Factors Driving Vegetation Trajectories of Post-Wildfire Landscapes in the Interior of British Columbia, Canada

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Oeggerli, Tara G. Martin, Suzanne W. Simard, Jennifer Grenz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6957462/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2026 Read the published version in Fire Ecology → Version 1 posted 13 You are reading this latest preprint version Abstract Background: Globally, shifting fire regimes which have increased in frequency and severity-- driven by climate change, land use changes, and fire suppression-- are altering vegetation dynamics. Little is known about the impacts of this new era of wildfire on seedbank dynamics, and native and non-native plants. Given constrained resources and the expanding extent of wildfire-affected areas, there is a pressing need to understand the factors influencing post-fire vegetation dynamics data to prioritize and guide landscape-level recovery interventions. The 46,000 ha McKay Creek Wildfire in the interior of British Columbia, Canada, provided a unique opportunity to assess how factors such as topography, burn severity, and pre-fire invasive plant presence drive post-wildfire vegetation trajectories due to its diverse ecosystems, representation of all burn severities, and extensive baseline data on the presence of invasive species. We hypothesized that both prior presence of non-native plants and high burn severity would result in increased presence of non-native plant populations on post-wildfire landscapes. Results: Foliar cover was recorded by species and grouped by native status (native or non-native), life cycle (annual, biennial, perennial), and life form (e.g., forb, grass, shrub) on 80 plots stratified by burn severity and pre-fire presence of invasive plants. Our findings showed that bare ground accounted for the greatest proportion of cover across all plots, remaining near or above 50% across all stratifications. Native cover ranged from just over 25% to 41%, varying with burn severity and prior invasive plant presence. Non-native cover remained below 5% across all conditions. Topography, particularly elevation and aspect, was the strongest driver of post-fire vegetation patterns with the proportion of native plant cover highest at higher elevations and on west-facing slopes. Plant lifecycle was an influential factor on non-native plants, with annuals most prevalent in high severity burns and at lower elevations, and perennials most abundant at higher elevations. Burn severity had limited influence on total non-native cover. Conclusions: At a time when wildfire is increasing in size, frequency and intensity, and resources are limited for recovery efforts, our study may contribute critical insights for land managers to prioritize and plan post-fire restoration activities such as monitoring, prevention and management of invasive species, and interventions such as planting. Wildfire invasive plants native plants vegetation trajectories ecological restoration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Fire regimes around the globe are shifting in frequency and severity, driven by the cumulative impacts of climate change, land use changes, and fire suppression. As a result, vegetation dynamics are changing, yet little is known about the impacts of this new era of wildfire on vegetation communities (Marlon et al. 2012, Balch et al. 2013, Flannigan et al. 2013, Parks et al. 2015, Abatzoglou and Williams 2016, Bowman et al. 2020, Archibald et al. 2018, Sayedi et al. 2024). While global patterns are important to understanding and anticipating changing fire regimes, they may not fully account for regionally specific processes. Local factors, such as historical ecology (Grenz and Armstrong 2023, Turner 1999), historic land management practices and policies (Dickson-Hoyle and John 2021, Lake et al. 2017), and plant community composition, may be more specific drivers of fire regime change and critical to the development of effective, place-based fire and restoration strategies (Sayedi et al. 2024). The interior of British Columbia, Canada, is characterized by dry forests, grasslands, and mountainous terrain. Over the past century, fire regimes in this region have changed dramatically, departing from the historical patterns that shaped these landscapes over millennia (Dickson-Hoyle and John 2021), a trend consistent with the ten-fold acceleration of fire regime change globally over the past two centuries (Sayedi et al. 2024). Local factors influencing fire regimes within British Columbia’s interior include a combined legacy of industrial logging practices, cattle grazing, increased temperatures and periods of drought, and the implementation of widespread fire exclusion policies across fire-dependent landscapes (Hoffman et al. 2022). Fire exclusion, introduced during colonization approximately 150 years ago, interrupted centuries of Indigenous fire stewardship and marked a major shift in land management policy (Lewis et al. 2018, Hagmann et al. 2013, Harvey et al. 2017). This suppression of fire has contributed to fuel load accumulation at the surface, ladder, and canopy levels due to increased tree density, homogenization of plant community composition, and a shift toward fire intolerant species (Hagmann et al. 2021). Western Canada is thus enduring a new era of wildfire, where catastrophic megafires, those characterized by extensive size, intense fire behavior, and a high proportion of high-severity or stand-replacing burns, are no longer outliers, but the current and likely future reality (Bedia et al. 2015, Coogan et al. 2019). For example, the 2023 fire season in British Columbia, Canada, saw 2.84 million ha of land burned, doubling the 2018 record fire season of 1.35 million ha (Government of British Columbia, 2024b). As we look toward the future, the realization that these megafires are going to continue to occur (Halofsky et al. 2020) necessitates a careful examination of their impacts to inform post-wildfire restoration and land management policies and approaches to reclaim fire resilient landscapes. Understanding vegetation trajectories of post-wildfire landscapes remains limited, particularly in regions where fire regimes are rapidly changing due to climate and land use pressures (Johnstone et al. 2016). Most studies have focused on short-term vegetation responses or broad-scale fire patterns, leaving a knowledge gap in how plant communities recover or fail to recover over time following high-severity fire (Johnstone et al. 2016, Coop et al. 2020). Wildfire can cause substantial changes to plant communities by altering species composition, reducing vegetation cover, and disrupting regeneration processes (Fornwalt and Kaufmann 2014). High-severity fires may result in the loss of mature vegetation and soil seed banks, while also modifying soil structure, moisture availability, and nutrient dynamics, all of which influence patterns of post-fire recovery (Neary et al. 1999, Certini 2005, Harvey et al. 2016). In the dry forests and grasslands of British Columbia’s Interior, these altered post-fire conditions are especially concerning given the long-standing presence of invasive plant species in many areas (Leung, 2002). Species such as Centaurea stoebe (spotted knapweed) and Bromus tectorum (cheatgrass) have been introduced and established over past decades, particularly along disturbed corridors, rangelands, and roadsides (Invasive Species Council of BC 2024a, 2024b, Leung 2002). These invaders are well positioned to capitalize on fire-created disturbances. Invasive plants, generally defined as a non-native species to a particular ecosystem that is causing some degree of harm to humans and/or the environment (Richardson and Pysek 2004, NISC 2005), employ highly competitive reproduction, dispersal, persistence, and evolution strategies that make them more adaptable to climatic changes than native forest species (Dukes et al. 2009, Birthisel et al. 2021, Shephard et al. 2022, Jones and Grenz 2023). This, combined with shifting fire regimes, may create opportunities for new and existing invasive plant species to establish and dominate habitats where they previously could not (Zouhar et al. 2008, Alba et al 2015). Increased potential for plant invasion and resulting changes to plant community composition and structure could introduce fire-invasion feedback loops that alter fire behavior in ways that further promote their spread and persistence (Brooks et al. 2004, Balch et al. 2013, Grenz and Clements 2023) and drive long-term ecological shifts (Chambers et al. 2014). Fire-invasion feedback loops have transformed entire ecosystems globally, such as the sagebrush steppe in the western United States, where non-native annual grasses such as Bromus tectorum (cheatgrass), a fine, flammable fuel, have replaced fire-resistant native bunchgrasses, leading to increased fire frequency and loss of native biodiversity (Balch et al. 2013, Brooks et al. 2004, D’Antonio and Vitousek 1992). Globally, similar patterns are seen in northern Australia’s tropical savannas, where non-native perennial grasses such as Andropogon gayanus (gamba grass), a tall, high-biomass species, have replaced native vegetation, resulting in increased fire intensity and frequency, and widespread degradation of native ecosystems (Setterfield et al. 2010). The impacts of wildfire on plant invasion, understanding whether post-fire invasions are a widespread phenomenon or are habitat specific, and the sources of variation of native and invasive plant responses to fire have not been rigorously tested (Alba et al. 2015). Evaluation of vegetation trajectories, both native and non-native plants, is needed to understand invasion dynamics on post-wildfire landscapes as the establishment of invasive plants does not occur in isolation. Research suggests that non-native invasive species fundamentally differ in their response to wildfire compared to native plants (Alba et al. 2015), and thus we need to examine contributing factors that may account for these differences such as wildfire severity. This is integrally important to understand as current fire regimes are resulting in higher proportions of burned areas being categorized as severe, whereas historically fires were more frequent at low or moderate intensities (Hagmann et al. 2021). For example, between 1985 and 2017, there was an eightfold increase in high severity burns annually in western US forests (Parks and Abatzoglou 2020). An additional potential contributing factor to post-wildfire vegetation trajectories could be the presence of non-native invasive plants prior to wildfire as invasion potential may be increased due to altered plant community composition prior to wildfire and remnant seedbanks. These relationships have been observed in various contexts, including Yellowstone National Park (Turner et al. 2016), Montana's Flathead National Forest (Rew & Johnson 2010), and Arizona ponderosa pine forests (Wolfson et al. 2005), highlighting the complexity and variability of post-fire outcomes. It remains unclear whether the same ecological and disturbance-related factors consistently drive post-wildfire vegetation recovery across different fire events and landscapes (Johnstone et al. 2016). This lack of generalizable understanding limits our ability to anticipate vegetation trajectories, especially under increasingly severe and frequent fire regimes. In this study, we investigated possible factors influencing post-wildfire vegetation trajectories two years after the McKay Creek Wildfire, a 46,000ha wildfire that occurred in June of 2021, 11km north of Lillooet, BC, Canada. This fire spanned four distinct Biogeoclimatic Ecosystem Classification (BEC) zones (British Columbia Ministry of Forests n.d., MacKenzie and Meidinger 2018), diverse topographical features (aspect, slope, elevation) and successional stages, had comprehensive pre-fire baseline data on invasive plant presence, and representation of three wildfire burn severities (low, moderate, and high). The confluence of these factors provided a unique opportunity to assess drivers influencing post-fire vegetation trajectories of both native and non-native species. We hypothesized that both pre-wildfire presence of non-native invasive plants and high burn severity would result in increased presence of non-native plant populations on post-wildfire landscapes. The overall goal of our study was to identify factors driving post-wildfire vegetation trajectories which could contribute to data-driven post-wildfire ecological restoration interventions, including the prevention and