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Material for manuscript submitted to Earth and Space Science "Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin"
<p>Configuration files for AROME Indian Ocean, NEMO and OASIS which are necessary to reproduce the results in the publication :</p> <p>Corale, L; Malardel S. , Bielli S. and M-N Bouin (2022) Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. <em>Earth and Space Science.</em></p>
Variation in arthropod responses to tropical landscape transformation: spiders 2015
<b>Description: </b><p>Postdoctoral project</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/61"><b>Ecosystem quality and herbivore dynamics in tropical rainforests fragmented by deforestation</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Australian Research Council (ARC Discovery Project, DP140101541)</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>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7252200">here</a></p><p><b>Files: </b>This consists of 1 file: Maunsell_spiders_220811.xlsx</p><p><b>Maunsell_spiders_220811.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Spider assemblage composition data 2015</b> (described in worksheet SpiderAssem)</p><p>Description: Spider assemblage composition data collected at the SAFE Project in 2015. Worksheet contains a site by morphospecies abundance matrix. Spiders were collected by beating plant foliage for a period of 20 minutes per location. Spiders were identified to family and seperated into morphospecies using a combination of genitalia dissections and DNA barcoding (CO1). </p><p>Number of fields: 219</p><p>Number of data rows: 48</p><p>Fields: </p><ul><li><b>Date</b>: Date of the collection (Field type: date)</li><li><b>Location</b>: SAFE Project location 2nd order (Field type: location)</li><li><b>Type</b>: Disturbance gradient (Field type: ordered categorical)</li><li><b>ANA01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA07_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA10_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA12_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA13_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA14_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA15_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA16_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>COR01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>COR02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>COR03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>COR04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>COR05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>DIC01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>GNA01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>HAH01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>HAH02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>HER01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>LIN01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>LIN02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MIM01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MIM02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MIM03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MYS01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MYS02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MYS03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON10_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO07_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO13_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PIS01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL07_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL10_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL12_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL13_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL14_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL15_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL16_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL17_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL18_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL19_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL20_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL21_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL22_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL23_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL24_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL25_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL26_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL27_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL28_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL29_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL30_