Next Article in Journal
A Forest Fire Occurrence Prediction Method for Guizhou Province, China, Based on the Ignition Component
Previous Article in Journal
Closed-Loop Multimodal Framework for Early Warning and Emergency Response for Overcharge-Induced Thermal Runaway in LFP Batteries
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Fire
Volume 8
Issue 11
10.3390/fire8110438
fire-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Fine-Scale Environmental Heterogeneity Shapes Post-Fire Macrofungal Richness in a Mediterranean Relict Forest

by
Celeste Santos-Silva
1,2,
Bruno Natário
1,2 and
Ricardo Pita
1,3,*
1
Mediterranean Institute for Agriculture, Environment and Development, CHANGE—Global Change and Sustainability Institute, University of Évora, P.O. Box 94, 7002-554 Évora, Portugal
2
MacroMycology Lab, Biology Department, Science & Technology School, University of Évora, P.O. Box 94, 7002-554 Évora, Portugal
3
UBC—Conservation Biology Lab, IIFA—Institute for Advanced Studies and Research, University of Évora, 7002-554 Évora, Portugal
*
Author to whom correspondence should be addressed.
Fire 2025, 8(11), 438; https://doi.org/10.3390/fire8110438 (registering DOI)
Submission received: 26 September 2025 / Revised: 29 October 2025 / Accepted: 5 November 2025 / Published: 9 November 2025
Browse Figures
Versions Notes

Abstract

Mediterranean relict forests, including Laurisilva and other humid forest refugia, are rare and ecologically distinctive habitats often embedded in fire-prone landscapes. Understanding how these ecosystems respond to disturbance is essential for biodiversity conservation and land management under increasing fire risk. However, the effects of fire on key components of these forests, such as macrofungi, remain poorly understood. Here, we examined how fine-scale spatial heterogeneity in fire severity, topography and vegetation shapes post-fire macrofungal communities in a Laurisilva relict forest in central Portugal. Fire severity reduced mycorrhizal richness while having negligible effects on saprotrophs, leading to shifts in the mycorrhizal-to-saprotrophic richness ratio along severity gradients. A similar shift toward saprotrophs also occurred from low to moderate–high elevations, consistent with more exposed, drier conditions at higher elevations. Aspect, topographic ruggedness, and wetness showed weaker, guild-specific associations with macrofungal richness, while vegetation cover and richness had more limited influence, possibly reflecting the complexity and vulnerability of post-fire plant–fungus interactions. Overall, these results highlight the importance of conserving humid and structurally complex environments to foster post-fire fungal diversity in relict forests. More broadly, our findings suggest that fine-scale environmental heterogeneity may help sustain relict forest resilience under intensifying wildfires and other disturbances associated with land-use and climate change.

1. Introduction

Across the globe, relict forests represent remnant fragments of ancient vegetation that persist under restricted environmental conditions and are widely recognized for their ecological distinctiveness and conservation importance [1]. In the Mediterranean context, these include Laurisilva and other humid refugia, once widespread across southern Europe, but now confined to rare, small and isolated patches within predominantly dry and fire-prone landscapes [2]. Laurisilva remnant forests in this region are maintained by humid microclimates that favor dense evergreen canopies dominated by laurel (Laurus nobilis L.), holly (Ilex aquifolium L.), and other broadleaved species [3,4], in sharp contrast to the surrounding vegetation. These relict ecosystems function as biodiversity hotspots, providing habitat for endemic species, regulating water quality, and contributing to ecological stability in landscapes otherwise dominated by drier and more seasonal vegetation [2]. However, their restricted extent and highly fragmented distribution make them particularly vulnerable to disturbance, elevating their conservation importance within Mediterranean landscapes.
Fire is one of the dominant drivers of disturbance in Mediterranean landscapes, where its frequency and intensity exceed those of most temperate forests [5,6]. In combination with land-use change and climate warming, wildfires create disturbance regimes that are increasingly severe and unpredictable [7,8,9], making relict forests, with their dense biomass and dependence on humid microclimates, potentially more vulnerable to these disturbances [10]. Despite this vulnerability, the impacts of fire on key components of these forests, such as macrofungal communities, remain poorly understood [11,12]. Macrofungi play essential roles in forest ecosystems, contributing to nutrient cycling, decomposition, and soil health, and sustaining key ecological processes such as forest regeneration and resilience to disturbance [13,14,15,16]. They also engage in complex interactions with plants, animals, and microorganisms, underpinning ecosystem services that are critical for both biodiversity and land management [14,17,18]. While the drivers of macrofungal diversity are relatively well documented in temperate forests [19], far less is known about their dynamics in Mediterranean systems, and even fewer studies have addressed post-fire responses in relict habitats such as Laurisilva forests (but see [20]). Post-fire recovery unfolds within fine-scale mosaics of topography and vegetation, which shape soil moisture, shading, and substrate availability, influencing fungal recolonization [11]. In relict Laurisilva forests, these environmental gradients should be particularly relevant, as the humid microclimates and often complex terrain of these forests likely affect both fire effects and subsequent recovery dynamics. Environmental heterogeneity in this context reflects both the pre-existing spatial structure of the landscape and the variability introduced by fire, particularly through differences in fire severity. While topographic features remain stable, fire alters how critical conditions for fungal recovery vary across topographic gradients, thereby altering the patterns of environmental heterogeneity [10]. Understanding how such heterogeneity shapes post-fire macrofungal diversity in Mediterranean relict forests thus requires assessing species responses along gradients of fire severity, topography, and vegetation. Such assessments may provide critical insights into the resilience and functional dynamics of these ecosystems, while informing management practices aiming their conservation under increasing land-use and climate pressures [2].
In this study, we investigated macrofungal diversity following the 2017 wildfire that burned nearly 90% of the Mata Nacional da Margaraça (MNM, ca. 68 ha), one of the few remaining Laurisilva forests in Mediterranean Europe [3,20]. To our knowledge, post-fire macrofungal communities have rarely been assessed in Mediterranean Laurisilva forests, despite their key role in ecosystem recovery after disturbance [8,20]. Macrofungal diversity in forests may be shaped by both biotic and abiotic factors, with vegetation richness and cover influencing resource availability [21,22,23], and topographic conditions affecting soil properties, moisture, and microclimates [24,25,26]. Building on this framework, and on fine-scale environmental variables describing spatial variation in vegetation, topography, and fire severity across MNM, we tested the hypotheses that: (H1) Fire severity impacts macrofungal diversity, reducing the richness of mycorrhizal fungi, while producing weaker and possibly slightly positive effects on saprotrophs at moderate severity, ultimately altering their relative proportions and changing community trophic structure along fire severity gradients [8,20,27]; (H2) Topographical factors (e.g., elevation, slope, aspect) significantly influence macrofungal diversity, with certain variables (such as slope or elevation) positively correlating with macrofungal richness, consistent with prior studies [24,25,28]; and (H3) Vegetation richness and cover positively influence macrofungal diversity, particularly for mycorrhizal fungi in areas with lower fire severity [7,9,29]. However, vegetation effects may be weaker than expected, due to the overriding influence of fire severity or other environmental factors. Overall, we expect our study will enhance understanding of the drivers of macrofungal diversity in relict Laurisilva forests in Mediterranean areas, particularly in relation to fire, and contribute to conservation strategies focused on supporting these biodiversity hotspots in the face of increasing fire frequency and other climate-related challenges [30].

2. Materials and Methods

2.1. Study Area

The MNM forest (40°12.9781′ N, 7°55.1349′ W) is located within the Serra do Açor Protected Landscape (Figure 1a,b), in a valley with a distinct Atlantic influence but embedded in a predominantly Mediterranean climate. The region is characterized by a three-month dry season (July to September), with annual precipitation averaging 1440 mm and temperatures ranging from 7.9 to 22.6 °C [31]. Elevations range from 450 to 800 m [32], and soils are mainly classified as Cambisols and Leptisols. Vegetation is mostly dominated by the Rusco aculeati-Quercetum roboris association, subassociation Viburnetosum tini, class Querco-Fagetea, order Quercetalia roboris and alliance Quercion robori-pyrenaicae, Quercus robur L. and Castanea sativa Mill. The understory is composed mainly of laurel species (e.g., Laurus nobilis L., Ilex aquifolium L., Viburnum tinus L., and Prunus lusitanica L. ssp. lusitanica), Cytisus spp., Erica spp., Calluna vulgaris (L.) Hull, Ulmus minor Mill., Prunus cerasus L., Prunus avium L., Corylus avellana L. and Arbutus unedo L. By the early 1960s all extractive activities in MNM had ceased, resulting in an accumulation of organic matter deposited on the forest floor, which may have contributed to the major wildfires in 1987 and more recently in October 2017 [20].

