A Systematic Approach for Variable Selection With Random Forests: Achieving Stable Variable Importance Values

Random Forests variable importance measures are often used to rank variables by their relevance to a classification problem and subsequently reduce the number of model inputs in high-dimensional data sets, thus increasing computational efficiency. However, as a result of the way that training data and predictor variables are randomly selected for use in constructing each tree and splitting each node, it is also well known that if too few trees are generated, variable importance rankings tend to differ between model runs. In this letter, we characterize the effect of the number of trees (ntree) and class separability on the stability of variable importance rankings and develop a systematic approach to define the number of model runs and/or trees required to achieve stability in variable importance measures. Results demonstrate that both a large ntree for a single model run, or averaged values across multiple model runs with fewer trees, are sufficient for achieving stable mean importance values. While the latter is far more computationally efficient, both the methods tend to lead to the same ranking of variables. Moreover, the optimal number of model runs differs depending on the separability of classes. Recommendations are made to users regarding how to determine the number of model runs and/or trees that are required to achieve stable variable importance rankings

Development of a forest structural complexity index based on multispectral airborne remote sensing and topographic data

This paper presents development of a multivariate forest structural complexity index based on relationships between field-based structural variables and geospatial data. Remote sensing has been widely used to model individual forest structural attributes at many scales. As opposed to, or in addition to, individual structural parameters such as leaf area index or tree height, overall structural complexity information can enhance forest inventories and provide a variety of information to forest managers, including identifying damage and disturbance as well as indicators of habitat or biodiversity. In this study, a multivariate modelling technique, redundancy analysis, was implemented to derive a model incorporating both horizontal and vertical structural attributes as predicted by an ensemble of high-resolution multispectral airborne imagery and topographic variables. The first redundancy analysis axis of the final model explained 35% of the total variance of the field variables and was used as the complexity index. With a root mean squared error of 19.9%, the model was capable of differentiating four to five relative levels of complexity. This paper presents the forest ecological and modelling aspects of the research. A related paper presents the remote sensing aspects, including application of the model to map predicted structural complexity, map validation, and testing of the method at multiple scales

Terrestrial ecosystem monitoring in Canada and the greater role for integrated earth observation

Ecosystems are valuable as well as aesthetic. The natural functions of ecosystems can have profound effects on the economy, and human and wildlife health. The aggregate value of these âœecosystem servicesâ? may far exceed the economic value derived from resource extraction or industrial development, especially when considering the costs of restoring ecosystems. There is increasing interest, therefore, in monitoring and protecting ecosystems, and accounting for the biodiversity and services they provide. In 2010, Canada undertook a review of ecosystem status and trends that identified the regions and ecosystems where management is most urgently needed. The authors concluded that more large-scale, long-term, standardized, and spatially complete information is needed for effective monitoring and management. Satellite-based earth observation (EO) tools were seen as a means of addressing this information need. In a separate exercise, a list of priority questions for conservation policy and management at a national level was produced: the resolution of three-quarters of those questions appears to depend on EO tools to a significant or critical extent. Canada has a long and successful history in all aspects of earth observation, placing it amongst the leaders in the international remote sensing community. Whereas the need for measuring ecosystem services to humans and wildlife is increasingly important, the challenges for doing so are increasingly significant and the technology required is increasingly complex. Overcoming these challenges is necessary to address emerging conservation priorities including measurement of ecosystem attributes to support habitat conservation for Species at Risk, measuring functional capacity of ecosystems to mitigate effects of climate change, monitoring and mitigating effects of resource extraction, and supporting industrial development in Canadaâ™s north. Addressing emerging priorities requires dialogue among ecologists and decision makers, coordinated at regional and national scales, and requires drawing on the best EO technologies and infrastructure available. This review highlights the urgency of a coordinated approach for innovative applications of EO tools toward conservation and discusses some of the key elements that might be included and opportunities and challenges that might be encountered, by such an approach

The homogenizing influence of agriculture on forest bird communities at landscape scales

Context: Agricultural expansion is a principal driver of biodiversity loss, but the impacts on community assembly in agro-ecosystems are less clear, especially across regional scales at which agricultural policies are applied. Objectives: Using forest-breeding birds, we (1) tested whether increased agricultural coverage resulted in species communities that were random or more similar than expected, (2) compared the relative influence of agriculture versus distance in structuring communities, and (3) tested whether different responses to agriculture among functional guilds leads to a change in functional diversity across gradients of agriculture. Methods: Species abundances were sampled along 229 transects, each 8 km, using citizen science data assembled across a broad region of eastern Canada. Agricultural and natural land cover were each summed within three different-sized buffers (landscapes) around each transect. A null modeling approach was used to measure community similarity. Results: Communities were most similar between landscapes that had high agricultural coverage and became more dissimilar as their respective landscapes differed more strongly in the amount of agriculture. Community dissimilarity increased with geographic distance up to about 200 km. Dissimilarity with increasing agriculture was largely due to the disappearance of Neotropical migrants, insectivores and foliage-gleaners from the community as agriculture increas

Optimizing landscape selection for estimating relative effects of landscape variables on ecological responses

