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Simple Summary: In this study, we investigated the population size of Amur softshell turtles (Pelodiscus maackii) around Jewellery Island, a river island in the mid-stream of the Ussuri River using continuous-time capture–recapture methods, and analyzed the spatial distribution of Amur softshell turtles based on cage trapping results. The results revealed the disturbingly low population density of Amur softshell turtle in this area, highlighting the urgent need for targeted conservation efforts. Our spatial distribution study indicates that juvenile turtles predominantly inhabit still-water channels and vegetated shoreline areas, emphasizing the critical importance of these habitats. This study establishes a foundational framework for future population assessments and pinpoints vital conservation areas. Our results fill a gap in the knowledge of the natural history of this endangered species. Freshwater turtle species preservation relies on understanding their population dynamics and geographical distribution. Amur softshell turtles (ASTs [Pelodiscus maackii]) are poorly protected due to insufficient awareness and the population in Northeastern China has experienced a steep decline compared to previous years. This study aims to investigate the population density and structure of ASTs in the Jewellery Island area of the Ussuri River in Northeast China using continuous-time capture–recapture methods in closed populations. A three-month mark–recapture study was conducted in 2022, resulting in 35 juvenile captures, including 12 recaptures from 23 marked individuals. The estimated population size in the study area was 40.79 ± 9.75 (95% confidence interval, 95% CI = 27–65), translating to 0.663 ± 0.158 turtles/ha (95% CI = 0.44–1.06 individuals/ha). Approximately 35.4–85.2% of the estimated population was marked. Additionally, we explored the influence of environmental variables on turtle distribution by dividing the surveyed sites into seven sections based on their natural characteristics. The ASTs distribution inferred from trapping successes was considerably different among sections, with most turtles (91%) captured at the vegetated shoreline and in water channel 2. This research offers essential baseline data to support future assessments of ASTs population on a larger scale and to inform the development of conservation strategies. [ABSTRACT FROM AUTHOR]

The majority of attempts to enumerate the homeless population rely on point-in-time or shelter counts, which can be costly and inaccurate. As an alternative, we use electronic health records from the Vancouver Island Health Authority, British Columbia, Canada from 2013 to 2022 to identify adults contending with homelessness based on their self-reported housing status. We estimate the annual population size of this population using a flexible open-population capture–recapture model that takes into account (1) the age and gender structure of the population, including aging across detection occasions, (2) annual recruitment into the population, (3) behavioural-response, and (4) apparent survival in the population, including emigration and incorporating known deaths. With this model, we demonstrate how to perform model selection for the inclusion of covariates. We then compare our estimates of annual population size with reported point-in-time counts of homeless populations on Vancouver Island over the same time period, and find that using data extracts from electronic health records gives comparable estimates. We find similarly comparable results using only a subset of interaction data, when using only ER interactions, suggesting that even if cross-continuum data is not available, reasonable estimates of population size can still be found using our method. [ABSTRACT FROM AUTHOR]

The Western Himalayan mountains, with several riverine systems, are considered one of the most fragile environments in the world. Among them is Beas, a primary river that provides essential ecosystem benefits to thousands of indigenous people in North India. One of the major pollutants, microplastics (MPs), are ubiquitous contaminants, yet their occurrence in the Beas and ecological risk factors remain largely unexplored. Due to extensive tourism and urban-related burdens, the usage and release of enormous amounts of plastics and MPs into the Beas are apparent. Here, we investigated the extent of MPs pollution and subsequent environmental risks in water and sediments from Beas along a stretch of 300 km. Our results showed that MPs were abundant and widely distributed, with the abundance range (mean ± SE) being 46–222 (112.27 ± 12.43) items/L in water and 36–896 (319.47 ± 49.25) items/kg in sediment samples. We found significant differences in MPs' abundance in water but not sediments among the five sampling sites. There was a significant positive correlation between population size and the abundance of MPs, with the highest abundance in populated Kullu and the lowest loads at the remote Dhundi Glacier. Fibers and film were common morphotypes; most items measured <1 mm. Of the eleven polymers identified, the majority were polyethylene. The pollution load index ranged up to 4.99 (low-risk category); however, the polymer hazard index exceeded 1000 (highest-risk category), and the potential ecological risk index was 13,761 (extreme-risk category) at selected sites. This study fills a crucial knowledge gap and raises concerns about the possible impact on human health, as many riparian residents depend on Beas as their primary source of potable water. Our findings may assist governmental agencies in formulating comprehensive eco-friendly policies and advancing environmentally sustainable strategies in vulnerable locales adjoining the Beas waterway. [Display omitted] • MPs in water and sediment samples from River Beas were quantified. • Fibers measuring <1 mm, comprised of polyethylene, were most abundant. • MPs abundance was significantly tied to population size and urban-related activities. • Urban areas along Beas showed higher pollution hazard index and ecological risks. • These data provide baseline information on MPs pollution in River Beas, India. [ABSTRACT FROM AUTHOR]

Considering the high impact of urbanization on climate change, it is pertinent to explore the nexus between household carbon emissions (HCEs), urban population size and population density in Indian cities so as to test whether larger or denser cities are carbon emission efficient. In this study, the statistical significance and variations of mean per capita HCEs are evaluated across six classes of Indian cities classified on the basis of population size and, five classes based on population density by conducting analysis of variance followed by post-hoc test. The results clearly demonstrate that larger or denser cities are not emission efficient. In addition to that, by revisiting scaling laws, it is analytically deducted that for a city to qualify for exhibiting simultaneous emission efficiency in relation to population size and density, population size exponent (α) must lie between 0 and 0.5 and the value of density exponent (β) must be greater than α and lower than unity. By quantifying, comparing and tracking carbon emission efficiencies of cities based on population size and density parameters, the findings of this study potentially provide new insights to policy makers towards approaching the highly ambitious decarbonization goals stated in India's Panchamrit commitment, 2070. • Emission efficiency index covering all Indian cities across six classes. • Larger or denser cities in India are not emission efficient. • Population size exponent (α) must lie between 0 and 0.5 for emission efficiency. • Density exponent (β) must lie between (α) and unity for emission efficient cities. [ABSTRACT FROM AUTHOR]