## Glossary

**Adaptability** is the ability to change the structure and/or the functioning of a system according to changes in the environment.

**Allometry** in general is defined as the growth of a part of an organism in relation to the growth of the whole organism or some part of it. Tree allometry narrows the definition to applications involving measurements of the growth or size of trees. The relationship between tree diameter and height is a common allometric relationship.

**Bayesian Network** models use a probabilistic, rather than a deterministic approach to describe the relationships among variables. This approach to characterizing knowledge allows 'driving' variables to be entered as a distribution of likely values (independent probability distributions). Outcomes are likewise expressed as probability distributions.

**Behaviours** in SORTIE-ND are the parameterized scientific models developed from individual field studies. For example, seedling growth as a function of light availability is a relatively simple behaviour, whereas, growth of individual canopy trees as a function of the local competitive neighbourhood is a more complex behaviour.

**Calibration** is the process of adjusting model parameters within defensible ranges until the resulting predictions give the best possible fit to observed data.

**Crown shyness** is the empty space that can be observed between individual crowns of similar-sized trees. Crown shyness is believed to be caused by breakage of branches caused by crown collisions during wind events and is believed to be more evident in boreal and sub-boreal forests where branches are brittle during cold winter conditions.

**Forest disturbance** is an event that causes change in structure and composition of the forest such as fire, flood, wind, or earthquake or mortality caused by insect or disease outbreaks. Forest disturbance can also be human caused such as timber harvesting.

**Empirical** denotes information gained by means of observation, experience, or experiment.

An **empirical model** is one where the structure is determined by the observed relationship among experimental data. These models can be used to develop relationships that are useful for forecasting and describing trends in behavior but they are not necessarily mechanistically relevant.

**Ecological resilience** is the capacity of an ecosystem to tolerate disturbance without collapsing into a qualitatively different state that is controlled by a different state of processes. Ecological resilience emphasizes persitence, adaptiveness, variability, and unpredictability.

**Forest dynamics** describes the underlying physical and biological forces that shape and change a forest. That is, the continuous state of change that alters the composition and structure of a forest. Two basic elements of forest dynamics are: forest succession (or ecological succession) and forest disturbance.

**Forest succession** is the shift in dominance of species on a site over time.

**Inverse modeling** uses the results of actual observations to infer the values of the parameters characterizing the system under investigation. For example, it can be effectively used to estimate the terms of a seed or seedling dispersal function, given the spatial distribution and sizes of potential parent trees around each sample location.

**landscape** is a relatively large area of land consisting of a mosaic of interacting ecosystems, but sharing a relatively uniform climate and disturbance regime.

**LIDAR (Light Detection and Ranging)** is an optical remote sensing technology that measures properties of scattered light to find range and/or other information of a distant target. The prevalent method to determine distance to an object or surface is to use laser pulses.

**Maximum likelihood estimation (MLE)** is a statistical method used for fitting a mathematical model to data. Using information theory it provides a basis for parameterizing and selecting among competing models, or in the simplest case, among competing point estimates of a parameter of a model. In contrast to traditional approaches, in which the statistical models are often constrained by the choice of a particular test statistic, a likelihood framework stresses the specification of both the "scientific" model that embodies the hypotheses and relationships to be tested, and the appropriate "probability" model that characterizes the statistical properties of the data and the error structure.

There are 4 general steps involved in a likelihood analysis:

1. model specification, including both alternate scientific models and appropriate error structures

2. maximum likelihood parameter estimation, using optimization methods

3. model comparison, using information theory, and

4. model evaluation, using a variety of metrics of precision, bias, and goodness of fit.

**A mechanistic model** is a model that has a structure that explicitly represents an understanding of physical, chemical, and/or biological processes. Mechanistic models quantitatively describe the relationship between some phenomenon and underlying first principles of cause. Hence, in theory, they are useful for inferring solutions outside of the domain that the initial data was collected and used to parameterize the mechanisms.

