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Intensification in agriculture affects many insect species, including butterflies. Insect‐resistant crops, such as Bt (Bacillus thuringiensis) maize, which produces a toxin active against Lepidoptera, are an alternative to insecticide sprays. Genetically modified crops are regulated in most countries and require an environmental risk assessment. In the European Union, such assessments include the use of simulation models to predict the effects on nontarget Lepidoptera (NTL). To support the assessment of protected NTL, we extended an individual‐based, stochastic, spatially explicit mathematical model (LepiX) to include a wider range of exposure scenarios, a species‐sensitivity distribution, and an option for repeated exposure of individuals. We applied the model to transgenic maize DAS‐1507, which expresses a high concentration of Bt toxin in pollen that may be consumed by NTL larvae on their host plants nearby. Even in the most conservative scenario without repeated exposure, mortality estimates for highly sensitive species ranged from 41% to 6% at distances of 10–1000 m from the nearest maize field. Repeated exposure can cause additional mortality and thus is relevant for the overall risk assessment. Uncertainties in both exposure and ecotoxicity estimates strongly influenced the predicted mortalities. Care should be taken to include these uncertainties in the model scenarios used for decision‐making. In accordance with other modeling results, our simulations demonstrated that mean mortality may not be safe for protected species. With its high pollen expression, DAS‐1507 maize may pose risks to sensitive and protected butterfly and moth species that may be difficult to manage. High expression of Bt toxin in pollen is unnecessary for controlling target pests. Consequently, we suggest that Bt maize with high pollen expression not be cultivated in regions where protected butterflies are to be conserved. [ABSTRACT FROM AUTHOR]

Over the last decades, the Amazon rainforest has been hit by multiple severe drought events. Here, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon region and their impacts on the regional carbon cycle. As an indicator of drought stress in the Amazon rainforest, we use the widely applied maximum cumulative water deficit (MCWD). Evaluating nine state-of-the-art precipitation datasets for the Amazon region, we find that the spatial extent of the drought in 2005 ranges from 2.2 to 3.0 (mean =2.7) ×106 km 2 (37 %–51 % of the Amazon basin, mean =45 %), where MCWD indicates at least moderate drought conditions (relative MCWD anomaly <-0.5). In 2010, the affected area was about 16 % larger, ranging from 3.0 up to 4.4 (mean =3.6) ×106 km 2 (51 %–74 %, mean =61 %). In 2016, the mean area affected by drought stress was between 2005 and 2010 (mean =3.2×106 km 2 ; 55 % of the Amazon basin), but the general disagreement between datasets was larger, ranging from 2.4 up to 4.1×106 km 2 (40 %–69 %). In addition, we compare differences and similarities among datasets using the self-calibrating Palmer Drought Severity Index (scPDSI) and a dry-season rainfall anomaly index (RAI). We find that scPDSI shows a stronger and RAI a much weaker drought impact in terms of extent and severity for the year 2016 compared to MCWD. We further investigate the impact of varying evapotranspiration on the drought indicators using two state-of-the-art evapotranspiration datasets. Generally, the variability in drought stress is most dependent on the drought indicator (60 %), followed by the choice of the precipitation dataset (20 %) and the evapotranspiration dataset (20 %). Using a fixed, constant evapotranspiration rate instead of variable evapotranspiration can lead to an overestimation of drought stress in the parts of Amazon basin that have a more pronounced dry season (for example in 2010). We highlight that even for well-known drought events the spatial extent and intensity can strongly depend upon the drought indicator and the data sources it is calculated with. Using only one data source and drought indicator has the potential danger of under- or overestimating drought stress in regions with high measurement uncertainty, such as the Amazon basin. [ABSTRACT FROM AUTHOR]

