Does time since fire drive live aboveground biomass and stand structure in low fire activity boreal forests? Impacts on their management.

Boreal forests subject to low fire activity are complex ecosystems in terms of structure and dynamics. They have a high ecological value as they contain important proportions of old forests that play a crucial role in preserving biodiversity and ecological functions. They also sequester important amounts of carbon at the landscape level. However, the role of time since fire in controlling the different processes and attributes of those forests is still poorly understood. The Romaine River area experiences a fire regime characterized by very rare but large fires and has recently been opened to economic development for energy and timber production. In this study, we aimed to characterize this region in terms of live aboveground biomass, merchantable volume, stand structure and composition, and to establish relations between these attributes and the time since the last fire. Mean live aboveground biomass and merchantable volume showed values similar to those of commercial boreal coniferous forests. They were both found to increase up to around 150 years after a fire before declining. However, no significant relation was found between time since fire and stand structure and composition. Instead, they seemed to mostly depend on stand productivity and non-fire disturbances. At the landscape level, this region contains large amounts of biomass and carbon stored resulting from the long fire cycles it experiences. Although in terms of merchantable volume these forests seemed profitable for the forest industry, a large proportion were old forests or presented structures of old forests. Therefore, if forest management was to be undertaken in this region, particular attention should be given to these old forests in order to protect biodiversity and ecological functions. Partial cutting with variable levels of retention would be an appropriate management strategy as it reproduces the structural complexity of old forests.

Plot size matters: Toward comparable species richness estimates across plot-based inventories.

To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot-based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample-based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway-Maxell-Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country-specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan-European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot-based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems.

Accounting for spatial autocorrelation improves the estimation of climate, physical environment and vegetation's effects on boreal forest's burn rates.

CONTEXT: Wildfires play a crucial role in maintaining ecological and societal functions of North American boreal forests. Because of their contagious way of spreading, using statistical methods dealing with spatial autocorrelation has become a major challenge in fire studies analyzing how environmental factors affect their spatial variability. OBJECTIVES: We aimed to demonstrate the performance of a spatially explicit method accounting for spatial autocorrelation in burn rates modelling, and to use this method to determine the relative contribution of climate, physical environment and vegetation to the spatial variability of burn rates between 1972 and 2015. METHODS: Using a 482,000 km2 territory located in the coniferous boreal forest of eastern Canada, we built and compared burn rates models with and without accounting for spatial autocorrelation. The relative contribution of climate, physical environment and vegetation to the burn rates variability was identified with variance partitioning. RESULTS: Accounting for spatial autocorrelation improved the models' performance by a factor of 1.5. Our method allowed the unadulterated extraction of the contribution of climate, physical environment and vegetation to the spatial variability of burn rates. This contribution was similar for the three groups of factors. The spatial autocorrelation extent was linked to the fire size distribution. CONCLUSIONS: Accounting for spatial autocorrelation can highly improve models and avoids biased results and misinterpretation. Considering climate, physical environment and vegetation altogether is essential, especially when attempting to predict future area burned. In addition to the direct effect of climate, changes in vegetation could have important impacts on future burn rates.

Elevation-dependent responses of tree mast seeding to climate change over 45 years.

We use seed count data from a New Zealand mono-specific mountain beech forest to test for decadal trends in seed production along an elevation gradient in relation to changes in climate. Seedfall was collected (1965 to 2009) from seed trays located on transect lines at fixed elevations along an elevation gradient (1020 to 1370 m). We counted the number of seeds in the catch of each tray, for each year, and determined the number of viable seeds. Climate variables were obtained from a nearby (<2 km) climate station (914-m elevation). Variables were the sum or mean of daily measurements, using periods within each year known to correlate with subsequent interannual variation in seed production. To determine trends in mean seed production, at each elevation, and climate variables, we used generalized least squares (GLS) regression. We demonstrate a trend of increasing total and viable seed production, particularly at higher elevations, which emerged from marked interannual variation. Significant changes in four seasonal climate variables had GLS regression coefficients consistent with predictions of increased seed production. These variables subsumed the effect of year in GLS regressions with a greater influence on seed production with increasing elevation. Regression models enforce a view that the sequence of climate variables was additive in their influence on seed production throughout a reproductive cycle spanning more than 2 years and including three summers. Models with the most support always included summer precipitation as the earliest variable in the sequence followed by summer maximum daily temperatures. We interpret this as reflecting precipitation driven increases in soil nutrient availability enhancing seed production at higher elevations rather than the direct effects of climate, stand development or rising atmospheric CO2 partial pressures. Greater sensitivity of tree seeding at higher elevations to changes in climate reveals how ecosystem responses to climate change will be spatially variable.