a></p> <h3> </h3> <p>The repository contains the following data, scripts, supplementary figures, and supplementary tables:</p> <p>1. Phylogenomic analyses<br>- Alignments<br>- Gene trees<br>- Species trees<br>- README file</p> <p>2. Divergence time estimation<br>- .xml files (containing both sequence data and analysis parameters)<br>- dated trees<br>- README file</p> <p>3. Ancestral range estimation<br>- input data<br>- scripts<br>- input data, intermediate data and scripts for the best model<br>- README file</p> <p>4. Downstream biogeographic analyses<br>- input data<br>- script<br>- README file</p> <p>5. Supplementary Figures</p> <p>6. Supplementary Tables</p>
Ficus trees with upregulated or downregulated defence did not impact predation on their neighbours in a tropical rainforest
<p>Trees can emit volatile organic compounds (VOCs) when under attack by herbivores, and these signals can also be detected by natural enemies and neighbouring trees. There is still limited knowledge of intra- and inter-specific communication in diverse habitats. We studied the effects of induced VOC emissions by three <em>Ficus</em> species on predation on the focal <em>Ficus</em> trees in a lowland tropical rainforest in Papua New Guinea. Further we assessed predation across a phylogenetically diverse set of neighbouring tree species. Two of the focal tree species, <em>Ficus pachyrrhachis</em> and <em>F. hispidioides</em>, have strong alkaloid-based constitutive defences while the third one, <em>F. wassa</em>, is lower in constitutive chemical defences. We experimentally manipulated the jasmonic acid signalling pathway by spraying the focal individuals with either methyl jasmonate (MeJA) or diethyldithiocarbamic acid (DIECA). These treatments induce increases or decreases in VOC emissions, respectively. We tested the possible effects of VOC emissions on each focal <em>Ficus</em> tree and two of its neighbours by measuring the predation rate of plasticine caterpillars. We found that predation increased after the MeJA application in only one focal tree species, <em>F. wassa</em>, while the DIECA application had no effect on any of the three focal species. Further, we did not detect an effect of our treatments on predation rates across neighbouring trees. Neither the phylogenetic distance of the neighbouring tree from the focal tree nor the physical distance from the focal tree had any effect on predation rates for any of the three focal <em>Ficus</em> species. These results suggest that even congeneric tree species vary in their response to the MeJA and DIECA treatment and subsequent response to VOC emissions by predators. Our results also suggest that MeJA effects did not spill over to neighbouring trees in highly diverse tropical rainforest vegetation.</p>
Small mammals reduce distance-dependence and increase seed predation risk in tropical rainforest fragments
Seed predation and reduced predation risk with distance from conspecific trees are important influences on tree regeneration in tropical forests. Shifts in animal communities, such as an increase in rodents and other small mammals due to forest fragmentation, could alter patterns of seed predation and affect tree regeneration and community dynamics in forest fragments. We performed a field experiment on four native rainforest tree species in the Western Ghats, India, to test whether fragmentation increases seed predation by mammals and alters the distance-dependence of seed predation. We monitored seed predation within open and mammal-exclosure plots, near and far from the canopies of conspecific trees, in contiguous and fragmented forests. Seed predation of Cullenia exarillata, Ormosia travancorica, and Syzygium rubicundum was markedly higher in forest fragments, and more so within open plots than exclosures, while the predominantly insect-predated Acronychia pedunculata experienced similar predation in contiguous forests and fragments. Seed predation of C. exarillata and S. rubicundum was unrelated to distance from conspecific trees in open plots in both contiguous forests and fragments, in contrast to exclosures that showed marked near versus far differences in seed predation. Our findings suggest that by increasing overall seed predation risk and imposing similar seed predation risk near and far from adults variably across the tree species, small mammals could alter processes that shape tree diversity and species composition in fragmented tropical rainforests.
Data from: Impact of human foraging on tree diversity, composition and abundance in a tropical rainforest
<p>These are summarised plot data from fifteen 40 m by 40 m sample plots established in Oban Division of Cross River National Park, Nigeria, between 23rd August 2019 and 9th September 2019. We have also included data summaries and RStudio codes used for analysis and generating results for the manuscript entitled: "Impact of human foraging on tree diversity, composition and abundance in a tropical rainforest", submitted for publication as an original research article in Biotropica. All data and R code required to generate the results as shown in the manuscript have been included. Complete tree species and plot data can be accessed at https://forestplots.net/.</p>
Figure 6 in Monthly variation of leaf litter Collembola in the tropical rainforest of Los Tuxtlas, Veracruz, Mexico
Figure 6. Relationship between precipitation, mean monthly temperature and abudance of collembola in pitfull traps for 2015.
