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. 2026 Apr 8;16(4):e73373. doi: 10.1002/ece3.73373

Richness and Composition of Mycorrhizal Fungi Varies by Flood Level and River Basin in Oligotrophic Amazonian Seasonally Flooded Forests

Maihyra Marina Pombo 1, Camila Duarte Ritter 2,3,✉, Florian Wittmann 1,4, Jadson José Souza de Oliveira 1,3,5, Maria Teresa Fernandez Piedade 1, Jochen Schongart 1, Alexander Zizka 6,✉
PMCID: PMC13062489  PMID: 41970348

ABSTRACT

Mycorrhizal fungi play a key role in supporting plants in nutrient‐poor environments, yet their diversity and distribution in Amazonian floodplain forests remain poorly understood. Here, we assessed the presence of mycorrhizal fungi across three seasonally flooded by black water forest (igapó) sites in Central Amazonia (RDS Uatumã, RDS Rio Negro, and PARNA Jaú) using DNA metabarcoding of roots. We examined how flood regimes and locality influence the richness and composition of mycorrhizal fungi and, particularly, for ectomycorrhizal fungi (ECM). We found 568 mycorrhizal ASVs, including 307 ECM and 238 arbuscular mycorrhizal fungi. Mycorrhizal richness was higher in sites that experienced prolonged inundation. However, ECM richness was more affected by site than flood level. Community composition varied across sites and flood levels, with distinct fungal assemblages associated with different host tree species. Our results suggest that a complex interplay of flood dynamics, local characteristics, and plant‐fungal interactions shapes the structure of mycorrhizal communities in igapós. Despite seasonal flooding, these forests harbor a diverse and specialized mycorrhizal biota, including many ECM lineages rarely documented in tropical wetlands. This study contributes with novel insights into Amazonian belowground biodiversity and highlights the importance of topographic and hydrological heterogeneity in maintaining adapted fungal diversity and function. Understanding these patterns is critical for forecasting the ecological impacts of climate‐driven changes to Amazonian flood regimes.

Keywords: I gapó forest, inundation, metabarcoding, Negro River, root‐fungi interaction, tropical forest

Conceptual representation of topographic gradients in Amazonian igapó forests. High, medium, and low igapós differ in elevation and flood duration, with low igapós experiencing the longest periods of inundation. Fungal mycelia and DNA symbols indicate sampling of root‐associated mycorrhizal communities across the gradient, used to assess the influence of flooding on fungal diversity.

graphic file with name ECE3-16-e73373-g003.jpg

1. Introduction

Plants form symbiotic relationships with fungi (mycorrhizae), increasing root nutrient uptake and improving drought resistance of individuals, and thereby the resilience of forest communities. In seasonally flooded environments, however, oxygen deprivation in the soil can lead to shifts in fungal diversity and taxonomic composition disrupting mycorrhizae associations (Das et al. 2025; Martínez‐Arias et al. 2022). In temperate ecosystems, flooding affects mycorrhizal activity and selects for flood‐tolerant taxa (Cho et al. 2021; Thomas 2021); however, these dynamics remain poorly understood in tropical forests. A better understanding of these dynamics is essential to predict forest responses to climate changes and develop conservation strategies, given the crucial role of mycorrhizae in nutrient‐poor tropical soils and the projected increase in extreme flooding events due to climate change (Martínez‐Arias et al. 2022).

In the major Amazonian rivers, the South American Monsoon System (Marengo et al. 2011) drives seasonal fluctuations in water and sediment discharge, creating an annual “flood pulse” that sustains the world's largest expanse of seasonally flooded forests (Junk et al. 1989). Part of these forests are inundated by nutrient‐poor black waters, known in Brazil as igapós, which cover approximately 302,000 km2 of the Amazon Basin (Junk et al. 2011, 2024; Wittmann et al. 2022). A characteristic example of these ecosystems is the Rio Negro basin, where around 125,000 km2 of predominantly woody vegetation are seasonally flooded (Hess et al. 2015). Flooding follows a marked topographic gradient, with lower areas remaining submerged for longer periods (low igapó), intermediate zones experiencing moderate inundation (medium igapó), and higher areas flooding for shorter durations (high igapó; Figure 1).

