cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.xx443u
title: Occurrence Download

description: A dataset containing 802 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Melaleuca fulgens"
 ]
}

 The dataset includes 802 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036723-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wzpnnv
title: Occurrence Download

description: A dataset containing 705 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Bee Biology and Systematics Laboratory, Snow Entomological Museum Collection, University of North Texas Elm Fork Insect Collection, Illinois Natural History Survey Insect Collection, Texas A&M University Insect Collection, Essig Museum of Entomology, C.P. Gillette Museum of Arthropod Diversity, NMNH Extant Specimen Records (USNM, US))",
 "GadmGid is USA.44_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Xylocopa"
 ]
}

 The dataset includes 705 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036725-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mpukp7
title: Occurrence Download

description: A dataset containing 9602 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erigeron divergens"
 ]
}

 The dataset includes 9602 records from 204 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036726-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3p5ug3
title: Occurrence Download

description: A dataset containing 1395 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erigeron tenuis"
 ]
}

 The dataset includes 1395 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036727-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12848755
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.q8enk9
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sorghum halepense",
 "Country is Tonga",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036730-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b6yuyf
title: Occurrence Download

description: A dataset containing 217 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Senecio teneriffae"
 ]
}

 The dataset includes 217 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036729-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rfbjjw
title: Occurrence Download

description: A dataset containing 1741 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Artemisia vallesiaca"
 ]
}

 The dataset includes 1741 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036728-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t7u36z
title: Occurrence Download

description: A dataset containing 30030 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erigeron strigosus"
 ]
}

 The dataset includes 30030 records from 426 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036731-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48339/2011-1-k67p1
title: Does grading affect educational attainment? A longitudinal study

description: U67
DOI: 10.15468/dl.rg9uwz
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Glyphonycteris behnii (Peters, 1865)"
 ]
}

 The dataset includes 7 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011149-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14288/1.0448473
title: Assessing the Impact of the University of British Columbia Campus Vision 2050 on Eastern Grey Squirrel Habitat Suitability in Acadia Park

description: Urban development is a leading driver of habitat fragmentation, with significant implications for wildlife in growing cities. This research investigates how projected urban expansion under the University of British Columbia’s Campus Vision 2050 may impact Eastern Grey Squirrel habitat suitability in Acadia Park, a residential neighborhood on the University of British Columbia Vancouver campus. The objective was to assess changes in habitat quality and connectivity using high-resolution Light Detection and Ranging data and visualize these changes through an interactive 3D model. Individual trees were identified using segmentation algorithms applied to 2022 Light Detection and Ranging data, and habitat suitability was evaluated using four ecological metrics: Nearest Neighbor Distance, Proximity Index, Convex Hull Area Index, and Height Index. These metrics were combined to produce a Habitat Suitability Index (HSI) for each tree. Results indicate a 4.0% reduction in tree count and an 8.2% loss in canopy volume between 2021 and 2050. High-suitability habitat (HSI ≥ 0.6) declined by 19.57%, while low-suitability areas increased by 1.9%, signaling greater habitat fragmentation. Although medium-suitability habitat remained relatively stable, connectivity between habitat patches weakened. A voxel-based visualization in Minetest revealed increasing gaps in the canopy, particularly in areas slated for future construction. These findings highlight that the loss of connectivity, rather than overall tree count, poses a greater risk to urban wildlife. By combining geospatial analysis with interactive modeling, this study offers a replicable approach for visualizing habitat change and supports the integration of ecological data into sustainable campus planning.
DOI: 10.14288/1.0448478
title: Integrating LiDAR (Light Detection and Ranging) and Camera Traps to Assess the Influence of Three-Dimensional Forest Structure on Mammal Habitat Use in Coastal Western Hemlock Forests