2.2. Macrofungal Surveys

Sixty-six non-overlapping 25 m2 (5 m × 5 m) square plots were randomly distributed across the MNM to survey macrofungal communities following the 2017 wildfire (Figure 1c). To account for the different fruiting phenologies of species [33], surveys were carried out between May 2018 and May 2019, covering the most favourable periods for sporocarp emergence, namely spring (March−May) and autumn (October−December), with each plot surveyed once per period. Within each plot, all aboveground sporocarps were collected from available substrates, including mineral soil, organic matter, downed logs, and live or dead stems [20]. Samples were stored at 4 °C and processed within 24 h for morphological identification to the species level whenever possible. Although molecular approaches are increasingly used in fungal diversity studies [34,35,36,37,38,39], we adopted a sporocarp-based methodology, which remains widely applied [40,41,42,43,44,45,46]. While molecular methods can detect non-fruiting and belowground fungi, they are constrained by the high proportion of unclassified taxa due to incomplete reference databases, reduced efficiency for rare species [47], and greater demands in terms of time and resources [48]. In contrast, sporocarp surveys provide reliable species-level identifications and are generally more time-efficient [40,45,49]. Identification was supported by the Macromycology Laboratory’s reference collection and specialized resources, including online databases, identification keys, and taxonomic literature (e.g., [50,51,52,53,54,55,56]). Nomenclature follows the taxonomic standards of [57,58]. Post-fire species classification was based on [8,59,60,61]. All collected specimens were preserved and deposited in the Évora University herbarium (UEVH-FUNGI)

2.3. Environmental Variables

Fire severity was estimated for each plot using ArcGIS version 10.5.1 (ESRI, Redlands, CA, USA) [62], based on the differenced Normalized Burn Ratio Index (dNBR). This index is derived from Sentinel-2 spectral reflectance in the near-infrared (NIR) and short-wave infrared (SWIR) bands, providing 10 m spatial resolution and 5-day revisit time [63]. Pre- and post-fire images acquired on 4 July and 22 October 2017, respectively, were used to compute dNBR. Higher values indicated greater fire damage, whereas negative values suggested post-fire regrowth. Although dNBR can be classified into severity categories, we treated it as a continuous variable to retain quantitative variation across plots [64]. Vegetation composition was recorded in the field during macrofungal surveys, with all vascular plant species identified and their percentage cover visually estimated within each 25 m2 sampling plot (Table S1, Supplementary Materials).
Topographic variables were derived from an ASTER digital elevation model (DEM), resampled to 30 m resolution using bilinear interpolation in ArcGIS version 10.5.1 (ESRI, Redlands, CA, USA), to characterize the topographic context around each 5 m × 5 m plot. This resolution provides hydrologically stable derivatives in steep, forested terrain and is widely adopted in landscape-scale ecological studies (e.g., for TWI/TRI) [65], representing the standard topographic scale in biodiversity and fungal ecology research, thus ensuring consistency and comparability across studies. In addition, this resolution provides spatial complementarity relative to fire severity data, avoiding pseudo-precision and redundancy that could result from using closely matched spatial scales, while capturing distinct aspects of fine-scale landscape variation [24]. For each plot, topography data were extracted from the raster cell nearest to the plot centroid, assuming this adequately represented local conditions given the 25 m2 plot size. Metrics were computed using the Geomorphometry and Gradient Metrics toolbox [66], DEM Surface Tools [67], and Land Facet Corridor Tools [68]. We considered elevation (m a.s.l.), slope (°; rate of change of elevation), and aspect (°; 0 = north, 90 = east, 180 = south, 270 = west), estimated using the 4-cell method [69,70] (Table 1). To better capture exposure to sunlight, we applied sine and cosine transformations of slope and aspect [71], with higher values reflecting greater exposure (e.g., south-facing slopes in the Northern Hemisphere) and lower values more shaded orientations. Additional terrain metrics included the topographic ruggedness index (TRI), quantifying the mean absolute difference in elevation between a focal cell and its surroundings within a 25-cell window [72], and the topographic wetness index (TWI) (Table 1), which expresses the balance between water accumulation and drainage potential [73,74]. TWI was derived using the SAGA GIS version 7.3.0 (Hamburg, Germany) implementation with a smoothing parameter set at 50 [75]. Both TRI and TWI are ecologically relevant, as rugged terrain and wetter microsites may enhance habitat heterogeneity and species diversity [72,74]. Given that topography and vegetation variables strongly mediate microclimatic variation [76], we did not include climatic variables, as available datasets describe regional macroclimatic gradients at ~1 km resolution [77,78], which would be insufficient to capture the fine-scale variation relevant to our plots.

2.4. Data Analyses

Macrofungal species were assigned to four trophic guilds (mycorrhizal, saprotrophs, biotrophs, parasitic). Species richness (S) was calculated for groups comprising at least two species or occurring in ≥15% of sampling plots, pooling data per plot to account for differences in fruiting phenology [33]. As only mycorrhizal and saprotrophic fungi met these criteria (see Section 3), we also computed the ratio between mycorrhizal and saprotrophic richness (Smyc/Ssap) as an index of trophic structure [79].
We assessed completeness for species richness with version 3.0.2 of ‘iNEXT rarefaction/extrapolation [80] using incidence-frequency data and treating each plot as a sampling unit. For each guild, we produced sample-size and sample-coverage curves with 95% CIs, interpolating within the observed effort and extrapolating to 2 × T (T = 66 plots per guild). Because equal effort does not imply equal completeness, we used the coverage-based view to compare guilds at a common level of completeness, checking the coverage attained and whether the guild ranking held within the observed range [81]. Having verified comparability, we considered observed plot-level richness (Smyc, Ssap) and the Smyc/Ssap ratio in subsequent analyses to assess among-plot variation under equal effort. We used observed responses because completeness procedures are assemblage-level diagnostics, rather than plot-level responses, and substituting estimator-derived values would dampen among-plot signals and introduce additional uncertainty [82].
We then assessed the effects of fire severity, vegetation, and topographic predictors on Smyc, Ssap, and the Smyc/Ssap ratio, following a two-stage procedure within an Information-Theoretic Approach [83]. First, we fitted single-predictor generalized linear models (GLMs) to evaluate the individual effect of each predictor. Negative binomial GLMs were used for Smyc and Ssap to account for overdispersion in count data [84], while the Smyc/Ssap ratio was modelled with Gaussian error. For each response variable, we compared single-predictor models against null models (with no predictors), evaluating alternative response curves (linear and second-order quadratic) while avoiding higher-order terms to reduce the risk of overfitting. Model selection was based on the Akaike Information Criterion corrected for small samples (AICc), with lower values indicating better fit [84]. Specifically, we first checked the support for the model with only the linear term relative to the null model, and then evaluated whether the inclusion of the quadratic term would lead to any further improvement in the model. For each response variable, we considered as candidate predictors for subsequent analyses only those that when included in the model resulted in a decrease in the AICc by more than two units relative to the model with no predictors (null model).
After identifying candidate predictors for each response variable, we examined multicollinearity using Pearson’s correlation coefficients. Highly correlated predictors (|r| > 0.7; [85]) were reduced by retaining only the variable with the lowest AICc in single-predictor models. To account for potential spatial autocorrelation, we calculated Moran’s I on model residuals using an inverse distance matrix using the R package ape version 5.0 [86]. Where significant (p < 0.05), we included the first axis of a principal coordinates of neighbour matrices (PCNM; [87]) as an additional fixed spatial predictor using the R package vegan version 2.7-2; [88]). In PCNMs, the truncation threshold was set to the shortest distance ensuring all sites were connected in a complete network. This approach incorporates spatial autocorrelation directly into the models, thereby controlling for spatial dependence in the response variables. Aspect and slope were treated as continuous variables, with low aspect values corresponding to more northerly exposures [89]. All predictors were standardized prior to modelling.
The final step of the analysis consisted in building a series of models for each response variable including all possible combinations of candidate predictors (main effects) and types of responses using ITA [83]. In each case, model ranking and selection were based on AICc. When multiple models were equally supported (ΔAICc < 2), we used conditional model averaging, reporting model-averaged coefficients with unconditional SEs and 95% confidence intervals (CI) to account for model-selection uncertainty, as well as summed Akaike weights (Σwi) as an index of variable importance [83]. When a single model had clear support (next-best ΔAICc ≥ 2), we reported its coefficients directly. As a measure of fit of supported models, we estimated the Nagelkerke’s likelihood-ratio pseudo-R2 for negative binomial GLMs and the conventional adjusted R2 for and Gaussian models, using the respective null models as reference. All models were implemented and compared in R version 4.5.0, using the R packages stats version 4.6.0 [90], mass version 7.3-65 [91] and MuMIn version 1.48-11 [92].

