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opencc-by-4.0Oct 2023View details →

Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India

TITLETree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India  DESCRIPTIONThis 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).PublicationsSpecific portions of the dataset have been used in the following publications:<ul><li>Mudappa, D. 2001. <a href="https://hdl.handle.net/10603/101890">Ecology of the brown palm civet Paradoxurus jerdoni 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>. Biodiversity and Conservation 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>. Journal of Tropical Ecology 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. BioRxiv 2022.10.22.513365. https://doi.org/10.1101/2022.10.22.513365  </li></ul>A related dataset is the following: 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. Dryad Dataset. https://doi.org/10.5061/dryad.4mw6m907q  Curation and correctionsData 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.AcknowledgementsWe 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. CONTACTS  CONTACT #1 1. Name: T. R. Shankar Raman 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: trsr@ncf-india.org 5. ORCID: https://orcid.org/0000-0002-1347-3953CONTACT #2 1. Name: Divya Mudappa 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India 3. Work Phone: +91 821 2515601 4. Email address: divya@ncf-india.org 5. ORCID: https://orcid.org/0000-0001-9708-4826CONTACT #3 1. Name: Hari Sridhar 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 3. Work Phone: +91 821 2515601 4. Email address: harisridhar1982@gmail.com 5. ORCID: https://orcid.org/0000-0003-3286-0120CONTACT #4 1. Name:  Akshay Surendra 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 3. Work Phone: +91 821 2515601 4. Email address: akshaysurendra1@gmail.com 5. ORCID: https://orcid.org/0000-0003-2719-7432   GEOGRAPHIC COVERAGE1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India2. 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) TEMPORAL COVERAGE1. Begins: 2000-01-01 (Year, Month, Day) 2. Ends: 2019-12-31 (Year, Month, Day) METHODSMethods involved are described in the publications listed above. The vegetation sampling methods are briefly described below.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).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. FILES INCLUDED 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.)  01) sites.csv -- Details of study sites verbatimLocality: Name of locality as originally used Fragment: Name of rainforest fragment or coffee plantation decimalLongitude: Longitude in decimal degrees North (WGS 84 datum) decimalLatitude: Latitude in decimal degrees East (WGS 84 datum) habitat: Habitat type as mature tropical rainforest, tropical rainforest fragment, or coffee plantation Description: Description of the place  02) allpcqdata.csv -- Tree data from point-centred quarter (PCQ) surveys Year: Year of survey for  bird and vegetation study verbatimLocality: Name of locality as originally used Fragment: Name of rainforest fragment or coffee plantation Point_name: Name ID of point-centred quarter (PCQ) point as used within a survey year pointID: Unique ID of point-centred quarter (PCQ) point including year of survey Tree_no: Tree number ID given to the four trees in each PCQ plot (T1 to T4) verbatimIdentification: Scientific name of tree species as originally written or identified scientificName: Scientific name as currently identified under updated taxonomy nativeAlien: Category indicating whether species is native or alien to the region/country kingdom: Taxonomic Kingdom phylum: Taxonomic Phylum Distance_eff: Distance in metres from centre of PCQ plot to centre of tree trunk 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 locationRemarks: Code for site name as originally used SpCode: Species code as originally used during data entry TreeHeight: Tree height in metres (only  available in 2019 survey) identificationRemarks: Notes related to identification if available occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and note on one possibly errorneous girth  03) pcqlocations.csv -- Locations of sample PCQ points pointID: Unique ID of point-centred quarter (PCQ) point including year of survey note: Site name code decimalLatitude: Latitude in decimal degrees East (WGS 84 datum) decimalLongitude: Longitude in decimal degrees North (WGS 84 datum) coordinateUncertaintyInMeters: Approximate uncertainty of the location in metres  04) allhabitat.csv -- Data on habitat structure variables Year: Year of survey for  bird and vegetation study