Zhaofei Fan

Estimating cavity tree abundance by stand age and basal area, Missouri, USA

Abstract We analyzed cavity tree distribution among Missouri Forest Inventory and Analysis (FIA) plots using the nonparametric classification and regression tree (CART) model and Weibull probability density function (pdf). Fifty-nine per cent (2370) of the 4052 Forest Inventory and Analysis plots (aged 1–160 years) had at least one cavity tree. The overall odds ratio of a plot with cavity trees (odds of a plot having cavity trees/odds of a plot with no cavity trees) across the five survey units of the entire state was 1.4. Three and four disjoint clusters (nodes) which differ significantly in cavity tree distribution were identified by CART using the two most discriminating stand level indicator variables: age and basal area, respectively. Cavity tree density distribution within each cluster was further described by the Weibull pdf. Cavity tree density per hectares varied considerably among stands (plots) of the same age or density, and the number of cavities for a given size or age class was distributed in an asymmetric form (primarily reverse-J shape). CART partitioning and Weibull fitting, in combination, provide an intuitive way to depict cavity tree distribution (variation) by important stand indicator variables such as age and basal area. This information can help forest managers and planners formulate management guidelines and results can be linked with forest landscape planning efforts, regional inventories, wildlife habitat modeling, and landscape simulation to evaluate or predict the consequences of different management alternatives.

Chapter 13 Application of landscape and habitat suitability models to conservation: the Hoosier National Forest land-management plan

We demonstrate an approach to integrated land-management planning and quantify differences in vegetation and avian habitat conditions among 5 management alternatives as part of the Hoosier National Forest planning process. The alternatives differed in terms of the type, extent, magnitude, frequency, and location of management activities. We modeled ecological processes of disturbance (e.g. tree harvest, prescribed fire, wildfire, windthrow) and succession using LANDIS, a spatially explicit landscape decision-support model, and applied habitat suitability models for six species of birds to the output from that model. In this way, we linked avian habitat suitability models to spatially explicit vegetation change models that include ecological processes affecting vegetation composition, horizontal and vertical structure, and configuration. The detailed and synthetic nature of our approach provides a framework and structure that (1) is readily conveyed to multiple constituencies, (2) is based on explicitly stated assumptions and relationships, (3) provides a basis for testing, refinement, and extension to other forest commodities and amenities, and (4) provides a way to consider cumulative effects of multiple forest attributes at multiple spatial and temporal scales.

Spatial Trends and Factors Associated with Hardwood Mortality in the Southeastern United States

Hardwood species play an integral role in forested ecosystems in the southeastern United States. This necessitates an assessment of mortality patterns in these species as well as factors associated with them. This study assessed mortality patterns for hardwood species utilizing Forest Inventory Analysis (FIA) data from the United States Forest Service for two consecutive inventory cycles using kernels smoothing and Classification and Regression Tree (CART) modeling. The first inventory cycle (2000–2004) reveals a patterns and associated factors that can be associated with decline events that have recurred throughout the region while the second inventory cycle (2005–2009) exhibits a different pattern in mortality than cycle one. Mortality patterns and their associated factors should be monitored in the hope that methods can be developed to mitigate extreme impacts to these vitally important species.

Spatial variability of heating profiles in windrowed poultry litter

SUMMARY In-house windrow composting of broiler litter has been suggested as a means to reduce microbial populations between flocks. Published time-temperature goals are used to determine the success of the composting process for microbial reductions. Spatial and temporal density of temperature measurement can influence the accuracy in determining what portion of a windrow section has achieved specified time-temperature goals. In this study, windrow section temperature was recorded every 2 min for 7 d on a 10 × 10-cm grid in 183 (width) × 91 cm (height) windrow sections. In 5 windrow sections, ordinary kriging was used to predict the mean portion of the windrow cross-sectional area reaching time-temperature goals of 40°C for 120 h, 50°C for 24 h, and 55°C for 4 h. Based on these results, 88.5 ± 2.0%, 80.8 ± 3.9%, and 38.4 ± 11.7% of the windrow cross-sectional area can be expected to reach published microbial reduction time-temperature goals of 40°C for 120 h, 50°C for 24 h, and 55°C for 4 h, respectively. This study illustrates the need to monitor temperature at multiple locations within windrowed litter to characterize heating profiles. Temporal and spatial sampling densities must be standardized to properly characterize temperature profiles in windrowed broiler litter. Additional research should be conducted to determine the degree of pathogen destruction achieved in the various time-temperature regions of the windrow pile. This study was useful in illustrating the efficacy (proportion of windrow cross-section) of windrow composting as a treatment method for reducing microbial populations as measured by time-temperature goals in used broiler litter.

Analysis of the Effect of Spatial and Temporal Sampling Densities on Accuracy of Predicting the Heating Profile in Windrowed Broiler Litter

A standard method for monitoring temperature in windrow piles of broiler litter to predict microbial population reductions is described. Temperature data collected every 2 min on a 10 cm x 10 cm spatial sampling grid in five identically-constructed litter windrow piles was utilized in this study. A Weibull distribution was fit to mean temperature response (MTR) curves of each pile. Curves were constructed at sample intervals parsed over a range of two to 1000 minutes. No difference in Weibull shape or scale parameters was observed among the analyzed sample intervals. A difference (P<0.05) in mean standard error of Weibull distribution fit parameters was identified between the 200- and 400-min sample intervals. Further analysis between the 200- and 400-minute sample intervals did not reveal a more appropriate value for optimal temporal sampling frequency. Optimal spatial sampling density was characterized using ordinary kriging analysis. Ordinary kriging was used to predict the cross-sectional areas of piles reaching specified time-temperature goals. Eight spatial sampling grid configurations were analyzed. Mean (n=5) predicted cross-sectional area (CSA) reaching 40°C for 120 h differed significantly (P<0.05) between the 30 cm x 20 cm and 30 cm x 30 cm grid spacing configurations. Accuracy of predicted pile CSA decreased as spatial sampling density decreased. This data will be beneficial when designing future windrow composting temperature monitoring studies.