**Mid-term timber supply** in the context of the mountain pine beetle epidemic that has been killing extensive swaths of lodgepole pine in the interior forests of British Columbia since the late 1990s is the projected timber supply in the mid-term (some 15-50 years from the early 2000s). Current projections suggest sharp drops in mid-term supply in all timber supply analysis units affected by the pine beetle. The release and future growth of healthy secondary structure in pine-leading stand types may help mitigate mid-term timber supply short falls. Part of the SORTIE-ND modeling program is to assess and predict growth rates in unsalvaged mountain pine beetle stands with abundant secondary structure.

**Model validation** is the task of demonstrating that the model is a reasonable representation of the actual system: that it reproduces system behavior with enough fidelity to satisfy analysis objectives. A model is considered valid for a set of experimental conditions if its accuracy is within its acceptable range, which is the amount of accuracy required for the model’s intended purpose. Model validation deals with building the right model. Model validation is possibly the most important step in the model building sequence. It is also one of the most overlooked.

**Model verification** checks whether the model implements the assumptions correctly. Verification is like debugging—it is intended to ensure that the model does what it is intended to do. Model verification deals with building the model right. It is important to remember that validation does not imply verification, nor verification imply validation. However, in practice, validation is often blended with verification, especially when measurement data is available for the system being modeled. If a comparison of system measurements and model results suggests that the results produced by the model are close to those obtained from the system, then the implemented model is assumed to be both a verified implementation of the assumptions and a valid representation of the system.

**Neighbourhood dynamics** refers to the interactions among individual trees and their spatially heterogeneous environment are inherently local in nature, acting at a neighbourhood scale over restricted distances (Gratzer et al. 2004; Canham and Uriarte 2006). Neighbourhood dynamics incorporates the mechanistic, spatially explicit interactions between species dynamics and ecosystem processes, including heterogeneity in the physical environment. Fine-scale spatial interactions regulate the demography of component tree species.

**Parameters** are terms in a scientific model that are estimated in the analysis of appropriate field data with, for example, model selection techniques.

**Partial cutting** refers to any harvest of mature trees leaving behind some portion of the overstorey for an extended period. This may include regeneration harvests such single-tree and group selection, shelterwood and irregular "partial retention" cuts, as well as intermediate harvests such as commercial thinning and salvage, improvement, and sanitation cuts.

**Probability Density Function** is the mathematical, graphical, or tabular expression of the relative likelihoods with which an unknown or variable quantity may take various values. The sum (or integral) of all likelihoods equals one for discrete (continous) random variables. These distributions arise from the fundamental properties of the quantities we are attempting to represent. Typical PDFs for continuous data are normal, lognormal and gamma; for discrete data typical PDFs are poisson, binomial, and negative binomial.

A **process-based model** explicitly simulates the numerous biological and ecological processes that operate to link the ecosystem together.

**Reliability** is the confidence that (potential) users have in a model and in the information derived from the model such that they are willing to use the model and the derived information. Specifically, reliability is a function of the performance record of a model and its conformance to best available, practicable science.

**Robustness** is the capacity of a model to perform equally well across the full range of environmental conditions for which it was designed.

**Secondary structure** is a term that was coined as a way to describe the abundance, composition and distribution of trees that will remain alive in stands impacted by the mountain pine beetle epidemic. The epidemic has been killing extensive swaths of lodgepole pine in the interior forests of British Columbia since the late 1990s. Secondary structure can be broken into two main components - understory and overstory trees. Understory trees include seedlings and saplings and can include smaller lodgepole pine trees that survive the epidemic. Overstory trees that survive the MPB epidemic are typically of non-host species (e.g., interior spruce, subalpine fir, Douglas-fir, or broadleaf species). Overstory pine can, and will, survive through the current epidemic, however, numbers will be highly variable and unpredictable.

**Sensitivity Analysis** is the computation of the effect of changes in input values or assumptions on the output values. Uncertainty in model output can be systematically apportioned to different sources of uncertainty in the model input. By investigating the “relative sensitivity” of model parameters, a user can become knowledgeable of the relative importance of parameters in the model.

A **Stand** is an area of forest dominated by a relatively uniform species composition and forest age class or stage of forest development