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL31_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL32_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL33_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL34_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL35_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL36_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL37_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL38_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL39_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL40_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL41_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL42_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL43_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL44_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL45_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL46_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL47_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SCY01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TEB01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD07_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD10_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD12_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD13_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD14_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD15_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD16_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD17_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD18_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD19_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD20_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD21_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD22_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD23_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD24_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD25_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD26_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD27_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD28_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD29_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD30_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD31_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD32_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD33_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD34_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD35_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD36_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD37_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD38_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD39_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD40_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD41_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD42_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD43_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD44_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD45_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD46_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD47_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD48_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD49_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD50_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD51_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD52_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD53_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD54_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD55_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD56_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD57_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD58_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD59_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD60_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD61_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD62_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD63_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD64_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD65_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD66_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD67_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO07_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO10_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO12_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO13_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO14_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO15_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO16_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO17_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO18_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO19_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO20_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THS01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ULO01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ULO02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ULO03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ULO04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ULO05_count</b>: Number collected in twenty minute period (Field type: abundance)</li></ul></li></ol><p><b>Date range: </b>2015-03-10 to 2015-04-11</p><p><b>Latitudinal extent: </b>4.6373 to 4.7714</p><p><b>Longitudinal extent: </b>116.9549 to 117.7021</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> -  -  Arthropoda <br> -  -  -  Arachnida <br> -  -  -  -  Araneae <br> -  -  -  -  -  Linyphiidae <br> -  -  -  -  -  -  [LIN01] <br> -  -  -  -  -  -  [LIN02] <br> -  -  -  -  -  Uloboridae <br> -  -  -  -  -  -  [ULO01] <br> -  -  -  -  -  -  [ULO02] <br> -  -  -  -  -  -  [ULO03] <br> -  -  -  -  -  -  [ULO04] <br> -  -  -  -  -  -  [ULO05] <br> -  -  -  -  -  Pisauridae <br> -  -  -  -  -  -  [PIS01] <br> -  -  -  -  -  Hersiliidae <br> -  -  -  -  -  -  [HER01] <br> -  -  -  -  -  Corinnidae <br> -  -  -  -  -  -  [COR01] <br> -  -  -  -  -  -  [COR02] <br> -  -  -  -  -  -  [COR03] <br> -  -  -  -  -  -  [COR04] <br> -  -  -  -  -  -  [COR05] <br> -  -  -  -  -  Salticidae <br> -  -  -  -  -  -  [SAL01] <br> -  -  -  -  -  -  [SAL02] <br> -  -  -  -  -  -  [SAL03] <br> -  -  -  -  -  -  [SAL04] <br> -  -  -  -  -  -  [SAL05] <br> -  -  -  -  -  -  [SAL06] <br> -  -  -  -  -  -  [SAL07] <br> -  -  -  -  -  -  [SAL08] <br> -  -  -  -  -  -  [SAL09] <br> -  -  -  -  -  -  [SAL10] <br> -  -  -  -  -  -  [SAL11] <br> -  -  -  -  -  -  [SAL12] <br> -  -  -  -  -  -  [SAL13] <br> -  -  -  -  -  -  [SAL14] <br> -  -  -  -  -  -  [SAL15] <br> -  -  -  -  -  -  [SAL16] <br> -  -  -  -  -  -  [SAL17] <br> -  -  -  -  -  -  [SAL18] <br> -  -  -  -  -  -  [SAL19] <br> -  -  -  -  -  -  [SAL20] <br> -  -  -  -  -  -  [SAL21] <br> -  -  -  -  -  -  [SAL22] <br> -  -  -  -  -  -  [SAL23] <br> -  -  -  -  -  -  [SAL24] <br> -  -  -  -  -  -  [SAL25] <br> -  -  -  -  -  -  [SAL26] <br> -  -  -  -  -  -  [SAL27] <br> -  -  -  -  -  -  [SAL28] <br> -  -  -  -  -  -  [SAL29] <br> -  -  -  -  -  -  [SAL30] <br> -  -  -  -  -  -  [SAL31] <br> -  -  -  -  -  -  [SAL32] <br> -  -  -  -  -  -  [SAL33] <br> -  -  -  -  -  -  [SAL34] <br> -  -  -  -  -  -  [SAL35] <br> -  -  -  -  -  -  [SAL36] <br> -  -  -  -  -  -  [SAL37] <br> -  -  -  -  -  -  [SAL38] <br> -  -  -  -  -  -  [SAL39] <br> -  -  -  -  -  -  [SAL40] <br> -  -  -  -  -  -  [SAL41] <br> -  -  -  -  -  -  [SAL42] <br> -  -  -  -  -  -  [SAL43] <br> -  -  -  -  -  -  [SAL44] <br> -  -  -  -  -  -  [SAL45] <br> -  -  -  -  -  -  [SAL46] <br> -  -  -  -  -  -  [SAL47] <br> -  -  -  -  -  Dictynidae <br> -  -  -  -  -  -  [DIC01] <br> -  -  -  -  -  Tetragnathidae <br> -  -  -  -  -  -  [TET01] <br> -  -  -  -  -  -  [TET02] <br> -  -  -  -  -  -  [TET03] <br> -  -  -  -  -  -  [TET04] <br> -  -  -  -  -  -  [TET05] <br> -  -  -  -  -  -  [TET06] <br> -  -  -  -  -  Mimetidae <br> -  -  -  -  -  -  [MIM01] <br> -  -  -  -  -  -  [MIM02] <br> -  -  -  -  -  -  [MIM03] <br> -  -  -  -  -  Scytodidae <br> -  -  -  -  -  -  [SCY01] <br> -  -  -  -  -  Pholcidae <br> -  -  -  -  -  -  [PHO01] <br> -  -  -  -  -  -  [PHO02] <br> -  -  -  -  -  -  [PHO03] <br> -  -  -  -  -  -  [PHO04] <br> -  -  -  -  -  -  [PHO05] <br> -  -  -  -  -  -  [PHO06] <br> -  -  -  -  -  -  [PHO07] <br> -  -  -  -  -  -  [PHO08] <br> -  -  -  -  -  -  [PHO09] <br> -  -  -  -  -  -  [PHO11] <br> -  -  -  -  -  -  [PHO13] <br> -  -  -  -  -  Mysmenidae <br> -  -  -  -  -  -  [MYS01] <br> -  -  -  -  -  -  [MYS02] <br> -  -  -  -  -  -  [MYS03] <br> -  -  -  -  -  Araneidae <br> -  -  -  -  -  -  [ARA01] <br> -  -  -  -  -  -  [ARA02] <br> -  -  -  -  -  -  [ARA03] <br> -  -  -  -  -  -  [ARA04] <br> -  -  -  -  -  -  [ARA05] <br> -  -  -  -  -  -  [ARA06] <br> -  -  -  -  -  -  [ARA07] <br> -  -  -  -  -  -  [ARA08] <br> -  -  -  -  -  -  [ARA09] <br> -  -  -  -  -  -  [ARA10] <br> -  -  -  -  -  -  [ARA11] <br> -  -  -  -  -  -  [ARA12] <br> -  -  -  -  -  -  [ARA13] <br> -  -  -  -  -  -  [ARA14] <br> -  -  -  -  -  -  [ARA15] <br> -  -  -  -  -  -  [ARA16] <br> -  -  -  -  -  Cheiracanthiidae <br> -  -  -  -  -  -  [EUT01] <br> -  -  -  -  -  -  [EUT02] <br> -  -  -  -  -  -  [EUT03] <br> -  -  -  -  -  -  [EUT04] <br> -  -  -  -  -  -  [EUT05] <br> -  -  -  -  -  -  [EUT06] <br> -  -  -  -  -  Gnaphosidae <br> -  -  -  -  -  -  [GNA01] <br> -  -  -  -  -  Oxyopidae <br> -  -  -  -  -  -  [OXY01] <br> -  -  -  -  -  -  [OXY02] <br> -  -  -  -  -  -  [OXY03] <br> -  -  -  -  -  -  [OXY04] <br> -  -  -  -  -  -  [OXY05] <br> -  -  -  -  -  -  [OXY06] <br> -  -  -  -  -  Tetrablemmidae <br> -  -  -  -  -  -  [TEB01] <br> -  -  -  -  -  Theridiosomatidae <br> -  -  -  -  -  -  [THS01] <br> -  -  -  -  -  Oonopidae <br> -  -  -  -  -  -  [OON01] <br> -  -  -  -  -  -  [OON02] <br> -  -  -  -  -  -  [OON03] <br> -  -  -  -  -  -  [OON05] <br> -  -  -  -  -  -  [OON06] <br> -  -  -  -  -  -  [OON08] <br> -  -  -  -  -  -  [OON09] <br> -  -  -  -  -  -  [OON10] <br> -  -  -  -  -  -  [OON11] <br> -  -  -  -  -  Anapidae <br> -  -  -  -  -  -  [ANA01] <br> -  -  -  -  -  Thomisidae <br> -  -  -  -  -  -  [THO01] <br> -  -  -  -  -  -  [THO02] <br> -  -  -  -  -  -  [THO03] <br> -  -  -  -  -  -  [THO04] <br> -  -  -  -  -  -  [THO05] <br> -  -  -  -  -  -  [THO06] <br> -  -  -  -  -  -  [THO07] <br> -  -  -  -  -  -  [THO08] <br> -  -  -  -  -  -  [THO09] <br> -  -  -  -  -  -  [THO10] <br> -  -  -  -  -  -  [THO11] <br> -  -  -  -  -  -  [THO12] <br> -  -  -  -  -  -  [THO13] <br> -  -  -  -  -  -  [THO14] <br> -  -  -  -  -  -  [THO15] <br> -  -  -  -  -  -  [THO16] <br> -  -  -  -  -  -  [THO17] <br> -  -  -  -  -  -  [THO18] <br> -  -  -  -  -  -  [THO19] <br> -  -  -  -  -  -  [THO20] <br> -  -  -  -  -  Hahniidae <br> -  -  -  -  -  -  [HAH01] <br> -  -  -  -  -  -  [HAH02] <br> -  -  -  -  -  Theridiidae <br> -  -  -  -  -  -  [THD01] <br> -  -  -  -  -  -  [THD02] <br> -  -  -  -  -  -  [THD03] <br> -  -  -  -  -  -  [THD04] <br> -  -  -  -  -  -  [THD05] <br> -  -  -  -  -  -  [THD06] <br> -  -  -  -  -  -  [THD07] <br> -  -  -  -  -  -  [THD08] <br> -  -  -  -  -  -  [THD09] <br> -  -  -  -  -  -  [THD10] <br> -  -  -  -  -  -  [THD11] <br> -  -  -  -  -  -  [THD12] <br> -  -  -  -  -  -  [THD13] <br> -  -  -  -  -  -  [THD14] <br> -  -  -  -  -  -  [THD15] <br> -  -  -  -  -  -  [THD16] <br> -  -  -  -  -  -  [THD17] <br> -  -  -  -  -  -  [THD18] <br> -  -  -  -  -  -  [THD19] <br> -  -  -  -  -  -  [THD20] <br> -  -  -  -  -  -  [THD21] <br> -  -  -  -  -  -  [THD22] <br> -  -  -  -  -  -  [THD23] <br> -  -  -  -  -  -  [THD24] <br> -  -  -  -  -  -  [THD25] <br> -  -  -  -  -  -  [THD26] <br> -  -  -  -  -  -  [THD27] <br> -  -  -  -  -  -  [THD28] <br> -  -  -  -  -  -  [THD29] <br> -  -  -  -  -  -  [THD30] <br> -  -  -  -  -  -  [THD31] <br> -  -  -  -  -  -  [THD32] <br> -  -  -  -  -  -  [THD33] <br> -  -  -  -  -  -  [THD34] <br> -  -  -  -  -  -  [THD35] <br> -  -  -  -  -  -  [THD36] <br> -  -  -  -  -  -  [THD37] <br> -  -  -  -  -  -  [THD38] <br> -  -  -  -  -  -  [THD39] <br> -  -  -  -  -  -  [THD40] <br> -  -  -  -  -  -  [THD41] <br> -  -  -  -  -  -  [THD42] <br> -  -  -  -  -  -  [THD43] <br> -  -  -  -  -  -  [THD44] <br> -  -  -  -  -  -  [THD45] <br> -  -  -  -  -  -  [THD46] <br> -  -  -  -  -  -  [THD47] <br> -  -  -  -  -  -  [THD48] <br> -  -  -  -  -  -  [THD49] <br> -  -  -  -  -  -  [THD50] <br> -  -  -  -  -  -  [THD51] <br> -  -  -  -  -  -  [THD52] <br> -  -  -  -  -  -  [THD53] <br> -  -  -  -  -  -  [THD54] <br> -  -  -  -  -  -  [THD55] <br> -  -  -  -  -  -  [THD56] <br> -  -  -  -  -  -  [THD57] <br> -  -  -  -  -  -  [THD58] <br> -  -  -  -  -  -  [THD59] <br> -  -  -  -  -  -  [THD60] <br> -  -  -  -  -  -  [THD61] <br> -  -  -  -  -  -  [THD62] <br> -  -  -  -  -  -  [THD63] <br> -  -  -  -  -  -  [THD64] <br> -  -  -  -  -  -  [THD65] <br> -  -  -  -  -  -  [THD66] <br> -  -  -  -  -  -  [THD67] <br></div><p></p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine tropical) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine tropical) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and marine tropical aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.</p>
Supporting Information for 'in situ fire emission factors for Malaysian tropical peatlands with the first investigation of the influence of physicochemical controls on peat fire emission factor variability'
<p>Two files containing:</p> <p>1. Raw mole fraction retrievals from our OP-FTIR spectra (as described in the paper)</p> <p>2. Calculated emission ratios used for the calculation of emission factors (as published in the paper)</p>
A Numerical Study of Tropical Cyclone and Ocean Responses to Air-sea Momentum Flux at High Winds
<p>The simulation data output from FIO-AOW for tropical cyclone study</p>
Intraspecific plant-soil feedback in four tropical tree species is inconsistent in a field experiment
