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Science-policy interfaces (SPIs) are social processes that are avenues for addressing sustainability challenges through strengthening collaborations between researchers, decision makers and social actors. These transdisciplinary experiences provide a framework wherein scientific advances, policy needs and societal concerns can be coupled to increase the understating of complex problems and identify collective solutions to solve them. However, many studies have highlighted the need to develop and refine tools and operational methods to operationalize SPIs. Here, we present a SPI experience for addressing day-to-day problems in the southeastern Spanish dryland (López- Rodríguez et al., 2015). To facilitate mutual understanding and generate trust between participants we used (1) a knowledge brokering approach based on six interlinked workshops, and (2) a context-specific boundary object specifically designed to put into practice the transdisciplinary process. The boundary object is a graphical tool (triangle) for diagnosing environmental problems using three gradients based on a standardized punctuation for each one (on a 0-3 scale), namely of: (i) the scientific knowledge (i.e. the scientific evidence available about the specific problem); (ii) the regulatory capacity (i.e. the current legislative framework relevant to articulating public administration solutions); (iii) public engagement (which reflects the social relevance of the specific problems to the general public). In this gradient 0 represents that scientific knowledge, regulatory capacity or public engagement not being relevant for solving the environmental problem in the short term; whereas 3 represents high scientific evidence, regulatory capacity and that public engagement is available to address the problem. Throughout the SPI, 12 environmental problems (5 related to water management and 7 related to biodiversity loss) were identified and agreed as priorities in the region. Then, each problem was, collectively, rated differently for each dimensionusing the boundary object. The use of this boundary object allowed (1) aligning scientific knowledge with specific management goals and societal demands, and (2) promoting the implementation of science-based actions through collaborative work between scientists, decision makers and social actors. These insights provide a useful contextual orientation for conducting similar experiences in other social-ecological and political-administrative contexts. Reference: López-Rodríguez, M.D., Castro, A.J., Castro, H, Jorreto, S., Cabello, J. 2015. Science-Policy interface for addressing environmental problems in arid Spain. Environmental Science and Policy 50: 1–14. ; peerReviewed

There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing). ; Siham Tabik was supported by the Ramón y Cajal Programme (RYC-2015-18136). ; The work was partially supported by the Spanish Ministry of Science and Technology under the projects: TIN2014-57251-P, CGL2014-61610-EXP, CGL2010-22314 and grant JC2015-00316, and ERDF and Andalusian Government under the projects: GLOCHARID, RNM-7033, P09-RNM-5048 and P11-TIC-7765. ; This research was also developed as part of project ECOPOTENTIAL, which received funding from the European Union Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, and by the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612.

Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management. ; S.T., E.G., D.A‐S., and F.H. were supported by the project DeepSCOP‐Ayudas Fundación BBVA a Equipos de Investigación Científica en Big Data 2018. E.G. is supported by the European Research Council grant agreement 647038 (BIODESERT). S.T. is supported by the Ramón y Cajal Program of the Spanish Government (RYC‐2015‐18136). S.T., E.G., and F.H. were supported by the Spanish Ministry of Science under the project TIN2017‐89517‐P. D.A‐S., E.G., and J.C. received support from project ECOPOTENTIAL, funded by European Union Horizon 2020 Research and Innovation Program, under grant agreement No. 641762, and from European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612. D.A‐S, S.T. and E.G. received support from Programa Operativo FEDER‐Andalucía 2014‐2020 under project DETECTOR A‐RNM‐256‐UGR18. D.A‐S. received support from ...

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. ; E.G. and D.A-S. received support from the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612, and from ERDF and the Andalusian Government under the project GLOCHARID (Global Change in Arid Zones - 852/2009/M/00). E.G. is supported by the European Research Council grant agreement nº 647038 (BIODESERT). J.B.-S. received funding from the European Union's Horizon 2020 research and innovation 514 programme under the Marie Sklodowska-Curie grant agreement No. 721995. D.A.-S. received support from the NASA Work Programme on Group on Earth Observations - Biodiversity Observation Network (GEOBON) under grant 80NSSC18K0446, from project ECOPOTENTIAL, funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, and from the Spanish Ministry of Science under project CGL2014-61610-EXP and grant JC2015-00316.

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. ; E.G. and D.A-S. received support from the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612, and from ERDF and the Andalusian Government under the project GLOCHARID (Global Change in Arid Zones - 852/2009/M/00). E.G. is supported by the European Research Council grant agreement nº 647038 (BIODESERT). J.B.-S. received funding from the European Union's Horizon 2020 research and innovation 514 programme under the Marie Sklodowska-Curie grant agreement No. 721995. D.A.-S. received support from the NASA Work Programme on Group on Earth Observations - Biodiversity Observation Network (GEOBON) under grant 80NSSC18K0446, from project ECOPOTENTIAL, funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, and from the Spanish Ministry of Science under project CGL2014-61610-EXP and grant JC2015-00316.

We are grateful to Javier Montes for providing help with the Python scripts to access Google Maps' API. ; Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management. ; S.T., E.G., D.A.-S., and F.H. were supported by the project DeepSCOP-Ayudas Fundación BBVA a Equipos de Investigación Científica en Big Data 2018. E.G. is supported by the European Research Council grant agreement 647038 (BIODESERT). S.T. is supported by the Ramón y Cajal Program of the Spanish Government (RYC-2015-18136). S.T., E.G., and F.H. were supported by the Spanish Ministry of Science under the project TIN2017-89517-P. D.A.-S., E.G., and J.C. received support from project ECOPOTENTIAL, funded by European Union Horizon 2020 Research and Innovation Program, under grant agreement No. 641762, and from European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612. D.A-S, S.T. and E.G. received support from Programa Operativo FEDER-Andalucía 2014-2020 under project DETECTOR A-RNM-256-UGR18. D.A-S. received support from NASA's Work Program on Group on Earth Observations—Biodiversity Observation Network (GEOBON) under grant 80NSSC18K0446.