description: Global forest degradation and habitat loss caused have induced the need for advanced methods to assess forest quality and how would these change impacts on animals. Although extensive research has focused on responses of birds to habitat change, the influence of forest structure on mammals has remained underexplored. In southwestern British Columbia, Malcolm Knapp Research Forest and Golden Ears Provincial Park were selected as representative study areas, characterized by temperate coniferous forests that are subject to both anthropogenic disturbances and natural variability. This study aimed to determine the relationship between mammal presence and abundance to forests structures by using camera traps with airborne laser scanning. Detailed three-dimensional measurements of canopy height, vertical complexity, and terrain ruggedness were derived from laser scanning data, while camera traps provided continuous, non-invasive records of mammalian activity. Results showed that black-tailed deer abundance increased with steeper slopes and rugged terrain. Bobcat abundance was higher in areas with taller, more complex canopies. Snowshoe hare abundance declined where understory density was greater. Shrub connectivity reduced overall mammal and black bear abundance, though larger shrub patches were associated with higher numbers. These results indicated that heterogeneity in forest structure is a key factor in sustaining wildlife populations. The use of remote sensing not only enhanced the spatial coverage and accuracy of habitat assessments compared to traditional field surveys but also provided a scalable approach for ecological monitoring. These findings offer important insights for conservation planning and forest management, emphasizing the need to consider structural habitat features when developing strategies to protect understudied mammalian species.
DOI: 10.14288/1.0448456
title: Influence of Aspen Serpentine Leafminer (Phyllocnistis populiella) on Burn Severity In Trembling Aspen (Populus tremuloides)

description: Trembling aspen (Populus tremuloides) is a fire-resilient tree species, making its role increasingly important as wildfire seasons intensify in British Columbia, Canada. However, forest pests like the aspen serpentine leafminer (Phyllocnistis populiella) raise concerns about their potential to alter fire behavior by affecting forest structure and fuel conditions. This study evaluates whether leafminer outbreaks influence burn severity in aspen-dominated stands of northeastern British Columbia. Using provincial and federal datasets from 2010 to 2020, fire-affected aspen stands were analyzed at a 200 m resolution, comparing areas with and without prior leafminer outbreaks. A random forest model assessed the effect of leafminer presence on burn severity, measured by the differenced Normalized Burn Ratio (dNBR), while controlling for 11 climate, topography, and vegetation variables. Among 10,326 aspen-dominated burn pixels, 21% showed leafminer presence. The model achieved an R² of 0.84, with surrounding mean dNBR exerting the greatest influence on model performance, while leafminer presence had the least effect. Burn severity was slightly lower in leafminer-affected areas, with a mean dNBR decrease of 0.03. Additionally, higher trembling aspen percentage and climate conditions favorable to aspen growth were strongly associated with reduced burn severity. These findings suggest that while the leafminer’s impact on burn severity is minimal, finer-scale analyses or investigations of outbreak severity may reveal more nuanced effects on aspen fire resilience.
DOI: 10.14288/1.0448466
title: Mapping Park and Greenspace Value Across the University of British Columbia (UBC) Vancouver Campus

description: Greenspaces on university campuses enhance well-being, yet their value varies due to accessibility and usability factors. This study mapped greenspace value across the University of British Columbia (UBC) Vancouver campus, assessing how lighting, accessibility infrastructure, topography and physical barriers influenced inclusivity and usability. Using a geospatial framework, data were collected from UBC infrastructure databases, LiDAR (Light Detection and Ranging) models, and on-site observations, analyzed through hotspot analysis, Kriging interpolation, and Weighted Overlay Analysis to generate a suitability map. Results showed central areas like West Mall and Lower Mall with high greenspace value due to dense lighting, flat terrain, and extensive accessibility features, while peripheral zones like Acadia Park scored lower due to inadequate lighting and steep slopes. The analysis revealed a central-peripheral divide, with central areas scoring 80% higher in suitability, highlighting systematic planning biases reducing equitable access. Targeted interventions like enhanced lighting and ramps were recommended to improve inclusivity. This work’s geospatial and LiDAR-based approach is transferable beyond UBC, offering a scalable model for urban parks, other campuses, and public spaces to assess accessibility. Cities globally could utilize it to address disparities in park equity, ensuring inclusivity for diverse populations, like those with mobility challenges or in underserved, remote areas. The focus on barriers and infrastructure applies to municipal planning, correcting imbalances where high-traffic zones overshadow peripheral regions. For example, suburban or rural communities could adapt this model to optimize limited greenspace resources. It also supports climate resilience by identifying areas requiring green enhancements. Applicable to sustainable development goals, this framework empowers planners to enhance social justice in urban design, making greenspaces equitable assets anywhere.
DOI: 10.15468/dl.7d52xx
title: Occurrence Download

description: A dataset containing 3920 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Caracterización de flora, edafofauna epígea, anfibios y aves del Complejo Páramos Chilí - Barragán, Tolima"
 ]
}

