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40 results for “Biodiversity indicator”
SHOWCASE Task 2.7 Biodiversity Indicators and Predictors data set
<p>The dataset contains five biodiversity (0-1 interval normalised) indicators from five case study areas (HU, ES, PT, NL and CH): wild bees abundance (field "Ind_WBA"), wild bees species richness (field "Ind_WBR"), spiders abundance (field "Ind_SpA"), spider species richness (field "Ind_SpR"), and vascular plants species richnes (field "Ind_PlaR"). Data are georeffered (EPSG: 3035) and coordinates are provided (field:" xcoord_EPSG3035" and "ycoord_EPSG3035"). Data were collected in two sampling rounds (field "Round"). Additionally the data set contains a set<span> of predictors for biodiversity indicators encompassing 27 variables, belonging to four different groups: 1. </span><span><span><span> </span></span></span><span>Landscape elements: proximity to roads and proximity to Small Woody Features (SWF, Copernicus land Monitoring Services, CLMS 2018); 2.<span> </span></span><span>Terrain descriptors: elevation, aspect, slope, and their derivatives (8 variables); 3. </span><span>Spectral signatures and Remote Sensing Indicatora (RSI) from Copernicus Sentinel 2 (14 variables); and </span><span><span>4.<span> </span></span></span><span>Biodiversity management (3 variables, dummy coded 0,1). </span></p> <table> <tbody> <tr> <td> <p><strong><span>Group </span></strong></p> </td> <td> <p><strong><span>Predictor </span></strong></p> </td> <td> <p><strong><span>Unit</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> <p><strong><span>Source</span></strong></p> </td> <td> <p><strong><span>Resolution</span></strong></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>Road proximity<sup>a</sup></span></p> </td> <td> <p><span>m </span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>SWF proximity<sup>b</sup></span></p> </td> <td> <p><span>m</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Elevation</span></p> </td> <td> <p><span>m a.s.l.</span></p> </td> <td> <p><span>Copernicus DEM</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Aspect</span></p> </td> <td> <p><span>degree from North</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Slope </span></p> </td> <td> <p><span>%</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Catchment slope </span></p> </td> <td> <p><span>%</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Catchment area </span></p> </td> <td> <p><span>m<sup>2</sup></span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Mod. Catchment area </span></p> </td> <td> <p><span>m<sup>2</sup></span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Topographic. wetness Index </span></p> </td> <td> <p><span>m/rad</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Valley depth</span></p> </td> <td> <p><span>m</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>BI, bare Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Blue (B2, 490 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Green (B3, 560 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>IR, infra-red (B8, 842 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDBSI, Norm. Diff. Bare Soil Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDSI, Normalized Diff. Soil Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDVI, Norm. Diff. Vegetation Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NIR, Near Infra-Red (B8A, 865 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Red (B4, 665 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSa, Soil Salinity </span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI1, Soil Salinity Index1</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI2, Soil Salinity Index2</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI3, Soil Salinity Index3</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SWIR Short Wave IR (B11,1610 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Biodiversity Intervention</span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Year of intervention</span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Round </span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> </tbody> </table> <p><sup><span>a</span></sup><span> Source of vector data: Open Street Map. © OpenStreetMap contributors. Available under the Open Database License from: openstreetmap.org. </span></p> <p><sup><span>b</span></sup><span> Source of raster data (res. 5 m): </span><span><a href="https://land.copernicus.eu/en/products/high-resolution-layer-small-woody-features/small-woody-features-2018"><span>https://land.copernicus.eu/en/products/high-resolution-layer-small-woody-features/small-woody-features-2018</span></a></span><span>. </span><span><a href="https://doi.org/10.2909/a8e683b1-2f96-45c8-827f-580a79413018"><span>https://doi.org/10.2909/a8e683b1-2f96-45c8-827f-580a79413018</span></a></span><span> </span></p>
Range-wide habitat use of the Harpy Eagle indicates four major tropical forest gaps in the Key Biodiversity Area network
<p>Quantifying habitat use is important for understanding how animals meet their requirements for survival and provides information for conservation planning. Currently, assessments of range-wide habitat use that delimit species distributions are incomplete for many taxa. The Harpy Eagle (Harpia harpyja) is a raptor of conservation concern, widely distributed across Neotropical lowland forests, that currently faces threats from habitat loss and fragmentation. Here, we use penalized logistic regression to identify species-habitat associations and predict habitat suitability based on a new International Union for the Conservation of Nature range metric, termed Area of Habitat. From the species-habitat model, we performed a gap analysis to identify areas of high habitat suitability in regions with limited coverage in the Key Biodiversity Area (KBA) network. Range-wide habitat use indicated that Harpy Eagles prefer areas of 70-75% evergreen forest cover, low elevation, and high vegetation species richness. Conversely, Harpy Eagles avoid areas of >10% cultivated landcover and mosaic forest, and topographically complex areas. Our species-habitat model identified a large continuous area of potential habitat across the pan-Amazonia region, and a habitat corridor from the Chocó-Darién ecoregion of Colombia running north along the Caribbean coast of Central America. Little habitat was predicted across the Atlantic Forest biome, which is now severely degraded. The current KBA network covered 18% of medium to high Harpy Eagle habitat exceeding a target biodiversity area representation of 10%, based on species range size. Four major areas of high suitability habitat lacking coverage in the KBA network were identified in north and west Colombia, western Guyana, and north-west Brazil. We recommend these multiple gaps of habitat as new KBAs for strengthening the current KBA network. Modelled area of habitat estimates as described here are a useful tool for large-scale conservation planning and can be readily applied to many taxa.</p>
Code&Data_'Choosing fit-for-purpose biodiversity impact indicators for agriculture in the Brazilian Cerrado ecoregion'
<p>File "R_Scripts_biodiversity_indicators.zip" includes codes for calculating the biodiversity impact on the terrestrial vertebrates of the Cerrado biome using the countryside Species Area Relationship (cSAR), the Species Threat Abatement and Restoration (STAR) metric and the Species Habitat Index (SHI).</p> <p>File "results_biodiversity_indicators.zip" includes the result tables of the calculations done with the above mentioned codes. </p>
Simulated population time series used to build and test a model of accuracy for population-based global biodiversity indicators
