scholarly journals Do we need demographic data to forecast plant population dynamics?

2015 ◽  
Author(s):  
Andrew T. Tredennick ◽  
Mevin B. Hooten ◽  
Peter B. Adler
Keyword(s):  
Environmental Change ◽  
Forecast Accuracy ◽  
Population Level ◽  
Population Models ◽  
Demographic Model ◽  
Percent Cover ◽  
Climate Effects ◽  
Individual Level ◽  
Level Data ◽  
Significant Difference

1. Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts. 2. Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction. 3. In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types. 4. In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist for many plant species. Modelers should exploit these data to predict the impacts of environmental change.

scholarly journals In search of opportunity and community: Internal migration of refugees in the United States

2020 ◽  
Vol 6 (32) ◽  
pp. eabb0295
Author(s):  
Nadwa Mossaad ◽  
Jeremy Ferwerda ◽  
Duncan Lawrence ◽  
Jeremy Weinstein ◽  
Jens Hainmueller
Keyword(s):  
United States ◽  
Population Level ◽  
The United States ◽  
Retention Rates ◽  
State Governments ◽  
Individual Level ◽  
Welfare Benefits ◽  
Level Data ◽  
Immigrant And Refugee ◽  
National Networks

At a time of heightened anxiety surrounding immigration, state governments have increasingly sought to manage immigrant and refugee flows. Yet the factors that influence where immigrants choose to settle after arrival remain unclear. We bring evidence to this question by analyzing population-level data for refugees resettled within the United States. Unlike other immigrants, refugees are assigned to initial locations across the country but are free to relocate and select another residence after arrival. Drawing on individual-level administrative data for adult refugees resettled between 2000 and 2014 (N = 447,747), we examine the relative desirability of locations by examining how retention rates and patterns of secondary migration differ across states. We find no discernible evidence that refugees’ locational choices are strongly influenced by state partisanship or the generosity of welfare benefits. Instead, we find that refugees prioritize locations with employment opportunities and existing co-national networks.

scholarly journals Hurricane-induced demographic changes in a non-human primate population

2020 ◽  
Vol 7 (8) ◽  
pp. 200173
Author(s):  
Dana O. Morcillo ◽  
Ulrich K. Steiner ◽  
Kristine L. Grayson ◽  
Angelina V. Ruiz-Lambides ◽  
Raisa Hernández-Pacheco
Keyword(s):  
Population Level ◽  
Matrix Population Models ◽  
Individual Level ◽  
Animal Populations ◽  
Level Data ◽  
Climatic Event ◽  
Free Ranging ◽  
Human Primate ◽  
Population Fitness

Major disturbance events can have large impacts on the demography and dynamics of animal populations. Hurricanes are one example of an extreme climatic event, predicted to increase in frequency due to climate change, and thus expected to be a considerable threat to population viability. However, little is understood about the underlying demographic mechanisms shaping population response following these extreme disturbances. Here, we analyse 45 years of the most comprehensive free-ranging non-human primate demographic dataset to determine the effects of major hurricanes on the variability and maintenance of long-term population fitness. For this, we use individual-level data to build matrix population models and perform perturbation analyses. Despite reductions in population growth rate mediated through reduced fertility, our study reveals a demographic buffering during hurricane years. As long as survival does not decrease, our study shows that hurricanes do not result in detrimental effects at the population level, demonstrating the unbalanced contribution of survival and fertility to population fitness in long-lived animal populations.

scholarly journals Impact of increasing vegetarian availability on meal selection and sales in cafeterias

2019 ◽  
Vol 116 (42) ◽  
pp. 20923-20929 ◽  
Author(s):  
Emma E. Garnett ◽  
Andrew Balmford ◽  
Chris Sandbrook ◽  
Mark A. Pilling ◽  
Theresa M. Marteau
Keyword(s):  
Large Scale ◽  
Population Level ◽  
Field Studies ◽  
Individual Level ◽  
Rebound Effects ◽  
Percentage Points ◽  
Physical Environments ◽  
Meat Meal ◽  
Level Data ◽  
The Impact