3. Results

We identified a total of 9122 macrofungal specimens belonging to 130 species, with 31 species found exclusively during spring surveys, 79 during autumn, and 20 in both seasons (Table S2, Supplementary Materials). Basidiomycota dominated the assemblage (100 species) relative to Ascomycota (30 species), encompassing 32 families. The most species-rich Basidiomycota families were Inocybaceae (10 species), Mycenaceae (10 species), and Russulaceae (8 species), whereas Pezizaceae (10 species) and Pyronemataceae (5 species) were the most diverse among Ascomycota. Based on published references, 15 species were classified as post-fire fungi, including 7 Basidiomycota and 8 Ascomycota (Figure 2).
Saprotrophs were the dominant guild, accounting for 82.9% of specimens recorded and 59.2% of the species identified, and were represented in all 66 sampling plots. The most abundant was Stereum hirsutum (4124 specimens, occurring in 77.3% of plots), followed by Pholiota brunnescens (513 specimens, 54.6% of plots) and Tephrocybe anthracophila (164 specimens, 42.4% of plots), all three classified as post-fire fungi. Other frequent saprotrophs included Stereum subtomentosum (688 specimens, 30.3% of plots), Psathyrella pennata (84 specimens, 30.3%), Hypholoma fasciculare (262 specimens, 22.7%), and Mycena galopus (96 specimens, 22.7%). The remaining 70 saprotrophic species each occurred in fewer than 15% of plots (Table S2, Supplementary Materials).
Mycorrhizal fungi were also relatively well represented, comprising 4.7% of all specimens and 37.7% of species, and occurring in 59.1% of the sampling plots. The most common mycorrhizal species was Laccaria laccata, with 238 specimens recorded in 31.8% of plots, while the remaining 48 species occurred in fewer than 15% of plots. Biotrophic fungi were much less frequent, with only two species identified (Table S2, Supplementary Materials). Among them, Anthracobia macrocystis was relatively abundant (1053 specimens across 20 plots), whereas Cuphophyllus russocoriaceus was represented by only six specimens in a single plot. Parasitic fungi were similarly scarce, with two mycoparasite species recorded: Phaeotremella foliacea (30 specimens in 13 plots) and Tremella mesenterica (15 specimens in 7 plots). Due to their low representation, richness was not calculated for either biotrophic or parasitic groups. Mean ± SE (range) species richness per plot was 4.86 ± 0.27 (1–12) for saprotrophs and 1.77 ± 0.28 (0–10) for mycorrhizal fungi, indicating lower mycorrhizal than saprotrophic richness per plot, with averaged Smyc/Ssap ratio of 0.57 ± 0.13 (0–5). Rarefaction/extrapolation curves showed no clear signs of saturation (Figure 3), with a total of 77 saprotrophic species and 49 mycorrhizal species observed across sampling plots. However, sample coverage at the observed effort indicated moderate completeness (0.861 for saprotrophs and 0.807 for mycorrhizal), consistent with ranges commonly reported in fungal surveys [93] and considered adequate for comparing guilds [80,81].
A total of seven potential predictors of mycorrhizal richness (Smyc) emerged from single-predictor modelling (Table S3, Supplementary Materials). After accounting for correlations among predictors (Table S4, Supplementary Materials), five were retained for model building: fire severity, plant richness, elevation, and aspect (all with negative effects), and the topographic wetness index (positive effect). For saprotrophic richness (Ssap), five potential predictors were initially identified (Table S3, Supplementary Materials), but only four were retained after correlation checks (Table S4, Supplementary Materials): fire severity, topographic ruggedness index, and cosine slope/aspect transformation (all with positive effects), and sine slope/aspect transformation (non-linear effect). For the Smyc/Ssap ratio, five predictors were indicated, of which four were retained after correlation analysis (Table S4): fire severity, elevation, and topographic wetness index (all with non-linear effects), and aspect (negative effect) (Table S3, Supplementary Materials).
Moran’s I tests revealed that all response variables (Smyc, Ssap, and Smyc/Ssap) exhibited stronger spatial clustering than expected under random spatial processes (Table S5, Supplementary Materials). Accordingly, the first axis of the Principal Coordinates of Neighbor Matrices (PCNM) was included as an additional predictor (pcnmmyc, pcnmsap, pcnmrms). This required removing the sin slope/aspect transformation from the list of candidate predictors of saprotrophic richness (Ssap), which was highly correlated with pcnmsap (Table S4, Supplementary Materials). The inclusion of the PCNM term allowed the models to account for spatial autocorrelation, thus minimizing the risk of spatially driven bias in parameter estimates. Overall, model building and selection considered 24 candidate models for Smyc, 9 for Ssap, and 15 for Smyc/Ssap (Tables S6–S8).
The AICc-based model ranking for Smyc revealed that three models are equally supported (see Table 2 and Table S6, Supplementary Materials), all including fire severity, one additionally including aspect and another including topographic wetness index (TWI). (Table 2). Model averaging showed a strong negative effect of fire severity on mycorrhizal richness, together with a weak negative effect of aspect (suggestive of higher diversity at lower angles, i.e., nearer north) and a weak positive effect of topographical wetness index, respectively (Table 3 and Figure 4a–c). For Ssap, a single best model was selected (Table 2; Table S7, Supplementary Materials), showing a positive effect of ruggedness index (Table 3; Figure 4d). For Smyc/Ssap, four models were equally supported (Table 2; Table S8 Supplementary Materials), all including a non-linear effect of fire severity, three including non-linear effect of elevation, one including non-linear effect of topographic wetness index and one including the linear effect of aspect. Model averaging indicated a sharp decline in Smyc/Ssap up to moderate–high fire severity, followed by a steady increase at the highest severity levels (Table 3; Figure 4e). This non-linear pattern was also apparent for elevation, while the topographic wetness index showed a weak non-linear effect characterized by a steady decline in Smyc/Ssap as this index increases (Table 3, Figure 4f,g). A weak, though consistent, negative effect was further observed for aspect (Table 3 and Figure 4h).

4. Discussion

Relict Laurisilva forest patches are rare in mainland Europe, persisting as refugia in humid, topographically buffered environments embedded within increasingly fire-prone Mediterranean landscapes [2]. Despite their ecological distinctiveness and high conservation value, the responses of fungal communities in these forests to wildfire and environmental heterogeneity remain poorly understood, constraining our ability to evaluate their vulnerability to disturbance [20]. Our study, carried out in the Mata Nacional da Margaraça (MNM) Laurisilva forest (central Portugal) after a severe wildfire, provides new insights into this gap. The results generally supported our initial hypotheses regarding the roles of fire severity, fine-scale topographical variation, and guild-specific responses in shaping macrofungal communities in relict forests. Overall, our findings highlight the sensitivity of these forests to fire and underscore the importance of conserving fine-scale environmental heterogeneity to sustain macrofungal diversity and resilience under intensifying disturbance regimes [27,93].

4.1. Fire Severity and Guild-Specific Responses

Our results showed that, in line with our predictions, fire severity had clear guild-specific effects on post-fire macrofungal richness. Specifically, mycorrhizal richness declined with increasing fire severity, a pattern that is consistent with other studies across Mediterranean and temperate biomes [94,95]. This likely reflects the vulnerability of mycorrhizal fungi to heat, organic matter loss, and host plant damage or mortality [18]. Because many taxa depend on living woody hosts, severe fires can disrupt these associations and cause local declines in diversity [12]. On the other hand, evidence for a fire-severity effect on saprotrophic richness was weak, suggesting a slight increase at moderate severities. This result is consistent with our expectation of weaker responses by saprotrophs and suggests that any direct influence of fire severity on this guild is small compared with the pronounced declines in mycorrhizal fungi. One possible explanation is that saprotrophs, which primarily feed on decaying organic matter, may be more resilient to fire disturbances compared to mycorrhizal fungi [8,27]. Unlike mycorrhizal fungi, saprotrophs might even experience modest, transient gains where fire increases dead organic substrates in the post-fire environments, potentially offsetting the negative impacts of fire severity [27]. In particular, fire may facilitate ecological release for early-successional saprotrophs, enabling the rapid colonization of burned areas by fast-growing, heat-tolerant species that can exploit newly available post-fire niches [96]. The contrasting responses by mycorrhizal and saprotrophic fungi to fire severity resulted in spatial variation in macrofungal community structure, as reflected by the changes in the mycorrhizal-to-saprotrophic richness ratio. In particular, this ratio declined from low to moderate fire severities, a pattern driven primarily by mycorrhizal losses rather than strong increases in saprotrophic species. This effect likely reflects the greater capacity of saprotrophs to persist or even thrive under moderate disturbance conditions, possibly due to increased availability of dead organic material [97]. However, at the highest levels of fire severity, the ratio showed a slight upturn, probably reflecting a threshold beyond which extreme conditions begin to limit saprotrophic diversity as well, due to substrate depletion or delayed colonization [98], thus narrowing the differences in species richness between the two guilds. Taken together, these patterns in macrofungal richness suggest that fire severity plays a key role in shaping the functional recovery of relict Laurisilva ecosystems by altering the balance between dominant fungal trophic guilds.

4.2. Environmental Drivers of Fungal Richness

Beyond fire severity, we found diverse effects of environmental heterogeneity on post-fire macrofungal communities in MNM relict Laurisilva forest, most notably for elevation, aspect, topographic wetness, and terrain complexity. In particular, elevation emerged as an important factor, with lower areas supporting higher mycorrhizal-to-saprotrophic ratios, likely reflecting more humid and sheltered microhabitats that favour mycorrhizal associations (e.g., [99]). Mycorrhizal richness also tended to be higher towards north-facing slopes (0º aspect) and wetter microsites (high topographic wetness index), consistent with the buffering role of humid, shaded environments [35,89,100]. Such conditions likely reduce local fire intensity, and maintain conditions for ectomycorrhizal colonization. These findings align with the patterns found in other moist or montane forest systems, as well as in Mediterranean type ecosystems, where fungal richness was positively correlated with topographic moisture and structural complexity (e.g., [101,102]).
Unlike mycorrhizal fungi, saprotrophic richness was more strongly linked to topographic ruggedness than moisture, suggesting that terrain heterogeneity enhances substrate availability and niche diversity for this trophic guild [24,103]. In post-disturbance settings like wildfires, topographic ruggedness may thus contribute to sustain saprotrophic diversity by offering refuge areas for slow-colonizing species, which may fail to establish in more disturbed or uniform environments [95,104]. These findings highlight the critical importance of spatial complexity in structuring post-fire macrofungal communities in Laurisilva relict forest patches, as also reflected by the relationship between topographic wetness and the ratio of mycorrhizal to saprotrophic fungi. These contrasting responses indicate that mycorrhizal and saprotrophic richness are shaped by partly distinct environmental gradients, primarily shaped by fire severity and further influenced by fine-scale, topographically driven heterogeneity, leading to shifts in the balance between trophic guilds across environmental conditions.
Contrary to initial predictions, macrofungal richness showed no strong relationship with vegetation variables such as plant cover or richness. Several non-exclusive factors may explain this apparent independence. First, vegetation in severely burned plots was in the early stages of recovery, dominated by the resprouting of native trees such as Laurus nobilis and Arbutus unedo, together with the expansion of fire-tolerant shrubs like Erica arborea and Cistus salviifolius, which serve as ectomycorrhizal hosts. This early regeneration gradually restores partial canopy cover and humid microclimatic conditions typical of Laurisilva forests, potentially reducing vegetation structure effects on fungal communities, which is typically observed in undisturbed environments [105]. Second, many mycorrhizal fungi form persistent spores or hyphal networks that can survive independently of aboveground vegetation for extended periods [106,107]. Third, the scale and resolution of our vegetation data may have been insufficient to capture relevant macrofungal hosts, particularly since some fungi may associate with specific host species or functional groups not adequately represented by cover-based metrics [108]. The lack of relationships could also reflect a delayed coupling between vegetation and fungal communities, a pattern that has been documented in post-fire systems elsewhere [109], and that reflects asynchronous recovery trajectories commonly reported among different biological groups, including vascular plants, bryophytes, lichens, and fungi [110,111]. This supports the idea that fungal responses may lag behind or proceed independently of vegetation recovery. Additionally, clearings created by severe burns may allow for the colonization of ruderal or early-successional plant species that are typically rare in later successional stages, potentially inflating plant diversity without a corresponding increase in fungal richness [10]. Overall, the early post-fire vegetation of Laurisilva forests, characterized by resprouting trees and pioneer shrubs, likely contributed to the partial recovery of canopy structure and local shading, but this regeneration did not override the influence of fire severity and topography-driven moisture gradients during the first year after fire.