management of non-native plants and revegetation of native plants in the current mega-wildfire reality. Methods Study Area In June of 2021, immediately after a historic heat wave broke records across the Pacific Northwest of North America, creating a “heat dome” and causing extreme fire conditions (Still et al. 2023), the McKay Creek Fire ignited. Located approximately 11km north of Lillooet, British Columbia (Figure 1), it burned over 46,000 ha. The fire occurred within the traditional territory of the St’át’imc Nation, specifically the traditional territories of six Northern St’át’imc communities, Sekw'el'was, T'ít'q'et, Tsal'alh, Ts'kw'aylaxw, Xaxli'p, and Xwísten. The study area encompassed the entirety of the McKay Creek wildfire, including major drainages of the Fraser River, Bridge River, and Seton Lake. This region is characterized by a warm, dry climate, average daytime high and low seasonal temperatures of 25.9°C and 12.6°C in the summer, and 4°C and -3.6°C in winter, approximately 31 days per year of temperatures exceeding 30°C, and annual precipitation of approximately 349mm (Environment and Climate Change Canada 2024). It has steep, rugged terrain with narrow valleys and high ridges. The highest elevation of the McKay Creek wildfire was at 1800 meters, and this dramatic change in elevation accounts for the region’s highly variable microclimates and diverse ecosystems. The fire spanned four Biogeoclimatic Ecosystem Classification (BEC) zones: Bunchgrass, Interior Douglas-fir, Montane Spruce, and Ponderosa Pine, ranging from low-elevation grasslands to high-elevation subalpine forests (Meidinger and Pojar 1991). The disturbance history of the study area is shaped by long-standing land-use activities, particularly forestry and cattle grazing. Local accounts indicate that grazing pressure has been consistent since at least the 1970s, following a seasonal pattern in which livestock move from low to high elevations (J. Rasmussen, Lillooet Regional Invasive Species Society, personal communication). Grazing in this region is managed under provincial grazing tenures that specify livestock numbers, timing, and duration of use (Government of British Columbia 2024a). Logging activity peaked in the 1980s, resulting in extensive clear-cutting within and around the study area. At the time, cut block burning was commonly used until shifting fire suppression policies in the early 1990s began to phase out this practice (J. Rasmussen, Lillooet Regional Invasive Species Society, personal communication). Experimental design Within the McKay creek wildfire area, 80 vegetation monitoring plots of 9m 2 size were established using a combination of preferential and stratified random sampling methods (Michalcová et al. 2011). Plots were stratified based on two key ecological factors: burn severity (low, moderate, high) and prior invasive species presence (yes/no). Burn severity was classified based on tree biomass mortality as follows: low (70%) (Hagmann et al. 2021). Using comprehensive mapping of priority non-native invasive plant locations that had taken place several years prior to the wildfire by the Lillooet Regional Invasive Species Society (LRISS), plots were located and categorized based on "prior presence". Using the three burn severity classes (low, medium, high) and presence or absence of invasive species, the plots were stratified into 6 unique treatments. In each of these treatments, 13 plots that were 3m x 3m plots in size established, except for the treatment group “low yes”, which included 15 plots. Plot locations were randomly located for each treatment combination using ArcGIS. Preferential sampling was incorporated due to the large size (46,000 ha) of the McKay creek wildfire, extreme nature (steep, loose slopes and cliffs) of its terrain, and therefore geographical and human safety considerations. While this may introduce spatial bias, efforts were made to maintain randomization within accessible areas to ensure representative coverage of burn severity categories. All plots were located within 500m of an access point by road to ensure researchers could reach the plots for monitoring within a reasonable timeframe. Post-wildfire salvage areas and roads were given a 30m buffer and riparian areas were given a 50m buffer from vegetation monitoring plots to limit external influences on the plant community composition. For the stratification of invasive species previous presence, a 100m buffer was applied between areas of prior presence areas and no prior presence to ensure clear separation. Detailed burn severity data (low, medium and high) for each plot were provided by the Province of British Columbia surveys and invasive plant species data (location, species and approximate size and density of infestations) were provided by LRISS. Data collection The predetermined plot GPS coordinates were located and the 3m x3m (9m 2 ) plots were constructed, where the coordinate corresponded with the northernmost corner point. Within each plot, three 1m 2 quadrats were monitored along the north to south diagonal (Figure 2). In each quadrat, each species was identified and percent cover recorded using nominal values ranging from 0-100% of foliar cover and bare ground. Data collection occurred between June 15-July 15, 2023, near or at the time of peak vegetative biomass (Applestein et al. 2018), two years post-fire. Plots at lower elevations with south facing slopes were sampled first, working towards north facing, high elevation plots at the end of the 2023 monitoring period to ensure sites were surveyed during a period of optimal plant condition above ground. Statistical analysis Percent cover plant groupings Overall percent cover of each species in each plot was determined by averaging the 3 quadrat replicates. Plant species were grouped into native (N) or non-native (NN) categories using E-Flora BC and Invasive Species Council of BC (ITIS) (Integrated Taxonomic Information System [ITIS], n.d.; Klinkenberg, n.d.;). Native and non-native groupings were further categorized by life cycle (annual (A), biennial (B), and perennial (P)) and life form (graminoid (G), forb (F), and shrub (S)) using Electronic atlas of the Flora of British Columbia (E-Flora BC), Integrated Taxonomic Information System (ITIS) and Fire effects Information System (FEIS) (Integrated Taxonomic Information System [ITIS] n.d.; Klinkenberg n.d.; U.S. Department of Agriculture, Forest Service n.d.). The individual plant species that fell within these categories were summed to provide the total percent cover for each combination of status, life cycle and life form within a plot. Vegetation trajectory models Generalized linear mixed models (GLMMs) were used to assess the direct and indirect effects of burn severity, prior invasive presence, slope, elevation, and aspect on post-wildfire vegetation trajectories. Plot-level variation was treated as a random effect (1|RE1) to account for spatial heterogeneity across the landscape. Models were selected by relevance to the research question and Akaike information criterion (AIC) rank to identify the best fitting model. Once the most appropriate model was identified, parameter estimates were examined to assess the effects of burn severity, prior invasive species presence, elevation, and other relevant factors on plant cover. The top ranked models were tested for uniformity and dispersion using the DHARMa package (Hartig 2022). Analyses were completed using R Studio (version 4.4.0) with the glmmTMB package for GLMMs (Brooks 2017, Magnusson 2017). The model formulas for each model chosen are as follows in Table 1. Table 1 Model formulas selected by plant grouping Plant grouping Formula Native plant cover Native cover (N) ~ burn severity + prior invasive presence + elevation + aspect + (1|RE1) Non-native plant cover Non-native cover (NN) ~ burn severity + prior invasive presence + elevation + (1|RE1) Bare ground Bare ground ~ burn severity + prior invasive presence + elevation + (1|RE1) Non-native biennial forb cover Non-native biennial forb cover (NNBF) ~ burn severity + prior invasive presence + slope + elevation + aspect + (1|RE1) Non-native annual cover Non-native annual cover (NNA) ~ burn severity + prior invasive presence + slope + elevation + (1|RE1) Non-native biennial cover Non-native biennial cover (NNB) ~ burn severity + prior invasive presence + slope + elevation + aspect + (1|RE1) Non-native perennial cover Non-native perennial cover (NNP) ~ burn severity + prior invasive presence + slope + elevation + aspect + (1|RE1) Native perennial cover Native perennial cover (NP) ~ burn severity + elevation + aspect + (1|RE1) Results Our analyses revealed that our hypothesis, that prior invasive plant presence and higher levels of burn severity would result in increased presence of non-native plant cover in the post-wildfire landscapes, was not supported by our results. Instead, our analyses suggest that topographical factors have the most significant effects on post-wildfire vegetation recovery two years after wildfire. Percent cover Percent cover native plant species Percent cover of native plant species varied with both burn severity and prior invasive species presence (Fig. 3 ). In the plots without prior presence of invasive plants prior to wildfire, the highest percent cover of native plants occurred within high burn severity plots (mean 32.56%, SE = 3.77), followed by low burn severity plots (mean 30.60%, SE = 3.38) and medium burn severity plots (mean 26.05%, SE = 4.78). In the plots with prior presence of invasive plants, the highest percent cover of native plants occurred within medium burn severity plots (mean 41.35%, SE = 4.44), followed by low burn severity plots (mean 26.17%, SE = 3.54), and high burn severity plots (mean 30.06%, SE = 4.32). Percent cover of non-native species The percent cover of non-native plant species was consistently low across all burn severities, regardless of prior invasive plant species presence (Fig. 3 ). In the plots without prior presence of invasive plants prior to wildfire, the highest percent cover of non-native plant species occurred within medium burn severity plots (mean 3.60%, SE = 1.96), while within low burn severity plots had the lowest cover (mean 0.36%, SE = 0.25). In the plots with prior presence of invasive plants prior to wildfire, the highest percent cover of non-native plant species also occurred within medium burn severity plots (mean 2.46%, SE = 1.00), with similar levels of percent cover in low burn severity plots (mean 2.08%, SE = 1.19) and high burn severity plots (mean 3.04%, SE = 1.34). Percent cover of bare ground The percent cover of bare ground represented almost or more than half of the percent cover across all strata (Fig. 3 ). In the plots without prior presence of invasive plants prior to wildfire, the highest percent cover of bare ground occurred within medium burn severity burn plots (mean 68.83%, SE = 4.02), followed by low severity burn plots (mean 59.19%, SE = 5.39), and high severity burn plots (mean = 46.82%, SE = 5.90). In plots with prior presence of invasive species, percent cover of bare ground was highest within low burn severity plots (mean 68.30%, SE = 4.19), followed by high severity burn plots (mean 58.50%, SE = 6.17), and medium severity burn plots (mean 53.01%, SE = 4.68). Vegetation trajectory models The native plant cover GLMM (Fig. 4 A) found that native plant cover was positively related to elevation, (estimate of 0.31 (SE = 0.07, p < 0.001)) and west-facing aspects (estimate 0.94 (SE = 0.38, p = 0.013)) when compared to the reference conditions. The non-native plant cover GLMM (Fig. 4 B) found that non-native plant cover was negatively related to elevation, indicating that as elevation increases, non-native plant cover decreases (estimate of -0.40 (SE = 0.19, p = 0.034)). The bare ground cover GLMM (Fig. 4 C) found that bare ground was negatively related to elevation indicating that as elevation increases, bare ground cover decreases (estimate of -0.18 (SE = 0.09, p = 0.047)). Percent cover of non-native biennial forbs and non-native perennial forbs The non-native perennial forbs GLMM (Fig. 5 A) found that in comparison to high burn severity, low burn severity was positively related to NNPF cover, (estimate of 1.10 (SE = 0.55, p = 0.047)). In contrast, medium burn severity was negatively related to NNPF cover (estimate = -1.41, (SE = 0.68, p = 0.038)), indicating that medium burn severity has lower NNPF cover than high burn severity. NNPF cover was positively related to elevation, (estimate of 0.46 (SE = 0.21, p = 0.030)), higher elevations are associated with increased NNPF cover. North aspects were