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

Tree crown ratio models for multi-species and multi-layered stands of southeastern British Columbia

The ratio of live crown length to tree height (crown ratio; CR) is often used as an important predictor variable for tree level growth equations, particularly for multi-species and multi-layered stands. Also, CR indicates tree vigour and can be an important habitat variable. Measurement of CR for each tree can be time-consuming and difficult to obtain in very dense stands and for very tall trees where the base of live crown is obscured. Models to predict CR from size, competition and site variables were developed for several coniferous and one hardwood tree species growing in multi-species and multi-layered forest stands (complex stands) of southeastern British Columbia. Simple correlations indicated the expected relationships of CR decreasing with increasing height, and with increasing competition. A logistic model form was used to constrain predicted CR values to the interval [0,1]. Also, predictors were divided into tree size, stand competition, and site measures, and the contribution of each set of contributors was examined. For all models, height was an important predictor. The stand competition measure, basal area of larger trees, contributed significantly to predicting CR given that crown competition factor was also included as a measure of competition. Logical trends in CR versus size and competition variable groups were reflected by the models; site variable slightly improved predictions for some species. Much of the variability in CR was not accounted for, indicating that other variables are important for explaining CR changes in these complex stands.Keywords: basal area of larger trees, multi-species stands, crown ratio, multi-layered stand

Regeneration imputation models for complex stands of southeastern British Columbia

Two imputation techniques for predicting natural regeneration in complex stands prevalent in southeastern British Columbia (BC) were compared using data from the Interior Cedar-Hemlock moist warm subzone variant 2 (ICHmw2) in the vicinity of Nelson, BC. Imputation approaches offer advantages over other modeling approaches in that they provide estimates of many variables at one time (multivariate) and there are no assumptions regarding the probability distributions of the variables to be predicted. For the tabular imputation, the average regeneration per ha was calculated for each combination of five site groups, two residual density classes, five time-since-disturbance intervals, species, and height classes. For Most Similar Neighbour (MSN) imputation, data with both regeneration information, and overstory trees and site information (called reference plots) were used to impute regeneration of plots with only overstory trees and site information (called target plots), by selecting the most similar plot. Of the two approaches studied, the MSN approach gave better results than tabular imputation. The tabular imputation approach is simpler to implement, since tables of results can be published and made available for use. However, the MSN software has been made freely available, resulting in greater ease of access.Keywords: multi-cohort, nonparametric imputation, multivariate prediction, multi-species, regeneration estimationKeywords: multi-cohort, nonparametric imputation, multivariate prediction, multi-species, regeneration estimatio

Hiring, Training, and Supporting Peer Research Associates: Operationalizing Community-Based Research Principles within Epidemiological Studies By, With, and For Women Living With HIV

Background A community-based research (CBR) approach is critical to redressing the exclusion of women—particularly, traditionally marginalized women including those who use substances—from HIV research participation and benefit. However, few studies have articulated their process of involving and engaging peers, particularly within large-scale cohort studies of women living with HIV where gender, cultural and linguistic diversity, HIV stigma, substance use experience, and power inequities must be navigated. Methods Through our work on the Canadian HIV Women’s Sexual and Reproductive Health Cohort Study (CHIWOS), Canada’s largest community-collaborative longitudinal cohort of women living with HIV (n = 1422), we developed a comprehensive, regionally tailored approach for hiring, training, and supporting women living with HIV as Peer Research Associates (PRAs). To reflect the diversity of women with HIV in Canada, we initially hired 37 PRAs from British Columbia, Ontario, and Quebec, prioritizing women historically under-represented in research, including women who use or have used illicit drugs, and women living with HIV of other social identities including Indigenous, racialized, LGBTQ2S, and sex work communities, noting important points of intersection between these groups. Results Building on PRAs’ lived experience, research capacity was supported through a comprehensive, multi-phase, and evidence-based experiential training curriculum, with mentorship and support opportunities provided at various stages of the study. Challenges included the following: being responsive to PRAs’ diversity; ensuring PRAs’ health, well-being, safety, and confidentiality; supporting PRAs to navigate shifting roles in their community; and ensuring sufficient time and resources for the translation of materials between English and French. Opportunities included the following: mutual capacity building of PRAs and researchers; community-informed approaches to study the processes and challenges; enhanced recruitment of harder-to-reach populations; and stronger community partnerships facilitating advocacy and action on findings. Conclusions Community-collaborative studies are key to increasing the relevance and impact potential of research. For women living with HIV to participate in and benefit from HIV research, studies must foster inclusive, flexible, safe, and reciprocal approaches to PRA engagement, employment, and training tailored to regional contexts and women’s lives. Recommendations for best practice are offered

The EPA's human exposure research program for assessing cumulative risk in communities

