OBJECTIVE: Climate change is a global phenomenon species are experiencing, which in arid regions will translate into more frequent and intense drought. The Sonoran Desert is becoming hotter and drier, and many organisms are rapidly changing in abundance and distribution. These population attributes directly depend on the dynamics of the population, which in turn depends on the vital rates of its individuals; yet few studies have documented the effects of climate change on the population dynamics of keystone species such as the saguaro cactus (Carnegiea gigantea). Although saguaros have traits that enable them to withstand present environmental conditions, climate change could make them vulnerable if forced beyond their tolerance limits.
METHODS: We evaluated the effect of climate change on 13 saguaro populations spanning most of the species\' distribution range. Using field data from 2014 to 2016, we built an integral projection model (IPM) describing the environmentally-explicit dynamics of the populations. We used this IPM, along with projections of two climate change and one no-change scenarios, to predict population sizes (N) and growth rates (λ) from 2017 to 2099 and compared these scenarios to demonstrate the effect of climate change on saguaro\'s future.
RESULTS: We found that all populations will decline, mainly due to future increases in drought, mostly hindering recruitment. However, the decline will be differential across populations, since those located near the coast will be affected by harsher drought events than those located further inland.
CONCLUSIONS: Our study demonstrates that climate change and its associated increase in drought pose a significant threat to the saguaro cactus populations in the Sonoran Desert. Our findings indicate that the recruitment of saguaros, vital for establishing new individuals, is particularly vulnerable to intensifying drought conditions. Importantly, regional climate trends will have different impacts on saguaro populations across their distribution range.

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

While considerable attention has been paid to the impact of Covid-19 on mortality and fertility, few studies have attempted to evaluate the pandemic\'s effect on international migration. We analyse the impact of Covid-19 on births, deaths, and international migration in Spain during 2020, comparing observed data with estimated values assuming there had been no pandemic. We also assess the consequences of three post-pandemic scenarios on the size and structure of the population to 2031. Results show that in 2020, excess mortality equalled 16.2 per cent and births were 6.5 per cent lower than expected. Immigration was the most affected component, at 36.0 per cent lower than expected, while emigration was reduced by 23.8 per cent. If net migration values recover to pre-pandemic levels in 2022, the size and structure of the population in 2031 will be barely affected. Conversely, if levels do not recover until 2025, there will be important changes to Spain\'s age structure.

A new dataset of population projections for local areas of Australia is described. The areas comprise SA3 areas of the Australian Statistical Geography Standard, which mostly range in population between 30,000 and 130,000. The projections are launched from the 2020 Estimated Resident Populations published by the Australian Bureau of Statistics and extend out to 2035. They are available by sex and five year age group up to 80-84 with 85+ as the final age group and in five year projection intervals. The projections were prepared using the synthetic migration cohort-component model, a new model for population projections which requires much less input data than conventional projection models, and therefore involves much lower costs and production time. Despite this, a recent evaluation demonstrated respectable forecast accuracy, and greater accuracy than equivalent simple projection models producing populations by age and sex. The age-sex projections are constrained to independent age-sex national projections and local area projections of total populations. The dataset consists of local area projections for the whole of the country which is consistent in methods, input data, and projection outputs due to the use of one model. This is rare in Australia because local area projections are most commonly prepared by individual State/Territory Governments using different methods, data sources, projection assumptions (which can be influenced by State/Territory population policies), and time periods. These nationally consistent projection data should be useful for a wide range of local area planning, policy, and research purposes, such as childcare demand, school enrolments, power and water usage, aged care provision, store and business site selection, living arrangements and household projections, labour force projections, and transport modelling.

Many ecological processes are profoundly influenced by abiotic factors, such as temperature and snow. However, despite strong evidence linking shifts in these ecological processes to corresponding shifts in abiotic factors driven by climate change, the mechanisms connecting population size to season-specific climate drivers are little understood. Using a 21-year dataset and a Bayesian state space model, we identified biologically informed seasonal climate covariates that influenced densities of snowshoe hares (Lepus americanus), a cold-adapted boreal herbivore. We found that snow and temperature had strong but conflicting season-dependent effects. Reduced snow duration in spring and fall and warmer summers were associated with lowered hare density, whereas warmer winters were associated with increased density. When modeled simultaneously and under two climate change scenarios, the negative effects of reduced fall and spring snow duration and warmer summers overwhelm the positive effect of warmer winters, producing projected population declines. Ultimately, the contrasting population-level impacts of climate change across seasons emphasize the critical need to examine the entire annual climate cycle to understand potential long-term population consequences of climate change.

to assess the effect of recent stalling of life expectancy and various scenarios for disability progression on projections of social care expenditure between 2018 and 2038, and the likelihood of reaching the Ageing Society Grand Challenge mission of five extra healthy, independent years at birth.
two linked projections models: the Population Ageing and Care Simulation (PACSim) model and the Care Policy and Evaluation Centre long-term care projections model, updated to include 2018-based population projections.
PACSim: about 303,589 individuals aged 35 years and over (a 1% random sample of the England population in 2014) created from three nationally representative longitudinal ageing studies.
Total social care expenditure (public and private) for older people, and men and women\'s independent life expectancy at age 65 (IndLE65) under five scenarios of changing disability progression and recovery with and without lower life expectancy.
between 2018 and 2038, total care expenditure was projected to increase by 94.1%-1.25% of GDP; men\'s IndLE65 increasing by 14.7% (range 11.3-16.5%), exceeding the 8% equivalent of the increase in five healthy, independent years at birth, although women\'s IndLE65 increased by only 4.7% (range 3.2-5.8%). A 10% reduction in disability progression and increase in recovery resulted in the lowest increase in total care expenditure and increases in both men\'s and women\'s IndLE65 exceeding 8%.
interventions that slow down disability progression, and improve recovery, could significantly reduce social care expenditure and meet government targets for increases in healthy, independent years.

