scholarly journals Identifying and Classifying Shrinking Cities Using Long-Term Continuous Night-Time Light Time Series

2021 ◽  
Vol 13 (16) ◽  
pp. 3142
Author(s):  
Baiyu Dong ◽  
Yang Ye ◽  
Shixue You ◽  
Qiming Zheng ◽  
Lingyan Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Population Decline ◽  
Population Data ◽  
Shrinking Cities ◽  
Economic Decline ◽  
Night Time ◽  
Geographically Weighted Regression Model ◽  
Stage 1 ◽  
Shrinking City

Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by long-term continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making.

scholarly journals Minimum time required to detect population trends: the need for long-term monitoring programs

2018 ◽  
Author(s):  
Easton R White
Keyword(s):  
Time Series ◽  
Statistical Power ◽  
Conservation Status ◽  
Species Conservation ◽  
Population Data ◽  
Iucn Red List ◽  
Series Length ◽  
Large Variability ◽  
Monitoring Programs

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. What is the minimum population time series length required to detect significant trends in abundance? I first present an overview of the theory and past work that has tried to address this question. As a test of these approaches, I then examine 822 populations of vertebrate species. I show that 72% of time series required at least 10 years of continuous monitoring in order to achieve a high level of statistical power. However, the large variability between populations casts doubt on commonly used simple rules of thumb, like those employed by the IUCN Red List. I argue that statistical power needs to be considered more often in monitoring programs. Short time series are likely under-powered and potentially misleading.

scholarly journals Long term population decline and the impacts of COVID-19 in South Australia’s regions

2021 ◽  
Vol 5 (2) ◽  
pp. 73-85
Author(s):  
Jacob Irving ◽  
Sandy Horne ◽  
Andrew Beer
Keyword(s):  
Age Structure ◽  
Economic Activity ◽  
Census Data ◽  
Population Change ◽  
Economic Impacts ◽  
Population Decline ◽  
South Australia ◽  
Population Data ◽  
Resident Population

Background   South Australian regions have been given little attention in discussions on population decline. Aims   This paper aims to examine the nature and incidence of population decline in South Australia as well as evaluate the potential impacts of COVID-19. Data and methods   Estimated Resident Population data from 2001 to 2020, and Census data from 2006 and 2016, were used to investigate demographic and economic change. Measures of population change, age structure, employment and components of population change were used. Results   Population decline has been a feature of South Australia’s regions for decades and continues to be so as more of the population concentrates in its capital and regional centres where greater opportunities of employment and greater provisions of amenities are available. COVID-19 has the potential to accelerate this change if South Australia’s vulnerable regions are not able to absorb the economic impacts the pandemic poses. Conclusions   A strong driver of population decline in the regions is employment loss in core industries. Strategies that support these industries or otherwise aim to stimulate economic activity in these communities are required to moderate further decline in South Australia’s regions especially as the economy recovers from the impacts of COVID-19.

scholarly journals Site-selection bias can drive apparent population declines in long-term studies

2019 ◽  
Author(s):  
Auriel M.V. Fournier ◽  
Easton R. White ◽  
Stephen B. Heard
Keyword(s):  
Selection Bias ◽  
Site Selection ◽  
Population Decline ◽  
Population Data ◽  
Spatiotemporal Variability ◽  
Population Declines ◽  
Substantial Impact ◽  
Real Population ◽  
Study Sites

Detecting population declines is a critical task for conservation biology. The spatiotemporal variability of populations, along with logistical difficulties in population estimation, makes this task difficult. Here we call attention to a possible bias in estimates of population decline: when study sites are chosen based on abundance of the focal species, for statistical reasons apparent declines are likely even without an underlying population trend. This “site-selection bias” has been mentioned in the literature but is not well known. We show using simulated and real population data that when site-selection biases are introduced, they have substantial impact on inferences about population trends. We use a left-censoring method to show patterns consistent with the operation of the site-selection bias in real population studies. The site-selection bias is, thus, an important consideration for conservation biologists, and we offer suggestions for minimizing or mitigating it in study design and analysis.

scholarly journals Comparison of measured residential black carbon levels outdoors and indoors with fixed-site monitoring data and with dispersion modelling

2020 ◽  
Author(s):  
Olena Gruzieva ◽  
Antonios Georgelis ◽  
Niklas Andersson ◽  
Tom Bellander ◽  
Christer Johansson ◽  
...  
Keyword(s):  
Time Series ◽  
Black Carbon ◽  
Health Effects ◽  
Dispersion Model ◽  
Monitoring Data ◽  
Dispersion Modelling ◽  
Night Time ◽  
Urban Background ◽  
Site Monitoring

