Disentangling legacy effects from environmental filters of postfire assembly of boreal tree assemblages

Abstract

Disturbance plays a key role in driving ecological responses by creating opportunities for new ecological communities to assemble and by directly influencing the outcomes of assembly. Legacy effects (such as seed banks) and environmental filters can both influence community assembly, but their effects are impossible to separate with observational data. Here, we used seeding experiments in sites covering a broad range of postdisturbance conditions to tease apart the effects of seed availability, environmental factors, and disturbance characteristics on early community assembly after fire. We added seed of four common boreal trees to experimental plots in 55 replicate sites in recently burned areas of black spruce forest in northwestern North America. Seed addition treatments increased the probability of occurrence for all species, indicating a widespread potential for seed limitation to affect patterns of recruitment after fire. Small-seeded. species (aspen and birch) were most sensitive to environmental factors such as soil moisture and organic layer depth, suggesting a role for niche-based environmental filtering in community assembly. Fire characteristics related to severity and frequency were also important drivers of seedling regeneration, indicating the potential for disturbance to mediate environmental filters and legacy effects on seed availability. Because effects of seed availability are typically impossible to disentangle from environmental constraints on recruitment in observational studies, legacy effects contingent on vegetation history may be misinterpreted as being driven by strong environmental filters. Results from the seeding experiments suggest that vegetation legacies affecting seed availability play a pivotal role in shaping patterns of community assembly after fire in these low-diversity boreal forests.

Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China

“…Indeed, vegetation legacies after fire affect seed availability and play a pivotal role in shaping patterns of community assembly after fire in these low-diversity boreal forests1213. As Larch forests are becoming increasingly seed-limited in response to intensifying fire regimes, lack of seed may potentially change forests to alternative vegetation-disturbance feedbacks under a future warmer and drier climate (e.g., shrubland or grassland with frequent, high-severity, fires), consistent with conclusions from a field-based study20.…”

“…Observational and experimental studies have found that fire interacts with tree species’ fire-adaptation strategies, strongly affects seed availability and site condition, and thus plays a dominant role in determining tree recruitment in the boreal forest ecosystem1213. In fire “embracer”-dominated forests, such as those dominated by Picea mariana , Pinus contorta , and Pinus banksiana , fires are typically of intermediate frequency and high severity.…”

Anticipating fire‐mediated impacts of climate change using a demographic framework

“…These rates are species‐specific and depend in part on seed size: smaller seeds generally require closer proximity to the mineral soil (Hesketh et al., ) due to lower seed reserves to support initial root development (Lazarus, Castanha, Germino, Kueppers, & Moyes, ). Therefore, where organic layer consumption is incomplete, larger‐seeded conifers generally exhibit higher postfire recruitment than smaller‐seeded broadleaved species (Brown, Liu, Yan, & Johnstone, ; Johnstone & Chapin, ; Johnstone & Kasischke, ). Among conifers, black spruce Pic.…”

Modelling the drivers of natural fire activity: the bias created by cropland fires

“…GLMs have many advantages over standard linear models or regression approaches; in particular, they are more flexible by assuming various families of probability distributions (Guisan et al 2002;O'Hara and Kotze 2010). As the fire activity data had excess zeros, we used a zero-inflated negative binomial generalised linear model (ZINB-GLM) to handle zeros and over-dispersion in the count data (Zeileis et al 2008;Brown et al 2015). For presence and absence data, we used a binomial GLM.…”