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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]

Forests mitigate climate change by storing carbon and reducing emissions via substitution effects of wood products. Additionally, they provide many other important ecosystem services (ESs), but are vulnerable to climate change; therefore, adaptation is necessary. Climate‐smart forestry combines mitigation with adaptation, whilst facilitating the provision of many ESs. This is particularly challenging due to large uncertainties about future climate. Here, we combined ecosystem modeling with robust multi‐criteria optimization to assess how the provision of various ESs (climate change mitigation, timber provision, local cooling, water availability, and biodiversity habitat) can be guaranteed under a broad range of climate futures across Europe. Our optimized portfolios contain 29% unmanaged forests, and implicate a successive conversion of 34% of coniferous to broad‐leaved forests (11% vice versa). Coppices practically vanish from Southern Europe, mainly due to their high water requirement. We find the high shares of unmanaged forests necessary to keep European forests a carbon sink while broad‐leaved and unmanaged forests contribute to local cooling through biogeophysical effects. Unmanaged forests also pose the largest benefit for biodiversity habitat. However, the increased shares of unmanaged and broad‐leaved forests lead to reductions in harvests. This raises the question of how to meet increasing wood demands without transferring ecological impacts elsewhere or enhancing the dependence on more carbon‐intensive industries. Furthermore, the mitigation potential of forests depends on assumptions about the decarbonization of other industries and is consequently crucially dependent on the emission scenario. Our findings highlight that trade‐offs must be assessed when developing concrete strategies for climate‐smart forestry. Plain Language Summary: Forests help mitigate climate change by storing carbon and via avoided emissions when wood products replace more carbon‐intensive materials. At the same time, forests provide many other "ecosystem services (ESs)" to society. For example, they provide timber, habitat for various species, and they cool their surrounding regions. They are, however, also vulnerable to ongoing climate change. Forest management must consider all these aspects, which is particularly challenging considering the uncertainty about future climate. Here, we propose how this may be tackled by computing optimized forest management portfolios for Europe for a broad range of future climate pathways. Our results show that changes to forest composition are necessary. In particular, increased shares of unmanaged and broad‐leaved forests are beneficial for numerous ESs. However, these increased shares also lead to decreases in harvest rates, posing a conflict between wood supply and demand. We further show that the mitigation potential of forests strongly depends on how carbon‐intensive the replaced materials are. Consequently, should these materials become "greener" due to new technologies, the importance of wood products in terms of climate change mitigation decreases. Our study highlights that we cannot optimize every aspect, but that trade‐offs between ESs need to be made. Key Points: Strategies for climate‐smart forestry under a range of climate scenarios always lead to trade‐offs between different ecosystem services (ESs)Higher shares of unmanaged and broad‐leaved forests are beneficial for numerous ESs, but lead to decreased timber provisionThe mitigation potential of forests strongly relies on substitution effects which depend on the carbon‐intensity of the alternative products [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]

Southeastern South America is subject to considerable precipitation variability on seasonal to decadal timescales and has undergone very heavy land‐cover changes (LCCs) since the middle of the past century. The influence of local LCC and precipitation as drivers of regional evapotranspiration (ET) long‐term trends and variability remains largely unknown in the region. Here, ensembles of stand‐alone dynamic global vegetation models (DGVMs) with different atmospheric forcings are used to disentangle the influence of those two drivers on austral summer ET from 1950 to 2010. This paper examines the influence of both the El Niño‐Southern Oscillation (ENSO) and the dipole‐like first‐mode of southeastern South American precipitation variability (EOF1) on regional ET. We found that in the lower La Plata Basin, ET was driven by precipitation variability and showed a positive summer trend. Moreover, the region showed marked seasonal anomalies during El Niño and La Niña summers but mainly during EOF1 phases. On the contrary, in the upper La Plata Basin, LCCs forced the negative summer ET trend and particularly reduced the summer anomalies of the late 1990s, a period of ENSO and EOF1‐positive phases. In the South Atlantic Convergence Zone region, the high ET uncertainty across ensemble members impeded finding robust results, which highlights the importance of using multiple DGVMs and atmospheric forcings instead of relying on single model/forcing results. [ABSTRACT FROM AUTHOR]