AbstractEpidemiologic studies on health effects of air pollution usually rely on time-series of ambient monitoring data or on spatially modelled levels. Little is known how well these estimate residential outdoor and indoor levels. We investigated the agreement of measured residential black carbon (BC) levels outdoors and indoors with fixed-site monitoring data and with levels calculated using a Gaussian dispersion model. One-week residential outdoor and indoor BC measurements were conducted for 15 families living in central Stockholm. Time-series from urban background and street-level monitors were compared to these measurements. The observed weekly concentrations were also standardized to reflect annual averages, using urban background levels, and compared spatially to long-term levels as estimated by dispersion modelling. Weekly average outdoor BC level was 472 ng/m3 (range 261–797 ng/m3). The corresponding fixed-site urban background and street levels were 313 and 1039 ng/m3, respectively. Urban background variation explained 50% of the temporal variation in residential outdoor levels averaged over 24 h. Modelled residential long-term outdoor levels were on average comparable with the standardized measured home outdoor levels, and explained 49% of the spatial variability. The median indoor/outdoor ratio across all addresses was 0.79, with no difference between day and night time. Common exposure estimation approaches in the epidemiology of health effects related to BC displayed high validity for residencies in central Stockholm. Urban background monitored levels explained half of the outdoor day-to-day variability at residential addresses. Long-term dispersion modelling explained half of the spatial differences in outdoor levels. Indoor BC concentrations tended to be somewhat lower than outdoor levels.

scholarly journals Site-selection bias can drive apparent population declines in long-term studies

2019 ◽  
Author(s):  
Auriel M.V. Fournier ◽  
Easton R. White ◽  
Stephen B. Heard
Keyword(s):  
Selection Bias ◽  
Site Selection ◽  
Population Decline ◽  
Population Data ◽  
Spatiotemporal Variability ◽  
Population Declines ◽  
Substantial Impact ◽  
Real Population ◽  
Study Sites

Detecting population declines is a critical task for conservation biology. The spatiotemporal variability of populations, along with logistical difficulties in population estimation, makes this task difficult. Here we call attention to a possible bias in estimates of population decline: when study sites are chosen based on abundance of the focal species, for statistical reasons apparent declines are likely even without an underlying population trend. This “site-selection bias” has been mentioned in the literature but is not well known. We show using simulated and real population data that when site-selection biases are introduced, they have substantial impact on inferences about population trends. We use a left-censoring method to show patterns consistent with the operation of the site-selection bias in real population studies. The site-selection bias is, thus, an important consideration for conservation biologists, and we offer suggestions for minimizing or mitigating it in study design and analysis.

scholarly journals Site-selection bias can drive apparent population declines in long-term studies

2019 ◽  
Author(s):  
Auriel M.V. Fournier ◽  
Easton R. White ◽  
Stephen B. Heard
Keyword(s):  
Selection Bias ◽  
Site Selection ◽  
Population Decline ◽  
Population Data ◽  
Spatiotemporal Variability ◽  
Population Declines ◽  
Substantial Impact ◽  
Real Population ◽  
Study Sites

Detecting population declines is a critical task for conservation biology. The spatiotemporal variability of populations, along with logistical difficulties in population estimation, makes this task difficult. Here we call attention to a possible bias in estimates of population decline: when study sites are chosen based on abundance of the focal species, for statistical reasons apparent declines are likely even without an underlying population trend. This “site-selection bias” has been mentioned in the literature but is not well known. We show using simulated and real population data that when site-selection biases are introduced, they have substantial impact on inferences about population trends. We use a left-censoring method to show patterns consistent with the operation of the site-selection bias in real population studies. The site-selection bias is, thus, an important consideration for conservation biologists, and we offer suggestions for minimizing or mitigating it in study design and analysis.

scholarly journals Minimum time required to detect population trends: the need for long-term monitoring programs

2018 ◽  
Author(s):  
Easton R White
Keyword(s):  
Time Series ◽  
Statistical Power ◽  
Conservation Status ◽  
Species Conservation ◽  
Population Data ◽  
Iucn Red List ◽  
Series Length ◽  
Large Variability ◽  
Monitoring Programs

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. What is the minimum population time series length required to detect significant trends in abundance? I first present an overview of the theory and past work that has tried to address this question. As a test of these approaches, I then examine 822 populations of vertebrate species. I show that 72% of time series required at least 10 years of continuous monitoring in order to achieve a high level of statistical power. However, the large variability between populations casts doubt on commonly used simple rules of thumb, like those employed by the IUCN Red List. I argue that statistical power needs to be considered more often in monitoring programs. Short time series are likely under-powered and potentially misleading.

scholarly journals Site-selection bias can drive apparent population declines in long-term studies

2019 ◽  
Author(s):  
Auriel M.V. Fournier ◽  
Easton R. White ◽  
Stephen B. Heard
Keyword(s):  
Selection Bias ◽  
Site Selection ◽  
Population Decline ◽  
Species Abundance ◽  
Population Data ◽  
Spatiotemporal Variability ◽  
Population Declines ◽  
Substantial Impact ◽  
Real Population