4.3. Conservation and Research Implications

This study provides novel insights into macrofungal responses to fire in a relict Laurisilva forest patch embedded within a Mediterranean landscape. While the patterns observed align with general post-fire successional models, the strong buffering role of local topography highlights the importance of microscale heterogeneity in conserving fungal diversity and hence ecosystem function and dynamics [24]. These findings are particularly important in the context of the increasing frequency in the occurrence of wildfires in Mediterranean and similar ecosystems [6,112], and the need to conserve relict habitats, which often represent unique biodiversity hotspots isolated within fragmented landscapes [8,113]. Protecting rugged, shaded, and wetter sites within areas that are threatened by the occurrence of wildfires may serve as a cost-effective strategy to help conserve diverse fungal communities, particularly symbiotic macrofungi, which are key to support forest regeneration and guarantee the long-term stability of these rare ecosystems [8,114]. In line with other studies on Mediterranean and relict ecosystems [20,115], our research suggests that targeted protection of specific microhabitats within these ecosystems may help buffer the impacts of fire, or other environmental pressures such as climate change, on fungal diversity and forest regeneration.
Despite the value of our findings, several limitations should be acknowledged. First, our data represent a post-fire snapshot collected just over a year after the event, so we could not properly track temporal trajectories in macrofungal community change (e.g., [95]). Fungal community dynamics may unfold over years, and short-term richness patterns may not reflect long-term shifts in composition or functional turnover [116]. Secondly, although sample coverage reached moderate levels of completeness for richness comparisons, accumulation curves did not plateau, as is typical for sporocarp surveys, suggesting that repeated, multi-season monitoring across years is warranted to increase survey completeness. Still, these moderate sample coverage levels are common in fungal inventories and, while they may limit detection of rare species, they provide an appropriate basis for interpreting differences between functional guilds. A further limitation concerns the potential influence of spore dispersal from unburned refugia, which was not explicitly quantified in this study. Since post-fire recolonization largely depends on the availability of nearby inoculum sources [117], the distance to unburned forest patches could affect the rate and extent of fungal recovery. Although the strong topographic and microclimatic heterogeneity of the study area likely provided local refuges for propagule persistence, future research incorporating spatial metrics of distance to unburned areas would help clarify the role of dispersal limitation in post-fire macrofungal dynamics. Lastly, we focused on aboveground fruiting bodies, which represent only a subset of the fungal community. Combining sporocarp surveys with molecular soil-based approaches would allow for a more comprehensive understanding of fungal diversity responses to disturbance [37,38,39]. Future research should therefore aim to track fungal succession over multiple years following fire, while integrating soil, microclimatic, and host dynamics, based on experimental designs conducted either in natural settings or under controlled manipulation of vegetation or substrate inputs (e.g., [118]). In this context, the use of in situ microclimate loggers would provide valuable data to quantify fine-scale temperature and humidity variation, improving understanding of how fine-scale heterogeneity mediates fungal diversity. Given the irreplaceable ecological functions of fungi [14,17], such research is urgently needed to inform conservation strategies that aim to protect fungal diversity and ensure the resilience of relict ecosystems in Mediterranean and similar regions [20,119].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fire8110438/s1, Table S1: Resume of vegetation % cover and richness recorded within each 66 sampling plot used to assess macrofungal communities in Mata Nacional da Margaraça (MNM) relict Laurissilva forest in central Portugal after the severe 2017 wildfire; Table S2: List of macrofungal species identified across 66 plots surveyed in Mata Nacional da Margaraça after the severe 2017 wildfire. Post-fire species are in bold; Table S3: Resume of the support of single-predictor models; Table S4: Pearson correlations (rp) among predictors considered (including the first axis of principal coordinates of neighbour matrix (pcnm) applied to each response variable; Table S5: Results Moran’s I autocorrelation test; Table S6: AICc-based model ranking considering all possible combination of main effects of candidate predictors for Mycorrhizal species richness, Smyc; Table S7: AICc-based model ranking considering all possible combination of main effects of candidate predictors for Saprotrophic species richness, Ssap; Table S8: AICc-based model ranking considering all possible combination of main effects of candidate predictors for the ration between Mycorrhizal richness and Saprotrophic richness, Smyc/Ssap.

Author Contributions

Conceptualization, C.S.-S. and R.P.; methodology, C.S.-S. and B.N.; validation C.S.-S. and R.P.; formal analysis, R.P.; investigation, C.S.-S., B.N. and R.P.; data curation, C.S.-S.; writing—original draft preparation, R.P.; writing—review and editing, C.S.-S. and R.P.; visualization, R.P.; project administration, C.S.-S.; funding acquisition, C.S.-S. All authors have read and agreed to the published version of the manuscript.

Funding

This work is funded by national funds through FCT—Fundação para a Ciência e Tecnologia (Portuguese Foundation for Science and Technology), under project UIDB/05183/2025. RP was supported by FCT thorough research contract 2022.02878.CEECIND and project UIDB/05183/2025.

Data Availability Statement

The datasets generated and analysed during the current study are available from Celeste Santos-Silva (css@uevora.pt) upon reasonable request.