<p class="MsoNormal">Global biodiversity is facing a crisis, which must be solved through effective policies and on-the-ground conservation. But governments, NGOs, and scientists need reliable indicators to guide research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. But without a basis for real-world comparison, there is no way to directly assess an indicator's accuracy or reliability. Instead, a modelling approach can be used.</p> <p class="MsoNormal">We developed a model of trend reliability, using simulated datasets as stand-ins for the "real world", degraded samples as stand-ins for indicator datasets (e.g. the Living Planet Database), and a distance measure to quantify reliability by comparing sampled to unsampled trends. The model revealed that the proportion of species represented in the database is not always indicative of trend reliability. Important factors are the number and length of time series, as well as their mean growth rates and variance in their growth rates, both within and between time series. We found that many trends in the Living Planet Index need more data to be considered reliable, particularly trends across the global south. In general, bird trends are the most reliable, while reptile and amphibian trends are most in need of additional data. We simulated three different solutions for reducing data deficiency, and found that collating existing data (where available) is the most efficient way to improve trend reliability, and that revisiting previously-studied populations is a quick and efficient way to improve trend reliability until new long-term studies can be completed and made available.</p>
Simulated population time series used to build and test a model of accuracy for population-based global biodiversity indicators
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Range-wide habitat use of the Harpy Eagle indicates four major tropical forest gaps in the Key Biodiversity Area network
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SHOWCASE Task 2.7 Biodiversity Indicators maps for the Hungarian case study area (Kiskunság)
<p>The data set <span>for the SHOWCASE Task 2.7 Hungarioan case study area of Kiskunság </span>encompasses 48 raster maps at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators:</p> <ol> <li>Wild bees abundance (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> <p>Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: arable land.</p>
SHOWCASE Task 2.7 Biodiversity Indicator maps for the Portuguese case study area (Evora, Portel, Cuba, Vidigueira and Beja)
<p>The data set <span>for the SHOWCASE Task 2.7 Portuguese<span> </span>case study area of the Municipalities of <span>Evora, Portel, Cuba, Vidigueira and Beja</span><span> </span></span>encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators:</p> <ol> <li>Wild bees abundance (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> <p>Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: permanent crops (olive orchards).</p>
SHOWCASE Task 2.7 Biodiversity Indicator maps for the Spanish case study area (Guadalquivida)
<p>The data set <span>for the SHOWCASE Task 2.7 Spanish case study area of Guadalquivida </span>encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators:</p> <ol> <li>Wild bees abundance (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> <p>Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: permane crops (stone fruit orchards)</p>
SHOWCASE Task 2.7 Biodiversity Indicator maps for the Swiss case study area (Solothurn)
<p>The data set for the SHOWCASE Task 2.7 Swiss case study area of Solothurn encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators:</p> <ol> <li>Wild bees abundance (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> <p>Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: arable land.</p>
SHOWCASE Task 2.7 Biodiversity Indicator maps for the Dutch case study area (Zuid-Limburg)
<p>The data set for the SHOWCASE Task 2.7 Dutch case study area of Zuid Limburg encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators:</p> <ol> <li>Wild bees abundance (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> <p>Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: arable land</p>
An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters: Data and R code
<p>These are the data and R code that accompany the Forest Ecology and Management publication titled "An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters". </p>
Biodiversity Habitat Index forest indicator
<p>This collection contains 30-arcsecond grid-resolution results for the Biodiversity Habitat Index associated with the publication 'Ecosystem extent is a necessary but not sufficient indicator of the state of global forest biodiversity.' </p>
