«HIFOMICETOS ACUÁTICOS» EN LAS CUENCAS ALTAS DE LOS RÍOS SEGURA Y GUADALQUIVIR

Foam samples from 62 sites along the upper basins of the rivers Segura and Guadalquivir were surveyed for conidia of «aquatich yphomycetes», 39 anamorph species and propagules of other doubtful or unknown taxa were detected. These are illustrated and discussed in relation to ecological parameters (altitude, water ternperature, pH, riparian vegetation and season) and possible taxonomic affinities.Se realiza un muestreo preliminar de los llamados «hifomicetos acuáticos» en las cuencas altas de los ríos Segura y Guadalquivir. En los 62 puntos muestreados se han reconocido 39 especies, así como varios propágulos de origen desconocido. Se comentan los resultados en relación a algunos parámetros ecológicos: altitud, temperatura del agua, pH, vegetación de ribera y estación anual

High-resolution global map of smallholder and industrial closed-canopy oil palm plantations

Oil seed crops, especially oil palm, are among the most rapidly expanding agricultural land uses, and their expansion is known to cause significant environmental damage. Accordingly, these crops often feature in public and policy debates which are hampered or biased by a lack of accurate information on environmental impacts. In particular, the lack of accurate global crop maps remains a concern. Recent advances in deep-learning and remotely sensed data access make it possible to address this gap. We present a map of closed-canopy oil palm (Elaeis guineensis) plantations by typology (industrial versus smallholder plantations) at the global scale and with unprecedented detail (10 m resolution) for the year 2019. The DeepLabv3+ model, a convolutional neural network (CNN) for semantic segmentation, was trained to classify Sentinel-1 and Sentinel-2 images onto an oil palm land cover map. The characteristic backscatter response of closed-canopy oil palm stands in Sentinel-1 and the ability of CNN to learn spatial patterns, such as the harvest road networks, allowed the distinction between industrial and smallholder plantations globally (overall accuracy =98.52±0.20 %), outperforming the accuracy of existing regional oil palm datasets that used conventional machine-learning algorithms. The user's accuracy, reflecting commission error, in industrial and smallholders was 88.22 ± 2.73 % and 76.56 ± 4.53 %, and the producer's accuracy, reflecting omission error, was 75.78 ± 3.55 % and 86.92 ± 5.12 %, respectively. The global oil palm layer reveals that closed-canopy oil palm plantations are found in 49 countries, covering a mapped area of 19.60 Mha; the area estimate was 21.00 ± 0.42 Mha (72.7 % industrial and 27.3 % smallholder plantations). Southeast Asia ranks as the main producing region with an oil palm area estimate of 18.69 ± 0.33 Mha or 89 % of global closed-canopy plantations. Our analysis confirms significant regional variation in the ratio of industrial versus smallholder growers, but it also confirms that, from a typical land development perspective, large areas of legally defined smallholder oil palm resemble industrial-scale plantings. Since our study identified only closed-canopy oil palm stands, our area estimate was lower than the harvested area reported by the Food and Agriculture Organization (FAO), particularly in West Africa, due to the omission of young and sparse oil palm stands, oil palm in nonhomogeneous settings, and semi-wild oil palm plantations. An accurate global map of planted oil palm can help to shape the ongoing debate about the environmental impacts of oil seed crop expansion, especially if other crops can be mapped to the same level of accuracy. As our model can be regularly rerun as new images become available, it can be used to monitor the expansion of the crop in monocultural settings. The global oil palm layer for the second half of 2019 at a spatial resolution of 10 m can be found at https://doi.org/10.5281/zenodo.4473715 (Descals et al., 2021)

Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra

Oil palm is rapidly expanding in Southeast Asia and represents one of the major drivers of deforestation in the region. This includes both industrial-scale and smallholder plantations, the management of which entails specific challenges, with either operational scale having its own particular social and environmental challenges. Although, past studies addressed the mapping of oil palm with remote sensing data, none of these studies considered the discrimination between industrial and smallholder plantations and, furthermore, between young and mature oil palm stands. This study assesses the feasibility of mapping oil palm plantations, by typology (industrial versus smallholder) and age (young versus mature), in the largest palm oil producing region of Indonesia (Riau province). The impact of using optical images (Sentinel-2) and radar scenes (Sentinel-1) in a Random Forest classification model was investigated. The classification model was implemented in a cloud computing system to map the oil palm plantations of Riau province. Our results show that the mapping of oil palm plantations by typology and age requires a set of optimal features, derived from optical and radar data, to obtain the best model performance (OA = 90.2% and kappa = 87.2%). These features are texture images that capture contextual information, such as the dense harvesting trail network in industrial plantations. The study also shows that the mapping of mature oil palm trees, without distinction between smallholder and industrial plantations, can be done with high accuracy using only Sentinel-1 data (OA = 93.5% and kappa = 86.9%) because of the characteristic backscatter response of palm-like trees in radar scenes. This means that researchers, certification bodies, and stakeholders can adequately detect mature oil palm stands over large regions without training complex classification models and with Sentinel-1 features as the only predictive variables. The results over Riau province show that smallholders represent 49.9% of total oil palm plantations, which is higher than reported in previous studies. This study is an important step towards a global map of oil palm plantations at different production scales and stand ages that can frequently be updated. Resulting insights would facilitate a more informed debate about optimizing land use for meeting global vegetable oil demands from oil palm and other oil crops