 The dataset includes 3920 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000518-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14288/1.0448477
title: Comparison Between Sentinel-2 and Landsat in Mapping Mangroves in The Gambia Using the Google Earth Engine Mangrove Mapping Methodology (GEM v2)

description: Mangroves play a vital role in coastal environments by supporting biodiversity, protecting shorelines, and storing carbon. However, these ecosystems are under growing pressure from land use change, development, and rising sea levels. Monitoring changes in mangrove cover is essential for guiding conservation and management efforts, but on the ground, methods are often time-consuming, expensive, and difficult to carry out across large or remote areas. This is where remote sensing offers a practical solution. By using satellite imagery, it becomes possible to track changes in mangrove extent over time, detect patterns of loss and regrowth, and support decision-making at both local and national levels. This study compared the effectiveness of Sentinel-2 and Landsat satellite data in mapping mangrove dynamics in The Gambia, using the Google Earth Engine Mangrove Mapping Methodology version 2 (GEM v2) from Blue Venture, a marine and conservation organization. A Random Forest classifier was applied to classify land cover types, and the accuracy of each dataset was evaluated. Sentinel-2 was better at detecting small, detailed changes, including narrow strips of loss and early signs of regrowth. Landsat, while less detailed, provided more stable classifications across broader areas. Sentinel-2 also showed greater variation in spectral data, which led to more classification errors in some mangrove classes, while Landsat maintained higher overall accuracy. The results show that both datasets have strengths, Sentinel-2 is well-suited for close-up monitoring of small-scale changes, while Landsat is reliable for long-term, large-scale analysis. A combined approach using both sensors may offer the complete overview of mangrove change. Future studies should consider using field data from the same area to improve classification accuracy and support more effective monitoring and protection of mangrove ecosystems.
DOI: 10.14288/1.0448453
title: Supervised Mangrove Classification in Madagascar Comparing Optical Imagery to Combined Optical and Synthetic Aperture Radar Imagery Using Random Forest on Sentinel-1 and Sentinel 2

description: Mangrove ecosystems are critical coastal habitats that provide essential services such as shoreline protection, carbon sequestration, and biodiversity support. Monitoring these ecosystems over large and often remote areas can be challenging, making remote sensing a valuable tool for effective observation and management. However, accurate classification of mangroves remains difficult due to spectral and structural complexities. This study evaluates the integration of Sentinel-1 (S1) synthetic aperture radar and Sentinel-2 (S2) optical data for supervised mangrove classification using a Random Forest (RF) algorithm across four sites in Madagascar: Velondriake, Mahajamba Bay, Belo Sur Mer, and Ambaro Bay. We compared classifications using S2 alone versus combined S1+S2 data to assess improvements in accuracy. Results showed that S1+S2 consistently outperformed S2 alone, achieving overall accuracies of 84.72%–95.53% compared to 84.09%–93.73%, with notable gains at Velondriake (92.61%vs. 89.07%) and Mahajamba Bay (95.53% vs. 93.73%). VH polarization emerged as a key discriminator, outperforming VV, contrasting with prior studies (e.g., Huang et al., 2022), likely due to our focus on seven land cover classes rather than binary mangrove/non-mangrove distinctions. The Optical and Synthetic Aperture Radar(SAR) Combined Mangrove Index (OSCMI) enhanced classification at three sites, though its importance varied (6.55%–9.89%). However, challenges persisted, particularly with closed-canopy mangroves, where user accuracy dropped below 70% at some sites, possibly due to spectral confusion, limited training data, and temporal mismatches between imagery and reference polygons. These findings highlight the value of combining S1 and S2 data for mangrove mapping, though site-specific factors and data alignment issues warrant further refinement. Enhanced field validation and updated reference data could further improve classification accuracy.
DOI: 10.14288/1.0448457
title: Mapping Equity Access to Green Spaces Across The University of British Columbia Vancouver Campus

