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China is a country with substantial differences in economic development, energy consumption mix, resources, and technologies, as well as the development path at the provincial level. Therefore, China’s provinces have different potential and degrees of difficulty to carry out carbon emission reduction (CER) requirements. In addition, interprovincial trade, with a large amount of embodied carbon emissions, has become the fastest growing driver of China’s total carbon emissions. A reasonable CER allocation plan is, therefore, crucial for realizing the commitment that China announced in the Paris Agreement. How to determine a fair way to allocate provincial CER duties has become a significant challenge for both policy-makers and researchers. In this paper, ecological network analysis (ENA), combined with a multi-regional input-output model (MRIO), is adopted to build an ecological network of embodied emissions across 30 provinces. Then, by using flow analysis and utility analysis based on the ENA model, the specific relationships among different provinces were determined, and the amount of responsibility that a certain province should take quantified, with respect to the embodied carbon emission (ECE) flows from interprovincial trade. As a result, we suggest a new CER allocation plan, based on the detailed data of interprovincial relationships and ECE flows.

The concept of 'resilience' was first adopted within systems ecology in the 1970s, where it marked a move away from the homeostasis of Cold War resource management toward the far-from-equilibrium models of second-order cybernetics or complex systems theory. Resilience as an operational strategy of risk management has more recently been taken up in financial, urban and environmental security discourses, where it reflects a general consensus about the necessity of adaptation through endogenous crisis. The generalization of complex systems theory as a methodology of power has ambivalent sources. While the redefinition of the concept can be directly traced to the work of the ecologist Crawford S. Holling, the deployment of complex systems theory is perfectly in accord with the later philosophy of the Austrian neoliberal Friedrich Hayek. This ambivalence is reflected in the trajectory of complex systems theory itself, from critique to methodology of power.

To highlight the potential value of network analysis for conservation biogeography and to focus attention on some of the challenges that lie ahead in applying it to conservation problems. Global. We briefly review existing literature and then focus on five important challenges for the further development of network-based approaches in the field. Our five challenges include (i) understanding cross-scale and cross-level linkages in ecological systems (top-down and bottom-up effects, such as trophic cascades, have been demonstrated in food webs but are poorly understood in nested hierarchies such as reserve networks and stream catchments), (ii) capturing dynamic aspects of ecological systems and networks (with a few exceptions we have little grasp of how important whole-network attributes change as the composition of nodes and links changes), (iii) integrating ecological aspects of network theory with metacommunity frameworks and multiple node functions and roles (can we link the spatial patterns of habitat patches in fragmented landscapes, the parallel networks of interacting species using those patches and community-level interactions as defined by metacommunity theory in a single framework?), (iv) integrating the analysis of social and ecological networks (particularly, can they be analysed as a single interacting system?) and (v) laying an empirical foundation for network analysis in conservation biogeography (this will require a larger data bank of well-studied networks from diverse habitats and systems). Recent research has identified a variety of approaches that we expect to contribute to progress in each of our five challenge areas. We anticipate that some of the most exciting outcomes of attempts to meet these challenges will be frameworks that unite areas of research, such as food web analysis and metacommunity theory, that have developed independently.

Aim: The assumption of equilibrium between organisms and their environment is a standard working postulate in species distribution models (SDMs). However, this assumption is typically violated in models of biological invasions where range expansions are highly constrained by dispersal and colonization processes. Here, we examined how stage of invasion affects the extent to which occurrence data represent the ecological niche of organisms and, in turn, influences spatial prediction of species' potential distributions. Location: Six ecoregions in western Oregon, USA. Methods: We compiled occurrence data from 697 field plots collected over a 9-year period (2001-09) of monitoring the spread of invasive forest pathogen Phytophthora ramorum. Using these data, we applied ecological-niche factor analysis to calibrate models of potential distribution across different years of colonization. We accounted for natural variation and uncertainties in model evaluation by further investigating three hypothetical scenarios of varying equilibrium in a simulated virtual species, for which the 'true' potential distribution was known. Results: We confirm our hypothesis that SDMs calibrated in early stages of invasion are less accurate than models calibrated under scenarios closer to equilibrium. SDMs that are developed in early stages of invasion tend to underpredict the potential range compared to models that are built in later stages of invasion. Main conclusions: A full environmental niche of invasive species cannot be effectively captured with data from a realized distribution that is restricted by processes preventing full occupancy of suitable habitats. If SDMs are to be used effectively in conservation and management, stage of invasion needs to be considered to avoid underestimation of habitats at risk of invasion.

