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Prescriptions for fuel management are universally applied across the forest types in British Columbia, Canada, to reduce the fire behaviour potential in the wildland–urban interface. Fuel thinning treatments have been advocated as a means of minimizing the likelihood of crown fire development in conifer forests. We hypothesized that these types of prescriptions are inappropriate for the coastal rainforests of the Whistler region of the province. Our study examined the impact of fuel thinning treatments in four stands located in the Whistler community forest. We measured several in-stand microclimatic variables beginning with snow melt in the spring up to the height of fire danger in late summer, at paired thinned and unthinned stand locations. We found that the thinning led to warmer, drier, and windier fire environments. The difference in mean soil moisture, ambient air temperature, and relative humidity between thinned and unthinned stands was significant in the spring with approximate p-values of 0.000217, 9.40 × 10−5, and 4.33 × 10−8, respectively, though there were no discernible differences in the late summer. The difference in mean solar radiation, average wind speed, and average cross wind between thinned and unthinned locations are significant in the spring and late summer (with approximate p-values for spring of 9.54 × 10−7, 0.02101, 1.92 × 10−9, and for late summer of 2.45 × 10−7, 4.08 × 10−6, and 2.45 × 10−5, respectively). [ABSTRACT FROM AUTHOR]

Abstract–Data sharpening in kernel regression has been shown to be an effective method of reducing bias while having minimal effects on variance. Earlier efforts to iterate the data sharpening procedure have been less effective, due to the employment of an inappropriate sharpening transformation. In the present paper, an iterated data sharpening algorithm is proposed which reduces the asymptotic bias at each iteration, while having modest effects on the variance. The efficacy of the iterative approach is demonstrated theoretically and via a simulation study. Boundary effects persist and the affected region successively grows when the iteration is applied to local constant regression. By contrast, boundary bias successively decreases for each iteration step when applied to local linear regression. This study also shows that after iteration, the resulting estimates are less sensitive to bandwidth choice, and a further simulation study demonstrates that iterated data sharpening with data-driven bandwidth selection via cross-validation can lead to more accurate regression function estimation. Examples with real data are used to illustrate the scope of change made possible by using iterated data sharpening and to also identify its limitations. [ABSTRACT FROM AUTHOR]

Local polynomial regression is a convenient method for smoothing scatterplots with readily available software. However, it is well known that variable amounts of bias are induced by the smoothing operation. This article proposes a simple visualization tool based on approximate confidence intervals which can alert the data analyst to regions of the regression function domain which might be susceptible to unacceptably large levels of bias, and possibly indicating a need for a less automatic smoothing approach. A simulation study verifies the accuracy of the confidence bands, and the method is illustrated with several real datasets. [ABSTRACT FROM AUTHOR]

Protecting human life and property from wildfires is a primary concern to wildland fire management agencies. Under certain environmental conditions and wildfire situations, burning embers can be lofted across a natural or man-made barrier devoid of fuel, such as a river or road, resulting in new ignitions downwind of the main advancing fire front. This phenomenon, referred to as a "spot fire" or "spotting," can put a considerable strain on firefighting operations and pose a grave risk to wildland-urban interface communities. In this article, we formulate the process of spot fire development, and derive the distribution of the time to the first spot fire occurring beyond a barrier to fire spread. A simulator is developed in the framework of generating burning embers from an active wildfire that may result in a spot fire. With the generated data, we demonstrate how to estimate the rate of developing spot fires and identify significant covariates based on data in two practical formats. [ABSTRACT FROM AUTHOR]

A method is proposed for shape-constrained density estimation under a variety of constraints, including but not limited to unimodality, monotonicity, symmetry, and constraints on the number of inflection points of the density or its derivative. The method involves computing an adjustment curve that is used to bring a pre-existing pilot estimate into conformance with the specified shape restrictions. The pilot estimate may be obtained using any preferred estimator, and the optimal adjustment can be computed using fast, readily-available quadratic programming routines. This makes the proposed procedure generic and easy to implement. [ABSTRACT FROM AUTHOR]

This article presents an analysis of Ontario Fire Weather Index (FWI) data†The data used is ©1963–2004, Queen’s Printer for Ontario, Canada, and was referenced under agreement with the Ontario Ministry of Natural Resources.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/lssp.using the block bootstrap for time series. Confidence intervals for parameters such as the first lag autocorrelation can have low coverage relative to the nominal level. Therefore, adjustments to the confidence intervals are necessary in order to achieve reasonable accuracy. We introduce a confidence interval calibration method in which the length of the confidence interval is adjusted according to an amount determined from a double bootstrap. We compare this method with the α-level adjustment method, and we find that the length-adjustment method is superior under scenarios similar to that of the FWI data: coverage proportions are slightly higher for the length-adjustment approach, and confidence interval widths are markedly smaller. Applying the length-adjustment method to the Ontario FWI data gives different results than would be obtained without adjustment. [ABSTRACT FROM AUTHOR]

• Memory based foraging patterns vary, giving optimal ecosystem services in different landscapes. • Flower visits differ by a factor of up to 3.5 depending on the location of wild flower enhancements. • Not all locations lead to equal use of wild flower enhancements, creating tradeoffs. • The top two wild flower locations are the centre of the crop field and side farthest from the nest. • Landscape design for enhancements should be influenced by memory-guided foragers' behaviour. Many studies examine how the landscape affects memory-informed movement patterns, but very few examine how memory-informed foragers influence the landscape. This reverse relationship is an important factor in preventing the continued decline of many ecosystem services. We investigate this question in the context of crop pollination services by wild bees, a critical ecosystem service that is in steep decline. Many studies suggest that adding wild flower patches near crops can result in higher crop pollination services, but specific advice pertaining to the optimal location and density of these wild flower patches is lacking, as well as any estimate of the expected change in crop pollination services. In this work, we seek to understand what is the optimal placement of a flower patch relative to a single crop field, during crop bloom and considering spatial factors alone. We develop an individual based model of memory-based foraging by bumble bees to simulate bee movement from a single nest while the crop is in bloom, and measure the resulting crop pollination services. We consider a single crop field enhanced with a wild flower patch in a variable location, and measure crop flower visitation over the course of a single day. We analyze the pollination intensity and spatial distribution of flower visits to determine optimal wild flower patch placement for an isolated crop field. We find that the spatial arrangement of crop and wild flower patch have a significant effect on the number of crop flower visits, and that these effects arise from the memory-informed foraging pattern. The most effective planting locations are either in the centre of the crop field or on the far side of the crop field, away from the single bumble bee nest. [ABSTRACT FROM AUTHOR]