Scenarios Of Population Change Research Articles

Demographic Trends in Albania for Social-economic Development After 1990

The paper evaluates the latest demographic trends in Albania for the socio-economic development of the space for the period 1990 - 2031. These indicators are calculated in terms of the existence of migration (Albania is a typical case), where the net migration rate is taken into account. The design methodology is presented on fertility, mortality and migration, which are combined in three scenarios of population change at the national level. The medium growth scenario is then regionalized and differentiated into three internal migration scenarios for projecting the population of Albania's counties. Forecasts at the national level are described and analyzed through the future demographic changes in Albania. In addition to the decline in birth and death rates, the population structure of Albania after the 1990s has been strongly influenced by the component processes of continuous emigration. The paper estimates that the population aging indicators would be up to 4% - 5% lower, if the emigration process was limited and controlled. From the analysis of statistical indicators, it is noted that, throughout the period from 2001 to the projection of 2031, the average annual growth of the population is estimated to be negative (-0.7%), while the annual growth of the population over the age of 65 is estimated to be positive ( +0.5%). In order to slow down the phenomenon of the aging population of Albania and to mitigate the negative consequences of this aging, the drafting and elaboration of rigorous and continuous immigration and family policies is recommended.

Sampling strategies for species with high breeding-site fidelity: A case study in burrow-nesting seabirds.

Sampling approaches used to census and monitor populations of flora and fauna are diverse, ranging from simple random sampling to complex hierarchal stratified designs. Usually the approach taken is determined by the spatial and temporal distribution of the study population, along with other characteristics of the focal species. Long-term monitoring programs used to assess seabird population trends are facilitated by their high site fidelity, but are often hampered by large and difficult to access colonies, with highly variable densities that require intensive survey. We aimed to determine the sampling effort required to (a) estimate population size with a high degree of confidence, and (b) detect different scenarios of population change in a regionally important species in the Atlantic, the Manx shearwater (Puffinus puffinus). Analyses were carried out using data collected from tape-playback surveys on four islands in the North Atlantic. To explore how sampling effort influenced confidence around abundance estimates, we used the heuristic approach of imagining the areas sampled represented the total population, and bootstrapped varying proportions of subsamples. This revealed that abundance estimates vary dramatically when less than half of all plots (n dependent on the size of the site) is randomly subsampled, leading to an unacceptable lack of confidence in population estimates. Confidence is substantially improved using a multi-stage stratified approach based on previous information on distribution in the colonies. In reality, this could lead to reducing the number of plots required by up to 80%. Furthermore, power analyses suggested that random selection of monitoring plots using a matched pairs approach generates little power to detect overall population changes of 10%, and density-dependent changes as large as 50%, because variation in density between plots is so high. Current monitoring programs have a high probability of failing to detect population-level changes due to inappropriate sampling efforts. Focusing sampling in areas of high density with low plot to plot variance dramatically increases the power to detect year to year population change, albeit at the risk of not detecting increases in low density areas, which may be an unavoidable strategy when resources are limited. We discuss how challenging populations with similar features to seabirds might be censused and monitored most effectively.

Quantifying excess deaths related to heatwaves under climate change scenarios: A multicountry time series modelling study.

BackgroundHeatwaves are a critical public health problem. There will be an increase in the frequency and severity of heatwaves under changing climate. However, evidence about the impacts of climate change on heatwave-related mortality at a global scale is limited.Methods and findingsWe collected historical daily time series of mean temperature and mortality for all causes or nonexternal causes, in periods ranging from January 1, 1984, to December 31, 2015, in 412 communities within 20 countries/regions. We estimated heatwave–mortality associations through a two-stage time series design. Current and future daily mean temperature series were projected under four scenarios of greenhouse gas emissions from 1971–2099, with five general circulation models. We projected excess mortality in relation to heatwaves in the future under each scenario of greenhouse gas emissions, with two assumptions for adaptation (no adaptation and hypothetical adaptation) and three scenarios of population change (high variant, median variant, and low variant). Results show that, if there is no adaptation, heatwave-related excess mortality is expected to increase the most in tropical and subtropical countries/regions (close to the equator), while European countries and the United States will have smaller percent increases in heatwave-related excess mortality. The higher the population variant and the greenhouse gas emissions, the higher the increase of heatwave-related excess mortality in the future. The changes in 2031–2080 compared with 1971–2020 range from approximately 2,000% in Colombia to 150% in Moldova under the highest emission scenario and high-variant population scenario, without any adaptation. If we considered hypothetical adaptation to future climate, under high-variant population scenario and all scenarios of greenhouse gas emissions, the heatwave-related excess mortality is expected to still increase across all the countries/regions except Moldova and Japan. However, the increase would be much smaller than the no adaptation scenario. The simple assumptions with respect to adaptation as follows: no adaptation and hypothetical adaptation results in some uncertainties of projections.ConclusionsThis study provides a comprehensive characterisation of future heatwave-related excess mortality across various regions and under alternative scenarios of greenhouse gas emissions, different assumptions of adaptation, and different scenarios of population change. The projections can help decision makers in planning adaptation and mitigation strategies for climate change.

Scenarios of population change in the coastal Ganges Brahmaputra Delta (2011-2051)

This paper provides an overview of population dynamics and scenarios of population change in the environmentally vulnerable coastal Ganges Brahmaputra Delta region. The main data sources used for the study include the most recent and historical census data, data from the Sample Vital Registration System (SVRS) and Demographic and Health Surveys (DHS). The research adopts the standard cohort component approach for population projections and provides population estimates for the period from 2011 to 2051. Research outcomes include scenarios of future population change in the coastal Ganges Brahmaputra Delta region and district-level population projections by age and sex. The results show that population growth is likely to continue in some, but not all, districts in the study area. The results also suggest that future migration flows are most likely to be the deciding component of population change in the study area. Given the expected shifts in population distribution and population structure, policy initiatives will have to address the challenges related to informal settlements and population ageing.

