Long-term monitoring of woody plants of Doñana shrublands (SW Spain) from 2008 to 2023.

Background: The long-term monitoring of the plant cover of Doñana shrublands is part of a harmonised protocol for the Long-term Ecological Monitoring Programme of Natural Resources and Processes targeting Terrestrial Vegetation. The general aim of this protocol is to monitor and assess the dynamics and trends of shrubland plant communities in Doñana. For shrublands, percentage cover is recorded annually, starting in 2008, by the Doñana Long-Term Monitoring Team in one field sampling campaign per year during the flowering season (between March and May) across 21 permanent square plots (15 m x 15 m).

Machine Learning and Feature Selection for soil spectroscopy. An evaluation of Random Forest wrappers to predict soil organic matter, clay, and carbonates.

Soil spectroscopy estimates soil properties using the absorption features in soil spectra. However, modelling soil properties with soil spectroscopy is challenging due to the high dimensionality of spectral data. Feature Selection wrapper methods are promising approaches to reduce the dimensionality but are barely used in soil spectroscopy.

Integrated modeling of labile and glycated hemoglobin with glucose for enhanced diabetes detection and short-term monitoring.

Metabolic biomarkers, particularly glycated hemoglobin and fasting plasma glucose, are pivotal in the diagnosis and control of diabetes mellitus. Despite their importance, they exhibit limitations in assessing short-term glucose variations. In this study, we propose labile hemoglobin as an additional biomarker, providing insightful perspectives into these fluctuations.

Variable selection for nonlinear dimensionality reduction of biological datasets through bootstrapping of correlation networks.

Identifying the most relevant variables or features in massive datasets for dimensionality reduction can lead to improved and more informative display, faster computation times, and more explainable models of complex systems. Despite significant advances and available algorithms, this task generally remains challenging, especially in unsupervised settings. In this work, we propose a method that constructs correlation networks using all intervening variables and then selects the most informative ones based on network bootstrapping.

Can Citizen Science Contribute to Avian Influenza Surveillance?

Global change is an important driver of the increase in emerging infectious diseases in recent decades. In parallel, interest in nature has increased, and different citizen science platforms have been developed to record wildlife observations from the general public. Some of these platforms also allow registering the observations of dead or sick birds.

Thirty-four years of Landsat monitoring reveal long-term effects of groundwater abstractions on a World Heritage Site wetland.

Wetlands are among the most biodiverse yet endangered ecosystems on Earth. Despite being the most important wetland in Europe, the Doñana National Park (southwestern Spain) is no exception, and the increase of nearby groundwater abstractions for intensive agriculture and human supply has raised international concerns about the conservation of this iconic wetland. It is thus needed to assess wetlands' long-term trends and responses to global and local factors to make informed management decisions.

Evaluating the BFAST method to detect and characterise changing trends in water time series: A case study on the impact of droughts on the Mediterranean climate.

Mediterranean climate regions are facing increased aridity conditions and water scarcity, thus needing integrated management of water resources. Detecting and characterising changes in water resources over time is the natural first step towards identifying the drivers of these changes and understanding the mechanism of change. The aim of this study is to evaluate the potential of Breaks For Additive Seasonal and Trend (BFAST) method to identify gradual (trend) and abrupt (step- change) changes in the freshwater resources time series over a long-term period.

Behavioural immune landscapes of inflammation.

Transcriptional and proteomic profiling of individual cells have revolutionized interpretation of biological phenomena by providing cellular landscapes of healthy and diseased tissues. These approaches, however, do not describe dynamic scenarios in which cells continuously change their biochemical properties and downstream 'behavioural' outputs. Here we used 4D live imaging to record tens to hundreds of morpho-kinetic parameters describing the dynamics of individual leukocytes at sites of active inflammation.

SURFWET: A biokinetic model for surface flow constructed wetlands.

Constructed wetlands are an alternative biotechnology for wastewater treatment that have several advantages over conventional systems. In this work, a biokinetic model for surface flow constructed wetlands is presented (SURFWET). SURFWET belongs to a class of models that are not only interesting from a theoretical viewpoint, as they allow to improve the understanding of the underlying processes; but also from a practical viewpoint, because they can be useful for optimal designs of constructed wetlands, complementing current empirical methods.

Effects of flooding regime and meteorological variability on the removal efficiency of treatment wetlands under a Mediterranean climate.

The modeling of free-water surface constructed wetlands (FWS-CWs) provides an improved understanding of their processes and constitutes a useful tool for the design and management of these systems. In this work, a dynamic simulation model for FWS-CWs was developed and used to simulate the operation of a FWS-CW proposed for improving the treatment of sewage effluents entering the Tablas de Daimiel National Park in central Spain. The process-based model simulates carbon, nitrogen and phosphorus dynamics, including key hydrological processes for wetlands under a fluctuating Mediterranean semiarid climate.

Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images.

We have used Landsat-5 TM and Landsat-7 ETM+ images together with simultaneous ground-truth data at sample points in the Doñana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630-690 nm), band 5 (1550-1750 nm) and the ratio between bands 1 (450-520 nm) and 4 (760-900 nm).