FIGURE 1.

FIGURE 1

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Location of the three study sites in the Brazilian Amazon: PARNA Jaú, RDS Uatumã, and RDS Rio Negro (red squares). The map shows major rivers (blue lines), national boundaries, and the extent of the Amazon biome (light gray). The inset map highlights the location of the study region within South America. Below is a schematic view of the topography of igapós. Low igapós are flooded for longer, supposedly having a more specialized biota adapted to stress by reduced oxygen, while high igapós are less stressed due to a shorter period.

This predictable yet intense seasonal inundation drives profound physicochemical changes in soils, resulting in acidic, nutrient‐poor conditions that strongly constrain biological communities. As a consequence, the nutrient‐depleted waters of the Rio Negro basin support ecosystems characterized by relatively low overall species richness but exceptionally high endemism and species turnover (Junk et al. 1989, 2011, 2024; Kubitzki 1989; Ríos‐Villamizar et al. 2020; Wittmann et al. 2022; Householder et al. 2024). These environmental filters operate consistently across igapó systems, shaping community assembly along the flooding gradient.

Despite pronounced floristic turnover among igapó forests from different river systems, comparative studies indicate that they share remarkably similar structural organization across topographic levels. Distinct tree assemblages associated with high, medium, and low igapós have been documented in multiple regions (Ferreira 2000; Lobo et al. 2019), while herbaceous communities exhibit particularly high dissimilarity among sites (Lopes et al. 2014). At the basin scale, arboreal composition is dominated by a relatively small number of plant families, including Fabaceae, Lecythidaceae, Annonaceae, Sapotaceae, Myrtaceae, Moraceae, Chrysobalanaceae, and Lauraceae (Ferreira and Stohlgren 1999; Montero et al. 2012; Scudeller and Villarrúbia 2018), suggesting convergence in functional strategies despite taxonomic differences.

The persistence of trees in these environments is made possible by a suite of morphological, anatomical, and physiological adaptations that enable survival under prolonged inundation. These include the development of aerenchyma, adventitious roots, and mechanisms for rhizosphere oxidation, which facilitate oxygen transport to belowground tissues (Wittmann and Parolin 2005; Wittmann et al. 2010; Haase and Rätsch 2010). Consequently, the distribution of tree species along the flooding gradient is largely determined by their tolerance to inundation. These adaptations not only allow trees to survive flooding but also directly influence their interactions with soil biota, particularly microorganisms involved in nutrient acquisition.

Among the most important belowground interactions in igapó forests are mutualistic associations with mycorrhizal fungi. Mycorrhizae are one of the most widespread plant–microbe interactions (Rinaldi et al. 2008; Voller et al. 2023). The symbiosis enhances the absorption of water and nutrients by the plant partner (N, P, K, and Ca) in return for photosynthesis products, thereby increasing plant tolerance to drought, high temperatures, and soil acidity (Watkinson et al. 2016). Also, mycorrhizae facilitate seedling establishment and growth (Watkinson et al. 2016), potentially promoting the dominance of some tree species in nutrient‐poor tropical ecosystems like igapós by reducing pathogen‐related negative feedback (Alexopoulos et al. 1996; Smith et al. 2011; Roy et al. 2016). Additionally, mycorrhizal fungi fulfill important functions on the ecosystem level, for instance improving soil structure, fertility, and salinity (Courty et al. 2010; Wang and Rengel 2024). Most vascular plants form some type of mycorrhiza, which differs in the anatomical structure and taxonomic group of the fungal interaction partner: ~72% of plant species interact with arbuscular mycorrhizal fungi (AMF), 2% with ectomycorrhizal fungi (ECM), whereas 10% form orchid mycorrhizae, and 1.5% form ericoid mycorrhizae (Brundrett and Tedersoo 2018).

In Amazonian trees, both AMF and ECM associations occur (Vasco‐Palacios et al. 2020). AMF belong to the monophyletic group Glomeromycota and are characterized by hyphae that penetrate root epidermal cells and extend into the cortex. ECM, in contrast, do not penetrate root cells; their hyphae form a mycelial sheath (mantle) around the roots and spread through intercellular spaces forming a specific structure, the Hartig net, in the outer cortex, expanding contact with the substrate and offering protection from pathogens (Alexopoulos et al. 1996; Watkinson et al. 2016; Halling 2001; Lutzoni et al. 2018).