Empirical studies of the relative effects of landscape variables may compromise inferential strength with common approaches to landscape selection. We propose a methodology for landscape sample selection that is designed to overcome some common statistical pitfalls that may hamper estimates of relative effects of landscape variables on ecological responses. We illustrate our proposed methodology through an application aimed at quantifying the relationships between farmland heterogeneity and biodiversity. For this project, we required 100 study landscapes that represented the widest possible ranges of compositional and configurational farmland heterogeneity, where these two aspects of heterogeneity were quantified as crop cover diversity (Shannon diversity index) and mean crop field size, respectively. These were calculated at multiple spatial extents from a detailed map of the region derived through satellite image segmentation and classification. Potential study landscapes were then selected in a structured approach such that: (1) they represented the widest possible range of both heterogeneity variables, (2) they were not spatially autocorrelated, and (3) there was independence (no correlation) between the two heterogeneity variables, allowing for more precise estimates of the regression coefficients that reflect their independent effects. All selection criteria were satisfied at multiple extents surrounding the study landscapes, to allow for multi-scale analysis. Our approach to landscape selection should improve the inferential strength of studies estimating the relative effects of landscape variables, particularly those with a view to developing land management guidelines

A national assessment of urban forest carbon storage and sequestration in Canada

Abstract During a time of rapid urban growth and development, it is becoming ever more important to monitor the carbon fluxes of our cities. Unlike Canada’s commercially managed forests that have a long history of inventory and modelling tools, there is both a lack of coordinated data and considerable uncertainty on assessment procedures for urban forest carbon. Nonetheless, independent studies have been carried out across Canada. To improve upon Canada’s federal government reporting on carbon storage and sequestration by urban forests, this study builds on existing data to develop an updated assessment of carbon storage and sequestration for Canada’s urban forests. Using canopy cover estimates derived from ortho-imagery and satellite imagery ranging from 2008 to 2012 and field-based urban forest inventory and assessment data from 16 Canadian cities and one US city, this study found that Canadian urban forests store approximately 27,297.8 kt C (− 37%, + 45%) in above and belowground biomass and sequester approximately 1497.7 kt C year−1 (− 26%, + 28%). In comparison with the previous national assessment of urban forest carbon, this study suggested that in urban areas carbon storage has been overestimated and carbon sequestration has been underestimated. Maximizing urban forest carbon sinks will contribute to Canada’s mitigation efforts and, while being a smaller carbon sink compared to commercial forests, will also provide important ecosystem services and co-benefits to approximately 83% of Canadian people

Influence of crop type, heterogeneity and woody structure on avian biodiversity in agricultural landscapes

Agriculture is a primary factor underlying world-wide declines in biodiversity. However, different agricultural systems vary in their effects depending on their resemblance to the natural ecosystem, coverage across the landscape, and operational intensity. We combined data from the North American Breeding Bird Survey with remotely sensed measures of crop type and linear woody feature (LWF) density to study how agricultural type, woody structure and crop heterogeneity influenced the avian community at landscape scales across a broad agricultural region of eastern Canada. Specifically, we examined whether 1) avian diversity and abundance differed between arable crop agriculture (e.g., corn, soy) and forage (e.g., hay) and pastoral agriculture, 2) whether increasing the density of LWF enhances avian diversity and abundance, and 3) whether increasing the heterogeneity of arable crop types can reduce negative effects of arable crop amount. Avian diversity was lower in landscapes dominated by arable crop compared to forage agriculture likely due to a stronger negative correlation between arable cropping and the amount of natural la

Nesting cormorants and temporal changes in Island habitat

Double-crested cormorant (Phalacrocorax auritus) populations have increased greatly across North America. The interior North America subpopulation is the largest with many birds nesting on the Laurentian Great Lakes. Lake Erie supports a large number of breeding pairs tha

Farmlands with smaller crop fields have higher within-field biodiversity

Simple rules for landscape management seem elusive because different species and species groups are associated with different land cover types; a change in landscape structure that increases diversity of one group may reduce diversity of another. On the other hand, if simple landscape-biodiversity relationships do exist despite this complexity, they would have great practical benefit to conservation management. With these considerations in mind, we tested for consistent relationships between landscape heterogeneity and biodiversity in farmland (the cropped areas in agricultural landscapes), with a view to developing simple rules for landscape management that could increase biodiversity within farmland. Our measures of farmland heterogeneity were crop diversity and mean crop field size, where increases in crop diversity and/or decreases in mean field size represent increasing landscape heterogeneity. We sampled the abundance, and alpha, gamma and beta diversity of birds, plants, butterflies, syrphids, bees, carabids and spiders, in crop fields within each of 93 1 km × 1 km agricultural landscapes. The landscapes were selected to represent three gradients in landscape composition and heterogeneity: proportion of the landscape in crop, mean crop field size and Shannon crop type diversity of the farmland. We found that mean crop field size had the strongest overall effect on biodiversity measures in crop fields, and this effect was consistently negative. Based on our results we suggest that, if biodiversity conservation in crop fields is a priority, policies and guidelines aimed at reducing crop field sizes should be considered