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174 results for “Oil palm”
Data from: Tree performance in a biodiversity enrichment experiment in an oil palm landscape
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Data from: The impacts of oil palm on recent deforestation and biodiversity loss
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Data from: Oil palm plantations fail to support mammal diversity
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Data from: Simplifying understory complexity in oil palm plantations is associated with a reduction in the density of a cleptoparasitic spider, Argyrodes miniaceus (Araneae: Theridiidae), in host (Araneae: Nephilinae) webs
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Data from: Managing Neotropical oil palm expansion to retain phylogenetic diversity
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Dataset from: Termite mounds house a diversity of taxa in oil palm plantations irrespective of understory management
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Complexity within an oil palm monoculture: the effects of habitat variability and rainfall on adult dragonfly (Odonata) communities.
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Global oil palm map
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Data from: Riparian reserves help protect forest bird communities in oil palm dominated landscapes
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Data from: Soil nitrogen-cycling responses to conversion of lowland forests to oil palm and rubber plantations in Sumatra, Indonesia
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Data from: Degradation of root community traits as indicator for transformation of tropical lowland rain forests into oil palm and rubber plantations
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Data from: Reframing the evidence base for policy-relevance to increase impact: a case study on forest fragmentation in the oil palm sector
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Removing understory vegetation in oil palm agroforestry reduces ground-foraging ant abundance but not species richness
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Soil VOC emission rates and associated parameters from forest and oil palm in the SAFE landscape
<p><strong>Description: </strong></p> <p>Monoterpene fluxes measured by the static chamber method including associated environmental parameters</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126"><strong>Characterising soil microbial communities and measuring associated biogeochemical fluxes</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (79))</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3698115">here</a></p> <p><strong>Files: </strong>This consists of 1 file: 4_VOC_jdrewer.xlsx</p> <p><strong>4_VOC_jdrewer.xlsx</strong></p> <p>This file contains dataset metadata and 2 data tables:</p> <ol> <li> <p><strong>data one off field</strong> (described in worksheet Data_one_off)</p> <p>Description: Soil and litter parameters</p> <p>Number of fields: 14</p> <p>Number of data rows: 28</p> <p>Fields:</p> <ul> <li><strong>Location</strong>: Location measurement was taken (Field type: location)</li> <li><strong>site</strong>: Location measurement was taken (Field type: id)</li> <li><strong>chamber_id</strong>: Chamber ID (Field type: id)</li> <li><strong>landuse</strong>: Land use of location (Field type: categorical)</li> <li><strong>pH</strong>: Soil pH (Field type: numeric)</li> <li><strong>bulk_density</strong>: dry weight of soil (Field type: numeric)</li> <li><strong>soil_N%</strong>: Percentage of soil N (Field type: numeric)</li> <li><strong>soil_C%</strong>: Percentage of soil C (Field type: numeric)</li> <li><strong>litter_N%</strong>: Percentage of leaf Nitrogen (Field type: numeric)</li> <li><strong>litter_C%</strong>: Percentage of leaf Carbon (Field type: numeric)</li> <li><strong>C/N_soil</strong>: Ratio of soil Carbon: Nitrogen (Field type: numeric)</li> <li><strong>Latitude</strong>: Latitude of sampling point (Field type: latitude)</li> <li><strong>Longitude</strong>: Longitude of sampling point (Field type: longitude)</li> <li><strong>Elevation</strong>: Elevation of sampling point (Field type: numeric)</li> </ul> </li> <li> <p><strong>data of repeated measures</strong> (described in worksheet Data_repeated_measures)</p> <p>Description: Soil VOCs and associated variables</p> <p>Number of fields: 15</p> <p>Number of data rows: 336</p> <p>Fields:</p> <ul> <li><strong>Location</strong>: Location measurement was taken (Field type: location)</li> <li><strong>site</strong>: Location measurement was taken (Field type: id)</li> <li><strong>chamber_id</strong>: Chamber ID (Field type: id)</li> <li><strong>landuse</strong>: Land use of location (Field type: categorical)</li> <li><strong>date</strong>: Date the measurement was taken (Field type: date)</li> <li><strong>time</strong>: Time the measurement was taken (Field type: time)</li> <li><strong>alpha pinene-C flux</strong>: alpha pinene flux (Field type: numeric)</li> <li><strong>beta pinene-C flux</strong>: beta pinene flux (Field type: numeric)</li> <li><strong>limonene-C flux</strong>: limonene flux (Field type: numeric)</li> <li><strong>3-carene-C flux</strong>: 3-carene flux (Field type: numeric)</li> <li><strong>camphene-C flux</strong>: camphene flux (Field type: numeric)</li> <li><strong>eucalyptol-C flux</strong>: eucalyptol flux (Field type: numeric)</li> <li><strong>air_temp</strong>: Air temperature around the flux chamber (Field type: numeric)</li> <li><strong>soil_temp</strong>: Soil temperature around the flux chamber (Field type: numeric)</li> <li><strong>soil_moisture</strong>: Soil moisture around the flux chamber (Field type: numeric)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2015-01-01 to 2016-12-31</p> <p><strong>Latitudinal extent: </strong>4.5000 to 5.0700</p> <p><strong>Longitudinal extent: </strong>116.7500 to 117.8200</p>
Bat activity in riparian reserves in forest and oil palm plantations