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) 647038 ; European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612 ; Junta de Andalucia P18-RT-1927 P18-RT-5130 ; DETECTOR A-RNM-256-UGR18 ; European Union Funds for Regional Development ; HIPATIA-UAL fellowship ; Spanish Government RYC-201518136 ; A-TIC-458-UGR18

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. ; This research was funded by the European Research Council (ERC Grant agreement 647038 [BIODESERT]), the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612, the RH2O-ARID (P18-RT-5130) and RESISTE (P18-RT-1927) funded by Consejería de Economía, Conocimiento, Empresas y Universidad from the Junta de Andalucía, 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 Ramón y Cajal Program of the Spanish Government (RYC-2015-18136).

Premise: Water is the most limiting factor in dryland ecosystems, and plants are adapted to cope with this constraint. Particularly vulnerable are phreatophytic plants from groundwater-dependent ecosystems (GDEs) in regions that have to face water regime alterations due to the impacts of climate and land-use changes. Methods: We investigated two aspects related to the water-use strategy of a keystone species that dominates one of the few terrestrial GDEs in European drylands (Ziziphus lotus): where it obtains water and how it regulates its use. We (1) evaluated plants' water sources and use patterns using a multiple-isotope approach (δH, δO, and ΔC); (2) assessed the regulation of plant water potential by characterizing the species on an isohydric–anisohydric continuum; and (3) evaluated plants' response to increasing water stress along a depth-to-groundwater (DTGW) gradient by measuring foliar gas exchange and nutrient concentrations. Results: Ziziphus lotus behaves as a facultative or partial phreatophyte with extreme anisohydric stomatal regulation. However, as DTGW increased, Z. lotus (1) reduced the use of groundwater, (2) reduced total water uptake, and (3) limited transpiration water loss while increasing water-use efficiency. We also found a physiological threshold at 14 m depth to groundwater, which could indicate maximum rooting length beyond which optimal plant function could not be sustained. Conclusions: Species such as Z. lotus survive by squandering water in drylands because of a substantial groundwater uptake. However, the identification of DTGW thresholds indicates that drawdowns in groundwater level would jeopardize the functioning of the GDE. ; This research was done in the framework of the LTSER Platform "The Arid Iberian South East LTSER Platform - Spain (LTER_EU_ES_027)" and supported by the European project LIFE Adaptamed (LIFE14349 CCA/ES/000612), the Spanish Ecological Transition Ministry (through Biodiversity Foundation) project CO-ADAPTA. (CA_CC_2016), and the RTI2018-102030-B-I00 project of the University of Almería (PPUENTE2020/001). F.G. was financially supported by the "HIPATIA" research program of the University of Almeria, and the Spanish government supported M.T.T. with a FPU predoctoral fellowship (16/02214)

Antimicrobial resistance is a global emerging public health issue whose presence and impact in wildlife are widely unknown. Antimicrobial resistance genes (ARGs) are considered environmental contaminants, suitable to evaluate the degree of anthropic impact on wildlife and the environment. We used a wild felid, the guigna (Leopardus guigna), as a sentinel for the presence of ARGs in anthropized and pristine areas across their entire distribution range in Chile. We evaluated fecal samples from 51 wild guignas, collected between 2009 and 2018. Real-time PCR essays were employed to detect and quantify 22 selected ARGs in their fecal microbiome. All animals (100%) were positive for at least one ARG. The most prevalent ARG families were those that confer resistance to tetracycline (88.2%) and beta-lactamase (68.9%), with tet (Q) (60.8%), tet(W) (60.8%), and bla(TEM) (66.7%) as the most prevalent ARGs. Multi-resistance profiles were observed in 43% of the guignas. Statistically significant differences were found between anthropized and pristine areas for tet(Q) (p = 0.014), tet(W) (p = 0.0037), tetracycline family (p = 0.027), multi-resistance profile prevalence (p = 0.043) and tet(W) quantification (p = 0.004). Two animals from anthropized landscapes were positive for mecA, a gene associated with Staphylococcus aureus and other staphylococci resistant to methicillin, while three animals from anthropized areas were positive for bla(CTX-M), that encodes class A extended-spectrum beta-lactamase. Both genes have been identified in bacteria causing relevant nosocomial infections worldwide. This is the first study on ARGs in wild felids from Chile and the first detection of mecA in South American wild felids. We observed an association between the degree of landscape anthropization and ARG prevalence, confirming that ARGs are important indicators of wildlife exposure to human activity/presence, with a widespread distribution. (C) 2019 Elsevier B.V. All rights reserved. ; Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) CONICYT FONDECYT Morris Animal Foundation (MAF) Fellowship Training Award National Geographic Society Mohamed bin Zayed Species Conservation Fund CONICYT PIA APOYO CCTE Wild Felid Association Fondo Interno UNAB Universidad Andrés Bello, Chile Morris Animal Foundation Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) CONICYT FONDECYT 2018 Endeavour Research Fellowship (Australian government)