This paper provides a theoretical and political critique of how the concept of resilience has been applied to places. It is based upon three main points. First, the ecological concept of resilience is conservative when applied to social relations. Second, resilience is externally defined by state agencies and expert knowledge. Third, a concern with the resilience of places is misplaced in terms of spatial scale, since the processes which shape resilience operate primary at the scale of capitalist social relations. In place of resilience, we offer the concept of resourcefulness as an alternative approach for community groups to foster.

Abstract The existence of the utilization of the Tanjung Lesung Special Economic Zone (SEZ) as connectivity, interaction relationships, and the balance of resource governance influence cultural ecosystem service. This research aimed to map out the social-ecological system components of coastal and marine cultural ecosystem services. The focus is on examining the connectivity network between resource governance (RG) components such as resource actors (RA), resource units (RU), and resource systems (RS). The data obtained were analyzed using the stages of social-ecological network analysis. The results show a significant influence and strong interaction between resource governance (RG) components and other components. The presence of institutional structures and typologies is a crucial component that serves as a guideline for SEZ management influenced by actor centrality through links. Several performance indicators are still lacking based on the interaction conditions, indicating the need for strategies to strengthen governance. However, a particular challenge that needs attention is the implementation of every governance strategy formulation. Cohesion among stakeholders in enhancing resource governance performance with the surrounding community is paramount. Improvement can be achieved through strong collaboration to ensure the sustainability of coastal and marine cultural ecosystem services. Highlight Research The components of the social-ecological system of cultural coastal and marine ecosystem services can be identified and analyzed in a case study of a special economic zone. The complexity of the social-ecological system was analyzed using a network perspective approach. Centrality analysis was used to determine the magnitude of influence of each component in the system. The performance condition of governance can be determined using analysis of the resource governance interactions.

The fourth joint inter-agency report on integrated analysis of antimicrobial consumption (AMC) and the occurrence of antimicrobial resistance (AMR) in bacteria from humans and food-producing animals (JIACRA) addressed data obtained by the Agencies' EU-wide surveillance networks for 2019-2021. The analysis also sought to identify whether significant trends in AMR and AMC were concomitant over 2014-2021. AMC in both human and animal sectors, expressed in mg/kg of estimated biomass, was compared at country and European level. In 2021, the total AMC was assessed at 125.0 mg/kg of biomass for humans (28 EU/EEA countries, range 44.3-160.1) and 92.6 mg/kg of biomass for food-producing animals (29 EU/EEA countries, range 2.5-296.5). Between 2014 and 2021, total AMC in food-producing animals decreased by 44%, while in humans, it remained relatively stable. Univariate and multivariate analyses were performed to study associations between AMC and AMR for selected combinations of bacteria and antimicrobials. Positive associations between consumption of certain antimicrobials and resistance to those substances in bacteria from both humans and food-producing animals were observed. For certain combinations of bacteria and antimicrobials, AMR in bacteria from humans was associated with AMR in bacteria from food-producing animals which, in turn, was related to AMC in animals. The relative strength of these associations differed markedly between antimicrobial class, microorganism and sector. For certain antimicrobials, statistically significant decreasing trends in AMC and AMR were concomitant for food-producing animals and humans in several countries over 2014-2021. Similarly, a proportion of countries that significantly reduced total AMC also registered increasing susceptibility to antimicrobials in indicator from food-producing animals and originating from human invasive infections (i.e., exhibited 'complete susceptibility' or 'zero resistance' to a harmonised set of antimicrobials). Overall, the findings suggest that measures implemented to reduce AMC in food-producing animals and in humans have been effective in many countries. Nevertheless, these measures need to be reinforced so that reductions in AMC are retained and further continued, where necessary. This also highlights the importance of measures that promote human and animal health, such as vaccination and better hygiene, thereby reducing the need for use of antimicrobials.