<b>Description: </b><p>Number of bat calls recorded by an Echometer-3 recorder during 10-minute point counts. Counts are classified within 5 acoustic call types, and several Rhinolophoid species where possible.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/12"><b>Investigating the importance of riparian reserves for insectivorous bat species in oil palm and forest estates in Sabah, Malaysia</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>UK Natural Environment Research Council (NERC) (Human Modified Tropical Forests programme & a PhD scholarship jointly funded by University of Kent & NERC & EnvEast DTP scholarship, NE/K016407/1 & NE/L002582/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2(374))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3971012">here</a></p><p><b>Files: </b>This consists of 1 file: SAFE_data_archive_Bat_Riparian_Acoustic_Struebig_Mullin_Yoh_v2_0308202.xlsx</p><p><b>SAFE_data_archive_Bat_Riparian_Acoustic_Struebig_Mullin_Yoh_v2_0308202.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>All data </b> (described in worksheet MasterData)</p><p>Description: All bat passes and their metadata inc frequencies & species identification for a subset of recordings when possible</p><p>Number of fields: 19</p><p>Number of data rows: 5696</p><p>Fields: </p><ul><li><b>Location</b>: where data was collected (Field type: location)</li><li><b>Type</b>: Habitat type (Field type: categorical)</li><li><b>Visit</b>: Visit number (Field type: numeric)</li><li><b>Date</b>: Date surveyed (Field type: date)</li><li><b>Sunset</b>: Sun set time (Field type: time)</li><li><b>Time</b>: Time data collected (Field type: time)</li><li><b>Mins_after_sunset</b>: Minutes after sunset (Field type: numeric)</li><li><b>Call_type</b>: Taxa (Field type: categorical)</li><li><b>Species_ID</b>: Species ID (Field type: taxa)</li><li><b>Buzz</b>: Presence of feeding buzz (Field type: numeric)</li><li><b>Av_High_freq</b>: Bat frequency (Field type: numeric)</li><li><b>Av_Low_freq</b>: Bat frequency (Field type: numeric)</li><li><b>Average_of_CallDuration</b>: Bat frequency (Field type: numeric)</li><li><b>Rip_res_width</b>: Riparian reserve width (Field type: numeric)</li><li><b>Canopy_gap</b>: Canopy gap within 50m radius buffer (Field type: numeric)</li><li><b>Avg_CH_50mR</b>: Average canopy height within 50m radius buffer (Field type: numeric)</li><li><b>Avg_Biomass_50mR</b>: Average biomass within 50m radius buffer (Field type: numeric)</li><li><b>Prop_Fcover_1km</b>: Proportion forest cover within 1km buffer (Field type: numeric)</li><li><b>Avg_Rug_50mR</b>: Average ruggedness within 50m radius buffer (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2014-04-28 to 2018-11-15</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Chiroptera <br> -  -  -  -  -  [BBFM1] <br> -  -  -  -  -  [BBFM2] <br> -  -  -  -  -  [BBFM3] <br> -  -  -  -  -  [BBFM4] <br> -  -  -  -  -  [BBFM5] <br> -  -  -  -  -  [BBFM6] <br> -  -  -  -  -  Hipposideridae <br> -  -  -  -  -  -  <i>Hipposideros</i> <br> -  -  -  -  -  -  -  <i>Hipposideros cervinus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros galeritus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ridleyi</i> <br> -  -  -  -  -  Rhinolophidae <br> -  -  -  -  -  -  <i>Rhinolophus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus acuminatus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus borneensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus sedulus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus trifoliatus</i> <br></div><p></p>
Data from: Predation of potential insect pests in oil palm plantations, rubber tree plantations and fruit orchards
<p class="MsoNoSpacing">In human-modified landscapes, important ecological functions such as predation are negatively affected by anthropogenic activities, including the use of pesticides and habitat degradation. Predation of insect pests is an indicator of healthy ecosystem functioning, which provides important ecosystem services, especially for agricultural systems. In this study, we compare predation attempts from arthropods, mammals, and birds on artificial caterpillars in the understory, between three tropical agricultural land-use types: oil palm plantations, rubber tree plantations, and fruit orchards. We collected a range of local and landscape-scale data including: undergrowth vegetation structure; elevation; proximity to forest; and canopy cover in order to understand how environmental variables can affect predation. In all three land-use types, our results showed that arthropods and mammals were important predators of artificial caterpillars and there was little predation by birds. We did not find any effect of the environmental variables on predation. There was an interactive effect between land-use type and predator type. Predation by mammals was considerably higher in fruit orchards and rubber tree than in oil palm plantations, likely due to their ability to support higher abundances of insectivorous mammals. In order to maintain or enhance natural pest-control in these common tropical agricultural land-use types, management practices that benefit insectivorous animals should be introduced, such as the reduction of pesticides, improvement of understory vegetation and local and landscape heterogeneity.</p>
Data from: Mining for single nucleotide polymorphisms and insertions / deletions in expressed sequence tag libraries of oil palm
The oil palm is a tropical oil bearing tree. Recently EST-derived SNPs and SSRs are a free by-product of the currently expanding EST (Expressed Sequence Tag) data bases. The development of high-throughput methods for the detection of SNPs (Single Nucleotide Polymorphism) and small indels (insertion / deletion) has led to a revolution in their use as molecular markers. Available (5452) Oil palm EST sequences were mined from dbEST of NCBI. CAP3 program was used to assemble EST sequences into contigs. Candidate SNPs and Indel polymorphisms were detected using the perl script auto_snip version 1.0 which has used 576 ESTs for detecting SNPs and Indel sites. We found 1180 SNP sites and 137 indel polymorphisms with frequency 1.36 SNPs / 100 bp. Among the six tissues from which the EST libraries had been generated, mesocarp had high frequency of 2.91 SNPs and indels per 100 bp whereas the zygotic embryos had lowest frequency of 0.15 per 100 bp. We also used the Shannon index to analyze the proportion of ten possible types of SNP/indels. ESTs from tissues of normal apex showed highest values of Shannon index (0.60) whereas abnormal apex had least value (0.02). The present report deals the use of Shannon index for comparing SNP/ indel frequencies mined from ESTlibraries and also confirm that the frequency of SNP occurrence in oil palm to use them as markers for genetic studies.
