A Multi-Temporal Object-Based Image Analysis to Detect Long-Lived Shrub Cover Changes in Drylands

Climate change and human actions condition the spatial distribution and structure of vegetation, especially in drylands. In this context, object-based image analysis (OBIA) has been used to monitor changes in vegetation, but only a few studies have related them to anthropic pressure. In this study, we assessed changes in cover, number, and shape of Ziziphus lotus shrub individuals in a coastal groundwater-dependent ecosystem in SE Spain over a period of 60 years and related them to human actions in the area. In particular, we evaluated how sand mining, groundwater extraction, and the protection of the area affect shrubs. To do this, we developed an object-based methodology that allowed us to create accurate maps (overall accuracy up to 98%) of the vegetation patches and compare the cover changes in the individuals identified in them. These changes in shrub size and shape were related to soil loss, seawater intrusion, and legal protection of the area measured by average minimum distance (AMD) and average random distance (ARD) analysis. It was found that both sand mining and seawater intrusion had a negative effect on individuals; on the contrary, the protection of the area had a positive effect on the size of the individuals’ coverage. Our findings support the use of OBIA as a successful methodology for monitoring scattered vegetation patches in drylands, key to any monitoring program aimed at vegetation preservation

Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

This research was funded by the European Research Council (ERC Grant agreement 647038 [BIODESERT]), the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612, the RH2OARID (P18-RT-5130) and RESISTE (P18-RT-1927) funded by Consejeria de Economia, Conocimiento, Empresas y Universidad from the Junta de Andalucia, and by projects A-TIC-458-UGR18 and DETECTOR (A-RNM-256-UGR18), with the contribution of the European Union Funds for Regional Development. E.R-C was supported by the HIPATIA-UAL fellowship, founded by the University of Almeria. S.T. is supported by the Ramon y Cajal Program of the Spanish Government (RYC-201518136).Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.European Research Council (ERC) 647038European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612Junta de Andalucia P18-RT-1927 P18-RT-5130DETECTOR A-RNM-256-UGR18European Union Funds for Regional DevelopmentHIPATIA-UAL fellowshipSpanish Government RYC-201518136A-TIC-458-UGR1

Spectral Diversity Successfully Estimates the α-Diversity of Biocrust-Forming Lichens

Biocrusts, topsoil communities formed by mosses, lichens, liverworts, algae, and cyanobacteria, are a key biotic component of dryland ecosystems worldwide. Experiments carried out with lichen- and moss-dominated biocrusts indicate that climate change may dramatically reduce their cover and diversity. Therefore, the development of reproducible methods to monitor changes in biocrust diversity and abundance across multiple spatio-temporal scales is key for evaluating how climate change may impact biocrust communities and the myriad of ecosystem functions and services that rely on them. In this study, we collected lichen-dominated biocrust samples from a semi-arid ecosystem in central Spain. Their α-diversity was then evaluated using very high spatial resolution hyperspectral images (pixel size of 0.091 mm) measured in laboratory under controlled conditions. Support vector machines were used to map the biocrust composition. Traditional α-diversity metrics (i.e., species richness, Shannon’s, Simpson’s, and Pielou’s indices) were calculated using lichen fractional cover data derived from their classifications in the hyperspectral imagery. Spectral diversity was calculated at different wavelength ranges as the coefficient of variation of different regions of the reflectance spectra of lichens and as the standard deviation of the continuum removal algorithm (SD_CR). The accuracy of the classifications of the images obtained was close to 100%. The results showed the best coefficient of determination (r2 = 0.47) between SD_CR calculated at 680 nm and the α-diversity calculated as the Simpson’s index, which includes species richness and their evenness. These findings indicate that this spectral diversity index could be used to track spatio-temporal changes in lichen-dominated biocrust communities. Thus, they are the first step to monitor α-diversity of biocrust-forming lichens at the ecosystem and regional levels, a key task for any program aiming to evaluate changes in biodiversity and associated ecosystem services in drylands.The research has received funding from the European Union’s Horizon 2020 research and innovation 514 program under the Marie Sklodowska-Curie grant agreement no. 721995. F.T.M. acknowledges support from the European Research Council grant agreement no. 647038 (BIODESERT)