Early surveys of fungi in Amazonian igapó forests were largely based on sporome observations and inferred associations. Singer and Aguiar (1986) reported 49 fungal species in the Tarumã‐Mirim River, although later work demonstrated that many of these taxa were not ectomycorrhizal. Nevertheless, families such as Amanitaceae, Boletaceae, and Russulaceae have been consistently reported as ectomycorrhizal in Amazonian forests (Tedersoo and Brundrett 2017; Brundrett and Tedersoo 2018; Corrales et al. 2018). More recently, metabarcoding studies have transformed our understanding of fungal diversity. Ritter et al. (2020) showed that habitat type is the primary determinant of fungal community composition across Amazonian ecosystems, surpassing soil and locality effects. In flooded forests, particularly várzea, the widespread distribution of fungal ASVs along flooding gradients has been attributed to DNA transport by floodwaters, suggesting that relative abundance and community composition may be more informative than presence–absence patterns alone (Bredin et al. 2021).

As observed for tree communities, ectomycorrhizal fungi exhibit high spatial turnover in tropical forests. Strong compositional changes have been documented over short distances and along fertility gradients (Corrales et al. 2018), patterns comparable to those described for igapó forests (Furch 1997). Globally, ectomycorrhizal fungi associate with a limited subset of plant lineages but infect a substantial proportion of tree species, with their distribution shaped by climatic, spatial, and edaphic factors (Tedersoo et al. 2014; Tedersoo and Brundrett 2017; Steidinger et al. 2019). In the Amazon, local dominance of ectomycorrhizal host trees can promote high fungal diversity, as observed in monodominant or host‐rich systems such as Dicymbe‐dominated forests and white‐sand ecosystems (Henkel et al. 2002; Roy et al. 2016; Ritter et al. 2020).

Recent studies highlight the high turnover of mycorrhizal fungi across soil moisture and fertility gradients in tropical regions (Corrales et al. 2018; Vasco‐Palacios et al. 2020) and global studies suggest that ectomycorrhizal diversity tends to be greater in forests dominated by one or a few tree species (Voller et al. 2023). This pattern contrasts with early work in Amazonian igapós, where higher ECM richness was reported in high igapós despite their greater tree diversity, while low igapós, with fewer tree species and more prolonged flooding, appeared to support lower ECM richness (Singer and Aguiar 1986). These results stress the Amazonian igapós as an exciting, but understudied, system to understand how flood duration and severity shape mycorrhizal communities. In particular, the local mycorrhizal fungi richness may peak in high igapós and decrease linearly toward low igapó as only more and more specialized species are able to cope with the prolonged flooding (Cho et al. 2021), or it may peak at intermediate disturbance combining species tolerant of both extremes (Medium igapós; Dial and Roughgarden 1998; Zhang et al. 2018). Furthermore, it is unclear whether ectomycorrhizal fungi partition niches across igapó topographies (Singer and Aguiar 1986). This knowledge gap complicates predictions of igapó tree responses to changes in the drought‐inundation cycle. The challenge partly stems from the difficulty of identifying mycorrhizal taxa using anatomical methods. In this context, DNA metabarcoding offers a more accurate alternative, particularly useful for remote areas (Bredin et al. 2021; Ritter et al. 2020). This technique enables precise fungal community analyses, detecting rare species and tracking ecosystem changes over time (Tedersoo, May, and Smith 2010; Tedersoo, Sadam, et al. 2010; Tedersoo et al. 2014, 2017, 2024; Tedersoo, Bahram, et al. 2022; Tedersoo, Mikryukov, et al. 2022; Corrales et al. 2018; Semenov 2021).

Here, we used DNA metabarcoding of root samples from three Amazonian igapó forests to characterize mycorrhizal fungal richness and community composition and to evaluate the influence of flood regimes using topographic position as a proxy, while explicitly accounting for geographic site effects. We hypothesized that (H1) mycorrhizal richness and diversity, as well as the relative contribution of ectomycorrhizal fungi, are higher in medium and high igapós than in low igapós, where flooding is more intense and tree communities are more strongly filtered; and (H2) mycorrhizal community composition differs among sites and among igapó topographic levels, reflecting spatial structuring driven by geographic context and flooding‐related environmental gradients in the Central Amazonia.