Detecting population declines is a critical task for conservation biology. The spatiotemporal variability of populations, along with logistical difficulties in population estimation, makes this task difficult. Here we call attention to a possible bias in estimates of population decline: when study-site selection is influenced by the focal species’ abundance, for statistical reason declines are likely even without an underlying population trend. This “site-selection bias” has been mentioned in the literature but is not well known. We show using simulated and real population data that when site-selection biases are introduced, they have substantial impact on inferences about population trends. We use a left-censoring method to show patterns consistent with the operation of the site-selection bias in real population studies. The site-selection bias is, thus, an important consideration for conservation biologists, and we offer suggestions for minimizing or mitigating it in study design and analysis.

scholarly journals Performance of imputation-based models in predicting breeding population trend of a near-threatened bird in changing water regime: A 36-year long-term case study of Painted Stork, Mycteria leucocephala

2021 ◽  
Vol 13 (3) ◽  
pp. 1072-1082
Author(s):  
Rajneesh Dwevedi ◽  
Vishal Deo ◽  
Janmejay Sethy ◽  
Renuka Gupta ◽  
Mahendiran Mylswamy
Keyword(s):  
Water Availability ◽  
Regional Scale ◽  
Population Decline ◽  
Population Data ◽  
Breeding Population ◽  
Population Census ◽  
Generalised Additive Model ◽  
Local Water ◽  
Near Threatened

The breeding population of birds are dynamic and are affected by multiple factors including climate and local environmental conditions. However, often to understand such relations requires long-term data modelling. Such long-term population data is either lacking or has data gaps. This study demonstrates the use of Multiple Imputation Chained Equation (MICE) to overcome the problem of missing data population census. This is also the first comprehensive study, modelling the 36-year (1980-2015) long-term breeding population data of a near-threatened bird, Painted Stork, from Keoladeo National Park, India. It tests the effect of local water availability, i.e., water released to the park, and regional rainfall, i.e, climatic condition, on the breeding population using Generalised Additive Model (GAM). Both imputation and observed data series-based GAM models identified the local water availability as the most important factor influencing the breeding population of Painted Stork. More than 80% population decline was observed, despite a slight increase in the rainfall at regional scale, suggesting local hydrological conditions are limiting to the breeding population and not the climate. According to the visual assessment of partial plot of GAM, minimum 200-300 million cubic feet of water is needed each nesting season to ensure  sustenance of breeding population. Post-1989, the breeding population was unable to match the long-term mean (~726) except in 1992, 1995, and 1996. The maximum decline was observed between 2000-2009, a decade of frequent droughts. The breeding population was stable until the end of this study, but it was far below the long term mean.

Registered Report: Transcriptional Analysis of Savings Memory Suggests Forgetting Is Due to Retrieval Failure

2020 ◽  
Author(s):  
Robert Calin-Jageman ◽  
Irina Calin-Jageman ◽  
Tania Rosiles ◽  
Melissa Nguyen ◽  
Annette Garcia ◽  
...  
Keyword(s):  
Memory Trace ◽  
Aplysia Californica ◽  
Transcriptional Response ◽  
Transcriptional Analysis ◽  
Retrieval Failure ◽  
Initial Learning ◽  
Rigorous Test ◽  
Stage 1 ◽  
Induced Changes

[[This is a Stage 2 Registered Report manuscript now accepted for publication at eNeuro. The accepted Stage 1 manuscript is posted here: https://psyarxiv.com/s7dft, and the pre-registration for the project is available here (https://osf.io/fqh8j, 9/11/2019). A link to the final Stage 2 manuscript will be posted after peer review and publication.]] There is fundamental debate about the nature of forgetting: some have argued that it represents the decay of the memory trace, others that the memory trace persists but becomes inaccessible due to retrieval failure. These different accounts of forgetting lead to different predictions about savings memory, the rapid re-learning of seemingly forgotten information. If forgetting is due to decay, then savings requires re-encoding and should thus involve the same mechanisms as initial learning. If forgetting is due to retrieval failure, then savings should be mechanistically distinct from encoding. In this registered report we conducted a pre-registered and rigorous test between these accounts of forgetting. Specifically, we used microarray to characterize the transcriptional correlates of a new memory (1 day after training), a forgotten memory (8 days after training), and a savings memory (8 days after training but with a reminder on day 7 to evoke a long-term savings memory) for sensitization in Aplysia californica (n = 8 samples/group). We found that the re-activation of sensitization during savings does not involve a substantial transcriptional response. Thus, savings is transcriptionally distinct relative to a newer (1-day old) memory, with no co-regulated transcripts, negligible similarity in regulation-ranked ordering of transcripts, and a negligible correlation in training-induced changes in gene expression (r = .04 95% CI [-.12, .20]). Overall, our results suggest that forgetting of sensitization memory represents retrieval failure.

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