On the delay between water temperature and invertebrate community response to warming climate

Melatonin injection and red light irradiation affect the antioxidant response and cell damage in disk abalone (Haliotis discus hannai) exposed to high water temperatures

The effects of red light-emitting diode (LED) light irradiation (630 nm, 0.5 W/m2) and melatonin (10-8 and 10-7 M) on oxidative stress and physiological responses in abalones exposed to high temperatures (28°C) were investigated. Changes in messenger RNA (mRNA) expressions of melatonin receptor (MT-R), heat shock protein 70 (HSP70), and antioxidant enzymes, as well as alterations in H2O2 levels in the hemolymph, were examined. The results revealed that high-temperature-stressed abalones treated with melatonin injections or exposed to red LED light showed a significant increase in MT-R mRNA expression, while HSP70 mRNA expression decreased. Notably, HSP70 mRNA expression levels in the red LED light-irradiated group were similar to those in the group injected with 10-8 M melatonin after 24 h exposure. Abalones treated with melatonin at 20°C or irradiated with red LED light exhibited decreased H2O2 levels and reduced antioxidant enzyme mRNA expression compared with those of the control group. However, the high-temperature environment induced oxidative stress in abalones, leading to increased antioxidant enzyme mRNA expression compared with that under 20°C conditions. Moreover, abalones exposed to high-temperature stress exhibited hepatopancreatic DNA damage, which was attenuated by melatonin treatment or red LED light irradiation. Hence, red LED light reduces oxidative stress, boosts antioxidant enzymes, and alleviates DNA damage in high-temperature-stressed abalones, akin to 10-8 M melatonin treatment. Therefore, considering the practical challenges of continuous melatonin administration to abalones, utilizing red LED light emerges as a practical, effective alternative to protect abalones from oxidative stress compared to 10-8 M melatonin treatment.

Optimizing backwash control using data on seasonal changes in the invertebrate community of granular activated carbon filters

Problems associated with the colonization and leakage of invertebrates in the granular activated carbon (GAC) filters of waterworks have received increased attention in recent years. To study the effect of environmental factors and water quality on invertebrate abundances, and the backwash control for minimizing invertebrate abundance. A survey of the invertebrate community of GAC filters was carried out monthly from March 2021 to May 2022. A pilot-scale GAC system established in the laboratory alongside a lake, with a volume of 35.3 L. 45 invertebrate species were detected, and 40 of these were rotifers. Significant variation in abundance was observed among seasons before and after GAC filtration, the average invertebrate abundance in the inlet water was 11.1 times that in the filtrate. The GAC filter contained invertebrates that might be responsible for the large number of organisms in the filtrate. Invertebrate abundance in the GAC filter decreased gradually with the carbon layer depth, which the mean invertebrate abundances were 6,926, 5,232, and 3818 ind./kg in the top layer (TL), middle layer (ML), and bottom layer (BL), respectively. Invertebrate abundance was correlated with water temperature and varied seasonally. Among eight water quality parameters, chlorophyll a (Chla) and the total plate count (TPC) were most significantly correlated with invertebrate abundance. According to the statistical modeling and the optimization process of response surface methodology (RSM). The predicted optimal values were a flow rate of 6.36 L/h, a backwash cycle of 3.26 d, and a backwash intensity of 14.97 L/(m2·s) for a minimum invertebrate abundance of 3013 ind./kg in the GAC filter. To maintain invertebrate abundance within an acceptable range, some of these measures might need to be modified depending on the actual conditions.

Molecular and morphological characterisation of larvae of the genus Diamesa Meigen, 1835 (Diptera: Chironomidae) in Alpine streams (Ötztal Alps, Austria)

Diamesa species (Diptera, Chironomidae) are widely distributed in freshwater ecosystems, and their life cycles are closely linked to environmental variables such as temperature, water quality, and sediment composition. Their sensitivity to environmental changes, particularly in response to pollution and habitat alterations, makes them valuable indicators of ecosystem health. The challenges associated with the morphological identification of larvae invoke the use of DNA barcoding for species determination. The mitochondrial cytochrome oxidase subunit I (COI) gene is regularly used for species identification but faces limitations, such as similar sequences in closely related species. To overcome this, we explored the use of the internal transcribed spacers (ITS) region in addition to COI for Diamesa larvae identification. Therefore, this study employs a combination of molecular markers alongside traditional morphological identification to enhance species discrimination. In total, 129 specimens were analysed, of which 101 were sampled from a glacier-fed stream in Rotmoostal, and the remaining 28 from spring-fed streams in the neighbouring valleys of Königstal and Timmelstal. This study reveals the inadequacy of utilizing single COI or ITS genes for comprehensive species differentiation within the genus Diamesa. However, the combined application of COI and ITS markers significantly enhances species identification resolution, surpassing the limitations faced by traditional taxonomists. Notably, this is evident in cases involving morphologically indistinguishable species, such as Diamesa latitarsis and Diamesa modesta. It highlights the potential of employing a multi-marker approach for more accurate and reliable Diamesa species identification. This method can be a powerful tool for identifying Diamesa species, shedding light on their remarkable adaptations to extreme environments and the impacts of environmental changes on their populations.

