Demographic Methods across the Tree of Life最新文献_第2页

Reproductive value and analyses of population dynamics of age-structured populations 年龄结构种群的生殖价值与种群动态分析

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0017

B. Sæther, S. Engen

{"title":"Reproductive value and analyses of population dynamics of age-structured populations","authors":"B. Sæther, S. Engen","doi":"10.1093/oso/9780198838609.003.0017","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0017","url":null,"abstract":"Many populations of especially long-lived species show large temporal variation in age structure, which can complicate estimating of important population parameters. This occurs because it can be difficult to disentangle whether variation in numbers is due to fluctuations in the environment or caused by changes in the age distribution. This chapter shows that fluctuations in the total reproductive value of the population, that is, the sum of all individual reproductive values, often provide a good description of the population dynamics but still is not confounded by fluctuations in age structure. Because the change in the total reproductive rate is exactly equal to the growth rate of the population, this quantity enables decomposition of the long-run growth rate into stochastic components caused by age-specific variation in demographic and environmental stochasticity. The chapter illustrates the practical application of this approach in stochastic demography by analyses of the dynamics of several populations of birds and mammals. It puts a strong focus on these methods being particularly useful in viability analyses of small populations of vulnerable or endangered species.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116250790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applying comparative methods to different databases: lessons from demographic analyses across mammal species 将比较方法应用于不同的数据库:从哺乳动物物种的人口统计分析中得到的教训

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0018

J. Gaillard, Victor Ronget, J. Lemaitre, C. Bonenfant, G. Péron, P. Capdevila, Marlène Gamelon, R. Salguero‐Gómez

{"title":"Applying comparative methods to different databases: lessons from demographic analyses across mammal species","authors":"J. Gaillard, Victor Ronget, J. Lemaitre, C. Bonenfant, G. Péron, P. Capdevila, Marlène Gamelon, R. Salguero‐Gómez","doi":"10.1093/oso/9780198838609.003.0018","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0018","url":null,"abstract":"Comparative demographic analyses aim to identify axes of variation in vital rates and the factors that determine the position of species along these axes. These analyses can be performed using different primary data sets, with marked heterogeneity in data quality and structure. Whether the outcome of demographic comparative analyses depends on the database used because theoretical predictions of evolutionary ecology are not that robust and depend on the set of species analysed or because data limitation prevents the identification of the expected patterns has never been investigated. This chapter fills this knowledge gap by performing a comparative demographic analysis across mammalian species from two distinct databases (Comadre and Malddaba) that were built for different purposes. The chapter first estimates some demographic metrics for each database, analyses their allometric relationships, and compares the estimates with theoretical expectations by performing phylogenetic regressions. Using Malddaba led to stronger allometric relationships closer to the expectation than Comadre. Moreover, the contribution of dimensionless demographic metrics to axes of variation in the shape of demographic trajectories was different between databases. The findings in the chapter demonstrate the key role of age dependence in vital rates for shaping demographic tactics across mammalian species and highlight the need for carefully choosing the database and the metrics to use depending on the question asked. Instead of opposing databases, the authors’ analysis nicely illustrates that different databases could be used to address different questions about life history variation.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131646830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Social data collection and analyses 社会数据收集和分析

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0003

M. Charpentier, M. Pele, J. Renoult, C. Sueur

引用次数: 1

Growth rings across the Tree of Life: demographic insights from biogenic time series data 跨越生命之树的年轮:来自生物时间序列数据的人口统计学见解

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0004

M. Evans, B. Black, D. Falk, C. L. Giebink, Emily L. Schultz

{"title":"Growth rings across the Tree of Life: demographic insights from biogenic time series data","authors":"M. Evans, B. Black, D. Falk, C. L. Giebink, Emily L. Schultz","doi":"10.1093/oso/9780198838609.003.0004","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0004","url":null,"abstract":"Biogenic time series data can be generated in a single sampling effort, offering an appealing alternative to the slow process of revisiting or recapturing individuals to measure demographic rates. Annual growth rings formed by trees and in the ear bones of fish (i.e. otoliths) are prime examples of such biogenic time series. They offer insight into not only the process of growth but also birth, death, movement, and evolution, sometimes at uniquely deep temporal and large spatial scales, well beyond 5–30 years of data collected in localised study areas. This chapter first reviews the fundamentals of how tree-ring and otolith time series data are developed and analysed (i.e. dendrochronology and sclerochronology), then surveys growth rings in other organisms, along with microstructural or microcompositional variation in growth rings, and other records of demographic processes. It highlights the answers to demographic questions revealed by these time series data, such as the influence of environmental (atmospheric or ocean) conditions, competition, and disturbances on demographic processes, and the genetic versus plastic basis of individual growth and traits that influence growth. Lastly, it considers how spatial networks of biogenic, annually resolved time series data can offer insights into the importance of macrosystem atmospheric and ocean dynamics on multispecies, trophic dynamics. The authors encourage demographers to integrate the complementary information contained in biogenic time series data into population models to better understand the drivers of vital rate variation and predict the impacts of global change.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132996810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Survival analyses 生存分析

