«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

Phytophthora polonica, a new species isolated from declining Alnus glutinosa stands in Poland

In a survey of Phytophthora associated with alder decline in Poland, several isolates of a homothallic Phytophthora spet al, which could not be assigned to other taxa including Phytophthora alni subspecies, were consistently recovered from rhizosphere soil samples. Their morphology and pathogenicity, as well as sequence data for three nuclear regions (internal transcribed spacer rDNA, elongation factor-1α and β-tubulin) and a coding mitochondrial DNA region (nadh1), were examined. The new Phytophthora species is characterized by the moderate to slow growth rate of its colony in carrot agar at 20°C, high optimal (c. 30°C) and maximum (c. 38°C) growth temperatures, formation of catenulate, often lateral, hyphal swellings, large chlamydospores in agar media and in soil extract, persistent, ovoid to ellipsoid nonpapillate sporangia and large oogonia with paragynous and sometimes amphigynous antheridia. Phytophthora polonica was slightly pathogenic to alder twigs and not pathogenic to trunks of several tree species. In a phylogenetic analysis using either Bayesian inference or maximum likelihood methods, P. polonica falls in clade 8 ‘sensu Kroon (2004)' of Phytophthor

Slowing deforestation in Indonesia follows declining oil palm expansion and lower oil prices

Much concern about tropical deforestation focuses on oil palm plantations, but their impacts remain poorly quantified. Using nation-wide interpretation of satellite imagery, and sample-based error calibration, we estimated the impact of large-scale (industrial) and smallholder oil palm plantations on natural old-growth ("primary") forests from 2001 to 2019 in Indonesia, the world's largest palm oil producer. Over nineteen years, the area mapped under oil palm doubled, reaching 16.24 Mha in 2019 (64% industrial; 36% smallholder), more than the official estimates of 14.72 Mha. The forest area declined by 11% (9.79 Mha), including 32% (3.09 Mha) ultimately converted into oil palm, and 29% (2.85 Mha) cleared and converted in the same year. Industrial plantations replaced more forest than detected smallholder plantings (2.13 Mha vs 0.72 Mha). New plantations peaked in 2009 and 2012 and declined thereafter. Expansion of industrial plantations and forest loss were correlated with palm oil prices. A price decline of 1% was associated with a 1.08% decrease in new industrial plantations and with a 0.68% decrease of forest loss. Deforestation fell below pre-2004 levels in 2017-2019 providing an opportunity to focus on sustainable management. As the price of palm oil has doubled since the start of the COVID-19 pandemic, effective regulation is key to minimising future forest conversion

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)

Impacts of global change on Mediterranean forests and their services

The increase in aridity, mainly by decreases in precipitation but also by higher temperatures, is likely the main threat to the diversity and survival of Mediterranean forests. Changes in land use, including the abandonment of extensive crop activities, mainly in mountains and remote areas, and the increases in human settlements and demand for more resources with the resulting fragmentation of the landscape, hinder the establishment of appropriate management tools to protect Mediterranean forests and their provision of services and biodiversity. Experiments and observations indicate that if changes in climate, land use and other components of global change, such as pollution and overexploitation of resources, continue, the resilience of many forests will likely be exceeded, altering their structure and function and changing, mostly decreasing, their capacity to continue to provide their current services. A consistent assessment of the impacts of the changes, however, remains elusive due to the difficulty of obtaining simultaneous and complete data for all scales of the impacts in the same forests, areas and regions. We review the impacts of climate change and other components of global change and their interactions on the terrestrial forests of Mediterranean regions, with special attention to their impacts on ecosystem services. Management tools for counteracting the negative effects of global change on Mediterranean ecosystem- services are finally discussed

Leaf litter decomposition of native and introduced tree species of contrasting quality in headwater streams: How does the regional setting matter?

Terrestrial plant litter is important in sustaining stream food webs in forested headwaters. Leaf litter quality often decreases when native species are replaced by introduced species, and a lower quality of leaf litter inputs may alter litter decomposition at sites afforested with non-native species. However, since detritivore composition and resource use plasticity may depend on the prevalent litter inputs, the extent of the alteration in decomposition can vary between streams. We tested 2 hypotheses using 2 native and 3 introduced species of tree differing in quality in 4 Iberian regions with contrasting vegetational traits: 1) decomposition rates of all plant species would be higher in regions where streams normally receive litter inputs of lower rather than higher quality; 2) a higher resource-use plasticity of detritivores in regions vegetated with plants of lower litter quality will cause a greater evenness in decomposition rates among plant species compared to regions where streams normally receive higher-quality plant litter inputs. Results showed a highly consistent interspecific ranking of decomposition rates across regions driven by litter quality, and a significant regional effect. Hypothesis 1 was supported: decomposition rates of the five litter types were generally higher in streams from regions vegetated with species producing leaf litter of low quality, possibly due to the profusion of caddisfly shredders in their communities. Hypothesis 2 was not supported: the relative differences in decomposition rates among leaf litter species remained essentially unaltered across regions. Our results suggest that, even in regions where detritivores can be comparatively efficient using resources of low quality, caution is needed particularly when afforestation programs introduce plant species of lower litter quality than the native species

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