Shifting people in higher income countries toward more plant-based diets would protect the natural environment and improve population health. Research in other domains suggests altering the physical environments in which people make decisions (“nudging”) holds promise for achieving socially desirable behavior change. Here, we examine the impact of attempting to nudge meal selection by increasing the proportion of vegetarian meals offered in a year-long large-scale series of observational and experimental field studies. Anonymized individual-level data from 94,644 meals purchased in 2017 were collected from 3 cafeterias at an English university. Doubling the proportion of vegetarian meals available from 25 to 50% (e.g., from 1 in 4 to 2 in 4 options) increased vegetarian meal sales (and decreased meat meal sales) by 14.9 and 14.5 percentage points in the observational study (2 cafeterias) and by 7.8 percentage points in the experimental study (1 cafeteria), equivalent to proportional increases in vegetarian meal sales of 61.8%, 78.8%, and 40.8%, respectively. Linking sales data to participants’ previous meal purchases revealed that the largest effects were found in the quartile of diners with the lowest prior levels of vegetarian meal selection. Moreover, serving more vegetarian options had little impact on overall sales and did not lead to detectable rebound effects: Vegetarian sales were not lower at other mealtimes. These results provide robust evidence to support the potential for simple changes to catering practices to make an important contribution to achieving more sustainable diets at the population level.

scholarly journals Greater wealth inequality, less polygyny: rethinking the polygyny threshold model

2018 ◽  
Vol 15 (144) ◽  
pp. 20180035 ◽  
Author(s):  
Cody T. Ross ◽  
Monique Borgerhoff Mulder ◽  
Seung-Yun Oh ◽  
Samuel Bowles ◽  
Bret Beheim ◽  
...  
Keyword(s):  
Choice Model ◽  
Threshold Model ◽  
Population Level ◽  
Wealth Inequality ◽  
Human Populations ◽  
Individual Level ◽  
Level Data ◽  
Mutual Mate Choice ◽  
Polygynous Marriage ◽  
Human Mating

Monogamy appears to have become the predominant human mating system with the emergence of highly unequal agricultural populations that replaced relatively egalitarian horticultural populations, challenging the conventional idea—based on the polygyny threshold model—that polygyny should be positively associated with wealth inequality. To address this polygyny paradox, we generalize the standard polygyny threshold model to a mutual mate choice model predicting the fraction of women married polygynously. We then demonstrate two conditions that are jointly sufficient to make monogamy the predominant marriage form, even in highly unequal societies. We assess if these conditions are satisfied using individual-level data from 29 human populations. Our analysis shows that with the shift to stratified agricultural economies: (i) the population frequency of relatively poor individuals increased, increasing wealth inequality, but decreasing the frequency of individuals with sufficient wealth to secure polygynous marriage, and (ii) diminishing marginal fitness returns to additional wives prevent extremely wealthy men from obtaining as many wives as their relative wealth would otherwise predict. These conditions jointly lead to a high population-level frequency of monogamy.

scholarly journals The 1968 Influenza Pandemic and COVID-19 Outcomes

2021 ◽  
Author(s):  
Charles A Taylor ◽  
Christopher Boulos ◽  
Matthew J Memoli
Keyword(s):  
Influenza Pandemic ◽  
Population Level ◽  
Related Factors ◽  
Death Rates ◽  
Individual Level ◽  
Age Cohorts ◽  
Differential Outcomes ◽  
Level Data ◽  
The Us ◽  
Before And After

Past pandemic experience at an individual or population level may affect health outcomes in future pandemics. In this study, we focus on how the influenza pandemic of 1968 (H3N2), which killed up to 100,000 people in the US, may have produced differential COVID-19 (SARS-CoV-2) outcomes. Our analysis finds that areas with high influenza-related mortality in 1968 experienced 1-2% lower COVID-19 death rates. We employ an identification strategy that isolates variation in COVID-19 rates across age cohorts born before and after 1968. Locales in the US with high 1968 influenza mortality have lower COVID-19 death rates among older cohorts relative to younger ones. The relationship holds using county-level and patient-level data, as well as data from hospitals and nursing homes. Results do not appear to be driven by systemic or policy-related factors that would affect a population, but instead suggest a potential individual-level response to prior influenza pandemic exposure. The findings merit substantial further investigation into potential biological, immunological, or other mechanisms that can account for these differential outcomes.

scholarly journals Multimethod, multidataset analysis reveals paradoxical relationships between sociodemographic factors, Hispanic ethnicity and diabetes