verbatimLocality: Name of locality as originally used Fragment: Name of rainforest fragment or coffee plantation Point: ID of replicate survey point within the Fragment 0-1m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 0-1 m above ground 1-2m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 1-2 m above ground 2-4m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 2-4 m above ground 4-8m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 4-8 m above ground 8-16m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 8-16 m above ground 16-24m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 16-24 m above ground 24-32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 24-32 m above ground over32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band greater than 32 m above ground VertStrata: Number of vertical strata with foliage (sum of preceding 8 columns) CanopyHeight: Canopy height in metres CanopyOpenness: Canopy openness in percentage as measured using a spherical densiometer CanopyCover: Canopy cover (closure) in percentage as measured using a spherical densiometer 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 UC: Canopy overlap rank as above, for understorey vegetation only MC: Canopy overlap rank as above, for the midstorey only CC: Canopy overlap rank as above, for the upper canopy only Altitude: Altitude above sea leavel in metres, measued from hand-held altimeter or GPS device RfShrub: Number of shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point Coffee: Number of coffee bushes (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point Maesopsis: Number of alien Maesopsis eminii stems (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point Strobilanthes: Number of Strobilanthes shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point TotalShrub: Total number of shrubs within 2 m radius of point Liana: Presence (1) or absence (0) of woody lianas within 5 m radius of point Cane: Presence (1) or absence (0) of cane (Calamus sp.) within 2 m radius of point Lantana: Presence (1) or absence (0) of Lantana camara shrubs within 2 m radius of point Bamboo: Presence (1) or absence (0) of bamboo culms within 2 m radius of point LeafLitter: Depth of leaf litter in cm (to 0.5 cm accuracy) measured using a calibrated wooden probe CutTrees: Number of cut trees within 5 m radius of point  05) gbifnames.csv -- Results of GBIF name matching tool sno: Serial number verbatimScientificName: Scientific name of tree species as originally written or identified scientificName: Scientific name after matching with Global Biodiversity Information Facility (GBIF) database to lowest taxonomic level sciNameWithAuthor: Scientific name with author as provided by GBIF name matching tool key: GBIF key as provided by GBIF name matching tool matchType: Type of match as provided by GBIF name matching tool confidence: Confidence as provided by GBIF name matching tool status: Status as accepted name or synonym as provided by GBIF name matching tool rank: Taxonomic rank as provided by GBIF name matching tool kingdom: Kingdom as provided by GBIF name matching tool phylum: Phylum as provided by GBIF name matching tool class: Class as provided by GBIF name matching tool order: Order as provided by GBIF name matching tool family: Family as provided by GBIF name matching tool genus: Genus as provided by GBIF name matching tool species: Species as provided by GBIF name matching tool canonicalName: Canonical name as provided by GBIF name matching tool authorship: Author of name as provided by GBIF name matching tool  06) plots2000.csv -- Data from 5 m radius circular plots in select sites verbatimLocality: Name of locality as originally used Fragment: Name of rainforest fragment or coffee plantation PlotID: ID of 5 m radius plot Treeno: Serial number of tree in the plot verbatimIdentification: Scientific name of tree species as originally written or identified scientificName: Scientific name as currently identified under updated taxonomy 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 nativeAlien: Category indicating whether species is native or alien to the region/country kingdom: Kingdom as provided by GBIF name matching tool phylum: Phylum as provided by GBIF name matching tool occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and identification 07) anampcqs4gbif.rmd -- Text file with code in the R statistical and programming language 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 Changes in Version 2In sites.csv, changed habitat from "Rainforest" to "Tropical rainforest fragment" for PuthuthottamAdded the anampcqs4gbif.rmd file with R code