<p>Soil microbes can influence patterns of diversity in plant communities via plant-soil feedbacks. Intraspecific plant-soil feedbacks occur when plant genotype causes variation in soil microbial composition, resulting in differences in the performance of seedlings growing near their maternal plants versus seedlings growing near non-maternal conspecific plants. How consistently such intraspecific plant-soil feedbacks occur in natural plant communities is unclear, especially under variable field conditions. We conducted an in situ experiment with four native tree species on Barro Colorado Island (BCI), Panama. Seedlings of each species were transplanted beneath their maternal tree or another conspecific tree in the BCI forest. Mortality and growth were assessed at the end of the wet season (~4 months post-transplant) and at the end of the experiment (~7 months post-transplant). Differences in seedling performance among field treatments were inconsistent among species and eroded over time. Effects of field environment were detected at the end of the wet season in two of the species: <em>Virola surinamensis</em> seedlings had higher survival beneath their maternal tree than other conspecific trees, while the opposite pattern was found in <em>Ormosia macrocalyx</em>. However, these differences disappeared by the end of the experiment. Our results suggest that intraspecific plant-soil feedbacks may not occur consistently under field conditions in tropical tree species and may have a limited role in determining seedling performance in tropical tree communities. Future studies are needed to elucidate the environmental and genetic factors that determine the incidence and direction of intraspecific plant-soil feedbacks in plant communities.</p>
Data for: "Different dry-wet pulses favor different functional strategies: a test using tropical dry forest tree species" by Vega-Ramos, Flor, Cifuentes Gómez, Lucas, Pineda-García, Fernando, Dawson, Todd, Paz, Horacio
<p>These data represent those published in "Different dry-wet pulses favor different functional strategies: a test using tropical dry forest tree species" by Vega-Ramos, Flor, Cifuentes Gómez, Lucas, Pineda-García, Fernando, Dawson, Todd, Paz, Horacio</p>
Data from: fire in the rainforest: a 3,200-year history of fire in a West Kalimantan, Indonesia tropical rainforest
<p>Despite its perceived historical rarity, fire is an important disturbance in tropical rainforests. Very large rainforest fires have been observed multiple times in recent decades, often during years of strong El Niño-Southern Oscillation droughts. Fire in rainforest has major short-term consequences for humans and wildlife by converting forest to fire-prone fern, shrub, and grass, but the long-term effects remain to be seen. Borneo's indigenous groups have been using fire to clear land for centuries, yet the prevalence and spatial patterns of pre-modern fire across forest types in Borneo is not well understood. This research set out to reconstruct fire in a 1500-ha primary rainforest spanning 800 m of elevation in Indonesian Borneo with the goal of elucidating the role humans have played in rainforest fire. We found that humans played an important role in the occurrence of fire in recent centuries. Evidence of fire—charcoal >2 mm—is more abundant in forest types where humans would be more likely to live and/or practice swidden agriculture. However, pyrogenic material is ubiquitous across the study area, showing that all forest types have experienced fire. A set of 50 radiocarbon dates showed that in lowland areas—where human-caused fire is most likely—fire occurred throughout the last 3,200 years, peaking 1300-1600 CE. The upland areas lacked evidence of fire before 1250 CE but otherwise had a similar pattern to the lowlands. The period of high fire coincides with regional demographic changes as well as regional droughts documented elsewhere in Southeast Asia. In upland areas, fires likely burned only under regional drought when fires could more easily spread upslope. Although forest plot studies at this site show little structural evidence of past fires, tree diversity is lower than expected in the most burned areas (alluvial benches). Thus, our results suggest that land clearance was a major source of fire, but the current intact state of these rainforests indicates that they were largely resilient to fires and land use hundreds of years ago. Recent fires mirror patterns of fire spread that occurred hundreds of years ago, though their severity and extent is likely much greater.</p>
Data for: Geolocation and immersion loggers reveal year-round residency and consequent nutrient deposition rates of adult red-footed boobies in the Chagos Archipelago, tropical Indian Ocean