Projected trends in high-mortality heatwaves under different scenarios of climate, population, and adaptation in 82 US communities.

Some rare heatwaves have extreme daily mortality impacts; moderate heatwaves have lower daily impacts but occur much more frequently at present and so account for large aggregated impacts. We applied health-based models to project trends in high-mortality heatwaves, including proportion of all heatwaves expected to be high-mortality, using the definition that a high-mortality heatwave increases mortality risk by ≥20 %. We projected these trends in 82 US communities in 2061-2080 under two scenarios of climate change (RCP4.5, RCP8.5), two scenarios of population change (SSP3, SSP5), and three scenarios of community adaptation to heat (none, lagged, on-pace) for large- and medium-ensemble versions of the National Center for Atmospheric Research's Community Earth System Model. More high-mortality heatwaves were expected compared to present under all scenarios except on-pace adaptation, and population exposure was expected to increase under all scenarios. At least seven more high-mortality heatwaves were expected in a twenty-year period in the 82 study communities under RCP8.5 than RCP4.5 when assuming no adaptation. However, high-mortality heatwaves were expected to remain <1 % of all heatwaves and heatwave exposure under all scenarios. Projections were most strongly influenced by the adaptation scenario- going from a scenario of on-pace to lagged adaptation or from lagged to no adaptation more than doubled the projected number of and exposure to high-mortality heatwaves. Based on our results, fewer high-mortality heatwaves are expected when following RCP4.5 versus RCP8.5 and under higher levels of adaptation, but high-mortality heatwaves are expected to remain a very small proportion of total heatwave exposure.

Detecting abundance trends under uncertainty: the influence of budget, observation error and environmental change

AbstractPopulation monitoring must robustly detect trends over time in a cost‐effective manner. However, several underlying ecological changes driving population trends may interact differently with observation uncertainty to produce abundance trends that are more or less detectable for a given budget and over a given time period. Errors in detecting these trends include failing to detect declines when they exist (type II), detecting them when they do not exist (type I), detecting trends in one direction when they are actually in another direction (type III) and incorrectly estimating the shape of the trend. Robust monitoring should be able to avoid each of these error types. Using monitoring of two contrasting ungulate species and multiple scenarios of population change (poaching, climate change and road development) in the Serengeti ecosystem as a case study, we used a ‘virtual ecologist’ approach to investigate monitoring effectiveness under uncertainty. We explored how the prevalence of different types of error varies depending on budgetary, observational and environmental conditions. Higher observation error and conducting surveys less frequently increased the likelihood of not detecting trends and misclassifying the shape of the trend. As monitoring period and frequency increased, observation uncertainty was more important in explaining effectiveness. Types I and III errors had low prevalence for both ungulate species. Greater investment in monitoring considerably decreased the likelihood of failing to detect significant trends (type II errors). Our results suggest that it is important to understand the effects of monitoring conditions on perceived trends before making inferences about underlying processes. The impacts of specific threats on population abundance and structure feed through into monitoring effectiveness; hence, monitoring programmes must be designed with the underlying processes to be detected in mind. Here we provide an integrated modelling framework that can produce advice on robust monitoring strategies under uncertainty.

Monitoring multiple species: Estimating state variables and exploring the efficacy of a monitoring program

Monitoring programs have the potential to identify population declines and differentiate among the possible cause(s) of these declines. Recent criticisms regarding the design of monitoring programs have highlighted a failure to clearly state objectives and to address detectability and spatial sampling issues. Here, we incorporate these criticisms to design an efficient monitoring program whose goals are to determine environmental factors which influence the current distribution and measure change in distributions over time for a suite of amphibians. In designing the study we (1) specified a priori factors that may relate to occupancy, extinction, and colonization probabilities and (2) used the data collected (incorporating detectability) to address our scientific questions and adjust our sampling protocols. Our results highlight the role of wetland hydroperiod and other local covariates in the probability of amphibian occupancy. There was a change in overall occupancy probabilities for most species over the first three years of monitoring. Most colonization and extinction estimates were constant over time (years) and space (among wetlands), with one notable exception: local extinction probabilities for Rana clamitans were lower for wetlands with longer hydroperiods. We used information from the target system to generate scenarios of population change and gauge the ability of the current sampling to meet monitoring goals. Our results highlight the limitations of the current sampling design, emphasizing the need for long-term efforts, with periodic re-evaluation of the program in a framework that can inform management decisions.

Monitoring Long‐Term Population Change: Why are there so Many Analysis Methods?

Monitoring long—term population change is an integral part of effective conservation—oriented research and management, and is central to the current debate on the status of Neotropical migrant land birds. However, the analysis of count data such as the Breeding Bird Survey is complicated by the subjective nature of trend estimation, and by limitations inherent to extensive, volunteer—based surveys, such as measurement error and missing data. A number of analysis methods have been used that differ in their approach to dealing with these complications and produce different estimates of population change when applied to the same data. There is, however, no consensus as to which method is the most suitable. Many analytical issues remain unresolved, such as model of trend, observer effects, treatment of missing observations, distribution of counts, and data selection criteria. These issues make it difficult to evaluate the relative merits of the methods, although a number of new approaches (nonlinear regression, Poisson regression, estimating equations estimates) offer promising solutions to some problems. I suggest the use of Monte Carlo simulations to empirically test the performance of the methods under realistic, spatially explicit scenarios of population change, and provide an example of the approach.