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An understanding of thermal limits and variation across geographic regions is central to predicting how any population may respond to global change. Latitudinal clines, in particular, have been used to demonstrate that populations can be locally adapted to their own thermal environment and, as a result, not all populations will be equally impacted by an increase in temperature. But how robust are these signals of thermal adaptation to the other ecological challenges that animals commonly face in the wild? Seasonal changes in population density, food availability, or photoperiod are common ecological challenges that could disrupt patterns of thermal tolerance along a cline if each population differentially used these signals to anticipate future temperatures and adjust their thermal tolerances accordingly. In this study, we aimed to test the robustness of a cline in thermal tolerance to simulated signals of seasonal heterogeneity. Experimental animals were derived from clones of the Australian water flea, Daphnia carinata, sampled from nine distinct populations along a latitudinal transect in Eastern Australia. We then factorially combined summer (18 h light, 6 h dark) and winter (6 h light, 18 h dark) photoperiods with high (5 million algal cells individual−1 day−1) and low (1 million algal cells individual−1 day−1) food availabilities, before performing static heat shock assays to measure thermal tolerance. We found that the thermal tolerances of the clonal populations were sensitive to both measures of seasonal change. In general, higher food availability led to an increase in thermal tolerances, with the magnitude of the increase varying by clone. In contrast, a switch in photoperiod led to rank‐order changes in thermal tolerances, with heat resistance increasing for some clones, and decreasing for others. Heat resistance, however, still declined with increasing latitude, irrespective of the manipulation of seasonal signals, with clones from northern populations always showing greater thermal resistance, most likely driven by adaptation to winter thermal conditions. While photoperiod and food availability can clearly shape thermal tolerances for specific populations, they are unlikely to overwhelm overarching signals of thermal adaptation, and thus, observed clines in heat resistance will likely have remained robust to these forms of seasonal heterogeneity. [ABSTRACT FROM AUTHOR]

• Proposing an assessment framework for complementarity and source-load matching. • Correlation and fluctuation measurement methods are incorporated. • Multi-perspective assessments from annual and seasonal scales are conducted. • Wind and solar power output exhibit relatively strong time complementarity. • Wind-solar power joint output improves the power supply–demand matching degree. Wind and solar energy are expected to become the main sources of electricity supply in China, which requires addressing the balance problem between intermittent generation and random fluctuating loads and overcoming the seasonal and spatial distribution differences between wind and solar resources and electricity demand. Therefore, analyzing the spatial and temporal complementarity of wind and solar power and their matching characteristics with electricity demand is of great significance for constructing reliable and cost-effective high-proportion renewable energy systems. The study develops an assessment framework for the temporal and spatial heterogeneity of wind and solar power complementarity and source-load matching characteristics at the provincial level in China, which incorporates correlation and fluctuation measurement methods and divides annual and seasonal scales. The results show that the temporal complementarity of wind and solar power among provinces is strong and exhibits significant seasonal differences, with the strongest complementarity in summer. Wind and solar power joint output can smooth individual output fluctuations, particularly in provinces and seasons with richer wind and solar resources. Wind power output between different provinces exhibits a certain degree of spatial complementarity, while there is no significant spatial complementarity for solar power. Electricity demand fluctuation is negatively correlated with wind power output but positively correlated with solar power output. However, solar power has a stronger exacerbating effect on the net load demand fluctuation compared to wind power, and the net load demand fluctuation is larger in provinces and seasons with richer wind and solar resources. Wind and solar power joint output can mitigate the anti-peak characteristic of wind power and the duck curve issue of solar power, improving the matching degree between power supply and demand. [ABSTRACT FROM AUTHOR]

The identification of the main sources and the impact of the current comprehensive water quality is important in improving the precision and pertinence of water quality management, which will have a significant bearing on the quality of the water environment and the ecological system. This paper focuses on the Yangtze River and Yellow River Basins, employs panel data from 2004–2019 from prefecture-level cities and water quality monitoring sections along the Yangtze and Yellow River main streams, and conducts an empirical study on the effects of pollution sources from cultivation, livestock farming, aquaculture, industry, and domestic activities on the overall water quality. It is found that: (1) Increased pollution from agriculture (cultivation, livestock farming and aquaculture), industry and domestic pollution all contribute to the deterioration of water quality. Industry is still the major source of China’s ambient water pollution. It is then followed by domestic source of pollution and agricultural source of pollution, with the latter having the least impact on water quality. (2) Impact of pollution sources on water quality vary from basin to basin significantly.The main sources of water pollution in the Yangtze and Yellow River Basins are industry and domestic life respectively. Cultivation and aquaculture are the main agricultural sources of water pollution in the two basins respectively. (3) Impact of cultivation pollution sources on water quality is subject to seasonal variations. It means that pollutants from cultivation enter the rivers through the carrying effect of precipitation, and this effect is more often reflected in the busy season. (4) In the long term, the negative impact of cultivation and livestock farming on water quality pollution form a cumulative effect. It grows as the number of lag periods increases. Therefore, in the subsequent environmental pollution control processes, priority should be given to industrial and urban pollution control with more targeted regional pollution management. It is also important to strengthen inter-basin and inter-regional synergistic control, taking top-level design of environmental policies and other conditions of the basin areas into full account to reduce the undesired environmental consequences caused by production.