negatively related to NNPF cover (estimate = -3.98, (SE = 0.78, p < 0.001)), indicating lower NNPF cover compared to south aspects. Similarly, northwest aspects were negatively related to NNPF, (estimate = -2.42, (SE = 0.77, p = 0.002)). West aspects were negatively related to NNPF, (estimate of -1.65 (SE = 0.56, p = 0.003)). Conversely, southwest aspects were positively related to NNPF, (estimate of 0.98 (SE = 0.38, p = 0.010)), indicating that NNPF cover is higher on these aspects in comparison to south aspects. The non-native biennial forbs GLMM (Fig. 5 B) found that NNBF cover was negatively related to slope, (estimate of -0.075 (SE = 0.032, p = 0.019)), suggesting that steeper areas have lower NNBF cover. Elevation was negatively related to NNBF, (estimate of -0.63 (SE = 0.28, p = 0.025)), indicating that higher elevations are associated with lower NNBF proportions. Compared to south aspects, northeast aspects were positively related to NNBF cover ((estimate = 2.31, (SE = 1.12, p = 0.04)), and north aspects were positively related to NNBF cover (estimate = 2.15, SE = 1.10, p = 0.0496). Vegetation trajectories of non-native and native plants by lifecycle Examining the mean percent cover of non-native and native plant species by plant life cycle under each treatment (no prior presence of invasive plants and burn severity) revealed differences between treatments and functional plant groupings (Fig. 6 ). Under no prior presence, non-native biennial and perennial plants showed similar cover at high burn severity, annual and biennial similar cover at medium burn severity with little perennial representation, and only biennial non-natives in the cover at low burn severity. Under no prior presence, perennial native plant cover was highest at high burn severity and was similar at medium and low burn severities. There were no annual or biennial native plants present at plots with no prior presence of invasive plants. Under prior presence of invasive plants, non-native biennial plants had the highest mean percent cover at high and medium burn severities, with annuals and perennials having similar cover. At low burn severity, non-native perennial plants had the highest mean percent cover with annuals and biennials have similar, low mean percent cover. For native species there were no biennial species for any burn severities, perennial native plants represented the highest mean percent cover for all three burn severities. Percent cover of non-native annual plants and non-native biennial plants The non-native annual GLMM (Fig. 7 A) found that low burn severity was negatively related to non-native annuals, (estimate of -2.36 (SE = 1.02 P = 0.021)). This suggests that compared to high burn severity, low burn severity is associated with a significant decrease in NNA cover. Medium burn severity was negatively related to non-native annuals, (estimate of -1.30 (SE = 0.54 p = 0.015)). This indicates that compared to high burn severity, medium burn severity has a decrease in NNA cover. Slope was negatively related to NNA cover, (estimate of -0.059 (SE = 0.03 p = 0.035)). As the slope increases, the predicted NNA cover decreases. Elevation was negatively related to NNA, (estimate of -2.04 (SE = 0.62 p = 0.001)), as elevation increased, the predicted NNA cover decreased. The non-native biennial GLMM (Fig. 7 B) found that slope was negatively related to NNB, (estimate of -0.078 (SE = 0.029, p = 0.008)). As the slope increases, the proportion of NNB decreases, suggesting that steeper areas tend to have lower NNB cover. Elevation was negatively related to NNB, (estimate of -0.66 (SE = 0.26, p = 0.012)), higher elevations have a decrease in NNB cover. In comparison to the south aspects, northeast aspects were positively related to NNB, (estimate of 2.34 (SE = 1.13, p = 0.038)), indicating that NNB cover in greater on northeast aspects than south aspects. Percent cover of non-native perennial plants and native perennial plants The non-native perennial GLMM (Fig. 8 A) found that elevation was positively related to NNP, (estimate of 0.53, (SE = 0.23 p = 0.02)). As elevation increases, the predicted NNP cover significantly increases. Compared to south-facing aspects, north (estimate = − 3.81, SE = 1.11, p < 0.001), northwest (estimate = − 2.22, SE = 0.91, p = 0.14), southeast (estimate = − 2.00, SE = 0.88, p = 0.023), and west-facing aspects (estimate = − 1.49, SE = 0.62, p = 0.016) were all negatively related with native non-native proportion (NNP).These negatively related aspects are associated with a lower NNP cover than south aspects. The native perennial GLMM (Figure 8 B) found that elevation was positively related to NP, (estimate of 0.321, (SE = 0.07, p < 0.001)). As elevation increases, the predicted NP cover significantly increases. The GLMM also found that the aspect played a significant role in native perennial plant cover, west aspects were positively related to NP, (estimate of 0.901, (SE = 0.39, p = 0.0195)) the proportion of NP cover is higher on west aspects in comparison to south aspects. Discussion The McKay Creek Wildfire area, with its diverse topography and habitat types, along with availability of baseline data regarding the pre-fire presence of invasive non-native plant species, and the detailed burn severity mapping, provided a unique opportunity to understand the factors driving post-wildfire vegetation trajectories and to evaluate the potential threat of 47 invasive, non-native species to post-wildfire landscapes. Neither of our hypotheses, that both prior presence of non-native invasive plants and higher levels of burn severity would result in increased presence of non-native plant populations on post-wildfire landscapes, were supported by our findings. Mean percent cover of native plants, non-native plants, and bare ground were similar across all treatments (Figure 3). Using generalized linear mixed effects models (GLMMs), our study uncovered key factors influencing post-wildfire vegetation trajectories of these status groupings; these were topographical factors, namely elevation and aspect. Percent cover of native plants, non-native plants, and bare ground had statistically significant relationships with elevation, with native plant percent cover increasing, non-native plant percent cover decreasing, and bare ground decreasing, with increasing elevation (Figure 4 A-C). When plant groups were further examined by life cycle, this pattern became more nuanced. Non-native annuals showed a negative relationship with burn severity and elevation (Figure 7 A), suggesting a particular vulnerability of these high burn severity low elevation areas to rapid colonization and spread. Non-native biennials decreased with elevation and slope (Figure 7 B), while non-native perennials increased with elevation (Figure 8 A), indicating tolerance for harsher, high-elevation environments. This last group poses a particular threat, as perennial forbs (Figure 5 A) are known for their resilience, deep roots, and ability to reproduce through multiple pathways. One explanation for the rejection of our hypothesis lies in the ecological mechanisms influencing plant recovery after wildfire. Fire affects both the physical environment and biological legacies. While it can eliminate seed banks in the upper soil layers, it may also stimulate the germination of deeply buried seeds (Santana et al. 2010, Roshan et al. 2022). These dynamics, combined with the survival and resprouting capabilities of perennial plants through rhizomes, may mask or override the effects of pre-fire invasive plant populations or burn severity (Figure 6). Additionally, vegetation recovery is influenced by the functional traits of plants, including their life cycles (Figure 6). Annual plants, which rely heavily on seed banks and disperse quickly, are more likely to establish rapidly in disturbed environments. In contrast, perennial species generally recover more slowly but persist through vegetative structures and complex ecological relationships. Given that our results contrast other findings, such as results from Yellowstone National Park that showed substantial effects of fire severity and existing invasive plant patch size on early post-fire plant cover and species (Turner et al. 2016), our study suggests that predictions regarding vegetation trajectories may not be widely applicable across differing landscapes. This difference may, in part, reflect the relatively early stage of recovery captured in our dataset. Several long-term studies have shown that vegetation responses to fire, particularly in relation to burn severity and invasive species, often unfold over a longer time frame. For example, Shinneman and Baker (2009) documented that Bromus tectorum cover increased gradually over seven to eight years post-fire in semiarid ecosystems, peaking well after the initial two-year period examined here. Similarly, Morgan et al. (2015) found that vegetation cover and species diversity remained suppressed on high-severity plots for at least six years following fire, highlighting the long-term influence of burn severity. Furthermore, Tepley et al. (2018) emphasize that high-severity burns can initiate fire–vegetation feedbacks that may not be detectable in early post-fire years but can eventually lead to landscape-scale shifts in vegetation composition. These studies collectively suggest that the muted effects of burn severity or previous presence of invasive species observed in our study may reflect temporal lag rather than a lack of eventual impact, underscoring the need for long-term monitoring to fully understand post-fire vegetation trajectories. Another explanation for this discrepancy may lie in sampling design or the biophysical differences of the McKay Creek area. Our sampling plots were located away from roads, riparian corridors, and other disturbance vectors, potentially underrepresenting invasion hotspots and overrepresenting conditions less prone to non-native colonization. Furthermore, the complex topography, diverse habitat types, and large size of the McKay Creek fire may dilute simple cause-effect relationships. These contrasting findings highlight the importance of regionally specific, long-term monitoring of vegetation trajectories of post-wildfire landscapes to inform restoration. Further limitations of our study must be acknowledged. While we benefited from strong pre-fire baseline data and high-resolution burn severity mapping, the scale and heterogeneity of the landscape limited sampling intensity. This low-intensity design, though spatially extensive, may have missed smaller-scale vegetation dynamics or rare species occurrences. Management and Policy Implications Our findings have several implications for land management and restoration policy in post-wildfire landscapes. First, while the desire to immediately intervene to restore post-wildfire landscapes, particularly by seeding, is a common response by agencies responsible for wildfire recovery, research shows that seeding can increase competition with desirable plant species and underestimate the potential for long-term recovery of native perennial cover (Copeland et al 2019). Land managers must exercise caution with interventions, particularly when there is urgency due to public pressure, to cover the wildfire scars on the landscape as quickly as possible. Further, they should also exercise caution in engaging in immediate interventions meant to serve the interests of industries that may be contributing to these destructive wildfire regimes such as forestry (the introduction of monotypic, high density tree plantations) and cattle grazing tenures. It is critical to ensure wildfire recovery is data-driven and carefully articulates restoration goals that build resiliency while also considering a future for the landscape that may not resemble pre-fire conditions. Second, they highlight the need for rapid response strategies, particularly in low-elevation, high-severity burn areas where bare ground and non-native annuals are most prevalent. Prevention measures such as restricting vehicle and livestock access, installing wash stations, and establishing exclusion zones should be prioritized. Monitoring programs should focus on early detection of invasive species in these vulnerable areas, coupled with careful, targeted application of herbicides where needed. Caution is warranted in the use of residual herbicides that could harm emerging native perennials. Finally, our study underscores the importance of locally tailored, data-driven restoration strategies. Generalized ecological theories such as niche theory may fail to account for the highly altered conditions of post-fire landscapes, including