Adaptive and plastic responses of Quercus petraea populations to climate across Europe

How temperate forests will respond to climate change is uncertain; projections range from severe decline to increased growth. We conducted field tests of sessile oak (Quercus petraea), a widespread keystone European forest tree species, including more than 150,000 trees sourced from 116 geographically diverse populations. The tests were planted on 23 field sites in six European countries, in order to expose them to a wide range of climates, including sites reflecting future warmer and drier climates. By assessing tree height and survival, our objectives were twofold: (1) to identify the source of differential population responses to climate (genetic differentiation due to past divergent climatic selection versus plastic responses to ongoing climate change), (2) to explore which climatic variables (temperature or precipitation) trigger the population responses. Tree growth and survival were modeled for contemporary climate and then projected using data from four regional climate models for years 2071-2100, using two greenhouse gas concentration trajectory scenarios each. Overall results indicated a moderate response of tree height and survival to climate variation, with changes in dryness (either annual or during the growing season) explaining the major part of the response. Whilst, on average, populations exhibited local adaptation, there was significant clinal population differentiation for height growth with winter temperature at the site of origin. The most moderate climate model (HIRHAM5-EC; rcp4.5) predicted minor decreases in height and survival, whilst the most extreme model (CCLM4-GEM2-ES; rcp8.5) predicted large decreases in survival and growth for southern and southeastern edge populations. Other non-marginal populations with continental climates were predicted to be severely and negatively affected, while populations at the contemporary northern limit (colder and humid maritime regions) will probably not show large changes in growth and survival in response to climate change

Mapping dominant leaf type based on combined Sentinel-1/-2 data – Challenges for mountainous countries

Countrywide winter and summer Sentinel-1 (S1) backscatter data, cloud-free summer Sentinel-2 (S2) images, an Airborne Laser Scanning (ALS)-based Digital Terrain Model (DTM) and a forest mask were used to model and subsequently map Dominant Leaf Type (DLT) with the thematic classes broadleaved and coniferous trees for the whole of Switzerland. A novel workflow was developed that is robust, cost-efficient and highly automated using reference data from aerial image interpretation. Two machine learning approaches based on Random Forest (RF) and deep learning (UNET) for the whole country with three sets of predictor variables were applied. 24 subareas based on aspect and slope categories were applied to explore effects of the complex mountainous topography on model performances. The reference data split into training, validation and test data sets was spatially stratified using a 25 km regular grid. Model accuracies of both RF and UNET were generally highest with Kappa (K) around 0.95 when predictors were included from both S1/S2 and the topographic variables aspect, elevation and slope from the DTM. While only slightly lower accuracies were obtained when using S2 and DTM data, lowest accuracies were obtained when only predictors from S1 and DTM were included, with RF performing worse than UNET. While on countrywide level RF and UNET performed overall similarly, substantial differences in model performances, i.e. higher variances and lower accuracies, were found in subareas with northwest to northeast orientations. The combined use of S1/S2 and DTM predictors mitigated these problems related to topography and shadows and was therefore superior to the single use of S1 and DTM or S2 and DTM data. The comparison with independent National Forest Inventory (NFI) plot data demonstrated precisions of K around 0.6 in the predictions of DLT and indicated a trend of increasing deviations in mixed forests. A comparison with the Copernicus High Resolution Layer (HRL) DLT 2018 revealed overall higher map accuracies with the exception of pure broadleaved forest. Although, spatial patterns of DTL were overall similar, UNET performed better than RF in areas with a distinct DLT on forest stand level, with the largest differences occurring when only S1 and DTM data was used. In contrast, predictions obtained from RF were more accurate in mixed stands. This study goes beyond the case study level and meets the requirements of countrywide data sets, in particular regarding repeatability, updating, costs and characteristics of training data sets. The 10 m countrywide DLT maps add complementary and spatially explicit information to the existing NFI estimates and are thus highly relevant for forestry practice and other related fields

Greening and browning of urban lawns in Geneva (Switzerland) as influenced by soil properties

Urban green spaces with healthy vegetation play a key role in improving the quality of life in cities. However, urban soils, the basis of the urban greenery, are under strong anthropogenic influence and can considerably differ from natural soils. In this study, we observed short-term greening and browning of lawns during one vegetation period in urban parks of Geneva (Switzerland). We related the temporal trajectory of seasonal Normalized Difference Vegetation Index (NDVI) (8 days median) as a proxy of vegetation condition at different test sites to the physical soil properties (soil depth, coarse material, bulk density, conditioned air and water content) and Soil Organic Carbon (SOC). Strong drops of NDVI during dry periods in summer were related to shallow soil depths (10%) as well as lower SOC. Bulk density of the fine earth and the soil structure quality (expressed by air and water content of soil cores conditioned at a soil water potential of −100 hPa) had a significant influence on grass growth in spring but not in summer. Dense soils with conditioned air content closer to the trigger value of degraded soil structure resulted in lower NDVI values in spring. Our approach of using Earth Observation (EO) data for observing short-term greening and browning patterns, in this case the rise and decline of NDVI values, revealed that the role of the soil properties changed with the season. This approach may contribute to digital soil mapping and the assessment of soil ecosystem services in urban contexts. Urban planners are advised to save natural soils from over-building and keep them for green spaces. If soil has to be restored to create new green spaces, it should be deep and should not contain much coarse material, even for grassy vegetation

Global pattern of phytoplankton diversity driven by temperature and environmental variability

Despite their importance to ocean productivity, global patterns of marine phytoplankton diversity remain poorly characterized. Although temperature is considered a key driver of general marine biodiversity, its specific role in phytoplankton diversity has remained unclear. We determined monthly phytoplankton species richness by using niche modeling and >540,000 global phytoplankton observations to predict biogeographic patterns of 536 phytoplankton species. Consistent with metabolic theory, phytoplankton richness in the tropics is about three times that in higher latitudes, with temperature being the most important driver. However, below 19°C, richness is lower than expected, with ~8°– 14°C waters (~35° to 60° latitude) showing the greatest divergence from theoretical predictions. Regions of reduced richness are characterized by maximal species turnover and environmental variability, suggesting that the latter reduces species richness directly, or through enhancing competitive exclusion. The nonmonotonic relationship between phytoplankton richness and temperature suggests unanticipated complexity in responses of marine biodiversity to ocean warming.ISSN:2375-254