2020 ◽  
Vol 8 (2) ◽  
pp. e001725
Author(s):  
Gabriel M Knight ◽  
Gabriela Spencer-Bonilla ◽  
David M Maahs ◽  
Manuel R Blum ◽  
Areli Valencia ◽  
...  
Keyword(s):  
Census Data ◽  
Characteristic Curve ◽  
Population Level ◽  
Sociodemographic Factors ◽  
Robert Wood Johnson Foundation ◽  
County Level ◽  
Ethnic Density ◽  
Hispanic Ethnicity ◽  
Individual Level ◽  
Level Data

IntroductionPopulation-level and individual-level analyses have strengths and limitations as do ‘blackbox’ machine learning (ML) and traditional, interpretable models. Diabetes mellitus (DM) is a leading cause of morbidity and mortality with complex sociodemographic dynamics that have not been analyzed in a way that leverages population-level and individual-level data as well as traditional epidemiological and ML models. We analyzed complementary individual-level and county-level datasets with both regression and ML methods to study the association between sociodemographic factors and DM.Research design and methodsCounty-level DM prevalence, demographics, and socioeconomic status (SES) factors were extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data. Analogous individual-level data were extracted from 2007 to 2016 National Health and Nutrition Examination Survey studies and corrected for oversampling with survey weights. We used multivariate linear (logistic) regression and ML regression (classification) models for county (individual) data. Regression and ML models were compared using measures of explained variation (area under the receiver operating characteristic curve (AUC) and R2).ResultsAmong the 3138 counties assessed, the mean DM prevalence was 11.4% (range: 3.0%–21.1%). Among the 12 824 individuals assessed, 1688 met DM criteria (13.2% unweighted; 10.2% weighted). Age, gender, race/ethnicity, income, and education were associated with DM at the county and individual levels. Higher county Hispanic ethnic density was negatively associated with county DM prevalence, while Hispanic ethnicity was positively associated with individual DM. ML outperformed regression in both datasets (mean R2 of 0.679 vs 0.610, respectively (p<0.001) for county-level data; mean AUC of 0.737 vs 0.727 (p<0.0427) for individual-level data).ConclusionsHispanic individuals are at higher risk of DM, while counties with larger Hispanic populations have lower DM prevalence. Analyses of population-level and individual-level data with multiple methods may afford more confidence in results and identify areas for further study.

scholarly journals A modelling exercise to show why population models should incorporate distinct life histories of dispersers

2018 ◽  
Author(s):  
Jacques A. Deere ◽  
Ilona van den Berg ◽  
Gregory Roth ◽  
Isabel M. Smallegange
Keyword(s):  
Life History ◽  
Population Growth ◽  
Life Histories ◽  
Population Level ◽  
Population Models ◽  
Integral Projection Model ◽  
Projection Model ◽  
Individual Level ◽  
Population Growth Rates ◽  
Dispersal Probability

AbstractDispersal is an important form of movement influencing population dynamics, species distribution, and gene flow between populations. In population models, dispersal is often included in a simplified manner by removing a random proportion of the population. Many ecologists now argue that models should be formulated at the level of individuals instead of the population-level. To fully understand the effects of dispersal on natural systems, it is therefore necessary to incorporate individual-level differences in dispersal behaviour in population models. Here we parameterised an integral projection model (IPM), which allows for studying how individual life histories determine population-level processes, using bulb mites, Rhizoglyphus robini, to assess to what extent dispersal expression (frequency of individuals in the dispersal stage) and dispersal probability affect the proportion of dispersers and natal population growth rate. We find that allowing for life-history differences between resident phenotypes and disperser phenotypes shows that multiple combinations of dispersal probability and dispersal expression can produce the same proportion of leaving individuals. Additionally, a given proportion of dispersing individuals results in different natal population growth rates. The results highlight that dispersal life histories, and the frequency with which disperser phenotypes occur in the natal population, significantly affect population-level processes. Thus, biological realism of dispersal population models can be increased by incorporating the typically observed life history differences between resident phenotypes and disperser phenotypes, and we here present a methodology to do so.

scholarly journals A framework for integrated clinical risk assessment using population sequencing data

2021 ◽  
Author(s):  
James D. Fife ◽  
Tho Tran ◽  
Jackson R. Bernatchez ◽  
Kiethen E. Shepard ◽  
Christopher Koch ◽  
...  
Keyword(s):  
Risk Ratio ◽  
Disease Risk ◽  
Population Level ◽  
Disease Genes ◽  
Sequencing Data ◽  
Individual Level ◽  
Clinical Risk ◽  
Pathogenic Variants ◽  
Significant Difference ◽  
Cad Risk