Cuentos del Olivar

En este apasionante viaje tenemos las puertas abiertas para todo aquel que se quiera sumar, ya sea trabajando en pos de la difusión de la cultura ligada al olivo o disfrutando de lecturas como las que se recogen en este libro, que seguro les depara un sabroso disfrute y les descubre una cultura del olivar que tiene a sus espaldas varios milenios de historia. Les deseo que paladeen cada uno de estos relatos, que conforman un excelente aperitivo literario.Área de Historia del Art

Remote Sensing-Based Monitoring of Postfire Recovery of Persistent Shrubs: The Case of Juniperus communis in Sierra Nevada (Spain)

Funding: This work has received funding from the European Research Council (ERC grant agreement 647038 [BIODESERT]), the ADAPTAMED LIFE14 CCA/ES/000612 project, the RH2O-ARID project (P18-RT-5130), and the RESISTE project (P18-RT-1927), funded by the Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía; and by the A-TIC-458-UGR18 and DETECTOR (A-RNM-256-UGR18) projects, with the contribution of the European Funds for Regional Development. E.G. was supported by the Generalitat Valenciana and the European Social Fund (APOSTD/2021/188). This work is part of the “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada” (LifeWatch-2019-10-UGR- 01) project, which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014–2020 (POPE), LifeWatch-ERIC action line, within theWorkpackages LifeWatch-2019-10-UGR-01_WP-8, LifeWatch-2019-10-UGR-01_WP-7, and LifeWatch-2019-10-UGR-01_WP-4.Wildfires affect the structure, functioning, and composition of ecosystems. Long-term monitoring of the occurrence, abundance, and growth of plant species is key to assessing the responses of the dynamics of plant populations with regard to environmental disturbances, such as wildfires. In this work, we evaluated the changes in the number of individuals and the canopy cover extent of a population of Juniperus communis L. during a four-decade period following a wildfire in a Mediterranean high-mountain ecosystem (Sierra Nevada, Spain). To do this, we used object-based image analysis (OBIA) applied to very high-resolution aerial images. Our study also provides a new approach to optimize the shrub identification process and to semi-automatically evaluate the accuracy of the number of shrubs and their canopy cover. From the 752 individuals present in 1977, only 433 remained immediately after a fire (1984), a few more disappeared one decade later (420 shrubs in 1997), while by 2008, the population had partially recovered to 578 shrubs. The wildfire decreased juniper canopy cover from 55,000 m2 to 40,000 m2, but two decades later it had already recovered to 57,000 m2. The largest shrubs were more resistant to fire than the smallest ones and recovered in a shorter time period. The protection measures introduced with the park declaration seemed to have contributed to the post-fire recovery. The potential of this methodology in the management and conservation of biodiversity in the future is also discussed.European Research Council (ERC grant agreement 647038 [BIODESERT])ADAPTAMED LIFE14 CCA/ES/000612 projectRH2O-ARID project (P18-RT-5130)RESISTE project (P18-RT-1927) funded by the Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía;A-TIC-458-UGR18 and DETECTOR (A-RNM-256-UGR18) projects, with the contribution of the European Funds for Regional DevelopmentGeneralitat Valenciana and the European Social Fund (APOSTD/2021/188)“Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada” (LifeWatch-2019-10-UGR- 01) project, which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014–2020 (POPE), LifeWatch-ERIC action line, within theWorkpackages LifeWatch-2019-10-UGR-01_WP-8, LifeWatch-2019-10-UGR-01_WP-7, and LifeWatch-2019-10-UGR-01_WP-

Remote Sensing-Based Monitoring of Postfire Recovery of Persistent Shrubs: The Case of Juniperus communis in Sierra Nevada (Spain)