Background Ecological analysis utilizes group-level aggregate measures to investigate the complex relationships between individuals or groups and their environment. Despite its extensive applications across various disciplines, this approach remains susceptible to several biases, including ecological fallacy. Methods Our study identified another significant source of bias in ecological analysis when using multiple sample datasets, a common practice in fields such as public health and medical research. We show this bias is proportional to the sampling fraction used during data collection. We propose two adjustment methods to address this bias: one that directly accounts for the sampling fraction and another based on measurement error models. The effectiveness of these adjustments is evaluated through formal mathematical derivations, simulations, and empirical analysis using data from the 2014 Kenya Demographic and Health Survey. Results Our findings reveal that the sampling fraction bias can lead to significant underestimation of true relationships when using aggregate measures from multiple sample datasets. Both adjustment methods effectively mitigate this bias, with the measurement-error-adjusted estimator showing particular robustness in real-world applications. The results highlight the importance of accounting for sampling fraction bias in ecological analyses to ensure accurate inference. Conclusion Beyond the ecological fallacy uncovered by Robinson’s seminar work, our research identified another critical bias in ecological analysis that is likely just as prevalent and consequential. The proposed adjustment methods provide potential tools for researchers to adjust for this bias, thereby improving the validity of ecological inferences. This study underscores the need for caution when pooling aggregate measures from multiple sample datasets and offers potential solutions to enhance the reliability of ecological analyses in various research domains. Clinical trial number Not applicable.

The second ECDC/EFSA/EMA joint report on the integrated analysis of antimicrobial consumption (AMC) and antimicrobial resistance (AMR) in bacteria from humans and food-producing animals addressed data obtained by the Agencies' EU-wide surveillance networks for 2013-2015. AMC in both sectors, expressed in mg/kg of estimated biomass, were compared at country and European level. Substantial variations between countries were observed in both sectors. Estimated data on AMC for pigs and poultry were used for the first time. Univariate and multivariate analyses were applied to study associations between AMC and AMR. In 2014, the average AMC was higher in animals (152 mg/kg) than in humans (124 mg/kg), but the opposite applied to the median AMC (67 and 118 mg/kg, respectively). In 18 of 28 countries, AMC was lower in animals than in humans. Univariate analysis showed statistically-significant (p < 0.05) associations between AMC and AMR for fluoroquinolones and in both sectors, for 3rd- and 4th-generation cephalosporins and in humans, and tetracyclines and polymyxins and in animals. In humans, there was a statistically-significant association between AMC and AMR for carbapenems and polymyxins in . Consumption of macrolides in animals was significantly associated with macrolide resistance in in animals and humans. Multivariate analyses provided a unique approach to assess the contributions of AMC in humans and animals and AMR in bacteria from animals to AMR in bacteria from humans. Multivariate analyses demonstrated that 3rd- and 4th-generation cephalosporin and fluoroquinolone resistance in from humans was associated with corresponding AMC in humans, whereas resistance to fluoroquinolones in spp. and spp. from humans was related to consumption of fluoroquinolones in animals. These results suggest that from a 'One-health' perspective, there is potential in both sectors to further develop prudent use of antimicrobials and thereby reduce AMR.

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated dependent and independent variables. SEM is commonly applied in ecology, but the spatial information commonly found in ecological data remains difficult to model in a SEM framework. Here we propose a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance/covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale and can be implemented using any standard SEM software package. We demonstrate the application of this method using three studies examining the relationships between environmental factors, plant community structure, nitrogen fixation, and plant competition. By design, these data sets had a spatial component, but were previously analyzed using standard SEM models. Using these data sets, we demonstrate the application of SE-SEM to regularly spaced, irregularly spaced, and ad hoc spatial sampling designs and discuss the increased inferential capability of this approach compared with standard SEM. We provide an R package, sesem, to easily implement spatial structural equation modeling.