Figure 2 in Monthly variation of leaf litter Collembola in the tropical rainforest of Los Tuxtlas, Veracruz, Mexico
Figure 2. Abudances and percentages of Collembola families collected in pitfall traps at Los Tuxtlas Tropical Biology Station, Veracruz, Mexico.
Figures 1-4 in Record of the dung beetle Canthon lucreciae Halffter & Halffter, 2009 (Coleoptera: Scarabaeinae) feeding on a millipede in a tropical rainforest
Figures 1-4. Canthon lucreciae feeding on an individual of the millipede Messicobolus magnificus. 1. Attacking directly on the wound opening. 2. Ventral part of the millipede showing diplosegments with mutilated legs (arrow). 3. Beetles attacking different ventral parts of the millipede. 4. Adult of C. lucreciae, dorsal habitus. / Canthon lucreciae alimentándose de un individuo del milpiés Messicobolus magnificus. 1. Atacando directamente en la abertura de la herida.2. Parte ventral del milpiés mostrando diplosegmentos con patas mutiladas (flecha). 3. Escarabajos atacando diferentes partes ventrales del milpiés. 4. Adulto de C. lucreciae, hábito dorsal.
Fig. 9. A in Diversity and host associations of Myrsidea chewing lice (Phthiraptera: Menoponidae) in the tropical rainforest of Malaysian Borneo
Fig. 9. A species delimitation of Bornean Myrsidea studied inferred from a partial sequence of the mitochondrial COI gene. Validation methods shown on Bayesian tree of hypothetical species. Colors represent unique partitions and correspond to operational taxonomic units (OTU) by various methods of species delimitation: morphological, Automatic Barcode Gap Discovery (ABGD), Generalized Mixed Yule Coalescent analyses (GMYC), and Poisson Tree Process analyses (PTP) respectively.
Fig. 10 in Diversity and host associations of Myrsidea chewing lice (Phthiraptera: Menoponidae) in the tropical rainforest of Malaysian Borneo
Fig. 10. Tanglegram of phylogenies of Bornean avian host (left) and associated Myrsidea lice (right) studied. Colored circles above nodes indicate cospeciation events recovered from Jane (they correspond to 83% of the solutions with p-value of 0.001). Arrow indicates a host switching event recovered by Jane. Red lines indicate significant host-parasite links estimated by the ParaFitLink1 test.
Fig. 7 in Diversity and host associations of Myrsidea chewing lice (Phthiraptera: Menoponidae) in the tropical rainforest of Malaysian Borneo
Fig. 7. Habitus. Myrsidea ramoni sp.n. A-B, holotype female (A), paratype male (B). Myrsidea victoriae sp.n. C-D, holotype female (C), paratype male (D).
Fig. 6 in Diversity and host associations of Myrsidea chewing lice (Phthiraptera: Menoponidae) in the tropical rainforest of Malaysian Borneo
Fig. 6. Habitus. Myrsidea carmenae sp.n. A-B, holotype female (A), paratype male (B). Myrsidea franciscae sp.n. C-D, holotype female (C), paratype male (D).
Fig. 8 in Diversity and host associations of Myrsidea chewing lice (Phthiraptera: Menoponidae) in the tropical rainforest of Malaysian Borneo
Fig. 8. Bayesian phylogeny of Bornean Myrsidea studied based on analysis of the mitochondrial cytochrome c oxidase subunit I (COI) and elongation factor-1α (EF- 1α) genes. Outgroup taxon was removed from this figure for readability. Hosts belong to family Pycnonotydae are indicated on the right.
Fig. 4 in Diversity and host associations of Myrsidea chewing lice (Phthiraptera: Menoponidae) in the tropical rainforest of Malaysian Borneo
Fig. 4. Myrsidea ramoni sp.n. A, dorso-ventral view of female thorax and abdomen; B, head shape; C, dorsal view of male abdomen; D, male metasternal plate and sternites I–II; E, male genital sac sclerite.
Fig. 3 in Diversity and host associations of Myrsidea chewing lice (Phthiraptera: Menoponidae) in the tropical rainforest of Malaysian Borneo
Fig. 3. Myrsidea franciscae sp.n. A, dorso-ventral view of female thorax and abdomen; B, head shape; C, male metasternal plate and sternites I–II; D, male genital sac sclerite.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.