description: Green spaces play an important role in providing ecosystem services such as air purification, temperature regulation, and recreational opportunities; however, their unequal distribution often highlights environmental inequities. This study examines the spatial distribution of green space coverage across the University of British Columbia (UBC) Vancouver campus to identify areas with limited access, termed Equity Initiative Zones (EIZs). A Geographic Information System (GIS)-based methodology was employed, integrating Light Detection and Ranging (LiDAR) data, green space data, a pedestrian walkway dataset, and a hexagonal grid framework to analyze pedestrian accessibility to green spaces using service area analysis, and to identify green space diversity and spatial equity disparities through statistical analysis. Results revealed that while the UBC campus has an average of 37.8% green space and 46.7% tree canopy coverage, approximately 6% of the campus falls within EIZs, primarily in northeast areas near academic buildings and parking lots—with mean green space coverage of approximately 1.8%. A moderate negative correlation between distance to green spaces and canopy coverage (r = −0.25, p = 1.96 × 10⁻⁷) suggested reduced canopy coverage in areas further away from green spaces. Additionally, green space diversity analysis showed that high-traffic areas like the academic zones are dominated by homogenized lawns, whereas peripheral areas such as those near the UBC Farm exhibit greater diversity. These findings highlight environmental inequities where EIZs may experience diminished access to ecosystem benefits, emphasizing the need for targeted interventions such as tree planting or the creation of pocket parks to promote a sustainable and inclusive campus environment.
DOI: 10.14288/1.0448479
title: Land Cover Mapping in Cable Bay with Sentinel 2 from 2018 to 2024

description: The Cable Bay area, south of Nanaimo, British Columbia, is an ecologically rich region with diverse ecosystems, including rare Garry oak meadows, increasingly threatened by urban expansion. This study mapped and analyzed land cover changes across six classes—Broadleaf, Conifer, Cropland, Developed, Meadow, and Water—from 2018 to 2024. We applied a Random Forest classifier to high-resolution Sentinel-2 imagery within a hybrid Python-R workflow, achieving classification accuracies of 97.0-98.6%. Spatiotemporal analyses, including transition matrices and hotspot detection, revealed a significant decline in Broadleaf forests and a net increase in Developed land. These shifts reflect urbanization’s pressure on natural habitats, threatening biodiversity and ecosystem services. The findings underscore the urgency of conservation efforts and informed land-use planning to preserve Cable Bay’s ecological integrity.
DOI: 10.14288/1.0448480
title: Tracking Forest Recovery: Early Indicators of Forest Regeneration Following the 2017 Elephant Hill Wildfire in British Columbia, Canada

description: Wildfire is an increasingly prominent disturbance across forested landscapes in British Columbia, yet ecological responses and long-term vegetation recovery remain complex and spatially variable. This study investigates post-fire vegetation recovery following the 2017 Elephant Hill wildfire using Landsat-derived remote sensing data from 2015 to 2022. By analyzing the Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI) we assess vegetation change across four time periods: pre-fire, immediate post-fire, two years post-fire, and five years post-fire. Vegetation recovery was defined as achieving at least 80% of pre-fire index values by 2022. Findings indicate that substantial portions of the burn scar remained below it five years after the fire. Slower recovery was often associated with higher initial burn severity, elevation, and climatic conditions. The use of remote sensing offers a unique advantage by enabling consistent, landscape-scale monitoring especially in remote or inaccessible areas while Landsat’s 30-meter spatial resolution facilitates the detection of fine-scale recovery patterns across heterogeneous terrain. These results underscore the importance of multi-temporal satellite imagery in evaluating ecosystem resilience and identifying areas that may benefit from targeted restoration efforts in the context of more frequent and severe wildfires.