soil degradation, hydrologic shifts, and climate-driven drought stress (Bakker et al. 2003, Groves & Brudvig 2019). Thus, species selection needs to take this new reality into consideration; planting plans should consider how to establish protective canopies, vertical structures, and appropriate timing should be established and adhered to for planting when regular precipitation is anticipated to ensure plant establishment. On-going stewardship activities after planting, such as monitoring for indicators of plant stress and establishment, will be necessary to not only give new plants the best possible chance of survival by informing when interventions may be needed, but to also contribute to the body of knowledge informing post-wildfire restoration best management practices. Conclusion This study offers new insights into the complex dynamics shaping post-wildfire vegetation recovery in British Columbia’s interior. Contrary to expectations, neither burn severity nor pre-fire invasive plant presence emerged as strong predictors of post-fire vegetation outcomes. Instead, elevation played a dominant role in structuring native and non-native plant cover, underscoring the need to account for topographic variation in restoration planning. The relatively high proportion of native species observed suggests some resilience among fire-adapted communities, while persistent bare ground highlights an urgent need for early monitoring and prevention of invasive plant spread. As fire regimes intensify under climate change, region-specific, long-term studies like this one will be essential for informing restoration efforts. Our results reinforce the importance of context-driven approaches and challenge assumptions that commonly guide post-fire intervention. By continuing to monitor vegetation trajectories at McKay Creek and similar sites, we can contribute to a more nuanced and adaptive understanding of fire ecology across diverse landscapes. Declarations Ethics Approval and Consent to Participate Not applicable. Consent for Publication Not applicable. Availability of Data and Materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare they have no competing interests. Funding Funding for this study was provided by the Lillooet Regional Invasive Species Society (LRISS), Squamish-Lillooet Regional District (SLRD), Mitacs Accelerate, and the Natural Sciences and Engineering Research Council of Canada (NSERC). Clinical trial number not applicable. Authors' contributions VO made substantial contributions to the study design, data collection, analyses, and drafting of the work. TM and SS contributed to the study design, analytical approaches and analyses, drafting, and revisions of the work. JG was responsible for the conception and funding of the study, development of the study design, assisted with data collection and analyses, and made substantial contributions to drafting and revisions. Acknowledgements We would like to acknowledge the contributions of the Ts'kw'aylaxw First Nation (Chief Justin Kane, Desmond Peters Jr., and Mike Mcewen), Xwísten First Nation (Travis Peters), Lillooet Tribal Council (Matt Manuel), St'át'imc Government Services (Darwyn John), Lillooet Regional Invasive Species Society (Jacquie Rasmussen and Dr. Sue Senger) for their time, knowledge, and guidance throughout this project. To the T'ít'q'et - P’egp’íg’lha community (Denise Antoine, Christian Ahrenkiel, Sam Copeland, Raymon Billy, and Luther Brigman) for contributions in the field. We would also like to acknowledge Dr. Lori Daniels, for sharing her expertise in wildfire, and Sarah Dickson-Hoyle and Sofie McComb for discussion regarding the study analyses. We would like to extend our thanks to all who assisted with the extensive fieldwork: Vanessa Jones, Emma Sneep, Kayla Poppy, Chanvre Oleman, Nicole Morgenstern, Nina Andrascik, Les Riley, and Denny Higginbottom. 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Simard","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Suzanne","middleName":"W.","lastName":"Simard","suffix":""},{"id":492138373,"identity":"fe264449-8777-4ebf-8408-6e4681edb1d0","order_by":3,"name":"Jennifer Grenz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACxgYgkWAD59swGBCnJQ3OTyOsBaYQBg4T1sLc3nvswYMEm3wG/rUPHxf8OZ+4nf0A44cf+BzWcy7dICEhzbJB4rmx8Qye24k7exKYJXvwaZmRYyaR+OOwAYPEMTZpHonbiRtuMLAx8ODTMv+NmURCwn+QFvbfPAbnwFoY/+C1hQek5YABA38bGzNPwgGwFma8tvTkgLQkG7BJsDFL8xxINt7Zk9gsLYNHi2H7GTPJHwl2Bvz8xxg/8/yxk93Ofvjgxzf4tDRAGWwSCXCbG7CphAN5OIv/AF6Fo2AUjIJRMIIBALRHRx+u9jSRAAAAAElFTkSuQmCC","orcid":"","institution":"University of British Columbia","correspondingAuthor":true,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Grenz","suffix":""}],"badges":[],"createdAt":"2025-06-23 13:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6957462/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6957462/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42408-026-00463-x","type":"published","date":"2026-03-05T15:59:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87939477,"identity":"c6c2fe61-ce19-4ac4-a3a5-cd0d0582d26d","added_by":"auto","created_at":"2025-07-30 15:04:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":409416,"visible":true,"origin":"","legend":"\u003cp\u003eMcKay Creek wildfire boundaries, burn severities, invasive plants and plot locations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6957462/v1/b831d65eef3872ed4ab35da4.png"},{"id":87940546,"identity":"b83a7b9e-d2bb-42b7-9943-99fa8caec4fc","added_by":"auto","created_at":"2025-07-30 15:12:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4342,"visible":true,"origin":"","legend":"\u003cp\u003ePlot diagram demonstrates where sampling occurred in the black squares. Sampling was conducted from North (N) to South (S).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6957462/v1/86e85763eb9552f8ddc72ea1.png"},{"id":87939482,"identity":"a3fda708-e84d-4c74-a306-643a24d599a5","added_by":"auto","created_at":"2025-07-30 15:04:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86034,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of burn severity and prior invasive species presence on native plants, non-native plants, and bare ground.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6957462/v1/9bb5a948cae10e63f740503d.png"},{"id":87939478,"identity":"063e53d7-691b-4d61-8464-376efcfa46a6","added_by":"auto","created_at":"2025-07-30 15:04:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67994,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated effects of predictors on native, non-native, and bare ground cover.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6957462/v1/e0705c9397a0f8eec046d0bc.png"},{"id":87939493,"identity":"48469a96-da05-4e03-90d0-5f7e91e263ae","added_by":"auto","created_at":"2025-07-30 15:04:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":122749,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated effects of predictors on non-native perennial forb, and non-native biennial forb cover.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6957462/v1/b110117c53cc2ae87bd361d6.png"},{"id":87939484,"identity":"8ca32dcf-8e52-405d-8d35-566547e45be1","added_by":"auto","created_at":"2025-07-30 15:04:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":103820,"visible":true,"origin":"","legend":"\u003cp\u003eMean cover of native and non-native plant life cycles across burn severity and invasion history.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6957462/v1/51f6a7b3313e36cf0251d055.png"},{"id":87940912,"identity":"b491321b-fb02-49e8-ad9d-9fb71e8a6559","added_by":"auto","created_at":"2025-07-30 15:20:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":67835,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated effects of predictors on non-native annuals, and non-native biennials cover.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6957462/v1/6fcc148842ef186d41af3061.png"},{"id":87939488,"identity":"64c7a775-f341-497f-ab2f-d9737c5cdfdd","added_by":"auto","created_at":"2025-07-30 15:04:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":78505,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated effects of predictors on non-native perennials, and native perennials cover.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6957462/v1/833107d22ecfc5899a9e6914.png"},{"id":104250708,"identity":"a555b661-6ed3-4d59-bf49-0f751f430c7d","added_by":"auto","created_at":"2026-03-09 16:06:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1555145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6957462/v1/246434d3-19a6-4d4c-a47d-15c9a9364e30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Driving Vegetation Trajectories of Post-Wildfire Landscapes in the Interior of British Columbia, Canada","fulltext":[{"header":"Background","content":"\u003cp\u003eFire regimes around the globe are shifting in frequency and severity, driven by the cumulative impacts of climate change, land use changes, and fire suppression. As a result, vegetation dynamics are changing, yet little is known about the impacts of this new era of wildfire on vegetation communities (Marlon et al. 2012, Balch et al. 2013, Flannigan et al. 2013, Parks et al. 2015, Abatzoglou and Williams 2016, Bowman et al. 2020, Archibald et al. 2018, Sayedi et al. 2024). While global patterns are important to understanding and anticipating changing fire regimes, they may not fully account for regionally specific processes. Local factors, such as historical ecology (Grenz and Armstrong 2023, Turner 1999), historic land management practices and policies (Dickson-Hoyle and John 2021, Lake et al. 2017), and plant community composition, may be more specific drivers of fire regime change and critical to the development of effective, place-based fire and restoration strategies (Sayedi et al. 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe interior of British Columbia, Canada, is characterized by dry forests, grasslands, and mountainous terrain. Over the past century, fire regimes in this region have changed dramatically, departing from the historical patterns that shaped these landscapes over millennia (Dickson-Hoyle and John 2021), a trend consistent with the ten-fold acceleration of fire regime change globally over the past two centuries (Sayedi et al. 2024). Local factors influencing fire regimes within British Columbia\u0026rsquo;s interior include a combined legacy of industrial logging practices, cattle grazing, increased temperatures and periods of drought, and the implementation of widespread fire exclusion policies across fire-dependent landscapes (Hoffman et al. 2022). Fire exclusion, introduced during colonization approximately 150 years ago, interrupted centuries of Indigenous fire stewardship and marked a major shift in land management policy (Lewis et al. 2018, Hagmann et al. 2013, Harvey et al. 2017). This suppression of fire has contributed to fuel load accumulation at the surface, ladder, and canopy levels due to increased tree density, homogenization of plant community composition, and a shift toward fire intolerant species (Hagmann et al. 2021). Western Canada is thus enduring a new era of wildfire, where catastrophic megafires, those characterized by extensive size, intense fire behavior, and a high proportion of high-severity or stand-replacing burns, are no longer outliers, but the current and likely future reality (Bedia et al. 2015, Coogan et al. 2019). For example, the 2023 fire season in British Columbia, Canada, saw 2.84 million ha of land burned, doubling the 2018 record fire season of 1.35 million ha (Government of British Columbia, 2024b). As we look toward the future, the realization that these megafires are going to continue to occur (Halofsky et al. 2020) necessitates a careful examination of their impacts to inform post-wildfire restoration and land management policies and approaches to reclaim fire resilient landscapes.\u003c/p\u003e\n\u003cp\u003eUnderstanding vegetation trajectories of post-wildfire landscapes remains limited, particularly in regions where fire regimes are rapidly changing due to climate and land use pressures (Johnstone et al. 2016). Most studies have focused on short-term vegetation responses or broad-scale fire patterns, leaving a knowledge gap in how plant communities recover or fail to recover over time following high-severity fire (Johnstone et al. 2016, Coop et al. 2020). Wildfire can cause substantial changes to plant communities by altering species composition, reducing vegetation cover, and disrupting regeneration processes (Fornwalt and Kaufmann 2014). High-severity fires may result in the loss of mature vegetation and soil seed banks, while also modifying soil structure, moisture availability, and nutrient dynamics, all of which influence patterns of post-fire recovery (Neary et al. 1999, Certini 2005, Harvey et al. 