Species exhibit large diversity in their seasonal metabolic patterns which are traditionally explained by habitat specific seasonal temperature changes and various thermoregulatory adaptations. However, due to seasonal changes in resource abundances, density-dependent, ecological processes can also be important selective forces shaping the evolution of metabolic patterns. In the present theoretical study, the combined effect of environmental temperature and resource availability on the evolutionarily stable metabolic strategies is investigated in a consumer–resource model. Our results suggest that, under a broad range of circumstances, density-dependent mechanisms favor the selection of active metabolic regulation, where the metabolic rate differs from the thermally optimal value. This effect may be temporary, limited only to a brief period at seasonal changes, or permanent depending on the implied energetic cost and the relative timescale of environmental changes as compared to the generation time. [ABSTRACT FROM AUTHOR]

Trend and seasonality are the most prominent features of economic time series that are observed at the subannual frequency. Modeling these components serves a variety of analytical purposes, including seasonal adjustment and forecasting. In this paper we introduce unobserved components models for which both the trend and seasonal components arise from systematically sampling a multivariate transition equation, according to which each season evolves as a random walk with a drift. By modeling the disturbance covariance matrix we can encompass traditional models for seasonal time series, like the basic structural model, and can formulate more elaborate ones, dealing with season specific features, such as seasonal heterogeneity and correlation, along with the different role of the nonstationary cycles defined at the fundamental and the harmonic frequencies in determining the shape of the seasonal pattern. [ABSTRACT FROM AUTHOR]

To investigate the seasonal heterogeneity of the periphytic ciliate communities, a 1-year baseline survey was conducted in the Karnaphuli River estuary, northern Bay of Bengal, Bangladesh. A total of 54 ciliate species were recorded, including seven common and 14 dominant species. Maximum species number was in autumn whereas maximum abundance was in winter; the minimum for both occurred in summer. Multivariate analyses, i.e., canonical analysis of principal coordinates (CAP) and principal co-ordinates analysis (PCoA), revealed a clear seasonal heterogeneity of community structure and environmental variables. Multivariate correlation analysis (RELATE) demonstrated that the community structure of the periphytic ciliate communities was significantly correlated with environmental variables, and best matching analysis (BIOENV) indicated that heterogeneity of community patterns was mainly driven by water temperature, pH, dissolved oxygen, total dissolved solids and nutrients. Species richness and diversity peaked in autumn whereas species evenness peaked in summer. These results suggest that environmental conditions shape periphytic ciliate community structure, which is a potentially useful bio-indicator of estuarine water quality. [ABSTRACT FROM AUTHOR]

• Industry is still the major source of China's ambient water pollution. • Impact of pollution sources on water quality vary from basin to basin significantly. • Cultivation's impact on water quality is seasonal. • Cultivation and livestock farming have a cumulative effect on water quality. The identification of the main sources and the impact of the current comprehensive water quality is important in improving the precision and pertinence of water quality management, which will have a significant bearing on the quality of the water environment and the ecological system. This paper focuses on the Yangtze River and Yellow River Basins, employs panel data from 2004–2019 from prefecture-level cities and water quality monitoring sections along the Yangtze and Yellow River main streams, and conducts an empirical study on the effects of pollution sources from cultivation, livestock farming, aquaculture, industry, and domestic activities on the overall water quality. It is found that: (1) Increased pollution from agriculture (cultivation, livestock farming and aquaculture), industry and domestic pollution all contribute to the deterioration of water quality. Industry is still the major source of China's ambient water pollution. It is then followed by domestic source of pollution and agricultural source of pollution, with the latter having the least impact on water quality. (2) Impact of pollution sources on water quality vary from basin to basin significantly.The main sources of water pollution in the Yangtze and Yellow River Basins are industry and domestic life respectively. Cultivation and aquaculture are the main agricultural sources of water pollution in the two basins respectively. (3) Impact of cultivation pollution sources on water quality is subject to seasonal variations. It means that pollutants from cultivation enter the rivers through the carrying effect of precipitation, and this effect is more often reflected in the busy season. (4) In the long term, the negative impact of cultivation and livestock farming on water quality pollution form a cumulative effect. It grows as the number of lag periods increases. Therefore, in the subsequent environmental pollution control processes, priority should be given to industrial and urban pollution control with more targeted regional pollution management. It is also important to strengthen inter-basin and inter-regional synergistic control, taking top-level design of environmental policies and other conditions of the basin areas into full account to reduce the undesired environmental consequences caused by production. [ABSTRACT FROM AUTHOR]