Search for: All records

Abstract QuestionsAnthropogenic climate change is causing increases in the severity of wildland fire in many parts of the world. At the same time, post‐fire succession is occurring under new, warmer temperatures that are projected to continue increasing. Despite this, the combined effects of uncharacteristically high burn severity and increased ambient temperature on post‐fire community composition remain poorly understood. We ask how post‐fire forest understorey community composition and species richness are influenced by (1) burn severity, (2) experimental warming, and (3) years since fire. LocationMuseum Fire Scar,Pinus ponderosaforest, Arizona, United States. MethodsWe established 120 1‐m²quadrats in unburned, low‐ and high‐severity locations nine months after a mixed‐severity fire. Half of the plots were subject to experimental warming via open‐top warming chambers that elevated daytime temperatures by 1.079°C, simulating near‐term anthropogenic warming. Plant composition data were collected annually for three years. Relationships between community composition, burn severity, and experimental warming were analyzed using repeated‐measures PERMANOVA and linear mixed‐effects models. ResultsComposition differed significantly according to burn severity, time since fire, and their interaction, while experimental warming did not significantly influence composition. Species richness significantly increased in burned areas compared to unburned control within two years of fire. ConclusionsOur results suggest that near‐term temperature increases, driven by anthropogenic climate change, will have little effect on community composition relative to fire severity. High‐severity fire drove large, rapid changes in plant composition compared to unburned controls, favoring exotic annuals in a historically perennial‐dominated plant community. 

A recentRestoration Ecologyarticle by Merchant et al. (2022) suggested that practitioners do not regularly use functional traits in restoration planning. We disagree and provide our collective experience that practitioners do leverage trait‐based approaches and information, but in ways that are different from researchers. Here, we provide an expanded perspective that incorporates practitioner voices to provide a more complete assessment of how traits are used in restoration practice. We highlight that a major challenge in the field of restoration ecology that leads to a disconnect between researchers and practitioners is a different set of knowledge systems, goals, incentives, and limitations. We provide approaches that researchers can use to connect with practitioners and leverage their knowledge. 

Abstract Human activities are altering ecological communities around the globe. Understanding the implications of these changes requires that we consider the composition of those communities. However, composition can be summarized by many metrics which in turn are influenced by different ecological processes. For example, incidence‐based metrics strongly reflect species gains or losses, while abundance‐based metrics are minimally affected by changes in the abundance of small or uncommon species. Furthermore, metrics might be correlated with different predictors. We used a globally distributed experiment to examine variation in species composition within 60 grasslands on six continents. Each site had an identical experimental and sampling design: 24 plots × 4 years. We expressed compositional variation within each site—not across sites—using abundance‐ and incidence‐based metrics of the magnitude of dissimilarity (Bray–Curtis and Sorensen, respectively), abundance‐ and incidence‐based measures of the relative importance of replacement (balanced variation and species turnover, respectively), and species richness at two scales (per plot‐year [alpha] and per site [gamma]). Average compositional variation among all plot‐years at a site was high and similar to spatial variation among plots in the pretreatment year, but lower among years in untreated plots. For both types of metrics, most variation was due to replacement rather than nestedness. Differences among sites in overall within‐site compositional variation were related to several predictors. Environmental heterogeneity (expressed as the CV of total aboveground plant biomass in unfertilized plots of the site) was an important predictor for most metrics. Biomass production was a predictor of species turnover and of alpha diversity but not of other metrics. Continentality (measured as annual temperature range) was a strong predictor of Sorensen dissimilarity. Metrics of compositional variation are moderately correlated: knowing the magnitude of dissimilarity at a site provides little insight into whether the variation is driven by replacement processes. Overall, our understanding of compositional variation at a site is enhanced by considering multiple metrics simultaneously. Monitoring programs that explicitly incorporate these implications, both when designing sampling strategies and analyzing data, will have a stronger ability to understand the compositional variation of systems and to quantify the impacts of human activities.