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27 results for “oil palm plantation”
Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2018
<p>This package supplements the following paper submitted to ESSD: <strong>Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016</strong>.<br> This dataset contains the updated version (v4) of the annual oil palm plantation maps for Malaysia and Indonesia from 2001 to 2018 at 100 resolution. </p>
Figure 2 in Comparison of dung beetle communities (Coleoptera: Scarabaeidae: Scarabaeinae) in oil palm plantations and native forest in the eastern Amazon, Brazil
Figure 2 Extrapolation and rarefaction of species richness in forest and oil palm plantation dung beetle communities. Shaded area represents 95% confidence limits. This figure is in color in the electronic version.
Figure 1 in Comparison of dung beetle communities (Coleoptera: Scarabaeidae: Scarabaeinae) in oil palm plantations and native forest in the eastern Amazon, Brazil
Figure 1 Location of the study area in the Brazilian Amazon, in the state of Pará. The right map represents the study area and the spatial distribution of 10 transects (red lines) in forest and oil palm habitats. Green and orange areas indicate primary forest and oil palm plantations, respectively (modified from Mendes-Oliveira et al., 2017). This figure is in color in the electronic version.
Fig. 1 in Native enemies of Strategus aloeus (Coleoptera: Scarabaeidae) in oil palm plantations in Colombia
Fig. 1. Natural enemies of Strategus aloeus. Metarhizium anisopliae infecting S. aloeus in: A. larvae, B. pupae, and C. adult of Phileurus didymus, D. searching for prey and E. preying on 3rd stage larvae of S. aloeus.
Fig. 1 in Giant rhinoceros beetle Golofa claviger (Linnaeus) (Coleoptera: Melolonthidae: Dynastini) is damaging North Brazilian oil palm plantations
Fig. 1. Golofa claviger on oil palm. A – specimens collected in the infested area; B – dead males of G. claviger sheltered on the rachis or stem angles of the young palm; C – adult male lying on a palm leaflet; D – ripped young frond; E and F – wedge-shaped cuts on young, not yet unfurled frond; G – ripped unfurled frond.
Data from: Shifting agriculture supports more tropical forest birds than oil palm or teak plantations in Mizoram, northeast India
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[VERSION 2] Data set and analytic codes supporting "How do management decisions impact butterfly assemblages in smallholding oil palm plantations in Peninsular Malaysia?"
<p>This is <strong>VERSION 2</strong> of data set and analytic codes (with a meta data [see the meta data from VERSION 1]) supporting "How do management decisions impact butterfly assemblages in smallholding oil palm plantations in Peninsular Malaysia?". We investigated the impacts of replanting and alternative replanting decisions (replanting with monoculture versus polyculture oil palm plantations) on within-plantation environmental conditions and butterfly assemblages (diversity, density, and composition). We also assessed the effects of habitat structure and complexity within plantations on butterfly assemblages. Apart from "BantingButterflies_ButterflyData", other data are the same as in VERSION 1.</p><p><strong>## List of changes:</strong></p><p># 1. <i>Tirumala septentrionis </i>was not included in the analyses because it should have been <i>Ideopsis vulgaris</i> (had been corrected),</p><p># 2. PC5 and PC6 (from PCA) were considered as predictors for the GLMs,</p><p># 3. The Mantel test was added.</p><p><strong>## Other notes:</strong></p><p># 1. Older version of ggiNEXT could work with facet.var = "site", now it needs to be "Assemblage"</p><p># 2. Older version of ggiNEXT could work with facet.var = "order", now it needs to be "Order.q"</p><p># 3. "set.seed(42)" function was used before running "iNEXT", ANOSIM, and the Mantel test to get reproducible outputs (exactly the same outputs every time each function is run).</p><p><strong>Funding and research permission:</strong> Jardine Foundation, the Cambridge Trust, and Tim Whitmore Fund provided funding for MFH, the Biotechnology and Biological Sciences Research Council (BBSRC) funded JS (USN: 304338625), and BBSRC (BB/T012366/1) provided funding for the establishment of the plots and surveys of environmental parameters. Research permission was provided by the Economic Planning Unit (EPU) of Malaysia's Prime Minister's Department for MFH (Ref: EPU 40/200/19/3727) and JS (Ref: MEA 40/200/19/3705).</p>
Output data for: Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia
<p>This repository contains the output data associated with the publication "Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia". It contains the FLAM modeled burned area and the GLOBIOM output, as well as the a downscaling grid.</p> <p>Descriptions of the results can be found in the publication (DOI will follow).</p>
Above-ground carbon density derived from LiDAR data over oil palm plantations in Malaysian Borneo, 2014
<b>Description: </b><p>The work was carried out in the oil palm plantations within the Stability of Altered Forest Ecosystem (SAFE) Project, located within lowland dipterocarp forest regions of East Sabah in Malaysian Borneo. Airborne LiDAR data were acquired on 5 November 2014 using a Leica LiDAR50-II flown at 1850 m altitude on a Dornier 228-201 travelling at 135 knots. The LiDAR sensor emitted pulses at 83.1 Hz with a field of view of 12.0°, and a footprint of about 40 cm diameter. The average pulse density was 7.3/m2. The Leica LiDAR50-II sensor records full waveform LiDAR, but for the purposes of this study the data were discretised, with up to four returns recorded per pulse. The LiDAR data were pre-processed by NERC's Data Analysis Node and delivered in standard LAS format. All further processing was undertaken using LAStools (Rapidlasso GmbH, LAStools). Points were classified as ground and non-ground, and a digital elevation model (DEM) was fitted to the ground returns, producing a raster of 1 m resolution. The DEM elevations were subtracted from elevations of all non-ground returns to produce a normalised point cloud, and a canopy height model (CHM) was constructed from this on a 0.5 m raster by averaging the first returns. Finally, holes in the raster were filled by averaging neighbouring cells. </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/25"><b>Influences of disturbance and environmental variation on biomass change in Malaysian Borneo</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant, JKM/MBS.1000-2/2 JLD.3 (128))</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 Centre (Research licence JKM/MBS.1000-2/2 JLD.3 (128))</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=3247699">here</a></p><p><b>Files: </b>This consists of 1 file: LiDAR_Aboveground_Carbon.xlsx</p><p><b>LiDAR_Aboveground_Carbon.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>LiDAR aboveground carbon in Oil palm plantations</b> (described in worksheet LiDAR_ Aboveground_Carbon)</p><p>Description: The output of a LiDAR50-II sensor records full waveform LiDAR, but for the purposes of this study the data were discretised, with up to four returns recorded per pulse. The LiDAR data was pre-processed by NERC's Data Analysis Node and delivered in standard LAS format. All further processing was undertaken using LAStools (Rapidlasso GmbH, LAStools). Points were classified as ground and non-ground, and a digital elevation model (DEM) was fitted to the ground returns, producing a raster of 1 m resolution. The DEM elevations were subtracted from elevations of all non-ground returns to produce a normalised point cloud, and a canopy height model (CHM) was constructed from this on a 0.5 m raster by averaging the first returns. Finally, holes in the raster were filled by averaging neighbouring cells. </p><p>Number of fields: 35</p><p>Number of data rows: 27</p><p>Fields: </p><ul><li><b>Year</b>: Year the oil palm trees were planted (Field type: Numeric)</li><li><b>Plot</b>: Plot number based on the SAFE project framework. Each plot is 25 metres x 25 metres size or 0.0625 hectares (Field type: Location)</li><li><b>meanH</b>: Average tree height per plot (Field type: Numeric)</li><li><b>TreeN_plot</b>: Number of trees per plot (Field type: Numeric)</li><li><b>TreeN_ha</b>: Number of trees per hectare obtained by upscaling the number of trees within each 25m x 25m (0.0625 ha) plot to 1 ha (Field type: Numeric)</li><li><b>ACD_plot</b>: Sum of the aboveground carbon density per plot (Field type: Numeric)</li><li><b>ACD_ha</b>: Sum of the aboveground carbon density per hectare obtained by upscaling the number of aboveground carbon density within each 25m x 25m (0.0625 ha) plot to 1 ha (Field type: Numeric)</li><li><b>CC1</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 1 metre height (Field type: Numeric)</li><li><b>CC2</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 2 metres height (Field type: Numeric)</li><li><b>CC3</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 3 metres height (Field type: Numeric)</li><li><b>CC4</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 4 metres height (Field type: Numeric)</li><li><b>CC5</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 5 metres height (Field type: Numeric)</li><li><b>CC6</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 6 metres height (Field type: Numeric)</li><li><b>CC7</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 7 metres height (Field type: Numeric)</li><li><b>CC8</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 8 metres height (Field type: Numeric)</li><li><b>CC9</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 9 metres height (Field type: Numeric)</li><li><b>CC10</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 10 metres height (Field type: Numeric)</li><li><b>CC11</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 11 metres height (Field type: Numeric)</li><li><b>CC12</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 12 metres height (Field type: Numeric)</li><li><b>CC13</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 13 metres height (Field type: Numeric)</li><li><b>CC14</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 14 metres height (Field type: Numeric)</li><li><b>CC15</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 15 metres height (Field type: Numeric)</li><li><b>CC16</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 16 metres height (Field type: Numeric)</li><li><b>CC17</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 17 metres height (Field type: Numeric)</li><li><b>CC18</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 18 metres height (Field type: Numeric)</li><li><b>CC19</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 19 metres height (Field type: Numeric)</li><li><b>CC20</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 20 metres height (Field type: Numeric)</li><li><b>CC21</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 21 metres height (Field type: Numeric)</li><li><b>CC22</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 22 metres height (Field type: Numeric)</li><li><b>CC23</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 23 metres height (Field type: Numeric)</li><li><b>TCH</b>: Top of canopy height: mean height of Canopy Height Model (CHM) pixels per hectare. (Field type: Numeric)</li><li><b>TreeN_itc</b>: Number of segmented trees per hectare obtained by using the itcSegment function implemented in R (Field type: Numeric)</li><li><b>meanH_itc</b>: Average tree height per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li><li><b>meanHc_itc</b>: Corrected average tree height per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li><li><b>ACDc_itc</b>: Sum of the aboveground carbon density per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2014-11-05 to 2014-11-05</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Limited evidence of biodiversity spillover from forest fragments into oil palm plantations in the Amazon
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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
Expansion of oil palm agriculture is currently one of the main drivers of habitat modification in Southeast Asia. Habitat modification can have significant effects on biodiversity, ecosystem function, and interactions between species by altering species abundances or the available resources in an ecosystem. Increasing complexity within modified habitats has the potential to maintain biodiversity and preserve species interactions. We investigated trophic interactions between Argyrodes miniaceus, a cleptoparasitic spider, and its Nephila spp. spider hosts in mature oil palm plantations in Sumatra, Indonesia. A. miniaceus co-occupy the webs of Nephila spp. females and survive by stealing prey items caught in the web. We examined the effects of experimentally manipulated understory vegetation complexity on the density and abundance of A. miniaceus in Nephila spp. webs. Experimental understory treatments included enhanced complexity, standard complexity, and reduced complexity understory vegetation, which had been established as part of the ongoing Biodiversity and Ecosystem Function in Tropical Agriculture (BEFTA) Project. A. miniaceus density ranged from 14.4 to 31.4 spiders per square meter of web, with significantly lower densities found in reduced vegetation complexity treatments compared with both enhanced and standard treatment plots. A. miniaceus abundance per plot was also significantly lower in reduced complexity than in standard and enhanced complexity plots. Synthesis and applications: Maintenance of understory vegetation complexity contributes to the preservation of spider host–cleptoparasite relationships in oil palm plantations. Understory structural complexity in these simplified agroecosystems therefore helps to support abundant spider populations, a functionally important taxon in agricultural landscapes. In addition, management for more structurally complex agricultural habitats can support more complex trophic interactions in tropical agroecosystems.