Acknowledgments

The authors are grateful to ICNF—Instituto para a Conservação da Natureza e das Florestas for the authorisation to harvest macrofungi specimens in A.P.P.S.A. The authors also acknowledge the R&D unit MED—Mediterranean Institute for Agriculture, Environment and Development (https://doi.org/10.54499/UIDB/05183/2020; https://doi.org/10.54499/UIDP/05183/2020) and the Associate Laboratory CHANGE—Global Change and Sustainability Institute (https://doi.org/10.54499/LA/P/0121/2020).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hampe, A.; Jump, A.S. Climate relicts: Past, present, future. Annu. Rev. Ecol. Evol. Syst. 2011, 42, 313–333. [Google Scholar] [CrossRef]
  2. Pignatti, E.; Pignatti, S.; D’Angeli, D.; De Nicola, C.; Maffei, L.; Testi, A.; Tinelli, A. The laurisilva as cultural heritage: Protection proposal for a laurel forest relict near Ponte Renaro. Rend. Lincei 2015, 26, 643–649. [Google Scholar] [CrossRef]
  3. Santos-Silva, C. Contribution to the Study of the Protected Landscape of Serra do Açor: Elements on the Bryological Flora and Vegetation Structure; Universidade de Évora: Évora, Portugal, 1985; Available online: http://hdl.handle.net/10174/5368 (accessed on 22 September 2025).
  4. Silveira, P.C. The flora of the Serra do Açor (Portugal). Guineana 2007, 13, 1–333. [Google Scholar]
  5. Pausas, J.G.; Keeley, J.E. A burning story: The role of fire in the history of life. BioScience 2009, 59, 593–601. [Google Scholar] [CrossRef]
  6. Pausas, J.G.; Keeley, J.E. Wildfires and global change. Front. Ecol. Environ. 2021, 19, 387–395. [Google Scholar] [CrossRef]
  7. Buscardo, E.; Rodríguez-Echeverría, S.; Freitas, H.; De Angelis, P.; Pereira, J.S.; Muller, L.A. Contrasting soil fungal communities in Mediterranean pine forests subjected to different wildfire frequencies. Fungal Divers. 2015, 70, 85–99. [Google Scholar] [CrossRef]
  8. Claridge, A.W.; Trappe, J.M.; Hansen, K. Do fungi have a role as soil stabilizers and remediators after forest fire? For. Ecol. Manag. 2009, 257, 1063–1069. [Google Scholar] [CrossRef]
  9. Gassibe, P.; Fabrero, R.; Hernández-Rodríguez, M.; Oria de Rueda, J.; Martín-Pinto, P. Fungal community succession following wildfire in a Mediterranean vegetation type dominated by Pinus pinaster in NW Spain. For. Ecol. Manag. 2011, 262, 655–662. [Google Scholar] [CrossRef]
  10. Keeley, J.E.; Bond, W.J.; Bradstock, R.A.; Pausas, J.G.; Rundel, P.W. Fire in Mediterranean Ecosystems: Ecology, Evolution and Management; Cambridge University Press: New York, NY, USA, 2012. [Google Scholar]
  11. Cairney, J.W.G.; Bastias, B.A. Influences of fire on forest soil fungal communities. Can. J. For. Res. 2007, 37, 207–215. [Google Scholar] [CrossRef]
  12. Taudière, A.; Richard, F.; Carcaillet, C. Review on fire effects on ectomycorrhizal symbiosis. For. Ecol. Manag. 2017, 391, 446–457. [Google Scholar] [CrossRef]
  13. Lindahl, B.D.; Tunlid, A. Ectomycorrhizal fungi—Potential organic matter decomposers, yet not saprotrophs. New Phytol. 2015, 205, 1443–1447. [Google Scholar] [CrossRef] [PubMed]
  14. Tedersoo, L.; Bahram, M.; Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 2020, 367, eaba1223. [Google Scholar] [CrossRef]
  15. Van der Heijden, M.G.A.; Martin, F.M.; Selosse, M.-A.; Sanders, I.R. Mycorrhizal ecology and evolution: The past, the present, and the future. New Phytol. 2015, 205, 1406–1423. [Google Scholar] [CrossRef]
  16. Zhang, X.; Dong, Y.; Li, Y.; Wu, X.; Chen, S.; Wang, M.; Li, Y.; Ge, Z.; Zhang, M.; Mao, L. The evolutionary adaptation of wood-decay macrofungi to host gymnosperms differs from that to host angiosperms. Ecol. Evol. 2024, 14, e70019. [Google Scholar] [CrossRef]
  17. Bahram, M.; Netherway, T. Fungi as mediators linking organisms and ecosystems. FEMS Microbiol. Rev. 2022, 46, fuab058. [Google Scholar] [CrossRef]
  18. Smith, S.E.; Read, D.J. Mycorrhizal Symbiosis, 3rd ed.; Academic Press: London, UK, 2008. [Google Scholar]
  19. Sridhar, K.R.; Deshmukh, S. (Eds.) Advances in Macrofungi: Diversity, Ecology and Biotechnology; CRC Press: Boca Raton, FL, USA, 2019. [Google Scholar] [CrossRef]
  20. Natário, B.; Louro, R.; Santos-Silva, C. Macrofungi of Mata da Margaraça (Portugal), a relic from the Tertiary Age. Biodivers. Data J. 2019, 7, e38177. [Google Scholar] [CrossRef]
  21. Deng, X.; Li, M.; Dai, Y.; Zhu, X.; Yan, X.; Wei, Z.; Yuan, Y. The diversity of macrofungi in the forests of Ningxia, western China. Diversity 2024, 16, 725. [Google Scholar] [CrossRef]
  22. Peay, K.; Baraloto, C.; Fine, P.V.A. Strong coupling of plant and fungal community structure across western Amazonian rainforests. ISME J. 2013, 7, 1852–1861. [Google Scholar] [CrossRef]
  23. Santos-Silva, C.; Gonçalves, A.; Louro, R. Canopy cover influence on macrofungal richness and sporocarp production in montado ecosystems. Agrofor. Syst. 2011, 82, 149–159. [Google Scholar] [CrossRef]
  24. Chen, Y.; Yang, Z.; Bi, S.; Wang, X.; Ye, Y.; Svenning, J.-C. Macrofungal species distributions depend on habitat partitioning of topography, light, and vegetation in a temperate mountain forest. Sci. Rep. 2018, 8, 13589. [Google Scholar] [CrossRef]
  25. Li, Z.; Wang, Z.; Zhang, W.; Zhu, J.; Chen, B.; Jiang, L.; Xu, D.; Li, W.; Liu, J.; He, Z. Soil environments regulate dominant soil fungal communities along an elevational gradient in subtropical forests. Forests 2024, 15, 643. [Google Scholar] [CrossRef]
  26. Rakić, M.; Marković, M.; Galić, Z.; Galović, V.; Karaman, M. Diversity and distribution of macrofungi in protected mountain forest habitats in Serbia and its relation to abiotic factors. J. Fungi 2022, 8, 1074. [Google Scholar] [CrossRef] [PubMed]
  27. Dove, N.C.; Hart, S.C. Fire reduces fungal species richness and in situ mycorrhizal colonization: A meta-analysis. Fire Ecol. 2017, 13, 37–65. [Google Scholar] [CrossRef]
  28. Alday, J.G.; De Aragón, J.M.; de-Miguel, S.; Bonet, J.A. Mushroom biomass and diversity are driven by different spatio-temporal scales along Mediterranean elevation gradients. Sci. Rep. 2017, 7, 45824. [Google Scholar] [CrossRef]
  29. Yu, H.; Wang, T.; Skidmore, A.; Heurich, M.; Bässler, C.; Kivlin, S. The critical role of tree species and human disturbance in determining macrofungal diversity in Europe. Glob. Ecol. Biogeogr. 2021, 30, 2084–2100. [Google Scholar] [CrossRef]
  30. Kelly, L.T.; Giljohann, K.M.; Duane, A.; Aquilué, N.; Archibald, S.; Batllori, E.; Bennett, A.F.; Buckland, S.T.; Canelles, Q.; Clarke, M.F.; et al. Fire and biodiversity in the Anthropocene. Science 2020, 370, eabb0355. [Google Scholar] [CrossRef]
  31. Silveira, P.C. Contribution to the Knowledge of the Vascular Flora of the Serra do Açor and Its Phytogeographic Interpretation. Ph.D. Thesis, University of Coimbra, Coimbra, Portugal, 2001. [Google Scholar]
  32. Lourenço, L. Serras de Xisto do Centro de Portugal–Contribuição para o Seu Conhecimento Geomorfológico e Geo-Ecológico. Ph.D. Thesis, Universidade de Coimbra, Coimbra, Portugal, 1996. Available online: https://hdl.handle.net/10316/627 (accessed on 22 September 2025).
  33. Lodge, D.J.; Ammirati, J.F.; O’Dell, T.E.; Mueller, G.M. Collecting and describing macrofungi. In Biodiversity of Fungi: Inventory and Monitoring Methods; Mueller, G.M., Bills, G.F., Foster, M.S., Eds.; Elsevier: Amsterdam, The Netherlands, 2004; pp. 128–158. [Google Scholar]
  34. Buée, M.; Courty, P.E.; Mignot, D.; Garbaye, J. Soil niche effect on species diversity and catabolic activities in an ectomycorrhizal fungal community. Soil Biol. Biochem. 2007, 39, 1947–1955. [Google Scholar] [CrossRef]
  35. Dickie, I.A.; Dentinger, B.M.; Avis, P.G.; McLaughlin, D.J.; Reich, P.B. Ectomycorrhizal fungal communities of oak savanna are distinct from forest communities. Mycologia 2009, 101, 473–483. [Google Scholar] [CrossRef]
  36. Durall, D.M.; Gamiet, S.; Simard, S.W.; Kudrna, L.; Sakakibara, S.M. Effects of clearcut logging and tree species composition on diversity and composition of epigeous ectomycorrhizal fruit bodies. Botany 2006, 84, 966–980. [Google Scholar] [CrossRef]
  37. Kebli, H.; Brais, S.; Kernaghan, G.; Drouin, P. Impact of harvesting intensity on wood-inhabiting fungi in boreal aspen forests of Eastern Canada. For. Ecol. Manag. 2012, 279, 45–54. [Google Scholar] [CrossRef]
  38. Louro, R.; Santos-Silva, C.; Nobre, T. What is in a name? Terfezia classification revisited. Fungal Biol. 2019, 123, 267–273. [Google Scholar] [CrossRef] [PubMed]
  39. Walker, A.K.; Robicheau, B.M. Fungal diversity and community structure from coastal and barrier island beaches in the United States Gulf of Mexico. Sci. Rep. 2021, 11, 81688. [Google Scholar] [CrossRef]
  40. Fernández-Toirán, L.M.; Ágreda, T.; Olano, J.M. Stand age and sampling year effect on the fungal fruit body community in Pinus pinaster forests in central Spain. Botany 2006, 84, 1249–1258. [Google Scholar] [CrossRef]