Wildfires affect the structure, functioning, and composition of ecosystems. Long-term monitoring of the occurrence, abundance, and growth of plant species is key to assessing the responses of the dynamics of plant populations with regard to environmental disturbances, such as wildfires. In this work, we evaluated the changes in the number of individuals and the canopy cover extent of a population of Juniperus communis L. during a four-decade period following a wildfire in a Mediterranean high-mountain ecosystem (Sierra Nevada, Spain). To do this, we used object-based image analysis (OBIA) applied to very high-resolution aerial images. Our study also provides a new approach to optimize the shrub identification process and to semi-automatically evaluate the accuracy of the number of shrubs and their canopy cover. From the 752 individuals present in 1977, only 433 remained immediately after a fire (1984), a few more disappeared one decade later (420 shrubs in 1997), while by 2008, the population had partially recovered to 578 shrubs. The wildfire decreased juniper canopy cover from 55,000 m2 to 40,000 m2, but two decades later it had already recovered to 57,000 m2. The largest shrubs were more resistant to fire than the smallest ones and recovered in a shorter time period. The protection measures introduced with the park declaration seemed to have contributed to the post-fire recovery. The potential of this methodology in the management and conservation of biodiversity in the future is also discussed.This work has received funding from the European Research Council (ERC grant agreement 647038 [BIODESERT]), the ADAPTAMED LIFE14 CCA/ES/000612 project, the RH2O-ARID project (P18-RT-5130), and the RESISTE project (P18-RT-1927), funded by the Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía; and by the A-TIC-458-UGR18 and DETECTOR (A-RNM-256-UGR18) projects, with the contribution of the European Funds for Regional Development. E.G. was supported by the Generalitat Valenciana and the European Social Fund (APOSTD/2021/188). This work is part of the “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada” (LifeWatch-2019-10-UGR-01) project, which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014–2020 (POPE), LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01_WP-8, LifeWatch-2019-10-UGR-01_WP-7, and LifeWatch-2019-10-UGR-01_WP-4

Spectral diversity successfully estimates the α-diversity of biocrust-forming lichens

Biocrusts, topsoil communities formed by mosses, lichens, liverworts, algae, and cyanobacteria, are a key biotic component of dryland ecosystems worldwide. Experiments carried out with lichen- and moss-dominated biocrusts indicate that climate change may dramatically reduce their cover and diversity. Therefore, the development of reproducible methods to monitor changes in biocrust diversity and abundance across multiple spatio-temporal scales is key for evaluating how climate change may impact biocrust communities and the myriad of ecosystem functions and services that rely on them. In this study, we collected lichen-dominated biocrust samples from a semi-arid ecosystem in central Spain. Their α-diversity was then evaluated using very high spatial resolution hyperspectral images (pixel size of 0.091 mm) measured in laboratory under controlled conditions. Support vector machines were used to map the biocrust composition. Traditional α-diversity metrics (i.e., species richness, Shannon's, Simpson's, and Pielou's indices) were calculated using lichen fractional cover data derived from their classifications in the hyperspectral imagery. Spectral diversity was calculated at different wavelength ranges as the coefficient of variation of different regions of the reflectance spectra of lichens and as the standard deviation of the continuum removal algorithm (SD_CR). The accuracy of the classifications of the images obtained was close to 100%. The results showed the best coefficient of determination (r2 = 0.47) between SD_CR calculated at 680 nm and the α-diversity calculated as the Simpson's index, which includes species richness and their evenness. These findings indicate that this spectral diversity index could be used to track spatio-temporal changes in lichen-dominated biocrust communities. Thus, they are the first step to monitor α-diversity of biocrust-forming lichens at the ecosystem and regional levels, a key task for any program aiming to evaluate changes in biodiversity and associated ecosystem services in drylands

Text mining and expert curation to develop a database on psychiatric diseases and their genes

