Demographic Methods across the Tree of Life最新文献

Adaptive management: making recurrent decisions in the face of uncertainty 适应性管理:在面对不确定性时反复做出决策

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0019

J. Nichols

{"title":"Adaptive management: making recurrent decisions in the face of uncertainty","authors":"J. Nichols","doi":"10.1093/oso/9780198838609.003.0019","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0019","url":null,"abstract":"The key to wise decision-making in disciplines such as conservation, wildlife management, and epidemiology is the ability to predict consequences of management actions on focal systems. Predicted consequences are evaluated relative to programme objectives in order to select the favoured action. Predictions are typically based on mathematical models developed to represent hypotheses about management effects on system dynamics. For populations ranging from large mammals to plant communities to bacterial pathogens, demographic modelling is often the approach favoured for model development. State variables of such models may be population abundance, density, occupancy, or species richness, with corresponding vital rates such as rates of reproduction, survival, local extinction, and local colonisation. A key source of uncertainty that characterises such modelling efforts is the nature of relationships between management actions and vital rates. Adaptive management is a form of structured decision-making developed for decision problems that are recurrent and characterised by such structural uncertainty. One approach to incorporating this uncertainty is to base decisions on multiple models, each of which makes different predictions according to its underlying hypothesis. An information state of model weights carries information about the relative predictive abilities of the models. Monitoring of system state variables provides information about system responses, and comparison of these responses with model-based predictions provides a basis for updating the information state. Decisions emphasise the better-predicting model(s), leading to better decisions as the process proceeds. Adaptive management can thus produce optimal decisions now, while simultaneously reducing uncertainty for even better management in the future.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":" 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120832859","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

Genetic data collection, pedigrees, and phylogenies 遗传数据收集、系谱和系统发育

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0001

Emily G. Simmonds, Alina K. Niskanen, H. Jensen, S. Smith

{"title":"Genetic data collection, pedigrees, and phylogenies","authors":"Emily G. Simmonds, Alina K. Niskanen, H. Jensen, S. Smith","doi":"10.1093/oso/9780198838609.003.0001","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0001","url":null,"abstract":"This chapter presents the role of genetic data in demographic studies. It focuses on two particular challenges faced in demographic analyses that can be solved using genetic data: estimating relatedness between individuals in a population and identifying drivers of cross-taxon variation in life history. The challenge of estimating relatedness is addressed with genetic pedigrees, and phylogenies allow comparisons of drivers of life history across taxa. These two different methods have several unifying features and histories. A past reliance on observational data in both cases limited taxonomic breadth of demographic analyses and reduced accuracy. With recent advances in genetic data collection and processing, in addition to improved computational methods, we are now in a position to use genetic data to expand demographic analyses across the Tree of Life. This chapter gives an overview of the whole process of constructing genetic pedigrees and reconstructing genetic phylogenies: beginning with the state-of-the-art, walking through the data collection steps required to obtain and process genetic material, and finishing with discussion and comparison of the diverse array of methods to construct genetic pedigrees and phylogenetic trees.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"32 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":"127547249","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

Longitudinal demographic data collection 纵向人口统计数据收集

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0005

Marlène Gamelon, Josh A. Firth, M. Le Moullec, William K. Petry, R. Salguero‐Gómez

引用次数: 0

Life tables: construction and interpretation 生命表:构建和解释

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0008

O. Jones

{"title":"Life tables: construction and interpretation","authors":"O. Jones","doi":"10.1093/oso/9780198838609.003.0008","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0008","url":null,"abstract":"Life tables, which describe how the risk of death (and sometimes fertility) changes with age, are a fundamental tool for describing and exploring the diversity of life histories. Numerous important life history metrics can be derived from them. This chapter provides a broad coverage of life table construction and use and use with a particular focus on nonhuman animals. The calculation of life tables can be divided into approaches: cohort-based, where the data are obtained from individuals born at (approximately) the same time that are followed until death; and period-based, where the data are obtained from a population of mixed ages followed for a particular time-frame (e.g. a year). Worked examples of both approaches are provided using data from published sources. Emphasis is placed on understanding concepts such as rates vs. probability, life expectancy, and generation time. Links are drawn between the survivorship curve (type I, type II, and type III survivorship) and entropy. The chapter also covers the concept of the Lexis diagram which is used to represent births and deaths for individuals in different cohorts. Finally, the assumptions and limitations of life tables are discussed, with pointers to further reading. Code and data are provided.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"5 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":"124212795","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

Transient analyses of population dynamics using matrix projection models 用矩阵投影模型分析种群动态