Industrial and Smallholder Oil Palm Plantation Expansion in Indonesia from 2001 to 2019
<p>The dataset contains wall-to-wall maps of the annual expansion of industrial and smallholder oil palm plantations from 2001 to 2019 generated from interpretation of annual LANDSAT composites, SPOT-6 and UAV imagery.</p> <p>We define an oil palm plantation as an area of land planted with oil palm trees (Elaeis guineensis Jacq.). </p> <p>Industrial plantations are intensively managed large-scale, typically covering several thousand hectares of land, plantations owned by companies. They exhibit distinctive linear boundaries while harvesting trails are laid out in grids on level land or follow contours on hilly terrain. Smallholder plantations are typically smaller — < 25 hectares according to government definition — although wealthy individuals sometimes own several hundred hectares — and their spatial patterns are less consistent. Smallholder landscapes sometimes form a mixed mosaic with one or more other crops and types of landcover , or a large homogeneous landscape , or resemble industrial plantings though generally smaller and with less consistent structure.</p> <p>The datasets are in shapefile format with the following columns:</p> <p><strong>Year:</strong> The year an area was converted to oil palm. This is the year an area of either Forest or Non-Forest transitioned to either industrial or smallholder oil palm.</p> <p><strong>Class:</strong> The type of plantation that exists as of year 2019. Class has category: ‘IOPP’ for Industrial Oil Palm Plantation or 'Smallholder'.</p> <p><strong>Gridcode:</strong> A code for the year the annual <em>Tree Loss</em> dataset (version 1.7) - developed at University of Maryland (Hansen et al. 2013) - recorded a loss, and whether this is loss of trees in the forest or loss of non-forested trees. This column can be ignored.</p> <ul> <li><strong>1xx :</strong> Year forest loss detected, where xx represents the last 2 digits of the year (from 2001 to 2019) when the loss happened. For example, 105 indicates forest loss in 2005.</li> <li><strong>300 :</strong> Non-forest since 2000.</li> <li><strong>3xx :</strong> Year tree (non-forest) loss detected, where xx represents the last 2 digits of the year (from 2001 to 2019) when the loss happened. For example, 305 indicates tree loss in 2005 in non-forest areas with trees.</li> </ul> <p><strong>F2000 to F2019:</strong> The annual land cover types (Forest or Non forest) that existed before oil palm plantations replaced them.</p> <p><strong>TDelay :</strong> The time delay (in number of years) beween the year an area lost its forest cover and the year it was developed as a plantation. If Tdelay = 0, the area was developed as a plantation in the same calendar year that it lost forest cover, or the area was already not forest in 2000.</p> <p><strong>CompDriven</strong> : The year an area was developed as a plantation and lost its forest cover. We reasoned that industrial plantations developed in the same year as forest clearance are likely to be responsible for that clearance, hence the term Company-driven deforestation. If CompDriven = 0 the area lost its forest cover several years (at one year) before it was developed as a plantation.</p> <p> </p> <p><strong>Region:</strong> The name of the region considered (Sumatra, Kalimantan, Java, Sulawesi, Maluku and Papua)</p> <p><strong>AreaHA:</strong> The area in hectares</p>
Data from: Soil nitrogen-cycling responses to conversion of lowland forests to oil palm and rubber plantations in Sumatra, Indonesia
Rapid deforestation in Sumatra, Indonesia is presently occurring due to the expansion of palm oil and rubber production, fueled by an increasing global demand. Our study aimed to assess changes in soil-N cycling rates with conversion of forest to oil palm (Elaeis guineensis) and rubber (Hevea brasiliensis) plantations. In Jambi Province, Sumatra, Indonesia, we selected two soil landscapes – loam and clay Acrisol soils – each with four land-use types: lowland forest and forest with regenerating rubber (hereafter, "jungle rubber") as reference land uses, and rubber and oil palm as converted land uses. Gross soil-N cycling rates were measured using the 15N pool dilution technique with in-situ incubation of soil cores. In the loam Acrisol soil, where fertility was low, microbial biomass, gross N mineralization and NH4+ immobilization were also low and no significant changes were detected with land-use conversion. The clay Acrisol soil which had higher initial fertility based on the reference land uses (i.e. higher pH, organic C, total N, effective cation exchange capacity (ECEC) and base saturation) (P≤0.05–0.09) had larger microbial biomass and NH4+ transformation rates (P≤0.05) compared to the loam Acrisol soil. Conversion of forest and jungle rubber to rubber and oil palm in the clay Acrisol soil decreased soil fertility which, in turn, reduced microbial biomass and consequently decreased NH4+ transformation rates (P≤0.05–0.09). This was further attested by the correlation of gross N mineralization and microbial biomass N with ECEC, organic C, total N (R=0.51–0. 76; P≤0.05) and C:N ratio (R=-0.71 – -0.75, P≤0.05). Our findings suggest that the larger the initial soil fertility and N availability, the larger the reductions upon land-use conversion. Because soil N availability was dependent on microbial biomass, management practices in converted oil palm and rubber plantations should focus on enriching microbial biomass.