Communities are faced with challenges in identifying and prioritizing environmental issues, taking actions to reduce their exposures, and determining their effectiveness for reducing human health risks. Additional challenges include determining what scientific tools are available and most relevant, and understanding how to use those tools; given these barriers, community groups tend to rely more on risk perception than science. The U.S. Environmental Protection Agency's Office of Research and Development, National Exposure Research Laboratory (NERL) and collaborators are developing and applying tools (models, data, methods) for enhancing cumulative risk assessments. The NERL's “Cumulative Communities Research Program” focuses on key science questions: (1) How to systematically identify and prioritize key chemical stressors within a given community?; (2) How to develop estimates of exposure to multiple stressors for individuals in epidemiologic studies?; and (3) What tools can be used to assess community-level distributions of exposures for the development and evaluation of the effectiveness of risk reduction strategies? This paper provides community partners and scientific researchers with an understanding of the NERL research program and other efforts to address cumulative community risks; and key research needs and opportunities. Some initial findings include the following: (1) Many useful tools exist for components of risk assessment, but need to be developed collaboratively with end users and made more comprehensive and user-friendly for practical application; (2) Tools for quantifying cumulative risks and impact of community risk reduction activities are also needed; (3) More data are needed to assess community- and individual-level exposures, and to link exposure-related information with health effects; and (4) Additional research is needed to incorporate risk-modifying factors (“non-chemical stressors”) into cumulative risk assessments. The products of this research program will advance the science for cumulative risk assessments and empower communities with information so that they can make informed, cost-effective decisions to improve public health

The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases

Almost universally, forest inventory and monitoring databases are incomplete, ranging from missing data for only a few records and a few variables, common for small land areas, to missing data for many observations and many variables, common for large land areas. For a wide variety of applications, nearest neighbor (NN) imputation methods have been developed to fill in observations of variables that are missing on some records (Y-variables), using related variables that are available for all records (X-variables). This review attempts to summarize the advantages and weaknesses of NN imputation methods and to give an overview of the NN approaches that have most commonly been used. It also discusses some of the challenges of NN imputation methods. The inclusion of NN imputation methods into standard software packages and the use of consistent notation may improve further development of NN imputation methods. Using X-variables from different data sources provides promising results, but raises the issue of spatial and temporal registration errors. Quantitative measures of the contribution of individual X-variables to the accuracy of imputing the Y-variables are needed. In addition, further research is warranted to verify statistical properties, modify methods to improve statistical properties, and provide variance estimators.Keywords: registration error, forest measurements, consistent notation, input data for forest planning, nearest neighbor imputation, sources of X-variablesKeywords: registration error, forest measurements, consistent notation, input data for forest planning, nearest neighbor imputation, sources of X-variable

Year-round foraging across large spatial scales suggest that bowhead whales have the potential to adapt to climate change

The ecological impact of environmental changes at high latitudes (e.g., increasing temperature, and decreased sea ice cover) on low-trophic species, such as bowhead whales, are poorly understood. Key to understanding the vulnerability of zooplanktivorous predators to climatic shifts in prey is knowing whether they can make behavioural or distributional adjustments to maintain sufficient prey acquisition rates. However, little is known about how foraging behaviour and associated environmental conditions fluctuate over space and time. We collected long-term movement (average satellite transmission days were 397 (± 204 SD) in 2012 and 484 (± 245 SD) in 2013) and dive behaviour data for 25 bowhead whales (Balaena mysticetus) equipped with time-depth telemetry tags, and used hierarchical switching-state-space models to quantify their movements and behaviours (resident and transit). We examined trends in inferred two-dimensional foraging behaviours based on dive shape of Eastern Canada-West Greenland bowhead whales in relation to season and sea ice, as well as animal sex and age via size. We found no differences with regards to whale sex and size, but we did find evidence that subsurface foraging occurs year-round, with peak foraging occurring in fall (7.3 hrs d-1 ± 5.70 SD; October) and reduced feeding during spring (2.7 hrs d-1 ± 2.55 SD; May). Although sea ice cover is lowest during summer foraging, whales selected areas with 65% (± 36.1 SD) sea ice cover. During winter, bowheads occurred in areas with 90% (± 15.5 SD) ice cover, providing some open water for breathing. The depth of probable foraging varied across seasons with animals conducting epipelagic foraging dives (< 200 m) during spring and summer, and deeper mesopelagic dives (> 400 m) during fall and winter that approached the sea bottom, following the seasonal vertical migration of lipid-rich zooplankton. Our findings suggest that, compared to related species (e.g., right whales), bowheads forage at relatively low rates and over a large geographic area throughout the year. This suggests that bowhead whales have the potential to adjust their behaviours (e.g., increased time allocated to feeding) and shift their distributions (e.g., occupy higher latitude foraging grounds) to adapt to climate-change induced environmental conditions. However, the extent to which energetic consumption may vary seasonally is yet to be determined

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