TA B L E 2 Identified R packages useful for taxonomic name harmonization. Square brackets indicate supplementary references in Harmonizing taxon names in biodiversity data: A review of tools, databases and best practices
TA B L E 2 Identified R packages useful for taxonomic name harmonization. Square brackets indicate supplementary references
Data from: Biodiversity assessment among two Nebraska prairies: a comparison between traditional and phylogenetic diversity indices
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Assessment of acoustic indices for monitoring phylogenetic and temporal patterns of biodiversity in tropical forests
<b>Description: </b><p>Acostic recordings of bird calls at SAFE in 2014. Four solar-powered Wildlife Song Meter SM3 bioacoustic recorders (Wildlife Acoustics Inc., Concord,<br>MA, USA) were deployed with omni-directional microphones (sensitivity: 20Hz - 20kHz). No exact locations recorded.</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/175"><b>Continuous bio-acoustic monitoring (2020 extension)</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7740620">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Trigg_safedata.xlsx, CLIPS.zip</p><p><b>Trigg_safedata.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Data</b> (described in worksheet Data)</p><p>Description: excel sheet with clips listed and birds identified</p><p>Number of fields: 7</p><p>Number of data rows: 837</p><p>Fields: </p><ul><li><b>FileNumber</b>: The audio files, which are labelled 1 to 120 (Field type: id)</li><li><b>StartTime</b>: approximate time when the species call began. (Field type: numeric)</li><li><b>EndTime</b>: approximate end time of the call. (Field type: numeric)</li><li><b>original_taxon_names</b>: species that can be heard in the recording. Original version as entered in to worksheet (Field type: comments)</li><li><b>Species</b>: species that can be heard in the recording. Corrected name, spelling mistakes and excess spaces removed etc. (Field type: taxa)</li><li><b>CallType</b>: Type of sound being identified (Field type: categorical)</li><li><b>IdCertainty</b>: Degree of certainty (Field type: categorical)</li></ul></li></ol><p><b>CLIPS.zip</b></p><p>Description: Zip file containing original audio clips used to generate these data</p><p><b>Date range: </b>2015-04-01 to 2015-06-30</p><p><b>Latitudinal extent: </b>4.6000 to 4.8000</p><p><b>Longitudinal extent: </b>117.5000 to 117.7000</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> -  -  -  -  Primates <br> -  -  -  -  -  Hylobatidae <br> -  -  -  -  -  -  <i>Hylobates</i> <br> -  -  -  -  -  -  -  <i>Hylobates funereus</i> <br> -  -  -  Aves <br> -  -  -  -  Galliformes <br> -  -  -  -  -  Phasianidae <br> -  -  -  -  -  -  <i>Argusianus</i> <br> -  -  -  -  -  -  -  <i>Argusianus argus</i> <br> -  -  -  -  Bucerotiformes <br> -  -  -  -  -  Bucerotidae <br> -  -  -  -  -  -  <i>Anorrhinus</i> <br> -  -  -  -  -  -  -  <i>Anorrhinus galeritus</i> <br> -  -  -  -  -  -  <i>Buceros</i> <br> -  -  -  -  -  -  -  <i>Buceros rhinoceros</i> <br> -  -  -  -  -  -  <i>Rhinoplax</i> <br> -  -  -  -  -  -  -  <i>Rhinoplax vigil</i> <br> -  -  -  -  Psittaciformes <br> -  -  -  -  -  Psittacidae <br> -  -  -  -  -  -  <i>Loriculus</i> <br> -  -  -  -  -  -  -  <i>Loriculus galgulus</i> <br> -  -  -  -  Passeriformes <br> -  -  -  -  -  Cisticolidae <br> -  -  -  -  -  -  <i>Orthotomus</i> <br> -  -  -  -  -  -  -  <i>Orthotomus ruficeps</i> <br> -  -  -  -  -  -  -  <i>Orthotomus atrogularis</i> <br> -  -  -  -  -  -  -  <i>Orthotomus sericeus</i> <br> -  -  -  -  -  -  <i>Prinia</i> <br> -  -  -  -  -  -  -  <i>Prinia flaviventris</i> <br> -  -  -  -  -  Chloropseidae <br> -  -  -  -  -  -  <i>Chloropsis</i> <br> -  -  -  -  -  -  -  <i>Chloropsis cyanopogon</i> <br> -  -  -  -  -  Aegithinidae <br> -  -  -  -  -  -  <i>Aegithina</i> <br> -  -  -  -  -  -  -  <i>Aegithina viridissima</i> <br> -  -  -  -  -  Pycnonotidae <br> -  -  -  -  -  -  <i>Pycnonotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus atriceps</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus simplex</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus erythropthalmos</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus goiavier</i> <br> -  -  -  -  -  -  <i>Alophoixus</i> <br> -  -  -  -  -  -  -  <i>Alophoixus bres</i> <br> -  -  -  -  -  -  <i>Iole</i> <br> -  -  -  -  -  -  -  <i>Iole olivacea</i> <br> -  -  -  -  -  Dicaeidae <br> -  -  -  -  -  -  <i>Prionochilus</i> <br> -  -  -  -  -  -  -  <i>Prionochilus xanthopygius</i> <br> -  -  -  -  -  -  <i>Dicaeum</i> <br> -  -  -  -  -  -  -  <i>Dicaeum trigonostigma</i> <br> -  -  -  -  -  Monarchidae <br> -  -  -  -  -  -  <i>Hypothymis</i> <br> -  -  -  -  -  -  -  <i>Hypothymis azurea</i> <br> -  -  -  -  -  -  <i>Rhipidura</i> <br> -  -  -  -  -  -  -  <i>Rhipidura javanica</i> <br> -  -  -  -  -  Irenidae <br> -  -  -  -  -  -  <i>Irena</i> <br> -  -  -  -  -  -  -  <i>Irena puella</i> <br> -  -  -  -  -  Muscicapidae <br> -  -  -  -  -  -  <i>Trichixos</i> <br> -  -  -  -  -  -  -  <i>Trichixos pyrropygus</i> <br> -  -  -  -  -  Tephrodornithidae <br> -  -  -  -  -  -  <i>Hemipus</i> <br> -  -  -  -  -  -  -  <i>Hemipus picatus</i> <br> -  -  -  -  -  -  -  <i>Hemipus hirundinaceus</i> <br> -  -  -  -  -  Pellorneidae <br> -  -  -  -  -  -  <i>Kenopia</i> <br> -  -  -  -  -  -  -  <i>Kenopia striata</i> <br> -  -  -  -  -  -  <i>Pellorneum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum capistratum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum bicolor</i> <br> -  -  -  -  -  -  <i>Malacopteron</i> <br> -  -  -  -  -  -  -  <i>Malacopteron affine</i> <br> -  -  -  -  -  -  <i>Malacocincla</i> <br> -  -  -  -  -  -  -  <i>Malacocincla malaccensis</i> <br> -  -  -  -  -  -  <i>Alcippe</i> <br> -  -  -  -  -  -  -  <i>Alcippe brunneicauda</i> <br> -  -  -  -  -  Corvidae <br> -  -  -  -  -  -  <i>Corvus</i> <br> -  -  -  -  -  -  -  <i>Corvus enca</i> <br> -  -  -  -  -  Acanthizidae <br> -  -  -  -  -  -  <i>Gerygone</i> <br> -  -  -  -  -  -  -  <i>Gerygone sulphurea</i> <br> -  -  -  -  -  Timaliidae <br> -  -  -  -  -  -  <i>Cyanoderma</i> <br> -  -  -  -  -  -  -  <i>Cyanoderma erythropterum</i> <br> -  -  -  -  -  -  -  <i>Cyanoderma rufifrons</i> <br> -  -  -  -  -  -  <i>Mixornis</i> <br> -  -  -  -  -  -  -  <i>Mixornis bornensis</i> <br> -  -  -  -  -  -  <i>Macronus</i> <br> -  -  -  -  -  -  -  <i>Macronus ptilosus</i> <br> -  -  -  -  -  -  <i>Stachyris</i> <br> -  -  -  -  -  -  -  <i>Stachyris maculata</i> <br> -  -  -  -  -  -  -  <i>Stachyris poliocephala</i> <br> -  -  -  -  -  -  <i>Pomatorhinus</i> <br> -  -  -  -  -  -  -  <i>Pomatorhinus montanus</i> <br> -  -  -  -  -  Nectariniidae <br> -  -  -  -  -  -  <i>Anthreptes</i> <br> -  -  -  -  -  -  -  <i>Anthreptes simplex</i> <br> -  -  -  -  -  -  <i>Arachnothera</i> <br> -  -  -  -  -  -  -  <i>Arachnothera longirostra</i> <br> -  -  -  -  -  -  -  <i>Arachnothera affinis</i> <br> -  -  -  -  -  Eurylaimidae <br> -  -  -  -  -  -  <i>Eurylaimus</i> <br> -  -  -  -  -  -  -  <i>Eurylaimus ochromalus</i> <br> -  -  -  -  Piciformes <br> -  -  -  -  -  Ramphastidae <br> -  -  -  -  -  -  <i>Megalaima</i> <br> -  -  -  -  -  -  -  <i>Megalaima australis</i> <br> -  -  -  -  -  -  -  -  <i>Megalaima australis duvaucelii</i> (as homotypic_synonym: <i>Psilopogon duvaucelii</i>)<br> -  -  -  -  -  Picidae <br> -  -  -  -  -  -  <i>Meiglyptes</i> <br> -  -  -  -  -  -  -  <i>Meiglyptes tukki</i> <br> -  -  -  -  -  -  <i>Dinopium</i> <br> -  -  -  -  -  -  -  <i>Dinopium rafflesii</i> <br> -  -  -  -  -  -  <i>Blythipicus</i> <br> -  -  -  -  -  -  -  <i>Blythipicus rubiginosus</i> <br> -  -  -  -  -  -  <i>Mulleripicus</i> <br> -  -  -  -  -  -  -  <i>Mulleripicus pulverulentus</i> <br> -  -  -  -  Cuculiformes <br> -  -  -  -  -  Cuculidae <br> -  -  -  -  -  -  <i>Cacomantis</i> <br> -  -  -  -  -  -  -  <i>Cacomantis sonneratii</i> <br> -  -  -  -  -  -  -  <i>Cacomantis merulinus</i> <br> -  -  -  -  -  -  <i>Rhinortha</i> <br> -  -  -  -  -  -  -  <i>Rhinortha chlorophaea</i> <br> -  -  -  -  -  -  <i>Cuculus</i> <br> -  -  -  -  -  -  -  <i>Cuculus vagans</i> <br> -  -  -  -  -  -  <i>Centropus</i> <br> -  -  -  -  -  -  -  <i>Centropus sinensis</i> <br> -  -  -  -  Columbiformes <br> -  -  -  -  -  Columbidae <br> -  -  -  -  -  -  <i>Chalcophaps</i> <br> -  -  -  -  -  -  -  <i>Chalcophaps indica</i> <br></div><p></p>