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the dry forests and grasslands of British Columbia\u0026rsquo;s Interior, these altered post-fire conditions are especially concerning given the long-standing presence of invasive plant species in many areas (Leung, 2002). Species such as \u003cem\u003eCentaurea stoebe\u003c/em\u003e (spotted knapweed) and \u003cem\u003eBromus tectorum\u003c/em\u003e (cheatgrass) have been introduced and established over past decades, particularly along disturbed corridors, rangelands, and roadsides (Invasive Species Council of BC 2024a, 2024b, Leung 2002). These invaders are well positioned to capitalize on fire-created disturbances. Invasive plants, generally defined as a non-native species to a particular ecosystem that is causing some degree of harm to humans and/or the environment (Richardson and Pysek 2004, NISC 2005), employ highly competitive reproduction, dispersal, persistence, and evolution strategies that make them more adaptable to climatic changes than native forest species (Dukes et al. 2009, Birthisel et al. 2021, Shephard et al. 2022, Jones and Grenz 2023). This, combined with shifting fire regimes, may create opportunities for new and existing invasive plant species to establish and dominate habitats where they previously could not (Zouhar et al. 2008, Alba et al 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIncreased potential for plant invasion and resulting changes to plant community composition and structure could introduce fire-invasion feedback loops that alter fire behavior in ways that further promote their spread and persistence (Brooks et al. 2004, Balch et al. 2013, Grenz and Clements 2023) and drive long-term ecological shifts (Chambers et al. 2014). Fire-invasion feedback loops have transformed entire ecosystems globally, such as the sagebrush steppe in the western United States, where non-native annual grasses such as \u003cem\u003eBromus tectorum\u003c/em\u003e (cheatgrass), a fine, flammable fuel, have replaced fire-resistant native bunchgrasses, leading to increased fire frequency and loss of native biodiversity (Balch et al. 2013, Brooks et al. 2004, D\u0026rsquo;Antonio and Vitousek 1992). Globally, similar patterns are seen in northern Australia\u0026rsquo;s tropical savannas, where non-native perennial grasses such as \u003cem\u003eAndropogon gayanus\u003c/em\u003e (gamba grass), a tall, high-biomass species, have replaced native vegetation, resulting in increased fire intensity and frequency, and widespread degradation of native ecosystems (Setterfield et al. 2010). The impacts of wildfire on plant invasion, understanding whether post-fire invasions are a widespread phenomenon or are habitat specific, and the sources of variation of native and invasive plant responses to fire have not been rigorously tested (Alba et al. 2015). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvaluation of vegetation trajectories, both native and non-native plants, is needed to understand invasion dynamics on post-wildfire landscapes as the establishment of invasive plants does not occur in isolation. Research suggests that non-native invasive species fundamentally differ in their response to wildfire compared to native plants (Alba et al. 2015), and thus we need to examine contributing factors that may account for these differences such as wildfire severity. This is integrally important to understand as current fire regimes are resulting in higher proportions of burned areas being categorized as severe, whereas historically fires were more frequent at low or moderate intensities (Hagmann et al. 2021). For example, between 1985 and 2017, there was an eightfold increase in high severity burns annually in western US forests (Parks and Abatzoglou 2020). An additional potential contributing factor to post-wildfire vegetation trajectories could be the presence of non-native invasive plants prior to wildfire as invasion potential may be increased due to altered plant community composition prior to wildfire and remnant seedbanks. These relationships have been observed in various contexts, including Yellowstone National Park (Turner et al. 2016), Montana\u0026apos;s Flathead National Forest (Rew \u0026amp; Johnson 2010), and Arizona ponderosa pine forests (Wolfson et al. 2005), highlighting the complexity and variability of post-fire outcomes.\u003cem\u003e\u0026nbsp;\u003c/em\u003eIt remains unclear whether the same ecological and disturbance-related factors consistently drive post-wildfire vegetation recovery across different fire events and landscapes (Johnstone et al. 2016). This lack of generalizable understanding limits our ability to anticipate vegetation trajectories, especially under increasingly severe and frequent fire regimes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we investigated possible factors influencing post-wildfire vegetation trajectories two years after the McKay Creek Wildfire, a 46,000ha wildfire that occurred in June of 2021, 11km north of Lillooet, BC, Canada. This fire spanned four distinct Biogeoclimatic Ecosystem Classification (BEC) zones (British Columbia Ministry of Forests n.d., MacKenzie and Meidinger 2018), diverse topographical features (aspect, slope, elevation) and successional stages, had comprehensive pre-fire baseline data on invasive plant presence, and representation of three wildfire burn severities (low, moderate, and high). \u0026nbsp;The confluence of these factors provided a unique opportunity to assess drivers influencing post-fire vegetation trajectories of both native and non-native species. We hypothesized that both pre-wildfire presence of non-native invasive plants and high burn severity would result in increased presence of non-native plant populations on post-wildfire landscapes. \u0026nbsp; The overall goal of our study was to identify factors driving post-wildfire vegetation trajectories which could contribute to data-driven post-wildfire ecological restoration interventions, including the prevention and management of non-native plants and revegetation of native plants in the current mega-wildfire reality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn June of 2021, immediately after a historic heat wave broke records across the Pacific Northwest of North America, creating a \u0026ldquo;heat dome\u0026rdquo; and causing extreme fire conditions (Still et al. 2023), the McKay Creek Fire ignited. Located approximately 11km north of Lillooet, British Columbia (Figure 1), it burned over 46,000 ha. The fire occurred within the traditional territory of the St\u0026rsquo;\u0026aacute;t\u0026rsquo;imc Nation, specifically the traditional territories of six Northern St\u0026rsquo;\u0026aacute;t\u0026rsquo;imc communities, Sekw\u0026apos;el\u0026apos;was, T\u0026apos;\u0026iacute;t\u0026apos;q\u0026apos;et, Tsal\u0026apos;alh, Ts\u0026apos;kw\u0026apos;aylaxw, Xaxli\u0026apos;p, and Xw\u0026iacute;sten.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study area encompassed the entirety of the McKay Creek wildfire, including major drainages of the Fraser River, Bridge River, and Seton Lake. This region is characterized by a warm, dry climate, average daytime high and low seasonal temperatures of 25.9\u0026deg;C and 12.6\u0026deg;C in the summer, and 4\u0026deg;C and -3.6\u0026deg;C in winter, approximately 31 days per year of temperatures exceeding 30\u0026deg;C, and annual precipitation of approximately 349mm (Environment and Climate Change Canada 2024). It has steep, rugged terrain with narrow valleys and high ridges. \u0026nbsp;The highest elevation of the McKay Creek wildfire was at 1800 meters, and this dramatic change in elevation accounts for the region\u0026rsquo;s highly variable microclimates and diverse ecosystems. The fire spanned four Biogeoclimatic Ecosystem Classification (BEC) zones: Bunchgrass, Interior Douglas-fir, Montane Spruce, and Ponderosa Pine, ranging from low-elevation grasslands to high-elevation subalpine forests (Meidinger and Pojar 1991).\u003c/p\u003e\n\u003cp\u003eThe disturbance history of the study area is shaped by long-standing land-use activities, particularly forestry and cattle grazing. Local accounts indicate that grazing pressure has been consistent since at least the 1970s, following a seasonal pattern in which livestock move from low to high elevations (J. Rasmussen, Lillooet Regional Invasive Species Society, personal communication). Grazing in this region is managed under provincial grazing tenures that specify livestock numbers, timing, and duration of use (Government of British Columbia 2024a). Logging activity peaked in the 1980s, resulting in extensive clear-cutting within and around the study area. At the time, cut block burning was commonly used until shifting fire suppression policies in the early 1990s began to phase out this practice (J. Rasmussen, Lillooet Regional Invasive Species Society, personal communication).\u003c/p\u003e\n\u003cp id=\"_Toc179816872\"\u003e\u003cstrong\u003eExperimental design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the McKay creek wildfire area, 80 vegetation monitoring plots of 9m\u003csup\u003e2\u003c/sup\u003e size were established using a combination of preferential and stratified random sampling methods (Michalcov\u0026aacute; et al. 2011). Plots were stratified based on two key ecological factors: burn severity (low, moderate, high) and prior invasive species presence (yes/no). Burn severity was classified based on tree biomass mortality as follows: low (\u0026lt;20%), moderate (20\u0026ndash;70%), and high (\u0026gt;70%) (Hagmann et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing comprehensive mapping of priority non-native invasive plant locations that had taken place several years prior to the wildfire by the Lillooet Regional Invasive Species Society (LRISS), plots were located and categorized based on \u0026quot;prior presence\u0026quot;. \u0026nbsp;Using the three burn severity classes (low, medium, high) and presence or absence of invasive species, the plots were stratified into 6 unique treatments. In each of these treatments, 13 plots that were 3m x 3m plots in size established, except for the treatment group \u0026ldquo;low yes\u0026rdquo;, which included 15 plots.\u003c/p\u003e\n\u003cp\u003ePlot locations were randomly located for each treatment combination using ArcGIS. Preferential sampling was incorporated due to the large size (46,000 ha) of the McKay creek wildfire, extreme nature (steep, loose slopes and cliffs) of its terrain, and therefore geographical and human safety considerations. While this may introduce spatial bias, efforts were made to maintain randomization within accessible areas to ensure representative coverage of burn severity categories. All plots were located within 500m of an access point by road to ensure researchers could reach the plots for monitoring within a reasonable timeframe. Post-wildfire salvage areas and roads were given a 30m buffer and riparian areas were given a 50m buffer from vegetation monitoring plots to limit external influences on the plant community composition. For the stratification of invasive species previous presence, a 100m buffer was applied between areas of prior presence areas and no prior presence to ensure clear separation.\u003c/p\u003e\n\u003cp\u003eDetailed burn severity data (low, medium and high) for each plot were provided by the Province of British Columbia surveys and invasive plant species data (location, species and approximate size and density of infestations) were provided by LRISS.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc179816873\"\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predetermined plot GPS coordinates were located and the 3m x3m (9m\u003csup\u003e2\u003c/sup\u003e) plots were constructed, where the coordinate corresponded with the northernmost corner point. \u0026nbsp;Within each plot, three 1m\u003csup\u003e2\u003c/sup\u003e quadrats were monitored along the north to south diagonal (Figure 2). In each quadrat, each species was identified and percent cover recorded using nominal values ranging from 0-100% of foliar cover and bare ground. Data collection occurred between June 15-July 15, 2023, near or at the time of peak vegetative biomass (Applestein et al. 2018), two years post-fire. Plots at lower elevations with south facing slopes were sampled first, working towards north facing, high elevation plots at the end of the 2023 monitoring period to ensure sites were surveyed during a period of optimal plant condition above ground.