Ecosystem services bundles:challenges and opportunities for implementation and further research

Background : the concept of ‘ecosystem services bundles’, i.e. ecosystem services that repeatedly appear together across space and/or time, has been developed and refined as part of an integrated approach to assess interactions between ecosystem services. Nevertheless, published evidence of actual use of bundles in decision-making is lacking. In the light of this gap, a review of what bundle approaches have shown and what they can bring to decision-making is timely. Method : we conducted two separate systematic reviews. The first one addressed emerging issues within what we identify as the diverse utilisation and definition of the concept of ‘bundle’ in the literature. The second one focused on papers dealing with bundles as sets of consistently associated services. Review Synthesis : the review first highlights that the confusion surrounding the term ‘bundle’ in ecosystem services literature threatens to weaken the potential for analysis of bundles to inform decision-making. Then, thanks to the review of peer-reviewed papers that detect bundles as sets of consistently associated services, we analyse the diversity of methodological choices and we detail the interactions observed between different ecosystem services across the literature. We also show that landscape features, socio-economic conditions and institutional factors are all potential drivers for the occurrence of specific bundles in a landscape. Discussion : overall, it appears that the analysis of bundles provides an opportunity to enhance policy effectiveness. Nevertheless, the methodological challenges linked to the identification and interpretation of bundles call for careful and reflective study designs. We anticipate that this review will lead to a better understanding by scientists and practitioners of the potential for bundle studies to inform decision-making

A Geospatial Approach To Identify Patterns of Antibiotic Susceptibility at a Neighborhood Level in Wisconsin, United States

The global threat of antimicrobial resistance (AMR) varies regionally. This study explores whether geospatial analysis and data visualization methods detect both clinically and statistically significant variations in antibiotic susceptibility rates at a neighborhood level. This observational multicenter geospatial study collected 10 years of patient-level antibiotic susceptibility data and patient addresses from three regionally distinct Wisconsin health systems (UW Health, Fort HealthCare, Marshfield Clinic Health System [MCHS]). We included the initial Escherichia coli isolate per patient per year per sample source with a patient address in Wisconsin (N = 100,176). Isolates from U.S. Census Block Groups with less than 30 isolates were excluded (n = 13,709), resulting in 86,467 E. coli isolates. The primary study outcomes were the results of Moran\u27s I spatial autocorrelation analyses to quantify antibiotic susceptibility as spatially dispersed, randomly distributed, or clustered by a range of - 1 to + 1, and the detection of statistically significant local hot (high susceptibility) and cold spots (low susceptibility) for variations in antibiotic susceptibility by U.S. Census Block Group. UW Health isolates collected represented greater isolate geographic density (n = 36,279 E. coli, 389 = blocks, 2009-2018), compared to Fort HealthCare (n = 5110 isolates, 48 = blocks, 2012-2018) and MCHS (45,078 isolates, 480 blocks, 2009-2018). Choropleth maps enabled a spatial AMR data visualization. A positive spatially-clustered pattern was identified from the UW Health data for ciprofloxacin (Moran\u27s I = 0.096, p = 0.005) and trimethoprim/sulfamethoxazole susceptibility (Moran\u27s I = 0.180, p \u3c 0.001). Fort HealthCare and MCHS distributions were likely random. At the local level, we identified hot and cold spots at all three health systems (90%, 95%, and 99% CIs). AMR spatial clustering was observed in urban areas but not rural areas. Unique identification of AMR hot spots at the Block Group level provides a foundation for future analyses and hypotheses. Clinically meaningful differences in AMR could inform clinical decision support tools and warrants further investigation for informing therapy options

Rolling resistance contribution to a road pavement life cycle carbon footprint analysis

Purpose Although the impact of road pavement surface condition on rolling resistance has been included in the life cycle assessment (LCA) framework of several studies in the last years, there is still a high level of uncertainty concerning the methodological assumptions and the parameters that can affect the results. In order to adopt pavement carbon footprint/LCA as a decision-making tool, it is necessary to explore the impact of the chosen methods and assumptions on the LCA results. Methods This paper provides a review of the main models describing the impact of the pavement surface properties on vehicle fuel consumption and analyses the influence of the methodological assumptions related to the rolling resistance on the LCA results. It compares the CO2 emissions, calculated with two different rolling resistance models existing in literature, and performs a sensitivity test on some specific input variables (pavement deterioration rate, traffic growth, and emission factors/fuel efficiency improvement). Results and discussion The model used to calculate the impact of the pavement surface condition on fuel consumption significantly affects the LCA results. The pavement deterioration rate influences the calculation in both models, while traffic growth and fuel efficiency improvement have a limited impact on the vehicle CO2 emissions resulting from the pavement condition contribution to rolling resistance. Conclusions and recommendations Existing models linking pavement condition to rolling resistance and hence vehicle emissions are not broadly applicable to the use phase of road pavement LCA and further research is necessary before a widely-used methodology can be defined. The methods of modelling and the methodological assumptions need to be transparent in the analysis of the impact of the pavement surface condition on fuel consumption, in order to be interpreted by decision makers and implemented in an LCA framework. This will be necessary before product category rules (PCR) for pavement LCA can be extended to include the use phase

Minimal Universal Extra Dimensions in CalcHEP/CompHEP

We present an implementation of the model of minimal universal extra dimensions (MUED) in CalcHEP/CompHEP. We include all level-1 and level-2 Kaluza-Klein (KK) particles outside the Higgs sector. The mass spectrum is automatically calculated at one loop in terms of the two input parameters in MUED: the radius of the extra dimension and the cut-off scale of the model. We implement both the KK number conserving and the KK number violating interactions of the KK particles. We also account for the proper running of the gauge coupling constants above the electroweak scale. The implementation has been extensively cross-checked against known analytical results in the literature and numerical results from other programs. Our files are publicly available and can be used to perform various automated calculations within the MUED model.Comment: 32 pages, 4 figures, 6 tables, invited contribution for New Journal of Physics Focus Issue on 'Extra Space Dimensions', the model file can be downloaded from http://home.fnal.gov/~kckong/mued

Dissolved organic matter quantity and quality response of tropical rainforest headwater rivers to the transition from dry to wet season

Dissolved organic matter (DOM) and its composition in aquatic ecosystems is a key indicator of ecosystem function and an important component of the global carbon cycle. Tropical rainforest headwaters play an important role in global carbon cycling. However, there is a large uncertainty on how DOM sources interact during mobilisation and the potential fate of associated carbon and nutrients. Using field techniques to measure dissolved organic carbon (DOC) concentration and composition, changes in DOM source from headwaters to larger downstream rivers were observed. This study shows that the hydrological connectivity, developed during the transition from dry to wet seasons, changes the DOM supply and transport across a tropical river catchment. The observed variability in the DOC-river discharge relationship provides further evidence of the changes in the DOM supply in a small headwater. This novel insight into the seasonal changes of the dynamics of DOM supply to the river helps understanding the mobilization of terrestrial DOM to tropical headwaters and its export from smaller to larger rivers. It also highlights the data gap in the study of smaller headwaters which may account for uncertainty in estimating the terrestrial carbon transported by inland waters