Putting the Ecosystem Services idea at work: applications on impact assessment and territorial planning

South America is experiencing profound land use and land cover changes. Their consequences on the Ecosystem Services (ES) supply and human well-being need to be diagnosed and monitored in order to support informed decisions both in management and territorial planning. The ES concept provides a key framework to evaluate human impacts on nature. The use of spatially explicit indicators able to characterize ES supply can turn operative the ES framework, enabling for sustainability assessment. The Ecosystem Services Supply Index (ESSI) is a synoptic indicator that estimates and maps supporting and regulating ES related to water and carbon dynamics from data provided by remote sensors of free access and wide spatial coverage. The ESSI merges two attributes of the Normalized Difference Vegetation Index (NDVI) annual dynamics: the annual average (NDVIMEAN, a proxy of total C gains) and the intra-annual coefficient of variation (NDVICV, an indicator of seasonality). In this article we proposed two objectives: 1) to describe the conceptual foundation of the ESSI and to gather the empirical support that shows its ability to explain the spatial-temporal variation in different ES, and to present a new case of empirical ESSI assessment, and 2) to synthesize the contribution of the ESSI in socio ecosystem diagnosis, monitoring and territorial planning stages in 8 existing cases of application. We also explored the links to the decision-making process by diverse stakeholders including local research and development institutions, NGOs and government agents. Cases corresponded to a wide range of situations from humid and dry forests to grasslands, and from local to subcontinental scales in southern South America. We found that ESSI was successfully applied for diagnosis, planning and monitoring processes which helped to better define interventions in management decisions and also to empower the most vulnerable stakeholders under territorial and environmental conflicts

¿Cuál es la situación de la Ley de Bosques en la Región Chaqueña a diez años de su sanción? Revisar su pasado para discutir su futuro

En un complejo escenario ambiental, productivo y socioeconómico, el 28 de noviembre de 2007 fue sancionada en Argentina la Ley Nacional Nº 26.331 de “Presupuestos Mínimos de Protección Ambiental de los Bosques Nativos" (conocida como "Ley de bosques") con el propósito de proteger los bosques nativos a escala nacional. En este artículo nos proponemos realizar una síntesis crítica de la información disponible acerca de esta ley a diez años de su sanción, con una aproximación que toma en cuenta aspectos ambientales, económicos y sociales. Caracterizamos el desempeño de esta ley en la Región Chaqueña en cuanto a diferentes dimensiones, identificamos sus principales desafíos y describimos una serie de propuestas que desde el sector de Ciencia y Técnica pueden contribuir a su (re)diseño e implementación en el contexto de las actualizaciones de los Ordenamientos Territoriales de Bosques Nativos provinciales. Para ello, integramos información disponible proveniente de distintas fuentes, tales como normativas (nacionales y provinciales), literatura científica, informes de organismos estatales y de ONG y artículos periodísticos. La Ley de Bosques instaló en la opinión pública de nuestro país la problemática vinculada a la pérdida de bosques nativos y se ha posicionado como el principal instrumento de política forestal nacional para su protección. Si bien hubo una reducción en las tasas de deforestación en la región Chaqueña, no existen evidencias certeras de que esta reducción se deba a su aplicación. La Ley de Bosques en la Región Chaqueña presenta una serie de desafíos para mejorar su desempeño en cuanto a su efectividad, equidad y legitimidad social. En este trabajo se presentan diez observaciones que emergen de la revisión realizada. Por otro lado, se esbozan una serie de propuestas de investigación y acción en torno a la ley vinculadas a esas observaciones.In a complex environmental, productive and socioeconomic scenario, on November 28th, 2007, the National Act N°26.331 of “Minimum Standards for the Environmental Protection of Native Forests” (known as the “Forest Law”) was sanctioned in Argentina with the purpose of protecting native forests at the national scale. In this article, we aim to critically synthesize the available information about this law ten years after its sanction from an approach that takes into account environmental, economic and social aspects. In particular, we characterize the performance of this law in the Chaco Region in different dimensions, identify its main challenges and describe a series of proposals that from the science and technology sector can contribute to its (re)design and implementation in the context of the revisions of the Provincial Native Forest Land Use Planning Programs. In order to accomplish these objectives, we integrated information available from different sources, such as laws and regulations (national and provincial), scientific literature, reports from government agencies and NGOs and newspaper articles. The Forest Law installed in the public opinion of our country the problems related to the loss of native forests and has positioned itself as the main national instrument for forest conservation. Although deforestation rates lowered in the Chaco Region, there is no clear evidence that this reduction was due to its application. The Forest Law in the Chaco Region presents a series of challenges to improve its performance in terms of its effectiveness, equity and social legitimacy. In this paper, we present ten observations that emerge from the review carried out. On the other hand, linked to these observations, we outline a series of research and action proposals for enhancing the performance of the law.EEA BalcarceFil: Aguiar, Sebastián. Universidad de Buenos Aires. Facultad de Agronomia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Mastrangelo, Matias Enrique. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales; Argentina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: García Collazo, María A. Ministerio de Ambiente y Desarrollo Sustentable de la Nación. Dirección Nacional de Bosques; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Camba Sans, Gonzalo H. Universidad de Buenos Aires. Facultad de Agronomia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Mosso, Clara E. Universidad de Buenos Aires. Facultad de Agronomía. Licenciatura en Ciencias Ambientales; ArgentinaFil: Ciuffoli, Lucía. Ministerio de Ambiente y Desarrollo Sustentable de la Nación. Dirección Nacional de Bosques; Argentina.Fil: Schmidt, Mariana. Universidad de Buenos Aires. Facultad de Ciencias Sociales. Instituto de Investigaciones Gino Germani. Grupo de Estudios Ambientales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Vallejos, María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Langbehn, Lorenzo. Universidad Nacional de Santiago del Estero. Facultad de Humanidades, Ciencias Sociales y de la Salud; ArgentinaFil: Brassiolo, Miguel. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Forestales; ArgentinaFil: Cáceres, Daniel. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Merlinsky, Gabriela. Universidad de Buenos Aires. Facultad de Ciencias Sociales. Instituto de Investigaciones Gino Germani. Grupo de Estudios Ambientales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Paruelo, José María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina. Instituto Nacional de Investigaciones Agropecuarias. INIA La Estanzuela; UruguayFil: Seghezzo, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; ArgentinaFil: Staiano, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Texeira González, Marcos Alexis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Volante, Jose Norberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Veron, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentin

Global Patterns and Controls of Nutrient Immobilization On Decomposing Cellulose In Riverine Ecosystems

Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low-nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low-nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature-dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature

Opportunities drive the global distribution of protected areas

Background Protected areas, regarded today as a cornerstone of nature conservation, result from an array of multiple motivations and opportunities. We explored at global and regional levels the current distribution of protected areas along biophysical, human, and biological gradients, and assessed to what extent protection has pursued (i) a balanced representation of biophysical environments, (ii) a set of preferred conditions (biological, spiritual, economic, or geopolitical), or (iii) existing opportunities for conservation regardless of any representation or preference criteria. Methods We used histograms to describe the distribution of terrestrial protected areas along biophysical, human, and biological independent gradients and linear and non-linear regression and correlation analyses to describe the sign, shape, and strength of the relationships. We used a random forest analysis to rank the importance of different variables related to conservation preferences and opportunity drivers, and an evenness metric to quantify representativeness. Results We find that protection at a global level is primarily driven by the opportunities provided by isolation and a low population density (variable importance = 34.6 and 19.9, respectively). Preferences play a secondary role, with a bias towards tourism attractiveness and proximity to international borders (variable importance = 12.7 and 3.4, respectively). Opportunities shape protection strongly in “North America & Australia–NZ” and “Latin America & Caribbean,” while the importance of the representativeness of biophysical environments is higher in “Sub-Saharan Africa” (1.3 times the average of other regions). Discussion Environmental representativeness and biodiversity protection are top priorities in land conservation agendas. However, our results suggest that they have been minor players driving current protection at both global and regional levels. Attempts to increase their relevance will necessarily have to recognize the predominant opportunistic nature that the establishment of protected areas has had until present times

Nature representation in South American protected areas: country contrasts and conservation priorities

Background South America faces strong environmental pressures as a result of agriculture and infrastructure expansion and also of demographic growth, demanding immediate action to preserve natural assets by establishing protected areas. Currently, 7.1% of the (sub)continent is under strict conservation categories (I to IV, IUCN), but the spatial distribution of these 1.3 × 106 km2 is poorly understood. We evaluated the representation of nature within the networks of protected areas, map conservation priorities and assess demographic, economic or geopolitical causes of existing protection patterns. Methods We characterized nature representation by looking at two components: the extent and the equality of protection. The first refers to the fraction of territory under protection, while the second refers to the homogeneity in the distribution along natural conditions of this protected fraction. We characterized natural conditions by either 113 biogeographical units (specifically, ecoregions) or a series of limited and significant climatic, topographic and edaphic traits. We analyzed representation every ten years since 1960 at national and continental levels. In the physical approach, histograms allowed us to map the degree of conservation priorities. Finally, we ranked the importance of different economic or geopolitical variables driving the observed distributions with a random forest technique. Results Nature representation varied across countries in spite of its priority in conservation agendas. In Brazil, Peru and Argentina there are still natural conditions with no formal protection, while in Bolivia and Venezuela, protected areas incorporate the natural diversity in a more balanced manner. As protected networks have increased their extent, so did their equality across and within countries over time. Our maps revealed as top continental priorities the southern temperate, subhumid and fertile lowland environments, and other country-specific areas. Protection extent was generally driven by a low population density and isolation, while other variables like distance to frontiers, were relevant only locally (e.g., in Argentina). Discussion Our description of the spatial distribution of protected areas can help societies and governments to improve the allocation of conservation efforts. We identified the main limitations that future conservation efforts will face, as protection was generally driven by the opportunities provided by low population density and isolation. From a methodological perspective, the physical approach reveals new properties of protection and provides tools to explore nature representation at different spatial, temporal and conceptual levels, complementing the traditional ones based on biodiversity or biogeographical attributes

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)