Summary1. The long-standing view that biomass growth in trees typically follows a rise-and-fall unimodal pattern has been challenged by studies concluding that biomass growth increases with size even among the largest stems in both closed forests and in open competition-free environments. We highlight challenges and pitfalls that influence such interpretations. 2. The ability to observe and calibrate biomass change in large stems requires adequate data regarding these specific stems. 3. Data checking and control procedures can bias estimates of biomass growth and generate false increases with stem size. 4. It is important to distinguish aggregate and individual-level trends: a failure to do so results in flawed interpretations. 5. Our assessment of biomass growth in 706 tropical forest stems indicates that individual biomass growth patterns often plateau for extended periods, with no significant difference in the number of stems indicating positive and negative trends in all but one of the 14 species. Nonetheless, when comparing aggregate growth during the most recent five years, 13 out of our 14 species indicate that biomass growth increases with size even among the largest sizes. Thus, individual and aggregate patterns of biomass growth with size are distinct. 6. Claims concerning general biomass growth patterns for large trees remain unconvincing. We suggest how future studies can improve our knowledge of growth patterns in and among large trees.

The interpretation and communication of fire danger warning levels based on fire weather index values are critical for fire management activities. A number of different indices have been developed for various environmental conditions, and many of them are currently applied in operational warning systems. To select an appropriate combination of such indices to work in different ecoregions in mountainous, hilly and flat terrain is challenging. This study analyses the performance of a total of 22 fire weather indices and two raw meteorological variables to predict wildfire occurrence for different ecological regions of Austria with respect to the different characteristics in climate and fire regimes. A median-based linear model was built based on percentile results on fire days and non-fire days to get quantifiable measures of index performance using slope and intercept of an index on fire days. We highlight the finding that one single index is not optimal for all Austrian regions in both summer and winter fire seasons. The summer season (May–November) shows that the Canadian build-up index, the Keetch Byram Drought Index and the mean daily temperature have the best performance; in the winter season (December–April), the M68dwd is the best performing index. It is shown that the index performance on fire days where larger fires appeared is better and that the uncertainties related to the location of the meteorological station can influence the overall results. A proposal for the selection of the best performing fire weather indices for each Austrian ecoregion is made.

Abstract. Over the past decade, several methods have been used to compare the performance of fire danger indices in an effort to find the most appropriate indices for particular regions or circumstances. Various authors have proposed comparators and demonstrated different responses of indices to their tests, but rarely has much effort been put into demonstrating the validity of the comparators themselves. We present a demonstration that many of the published comparators are sensitive to the different frequency distributions, that may be inherent in the performance of the different indices, and outline a non-parametric method that may be useful for future work. We compare four hypothetical fire danger indices, three of which are simple mathematical transformations of each other. The hypothesis tested is that the comparators often used in such studies may indicate spurious performance differences between these indices, which is found to be the case. Non-parametric methods are robust to differences in index value frequency distribution and may allow more valid comparisons of fire danger indices. The new comparison method is shown to have advantages over other non-parametric comparators.

The aim of this paper is to determine whether a detectable impact of climate change is apparent in Austrian forests. In regions of complex terrain such as most of Austria, climatic trends over the past 50 years show marked geographic variability. As climate is one of the key drivers of forest growth, a comparison of growth characteristics between regions with different trends in temperature and precipitation can give insights into the impact of climatic change on forests. This study uses data from several hundred climate recording stations, interpolated to measurement sites of the Austrian National Forest Inventory (NFI). Austria as a whole shows a warming trend over the past 50 years and little overall change in precipitation. The warming trends, however, vary considerably across certain regions and regional precipitation trends vary widely in both directions, which cancel out on the national scale These differences allow the delineation of 'climatic change zones' with internally consistent climatic trends that differ from other zones. This study applies the species-specific adaptation of the biogeochemical model BIOME-BGC to Norway spruce (Picea abies (L.) Karst) across a range of Austrian climatic change zones, using input data from a number of national databases. The relative influence of extant climate change on forest growth is quantified, and compared with the far greater impact of non-climatic factors. At the national scale, climate change is found to have negligible effect on Norway spruce productivity, due in part to opposing effects at the regional level. The magnitudes of the modeled non-climatic influences on aboveground woody biomass increment increases are consistent with previously reported values of 20-40 kg of added stem carbon sequestration per kilogram of additional nitrogen deposition, while climate responses are of a magnitude difficult to detect in NFI data.