Psychiatric disorders constitute one of the main causes of disability worldwide. During the past years, considerable research has been conducted on the genetic architecture of such diseases, although little understanding of their etiology has been achieved. The difficulty to access up-to-date, relevant genotype-phenotype information has hampered the application of this wealth of knowledge to translational research and clinical practice in order to improve diagnosis and treatment of psychiatric patients. PsyGeNET (http://www.psygenet.org/) has been developed with the aim of supporting research on the genetic architecture of psychiatric diseases, by providing integrated and structured accessibility to their genotype-phenotype association data, together with analysis and visualization tools. In this article, we describe the protocol developed for the sustainable update of this knowledge resource. It includes the recruitment of a team of domain experts in order to perform the curation of the data extracted by text mining. Annotation guidelines and a web-based annotation tool were developed to support the curators' tasks. A curation workflow was designed including a pilot phase and two rounds of curation and analysis phases. Negative evidence from the literature on gene-disease associ- ations (GDAs) was taken into account in the curation process. We report the results of the application of this workflow to the curation of GDAs for PsyGeNET, including the analysis of the inter-annotator agreement and suggest this model as a suitable approach for the sustainable development and update of knowledge resources. Database URL: http://www.psygenet.org. PsyGeNET corpus: http://www.psygenet.org/ds/PsyGeNET/results/psygenetCorpus.tarWe received support from ISCIII-FEDER (PI13/00082, CP10/00524, CPII16/00026), IMI-JU under grants agreements no. 115191 (Open PHACTS)] and no. 115372 (EMIF), resources of which are composed of financial contribution from the EU-FP7 (FP7/2007-2013) and EFPIA companies in kind contribution, and the EU H2020 Programme 2014-2020 under grant agreements no. 634143 (MedBioinformatics) and no. 676559 (Elixir-Excelerate). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I + D+i 2013-2016, funded by ISCIII and FEDER. MRA, SMR and MCBG are supported RD16/0017/0007; OV, FF and MT are supported by RD16/0017/0010; and AS and FJP are supported by RD16/0017/0001, by Instituto de Salud Carlos III, Red de Trastornos Adictivos (RTA-Retics-ISCIII). AGS acknowledges financial support from the Spanish Ministry of Economy and Competitiveness, through the 'María de Maeztu' Programme for Units of Excellence in R&D (MDM-2014-0370). Funding for open access: EU H2020 Programme 2014-2020 under grant agreements no. 634143 (MedBioinformatics)

Text mining and expert curation to develop a database on psychiatric diseases and their genes

Altres ajuts: We received support from IMI-JU under grants agreements no. 115191 (Open PHACTS)] and no. 115372 (EMIF), resources of which are composed of financial contribution from the EU-FP7 (FP7/2007-2013) and EFPIA companies in kind contribution, and the EU H2020 Programme 2014-2020 under grant agreements no. 634143(MedBioinformatics) and no. 676559 (Elixir-Excelerate). , of the PE I þ Dþi 2013- 2016, funded by ISCIII and FEDER. Funding for open access: EU H2020 Programme 2014-2020 under grant agreements no. 634143 (MedBioinformatics).Psychiatric disorders constitute one of the main causes of disability worldwide. During the past years, considerable research has been conducted on the genetic architecture of such diseases, although little understanding of their etiology has been achieved. The difficulty to access up-to-date, relevant genotype-phenotype information has hampered the application of this wealth of knowledge to translational research and clinical practice in order to improve diagnosis and treatment of psychiatric patients. PsyGeNET () has been developed with the aim of supporting research on the genetic architecture of psychiatric diseases, by providing integrated and structured accessibility to their genotype-phenotype association data, together with analysis and visualization tools. In this article, we describe the protocol developed for the sustainable update of this knowledge resource. It includes the recruitment of a team of domain experts in order to perform the curation of the data extracted by text mining. Annotation guidelines and a web-based annotation tool were developed to support the curators' tasks. A curation workflow was designed including a pilot phase and two rounds of curation and analysis phases. Negative evidence from the literature on gene-disease associations (GDAs) was taken into account in the curation process. We report the results of the application of this workflow to the curation of GDAs for PsyGeNET, including the analysis of the inter-annotator agreement and suggest this model as a suitable approach for the sustainable development and update of knowledge resources. Database URL : PsyGeNET corpus