Lifting the veil on the correction of double counting incidents in hybrid life cycle assessment

Life cycle assessment (LCA) and environmentally extended input–output analyses (EEIOA) are two techniques commonly used to assess environmental impacts of an activity/product. Their strengths and weaknesses are complementary, and they are thus regularly combined to obtain hybrid LCAs. A number of approaches in hybrid LCA exist, which leads to different results. One of the differences is the method used to ensure that mixed LCA and EEIOA data do not overlap, which is referred to as correction for double counting. This aspect of hybrid LCA is often ignored in reports of hybrid assessments and no comprehensive study has been carried out on it. This article strives to list, compare, and analyze the different existing methods for the correction of double counting. We first harmonize the definitions of the existing correction methods and express them in a common notation, before introducing a streamlined variant. We then compare their respective assumptions and limitations. We discuss the loss of specific information regarding the studied activity/product and the loss of coherent financial representation caused by some of the correction methods. This analysis clarifies which techniques are most applicable to different tasks, from hybridizing individual LCA processes to integrating complete databases. We finally conclude by giving recommendations for future hybrid analyses.     

Origin of volatile organic compound emissions from subarctic tundra under global warming

Warming occurs in the Arctic twice as fast as the global average, which in turn leads to a large enhancement in terpenoid emissions from vegetation. Volatile terpenoids are the main class of biogenic volatile organic compounds (VOCs) that play crucial roles in atmospheric chemistry and climate. However, the biochemical mechanisms behind the temperature‐dependent increase in VOC emissions from subarctic ecosystems are largely unexplored. Using ¹³CO₂‐labeling, we studied the origin of VOCs and the carbon (C) allocation under global warming in the soil–plant–atmosphere system of contrasting subarctic heath tundra vegetation communities characterized by dwarf shrubs of the genera Salix or Betula. The projected temperature rise of the subarctic summer by 5°C was realistically simulated in sophisticated climate chambers. VOC emissions strongly depended on the plant species composition of the heath tundra. Warming caused increased VOC emissions and significant changes in the pattern of volatiles toward more reactive hydrocarbons. The ¹³C was incorporated to varying degrees in different monoterpene and sesquiterpene isomers. We found that de novo monoterpene biosynthesis contributed to 40%–44% (Salix) and 60%–68% (Betula) of total monoterpene emissions under the current climate, and that warming increased the contribution to 50%–58% (Salix) and 87%–95% (Betula). Analyses of above‐ and belowground ^12/13C showed shifts of C allocation in the plant–soil systems and negative effects of warming on C sequestration by lowering net ecosystem exchange of CO₂ and increasing C loss as VOCs. This comprehensive analysis provides the scientific basis for mechanistically understanding the processes controlling terpenoid emissions, required for modeling VOC emissions from terrestrial ecosystems and predicting the future chemistry of the arctic atmosphere. By changing the chemical composition and loads of VOCs into the atmosphere, the current data indicate that global warming in the Arctic may have implications for regional and global climate and for the delicate tundra ecosystems.     

Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species

Species distribution models are required for the research and management of biodiversity in the hyperdiverse tropical forests, but reliable and ecologically relevant digital environmental data layers are not always available. We here assess the usefulness of multispectral canopy reflectance (Landsat) relative to climate data in modelling understory plant species distributions in tropical rainforests. We used a large dataset of quantitative fern and lycophyte species inventories across lowland Amazonia as the basis for species distribution modelling (SDM). As predictors, we used CHELSA climatic variables and canopy reflectance values from a recent basin-wide composite of Landsat TM/ETM+ images both separately and in combination. We also investigated how species accumulate over sites when environmental distances were expressed in terms of climatic or surface reflectance variables. When species accumulation curves were constructed such that differences in Landsat reflectance among the selected plots were maximised, species accumulated faster than when climatic differences were maximised or plots were selected in a random order. Sixty-nine species were sufficiently frequent for species distribution modelling. For most of them, adequate SDMs were obtained whether the models were based on CHELSA data only, Landsat data only or both combined. Model performance was not influenced by species’ prevalence or abundance. Adding Landsat-based environmental data layers overall improved the discriminatory capacity of SDMs compared to climate-only models, especially for soil specialist species. Our results show that canopy surface reflectance obtained by multispectral sensors can provide studies of tropical ecology, as exemplified by SDMs, much higher thematic (taxonomic) detail than is generally assumed. Furthermore, multispectral datasets complement the traditionally used climatic layers in analyses requiring information on environmental site conditions. We demonstrate the utility of freely available, global remote sensing data for biogeographical studies that can aid conservation planning and biodiversity management.     

Digital transformation—life cycle assessment of digital services,multifunctional devices and cloud computing

The International Journal of Life Cycle Assessment -     

Occupancy versus colonization–extinction models for projecting population trends at different spatial scales

Understanding spatiotemporal population trends and their drivers is a key aim in population ecology. We further need to be able to predict how the dynamics and sizes of populations are affected in the long term by changing landscapes and climate. However, predictions of future population trends are sensitive to a range of modeling assumptions. Deadwood‐dependent fungi are an excellent system for testing the performance of different predictive models of sessile species as these species have different rarity and spatial population dynamics, the populations are structured at different spatial scales, and they utilize distinct substrates. We tested how the projected large‐scale occupancies of species with differing landscape‐scale occupancies are affected over the coming century by different modeling assumptions. We compared projections based on occupancy models against colonization–extinction models, conducting the modeling at alternative spatial scales and using fine‐ or coarse‐resolution deadwood data. We also tested effects of key explanatory variables on species occurrence and colonization–extinction dynamics. The hierarchical Bayesian models applied were fitted to an extensive repeated survey of deadwood and fungi at 174 patches. We projected higher occurrence probabilities and more positive trends using the occupancy models compared to the colonization–extinction models, with greater difference for the species with lower occupancy, colonization rate, and colonization:extinction ratio than for the species with higher estimates of these statistics. The magnitude of future increase in occupancy depended strongly on the spatial modeling scale and resource resolution. We encourage using colonization–extinction models over occupancy models, modeling the process at the finest resource‐unit resolution that is utilizable by the species, and conducting projections for the same spatial scale and resource resolution at which the model fitting is conducted. Further, the models applied should include key variables driving the metapopulation dynamics, such as the availability of suitable resource units, habitat quality, and spatial connectivity.