Connecting Inventory Information Sources for Landscape Level Analyses

In forest landscape level analyses, forest information is commonly represented by separate polygons, defined by differences in species composition, stand structure, crown closure, and productivity. The simplest approach to projecting yield of stands over the land base is to create an aggregated yield table, weighted by area of each stand type (groups of polygons with similar attributes) as a means of projecting future volume per ha and other attributes. At the other end of complexity, each polygon is projected forward, using a particular management pathway where a record of each tree (and other elements) is maintained. Polygons may also be subdivided and/or recombined based on changes over time, and on features identified on other data sources (e.g., soils maps). As information needs increase, the trend has been toward the more complex approach to landscape level analysis. However, data are commonly limited, in terms of attributes, space, time, and management pathways represented. As a result, most resource managers rely on the very simple projection of forests in time, using an aggregated yield table. Others try to represent this spatial complexity via spatial mapping using polygons defined on aerial photography or other remotely sensed media. Gains have been made in presenting the spatial maps in Geographic Information Systems, and in producing models for a variety of attributes and management pathways, often by producing hybrid models. However, improved linkages between models, ground data, and spatial maps are needed, as are statements of model accuracy at larger spatial and temporal scales. For Canada, the spatial and temporal scales are particularly of interest, since the forested area is very large, and tree species have long life spans. This study discusses and compares commonly used methods to link data sources, using a small land area of about 5,000 ha located in British Columbia, Canada.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the FBMIS Group and can be found at: http://cms1.gre.ac.uk/conferences/iufro/fbmis/FBMISCov.htm.Keywords: projection of forest land, landscape level analysis, linkages across scale

Human footprint and protected areas shape elephant range across Africa

Over the last two millennia, and at an accelerating pace, the African elephant (Loxodonta spp. Lin.) has been threatened by human activities across its range. We investigate the correlates of elephant home range sizes across diverse biomes. Annual and 16-day elliptical time density home ranges were calculated by using GPS tracking data collected from 229 African savannah and forest elephants (L. africana and L. cyclotis, respectively) between 1998 and 2013 at 19 sites representing bushveld, savannah, Sahel, and forest biomes. Our analysis considered the relationship between home range area and sex, species, vegetation productivity, tree cover, surface temperature, rainfall, water, slope, aggregate human influence, and protected area use. Irrespective of these environmental conditions, long-term annual ranges were overwhelmingly affected by human influence and protected area use. Only over shorter, 16-day periods did environmental factors, particularly water availability and vegetation productivity, become important in explaining space use. Our work highlights the degree to which the human footprint and existing protected areas now constrain the distribution of the world’s largest terrestrial mammal. A habitat suitability model, created by evaluating every square kilometer of Africa, predicts that 18,169,219 km2 would be suitable as elephant habitat—62% of the continent. The current elephant distribution covers just 17% of this potential range of which 57.4% falls outside protected areas. To stem the continued extirpation and to secure the elephants’ future, effective and expanded protected areas and improved capacity for coexistence across unprotected range are essential

Comparison of fitting techniques for systems of forestry equations

In order to describe forestry problems, a system of equations is commonly used. The chosen system may be simultaneous, in that a variable which appears on the left hand side of an equation also appears on the right hand side of another equation in the system. Also, the error terms among equations of the system may be contemporaneously correlated, and error terms within individual equations may be non-iid in that they may be dependent (serially correlated) or not identically distributed (heteroskedastic) or both. Ideally, the fitting technique used to fit systems of equations should be simple; estimates of coefficients and their associated variances should be unbiased, or at least consistent, and efficient: small and large sample properties of the estimates should be known; and logical compatibility should be present in the fitted system. The first objective of this research was to find a fitting technique from the literature which meets the desired criteria for simultaneous, contemporaneously correlated systems of equations, in which the error terms for individual equations are non-iid. This objective was not met in that no technique was found in the literature which satisfies the desired criteria for a system of equations with this error structure. However, information from the literature was used to derive a new fitting technique as part of this research project, and labelled multistage least squares (MSLS). The MSLS technique is an extension of three stage least squares from econometrics research, and can be used to find consistent and asymptotically efficient estimates of coefficients, and confidence limits can also be calculated for large sample sizes. For small sample sizes, an iterative routine labelled iterated multistage least squares (IMSLS) was derived. The second objective was to compare this technique to the commonly used techniques of using ordinary least squares (simple or multiple linear regression and nonlinear least squares regresion), and of substituting all of the equations into a composite model and using ordinary least squares to fit the composite model. The three techniques were applied to three forestry problems for which a system of equations is used. The criteria for comparing the results included comparing goodness-of-fit measures (Fit Index, Mean Absolute Deviation, Mean Deviation), comparing the traces of the estimated coefficient co variance matrices, and calculating a summed rank, based on the presence or absence of desired properties of the estimates. The comparison indicated that OLS results in the best goodness-of-fit measures for all three forestry- problems; however, estimates of coefficients are biased and inconsistent for simultaneous systems. Also, the estimated coefficient covariance matrix cannot be used to calculate confidence intervals for the true parameters, or to test hypothesis statements. Finally, compatibility among equations is not assured. The fit of the composite model was attractive for the systems tested; however, only one left hand side variable was estimated, and, for larger systems with more variables and more equations, this technique may not be appropriate. The MSLS technique resulted in goodness-of-fit measures which were close to the OLS goodness-of-fit measures. Of most importance, however, is that the MSLS fit ensures compatibility among equations, estimates of coefficients and their variances are consistent, estimates are asymptotically efficient, and confidence limits can be calculated for large sample sizes using the estimated variances and probabilities from the normal distribution. Also, the number and difficulty of steps required for the MSLS technique were similar to the OLS fit of individual equations. The main disadvantage to using the MSLS technique is that a large amount of computer memory is required; for some forestry problems with very large sample sizes, the use of a subsample or the exclusion of the final step of the MSLS fit were suggested. This would result in some loss of efficiency, but estimated coefficients and their variances would be consistent.Forestry, Faculty ofGraduat