<p>Bio-logging has revealed much about high-latitude seabird migratory strategies, but migratory behaviour in tropical species may differ, with implications for understanding nutrient deposition. Here we use combined light-level and saltwater immersion loggers to study the year-round movement behaviour of adult red-footed boobies (<em>Sula sula rubripes</em>) from the Chagos Archipelago, tropical Indian Ocean to assess migratory movements and estimate nutrient deposition rates based on the number of days they spent ashore. Light levels suggest that red-footed boobies are resident in the Chagos Archipelago year-round, although there are large latitudinal errors this close to the equator. Immersion data also indicate residency with tracked birds returning to land every one or two days. Spending an average of 79.86 ± 2.80 days and 280.84 ± 2.64 nights per year on land allows us to estimate that the 21,670 pairs of red-footed boobies deposit 37.34 ± 0.56 tonnes year<sup>-1</sup> of guano-derived nitrogen throughout the archipelago. Our findings have implications for tropical seabird conservation and phylogenetics, as well as for assessing the impact of seabird nutrients on coral reef ecosystems.</p>
Fruiting trees provide fruit and insect resources for four tropical deer species
<p>Fruiting trees provide important fruit and seed resources for various animal species, but rarely are they considered to be rich sources of insects as well. During a study of seed dispersal of <em>Trewia nudiflora</em> (Euphorbiaceae) using camera-traps in Nepal, we observed four tropical deer species (<em>Axis axis</em>, <em>Muntiacus vaginalis</em>, <em>Rusa unicolor</em>, <em>Axis porcinus</em>) foraging for insects, rather than fruits under the trees. These herbivorous deer are proven seed dispersers of <em>Trewia</em>, but 8-38% of video captures (across each species) were of insectivory, rather than frugivory. The deer chased and consumed red cotton bugs (<em>Dysdercus</em> sp. family Pyrrhocoridae), which were seed predators that hoarded <em>Trewia</em> seeds. It is likely that other unidentified insect species were also consumed. Tropical deer species are considered to be fully herbivorous, so our observations extend their known diets and possibly indicate a seasonal requirement for protein. These findings also highlight tri-trophic interactions among deer, insects, and fruits that could have important implications for seedling recruitment if seed predators are being consumed by seed dispersers (in addition to fruit consumption).</p>
Effects of fire and invasive plants on tropical dry forest restorartion
<p>The data collected is part of monitoring the ecological restoration process in a tropical dry forest. In this process, my research team conducted an experimental exercise, aiming to assess the natural regeneration around planted seedlings in burned sites compared to unburned sites. Additionally, we aimed to examine the effects of invasive species recruits and ruderal vegetation removal on seedlings and natural regeneration in both burned and unburned sites. For this research, we measured the planted seedlings and recorded the following data: height, weight, crown diameter, amount of radiation, and distance from the remaining forest edge. Furthermore, we counted the number of recruits, identified and searched for invasive potential in another database such as CABI.</p> <p>The planted seedlings were measured at two times; the first measurement was taken four months after planting, immediately after removing the ruderal vegetation around them. Forty-five days later, another round of ruderal vegetation removal in the plots was conducted, and after an additional 75 days, a final characterization of regeneration was performed</p>
Raw data: Temperature and water availability drive insect seasonality across a temperate and a tropical region
<p>The more insects there are, the more food there is for insectivores and the higher the likelihood of insect-associated ecosystem services. Yet, we lack insights into the drivers of insect biomass over space and seasons, both for tropical and temperate zones. We used 245 Malaise traps, managed by 191 volunteers and park guards, to characterise year-round flying insect biomass in a temperate (Sweden) and a tropical (Madagascar) country. Surprisingly, we found that local insect biomass was similar across zones. In Sweden, local insect biomass increased with accumulated heat and varied across habitats, while biomass in Madagascar was unrelated to the environmental predictors measured. Drivers behind seasonality partly converged: In both countries, the seasonality of insect biomass differed between warmer and colder sites and wetter and drier sites. In Sweden, short-term deviations from expected season-specific biomass were explained by week-to-week fluctuations in accumulated heat, rainfall, and soil moisture, whereas in Madagascar, weeks with higher soil moisture had higher insect biomass. Overall, our study identifies key drivers of the seasonal distribution of flying insect biomass in a temperate and tropical climate. This knowledge is key to understanding the spatial and seasonal availability of insects — as well as predicting future scenarios of insect biomass change.</p>