Dataset from: Termite mounds house a diversity of taxa in oil palm plantations irrespective of understory management
<p>We investigated the effects of oil palm understory vegetation management on termite mound activity and non-termite inhabitants. We found a diversity of taxa, most of which were unaffected by understory management. Mound volume and termite activity had taxa-specific effects on abundance. Preserving mounds in oil palm plantations will benefit biodiversity.</p>
Estimation of worker population size-density by nest counting in the Asian weaver ant, Oecophylla smaragdina (Hymenoptera: Formicidae) and it's dynamic in oil palm plantations industry
<p>Supplementary material-information supporting the article of research related to the Asian weaver ant population size-density in the oil palm plantations. The findings suggested an abundant numerical amount of individual workers per colony. The weaver ant were self-sustainable surviving during long years i.e. more than 20 years. This is the first study carried out on a large scale in oil palm plantation directly in the field by gathering only empirical data and monitor the population dynamic on a long term basis. </p>
Data set and analytic codes supporting "Direct observation to assess the effects of habitat structure and complexity on resource-use behaviour of butterflies: a study case in smallholding oil palm plantations in Peninsular Malaysia"
<p>This deposit contains data set and analytic codes (with a meta data) supporting "Direct observation to assess the effects of habitat structure and complexity on resource-use behaviour of butterflies: a study case in smallholding oil palm plantations in Peninsular Malaysia".</p> <p>We investigated how habitat structure and complexity within smallholding oil palm plantations affected resource-use behaviours of two common butterfly species in the study areas (oil palm plantations in Banting, Selangor, Malaysia): Leptosia nina (Pieridae) and Ypthima spp. (Nymphalidae). Using direct-observation methods we developed, we followed seven and nine individuals of each species respectively, for three minutes in smallholder-owned immature monoculture and polyculture oil palm, and mature monoculture oil palm plantations. We recorded distance travelled by each individual from both straight line and the sums of distances between all perching points, and the position and characteristics of each perch location, where the individual landed. We compared our observations to control runs, generated by pairing observed distances travelled, but selecting the direction for each movement at random. By comparing the distance travelled and characteristics of locations used by butterflies and paired control points across habitats, we assessed how individuals in the two species used the local environment and whether this differed with habitat structure and complexity.</p> <p>Funding and research permission: Jardine Foundation, the Cambridge Trust, and Tim Whitmore Fund funded MFH, the Biotechnology and Biological Sciences Research Council (BBSRC) funded JS (USN: 304338625), and BBSRC (BB/T012366/1) funded the establishment of the plots and surveys of environmental parameters. Research permission was granted by the Economic Planning Unit (EPU) of Malaysia’s Prime Minister’s Department for MFH (Ref: EPU 40/200/19/3727) and JS (Ref: MEA 40/200/19/3705).</p>
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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Dataset from: Termite mounds house a diversity of taxa in oil palm plantations irrespective of understory management
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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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