Metal enrichment in ice-melt water and uptake by chironomids as possible legacy of World War One in the Italian Alps

Relics of World War One (WW1) were buried in alpine glaciers around 100 years ago. Today, these are emerging from the ice due to widespread glacier retreat, and are in direct contact with glacial meltwater-fed streams. To address a possible emergent contamination, we quantified major and trace elements (M-TEs) by mass spectrometry in water and larvae of Diamesa zernyi from three glacial streams fed by glaciers differently impacted by the Italian Austro-Hungarian war, in the Adamello-Presanella mountain range (Italian Alps): Lares and Presena, the two main battlefields, and Amola, 8 km from the front. M-TEs in stream water were interpreted using the crustal enrichment factor (EFc) while larval uptake was quantified by adopting the bioaccumulation factor (BAF). Despite low M-TEs concentrations in the water, in a range between 1 ng L-1 (Ag, Ta) and 1-2 mg L-1 (Al, Fe, Mg), low to moderate enrichments (10 ≥ EFc≥ 6) were observed for Sb and U in Presena and for Ag, As, Bi, Cd, Li, Mo, Pb, Sb and U in Lares. In addition, M-TE mass concentrations in larvae were up to ninety thousand times higher than in water, from 20 to 50 ng g-1 dry weight (d.w.; for Bi, Sb, Ta, Tl) to 1-4 mg g-1 d.w. (for Al, Fe, Na, and Mg). Larvae from Lares accumulated the largest amount of metals and metalloids, including those mostly used in the manufacture of artillery shells (As, Cu, Ni, Pb, Sb; BAFs from 375 to about 11,500). This was expected as most of the WW1 battles in this mountain range were fought on the Lares glacier, where the greatest number of war relics are emerging. These results provide preliminary evidence of water contamination and bioaccumulation of metals and metalloids by glacial fauna as a possible legacy of WW1 in the Alps.

A catchment-scale model of river water quality by Machine Learning

Water quality is a concern in most river basins worldwide due to the widespread release of pollutants which impacts the freshwater ecosystems. Exploring the relationships between drivers and water quality parameters at the regional scale is key in the identification of appropriate actions for the reduction of water pollution. Regional models are the appropriate tool to achieve this, though their development poses relevant challenges because of the complexity and non-linearity of such relationships. Among the available approaches, Machine Learning (ML) is promising because of its capability to detect complex nonlinear relationships and flexibility in the parameterization, which is learned from data. In this work, we developed regional models of water temperature, dissolved oxygen, arsenic, sulfate and chloride concentrations, as well as electrical conductivity, by using two ML algorithms, Random Forest and Deep feed-forward Neural Network, and compared their performances against the standard Linear Regression model. Our results indicate that the two ML algorithms are much more accurate models for such variables than the classical Linear Regression model, with Deep feed-forward Neural Network being the most effective in identifying the reciprocal importance of the drivers and capturing nonlinear relationships between drivers and water quality variables. Our analysis also revealed that the Julian day and year at which the sample was taken surrogate the air temperature in modeling water temperature and dissolved oxygen, with only a slight performance reduction. Arsenic, sulfate, and chloride show more complex behaviors in which geogenic and anthropogenic sources are intertwined. Dilution exerts a role chiefly for arsenic concentration, which suggests a non-uniform, in space, geogenic origin for this variable.

Effects of thermal acclimation on the proteome of the planarian Crenobia alpina from an alpine freshwater spring

Species' acclimation capacity and their ability to maintain molecular homeostasis outside ideal temperature ranges will partly predict their success following climate change-induced thermal regime shifts. Theory predicts that ectothermic organisms from thermally stable environments have muted plasticity, and that these species may be particularly vulnerable to temperature increases. Whether such species retained or lost acclimation capacity remains largely unknown. We studied proteome changes in the planarian Crenobia alpina, a prominent member of cold-stable alpine habitats that is considered to be a cold-adapted stenotherm. We found that the species' critical thermal maximum (CT_max) is above its experienced habitat temperatures and that different populations exhibit differential CT_max acclimation capacity, whereby an alpine population showed reduced plasticity. In a separate experiment, we acclimated C. alpina individuals from the alpine population to 8, 11, 14 or 17°C over the course of 168 h and compared their comprehensively annotated proteomes. Network analyses of 3399 proteins and protein set enrichment showed that while the species' proteome is overall stable across these temperatures, protein sets functioning in oxidative stress response, mitochondria, protein synthesis and turnover are lower in abundance following warm acclimation. Proteins associated with an unfolded protein response, ciliogenesis, tissue damage repair, development and the innate immune system were higher in abundance following warm acclimation. Our findings suggest that this species has not suffered DNA decay (e.g. loss of heat-shock proteins) during evolution in a cold-stable environment and has retained plasticity in response to elevated temperatures, challenging the notion that stable environments necessarily result in muted plasticity.

Summary: The proteome of an alpine Crenobia alpina population shows plasticity in response to acclimation to warmer temperatures.