Replication of manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates"
<p>Replication of manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates".</p> <p>This page contains datasets used to replicate figures in a manuscript entitled "Multi-decadal climate variability and satellite biases have amplified model-observation discrepancies in tropical troposphere warming estimates". These ascii or netcdf files can be easily read by NCL, Fortran and others.</p> <p> </p>
Ants may buffer the Janzen–Connell effect in a tropical forest in Southwest China
<p>These files contain the datasets and R codes used in the data analyses in the paper " Ants may buffer the Janzen–Connell effect in a tropical forest in Southwest China" that will be published in Ecology</p>
Dataset for "Multi-model evidence of future tropical Atlantic rainfall change modulated by AMOC decline"
<p>This dataset contains EC-Earth3 experiments used for the paper Multi-model evidence of future tropical Atlantic rainfall change modulated by AMOC decline (G. Cerato, K. Bellomo, R. D'Agostino, J. von Hardenberg, 2024) submitted to <em>Journal of Climate. </em>Data uploaded here allows the user to reproduce the figures related to the EC-Earth3 experiments from the journal article.</p>
Data for "Examining outer band supercell environments in landfalling tropical cyclones using ground-based radar analyses" v3
<p>The data here are archived for open data access for the publication entitled "Examining outer band supercell environments in landfalling tropical cyclones using ground-based radar analyses" submitted to <em>Monthly Weather Review</em>.</p> <p>Radar data are archived in netCDF format in which variables are identified by their radar moment. The radar data are separated by SR1 and KLCH for Hurricane Laura. For Hurricane Frances, the relevant SR data are contained in the frances_sr_data.tar.gz file.</p> <p>The csv archive contains the track information for objectively identified supercell storms from the manuscript.</p> <p>Questions about the data may be directed to addison.alford@noaa.gov.</p>
Acoustic phenology of tropical resident birds differs between native forest species and parkland colonizer species
<p>Most birds are characterized by a seasonal phenology closely adapted to local climatic conditions, even in tropical habitats where climatic seasonality is slight. In order to better understand the phenologies of resident tropical birds, and how phenology may differ among species at the same site, we used ~70,000 hours of audio recordings collected continuously for two years at four recording stations in Singapore and nine custom-made machine learning classifiers to determine the vocal phenology of a panel of nine resident bird species. We detected distinct seasonality in vocal activity in some species but not others. Native forest species sang seasonally. In contrast, species which have had breeding populations in Singapore only for the last few decades exhibited seemingly aseasonal or unpredictable song activity throughout the year. Urbanization and habitat modification over the last 100 years have altered the composition of species in Singapore, which appears to have influenced phenological dynamics in the avian community. It is unclear what is driving the differences in phenology between these two groups of species, but it may be due to either differences in seasonal availability of preferred foods, or newly established populations may require decades to adjust to local environmental conditions. Our results highlight the ways that anthropogenic habitat modification may disrupt phenological cycles in tropical regions in addition to altering the species community.</p>
Supplementary data for "Seasonal emergence and circulation coupling of moist layers over the tropical Atlantic"
<p>The data provided here contains the derived moist layer characteristics from ERA5 reanalysis, as presented in the associated publication. Data is given on a 0.25° latitude/longitude grid between 30°N/S on the native vertical levels of the ERA5 dataset, cut off at 50 hPa. When a moist layer is detected at a given location and altitude, as identified by the method described in Sect. 2.2 of the publication, the moist layer characteristics (strength, pressure level, upper bound pressure, lower bound pressure) are written in the associated files at the given location (lat/lon) and pressure level of the moist layer. Here, we provide the 15 year means and standard deviations for the months January and July, and the 3 hourly data along the equatorial and 15°N cross-sections of the Atlantic used for the Hovmoller diagrams in Fig. 4 and Supporting Figure S1 of the publication.</p> <p>Files containing "geographical_mean" indicate monthly means on the latitude/longitude grid. Files containing "moisture_space" indicate monthly means within moisture space over the tropical Atlantic, as defined in the paper. <br><br>Code used to generate the data and to conduct the analysis presented in the publication are provided in the github repository URL listed in the Software section.</p>