opencc-by-4.0Nov 2024View details →

Spent coffee grounds as biofiller

This data set corresponds to the analyses carried out in the following article: Lage‐Rivera, S., Ares‐Pernas, A., Dopico‐García, M. S., Covas, J., & Abad, M. J. (2024).  Comparing lignin and spent coffee grounds as bio‐fillers in PLA 3D‐printable filaments. Polymer Composites. https://doi.org/10.1002/pc.28782

opencc-by-4.0Jul 2022View details →

A chromosome-level genome resource for studying virulence mechanisms and evolution of the coffee rust pathogen Hemileia vastatrix

Recurrent epidemics of coffee leaf rust, caused by the fungal pathogen Hemileia vastatrix, have constrained the sustainable production of Arabica coffee for over 150 years. The ability of H. vastatrix to overcome resistance in coffee cultivars and evolve new races is inexplicable for a pathogen that supposedly only utilizes clonal reproduction. Understanding the evolutionary complexity between H. vastatrix and its only known host, including determining how the pathogen evolves virulence so rapidly is crucial for disease management. Achieving such goals relies on the availability of a comprehensive and high-quality genome reference assembly. To date, two reference genomes have been assembled and published for H. vastatrix that, while useful, remain fragmented and do not represent chromosomal scaffolds. Here, we present a complete scaffolded pseudochromosome-level genome resource for H. vastatrix strain 178a (Hv178a). Our initial assembly revealed an unusually high degree of gene duplication (over 50% BUSCO basidiomycota_odb10 genes). Upon inspection, this was predominantly due to a single scaffold that itself showed 91.9% BUSCO Completeness. Taxonomic analysis of predicted BUSCO genes placed this scaffold in Exobasidiomycetes and suggests it is a distinct genome, which we have named Hv178a associated fungal genome (Hv178a AFG). The high depth of coverage and close association with Hv178a raises the prospect of symbiosis, although we cannot completely rule out contamination at this time. The main Ca. 546 Mbp Hv178a genome was primarily (97.7%) localised to 11 pseudochromosomes (51.5 Mb N50), building the foundation for future advanced studies of genome structure and organization. Citation: https://doi.org/10.1101/2022.07.29.502101

opencc-by-4.0Feb 2018View details →

21 coffee makers energy consumption dataset

This dataset represents the use of 21 coffee machines over time, with each line representing an energy consumption event of a specific machine.

opencc-by-3.0Jul 2019View details →

Research data for: Preventing the coffee-ring effect and aggregate sedimentation by in situ gelation of monodisperse materials

Raw data for the publication: Preventing the coffee-ring effect and aggregate sedimentation by in situ gelation of monodisperse materials

opencc-by-4.0Sep 2024View details →

Data for Publication - Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo

Data used for the publication: "Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo" - Ieben Broeckhoven, Jonas Depecker, Trésor Kasereka Muliwambene, Olivier Honnay, Roel Merckx and Bruno Verbist

opencc-by-4.0Jan 2021View details →

Coffee Consumption per Capita and Covid-19 Mortality Rate

There is a correlation between average of "Coffee Consumption per capita" and average of "Covid-19 Mortality Rate" for countries with high coffee consumption per capita (the countries that has more than 5.4 kg per capita per year consumption). The Details of computations and data are provided in an attached supplementary file (Excel File Format). Data gathered on 10 Aug 2021

opencc-by-4.0Aug 2023View details →

Qualitative dataset based on ancestral knowledge about coffee crops

The qualitative dataset is about coffee pests based on the ancestral knowledge of coffee farmers in the Department of Cauca, Colombia. The dataset has been obtained from a survey applied to coffee growers with 432 records and 41 variables collected weekly from September 2020 to August 2021. The qualitative dataset includes climatic conditions, productive activities, external conditions, and coffee bio-aggressors. This dataset allows researchers to find patterns for coffee crop protection by means of ancestral knowledge not detected by real-time agricultural sensors. As far as we are concerned, there are no datasets like the one presented in this paper with similar characteristics of qualitative value that express the empirical knowledge of coffee farmers used to detect triggers of causal behaviors of pests and diseases in coffee crops.

opencc-by-4.0Apr 2018View details →

Figure 9 in A new Colombian pest species of the genus Poecilocloeus Bruner (Orthoptera: Acrididae: Proctolabinae) on coffee, with a key to the Neotropical species

Figure 9. Accumulated mortality of Poecilocloeus coffeaphilus nymphs with a strain of Metarhizium acridum by the immersion method (T1) and spraying (T2), spraying with a commercial formulation of Metarhizium anisopliae (T3) and control treatment with water (T4).

opencc-by-4.0Apr 2018View details →

Figure 7 in A new Colombian pest species of the genus Poecilocloeus Bruner (Orthoptera: Acrididae: Proctolabinae) on coffee, with a key to the Neotropical species

Figure 7. Damage caused by adults of Poecilocloeus coffeaphilus n. sp. on coffee plants. A) Close-up of leaf damage. B) Scrapings on the bark of stems and branches. C) Damage of ripe fruit. D-E) Damage of unripe and near ripe fruits. F) Fruits with the pulp consumed. G) Coffee fruits with the exocarp and pulp completely eaten off and the grains exposed.

opencc-by-4.0Apr 2018View details →

Figure 5 in A new Colombian pest species of the genus Poecilocloeus Bruner (Orthoptera: Acrididae: Proctolabinae) on coffee, with a key to the Neotropical species