Clinical risk prediction for genetic variants remains challenging even in established disease genes, as many are so rare that epidemiological assessment is not possible. Using data from 200,625 individuals, we integrate individual-level, variant-level, and protein region risk factors to estimate personalized clinical risk for individuals with rare missense variants. These estimates are highly concordant with clinical outcomes in breast cancer (BC) and familial hypercholesterolemia (FH) genes, where we distinguish between those with elevated versus population-level disease risk (logrank p<10-5, Risk Ratio=3.71 [3.53, 3.90] BC, Risk Ratio=4.71 [4.50, 4.92] FH), validated in an independent cohort (χ2 p=9.9x10-4 BC, χ2 p=3.72x10-16 FH). Notably in FH genes, we predict that 64% of biobank patients with laboratory-classified pathogenic variants are not at increased coronary artery disease (CAD) risk when considering all patient and variant characteristics. These patients have no significant difference in CAD risk from individuals without a monogenic variant (logrank p=0.68). Such assessments may be useful for optimizing clinical surveillance, genetic counseling, and intervention, and demonstrate the need for more nuanced approaches in population screening.

scholarly journals Key data for outbreak evaluation: building on the Ebola experience

2017 ◽  
Vol 372 (1721) ◽  
pp. 20160371 ◽  
Author(s):  
Anne Cori ◽  
Christl A. Donnelly ◽  
Ilaria Dorigatti ◽  
Neil M. Ferguson ◽  
Christophe Fraser ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Outbreaks ◽  
Population Level ◽  
West African ◽  
Public Health Response ◽  
The West ◽  
Individual Level ◽  
Infectious Disease Outbreaks ◽  
Exposure Data ◽  
Level Data

Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks. This article is part of the themed issue ‘The 2013–2016 West African Ebola epidemic: data, decision-making and disease control’.

scholarly journals Associations between state intimate partner violence-related firearm policies and injuries among women and men who experience intimate partner violence

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tiara C. Willie ◽  
Trace Kershaw ◽  
Rachel Perler ◽  
Amy Caplon ◽  
Marina Katague ◽  
...  
Keyword(s):  
Intimate Partner Violence ◽  
Partner Violence ◽  
State Level ◽  
Health Impact ◽  
Intimate Partner ◽  
Individual Level ◽  
Representative Study ◽  
Level Data ◽  
Significant Difference ◽  
Men Survivors

Abstract Background Comprehensive state firearm policies related to intimate partner violence (IPV) may have a significant public health impact on non-lethal IPV-related injuries. Research indicates that more restrictive firearm policies may reduce risk for intimate partner homicide, however it is unclear whether firearm policies prevent or reduce the risk of non-lethal IPV-related injuries. This study sought to examine associations between state-level policies and injuries among U.S. IPV survivors. Methods Individual-level data were drawn from the National Intimate Partner and Sexual Violence Survey, a nationally-representative study of noninstitutionalized adults. State-level data were drawn from a firearm policy compendium. Multivariable regressions were used to test associations of individual policies with non-fatal IPV-related injuries (N = 5493). Regression models were also conducted to explore differences in the policy-injury associations among women and men survivors. Results Three categories of policies were associated with IPV-related injuries. The odds of injuries was lower for IPV survivors living in states that prohibited firearm possession and require firearm relinquishment among persons convicted of IPV-related misdemeanors (aOR [95% CI] = .76 [.59, .97]); prohibited firearm possession and require firearm relinquishment among persons subject to IPV-related restraining orders (aOR [95% CI] = .81 [.66, .98]); and prohibited firearm possession among convicted of stalking (aOR [95% CI] = .82 [.68, .98]) than IPV survivors living in states without these policies. There was a significant difference between women and men survivors in the association between IPV-related misdemeanors policy and injuries (B [SE] = .60 [.29]), such that the association was stronger for men survivors (aOR [95% CI] = .10 [.06, .17]) than women survivors (aOR [95% CI] = .60 [.48, .76]). Conclusions Restrictive state firearm policies regarding IPV may provide unique opportunities to protect IPV survivors from injuries.

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