Biomaterial Database

Trend analyses of hierarchical pin-point cover data. 2012

Christian Damgaard

Bioscience, Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark. cfd@dmu.dk

The use of state-space models for analyzing longitudinal hierarchical pin-point plant cover data is demonstrated. The main advantages of using a state-space model are (1) that the observed variance is separated into sampling variance and the more interesting structural variance which are needed for quantifying prediction uncertainty, (2) that missing values or an unbalanced sampling design readily may be accounted for, and (3) that the structural equation easily may be expanded and made as complex as necessary for modeling longitudinal pin-point cover data, thus allowing the incorporation of the most important ecological processes in the state-space model without technical difficulties. Typically, there is considerable spatial variation in plant abundance, and this variation is modeled using the Pólya-Eggenberger distribution (a generalization of the beta-binomial distribution). To illustrate this method, longitudinal hierarchical pin-point data of Erica tetralix in wet Danish heathlands were analyzed, including and excluding autocorrelation and an environmental covariable in the state-space model. The pin-point plant cover data showed a significant decrease in the plant cover of E. tetralix in the period from 2004 to 2009, with an annual decrease of about 10% in the logit-transformed cover. The distribution of predicted plant cover at a given site the following year was calculated, including and excluding the information of an environmental covariable.

UI	MeSH Term	Description	Entries
D008954	Models, Biological	Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.	Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D003710	Demography	Statistical interpretation and description of a population with reference to distribution, composition, or structure.	Demographer,Demographic,Demographic and Health Survey,Population Distribution,Accounting, Demographic,Analyses, Demographic,Analyses, Multiregional,Analysis, Period,Brass Technic,Brass Technique,Demographers,Demographic Accounting,Demographic Analysis,Demographic Factor,Demographic Factors,Demographic Impact,Demographic Impacts,Demographic Survey,Demographic Surveys,Demographic and Health Surveys,Demographics,Demography, Historical,Demography, Prehistoric,Factor, Demographic,Factors, Demographic,Family Reconstitution,Historical Demography,Impact, Demographic,Impacts, Demographic,Multiregional Analysis,Period Analysis,Population Spatial Distribution,Prehistoric Demography,Reverse Survival Method,Stable Population Method,Survey, Demographic,Surveys, Demographic,Analyses, Period,Analysis, Demographic,Analysis, Multiregional,Demographic Analyses,Demographies, Historical,Demographies, Prehistoric,Distribution, Population,Distribution, Population Spatial,Distributions, Population,Distributions, Population Spatial,Family Reconstitutions,Historical Demographies,Method, Reverse Survival,Method, Stable Population,Methods, Reverse Survival,Methods, Stable Population,Multiregional Analyses,Period Analyses,Population Distributions,Population Methods, Stable,Population Spatial Distributions,Prehistoric Demographies,Reconstitution, Family,Reconstitutions, Family,Reverse Survival Methods,Spatial Distribution, Population,Spatial Distributions, Population,Stable Population Methods,Technic, Brass,Technique, Brass
D004784	Environmental Monitoring	The monitoring of the level of toxins, chemical pollutants, microbial contaminants, or other harmful substances in the environment (soil, air, and water), workplace, or in the bodies of people and animals present in that environment.	Monitoring, Environmental,Environmental Surveillance,Surveillance, Environmental
D000818	Animals	Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA.	Animal,Metazoa,Animalia
D013997	Time Factors	Elements of limited time intervals, contributing to particular results or situations.	Time Series,Factor, Time,Time Factor
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D017753	Ecosystem	A functional system which includes the organisms of a natural community together with their environment. (McGraw Hill Dictionary of Scientific and Technical Terms, 4th ed)	Ecosystems,Biome,Ecologic System,Ecologic Systems,Ecological System,Habitat,Niche, Ecological,System, Ecological,Systems, Ecological,Biomes,Ecological Niche,Ecological Systems,Habitats,System, Ecologic,Systems, Ecologic
D029791	Ericaceae	The heath plant family of the order Ericales, subclass Dilleniidae, class Magnoliopsida that are generally shrubs or small trees. Leaves are alternate, simple, and leathery; flowers are symmetrical with a 4- or 5-parted corolla of partly fused petals.