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0011

D. Koons, D. Iles, I. Stott

引用次数: 0

Integral projection models 积分投影模型

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0010

Edgar J. González, D. Childs, P. Quintana‐Ascencio, R. Salguero‐Gómez

引用次数: 4

Evolutionary Demography 进化的人口

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0016

S. Tuljapurkar, Wenyun Zuo

{"title":"Evolutionary Demography","authors":"S. Tuljapurkar, Wenyun Zuo","doi":"10.1093/oso/9780198838609.003.0016","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0016","url":null,"abstract":"Evolutionary demography has grown rapidly in recent years, as the biological topics of life history evolution and evolution in population with complex life cycles have benefitted from and contributed to a broader focus on evolutionary biodemography. This chapter provides a critical summary of the central ideas and methods. The authors emphasise theoretical methods, starting with the main ideas that have attracted attention in the field, the assumptions behind these, and efforts to relax those assumptions, and provide a short account of some new directions. The chapter begins with the classic work of Peter Medawar and William Hamilton and discusses the connections, applications, assumptions, and limitations related to their ideas and results, e.g. sensitivity and corresponding elasticity of growth rate on fertility and survival. It highlights extensions to variable environments and the large body of theory around that topic. Next the chapter discusses how these theoretical methods are related to analyses and theories of post-reproductive life, via the general concept of ‘borrowing fitness’. Finally, the chapter discusses nonlinear models of mutation and selection and density-dependent models.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"17 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":"127864599","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: 10

Demographic processes in socially structured populations 社会结构人口的人口统计过程

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0021

Maria Paniw, G. Cozzi, S. Sommer, A. Ozgul

{"title":"Demographic processes in socially structured populations","authors":"Maria Paniw, G. Cozzi, S. Sommer, A. Ozgul","doi":"10.1093/oso/9780198838609.003.0021","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0021","url":null,"abstract":"In socially structured animal populations, vital rates such as survival and reproduction, are affected by complex interactions among individuals of different social ranks and among social groups. Due to this complexity, mechanistic approaches to model vital rates may be preferred over commonly used structured population models. However, mechanistic approaches come at a cost of increased modelling complexity, computational requirements, and reliance on simulated metrics, while structured population models are analytically tractable. This chapter compares different approaches to modelling population dynamics of socially structured populations. It first simulates individual-based data based on the life cycle of a hypothetical cooperative breeder and then projects population dynamics using a matrix population model (MPM), an integral projection model (IPM), and an individual-based model (IBM). The authors demonstrate that, when projecting population size or structure, the relatively simpler MPM can outperform both the IPM and IBM. However, mechanistic details parametrised in the more complex IBM are required to accurately project interactions within social groups. The R scripts in this chapter provide a roadmap to both simulate data that best describe a socially structured system and assess the level of model complexity needed to capture the dynamics of the system.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"20 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":"132648677","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: 2

Abundance-based approaches Abundance-based方法

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0007

J. Knape, Andreas Lindén

引用次数: 0

Biochemical and physiological data collection 生化和生理数据收集

Demographic Methods across the Tree of Life Pub Date : 2021-08-31 DOI: 10.1093/oso/9780198838609.003.0002

Oldřich Tomášek, A. Cohen, E. Fenollosa, Maurizio Mencuccini, S. Munné‐Bosch, F. Pelletier

{"title":"Biochemical and physiological data collection","authors":"Oldřich Tomášek, A. Cohen, E. Fenollosa, Maurizio Mencuccini, S. Munné‐Bosch, F. Pelletier","doi":"10.1093/oso/9780198838609.003.0002","DOIUrl":"https://doi.org/10.1093/oso/9780198838609.003.0002","url":null,"abstract":"Physiological and biochemical traits hold great promise for demographic research as potential proxies (biomarkers) of various biotic and environmental variables that determine individual fitness and ultimately demographic rates. Integrating such biomarkers into demographic models can thus provide insights into drivers of population dynamics or increase predictive power of the models by refining estimation of vital rates. Biomarkers also represent promising means to characterise population structure and dynamics on much shorter time-scales compared to classical demographic approaches. Functional traits further emerge as direct targets of conservation efforts directed towards conserving functional diversity. Yet, biomarkers and functional traits remain underutilised in demography and population ecology, indicating that their benefits still await wider recognition. This chapter briefly reviews the most prominent physiological and biochemical traits (e.g. metabolic rates, hormones, oxidative stress markers, telomeres) that may be of interest in animal and plant demographic research, including the methods for collection, storage, and analysis, and the criteria to be met before the trait is validated as a biomarker. Hopefully, this effort will stimulate further integration of physiological and biochemical data into demographic framework.","PeriodicalId":442239,"journal":{"name":"Demographic Methods across the Tree of Life","volume":"1 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":"131051404","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