Data from: Replanting of first-cycle oil palm results in a second wave of biodiversity loss
1. Conversion of forest to oil palm plantations results in a significant loss of biodiversity. Despite this, first-cycle oil palm plantations can sustain relatively high biodiversity compared to other crops. However, the long-term effects of oil palm agriculture on flora and fauna are unknown. Oil palm has a 25-year commercial lifespan before it must be replanted, due to reduced productivity and difficulty of harvesting. Loss of the complex vegetation structure of oil palm plantations during the replanting process will likely have impacts on the ecosystem at a local and landscape scale. However, the effect of replanting on biodiversity is poorly understood. 2 Here, we investigate the effects of replanting oil palm on soil macrofauna communities. We assessed ordinal richness, abundance and community composition of soil macrofauna in first (25-27-years-old) and second-cycle oil palm (freshly cleared, 1-year-old, 3-year-old and 7-year-old mature). 3. Macrofauna abundance and richness drastically declined immediately after replanting. Macrofauna richness showed some recovery 7-years after replanting, but was still 19% lower than first-cycle oil palm. Macrofauna abundance recovered to similar levels to that of first-cycle oil palm plantations, one-year after replanting. This was mainly due to high ant abundance, possibly due to the increased understory vegetation as herbicides are not used at this age. However, there were subsequent declines in macrofauna abundance 3 and 7-years after replanting, resulting in a 59% drop in macrofauna abundance compared to first-cycle levels. Furthermore, soil macrofauna community composition in all ages of second-cycle oil palm was different to first-cycle plantations, with decomposers suffering particular declines. 4. After considerable biodiversity loss due to forest conversion for oil palm; belowground invertebrate communities suffer a second wave of biodiversity loss due to replanting. This is likely to have serious implications for soil invertebrate diversity and agricultural sustainability in oil palm landscapes, due to the vital ecosystem functions that soil macrofauna provide.
Data from: The impact of tropical forest logging and oil palm agriculture on the soil microbiome
Selective logging and forest conversion to oil palm agriculture are rapidly altering tropical forests. However, functional responses of the soil microbiome to these land-use changes are poorly understood. Using 16S rRNA gene and shotgun metagenomic sequencing, we compared composition and functional attributes of soil biota between unlogged, once-logged and twice-logged rainforest, and areas converted to oil palm plantations in Sabah, Borneo. Although there was no significant effect of logging history, we found a significant difference between the taxonomic and functional composition of both primary and logged forests and oil palm. Oil palm had greater abundances of genes associated with DNA, RNA, protein metabolism and other core metabolic functions, but conversely, lower abundance of genes associated with secondary metabolism and cell–cell interactions, indicating less importance of antagonism or mutualism in the more oligotrophic oil palm environment. Overall, these results show a striking difference in taxonomic composition and functional gene diversity of soil microorganisms between oil palm and forest, but no significant difference between primary forest and forest areas with differing logging history. This reinforces the view that logged forest retains most features and functions of the original soil community. However, networks based on strong correlations between taxonomy and functions showed that network complexity is unexpectedly increased due to both logging and oil palm agriculture, which suggests a pervasive effect of both land-use changes on the interaction of soil microbes.
Figure 1 from: Pradana AT, Ritthidej GC, Limprasutr V, Wongtayan A, Lipipun V, Iksen (2024) Antihypertensive activity of spray-dried nanoemulsion containing Asiatic acid-Palm oil in high salt diet-fed rats. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e115091
Figure 1 Antihypertensive activity study in rats: Study timeline (A), Oral administration (B), Non-invasive blood pressure instrument (C), Animal handling on indirect blood pressure test (D).
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OpenNeuro
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