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc179816875\"\u003e\u003cstrong\u003ePercent cover plant groupings\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall percent cover of each species in each plot was determined by averaging the 3 quadrat replicates. Plant species were grouped into native (N) or non-native (NN) categories using E-Flora BC and Invasive Species Council of BC (ITIS) (Integrated Taxonomic Information System [ITIS], n.d.; Klinkenberg, n.d.;). \u0026nbsp;Native and non-native groupings were further categorized by life cycle (annual (A), biennial (B), and perennial (P)) and life form (graminoid (G), forb (F), and shrub (S)) using Electronic atlas of the Flora of British Columbia (E-Flora BC), Integrated Taxonomic Information System (ITIS) and Fire effects Information System (FEIS) (Integrated Taxonomic Information System [ITIS] n.d.; Klinkenberg n.d.; U.S. Department of Agriculture, Forest Service n.d.). The individual plant species that fell within these categories were summed to provide the total percent cover for each combination of status, life cycle and life form within a plot.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc179816877\"\u003e\u003cstrong\u003eVegetation trajectory models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneralized linear mixed models (GLMMs) were used to assess the direct and indirect effects of burn severity, prior invasive presence, slope, elevation, and aspect on post-wildfire vegetation trajectories. Plot-level variation was treated as a random effect (1|RE1) to account for spatial heterogeneity across the landscape. Models were selected by relevance to the research question and Akaike information criterion (AIC) rank to identify the best fitting model. Once the most appropriate model was identified, parameter estimates were examined to assess the effects of burn severity, prior invasive species presence, elevation, and other relevant factors on plant cover. The top ranked models were tested for uniformity and dispersion using the DHARMa package (Hartig 2022). Analyses were completed using R Studio (version 4.4.0) with the glmmTMB package for GLMMs (Brooks 2017, Magnusson 2017). The model formulas for each model chosen are as follows in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1 Model formulas selected by plant grouping \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.8812%;\"\u003e\n \u003cp\u003ePlant grouping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62.1188%;\"\u003e\n \u003cp\u003eFormula\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.8812%;\"\u003e\n \u003cp\u003eNative plant cover \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62.1188%;\"\u003e\n \u003cp\u003eNative cover (N) ~ burn severity + prior invasive presence + elevation + aspect + (1|RE1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.8812%;\"\u003e\n \u003cp\u003eNon-native plant cover\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62.1188%;\"\u003e\n \u003cp\u003eNon-native cover (NN) ~ burn severity + prior invasive presence + elevation + (1|RE1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.8812%;\"\u003e\n \u003cp\u003eBare ground\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62.1188%;\"\u003e\n \u003cp\u003eBare ground ~ burn severity + prior invasive presence + elevation + (1|RE1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.8812%;\"\u003e\n \u003cp\u003eNon-native biennial forb cover\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62.1188%;\"\u003e\n \u003cp\u003eNon-native biennial forb cover (NNBF) ~ burn severity + prior invasive presence + slope + elevation + aspect + (1|RE1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.8812%;\"\u003e\n \u003cp\u003eNon-native annual cover\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62.1188%;\"\u003e\n \u003cp\u003eNon-native annual cover (NNA) ~ burn severity + prior invasive presence + slope + elevation + (1|RE1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.8812%;\"\u003e\n \u003cp\u003eNon-native biennial cover\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62.1188%;\"\u003e\n \u003cp\u003eNon-native biennial cover (NNB) ~ burn severity + prior invasive presence + slope + elevation + aspect + (1|RE1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.8812%;\"\u003e\n \u003cp\u003eNon-native perennial cover\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62.1188%;\"\u003e\n \u003cp\u003eNon-native perennial cover (NNP) ~ burn severity + prior invasive presence + slope + elevation + aspect + (1|RE1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.8812%;\"\u003e\n \u003cp\u003eNative perennial cover\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62.1188%;\"\u003e\n \u003cp\u003eNative perennial cover (NP) ~ burn severity + elevation + aspect + (1|RE1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results","content":"\u003cp\u003eOur analyses revealed that our hypothesis, that prior invasive plant presence and higher levels of burn severity would result in increased presence of non-native plant cover in the post-wildfire landscapes, was not supported by our results. Instead, our analyses suggest that topographical factors have the most significant effects on post-wildfire vegetation recovery two years after wildfire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePercent cover\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePercent cover native plant species\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePercent cover of native plant species varied with both burn severity and prior invasive species presence (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In the plots without prior presence of invasive plants prior to wildfire, the highest percent cover of native plants occurred within high burn severity plots (mean 32.56%, SE\u0026thinsp;=\u0026thinsp;3.77), followed by low burn severity plots (mean 30.60%, SE\u0026thinsp;=\u0026thinsp;3.38) and medium burn severity plots (mean 26.05%, SE\u0026thinsp;=\u0026thinsp;4.78). In the plots with prior presence of invasive plants, the highest percent cover of native plants occurred within medium burn severity plots (mean 41.35%, SE\u0026thinsp;=\u0026thinsp;4.44), followed by low burn severity plots (mean 26.17%, SE\u0026thinsp;=\u0026thinsp;3.54), and high burn severity plots (mean 30.06%, SE\u0026thinsp;=\u0026thinsp;4.32).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePercent cover of non-native species\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe percent cover of non-native plant species was consistently low across all burn severities, regardless of prior invasive plant species presence (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In the plots without prior presence of invasive plants prior to wildfire, the highest percent cover of non-native plant species occurred within medium burn severity plots (mean 3.60%, SE\u0026thinsp;=\u0026thinsp;1.96), while within low burn severity plots had the lowest cover (mean 0.36%, SE\u0026thinsp;=\u0026thinsp;0.25). In the plots with prior presence of invasive plants prior to wildfire, the highest percent cover of non-native plant species also occurred within medium burn severity plots (mean 2.46%, SE\u0026thinsp;=\u0026thinsp;1.00), with similar levels of percent cover in low burn severity plots (mean 2.08%, SE\u0026thinsp;=\u0026thinsp;1.19) and high burn severity plots (mean 3.04%, SE\u0026thinsp;=\u0026thinsp;1.34).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePercent cover of bare ground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe percent cover of bare ground represented almost or more than half of the percent cover across all strata (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In the plots without prior presence of invasive plants prior to wildfire, the highest percent cover of bare ground occurred within medium burn severity burn plots (mean 68.83%, SE\u0026thinsp;=\u0026thinsp;4.02), followed by low severity burn plots (mean 59.19%, SE\u0026thinsp;=\u0026thinsp;5.39), and high severity burn plots (mean\u0026thinsp;=\u0026thinsp;46.82%, SE\u0026thinsp;=\u0026thinsp;5.90). In plots with prior presence of invasive species, percent cover of bare ground was highest within low burn severity plots (mean 68.30%, SE\u0026thinsp;=\u0026thinsp;4.19), followed by high severity burn plots (mean 58.50%, SE\u0026thinsp;=\u0026thinsp;6.17), and medium severity burn plots (mean 53.01%, SE\u0026thinsp;=\u0026thinsp;4.68).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVegetation trajectory models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe native plant cover GLMM (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA) found that native plant cover was positively related to elevation, (estimate of 0.31 (SE\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)) and west-facing aspects (estimate 0.94 (SE\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;0.013)) when compared to the reference conditions. The non-native plant cover GLMM (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB) found that non-native plant cover was negatively related to elevation, indicating that as elevation increases, non-native plant cover decreases (estimate of -0.40 (SE\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;=\u0026thinsp;0.034)). The bare ground cover GLMM (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC) found that bare ground was negatively related to elevation indicating that as elevation increases, bare ground cover decreases (estimate of -0.18 (SE\u0026thinsp;=\u0026thinsp;0.09, p\u0026thinsp;=\u0026thinsp;0.047)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePercent cover of non-native biennial forbs and non-native perennial forbs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-native perennial forbs GLMM (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA) found that in comparison to high burn severity, low burn severity was positively related to NNPF cover, (estimate of 1.10 (SE\u0026thinsp;=\u0026thinsp;0.55, p\u0026thinsp;=\u0026thinsp;0.047)). In contrast, medium burn severity was negatively related to NNPF cover (estimate = -1.41, (SE\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;=\u0026thinsp;0.038)), indicating that medium burn severity has lower NNPF cover than high burn severity. NNPF cover was positively related to elevation, (estimate of 0.46 (SE\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;=\u0026thinsp;0.030)), higher elevations are associated with increased NNPF cover. North aspects were negatively related to NNPF cover (estimate = -3.98, (SE\u0026thinsp;=\u0026thinsp;0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)), indicating lower NNPF cover compared to south aspects. Similarly, northwest aspects were negatively related to NNPF, (estimate = -2.42, (SE\u0026thinsp;=\u0026thinsp;0.77, p\u0026thinsp;=\u0026thinsp;0.002)). West aspects were negatively related to NNPF, (estimate of -1.65 (SE\u0026thinsp;=\u0026thinsp;0.56, p\u0026thinsp;=\u0026thinsp;0.003)). Conversely, southwest aspects were positively related to NNPF, (estimate of 0.98 (SE\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;0.010)), indicating that NNPF cover is higher on these aspects in comparison to south aspects.\u003c/p\u003e\n\u003cp\u003eThe non-native biennial forbs GLMM (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB) found that NNBF cover was negatively related to slope, (estimate of -0.075 (SE\u0026thinsp;=\u0026thinsp;0.032, p\u0026thinsp;=\u0026thinsp;0.019)), suggesting that steeper areas have lower NNBF cover. Elevation was negatively related to NNBF, (estimate of -0.63 (SE\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;=\u0026thinsp;0.025)), indicating that higher elevations are associated with lower NNBF proportions. Compared to south aspects, northeast aspects were positively related to NNBF cover ((estimate\u0026thinsp;=\u0026thinsp;2.31, (SE\u0026thinsp;=\u0026thinsp;1.12, p\u0026thinsp;=\u0026thinsp;0.04)), and north aspects were positively related to NNBF cover (estimate\u0026thinsp;=\u0026thinsp;2.15, SE\u0026thinsp;=\u0026thinsp;1.10, p\u0026thinsp;=\u0026thinsp;0.0496).