Figure 5. Different stages of development of Poecilocloeus coffeaphilus n. sp. A) First instar. B) Second instar. C) Third instar. D) Fourth instar. E) Fifth instar. F) Sixth instar. G) Adult male. H) Adult female.

dryad40/100

Data from: Habitat selection in transformed landscapes and the role of forest remnants and shade coffee in the conservation of resident birds

1. Biodiversity conservation in transformed landscapes is becoming increasingly important. However, most assessments of the value of modified habitats rely heavily on species presence and/or abundance, masking ecological processes such as habitat selection and phenomena like ecological traps, which may render species persistence uncertain. High species richness has been documented in tropical agroforestry systems but comparisons with native habitat remnants generally lack detailed information on species demography and habitat use. 2. We generated a multi-species, multi-measure framework to evaluate the role of habitat selection in the adaptation of species to transformed landscapes, and demonstrate that its use could affect how we value the contribution different land uses make to biodiversity conservation. 3. We analyzed seven years of capture-mark-recapture and observation data for twelve species of resident birds present in native forest remnants and shade coffee plantations in a mega-diverse region. We assessed whether species behaved adaptively by evaluating the correlation between measures of habitat preference (occurrence, abundance, fidelity, inter-seasonal variance and age) and performance (body condition, muscle, primary molt, breeding and juveniles) in forest and coffee, and generated hypotheses about their role in species persistence. 4. We documented adaptive habitat selection for seven species, non-ideal selection for four, and maladaptive selection for one. While many species showed equal-preference and/or equal performance in many traits, in general we found more evidence for birds preferring and/or performing better in forest than coffee, although relationships between our indicators and population adaptation need to be studied further before our proposed framework can be applied to more species and landscapes. 5. While shade coffee can act as a biodiversity-friendly matrix providing complementary or supplementary habitat to a wide range of resident bird species, protecting remnants of native vegetation is still of paramount importance for biodiversity conservation in agricultural landscapes. 28-Aug-2019

opencc-zeroDec 2019View details →

opencc-by-4.0Sep 2023View details →

Yield increases mediated by pollination and carbon payments can offset restoration costs in coffee landscapes

This dataset and code can be used to reproduce results from the article with the same title The scripts to generate the farm-level scenarios and the regional-level scenarios are as following: <ul> <li>farm-level = optimization_scenarios.R</li> <li>regional-level = optimization_scenarios_rl.R</li> </ul> The properties boundaries and APP limits for the municipalities considered in this study can be obtained from the adress: <ul> <li><a href="https://www.car.gov.br/publico/imoveis/index">https://www.car.gov.br/publico/imoveis/index</a>.</li> </ul> The list of municipalities is located at the file "list_mun.csv" within the tables folder. The raster with land-use and land-cover classes is classified as follows: <ul> <li>class 0: all other uses;</li> <li>class 1: natural vegetation;</li> <li>class 2: coffee;</li> </ul> The estimated yield per property is at the yield_estimation.csv file. Each property (n=507) is identified with an unique ID. The dataframe contains: <ul> <li>the predicted yield in coffee bags/ha (predictec_coffee_bags);</li> <li>yield in USD/ha (yield_USD_future); the difference in yield bewteen the future scenarios and the baseline scenario (diff_USD);</li> <li>the net present value of the difference between the future scenarios and the baseline (npv_yield_USD_future)</li> </ul> The script "merging_scen_land_use.R" was used to combine the restoration scenarios generated by the optimization model with the baseline land-use and land-cover;

Figure 6 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 6. Moth (A); coffee leaf damage (B and C); caterpillar at the beginning of pupal stage (D); and the characteristic X-shaped cocoon of coffee tree miner (E).

Figure 1 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 1. Rosette with coffee berry borer attack symptom (A); and detail of the pest hole in the crown region of the fruit (B).

Figure 9 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 9. Orthezia colony (Praelongorthezia praelonga) on coffee leaves (A); leaf covered with dark- colored fungus, commonly referred to as sooty mold (B); and coffee plant with high defoliation caused by the pest (C).

Figure 7 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 7. Flower bud (A), branches (B) and rosette (C) of the coffee tree infested with citrus mealybug.

Figure 3 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 3. Prorops nasuta - Uganda wasp (A); C. stephanoderis - Ivory Coast Wasp (B); and C. hyalinipennis (C).

Figure 12 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 12. Young form and pupa of the honeydew moth caterpillar and damages the coffee tree rosette.