Fifty-year study of microplastics ingested by brachyuran and fish larvae in the central English North Sea.

Microplastics (MPs) are ubiquitous pollutants in marine environments. Among the many detrimental consequences of microplastic pollution, its consumption by marine biota is of particular relevance for human health, due to exposure through the food web. Long-term time-series biotic samples are overlooked sources of information for microplastics research. These collections are extremely valuable for the detection and monitoring of changes in marine environments. However, there are very few long-term studies (>10 years) of the uptake of microplastics by biota. Here, we used Dove Time Series planktonic samples (from 1971 to 2020) to assess the presence and prevalence of microplastics in the English North Sea coast over time. Fish and brachyuran larvae were selected due to their commercial importance and consequent implications for human health. A custom enzymatic digestion method was used to extract microplastics for FTIR-ATR polymer identification. An increasing cumulative trend in MP ingestion was identified. Cellophane and polyethylene terephthalate were the polymer types found most frequently in both taxa. Although a total higher microplastics uptake was observed in fish, consumption was not significantly different between taxa over time. Equally, results were not clearly related to microplastics shape or polymer type. This work did not find significant long-term evidence on the increasing uptake of microplastic particles by zooplankton over time. However, the results of this report identified additives, plasticisers, and other more complex and hazardous compounds that should not be released to the environment (e.g., bis-(2-hydroxyethyl) dimerate, propylene glycol ricinoleate) inside marine biota. The study detailed herein provides a case study for the use of long-term time-series in providing accurate assessments of microplastic pollution in marine biota.

Invasion impacts and dynamics of a European-wide introduced species.

Globalization has led to the introduction of thousands of alien species worldwide. With growing impacts by invasive species, understanding the invasion process remains critical for predicting adverse effects and informing efficient management. Theoretically, invasion dynamics have been assumed to follow an "invasion curve" (S-shaped curve of available area invaded over time), but this dynamic has lacked empirical testing using large-scale data and neglects to consider invader abundances. We propose an "impact curve" describing the impacts generated by invasive species over time based on cumulative abundances. To test this curve's large-scale applicability, we used the data-rich New Zealand mud snail Potamopyrgus antipodarum, one of the most damaging freshwater invaders that has invaded almost all of Europe. Using long-term (1979-2020) abundance and environmental data collected across 306 European sites, we observed that P. antipodarum abundance generally increased through time, with slower population growth at higher latitudes and with lower runoff depth. Fifty-nine percent of these populations followed the impact curve, characterized by first occurrence, exponential growth, then long-term saturation. This behaviour is consistent with boom-bust dynamics, as saturation occurs due to a rapid decline in abundance over time. Across sites, we estimated that impact peaked approximately two decades after first detection, but the rate of progression along the invasion process was influenced by local abiotic conditions. The S-shaped impact curve may be common among many invasive species that undergo complex invasion dynamics. This provides a potentially unifying approach to advance understanding of large-scale invasion dynamics and could inform timely management actions to mitigate impacts on ecosystems and economies.

Global patterns of resilience decline in vertebrate populations.

Maintaining the resilience of natural populations, their ability to resist and recover from disturbance, is crucial to prevent biodiversity loss. However, the lack of appropriate data and quantitative tools has hampered our understanding of the factors determining resilience on a global scale. Here, we quantified the temporal trends of two key components of resilience-resistance and recovery-in >2000 population time-series of >1000 vertebrate species globally. We show that the number of threats to which a population is exposed is the main driver of resilience decline in vertebrate populations. Such declines are driven by a non-uniform loss of different components of resilience (i.e. resistance and recovery). Increased anthropogenic threats accelerating resilience loss through a decline in the recovery ability-but not resistance-of vertebrate populations. These findings suggest we may be underestimating the impacts of global change, highlighting the need to account for the multiple components of resilience in global biodiversity assessments.

Long-Term Water Quality Patterns in an Estuarine Reservoir and the Functional Changes in Relations of Trophic State Variables Depending on the Construction of Serial Weirs in Upstream Reaches.

