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Guirado, E.

Publications and source records attributed to Guirado, E..

3 recordsLinked to original sources

Environmental thresholds in the functional mycobiome of global drylands

Fungi are major drivers of ecosystem functions. Increases in aridity are known to negatively impact fungal communities in dryland ecosystems globally, however, much less is known on the potential influence of other environmental drivers. To fill this knowledge gap, we reanalyzed fungal data from 912 soil samples, providing the largest and most complete fungal community dataset from global drylands. We used machine learning tools to examine geographical patterns in community composition and spatial, edaphic, and climatic factors driving them. Further, we determined critical thresholds of community turnover along those gradients. Our analysis identifies UV index, climate seasonality, and sand content as the most important environmental predictors of community shifts, harbouring greatest association with the richness of putative plant pathogens and saprobes. Important nonlinear relationships existed with each of these fungal guilds, with increases in UV and temperature seasonality above 7.5 and 900 SD, respectively, being associated with an increased probability of plant pathogens and unspecified saprotrophs occurrence. Conversely, these environmental parameters had a negative relationship with litter and soil saprotrophs richness. Consequently, these functional groups might be differentially sensitive to environmental changes, which might result in an inevitable disturbance of current plant-soil dynamics in drylands.

ecology↗

Sentinel2GlobalLULC: A deep-learning-ready Sentinel-2 RGB image dataset for global land use/cover mapping

Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there is still low consistency among LULC products due to low accuracy for some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the consensus of 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v1.1 contains 195572 RGB images organized into 29 global LULC mapping classes. Each image is a tile that has 224 x 224 pixels at 10 x 10 m spatial resolution and was built as a cloud-free composite from all Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC type annotation per image, together with level of consensus, reverse geo-referencing, and global human modification index. Sentinel2GlobalLULC is optimized for the state-of-the-art Deep Learning models to provide a new gate towards building precise and robust global or regional LULC maps.

ecology↗

Automatic whale counting in satellite images with deep learning

Despite their interest and threat status, the number of whales in worlds oceans remains highly uncertain. Whales detection is normally carried out from costly sighting surveys, acoustic surveys or through high-resolution orthoimages. Since deep convolutional neural networks (CNNs) achieve great performance in object-recognition in images, here we propose a robust and generalizable CNN-based system for automatically detecting and counting whales from space based on open data and tools. A test of the system on Google Earth images in ten global whale-watching hotspots achieved a performance (F1-measure) of 84% in detecting and 97% in counting 80 whales. Applying this cost-effective method worldwide could facilitate the assessment of whale populations to guide conservation actions. Free and global access to high-resolution imagery for conservation purposes would boost this process.

ecology↗