The human population is at the centre of research on global environmental change. On the one hand, population dynamics influence the environment and the global climate system through consumption-based carbon emissions. On the other hand, the health and well-being of the population are already being affected by climate change. A knowledge of population dynamics and population heterogeneity is thus fundamental to improving our understanding of how population size, composition, and distribution influence global environmental change and how these changes affect population subgroups differentially by demographic characteristics and spatial distribution. The increasing relevance of demographic research on the topic, coupled with availability of theoretical concepts and advancement in data and computing facilities, has contributed to growing engagement of demographers in this field. In the past 25 years, demographic research has enriched climate change research-with the key contribution being in moving beyond the narrow view that population matters only in terms of population size-by putting a greater emphasis on population composition and distribution, through presenting both empirical evidence and advanced population forecasting to account for demographic and spatial heterogeneity. What remains missing in the literature is research that investigates how global environmental change affects current and future demographic processes and, consequently, population trends. If global environmental change does influence fertility, mortality, and migration, then population estimates and forecasts need to adjust for climate feedback in population projections. Indisputably, this is the area of new research that directly requires expertise in population science and contribution from demographers.

OBJECTIVE: In this study, we evaluate the impact of population ageing on the required hospital capacity.
METHODS: We used hospital discharge (years 2003-2014) and population data to estimate the required hospital capacity by 2025 for older inpatients (≥ 75 years) taking into account population changes and trends in hospital admission rates and length of stay. In addition, we developed an alternative scenario to evaluate the impact of accelerated ageing based on the peaks in population ageing from 2030 onwards.
RESULTS: The number of inpatient stays for our study population is expected to increase from 478,027 in 2014 to 590,313 in 2025 (+ 23.5%). The average length of stay is expected to decrease by 18.4% (- 2.3 days). As a consequence, the number of inpatient days and the required bed capacity will only increase by 42,709 days (+ 0.7%) and 72 beds (+ 0.4%), respectively. The accelerated ageing scenario shows that the increase between 2014 and 2025 is more pronounced for inpatient stays (+ 50.5%), inpatient days (+ 21.9%) and hospital beds (+ 21.1%).
CONCLUSIONS: Ageing will, if no drastic policy actions are taken, impact the required hospital capacity. This can initially (by 2025) be more or less controlled by further reductions in length of stay. From 2030, it is expected that the required hospital bed capacity will increase exponentially with a pronounced shift between general acute care beds towards geriatric and chronic care beds. If policy makers want to revert this trend, substantial investments in hospital alternatives will be required.

OBJECTIVE: This paper focuses on the older Latino undocumented population and anticipates how their current demographic characteristics and health insurance coverage might impact future population size and health insurance trends.
METHODS: We use the 2013-2018 American Community Survey as a baseline to project growth in the Latino 55+ undocumented population over the next 20 years. We use the cohort component method to estimate population size across different migration scenarios and distinguish between aging in place and new in-migration. We also examine contemporary health insurance coverage and chronic health conditions among 55+ undocumented Latinos from the 2003-2014 California Health Interview Survey. We then project health insurance rates in 2038 among Latino immigrants under different migration and policy scenarios.
RESULTS: If current mortality, migration, and policy trends continue, projections estimate that 40% of undocumented Latino immigrants will be 55 years or older by 2038 - nearly all of whom will have aged in place. Currently, 40% of older Latino undocumented immigrants do not have insurance. Without policies that increase access to insurance, projections estimate that the share who are uninsured among all older Latinos immigrants will rise from 15% to 21%, and the share that is both uninsured and living with a chronic health condition will rise from 5% to 9%.
CONCLUSIONS: Without access health care, older undocumented immigrants may experience delayed care and more severe morbidity. Our projections highlight the need to develop and enact policies that can address impending health access concerns for an increasingly older undocumented Latino population.

Species extinction risk is accelerating due to anthropogenic climate change, making it urgent to protect vulnerable species through legal frameworks in order to facilitate conservation actions that help mitigate risk. Here, we discuss fundamental concepts for assessing climate change risks to species using the example of the emperor penguin (Aptenodytes forsteri), currently being considered for protection under the US Endangered Species Act (ESA). This species forms colonies on Antarctic sea ice, which is projected to significantly decline due to ongoing greenhouse gas (GHG) emissions. We project the dynamics of all known emperor penguin colonies under different GHG emission scenarios using a climate-dependent meta-population model including the effects of extreme climate events based on the observational satellite record of colonies. Assessments for listing species under the ESA require information about how species resiliency, redundancy and representation (3Rs) will be affected by threats within the foreseeable future. Our results show that if sea ice declines at the rate projected by climate models under current energy system trends and policies, the 3Rs would be dramatically reduced and almost all colonies would become quasi-extinct by 2100. We conclude that the species should be listed as threatened under the ESA.