Water quality degradation is one of the major problems with artificial lakes in estuaries. Long-term spatiotemporal patterns of water quality in a South Korean estuarine reservoir were analyzed using seasonal datasets from 2002 to 2020, and some functional changes in relations of trophic state variables due to the construction of serial weirs in the upper river were also investigated. A total of 19 water quality parameters were used for the study, including indicators of organic matter, nutrients, suspended solids, water clarity, and fecal pollution. In addition, chlorophyll-a (CHL-a) was used to assess algal biomass. An empirical regression model, trophic state index deviation (TSID), and principal component analysis (PCA) were applied. Longitudinal fluctuations in nutrients, organic matter, sestonic CHL-a, and suspended solids were found along the axis of the riverine (Rz), transition (Tz), and lacustrine zones (Lz). The degradation of water quality was seasonally caused by resuspension of sediments, monsoon input due to rainfall inflow, and intensity of Asian monsoon, and was also related to intensive anthropic activities within the catchment. The empirical model and PCA showed that light availability was directly controlled by non-algal turbidity, which was a more important regulator of CHL-a than total nitrogen (TN) and total phosphorus (TP). The TSID supported our hypothesis on the non-algal turbidity. We also found that the construction of serial upper weirs influenced nutrient regime, TSS, CHL-a level, and trophic state in the estuarine reservoir, resulting in lower TP and TN but high CHL-a and high TN/TP ratios. The proportions of both dissolved color clay particles and blue-green algae in the TSID additionally increased. Overall, the long-term patterns of nutrients, suspended solids, and algal biomass changed due to seasonal runoff, turnover time, and reservoir zones along with anthropic impacts of the upper weir constructions, resulting in changes in trophic state variables and their mutual relations in the estuarine reservoir.

Linking consumer physiological status to food-web structure and prey food value in the Baltic Sea.

Declining physiological status in marine top consumers has been observed worldwide. We investigate changes in the physiological status and population/community traits of six consumer species/groups in the Baltic Sea (1993-2014), spanning four trophic levels and using metrics currently operational or proposed as indicators of food-web status. We ask whether the physiological status of consumers can be explained by food-web structure and prey food value. This was tested using partial least square regressions with status metrics for gray seal, cod, herring, sprat and the benthic predatory isopod Saduria as response variables, and abundance and food value of their prey, abundance of competitors and predators as predictors. We find evidence that the physiological status of cod, herring and sprat is influenced by competition, predation, and prey availability; herring and sprat status also by prey size. Our study highlights the need for management approaches that account for species interactions across multiple trophic levels.

Citizen science observations reveal rapid, multi-decadal ecosystem changes in eastern Long Island Sound.

Long-term environmental records are among the most valuable assets for understanding the trajectory and consequences of climate change. Here we report on a newly recovered time-series from Project Oceanology, a non-profit ocean science organization serving New England schools (USA) since 1972. As part of its educational mission, Project Oceanology has routinely and consistently recorded water temperature, pH, and oxygen as well as invertebrate and fish abundance in nearshore waters of the Thames River estuary in eastern Long Island Sound (LIS). We digitized these long-term records to test for decadal trends in abiotic and biotic variables including shifts in species abundance, richness, and diversity. Consistent with previous studies, the data revealed an above-average warming rate of eastern LIS waters over the past four decades (+0.45 °C decade-1), a non-linear acidification trend twice the global average (-0.04 pH units decade-1), and a notable decline in whole water-column dissolved oxygen concentrations (-0.29 mg L-1 decade-1). Trawl catches between 1997 and 2016 suggested a significant decrease in overall species diversity and richness, declines in cold-water adapted species such as American lobster (Homarus americanus), rock crab (Cancer irroratus), and winter flounder (Pseudopleuronectes americanus), but concurrent increases in the warm-water decapod Libinia emarginata (spider crab). Our study confirmed that Long Island Sound is a rapidly changing urban estuary, while demonstrating the value of long-term observations made by citizen-scientists, educators, and other stakeholders.

Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?

Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.

Association between energy use and poor visibility in Hong Kong SAR, China.

A city's reliance on energy increases when it is developed. Moreover, the combustion of fossil fuels inevitably generates air pollutants including carbon dioxide, nitrogen oxides, sulfur dioxide, particulate matter, and others. Combining with other anthropogenic air pollutants, visibility in many Asian cities including Hong Kong have deteriorated rapidly in the past decades. This paper explores the relationships between energy use, meteorological factors, and change in visibility in Hong Kong using long-term time-series data. The total use of primary energy increased from 146,700 TJ in 1971 to 1,270,865 TJ in 2011 while the number of hours of reduced visibility increased from 184 h to 1398 h during the same period of time. Bivariate correlations show that poor visibility was significantly associated with energy use and annual mean air temperature. Multiple regression analysis indicates that the burning of aviation gasoline significantly, adversely affect visibility. Results illustrate that the number of clear days in Hong Kong will decrease, in particular due to the increase in air traffic.