High-resolution global map of closed-canopy coconut palm

Demand for coconut is expected to rise, but the global distribution of coconut palm has been studied little, which hinders the discussion of its impacts. Here, we produced the first 20m global coconut palm layer using a U-Net model that was trained on annual Sentinel-1 and Sentinel-2 composites for the year 2020. The overall accuracy was 99.04±0.21%, which was significantly higher than the no-information rate. The producer's accuracy for coconut palm was 71.51±23.11% when only closed-canopy coconut palm was considered in the validation, but this decreased to 11.30±2.33% when sparse and dense open-canopy coconut palm was also taken into account. This indicates that sparse and dense open-canopy coconut palm remains difficult to map with accuracy. We report a global coconut palm area of 12.66±3.96×106ha for dense open-and closed-canopy coconut palm, but the estimate is 3 times larger (38.93±7.89×106ha) when sparse coconut palm is included in the area estimation. The large area of sparse coconut palm is important as it indicates that production increases can likely be achieved on the existing lands allocated to coconut. The Philippines, Indonesia, and India account for most of the global coconut palm area, representing approximately 82% of the total mapped area. Our study provides the high-resolution, quantitative, and precise data necessary for assessing the relationships between coconut production and the synergies and trade-offs between various sustainable development goal indicators. The global coconut palm layer is available at 10.5281/zenodo.8128183 (Descals, 2023)

Cognitive decline in Huntington's disease expansion gene carriers

BACKGROUND: In Huntington's Disease (HD) cognitive decline can occur before unequivocal motor signs become apparent. As cognitive decline often starts early in the course of the disease and has a progressive nature over time, cognition can be regarded as a key target for symptomatic treatment. The specific progressive profile of cognitive decline over time is unknown. OBJECTIVE: The aim of this study is to quantify the progression of cognitive decline across all HD stages, from pre-motormanifest to advanced HD, and to investigate if CAG length mediates cognitive decline. METHODS: In the European REGISTRY study 2669 HD expansion gene carriers underwent annual cognitive assessment. General linear mixed models were used to model the cognitive decline for each cognitive task across all disease stages. Additionally, a model was developed to evaluate the cognitive decline based on CAG length and age rather than disease stage. RESULTS: There was significant cognitive decline on all administered tasks throughout pre-motormanifest (close to estimated disease onset) participants and the subsequent motormanifest participants from stage 1 to stage 4. Performance on the Stroop Word and Stroop Color tests additionally declined significantly across the two pre-motormanifest groups: far and close to estimated disease onset. The evaluation of cognition performance in relation to CAG length and age revealed a more rapid cognitive decline in participants with longer CAG length than participants with shorter CAG length over time. CONCLUSION: Cognitive performance already shows decline in pre-motormanifest HD gene expansion carriers and gradually worsens to late stage HD. HD gene expansion carriers with certain CAG length have their own cognitive profile, i.e., longer CAG length is associated with more rapid decline

The V471A polymorphism in autophagy-related gene ATG7 modifies age at onset specifically in Italian Huntington disease patients

The cause of Huntington disease (HD) is a polyglutamine repeat expansion of more than 36 units in the huntingtin protein, which is inversely correlated with the age at onset of the disease. However, additional genetic factors are believed to modify the course and the age at onset of HD. Recently, we identified the V471A polymorphism in the autophagy-related gene ATG7, a key component of the autophagy pathway that plays an important role in HD pathogenesis, to be associated with the age at onset in a large group of European Huntington disease patients. To confirm this association in a second independent patient cohort, we analysed the ATG7 V471A polymorphism in additional 1,464 European HD patients of the “REGISTRY” cohort from the European Huntington Disease Network (EHDN). In the entire REGISTRY cohort we could not confirm a modifying effect of the ATG7 V471A polymorphism. However, analysing a modifying effect of ATG7 in these REGISTRY patients and in patients of our previous HD cohort according to their ethnic origin, we identified a significant effect of the ATG7 V471A polymorphism on the HD age at onset only in the Italian population (327 patients). In these Italian patients, the polymorphism is associated with a 6-years earlier disease onset and thus seems to have an aggravating effect. We could specify the role of ATG7 as a genetic modifier for HD particularly in the Italian population. This result affirms the modifying influence of the autophagic pathway on the course of HD, but also suggests population-specific modifying mechanisms in HD pathogenesis