Scenario description and parameter definition.

a<p>Parameters definition: <i>m,</i> growth rate of GDP per capita (%); <i>r</i>, population growth rate (%); <i>k</i>, technology progress rate (%); <i>f</i>, energy structure optimization rate (%).</p>b<p>Data sources: The values of parameters are calculated or assumed based on the references from CCAP <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone.0077699-Center1" target="_blank">[44]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone.0077699-Center2" target="_blank">[45]</a>, CAS <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone.0077699-Study1" target="_blank">[46]</a>, SCPRC <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone.0077699-State1" target="_blank">[47]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone.0077699-State2" target="_blank">[48]</a>.</p

China’s GDP growth under BAU, EEI, LC and ELC scenarios (Scenarios are defined in Table 2).

<p>China’s GDP growth under BAU, EEI, LC and ELC scenarios (Scenarios are defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone-0077699-t002" target="_blank">Table 2</a>).</p

China’s energy intensity under BAU, EEI, LC and ELC scenarios. (Scenarios are defined in Table 2).

<p>China’s energy intensity under BAU, EEI, LC and ELC scenarios. (Scenarios are defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone-0077699-t002" target="_blank">Table 2</a>).</p

China’s total energy consumption under BAU, EEI, LC and ELC scenarios. (Scenarios are defined in Table 2).

<p>China’s total energy consumption under BAU, EEI, LC and ELC scenarios. (Scenarios are defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone-0077699-t002" target="_blank">Table 2</a>).</p

China’s energy-related CO₂ emissions under BAU, EEI, LC and ELC scenarios.

<p>(Scenarios are defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0077699#pone-0077699-t002" target="_blank">Table 2</a>).</p

Prediction result of scenario indicators (2007–2020).

c<p>Indexes definition: <i>P</i>, Population (<i>10⁸ persons</i>); <i>G</i>, GDP (<i>10¹² yuan</i>); <i>A</i>, GDP per capita (<i>10⁴ yuan</i>); <i>E</i>, Total energy consumption (10⁸ tce); <i>I</i>, Energy intensity (<i>tce/10⁴yuan</i>); <i>C</i>, CO₂ emissions (<i>10⁸ tons</i>); <i>c</i>, CO₂ emission intensity (<i>tC/10⁴yuan</i>).</p

Definitions of each variable in Eq. (1–3).

<p>Definitions of each variable in Eq. (1–3).</p

Ecological Network Analysis on Global Virtual Water Trade

Global water interdependencies are likely to increase with growing virtual water trade. To address the issues of the indirect effects of water trade through the global economic circulation, we use ecological network analysis (ENA) to shed insight into the complicated system interactions. A global model of virtual water flow among agriculture and livestock production trade in 1995–1999 is also built as the basis for network analysis. Control analysis is used to identify the quantitative control or dependency relations. The utility analysis provides more indicators for describing the mutual relationship between two regions/countries by imitating the interactions in the ecosystem and distinguishes the beneficiary and the contributor of virtual water trade system. Results show control and utility relations can well depict the mutual relation in trade system, and direct observable relations differ from integral ones with indirect interactions considered. This paper offers a new way to depict the interrelations between trade components and can serve as a meaningful start as we continue to use ENA in providing more valuable implications for freshwater study on a global scale

How Does Predation Affect the Bioaccumulation of Hydrophobic Organic Compounds in Aquatic Organisms?

It is well-known that the body burden of hydrophobic organic compounds (HOCs) increases with the trophic level of aquatic organisms. However, the mechanism of HOC biomagnification is not fully understood. To fill this gap, this study investigated the effect of predation on the bioaccumulation of polycyclic aromatic hydrocarbons (PAHs), one type of HOC, in low-to-high aquatic trophic levels under constant freely dissolved PAH concentrations (1, 5, or 10 μg L^–1) maintained by passive dosing systems. The tested PAHs included phenanthrene, anthracene, fluoranthene, and pyrene. The test organisms included zebrafish, which prey on <i>Daphnia magna</i>, and cichlids, which prey on zebrafish. The results revealed that for both zebrafish and cichlids, predation elevated the uptake and elimination rates of PAHs. The increase of uptake rate constant ranged from 20.8% to 39.4% in zebrafish with the amount of predation of 5 daphnids per fish per day, and the PAH uptake rate constant increased with the amount of predation. However, predation did not change the final bioaccumulation equilibrium; the equilibrium concentrations of PAHs in fish only depended on the freely dissolved concentration in water. Furthermore, the lipid-normalized water-based bioaccumulation factor of each PAH was constant for fish at different trophic levels. These findings infer that the final bioaccumulation equilibrium of PAHs is related to a partition between water and lipids in aquatic organisms, and predation between trophic levels does not change bioaccumulation equilibrium but bioaccumulation kinetics at stable freely dissolved PAH concentrations. This study suggests that if HOCs have not reached bioaccumulation equilibrium, biomagnification occurs due to enhanced uptake rates caused by predation in addition to higher lipid contents in higher trophic organisms. Otherwise, it is only due to the higher lipid contents in higher trophic organisms

Environmental DNA Biomonitoring Reveals the Interactive Effects of Dams and Nutrient Enrichment on Aquatic Multitrophic Communities

Dam construction and nutrient enrichment are two pervasive stressors in rivers worldwide, which trigger a sharp decline in biodiversity and ecosystem services. However, the interactive effects of both stressors on multitrophic taxonomic groups remain largely unclear. Here, we used the multitrophic datasets captured by the environmental DNA (eDNA) approach to reveal the interactions between dams and nutrient enrichment on aquatic communities from the aspects of taxonomic α diversity, β diversity, and food webs. First, our data showed that dams and nutrient enrichment jointly shaped a unique spatial pattern of aquatic communities across the four river systems, and the dissimilarity of community structure significantly declined (i.e., structural homogenization) under both stressors. Second, dams and nutrients together explained 40–50% of the variations in aquatic communities, and dams had a stronger impact on fish, aquatic insects, and bacteria, yet nutrients had a stronger power to drive protozoa, fungi, and eukaryotic algae. Finally, we found that additive, synergistic, and antagonistic interactions of dams and nutrient enrichment were common and coexisted in river systems and led to significantly simplified aquatic food webs, with decreases in modularity (synergistic) and robustness (additive) and an increase in coherence (synergistic). Overall, our study highlights that eDNA-based datasets can provide multitrophic perspectives for fostering the understanding of the interactive effects of multiple stressors on rivers