Recent publications indicate that the Amazon may be acting more as a carbon source than a sink in some regions. Moreover, the Amazon is a source of moisture for other regions in the continent, and deforestation over the years may be reducing this function. In this work, we analyze the impacts of elevated CO2 (eCO2) and land use change (LUC) on gross primary productivity (GPP) and evaporation in the southern Amazon (7°S 14°S, 66°W 51°W), which suffered strong anthropogenic influence in the period of 1981‒2010. We ran four dynamic global vegetation models (DGVMs), isolating historical CO2, constant CO2, LUC, and potential natural vegetation scenarios with three climate variable data sets: precipitation, temperature, and shortwave radiation. We compared the outputs to five "observational" data sets obtained through eddy covariance, remote sensing, meteorological measurements, and machine learning. The results indicate that eCO2 may have offset deforestation, with GPP increasing by ∼13.5% and 9.3% (dry and rainy seasons, respectively). After isolating the LUC effect, a reduction in evaporation of ∼4% and ∼1.2% (dry and rainy seasons, respectively) was observed. The analysis of forcings in subregions under strong anthropogenic impact revealed a reduction in precipitation of ∼15 and 30 mm, and a temperature rise of 1°C and 0.6°C (dry and rainy seasons, respectively). Differences in the implementation of plant physiology and leaf area index in the DGVMs introduced some uncertainties in the interpretation of the results. Nevertheless, we consider that it was an important exercise given the relevance. Key Points: The elevated atmospheric CO2 had a strong positive influence on gross primary productivity (GPP) and also offset deforestation, when native forest was converted to grassesLand use change also had a negative effect on evaporation and most intense effects of climate change (as temperature increasing) were on deforested areasUncertainties were found in the outputs of the models and in the climatic forcings. Divergences were also observed among the models [ABSTRACT FROM AUTHOR]

Over the last decades, the Amazon rainforest was hit by multiple severe drought events. Here we assess the severity and spatial extent of the extreme drought years 2005, 2010, and 2015/2016 in the Amazon region and their impacts on the carbon cycle. As an indicator of drought stress in the Amazon rainforest, we use the widely applied maximum cumulative water deficit (ΔMCWD). Evaluating an ensemble of ten state-of-the-art precipitation datasets for the Amazon region, we find that the spatial extent of the drought in 2005 ranges from 2.8 to 4.2 (mean = 3.2) million km2 (46-71 % of the Amazon basin, mean = 53 %) where ΔMCWD indicates at least moderate drought conditions (ΔMCWD anomaly < 25 mm). In 2010, the affected area was about 16 % larger, ranging from 3.1 up to 4.6 (mean = 3.7) million km2 (52-78 %, mean = 63 %). In 2016, the mean area affected by drought stress was similar to 2005 (mean = 3.2 million km2; 55 % of the Amazon basin), but the general disagreement between data sets was larger, ranging from 2.4 up to 4.1 million km2 (40-70 %). In addition, we compare differences and similarities among datasets using the self-calibrating Palmer Drought Severity Index (scPDSI) and a rainfall anomaly index (RAI). We find that scPDSI shows a much stronger, and RAI a much weaker drought impact in terms of extent and severity for 2016 compared to ΔMCWD. Using an empirical ΔMCWD-mortality relationship, we calculate biomass losses of the three drought events. We show that eight of ten datasets agree on biomass losses of about 1.8 PgC for the drought years 2005 and 2010, indicating that the more intense drought in 2005 equals a larger total area of the 2010 drought regarding biomass loss. For the 2015/2016 drought event, datasets show a large variability of biomass loss induced by drought stress ranging from 1.3 to 2.7 PgC with a mean loss of 1.8 PgC. Disagreement across datasets increased, (1) when comparing the total area of more severe and extreme drought signals and (2) when comparing spatial drought location across datasets. Generally, only half of the datasets agreed on the location of a drought event. We conclude that for deriving impacts of droughts to the Amazon Basin based on precipitation, an ensemble of datasets should be considered. This is especially relevant when assessing the impact of drought on the Amazon rainforest and its carbon cycle. [ABSTRACT FROM AUTHOR]

Vegetation responds to drought through a complex interplay of plant hydraulic mechanisms, posing challenges for model development and parameterization. We present a mathematical model that describes the dynamics of leaf water-potential over time while considering different strategies by which plant species regulate their water-potentials. The model has two parameters: the parameter λ describing the adjustment of the leaf water potential to changes in soil water potential, and the parameter Δψww describing the typical 'well-watered' leaf water potentials at non-stressed (near-zero) levels of soil water potential. Our model was tested and calibrated on 110 time-series datasets containing the leaf- and soil water potentials of 66 species under drought and non-drought conditions. Our model successfully reproduces the measured leaf water potentials over time based on three different regulation strategies under drought. We found that three parameter sets derived from the measurement data reproduced the dynamics of 53% of an drought dataset, and 52% of a control dataset [root mean square error (RMSE) < 0.5 MPa)]. We conclude that, instead of quantifying water-potential-regulation of different plant species by complex modeling approaches, a small set of parameters may be sufficient to describe the water potential regulation behavior for large-scale modeling. Thus, our approach paves the way for a parsimonious representation of the full spectrum of plant hydraulic responses to drought in dynamic vegetation models. [ABSTRACT FROM AUTHOR]