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVegetation trajectories of non-native and native plants by lifecycle\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExamining the mean percent cover of non-native and native plant species by plant life cycle under each treatment (no prior presence of invasive plants and burn severity) revealed differences between treatments and functional plant groupings (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Under no prior presence, non-native biennial and perennial plants showed similar cover at high burn severity, annual and biennial similar cover at medium burn severity with little perennial representation, and only biennial non-natives in the cover at low burn severity. Under no prior presence, perennial native plant cover was highest at high burn severity and was similar at medium and low burn severities. There were no annual or biennial native plants present at plots with no prior presence of invasive plants.\u003c/p\u003e\n\u003cp\u003eUnder prior presence of invasive plants, non-native biennial plants had the highest mean percent cover at high and medium burn severities, with annuals and perennials having similar\u003c/p\u003e\n\u003cp\u003ecover. At low burn severity, non-native perennial plants had the highest mean percent cover with annuals and biennials have similar, low mean percent cover. For native species there were no biennial species for any burn severities, perennial native plants represented the highest mean percent cover for all three burn severities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePercent cover of non-native annual plants and non-native biennial plants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-native annual GLMM (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA) found that low burn severity was negatively related to non-native annuals, (estimate of -2.36 (SE\u0026thinsp;=\u0026thinsp;1.02 P\u0026thinsp;=\u0026thinsp;0.021)). This suggests that compared to high burn severity, low burn severity is associated with a significant decrease in NNA cover. Medium burn severity was negatively related to non-native annuals, (estimate of -1.30 (SE\u0026thinsp;=\u0026thinsp;0.54 p\u0026thinsp;=\u0026thinsp;0.015)). This indicates that compared to high burn severity, medium burn severity has a decrease in NNA cover. Slope was negatively related to NNA cover, (estimate of -0.059 (SE\u0026thinsp;=\u0026thinsp;0.03 p\u0026thinsp;=\u0026thinsp;0.035)). As the slope increases, the predicted NNA cover decreases. Elevation was negatively related to NNA, (estimate of -2.04 (SE\u0026thinsp;=\u0026thinsp;0.62 p\u0026thinsp;=\u0026thinsp;0.001)), as elevation increased, the predicted NNA cover decreased.\u003c/p\u003e\n\u003cp\u003eThe non-native biennial GLMM (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB) found that slope was negatively related to NNB, (estimate of -0.078 (SE\u0026thinsp;=\u0026thinsp;0.029, p\u0026thinsp;=\u0026thinsp;0.008)). As the slope increases, the proportion of NNB decreases, suggesting that steeper areas tend to have lower NNB cover. Elevation was negatively related to NNB, (estimate of -0.66 (SE\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.012)), higher elevations have a decrease in NNB cover. In comparison to the south aspects, northeast aspects were positively related to NNB, (estimate of 2.34 (SE\u0026thinsp;=\u0026thinsp;1.13, p\u0026thinsp;=\u0026thinsp;0.038)), indicating that NNB cover in greater on northeast aspects than south aspects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePercent cover of non-native perennial plants and native perennial plants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-native perennial GLMM (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA) found that elevation was positively related to NNP, (estimate of 0.53, (SE\u0026thinsp;=\u0026thinsp;0.23 p\u0026thinsp;=\u0026thinsp;0.02)). As elevation increases, the predicted NNP cover significantly increases. Compared to south-facing aspects, north (estimate\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.81, SE\u0026thinsp;=\u0026thinsp;1.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), northwest (estimate\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.22, SE\u0026thinsp;=\u0026thinsp;0.91, p\u0026thinsp;=\u0026thinsp;0.14), southeast (estimate\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.00, SE\u0026thinsp;=\u0026thinsp;0.88, p\u0026thinsp;=\u0026thinsp;0.023), and west-facing aspects (estimate\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.49, SE\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;=\u0026thinsp;0.016) were all negatively related with native non-native proportion (NNP).These negatively related aspects are associated with a lower NNP cover than south aspects.\u003c/p\u003e\n\u003cp\u003eThe native perennial GLMM (Figure 8 B) found that elevation was positively related to NP, (estimate of 0.321, (SE = 0.07, p \u0026lt; 0.001)). As elevation increases, the predicted NP cover significantly increases. The GLMM also found that the aspect played a significant role in native perennial plant cover, west aspects were positively related to NP, (estimate of 0.901, (SE = 0.39, p = 0.0195)) the proportion of NP cover is higher on west aspects in comparison to south aspects.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe McKay Creek Wildfire area, with its diverse topography and habitat types, along with availability of baseline data regarding the pre-fire presence of invasive non-native plant species, and the detailed burn severity mapping, provided a unique opportunity to understand the factors driving post-wildfire vegetation trajectories and to evaluate the potential threat of 47 invasive, non-native species to post-wildfire landscapes. Neither of our hypotheses, that both prior presence of non-native invasive plants and higher levels of burn severity would result in increased presence of non-native plant populations on post-wildfire landscapes, were supported by our findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMean percent cover of native plants, non-native plants, and bare ground were similar across all treatments (Figure 3). Using generalized linear mixed effects models (GLMMs), our study uncovered key factors influencing post-wildfire vegetation trajectories of these status groupings; these were topographical factors, namely elevation and aspect. Percent cover of native plants, non-native plants, and bare ground had statistically significant relationships with elevation, with native plant percent cover increasing, non-native plant percent cover decreasing, and bare ground decreasing, with increasing elevation (Figure 4 A-C). When plant groups were further examined by life cycle, this pattern became more nuanced. Non-native annuals showed a negative relationship with burn severity and elevation (Figure 7 A), suggesting a particular vulnerability of these high burn severity low elevation areas to rapid colonization and spread. Non-native biennials decreased with elevation and slope (Figure 7 B), while non-native perennials increased with elevation (Figure 8 A), indicating tolerance for harsher, high-elevation environments. This last group poses a particular threat, as perennial forbs (Figure 5 A) are known for their resilience, deep roots, and ability to reproduce through multiple pathways.\u003c/p\u003e\n\u003cp\u003eOne explanation for the rejection of our hypothesis lies in the ecological mechanisms influencing plant recovery after wildfire. Fire affects both the physical environment and biological legacies. While it can eliminate seed banks in the upper soil layers, it may also stimulate the germination of deeply buried seeds (Santana et al. 2010, Roshan et al. 2022). These dynamics, combined with the survival and resprouting capabilities of perennial plants through rhizomes, may mask or override the effects of pre-fire invasive plant populations or burn severity (Figure 6). Additionally, vegetation recovery is influenced by the functional traits of plants, including their life cycles (Figure 6). Annual plants, which rely heavily on seed banks and disperse quickly, are more likely to establish rapidly in disturbed environments. In contrast, perennial species generally recover more slowly but persist through vegetative structures and complex ecological relationships.\u003c/p\u003e\n\u003cp\u003eGiven that our results contrast other findings, such as results from Yellowstone National Park that showed substantial effects of fire severity and existing invasive plant patch size on early post-fire plant cover and species (Turner et al. 2016), our study suggests that predictions regarding vegetation trajectories may not be widely \u003cem\u003e\u0026nbsp;\u003c/em\u003eapplicable across differing landscapes. This difference may, in part, reflect the relatively early stage of recovery captured in our dataset. Several long-term studies have shown that vegetation responses to fire, particularly in relation to burn severity and invasive species, often unfold over a longer time frame. For example, Shinneman and Baker (2009) documented that \u003cem\u003eBromus tectorum\u003c/em\u003e cover increased gradually over seven to eight years post-fire in semiarid ecosystems, peaking well after the initial two-year period examined here. Similarly, Morgan et al. (2015) found that vegetation cover and species diversity remained suppressed on high-severity plots for at least six years following fire, highlighting the long-term influence of burn severity. Furthermore, Tepley et al. (2018) emphasize that high-severity burns can initiate fire–vegetation feedbacks that may not be detectable in early post-fire years but can eventually lead to landscape-scale shifts in vegetation composition. These studies collectively suggest that the muted effects of burn severity or previous presence of invasive species observed in our study may reflect temporal lag rather than a lack of eventual impact, underscoring the need for long-term monitoring to fully understand post-fire vegetation trajectories. Another explanation for this discrepancy may lie in sampling design or the biophysical differences of the McKay Creek area. Our sampling plots were located away from roads, riparian corridors, and other disturbance vectors, potentially underrepresenting invasion hotspots and overrepresenting conditions less prone to non-native colonization. Furthermore, the complex topography, diverse habitat types, and large size of the McKay Creek fire may dilute simple cause-effect relationships. These contrasting findings highlight the importance of regionally specific, long-term monitoring of vegetation trajectories of post-wildfire landscapes to inform restoration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther limitations of our study must be acknowledged. While we benefited from strong pre-fire baseline data and high-resolution burn severity mapping, the scale and heterogeneity of the landscape limited sampling intensity. This low-intensity design, though spatially extensive, may have missed smaller-scale vegetation dynamics or rare species occurrences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eManagement and Policy Implications\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur findings have several implications for land management and restoration policy in post-wildfire landscapes. First, while the desire to immediately intervene to restore post-wildfire landscapes, particularly by seeding, is a common response by agencies responsible for wildfire recovery, research shows that seeding can increase competition with desirable plant species and underestimate the potential for long-term recovery of native perennial cover (Copeland et al 2019). Land managers must exercise caution with interventions, particularly when there is urgency due to public pressure, to cover the wildfire scars on the landscape as quickly as possible. Further, they should also exercise caution in engaging in immediate interventions meant to serve the interests of industries that may be contributing to these destructive wildfire regimes such as forestry (the introduction of monotypic, high density tree plantations) and cattle grazing tenures. It is critical to ensure wildfire recovery is data-driven and carefully articulates restoration goals that build resiliency while also considering a future for the landscape that may not resemble pre-fire conditions.\u003c/p\u003e\n\u003cp\u003eSecond, they highlight the need for rapid response strategies, particularly in low-elevation, high-severity burn areas where bare ground and non-native annuals are most prevalent. Prevention measures such as restricting vehicle and livestock access, installing wash stations, and establishing exclusion zones should be prioritized. Monitoring programs should focus on early detection of invasive species in these vulnerable areas, coupled with careful, targeted application of herbicides where needed. Caution is warranted in the use of residual herbicides that could harm emerging native perennials.