Abstract. Human land use practices, altered climates, and shifting forest and fire management policies have increased the frequency of large wildfires several-fold. Mitigation of potential fire behaviour and fire severity have increasingly been attempted through pre-fire alteration of wildland fuels using mechanical treatments and prescribed fires. Despite annual treatment of more than a million hectares of land, quantitative assessments of the effectiveness of existing fuel treatments at reducing the size of actual wildfires or how they might alter the risk of burning across landscapes are currently lacking. Here, we present a method for estimating spatial probabilities of burning as a function of extant fuels treatments for any wildland fire-affected landscape. We examined the landscape effects of more than 72 000 ha of wildland fuel treatments involved in 14 large wildfires that burned 314 000 ha of forests in nine US states between 2002 and 2010. Fuels treatments altered the probability of fire occurrence both positively and negatively across landscapes, effectively redistributing fire risk by changing surface fire spread rates and reducing the likelihood of crowning behaviour. Trade offs are created between formation of large areas with low probabilities of increased burning and smaller, well-defined regions with reduced fire risk.

Abstract. Understanding the influences of forest management practices on wildfire severity is critical in fire-prone ecosystems of the western United States. Newly available geospatial data sets characterizing vegetation, fuels, topography, and burn severity offer new opportunities for studying fuel treatment effectiveness at regional to national scales. In this study, we used ordinary least-squares (OLS) regression and sequential autoregression (SAR) to analyze fuel treatment effects on burn severity for three recent wildfires: the Camp 32 fire in western Montana, the School fire in southeastern Washington, and the Warm fire in northern Arizona. Burn severity was measured using differenced normalized burn ratio (dNBR) maps developed by the Monitoring Trends in Burn Severity project. Geospatial data sets from the LANDFIRE project were used to control for prefire variability in canopy cover, fuels, and topography. Across all three fires, treatments that incorporated prescribed burning were more effective than thinning alone. Treatment effect sizes were lower, and standard errors were higher in the SAR models than in the OLS models. Spatial error terms in the SAR models indirectly controlled for confounding variables not captured in the LANDFIRE data, including spatiotemporal variability in fire weather and landscape-level effects of reduced fire severity outside the treated areas. This research demonstrates the feasibility of carrying out assessments of fuel treatment effectiveness using geospatial data sets and highlights the potential for using spatial autoregression to control for unmeasured confounding factors.

The growth data of a commercial aquaculture recirculation system were analysed to investigate the growth performance of reared turbot (Psetta maxima). Three common growth models (von Bertalanffy, Gompertz and Schnute) were fitted to the growth data documented over a time period of 6 years. To determine the most suitable model, three different criteria were used: (1) the Akaike index criterion, (2) the sum of squared residuals and (3) the average daily deviation between the estimated final weight and the observed final weight. The evaluation of the growth models showed that the Schnute model had the lowest Akaike index, the lowest sum of squared residuals and the lowest daily deviation between estimated and real weight of all tested growth models. The Schnute model produced sigmoid growth curves. The estimated growth coefficients were the most realistic ones in regard to biological interpretation. In contrast, the von Bertalanffy growth model and the Gompertz model estimated inaccurate exponential growth curves and are therefore unable to simulate the growth data as well as the Schnute model. The results indicate that the von Bertalanffy growth model is not the optimal model to simulate the present growth data and that the growth potential of reared turbot has probably not yet been fully exploited in the aquaculture system(s) examined (so far).

Aim To compare the geographical distributions of two tick-borne pathogens vectored by different tick species, to examine the relative importance of climate, land cover and host density in structuring these distributions, and to assess the spatial variability of these environmental constraints across the species ranges.Location South-central and south-eastern North America.Methods Presence/absence data for two tick-borne pathogens, Ehrlichia chaffeensis and Anaplasma phagocytophilum , were obtained for 567 counties from a regional data base based on white-tailed deer ( Odocoileus virginianus ) serology. Environmental variables describing climate, land cover and deer density were calculated for these counties. Global logistic regression analysis was used to screen the environmental variables and select a parsimonious subset of predictors. Local analysis was carried out using geographically weighted regression (GWR) to explore spatial variability in the parameters of the regression models. Cluster analysis was applied to the GWR output to identify zones with distinctive species-habitat relationships.Results Global habitat models for E. chaffeensis and A. phagocytophilum included temperature, humidity, precipitation and forest cover as explanatory variables. The E. chaffeensis model also included forest fragmentation, whereas the A. phagocytophilum model included deer density. Local analyses revealed that climate was the primary correlate of pathogen presence in the eastern portion of the study area, whereas forest cover and fragmentation constrained the western range boundaries. Habitat relationships for all variables were weak in and around the Mississippi Delta.Main conclusions Efforts to model pathogen and disease ranges, and to predict shifts in response to global change should consider future scenarios of land-cover change as well as climate change, and should address the possibility of spatial heterogeneity in species-habitat relationships. The methods presented here outline an approach for objectively delineating geographical zones with similar speciesenvironment relationships, which can then be used to stratify landscapes for the purposes of further explanatory and predictive modelling.

Background: Disease maps are used increasingly in the health sciences, with applications ranging from the diagnosis of individual cases to regional and global assessments of public health. However, data on the distributions of emerging infectious diseases are often available from only a limited number of samples. We compared several spatial modelling approaches for predicting the geographic distributions of two tick-borne pathogens: Ehrlichia chaffeensis, the causative agent of human monocytotropic ehrlichiosis, and Anaplasma phagocytophilum, the causative agent of human granulocytotropic anaplasmosis. These approaches extended environmental modelling based on logistic regression by incorporating both spatial autocorrelation (the tendency for pathogen distributions to be clustered in space) and spatial heterogeneity (the potential for environmental relationships to vary spatially).