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Abundance indices play a crucial role in monitoring and assessing fish population dynamics. Fishery-independent surveys are commonly favored for deriving abundance indices because they follow standardized or randomized designs, ensuring spatiotemporal consistency in representative and unbiased sampling. However, modifications to the survey protocol may be necessary to accommodate changes in survey goals and logistic difficulty. When the survey undergoes changes, calibration is often needed to remove variability that is unrelated to changes in abundance. We evaluated a long-term monitoring program, the Long River Survey (LRS) in the Hudson River Estuary (HRE), to illustrate the process of calibrating survey data to account for the effects of changing sampling protocol. The LRS provided valuable ichthyoplankton data from 1974 to 2017, but inconsistencies in sampling timing, location, and gears resulted in challenges in interpreting and comparing the fish abundance data in the HRE. Generalized Additive Models were developed for five species at various life stages, aiming to mitigate the impact of sampling protocol changes. Model validation results suggest the consistent performance of the developed models with varying lengths of time series. This study indicates that changes in the sampling protocol can introduce biases in the estimates of abundance indices and that the model-based estimates can improve the reliability and accuracy of the survey abundance indices. The model-estimated sampling effects for each species and life stage provide critical information and valuable insights for designing future sampling protocols.

Climate change is known to have significant impacts on coastal marine ecosystems. However, a good understanding of the exact impacts requires consistent long-term monitoring programs, which are not available for many coastal marine ecosystems. The Hudson River Estuary (HRE) provides critical habitats for many estuarine, freshwater, and diadromous species. The Longitudinal River Ichthyoplankton Survey and Water Quality Survey in the Hudson River Biological Monitoring Program provided a long-term time series (1974-2017) for examining the changes in salinity and other environmental variables and identifying the temporal variability of fish diversity. In this study, we examined the environmental data and found a 2-phase pattern in the salinity time series data with a change point of 1984. We calculated a Margalef index and a Simpson index to examine species richness and evenness for early and older life stages in the upper and lower HRE, and explored the potential effects of rising sea level and salinity on fish biodiversity. This study reveals that sea level and salinity may play an important part in explaining increased species richness and evenness. The increased occurrence, establishment and range expansion of marine species as well as possible displacement of freshwater species may be associated with sea level-induced salinity change observed in the HRE. This study highlights a need for a long-term consistent monitoring program and a better understanding of climate change effects on the biota in coastal estuary ecosystems such as the HRE in order to enhance management, conservation, and restoration plans.

There have been no reports on the association of hyperthyroidism with hyperlipidemia in patients undergoing treatment especially in Asia. To determine the association between hyperthyroidism and the risk of hyperlipidemia in patients, we conducted a retrospective cohort study using Longitudinal Health Insurance Database (LHID) from Taiwan, R.O.C. We also evaluate the influence of 6-n-propyl-2-thiouracil (PTU) and methimazole (MMI) on hepatic genes to explain changes in blood lipid levels in a hepatic cell line model. The cohort study involved 13,667 patients with hyperthyroidism, and the corresponding comparison cohort had four times as many patients. Using Kaplan-Meier analysis method, the results showed that, compared to patients without hyperthyroidism, the overall incidence of hyperlipidemia was significantly higher in the hyperthyroidism patients (18.7 vs. 11.8 cases/1,000 persons-years; adjusted HR 1.5; 95% CI, 1.41–1.59). With only PTU or MMI/carbimazole (CBM) treatment, patients with hyperthyroidism showed a 1.78-fold (95% CI, 1.50–2.11) and 1.43-fold (95% CI, 1.27–1.60) higher risk of hyperlipidemia than those without hyperthyroidism, respectively. Additionally, hyperthyroidism patients that received surgery only or surgery with I131 therapy tended to have a higher risk of hyperlipidemia. Although PTU and MMI treatment decreased the expression levels of genes responsible for circulating remnant lipoproteins, they increased the levels of lipogenic gene expression in hepatic cells. Thus, treatment of hyperthyroid patients with anti-thyroid drugs (ATDs), I131, or surgery is likely to induce hyperlipidemia. ATDs downregulate the expression of genes involved in lipoproteins clearance; increases lipogenic genes expression, which may partly contribute to abnormal blood lipid profiles.