  41. Juutilainen, K.; Monkkonen, M.; Kotiranta, H.; Halme, P. The role of novel forest ecosystems in conserving wood-inhabiting fungi in boreal broadleaved forests. Ecol. Evol. 2016, 6, 6943–6954. [Google Scholar] [CrossRef]
  42. Kranabetter, J.M.; Friesen, J.; Gamiet, S.; Kroeger, P. Ectomycorrhizal mushroom distribution by stand age in western hemlock–lodgepole pine forests of northwestern British Columbia. Can. J. For. Res. 2005, 35, 1527–1539. [Google Scholar] [CrossRef]
  43. Müller, J.; Engel, H.; Blaschke, M. Assemblages of wood-inhabiting fungi related to silvicultural management intensity in beech forests in southern Germany. Eur. J. For. Res. 2007, 126, 513–527. [Google Scholar] [CrossRef]
  44. Olsson, J.; Jonsson, B.G.; Hjältén, J.; Ericson, L. Addition of coarse woody debris—Early fungal succession on Picea abies logs in managed forests and reserves. Biol. Conserv. 2011, 144, 1100–1110. [Google Scholar] [CrossRef]
  45. Oria-de-Rueda, J.A.; Hernández-Rodríguez, M.; Martín-Pinto, P.; Pando, V.; Olaizola, J. Could artificial reforestations provide as much fungal production and diversity as natural forest stands in marginal Mediterranean areas? For. Ecol. Manag. 2010, 260, 171–180. [Google Scholar] [CrossRef]
  46. Santos-Silva, C.; Louro, R. Assessment of diversity of epigeous Basidiomycota under different soil-management systems in a Montado ecosystem: A case study from Alentejo. Agrofor. Syst. 2016, 90, 117–126. [Google Scholar] [CrossRef]
  47. Horton, T.R.; Bruns, T.D. The molecular revolution in ectomycorrhizal ecology: Peeking into the black box. Mol. Ecol. 2001, 10, 1855–1871. [Google Scholar] [CrossRef]
  48. Peay, K.; Kennedy, P.; Bruns, T. Fungal community ecology: A hybrid beast with a molecular master. BioScience 2008, 58, 799–810. [Google Scholar] [CrossRef]
  49. Nordén, B.; Ryberg, M.; Götmark, F.; Olausson, B. Relative importance of coarse and fine woody debris for wood-inhabiting fungi diversity in temperate broadleaf forests. Biol. Conserv. 2004, 117, 1–10. [Google Scholar] [CrossRef]
  50. Ascomycete. Ascomycete.org. 2018. Available online: https://ascomycete.org/ (accessed on 22 September 2018).
  51. Bon, M. The Mushrooms and Toadstools of Britain and North-Western Europe; Hodder & Stoughton: London, UK, 1987. [Google Scholar]
  52. Breitenbach, J.; Kränzlin, F. Fungi of Switzerland; Edition Mykologia: Lucerne, Switzerland, 2000; Volume I–V. [Google Scholar]
  53. Courtecuisse, R.; Duhem, B. Collins Field Guide–Mushrooms and Toadstools of Britain and Europe, 1st ed.; HarperCollins: London, UK, 1995. [Google Scholar]
  54. Champignons de Charente-Maritime, Charente et Deux-Sèvres. Species Index. 2018. Available online: https://www.mycocharentes.fr/index.php?page=Alpha (accessed on 22 September 2018).
  55. Moser, M. Keys to Agarics and Boleti, 4th ed.; Roger Phillips: London, UK, 1983. [Google Scholar]
  56. Sarnari, G. Monografia Illustrata del Genere Russula in Europa; Associazione Micologica Bresadola, Fondazione Centro Studi Micologici: Trento, Italy, 2005; Volume 1–2. [Google Scholar]
  57. Bánki, O.; Roskov, Y.; Döring, M.; Ower, G.; Hernández Robles, D.R.; Plata Corredor, C.A.; Stjernegaard Jeppesen, T.; Örn, A.; Pape, T.; Hobern, D.; et al. Catalogue of Life (Version 2025-09-11); Catalogue of Life Foundation: Amsterdam, The Netherlands, 2025. [Google Scholar] [CrossRef]
  58. Kirk, P.M. Species Fungorum. In Species 2000 & ITIS Catalogue of Life, 2019 Annual Checklist; Roskov, Y., Ower, G., Orrell, T., Nicolson, D., Bailly, N., Kirk, P.M., Bourgoin, T., DeWalt, R.E., Decock, W., van Nieukerken, E., et al., Eds.; Species 2000; Naturalis: Leiden, The Netherlands, 2019; Available online: https://www.catalogueoflife.org/annual-checklist/2019 (accessed on 22 September 2025).
  59. Dix, N.J.; Webster, J. Phoenicoid fungi. In Fungal Ecology; Dix, N.J., Webster, J., Eds.; Chapman and Hall: London, UK, 1995; pp. 302–321. [Google Scholar] [CrossRef]
  60. Robinson, R.M.; Mellican, A.E.; Smith, R.H. Epigeous macrofungal succession in the first five years following a wildfire in karri (Eucalyptus diversicolor) regrowth forest in Western Australia. Austral Ecology 2008, 33, 807–820. [Google Scholar] [CrossRef]
  61. Sumorok, B. Post-fire macrofungi in the burnt area in the Jelonka reserve (Bialowieza region). Acta Mycol. 2001, 36, 149–158. [Google Scholar] [CrossRef]
  62. ESRI. ArcGIS Desktop: Release 10.5.1; Environmental Systems Research Institute: Redlands, CA, USA, 2017. [Google Scholar]
  63. Gupta, P.; Shukla, A.K.; Shukla, D.P. Sentinel-2 based burn severity mapping and post-fire impact assessment in Mizoram. Remote Sens. Appl. Soc. Environ. 2024, 36, 101279. [Google Scholar] [CrossRef]
  64. Miller, J.D.; Thode, A.E. Quantifying burn severity with a relative version of the dNBR. Remote Sens. Environ. 2007, 109, 66–80. [Google Scholar] [CrossRef]
  65. Sørensen, R.; Zinko, U.; Seibert, J. On the Calculation of the Topographic Wetness Index: Evaluation of Different Methods Based on Field Observations. Hydrol. Earth Syst. Sci. 2006, 10, 101–112. [Google Scholar] [CrossRef]
  66. Evans, J.S.; Oakleaf, J.; Cushman, S.A.; Theobald, D. Geomorphometry and Gradient Metrics Toolbox: A Toolbox for Surface Gradient Modelling. 2013. Available online: https://evansmurphy.wixsite.com/evansspatial (accessed on 22 September 2018).
  67. Jenness, J.S. DEM Surface Tools: An ArcGIS Extension for Analysing Raster Elevation Datasets. 2011. Available online: http://www.jennessent.com/arcgis/surface_area.htm (accessed on 22 September 2019).
  68. Jenness, J.S.; Brost, B.; Beier, P. Land Facet Corridor Designer—Extension for ArcGIS. 2013. Available online: http://www.jennessent.com/arcgis/land_facets.htm (accessed on 22 September 2019).
  69. Fleming, M.D.; Hoffer, R.M. Machine processing of Landsat MSS data and DMA topographic data for forest cover type mapping. In Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN, USA, 27–29 June 1979; p. 062879. Available online: http://docs.lib.purdue.edu/lars_symp/302 (accessed on 22 September 2025).
  70. Jones, K.H. A comparison of algorithms used to compute hill slope as a property of the DEM. Comput. Geosci. 1998, 24, 315–323. [Google Scholar] [CrossRef]
  71. Stage, A.R. An expression for the effect of aspect, slope, and habitat type on tree growth. For. Sci. 1976, 22, 457–460. [Google Scholar] [CrossRef]
  72. Riley, S.J.; DeGloria, S.D.; Elliot, R. A terrain ruggedness index that quantifies topographic heterogeneity. Intermt. J. Sci. 1999, 5, 23–27. [Google Scholar]
  73. Beven, K.J.; Kirkby, M.J. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. J. 1979, 24, 43–69. [Google Scholar] [CrossRef]
  74. Schmidt, F.; Persson, A. Comparison of DEM data capture and topographic wetness indices. Precis. Agric. 2003, 4, 179–192. [Google Scholar] [CrossRef]
  75. Conrad, O.; Bechtel, B.; Bock, M.; Dietrich, H.; Fischer, E.; Gerlitz, L.; Wehberg, J.; Wichmann, V.; Böhner, J. System for Automated Geoscientific Analyses (SAGA) v.2.1.4. Geosci. Model Dev. 2015, 8, 1991–2007. [Google Scholar] [CrossRef]
  76. Dobrowski, S.Z. A Climatic Basis for Microrefugia: The Influence of Terrain on Climate. Glob. Change Biol. 2011, 17, 1022–1035. [Google Scholar] [CrossRef]
  77. Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km Spatial Resolution Climate Surfaces for Global Land Areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  78. Karger, D.N.; Conrad, O.; Böhner, J.; Kawohl, T.; Kreft, H.; Soria-Auza, R.W.; Zimmermann, N.E.; Linder, H.P.; Kessler, M. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 2017, 4, 170122. [Google Scholar] [CrossRef] [PubMed]
  79. Laganà, A.; Loppi, S.; De Dominicis, V. Environmental factors and proportions of fungal trophic groups in Mediterranean forests. For. Ecol. Manag. 1999, 124, 145–151. [Google Scholar] [CrossRef]
  80. Hsieh, T.C.; Ma, K.H.; Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 2016, 7, 1451–1456. [Google Scholar] [CrossRef]
  81. Chao, A.; Jost, L. Coverage-based rarefaction and extrapolation: Standardizing samples by completeness rather than size. Ecology 2012, 93, 2533–2547. [Google Scholar] [CrossRef]
  82. Chao, A.; Gotelli, N.J.; Hsieh, T.C.; Sander, E.L.; Ma, K.H.; Colwell, R.K.; Ellison, A.M. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 2014, 84, 45–67. [Google Scholar] [CrossRef]
  83. Burnham, K.P.; Anderson, D.R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed.; Springer: New York, NY, USA, 2002. [Google Scholar]
  84. Lindén, A.; Mäntyniemi, S. Using the negative binomial distribution to model overdispersion in ecological count data. Ecology 2011, 92, 1414–1421. [Google Scholar] [CrossRef]
  85. Dormann, C.F.; Elith, J.; Bacher, S.; Buchmann, C.; Carl, G.; Carré, G.; Marquéz, J.R.G.; Gruber, B.; Lafourcade, B.; Leitão, P.J.; et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 2013, 36, 27–46. [Google Scholar] [CrossRef]
  86. Paradis, E.; Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analysis in R. Bioinformatics 2019, 35, 526–528. [Google Scholar] [CrossRef]
