Spatial point pattern analysis provides a statistical method to compare an observed spatial pattern against a hypothesized spatial process model. The G statistic, which considers the distribution of nearest neighbor distances, and the K statistic, which evaluates the distribution of all neighbor distances, are commonly used in such analyses. One method of employing these statistics involves building a simulation envelope from the result of many simulated patterns of the hypothesized model. Specifically, a simulation envelope is created by calculating, at every distance, the minimum and maximum results computed across the simulated patterns. A statistical test is performed by evaluating where the results from an observed pattern fall with respect to the simulation envelope. However, this method, which differs from P. Diggle's suggested approach, is invalid for inference because it violates the assumptions of Monte Carlo methods and results in incorrect type I error rate performance. Similarly, using the simulation envelope to estimate the range of distances over which an observed pattern deviates from the hypothesized model is also suspect. The technical details of why the simulation envelope provides incorrect type I error rate performance are described. A valid test is then proposed, and details about how the number of simulated patterns impacts the statistical significance are explained. Finally, an example of using the proposed test within an exploratory data analysis framework is provided.

No abstract

No abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. SUMMARY(1) The partitioning of incident precipitation into throughfall, stemflow and interception loss was assessed in a 14-year-old Picea sitchensis plantation in South Scotland. The tree crowns overlapped to form a dense canopy with 25, 44, 28 and 3% of ground area covered respectively by foliage from one, two, three and four trees. All main branches sloped down inwards to the tree trunk.(2) Over a single calendar year the incident precipitation was 1639 mm. Stemflow accounted for 27% of this, throughfall 43% and interception loss, estimated by difference, accounted for 30%. Percentage stemflow was consistent throughout the year but, during the winter period (January-March), throughfall increased to 57% and interception loss decreased to 15%.(3) The amount of stemflow for individual trees was positively correlated (P=0 05) with the projected area of tree crowns.(4) The amount of throughfall was greatest close to the tree trunks and between trees in the same row, particularly for intermediate levels of rainfall (20-40 mm week -).(5) The spatial pattern of throughfall parallels the distribution of fine roots in the soil.

The model of competition for light presented here uses modular autonomy to incorporate plasticity in plant growth under competition. Once plants are characterized as composed of modules, then model structure for competition changes in a fundamental way. Interactions between the plant module and its local resource environment must be modeled rather than the traditionally viewed interactions between whole plants and their neighbors. We assume that a plant module interacts with its local resource environment regardless of whether this environment was altered by a neighbor or by the same plant. Two spatial processes are considered: resource acquisition and growth. The spatial pattern of resource acquisition by a module determines a growth and allocation pattern, e.g., the elongation of branches into a gap. The spatial structure of a module and its connection to the whole tree then determines the pattern of resource distribution and resource acquisition of the next time step.Plasticity of plant growth is incorporated by variation in both the efficiency of resource capture of modules and patterns of resource allocation for individuals of different canopy positions and results in individuals in the community having different spatial structures. The model simulates the three—dimensional development of tree crown structure over time. It is applied to the 30—yr development of a dense, spatially aggregated stand of Abies amabilis beginning with an initial pattern of seedlings. The importance of incorporation of plasticity is apparent when the model output is compared to observed height distribution and crown structure data. Simulations indicate that asymmetrical crown development, one form of plasticity, is advantageous to stand productivity and becomes more advantageous as the degree of spatial aggregation in the initial spacing of trees increases.

No abstract

Summary 1.Effective decision-making in environmental management requires the consideration of multiple objectives that may conflict. Common optimization methods use weights on the multiple objectives to aggregate them into a single value, neglecting valuable insight into the relationships among the objectives in the management problem. 2. We present a multi-objective optimization procedure that approximates the non-dominated Pareto frontier without the use of weightings, allowing for visualization of the trade-offs among objectives. The non-dominated Pareto frontier is approximated by the simultaneous optimization of a vector objective function; two vector objective functions are defined as non-dominated if improvement with respect to one objective is at the detriment of another objective. 3. We demonstrate the method with a case study for the optimum distribution of forest fuels treatments that reduce the impact of fire on a forest. The multiple objectives are to protect habitat of an endangered species, protect late successional forest reserves and minimize the total area treated. In the comparison of three optimization searches, the number of non-dominated solutions increases with the dimensions of the objective space, but with only two objectives the search is ineffective in minimizing fire impact in the different landscape types. Key challenges include the extensive computation time required to approximate the non-dominated set, and reducing the number of solutions that are analysed in detail. 4. Synthesis and applications . The multi-objective optimization program presented can be adapted to other environmental management problems, and easily incorporates a wide range of quantifiable objectives. This tool provides decision-makers with a set of alternatives that estimates the full range of trade-offs among multiple objectives and provides a common ground from which dialogue can come to an informed compromise and decision in environmental management problems.

No abstract