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0013

S. Cubaynes, S. Galas, Myriam Richaud, Ana Sanz Aguilar, R. Pradel, G. Tavecchia, F. Colchero, S. Roques, Richard P. Shefferson, C. Camarda

{"title":"Survival analyses","authors":"S. Cubaynes, S. Galas, Myriam Richaud, Ana Sanz Aguilar, R. Pradel, G. Tavecchia, F. Colchero, S. Roques, Richard P. Shefferson, C. Camarda","doi":"10.1093/oso/9780198838609.003.0013","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0013","url":null,"abstract":"Survival analyses are a key tool for demographers, ecologists, and evolutionary biologists. This chapter presents the most common methods and illustrates their use for species across the Tree of Life. It discusses the challenges associated with various types of survival data, how to model species with a complex life cycle, and includes the impact of environmental factors and individual heterogeneity. It covers the analysis of ‘known-fate’ data collected in lab conditions, using the Kaplan–Meier estimator and Cox’s proportional hazard regression analysis. Alternatively, survival data collected on free-ranging populations usually involve individuals missing at certain monitoring occasions and unknown time at death. The chapter provides an overview of capture–mark–recapture (CMR) models, from single-state to multi-state and multi-event models, and their use in animal and plant demography to estimate demographic parameters while correcting for imperfect detection of individuals. It discusses various inference frameworks available to implement CMR models using a frequentist or Bayesian approach. Only humans are an exception among free-ranging populations, with the existence of several consequent databases with perfect knowledge of age and cause of death for all individuals. The chapter presents an overview of the most common models used to describe mortality patterns over age and time using human mortality data. Throughout, focus is placed on eight case studies, which involve lab organisms, free-ranging animal populations, plant populations, and human populations. Each example includes data and codes, together with step-by-step guidance to run the survival analysis.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129704515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Efficient use of demographic data: integrated population models 有效利用人口统计数据:综合人口模型

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0014

Marlène Gamelon, S. Vriend, M. Visser, C. Hallmann, S. Lommen, E. Jongejans

{"title":"Efficient use of demographic data: integrated population models","authors":"Marlène Gamelon, S. Vriend, M. Visser, C. Hallmann, S. Lommen, E. Jongejans","doi":"10.1093/oso/9780198838609.003.0014","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0014","url":null,"abstract":"Various types of demographic data can be collected in the field: population censuses, capture–mark–recapture data, and so on. These data sources share common demographic information about the studied population. Bayesian integrated population models (IPM) make efficient use of these different types of demographic data by jointly analysing them. This chapter discusses the advantages and the possibilities offered by this integrated approach. It describes the different steps required to build an IPM and illustrates the usefulness of this approach using two case studies. The first case study is a short-lived bird species, the blue tit, taking advantage of different data sources collected in a Dutch population to highlight how an integrated analysis might help to obtain a comprehensive picture of its dynamics. This IPM also assesses whether and how beech crop size might influence vital rates. The second case study is an invasive plant species, the common ragweed. The chapter illustrates how seedling data, plant data, and seed bank data could be analysed simultaneously to estimate key vital rates such as the probability that a seedling survives up to flowering.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121267757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Demographic methods in epidemiology 流行病学中的人口统计学方法

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0022

P. Klepac, C. Metcalf

引用次数: 0

Heritability, polymorphism, and population dynamics: individual-based eco-evolutionary simulations 遗传力、多态性和种群动态:基于个体的生态进化模拟

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0020

A. Kuparinen

引用次数: 0

Spatial demography 空间统计学

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0015

G. Péron

引用次数: 2

Drivers of demography: past challenges and a promise for a changed future 人口的驱动因素:过去的挑战和改变未来的希望

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0006

P. Quintana‐Ascencio, E. Menges, Geoffrey S. Cook, J. Ehrlén, Michelle E. Afkhami

{"title":"Drivers of demography: past challenges and a promise for a changed future","authors":"P. Quintana‐Ascencio, E. Menges, Geoffrey S. Cook, J. Ehrlén, Michelle E. Afkhami","doi":"10.1093/oso/9780198838609.003.0006","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0006","url":null,"abstract":"There is an urgent need to understand how populations and metapopulations respond to shifts in the environment to mitigate the consequences of human actions and global change. Identifying environmental variables/factors affecting population dynamics and the nature of their impacts is fundamental to improve projections and predictions. This chapter examines how environmental drivers, both continuous (stress) and episodic (disturbance), are incorporated in demographic modelling across many types of organisms and environments, using both observational and experimental approaches to characterise drivers. It critically summarises examples of the main approaches and identifies major accomplishments, challenges, and limitations. The chapter points to promising approaches and possible future developments. In the initial sections, models in closed systems without migration among populations are considered. The chapter then focuses on metapopulation models, emphasising the importance of understanding drivers affecting migration and differential extinction among populations. Finally, it concludes with a discussion of some important and general problems associated with assessing how population dynamics may be affected by environmental drivers that are dynamic, nonlinear, and with indirect and/or interacting effects with other drivers..","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125318114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1