\u003c/p\u003e\n\u003cp\u003eFinally, our study underscores the importance of locally tailored, data-driven restoration strategies. Generalized ecological theories such as niche theory may fail to account for the highly altered conditions of post-fire landscapes, including soil degradation, hydrologic shifts, and climate-driven drought stress (Bakker et al. 2003, Groves \u0026amp; Brudvig 2019). Thus, species selection needs to take this new reality into consideration; planting plans should consider how to establish protective canopies, vertical structures, and appropriate timing should be established and adhered to for planting when regular precipitation is anticipated to ensure plant establishment. On-going stewardship activities after planting, such as monitoring for indicators of plant stress and establishment, will be necessary to not only give new plants the best possible chance of survival by informing when interventions may be needed, but to also contribute to the body of knowledge informing post-wildfire restoration best management practices.\u0026nbsp;\u003c/p\u003e\n\n\n\n"},{"header":"Conclusion","content":"\u003cp\u003eThis study offers new insights into the complex dynamics shaping post-wildfire vegetation recovery in British Columbia\u0026rsquo;s interior. Contrary to expectations, neither burn severity nor pre-fire invasive plant presence emerged as strong predictors of post-fire vegetation outcomes. Instead, elevation played a dominant role in structuring native and non-native plant cover, underscoring the need to account for topographic variation in restoration planning. The relatively high proportion of native species observed suggests some resilience among fire-adapted communities, while persistent bare ground highlights an urgent need for early monitoring and prevention of invasive plant spread.\u003c/p\u003e\n\u003cp\u003eAs fire regimes intensify under climate change, region-specific, long-term studies like this one will be essential for informing restoration efforts. Our results reinforce the importance of context-driven approaches and challenge assumptions that commonly guide post-fire intervention. By continuing to monitor vegetation trajectories at McKay Creek and similar sites, we can contribute to a more nuanced and adaptive understanding of fire ecology across diverse landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics Approval and Consent to Participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for Publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of Data and Materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this study was provided by the Lillooet Regional Invasive Species Society (LRISS), Squamish-Lillooet Regional District (SLRD), Mitacs Accelerate, and the Natural Sciences and Engineering Research Council of Canada (NSERC).\u003c/p\u003e\n\u003cp\u003eClinical trial number not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eVO made substantial contributions to the study design, data collection, analyses, and drafting of the work. TM and SS contributed to the study design, analytical approaches and analyses, drafting, and revisions of the work. JG was responsible for the conception and funding of the study, development of the study design, assisted with data collection and analyses, and made substantial contributions to drafting and revisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the contributions of the Ts'kw'aylaxw First Nation (Chief Justin Kane, Desmond Peters Jr., and Mike Mcewen), Xwísten First Nation (Travis Peters), Lillooet Tribal Council (Matt Manuel), St'át'imc Government Services (Darwyn John), Lillooet Regional Invasive Species Society (Jacquie Rasmussen and Dr. Sue Senger) for their time, knowledge, and guidance throughout this project. To the T'ít'q'et - P’egp’íg’lha community (Denise Antoine, Christian Ahrenkiel, Sam Copeland, Raymon Billy, and Luther Brigman) for contributions in the field. We would also like to acknowledge Dr. Lori Daniels, for sharing her expertise in wildfire, and Sarah Dickson-Hoyle and Sofie McComb for discussion regarding the study analyses. We would like to extend our thanks to all who assisted with the extensive fieldwork: Vanessa Jones, Emma Sneep, Kayla Poppy, Chanvre Oleman, Nicole Morgenstern, Nina Andrascik, Les Riley, and Denny Higginbottom. \u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbatzoglou, John T., and A. Park Williams. 2016. \u0026ldquo;Impact of Anthropogenic Climate Change on Wildfire across Western US Forests.\u0026rdquo; \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e 113 (42): 11770\u0026ndash;75.\u003c/li\u003e\n \u003cli\u003eAlba, Christina, Hana Sk\u0026aacute;lov\u0026aacute;, Kirsty F. 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Williams. 2015. \u0026ldquo;Vegetation Response to Burn Severity, Native Grass Seeding, and Salvage Logging.\u0026rdquo; \u003cem\u003eFire Ecology\u003c/em\u003e 11 (2): 31\u0026ndash;58. https://doi.org/10.4996/fireecology.1102031.\u003c/li\u003e\n \u003cli\u003eNeary, Daniel G, Carole C Klopatek, Leonard F DeBano, and Peter F Ffolliott. 1999. \u0026ldquo;Fire Effects on Belowground Sustainability: A Review and Synthesis.\u0026rdquo; \u003cem\u003eForest Ecology and Management\u003c/em\u003e 122 (1): 51\u0026ndash;71. https://doi.org/10.1016/S0378-1127(99)00032-8.\u003c/li\u003e\n \u003cli\u003eParks, S. A., and J. T. 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Anderson-Teixeira, Andrs Holz, Juan Paritsis, and Thomas Kitzberger. 2018. \u0026ldquo;Influences of Fire-Vegetation Feedbacks and Post-Fire Recovery Rates on Forest Landscape Vulnerability to Altered Fire Regimes.\u0026rdquo; \u003cem\u003eJournal of Ecology\u003c/em\u003e 106 (5): 1925\u0026ndash;40.\u003c/li\u003e\n \u003cli\u003eTurner, Monica G., Timothy G. Whitby, Daniel B. Tinker, and William H. Romme. 2016. \u0026ldquo;Twenty-Four Years after the Yellowstone Fires: Are Postfire Lodgepole Pine Stands Converging in Structure and Function?\u0026rdquo; \u003cem\u003eEcology\u003c/em\u003e 97 (5): 1260\u0026ndash;73.\u003c/li\u003e\n \u003cli\u003eTurner, Nancy J. 1999. \u0026ldquo;Time to Burn: Traditional Use of Fire to Enhance Resource Production by Aboriginal Peoples in British Columbia.\u0026rdquo; In \u003cem\u003eIndians, Fire and the Land in the Pacific Northwest\u003c/em\u003e, edited by R. Boyd, 185\u0026ndash;218. Corvallis: Oregon State University Press.\u003c/li\u003e\n \u003cli\u003eU.S. Department of Agriculture, Forest Service. n.d. \u003cem\u003eFire Effects Information System (FEIS)\u003c/em\u003e. Rocky Mountain Research Station, Missoula Fire Sciences Laboratory. https://www.feis-crs.org/feis/.\u003c/li\u003e\n \u003cli\u003eZouhar, Kristin, Jane Kapler Smith, and Steve Sutherland. 2008. \u0026ldquo;Effects of Fire on Nonnative Invasive Plants and Invasibility of Wildland Ecosystems.\u0026rdquo; In \u003cem\u003eWildland Fire in Ecosystems: Fire and Nonnative Invasive Plants\u003c/em\u003e, edited by Kristin Zouhar, Jane Kapler Smith, Steve Sutherland, and Matthew L. Brooks, 7\u0026ndash;32, 42. Gen. Tech. Rep. RMRS-GTR-42, vol. 6. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"fire-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"feco","sideBox":"Learn more about [Fire Ecology](https://www.springer.com/journal/42408)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/feco/default.aspx","title":"Fire Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Wildfire, invasive plants, native plants, vegetation trajectories, ecological restoration","lastPublishedDoi":"10.21203/rs.3.rs-6957462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6957462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Globally, shifting fire regimes which have increased in frequency and severity-- driven by climate change, land use changes, and fire suppression-- are altering vegetation dynamics. Little is known about the impacts of this new era of wildfire on seedbank dynamics, and native and non-native plants. Given constrained resources and the expanding extent of wildfire-affected areas, there is a pressing need to understand the factors influencing post-fire vegetation dynamics data to prioritize and guide landscape-level recovery interventions. The 46,000 ha McKay Creek Wildfire in the interior of British Columbia, Canada, provided a unique opportunity to assess how factors such as topography, burn severity, and pre-fire invasive plant presence drive post-wildfire vegetation trajectories due to its diverse ecosystems, representation of all burn severities, and extensive baseline data on the presence of invasive species. We hypothesized that both prior presence of non-native plants and high burn severity would result in increased presence of non-native plant populations on post-wildfire landscapes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFoliar cover was recorded by species and grouped by native status (native or non-native), life cycle (annual, biennial, perennial), and life form (e.g., forb, grass, shrub) on 80 plots stratified by burn severity and pre-fire presence of invasive plants. Our findings showed that bare ground accounted for the greatest proportion of cover across all plots, remaining near or above 50% across all stratifications. Native cover ranged from just over 25% to 41%, varying with burn severity and prior invasive plant presence. Non-native cover remained below 5% across all conditions. Topography, particularly elevation and aspect, was the strongest driver of post-fire vegetation patterns with the proportion of native plant cover highest at higher elevations and on west-facing slopes. Plant lifecycle was an influential factor on non-native plants, with annuals most prevalent in high severity burns and at lower elevations, and perennials most abundant at higher elevations. Burn severity had limited influence on total non-native cover.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eAt a time when wildfire is increasing in size, frequency and intensity, and resources are limited for recovery efforts, our study may contribute critical insights for land managers to prioritize and plan post-fire restoration activities such as monitoring, prevention and management of invasive species, and interventions such as planting.\u003c/p\u003e","manuscriptTitle":"Factors Driving Vegetation Trajectories of Post-Wildfire Landscapes in the Interior of British Columbia, Canada","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 15:04:00","doi":"10.21203/rs.3.rs-6957462/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-24T23:13:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T01:01:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T18:25:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61494605232458124782218885729703301533","date":"2025-09-08T17:58:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236551757514381179226173783082258306820","date":"2025-09-04T15:25:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T22:43:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111605232569704760620238657216330184087","date":"2025-08-19T13:54:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329864878522716698029509824426199664045","date":"2025-08-13T02:07:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81772522023847971373561785502613006728","date":"2025-07-28T16:45:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T16:42:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-01T00:02:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-01T00:01:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Fire Ecology","date":"2025-06-23T13:42:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"fire-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"feco","sideBox":"Learn more about [Fire Ecology](https://www.springer.com/journal/42408)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/feco/default.aspx","title":"Fire Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"484ef087-85e1-4e49-a068-30f057f87a5e","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:02:37+00:00","versionOfRecord":{"articleIdentity":"rs-6957462","link":"https://doi.org/10.1186/s42408-026-00463-x","journal":{"identity":"fire-ecology","isVorOnly":false,"title":"Fire Ecology"},"publishedOn":"2026-03-05 15:59:04","publishedOnDateReadable":"March 5th, 2026"},"versionCreatedAt":"2025-07-30 15:04:00","video":"","vorDoi":"10.1186/s42408-026-00463-x","vorDoiUrl":"https://doi.org/10.1186/s42408-026-00463-x","workflowStages":[]},"version":"v1","identity":"rs-6957462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6957462","identity":"rs-6957462","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}