This study explores the impact of phosphorus on the functional diversity (FD) of freshwater fish globally. The FD metrics functional richness (FRic), functional evenness (FEve) and functional divergence (FDiv) are calculated using a compiled dataset of 36,868 total phosphorus (TP) stations and 4,478,635 fish occurrences, leading to a FD analysis for 2498 buffers.
Using linear mixed models and various potential covariates, the effects of TP and freshwater realm on FD was assessed. The best-performing model for FRic, selected based on ANOVA, marginal R2 and conditional R2, included ecoregions as a random factor and temperature as a covariate. This model explained 57.5% of the variance in FRic, but TP's effect was not significant. The models for FEve and FDiv are singular, therefore no response to TP has been found. Contrary to prior research suggesting a significant effect of phosphorus on aquatic ecosystems, these findings reveal no significant relationship between TP and the FD metrics. This result challenges the expectation of decreasing FRic based on findings of decrease in species richness, highlighting that species richness does not necessarily correlate with functional diversity.
These findings suggest that phosphorus alone may not be a reliable indicator of eutrophication's impact on functional diversity. The results underscore the importance of considering additional factors, such as the nitrogen-to-phosphorus (N/P) ratio, in assessing the ecological effects of eutrophication. Consequently, phosphorus should be reconsidered as the sole metric for evaluating freshwater eutrophication in life cycle assessments, especially when using FD as impact category.

The CONE-LCA framework is a promising framework that can be used to assess the impact of food items on human health and the environment. However, the nutrition-health assessment in the CONE-LCA framework quantifies the health impacts of food at the level of food items with the use of an indicator that is based on only few nutrients and otherwise broad food groups. Given that food items within food groups can have different impacts on health, disaggregating health impacts can be of added value and was explored here. An extended version of the Nutrients Rich Food index is used for both downscaling and cluster analysis.

The correlation coefficient at the aggregated levels with the NRF index (-0.93) was substantially higher than the original health impact indicator of food items with the NRF index (0.36). These results illustrate that the use of a downscaled indicator improves the health impact assessment of food items. The downscaling analysis showed that downscaled health impacts can differ substantially between food items within food groups. Furthermore, the cluster analysis showed that some food groups (e.g. fruits, vegetables and red meat) are highly variable in nutrient density and therefore prioritizing these food groups for clustering can be of added value in future epidemiological studies.

The improved nutrition health assessment model is not only relevant within the CONE-LCA framework but can be used on its own to assess health impacts of food items. The model may be very useful in policy applications as it enables comparison of food items on impact on human health and the environment, which can be the basis for sustainable dietary guidelines. Future research can focus on further development and validation of the model.

With the current trend in emissions, the pH levels of the oceans are expected to decrease from 8.1 to 7.8 by the end of the century. In combination with the other stressors, it is projected that OA will have a wide range of impacts on marine life and its services to humanity. The representation of these implications is limited in environmental assessment tools such as Life Cycle Assessment. This research explores the relationship between the changing acidity of the oceans and marine biodiversity loss. This relation is quantified through utilising the ecotoxicology impact assessment approach for LCA. Following this approach, an endpoint characterization model is developed for ocean acidification. 3 different categorisations (climate zones, calcification, exposure duration) were made to assess their effects on species responses. The results revealed that there is no significant difference in responses based on different exposure durations or climate zones. Calcifying species on the other hand is found to have a higher sensitivity to ocean acidification as the change in carbonate chemistry directly influences the shell and skeleton formation of these organisms. Lastly, these models were integrated into an endpoint characterization model for ocean acidification. From the 3 GHG emissions included within the scope of this research (CO2, CO, CH4), CO2 has the highest (CFCO2 = 4.883 × 104 (𝑃𝐷𝐹)𝑚3/𝑘𝑔𝐺𝐻𝐺) and CH4 has the lowest (CFCH4 = 4.072 × 104 𝑃𝐷𝐹)𝑚3/𝑘𝑔𝐺𝐻𝐺) impact on marine biodiversity loss due to OA. These ecosystem damage indicators can be utilised in the impact assessment phase of the Life Cycle Assessment to translate the inventory results into impact on marine biodiversity. Through the quantification of the impacts of ocean acidification, the effects of this major stressor on marine life can be better understood and targeted strategies can be developed. However, more research is required to increase the robustness of these models through expanding the species scope and incorporating temporal and geographical aspects into the models. Furthermore, the cascading effects of the changing ocean pH are still unknown and its consequences on ecosystems and socio-economic structures are unprecedented. To establish science-based targets and strategies to conserve the species richness in marine life, the extent of our understanding of the damage caused by anthropogenic actions needs to be further explored and estimated.

The food sector plays a crucial role in exceeding several planetary boundaries. Currently, humanity has the challenge to stay between these boundaries. Food forests are an approach for improving ecosystems in soil quality, sequestering carbon and enhancing biodiversity while providing food. However, this concept is yet nearly unstudied in temperate regions. Accordingly, this study attempted to investigate aboveground and belowground storage, carbon fluxes, and the build-up rate in the transition from grassland to a food forest through an in-depth case study in a temperate region. In order to analyse all key carbon pools, a plotless method was applied for the coppice, hedgerows, fruit and nut-bearing trees, and a plot-based method for the grass, herbs, litter and soil organic carbon (SOC).
Overall, the results showed exponential growth in aboveground carbon in the living biomass, though remarkably different patterns for SOC over the first 5.5 years. The ground layer (e.g. grass, herbs, and litter) is a significant source of aboveground carbon in young food forests (95% in year 3.5 to 36% in year 5.5). The trees compartments showed an exponential increase, and the total stored carbon biomass in the food forests at the age of 5.5 is 6.0 t C ha-1 yr-1. The SOC differed over the years, which is 115 t C ha-1 in the grassland and depicts 78 to 136 t C ha-1in year 5.5. Finally, the food forest has the potential to sequester around 4.4 t C ha-1 yr-1 in above and belowground biomass, functioning as active carbon sinks already within the first five years of the case study. Coppice and hedgerows have proven to be vital in sequestering carbon in living biomass. Further research should investigate older food forests in temperate regions while bearing in mind the potential of coppices and hedgerows. In addition, a better understanding of belowground carbon is essential for assessing the net carbon impact of carbon farming initiatives, as the soil stores most of the carbon.

Assessing the human impacts on biodiversity is important for conserving biodiversity. Life cycle assessment (LCA) is a tool to assess the impact product service systems have on the environment. To critically asses the human impact of land use on biodiversity in LCA, characterisation factors (CF) are needed to translate area and type of land use into loss of biodiversity. Most CFs are based on species richness but another biodiversity indicator, functional diversity (FD), better represents ecosystem functioning compared to taxonomic measures such as species richness. This study proposes a new method for calculating CFs, based on FD, for assessing impact of land use on plant biodiversity. To demonstrate the applicability of the method that is proposed, CFs were calculated based on data from Germany. The data was divided into four types, being land use, plant abundance, trait data and environmental data. The CFs developed show trends in impact on biodiversity in accordance with other studies. The proposed method in this study can guide in the development of more precise and geographically diverse CFs for assessing the impact of land use in biodiversity in LCA.