Data from: Environmental conditions differently shape leaf, seed and seedling trait composition between and within elevations of tropical montane forests
<p>The composition of plant functional traits varies in response to environmental conditions due to processes of community assembly and species sorting. However, there is a lack of understanding of how plant trait composition responds to environmental conditions at different spatial scales and across the plant life cycle. We investigated the trait composition of leaves (specific leaf area), seeds (seed mass) and seedlings (initial seedling height) across elevations and within elevations in relation to soil and light conditions in a tropical montane forest in southern Ecuador. We surveyed traits and communities of adult trees, seeds and seedlings on nine plots at three elevations (1000-3000 m a.s.l.) and calculated community-weighted mean trait values to analyse trait variation across and within elevations. In addition, we measured two environmental factors (soil C/N ratio and canopy openness) to quantify local-scale variation in environmental conditions within elevations. We found that community-weighted means of specific leaf area, seed mass and initial seedling height decreased consistently with increasing elevation. Within elevations, mean trait values of trees, seeds and seedlings responded differently to local-scale environmental conditions. Specific leaf area decreased with increasing soil C/N ratio, and initial seedling height decreased with increasing canopy openness. Seed mass was associated neither with soil nor with light conditions. Our findings show that broad-scale and local-scale processes differently shape the composition of leaf, seed and seedling traits in tropical forests, indicating a scale-dependence in trait-environment associations. Furthermore, plant traits corresponding to different life stages were related differently to environmental conditions within elevations. Community assembly processes may therefore lead to differences in species sorting at early and late plant life stages.</p>
Data from: Tracking shifts in forest structural complexity through space and time in human-modified tropical landscapes
<p>Habitat structural complexity is an emergent property of ecosystems that directly shapes their biodiversity, functioning and resilience to disturbance. Yet despite its importance, we continue to lack consensus on how best to define structural complexity, nor do we have a generalised approach to measure habitat complexity across ecosystems. To bridge this gap, here we adapt a geometric framework developed to quantify the surface complexity of coral reefs and apply it to the canopies of tropical rainforests. Using high-resolution, repeat-acquisition airborne laser scanning data collected over 450 km2 of human-modified tropical landscapes in Borneo, we generated 3D canopy height models of forests at varying stages of recovery from logging. We then tested whether the geometric framework of habitat complexity – which characterises 3D surfaces according to their height range, rugosity and fractal dimension – was able to detect how both human and natural disturbances drive variation in canopy structure through space and time across these landscapes. We found that together, these three metrics of surface complexity captured major differences in canopy 3D structure between highly-degraded, selectively logged and old-growth forests. Moreover, the three metrics were able to track distinct temporal patterns of structural recovery following logging and wind disturbance. However, in the process we also uncovered several important conceptual and methodological limitations with the geometric framework of habitat complexity. We found that fractal dimension was highly sensitive to small variations in data inputs and was ecologically counteractive (e.g., higher fractal dimension in oil palms than old-growth forests), while rugosity and height range were tightly correlated (r=0.75) due to their strong dependency on maximum tree height. Our results suggest that forest structural complexity cannot be summarised using these three descriptors alone, as they overlook key features of canopy vertical and horizontal structure that arise from the way trees fill 3D space.</p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.