Statistics for Spatial Data.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Generalized Linear Models (2nd ed.)

Forests are of major importance to human society, contributing several crucial ecosystem services. Biodiversity is suggested to positively influence multiple services but evidence from natural systems at scales relevant to management is scarce. Here, across a scale of 400,000 km2, we report that tree species richness in production forests shows positive to positively hump-shaped relationships with multiple ecosystem services. These include production of tree biomass, soil carbon storage, berry production and game production potential. For example, biomass production was approximately 50% greater with five than with one tree species. In addition, we show positive relationships between tree species richness and proxies for other biodiversity components. Importantly, no single tree species was able to promote all services, and some services were negatively correlated to each other. Management of production forests will therefore benefit from considering multiple tree species to sustain the full range of benefits that the society obtains from forests.

Higher levels of multiple ecosystem services are found in forests with more tree species, Suppl info

Comparison of a physiological model and a statistical model for prediction of growth and yield in boreal forests

Empirical models for the abundance and berry yield of V. myrtillus were constructed using generalized linear mixed model (GLMM) techniques. The percentage coverage of bilberry was predicted as a function of site and stand characteristics using the permanent sample plots of the National Forest Inventory (NFI) in 1995. The number of bilberries was predicted as a function of percentage coverage and stand characteristics using a sub-sample of the NFI plots in North Karelia. The between-year variation in the bilberry yield was analysed using the permanent experimental plots (MASI) established in different areas of Finland and measured in 2001–2007. The highest coverage of bilberry was found on mesic heath sites; on sub-xeric and herb-rich heath sites the values were 62% of that for mesic sites. The decreasing effect of deciduous trees (compared to spruce) was significant only on herb-rich heath sites. The coverage increased along with stand development up to certain stand ages and basal areas. The bilberry yields were higher in pine-dominated stands than in spruce-dominated ones. In spruce stands, the coverage of bilberry and stand basal area significantly affected the number of berries, whereas in pine stands only the coverage was a significant predictor. In the MASI data, the bilberry yield of pine stands was two times higher than that of spruce stands; however, the between-year variation in bilberry yield was higher in the spruce than in the pine stands. The estimated models were used to predict the bilberry coverage and yield along with stand development. On mesic heath sites in southern Finland (1200 dd.), the predicted annual yield of bilberry was about 25 kg ha–1 (95% confidence interval 9–73 kg ha–1) in a mature pine stand and about 10 kg ha–1 (3–35 kg ha–1) in a mature spruce stand. The models can be included in stand simulators, where they would facilitate the prediction of bilberry abundance and yields for silvicultural and forest planning purposes.

Modelling the abundance and temporal variation in the production of bilberry (Vaccinium myrtillus L.) in Finnish mineral soil forests.

Dependence of tree-ring, earlywood and latewood indices of Scots pine and Norway spruce on climatic factors in eastern Finland

A method for stochastic multiobjective optimization of stand management

Multiple-use forestry requires comprehensive planning to maximize the utilization and sustainability of many forest resources whose growth and productivity are interconnected Forest fungi represen

Jari Miina

Papers

Comparison of a physiological model and a statistical model for prediction of growth and yield in boreal forests

Modelling the abundance and temporal variation in the production of bilberry (Vaccinium myrtillus L.) in Finnish mineral soil forests.

Dependence of tree-ring, earlywood and latewood indices of Scots pine and Norway spruce on climatic factors in eastern Finland

A method for stochastic multiobjective optimization of stand management

Modelling the production and species richness of wild mushrooms in pine forests of the Central Pyrenees in northeastern Spain