Knowledge Management

The Grid 2: Blueprint for a New Computing Infrastructure

Concepts of spatial scale, such as extent, grain, resolution, range, footprint, support and cartographic ratio are not interchangeable. Because of the potential confusion among the definitions of these terms, we suggest that authors avoid the term "scale" and instead refer to specific concepts. In particular, we are careful to discriminate between observation scales, scales of ecological phenomena and scales used in spatial statistical analysis. When scales of observation or analysis change, that is, when the unit size, shape, spacing or extent are altered, statistical results are expected to change. The kinds of results that may change include estimates of the population mean and variance, the strength and character of spatial autocorrelation and spatial anisotropy, patch and gap sizes and multivariate relationships, The First three of these results (precision of the mean, variance and spatial autocorrelation) can sometimes be estimated using geostatistical support-effect models. We present four case studies of organism abundance and cover illustrating some of these changes and how conclusions about ecological phenomena (process and structure) may be affected. We identify the influence of observational scale on statistical results as a subset of what geographers call the Modifiable Area Unit Problem (MAUP). The way to avoid the MAUP is by careful construction of sampling design and analysis. We recommend a set of considerations for sampling design to allow useful tests for specific scales of a phenomenon under study. We further recommend that ecological studies completely report all components of observation and analysis scales to increase the possibility of cross-study comparisons.

/pdf/a-balanced-view-of-scale-in-spatial-statistical-analysis-4ctzco3pcl.pdf

A balanced view of scale in spatial statistical analysis

http://www.sysecol2.ethz.ch/Refs/EntClim/F/Fr035.pdf

A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0)

1. Introduction.- 2. Tree Form and Stem Taper.- 3. Tree-stem Volume Equations.- 4. Tree Weight and Biomass Estimation.- 5. Quantifying Tree Crowns.- 6. Growth Functions.- 7. Evaluating Site Quality.- 8. Quantifying Stand Density.- 9. Indices of Individual-tree Competition.- 10. Modeling Forest Stand Development.- 11. Whole-stand Models for Even-aged Stands.- 12. Diameter-distribution Models for Even-aged Stands.- 13. Size-class Models for Even-aged Stands.- 14. Individual-tree Models for Even-aged Stands.- 15. Growth and Yield Models for Uneven-aged Stands.- 16. Modeling Response to Silvicultural Treatments.- 17. Modeling Wood Characteristics.- 18. Model Implementation and Evaluation.-

Modeling Forest Trees and Stands

Johnson's SB distribution is a four-parameter distribution that is transformed into a normal distribution by a logit transformation. By replacing the normal distribution of Johnson's SB with the lo...

Tree diameter distribution modelling: introducing the logitlogistic distribution

Data from a hilly forest study site at Batang Ule, Sumatra, are organ- ized into 30 100-m x 10-m subplots lying perpendicular to the line of maximal topographic gradient, from the valley to the plateau/ridge. The following methodo- logical question is addressed: what species diversity measures are best used in order to reveal the ecologically distinct regions in the site. The main tool used to answer this question is the a-diversity curve (Ha). Graphical examination of tree and species densities, and a-diversity curves identifies an anomalous species divers- ity behaviour of the 'ridge above the slope' subplots which may have implications on land-facet class definitions. Factor analysis of the a-diversity curves indicates that the diversity space is two-dimensional: i.e. two diversity measures are suffi- cient to characterize the site; the species density (Ho), and the Berger-Parker index (H.,). In the two-dimensional diversity-space three distinct species diversity groups are found which relate to the topographic gradient at the Batang Ule site. The results are compared with those for a flat homogeneous site at Pasirmayang, Sumatra. The implications of the results on land-classifications in species-diversity mapping and conservation strategy are discussed.

Species diversity structure analysis at two sites in the tropical rain forest of Sumatra

Forest height growth modelling

The SB distributional model of Johnson's 1949 paper was introduced by a transformation to normality, that is, z ~ N(0, 1), consisting of a linear scaling to the range (0, 1), a logit transformation, and an affine transformation, z = γ + δu. The model, in its original parameterization, has often been used in forest diameter distribution modelling. In this paper, we define the SB distribution in terms of the inverse transformation from normality, including an initial linear scaling transformation, u = γ′ + δ′z (δ′ = 1/δ and γ′ = γ/δ). The SB model in terms of the new parameterization is derived, and maximum likelihood estimation schema are presented for both model parameterizations. The statistical properties of the two alternative parameterizations are compared empirically on 20 data sets of diameter distributions of Changbai larch (Larix olgensis Henry). The new parameterization is shown to be statistically better than Johnson's original parameterization for the data sets considered here.

A new parameterization of Johnson's SB distribution with application to fitting forest tree diameter data

1. The effect of spatial scale on the interactions between three hymenopteran parasitoids and their weevil hosts was investigated. The parasitoid Mesopolobus incultus (Walker) parasitised Gymnetron pascuorum Gyll.; the parasitoids Entodon sparetus (Walker) and Bracon sp. parasitised Mecinus pyraster Herbst. Both of these weevils develop inside the seedhead of Plantago lanceolata L. but occupy different niches. Seedheads were sampled annually from 162 plants at each of two experimental sites consisting of a series of habitat patches of two distinct sizes. Data were analysed from three site-years.


2. Parasitoid densities at each site-year were closely related to the abundance of their respective weevil hosts. The overall proportion of hosts parasitised was more variable for M. incultus than for E. sparetus and Bracon sp.


3. Changes in spatial scale affected the variability of parasitoid densities. For M. incultus, there was generally a greater degree of additional heterogeneity for all increases of scale; for E. sparetus, this was true only at the largest scales; for Bracon sp., all components of variance were negative.


4. The rate of parasitism was related to host density in different ways at different spatial scales. Mesopolobus incultus exhibited inverse density dependence at the finest (seedhead) scale, direct density dependence at the intermediate (plant) scale, and density independence at the large (habitat area 729 m2) scale. Entodon sparetus showed no response to variation in host density at any spatial scale. Bracon sp. showed direct density dependence only at the intermediate and largest scales.


5. Parasitoids E. sparetus and Bracon sp. seemed able to detect more than one M. pyraster individual in seedheads with multiple host occupancy; a greater incidence of conspecific parasitoids than expected emerged from such seedheads.

Keith Rennolls

Papers

Tree diameter distribution modelling: introducing the logitlogistic distribution

Species diversity structure analysis at two sites in the tropical rain forest of Sumatra

Forest height growth modelling

A new parameterization of Johnson's SB distribution with application to fitting forest tree diameter data

The effect of spatial scale on interactions between two weevils and their parasitoid