Data from: Assessing the sensitivity of biodiversity indices used to inform fire management
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Data from: Ecoacoustic indices as proxies for biodiversity on temperate reefs
Diversity measurement techniques can present logistical and financial obstacles to conservation efforts. Ecoacoustics has recently emerged as a promising solution to these issues, providing a mechanism for measuring diversity using acoustic indices, which have proven to be beneficial in terrestrial habitats. This study investigates the application of acoustic measures as a tool for quick and effective marine diversity monitoring via direct, in situ comparison of ecoacoustics indices with species assemblage diversity measures from temperate rocky reefs. Acoustic recordings and visual surveys of reef fish abundance were collected at nine sites in north-eastern New Zealand. Three ecoacoustic indices originally developed for terrestrial use were then compared to three species assemblage diversity measures and compared using Pearson correlations. Additionally, four criteria for successful ecoacoustic indices were developed and tested as a means of standardizing future evaluation and use of acoustic indices: (i) positive correlations between species diversity and ecoacoustic indices in relevant frequency ranges, (ii) robustness to changes in spectral resolution, (iii) robustness to the presence of natural noise interference (i.e. wind) and (iv) robustness to the presence of anthropogenic noise. Acoustic Complexity Index (ACI) was significantly correlated with Pielou's Evenness (J′) and Shannon's index (H′). Neither Acoustic Richness (AR) nor ACI was impacted by changes in spectral resolution, but values of the Acoustic Entropy Index (H) increased significantly between fast Fourier transformation (FFT) sizes 512 and 1024. H was consistently positively correlated with both H′ and estimated number of species (S) above a spectral resolution of c. 140·6 Hz (FFT size 1024). Wind did not affect any of the acoustic indices. As anthropogenic noise was included in these investigations, both ACI and H were considered robust to its presence. While AR failed to meet all four criteria for a successful ecoacoustic indices, both ACI and H appeared to be appropriate for use on temperate reefs. In a time of accelerated global diversity loss, these two ecoacoustic indices show strong potential for use as efficient, non-invasive marine diversity measures.
Data from: Ecoacoustic indices as proxies for biodiversity on temperate reefs
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Abbreviations: ILK: indigenous and local knowledge; IPLCs: indigenous peoples and local communities; NBSAPs: national biodiversity strategies and action plans. a Strategic Plan for Biodiversity 2011–2020. Figure 6. Summary of progress towards the Aichi Targets. Scores are based on quantitative analysis of indicators, a systematic review of the literature, fifth National Reports to the CBD, and available information on countries' stated intentions to implement additional actions by 2020. Progress towards target elements is scored as "Good" (substantial positive trends at a global scale relating to in Summary for policymakers of the global assessment report on biodiversity and ecosystem services - unedited advance version
Abbreviations: ILK: indigenous and local knowledge; IPLCs: indigenous peoples and local communities; NBSAPs: national biodiversity strategies and action plans. a Strategic Plan for Biodiversity 2011–2020. Figure 6. Summary of progress towards the Aichi Targets. Scores are based on quantitative analysis of indicators, a systematic review of the literature, fifth National Reports to the CBD, and available information on countries' stated intentions to implement additional actions by 2020. Progress towards target elements is scored as "Good" (substantial positive trends at a global scale relating to
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