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model–data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model–data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model–data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. [ABSTRACT FROM AUTHOR]

Understanding Earth system dynamics in the light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing inter-disciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept ofEarth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple time-scales; and (3) data-model integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. Latest developments in machine learning, causal inference, and model data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. [ABSTRACT FROM AUTHOR]

Comparing model output and observed data is an important step for assessing model performance and quality of simulation results. However, such comparisons are often hampered by differences in spatial scales between local point observations and large-scale simulations of grid cells or pixels. In this study, we propose a generic approach for a pixel-to-point comparison and provide statistical measures accounting for the uncertainty resulting from landscape variability and measurement errors in ecosystem variables. The basic concept of our approach is to determine the statistical properties of small-scale (within-pixel) variability and observational errors, and to use this information to correct for their effect when large-scale area averages (pixel) are compared to small-scale point estimates. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity (woody net primary productivity, NPP) and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest, a region with the typical problem of low data availability, potential scale mismatch and thus high model uncertainty. We find that the DGVMs under- and overestimate aboveground biomass by 25 % and up to 60 %, respectively. Our comparison metrics provide a quantitative measure for model–data agreement and show moderate to good agreement with the region-wide spatial biomass pattern detected by plot observations. However, all four DGVMs overestimate woody productivity and underestimate residence time of woody biomass even when accounting for the large uncertainty range of the observational data. This is because DGVMs do not represent the relation between productivity and residence time of woody biomass correctly. Thus, the DGVMs may simulate the correct large-scale patterns of biomass but for the wrong reasons. We conclude that more information about the underlying processes driving biomass distribution are necessary to improve DGVMs. Our approach provides robust statistical measures for any pixel-to-point comparison, which is applicable for evaluation of models and remote-sensing products. [ABSTRACT FROM AUTHOR]

The cultivation of Bt maize, genetically modified to be resistant to insect pests, has led to intense scientific and political debate about its possible adverse impacts on biodiversity. To better address this question we developed an individual-based simulation model ( LepiX ). LepiX considers the temporal dynamics of maize pollen shedding and larval phenology, and pollen deposition on host plants related to distance from the maize field, in order to estimate mortality of lepidopteran larvae exposed to toxic Bt pollen. We employed a refined exposure analysis, comparison to previous approaches, using recent evidence on leaf pollen deposition and accounting for the spatial heterogeneity of pollen on leaves. Moreover, we used a stochastic approach, considering literature data on a minimum dataset for butterfly biology in combination with historic data on temporal pollen deposition to predict the coincidence between larval phenology and pollen deposition. Since conservation management actions may act at the level of the individual for protected species, LepiX , as an individual based spatially explicit model, is suited to assist both risk assessment and management measures based on threshold mortalities. We tested our model using Inachis io (Lepidoptera: Nymphalidae) as butterfly species and the cultivation of insect resistant MON810 maize. In accordance to predictions based on other models we identified mortality risks of I. io larvae for the second larval generation. An analysis of the sensitivity of input parameters stressed the importance of both the slope and the LC 50 value of the dose-response curve as well as the earliest day of larval hatching. Using different published data to characterize the dose-response of MON810 pollen to I. io we revealed consequences due to uncertainties in ecotoxicological parameters and thus highlight the importance of key biological parameters for reliable estimates of effects, and decision making (e.g. isolation distances) in risk assessment. [ABSTRACT FROM AUTHOR]

Thermal melanism theory states that dark-colored ectotherm organisms are at an advantage at low temperature due to increased warming. This theory is generally supported for ectotherm animals, however, the function of colors in the fungal kingdom is largely unknown. Here, we test whether the color lightness of mushroom assemblages is related to climate using a dataset of 3.2 million observations of 3,054 species across Europe. Consistent with the thermal melanism theory, mushroom assemblages are significantly darker in areas with cold climates. We further show differences in color phenotype between fungal lifestyles and a lifestyle differentiated response to seasonality. These results indicate a more complex ecological role of mushroom colors and suggest functions beyond thermal adaption. Because fungi play a crucial role in terrestrial carbon and nutrient cycles, understanding the links between the thermal environment, functional coloration and species' geographical distributions will be critical in predicting ecosystem responses to global warming.