  87. Borcard, D.; Legendre, P. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 2002, 153, 51–68. [Google Scholar] [CrossRef]
  88. Oksanen, J.; Simpson, G.L.; Blanchet, F.G.; Kindt, R.; Legendre, P.; Minchin, P.R.; O’Hara, R.B.; Solymos, P.; Stevens, M.H.H.; Szoecs, E.; et al. vegan: Community Ecology Package, R Package Version 2.6-4. 2022. Available online: https://cran.r-project.org/web/packages/vegan (accessed on 22 September 2025).
  89. Marshall, L.A.E.; Fornwalt, P.J.; Stevens-Rumann, C.S.; Rodman, K.C.; Rhoades, C.C.; Zimlinghaus, K.; Chapman, T.B.; Schloegel, C.A. North-facing aspects, shade objects, and microtopographic depressions promote the survival and growth of tree seedlings planted after wildfire. Fire Ecol. 2023, 19, 26. [Google Scholar] [CrossRef]
  90. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2023; Available online: https://www.r-project.org/ (accessed on 22 September 2025).
  91. Ripley, B.; Venables, B.; Bates, D.M.; Hornik, K.; Gebhardt, A.; Firth, D.; Ripley, M.B. MASS, R Package. 2013. Available online: https://cran.r-project.org/web/packages/MASS/ (accessed on 22 September 2025).
  92. Barton, K.; Barton, M.K. MuMIn: Multi-Model Inference, R Package Version 1.46.0. 2015. Available online: https://CRAN.R-project.org/package=MuMIn (accessed on 22 September 2025).
  93. Raimbault, A.; Brin, A.; Manzi, S.; Savoie, J.M.; Gandois, L.; Oliva, P.; Fogliani, O.; Roy-Camille, C.; Gratacap, L.; Roy, M. Influence of habitat fragmentation and habitat amount on soil fungi communities in ancient forests. Landsc. Ecol. 2024, 39, 19. [Google Scholar] [CrossRef]
  94. Dahlberg, A. Effects of fire on ectomycorrhizal fungi in Fennoscandian boreal forests. Silva Fenn. 2002, 36, 69–80. [Google Scholar] [CrossRef]
  95. Hernández-Rodríguez, M.; Oria-de-Rueda, J.A.; Martín-Pinto, P. Post-fire fungal succession in a Mediterranean ecosystem dominated by Cistus ladanifer L. For. Ecol. Manag. 2013, 289, 48–57. [Google Scholar] [CrossRef]
  96. Fox, S.; Sikes, B.A.; Brown, S.P.; Cripps, C.L.; Glassman, S.I.; Hughes, K.; Semenova-Nelsen, T.; Jumpponen, A. Fire as a driver of fungal diversity—A synthesis of current knowledge. Mycologia 2022, 114, 215–241. [Google Scholar] [CrossRef]
  97. Semenova-Nelsen, T.A.; Platt, W.J.; Patterson, T.R.; Huffman, J.; Sikes, B.A. Frequent fire reorganizes fungal communities and slows decomposition across a heterogeneous pine savanna landscape. New Phytol. 2019, 224, 916–927. [Google Scholar] [CrossRef]
  98. Owen, S.M.; Patterson, A.M.; Gehring, C.A.; Sieg, C.H.; Baggett, L.S.; Fulé, P.Z. Large, high-severity burn patches limit fungal recovery 13 years after wildfire in a ponderosa pine forest. Soil Biol. Biochem. 2019, 139, 107616. [Google Scholar] [CrossRef]
  99. Querejeta, J.I.; Egerton-Warburton, L.M.; Allen, M.F. Topographic position modulates the mycorrhizal response of oak trees to interannual rainfall variability. Ecology 2009, 90, 649–662. [Google Scholar] [CrossRef]
  100. Claridge, A.W.; Barry, S.C.; Cork, S.J.; Trappe, J.M. Diversity and habitat relationships of hypogeous fungi. II. Factors influencing the occurrence and number of taxa. Biodivers. Conserv. 2000, 9, 175–199. [Google Scholar] [CrossRef]
  101. Dove, N.C.; Keeton, W.S. Structural complexity enhancement increases fungal species richness in northern hardwood forests. Fungal Ecol. 2015, 13, 181–192. [Google Scholar] [CrossRef]
  102. Fernández-Ruiz, A.; Vicente-Villardón, J.L.; Sánchez-Sánchez, J.; García-Jiménez, P.; Sánchez-Durán, S.; Rodríguez-de la Cruz, D. A statistical approach to macrofungal diversity in a Mediterranean ecosystem dominated by the holm oak. Forests 2023, 14, 1662. [Google Scholar] [CrossRef]
  103. Tsujino, R.; Sato, H.; Imamura, A.; Yumoto, T. Topography-specific emergence of fungal fruiting bodies in warm temperate evergreen broad-leaved forests on Yakushima Island, Japan. Mycoscience 2009, 50, 388–399. [Google Scholar] [CrossRef]
  104. Wang, Y.; Li, S.; Lang, X.; Huang, X.; Su, J. Effects of microtopography on soil fungal community diversity, composition and assembly in a subtropical monsoon evergreen broadleaf forest of Southwest China. Catena 2022, 211, 106025. [Google Scholar] [CrossRef]
  105. Lumley, T.C.; Gignac, L.D.; Currah, R.S. Microfungus communities of spruce and aspen logs at different decay stages in disturbed/undisturbed sites. Can. J. Bot. 2001, 79, 76–92. [Google Scholar] [CrossRef]
  106. Nguyen, N.H.; Hynson, N.A.; Bruns, T.D. Stayin’ alive: Survival of mycorrhizal fungal propagules from 6-yr-old forest soil. Fungal Ecol. 2012, 5, 741–746. [Google Scholar] [CrossRef]
  107. Pepe, A.; Giovannetti, M.; Sbrana, C. Lifespan and functionality of mycorrhizal fungal mycelium are uncoupled from host plant lifespan. Sci. Rep. 2018, 8, 10235. [Google Scholar] [CrossRef]
  108. Siknia, M.; Skiada, V.; Ipsilantis, I.; Vasileiadis, S.; Kavroulakis, N.; Genitsaris, S.; Papadopoulou, K.K.; Hart, M.; Klironomos, J.; Karpouzas, D.G.; et al. Strong host-specific selection and over-dominance characterize arbuscular mycorrhizal fungal root colonizers of coastal sand dune plants of the Mediterranean region. FEMS Microbiol. Ecol. 2021, 97, fiab109. [Google Scholar] [CrossRef]
  109. Yang, T.; Tedersoo, L.; Lin, X.; Fitzpatrick, M.C.; Jia, Y.; Liu, X.; Shen, Z. Distinct fungal successional trajectories following wildfire between soil horizons in a cold-temperate forest. New Phytol. 2020, 227, 572–587. [Google Scholar] [CrossRef] [PubMed]
  110. Orumaa, A.; Köster, K.; Tullus, A.; Tullus, T.; Metslaid, M. Forest Fires Have Long-Term Effects on the Composition of Vascular Plants and Bryophytes in Scots Pine Forests of Hemiboreal Estonia. Silva Fenn. 2022, 56, 10598. [Google Scholar] [CrossRef]
  111. Giordani, P.; Calderisi, G.; Cogoni, D.; Fenu, G. Asynchronous Postfire Recovery Dynamics between Epilithic Lichens and Vascular Plants in Mediterranean Ecosystems. J. Environ. Manag. 2025, 394, 127645. [Google Scholar] [CrossRef] [PubMed]
  112. Ruffault, J.; Curt, T.; Moron, V.; Trigo, R.M.; Mouillot, F.; Koutsias, N.; Pimont, F.; Martin-StPaul, N.; Barbero, R.; Dupuy, J.-L.; et al. Increased likelihood of heat-induced large wildfires in the Mediterranean Basin. Sci. Rep. 2020, 10, 13790. [Google Scholar] [CrossRef]
  113. Mansourian, S.; Rossi, M.; Vallauri, D. Ancient Forests in the Northern Mediterranean: Neglected High Conservation Value Areas; WWF: Marseille, France, 2013; 80p. [Google Scholar]
  114. Niego, A.G.; Rapior, S.; Thongklang, N.; Raspé, O.; Hyde, K.; Mortimer, P. Reviewing the contributions of macrofungi to forest ecosystem processes and services. Fungal Biol. Rev. 2022, 44, 100294. [Google Scholar] [CrossRef]
  115. Mediavilla, O.; Oria-de-Rueda, J.A.; Martín-Pinto, P. Changes in sporocarp production and vegetation following wildfire in a Pinus nigra forest in northern Spain. For. Ecol. Manag. 2014, 331, 85–92. [Google Scholar] [CrossRef]
  116. Orumaa, A.; Agan, A.; Anslan, S.; Drenkhan, T.; Drenkhan, R.; Kauer, K.; Tedersoo, L.; Metslaid, M. Long-term effects of forest fires on fungal community and soil properties along a Scots pine fire chronosequence. Sci. Total Environ. 2022, 851, 158173. [Google Scholar] [CrossRef]
  117. Hewitt, R.E.; Day, N.J.; DeVan, M.R.; Taylor, D.L. Wildfire Impacts on Root-Associated Fungi and Predicted Plant–Soil Feedbacks in the Boreal Forest: Research Progress and Recommendations. Funct. Ecol. 2023, 37, 2110–2125. [Google Scholar] [CrossRef]
  118. Morman, K.E.; Buckley, H.L.; Higgins, C.M.; Tosi, M.; Dunfield, K.E.; Day, N.J. Simulated fire and plant–soil feedback effects on mycorrhizal fungi and invasive plants. iScience 2024, 27, 111193. [Google Scholar] [CrossRef]
  119. Zak, J.C.; Wildman, H.G. Fungi in stressful environments. In Biodiversity of Fungi: Inventory and Monitoring Methods; Mueller, G.M., Bills, G.F., Foster, M.S., Eds.; Elsevier: London, UK, 2004; pp. 303–331. [Google Scholar]
Fire 08 00438 g001
Figure 1. Study area showing (a) the Serra do Açor Protected Landscape in central Portugal, (b) the Mata Nacional da Margaraça, and (c) the 66 sampling plots (red dots) surveyed for macrofungal diversity after the 2017 wildfire.
Figure 1. Study area showing (a) the Serra do Açor Protected Landscape in central Portugal, (b) the Mata Nacional da Margaraça, and (c) the 66 sampling plots (red dots) surveyed for macrofungal diversity after the 2017 wildfire.
Fire 08 00438 g001
Fire 08 00438 g002
Figure 2. Images of MNM before the wildfire (30 September 2017) (a) and after the wildfire (7 April 2018) (b). Examples of macrofungi recorded include: Russula pelargolina, a rare species (c); Anthracobia macrocystis, a post-fire species (d); Mycena galopus, a frequent species (e); Psathyrella pennata, a post-fire species (f); and Plicaria endocarpoides, also a post-fire species (g).
