Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/756163

Long-term effects of fire on Arctic tundra vegetation in Western Siberia

Heim, R. J.·Bucharova, A.·Rieker, D.·Yurtaev, A.·Kamp, J.·Hoelzel, N.
View original source ↗Export metadata ↓

Abstract

Wildfires are relatively rare in subarctic tundra ecosystems, but they can strongly change ecosystem properties. Short-term fire effects on subarctic tundra vegetation are well documented, but long-term vegetation recovery has been studied less. The frequency of tundra fires will increase with climate warming. Understanding the long-term effects of fire is necessary to predict future ecosystem changes. We used a space-for-time approach to assess vegetation recovery after fire over more than four decades. We studied soil and vegetation patterns on three large fire scars (>44, 28 and 12 years old) in dry, lichen-dominated forest tundra in Western Siberia. On 60 plots, we determined soil temperature and permafrost thaw depth, sampled vegetation and measured plant functional traits. We assessed trends in NDVI to support the field-based results on vegetation recovery. Soil temperature, permafrost thaw depth and total vegetation cover had recovered to pre-fire levels after >44 years, as well as total vegetation cover. In contrast, after >44 years, functional groups had not recovered to the pre-fire state. Burnt areas had lower lichen and higher bryophyte and shrub cover. The dominating shrub species, Betula nana, exhibited a higher vitality (higher specific leaf area and plant height) on burnt compared with control plots, suggesting a fire legacy effect in shrub growth. Our results confirm patterns of shrub encroachment after fire that were detected before in other parts of the Arctic and Subarctic. In the so far poorly studied Western Siberian forest tundra we demonstrate for the first time, long-term fire-legacies on the functional composition of relatively dry shrub- and lichen-dominated vegetation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/756163v3_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@3ffa0borg.highwire.dtl.DTLVardef@39198borg.highwire.dtl.DTLVardef@bbf8e4org.highwire.dtl.DTLVardef@d1704c_HPS_FORMAT_FIGEXP M_FIG C_FIG

Source connections

Explore related subjects

ecologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Heim, R. J., Bucharova, A., Rieker, D., Yurtaev, A., Kamp, J., Hoelzel, N.. 2019-09-05. Long-term effects of fire on Arctic tundra vegetation in Western Siberia. https://doi.org/10.1101/756163

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Invasion analysis for structured populations: analytical principles and numerical workflows

All real populations show some form of structure--differences among individuals, such as life stage or physiological condition, that shape a species' ability to persist. Yet it remains challenging to incorporate population structure into models of species interactions. We provide a general framework for studying competition in structured populations by quantifying species' invasion growth rates. The crux of our approach is separation of timescales: the initial dynamics of a structured population depend on how exactly it is introduced, but these transient differences do not affect long-term invasion success. Depending on the research question, the relevant long-term growth rate when rare may equivalently be derived using analytical separation of timescales, matrix population theory, or numerical simulations. Using case studies spanning developmental stage, environmental state, and infection status, we illustrate how our methodology derives simple, ecologically meaningful invasion conditions for a variety of systems. Finally, we extend the approach to fluctuating resident dynamics, showing how to use simulations or a general mathematical framework known as Floquet theory to correctly quantify invasion growth. By unifying the quantitative methods of population biology and community ecology, our framework will allow ecologists to study species persistence and coexistence across a greater variety of demographically complex systems.

ecology↗
bioRxiv2026

Interspecific social contacts spread gut microbiota in wild rodent communities

Background: The mammalian gut microbiota influences a wide array of host phenotypes and varies substantially among individuals. Among conspecifics, microbiota composition is strongly shaped by microbial transmission through social interactions, yet the extent to which such transmission also occurs across species boundaries remains poorly understood. Here, we combine high-resolution tracking data collected over more than 10 months with gut microbiota profiling to examine social associations, space use, and microbiota similarity among individuals of three sympatric rodent species - wood mice (Apodemus sylvaticus), yellow-necked mice (A. flavicollis) and bank voles (Myodes glareolus), in two woodlands. By integrating multi-species social networks and spatial overlap with individual microbiota data, we test how both conspecific and interspecific interactions shape gut microbiota composition. Results: At both study sites, social associations strongly predicted microbiota similarity among conspecific individuals, whereas spatial proximity consistently predicted microbiota similarity among interspecific pairs. In addition, social associations between mice of different species predicted microbiota similarity, although this effect was weaker than that observed within species and was asymmetric, with evidence for stronger microbial transmission from wood mice to yellow-necked mice than vice versa. By focusing on species-indicative bacterial taxa, we further show that wood mice with more social connections to yellow-necked mice harboured a greater richness of gut bacteria typical of yellow-necked mice. Conclusions: Together, these results demonstrate that individual-level social behaviour can influence gut microbial transmission across species boundaries, particularly among closely related species with overlapping ecological niches. Interspecific social interactions may therefore represent an underappreciated pathway for the acquisition of novel gut symbionts in multi-species communities.

ecology↗
bioRxiv2026

Microscale nonrandomness revisited: An overlooked predictor of ecological change

Abrupt ecological transitions have driven the development of early warning signal metrics that track how ecological systems respond as external conditions gradually change and systems approach transitions between states. However, their generality and applicability remain uncertain, particularly for short-term ecological dynamics that can occur without directional external change. To address this gap, we propose a general framework based on the hypothesis that microscale nonrandomness, quantified from frequency distributions or spatial patterns, predicts subsequent short-term rates of change in populations and interactions, as well as current macroscale ecosystem states. We applied the framework to four datasets: time series from an incubation experiment of phytoplankton-parasitic chytrid interactions; spatiotemporal field observations of leaf beetles on willow trees and aphids in a crop system; and spatial snapshot data linking soil surface characteristics with aboveground and soil ecosystem properties in a forest vegetations-soil system. We quantified microscale nonrandomness using Morisita I_{delta} from frequency distributions and Moran's I and SADIE from spatial patterns. Across all systems, models incorporating microscale nonrandomness metrics consistently showed greater cross-validated predictive performance than those based on macroscale variables or spatial averages alone. Although their relative performance varied among systems, frequency-based metrics performed as well as or better than spatially explicit metrics in some cases. The consistent improvement across contrasting systems supports the broad applicability of this framework, including under limited data conditions. This framework broadens ecological forecasting to short-term, continuous dynamics beyond those associated with directional environmental change.

ecology↗