Comparison of spatially explicit forest landscape fire disturbance models

Comparisons of model behaviors are an efficient way of understanding the differences amongst various models and thus providing guidance to model users for selecting suitable models for their own purposes. This study focuses on a comparison of the most commonly used fire spread algorithms used in scenario fire regime models. This paper provides an overview of fire regime modeling and describes a simulation model, Ecological Disturbance Model, as a simulation shell for such a comparison using the Fort A La Corne forest area in central Saskatchewan, Canada, as the study area. Simulation results suggested that for a fire scenario modeling approach, various fire spread algorithms such as DISPATCH, percolation, and cellular automata (CA) may not result in significant differences between user-defined and simulated fire frequencies; however, significant differences in simulated forest dynamics could result when using different fire spread algorithms. The simulation results from DISPATCH and CA are more similar than those from the percolation algorithm; however, the latter appeared to be a better representative of observed fire spread processes due to its underlying assumption of fire spread mechanisms. Simulation results also suggested that the fire spread algorithms that replicate four or eight direction fire spread in percolation and CA will not make a significant difference in simulated fire regimes or forest dynamics. It is thus recommend that using simulation shells as a tool to take alternative assumptions or models into account to narrow down the uncertainty parameters and avoid the paradoxes in the modeling of natural resource management.     

Handling missingness when modeling the force of infection from clustered seroprevalence data

Modeling infectious diseases data is a relatively young research area in which clustering and stratification are key features. It is not unlikely for these data to have missing values. If values are missing completely at random, the analysis on the complete cases is valid. However, in practice this assumption is usually not fulfilled. This article shows the effect of ignoring missing data in modeling the force of infection of the bovine herpesvirus-1 in Belgian cattle and proposes the use of weighted generalized estimating equations with constrained fractional polynomials as a flexible modeling tool.     

Organic‐carbon fractions in an agricultural topsoil assessed by the determination of the soil mineral surface area

According to recent conceptual models, the organic carbon (OC) of soils can be divided into OC fractions of increasing stability from labile free OC to resistant OC associated with the soil mineral phase. In this study, we present a method for quantifying two OC fractions based on soil aggregate–size fractionation and the N₂ gas–adsorption method. For this purpose, we analyzed soil material of the plow layer of a Haplic Chernozem subjected to different fertilizer treatments (no fertilizer, mineral fertilizer, mineral and organic fertilizer). The total organic‐C concentration (TOC) and the clay content of the different size fractions were determined as well as the specific surface area (SSA_mineral) and the sample pore volume after thermal oxidation (OC‐free). The TOC of the different soil‐aggregate fractions was linearly related to SSA_mineral. Clay‐associated OC and nonassociated OC fractions of the different soil samples were quantified using two methods based on the OC surface loading at the clay fraction. The application of organic fertilizer increased the amount of nonassociated OC but hardly affected the concentration of clay‐associated OC. This finding agrees with previous studies on C dynamics in soils and indicates a finite capacity of soil materials to sequester OC. Even without any addition of organic fertilizer, the mineral phase of the analyzed soil material appears to be C‐saturated.     

Nitrogen fertilizer–induced mineralization of soil organic C and N in six contrasting soils of Bangladesh

A 90‐day laboratory incubation study was carried out using six contrasting subtropical soils (calcareous, peat, saline, noncalcareous, terrace, and acid sulfate) from Bangladesh. A control treatment without nitrogen (N) application was compared with treatments where urea, ammonium sulfate (AS), and ammonium nitrate (AN) were applied at a rate of 100 mg N (kg soil)^–1. To study the effect of N fertilizers on soil carbon (C) turnover, the CO₂‐C flux was determined at nine sampling dates during the incubation, and the total loss of soil carbon (TC) was calculated. Nitrogen turnover was characterized by measuring net nitrogen mineralization (NNM) and net nitrification (NN). Simple and stepwise multiple regressions were calculated between CO₂‐C flux, TC, NNM, and NN on the one hand and selected soil properties (organic C, total N, C : N ratio, CEC, pH, clay and sand content) on the other hand. In general, CO₂‐C fluxes were clearly higher during the first 2 weeks of the incubation compared to the later phases. Soils with high pH and/or indigenous C displayed the highest CO₂‐C flux. However, soils having low C levels (i.e., calcareous and terrace soils) displayed a large relative TC loss (up to 22.3%) and the added N–induced TC loss from these soils reached a maximum of 10.6%. Loss of TC differed depending on the N treatments (urea > AS > AN >> control). Significantly higher NNM was found in the acidic soils (terrace and acid sulfate). On average, NNM after urea application was higher than for AS and AN (80.3 vs. 71.9 and 70.9 N (kg soil)^–1, respectively). However, specific interactions between N‐fertilizer form and soil type have to be taken into consideration. High pH soils displayed larger NN (75.9–98.1 mg N (kg soil)^–1) than low pH soils. Averaged over the six soils, NN after application of urea and AS (83.3 and 82.2 mg N (kg soil)^–1, respectively) was significantly higher than after application of AN (60.6 mg N (kg soil)^–1). Significant relationships were found between total CO₂ flux and certain soil properties (organic C, total N, CEC, clay and sand content). The most important soil property for NNM as well as NN was soil pH, showing a correlation coefficient of –0.33** and 0.45***, respectively. The results indicate that application of urea to acidic soils and AS to high‐pH soils could be an effective measure to improve the availability of added N for crop uptake.