Figure 2. Images of MNM before the wildfire (30 September 2017) (a) and after the wildfire (7 April 2018) (b). Examples of macrofungi recorded include: Russula pelargolina, a rare species (c); Anthracobia macrocystis, a post-fire species (d); Mycena galopus, a frequent species (e); Psathyrella pennata, a post-fire species (f); and Plicaria endocarpoides, also a post-fire species (g).
Fire 08 00438 g002
Fire 08 00438 g003
Figure 3. Rarefaction/extrapolation of species richness for saprotrophs (orange) and mycorrhizal (blue). (a) Sample-size curves (plots as sampling units); (b) coverage-based curves. Solid lines and filled symbols (triangle for saprotrophs and circle for mycorrhizal) indicate the observed range (T = 66); dashed lines indicate extrapolation to 2 × T; shaded areas are 95% CIs.
Figure 3. Rarefaction/extrapolation of species richness for saprotrophs (orange) and mycorrhizal (blue). (a) Sample-size curves (plots as sampling units); (b) coverage-based curves. Solid lines and filled symbols (triangle for saprotrophs and circle for mycorrhizal) indicate the observed range (T = 66); dashed lines indicate extrapolation to 2 × T; shaded areas are 95% CIs.
Fire 08 00438 g003
Fire 08 00438 g004
Figure 4. Model-based partial response curves for macrofungal richness and trophic structure. (a–c) Mycorrhizal richness (Smyc) from model averaging shown against (a) fire severity, (b) aspect, and (c) topographic wetness index (TWI). (d) Saprotrophic richness (Ssap) from the single supported model against topographic ruggedness index (TRI). (e–h) ratio between mycorrhizal and saprotrophic species richness (Smyc/Ssap) from model averaging against (e) fire severity, (f) elevation, (g) TWI, and (h) aspect. Solid lines are fitted values on the response scale; shaded ribbons show 95% confidence intervals. For each panel, the focal predictor varies along the x-axis while all other predictors—including the first PCNM axis used as a spatial control—are held at their sample means; predictors were standardized prior to fitting. Color scheme: blue = Smyc, orange = Ssap, green = Smyc/Ssap.
Figure 4. Model-based partial response curves for macrofungal richness and trophic structure. (a–c) Mycorrhizal richness (Smyc) from model averaging shown against (a) fire severity, (b) aspect, and (c) topographic wetness index (TWI). (d) Saprotrophic richness (Ssap) from the single supported model against topographic ruggedness index (TRI). (e–h) ratio between mycorrhizal and saprotrophic species richness (Smyc/Ssap) from model averaging against (e) fire severity, (f) elevation, (g) TWI, and (h) aspect. Solid lines are fitted values on the response scale; shaded ribbons show 95% confidence intervals. For each panel, the focal predictor varies along the x-axis while all other predictors—including the first PCNM axis used as a spatial control—are held at their sample means; predictors were standardized prior to fitting. Color scheme: blue = Smyc, orange = Ssap, green = Smyc/Ssap.
Fire 08 00438 g004
Table 1. List of predictors considered in the analysis of macrofungi diversity after the 2017 wildfire in the MNM Laurisilva relict forest.
Table 1. List of predictors considered in the analysis of macrofungi diversity after the 2017 wildfire in the MNM Laurisilva relict forest.
Category Variable Units Mean ± SE Range
FireSeverity index (dNBR)Numeric0.471 ± 0.0410.025–1.133
VegetationPlant richnessInteger11.546 ± 05163–21
Plant coverProportion0.320 ± 0.0380.010–1.090
TopographyElevationm a.s.l.623.848 ± 9.513469–775
SlopeDegrees23.801 ± 0.8833.931–37.793
AspectDegrees165.204 ± 19.3580.000–357.879
Sine slope/aspect transformationNumeric0.029 ± 0.021−0.292–0.392
Cosine slope/aspect transformationNumeric0.403 ± 0.022−0.138–0.763
Topographic ruggedness index (TRI)Numeric10.731 ± 0.0819.392–11.720
Topographic wetness index (TWI)Numeric5.192 ± 0.0614.562–6.312
Table 2. Summary of AICc-based model selection considering all possible combinations of candidate predictors for each response variable (see text for details). Only the best-supported models (ΔAICc < 2) are shown here (see Tables S6–S8 Supplementary Materials for the complete list of models). TWI refers to the Topographic Wetness Index, and TRI to the Topographic Ruggedness Index. Pcnm variables correspond to the first axis of a principal coordinates of neighbour matrix (pcnm) included as an additional predictor to account for spatial autocorrelation in each response variable. Predictors included with both linear and quadratic terms are indicated by superscript 2. R2 is Nagelkerke’s likelihood-ratio pseudo-R2 for negative binomial models (Smyc, Ssap) and adjusted R2 for the Gaussian model (Smyc/Ssap), each relative to the null model.
Table 2. Summary of AICc-based model selection considering all possible combinations of candidate predictors for each response variable (see text for details). Only the best-supported models (ΔAICc < 2) are shown here (see Tables S6–S8 Supplementary Materials for the complete list of models). TWI refers to the Topographic Wetness Index, and TRI to the Topographic Ruggedness Index. Pcnm variables correspond to the first axis of a principal coordinates of neighbour matrix (pcnm) included as an additional predictor to account for spatial autocorrelation in each response variable. Predictors included with both linear and quadratic terms are indicated by superscript 2. R2 is Nagelkerke’s likelihood-ratio pseudo-R2 for negative binomial models (Smyc, Ssap) and adjusted R2 for the Gaussian model (Smyc/Ssap), each relative to the null model.
Response Candidate Model Code AICc ΔAICc AICc-Weights R2
SmycFire severity + pcnmmycM-1183.610.000.310.637
Fire severity + Aspect + pcnmmycM-2184.651.050.180.644
Fire severity + TWI + pcnmmycM-3185.061.450.150.642
SsapTRI + pcnmsapS-1278.440.000.470.248
Smyc/SsapFire severity 2 + Elevation 2 + pcnmrmsMSr-1148.990.000.290.619
Fire severity 2 + Elevation 2 + TWI 2 + pcnmrmsMSr-2150.501.510.140.644
Fire severity 2 + Elevation 2 + Aspect + pcnmrmsMSr-3150.631.650.130.626
Fire severity 2 + pcnmrmsMSr-4150.811.820.120.572
Table 3. Results of the selected GLMs from Table 2, with mean, standard error (SE) and 95% confidence intervals (CI) of effect sizes estimated from model averaging in the case of Smyc and Smyc/Ssap, or from a single supported model in the case of Ssap. Variable importance is also shown for model-averaged estimates. TWI refers to the Topographic Wetness Index, and TRI to the Topographic Ruggedness Index. Pcnm variables correspond to the first axis of a principal coordinates of neighbour matrix (pcnm) included as an additional predictor to account for spatial autocorrelation in each response variable. Quadratic terms are indicated by superscript 2.
Table 3. Results of the selected GLMs from Table 2, with mean, standard error (SE) and 95% confidence intervals (CI) of effect sizes estimated from model averaging in the case of Smyc and Smyc/Ssap, or from a single supported model in the case of Ssap. Variable importance is also shown for model-averaged estimates. TWI refers to the Topographic Wetness Index, and TRI to the Topographic Ruggedness Index. Pcnm variables correspond to the first axis of a principal coordinates of neighbour matrix (pcnm) included as an additional predictor to account for spatial autocorrelation in each response variable. Quadratic terms are indicated by superscript 2.
Response Inference Predictor Mean ± SE Effect Size 95% CI Variable Importance
SmycModel averagingFire severity−0.856 ± 0.167−1.130–−0.5821
Aspect−0.148 ± 0.134−0.369–0.0730.285
TWI0.093 ± 0.099−0.070–0.2550.234
pcnmmyc0.499 ± 0.0910.349–0.6491
SsapSingle modelTRI0.176 ± 0.0690.062–0.290-
pcnmsap0.111 ± 0.0660.002–0.220-
Smyc/SsapModel averagingFire severity−0.447 ± 0.167−0.721–−0.1731
Fire severity 20.247 ± 0.1310.031–0.462
Elevation−0.315 ± 0.151−0.563–−0.0670.826
Elevation 20.171 ± 0.1000.006–0.335
TWI−0.129 ± 0.160−0.392–0.1340.204
TWI 2−0.085 ± 0.118−0.280–0.109
Aspect−0.090 ± 0.099−0.254–0.0730.19
pcnmrms0.459 ± 0.0930.306–0.6121
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Santos-Silva, C.; Natário, B.; Pita, R. Fine-Scale Environmental Heterogeneity Shapes Post-Fire Macrofungal Richness in a Mediterranean Relict Forest. Fire 2025, 8, 438. https://doi.org/10.3390/fire8110438

AMA Style

Santos-Silva C, Natário B, Pita R. Fine-Scale Environmental Heterogeneity Shapes Post-Fire Macrofungal Richness in a Mediterranean Relict Forest. Fire. 2025; 8(11):438. https://doi.org/10.3390/fire8110438

Chicago/Turabian Style

Santos-Silva, Celeste, Bruno Natário, and Ricardo Pita. 2025. "Fine-Scale Environmental Heterogeneity Shapes Post-Fire Macrofungal Richness in a Mediterranean Relict Forest" Fire 8, no. 11: 438. https://doi.org/10.3390/fire8110438

APA Style

Santos-Silva, C., Natário, B., & Pita, R. (2025). Fine-Scale Environmental Heterogeneity Shapes Post-Fire Macrofungal Richness in a Mediterranean Relict Forest. Fire, 8(11), 438. https://doi.org/10.3390/fire8110438

Article Metrics

Back to TopTop