2. Material and Methods

2.1. Study Area and Sampling Design

To verify the effect of flooding on tree‐mycorrhizal fungus interactions, we selected three igapó sites in the Negro River basin, with a well‐documented flora (Figure 1; PELD—MAUA/INPA database—Long‐term Monitoring Program for Wetlands). At each site, we sampled roots in rectangular plots (Table S1), where all trees with diameter at breast height (DBH) ≥ 10 cm were identified and mapped. The first area was RDS Uatumã (Uatumã Sustainable Development Reserve) at the Abacate River, located on the border between the municipalities of Presidente Figueiredo and São Sebastião do Uatumã, Amazonas state (AM), Brazil. The second area was the RDS Rio Negro (Rio Negro Sustainable Development Reserve) at the Cuieiras River, situated near Manaus, AM, Brazil. The third area was the PARNA Jaú (Jaú National Park) at the Jaú River, located in the municipality of Novo Airão, AM, Brazil. The Jaú River drains 10,000 km2 of tropical forest and meets the main channel of the Rio Negro about 300 km upstream from its mouth. Its smaller tributaries are flooded annually, with varying frequency and duration (Forsberg et al. 2001; Junk et al. 2011, 2014). The Uatumã floodplain is wide and experiences strong seasonal pulses (Walker et al. 1999; Kasper et al. 2014). The Cuieiras River, similar to the Uatumã, is also heavily impacted by the flood pulse.

For root sampling, we targeted host trees selected through a stratified approach to ensure a representative yet biologically meaningful collection of tree species following three criteria: (1) abundance, including dominant and rare tree species within each igapó locality and topographic level, as quantified by field surveys and the long‐term PELD‐MAUA forest inventory (Table 1); (2) taxonomic predisposition as ECM host, prioritizing species from genera or families with documented ectomycorrhizal associations in the literature; and (3) direct fungal evidence, focusing on trees in immediate proximity to basidiomes of known ectomycorrhizal taxa (e.g., Russula, Entoloma, Amanita, boletes genera). We followed this tripartite strategy to maximize the probability of finding mycorrhizal fungi and to address the inherent ecological variability of igapó forests, where flood‐mediated turnover in tree community composition creates heterogeneous host availability across sites. The inclusion of rare species alongside dominant taxa increased the potential to capture niche‐specific partnerships, while the basidiome proximity criterion allowed field‐verifiable confirmation of active symbioses, minimizing false negatives in root sampling. Variable sampling effort per species arose from this framework: host abundance differed among igapós, and ectomycorrhizal associations were detectable only where fungal fruiting bodies occurred or records already exist in literature. Consequently, sample sizes reflect host distribution and symbiosis frequency rather than a balanced experimental design. This approach aligns with the study's goal of characterizing ectomycorrhizal networks under natural conditions, where flood‐driven disturbances and host specificity jointly shape fungal colonization patterns.

TABLE 1.

Sampling sites and corresponding flood regime.

Locality Plot Topography Flooding level± (m)
RDS UATUM×Abacate River A1A Low ≥ 5
RDS UATUM×Abacate River A1B Low ≥ 5
RDS UATUM×Abacate River A2A Medium 3–4
RDS UATUM×Abacate River A2B Medium 3–4
RDS UATUM×Abacate River A3A High 1–2
RDS RIO NEGRO—Cuieiras River CA1 High 1–2
RDS RIO NEGRO— Cuieiras River CA2 Medium 3–4
RDS RIO NEGRO—Cuieiras River CA3 Low ≥ 5
PARNA JAU—Jaú River JP1 Low ≥ 5
PARNA JAU—Jaú River JP5 Medium 3–4
PARNA JAU—Jaú River JP6 Medium 3–4
PARNA JAU—Jaú River JP7 High 1–2
PARNA JAU—Jaú River JP9 High 1–2
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We collected fine root samples (30 mg) from 157 tree individuals belonging to 68 species (Table 2) between August and December (the dry season when igapós were drained) from 2019 to 2021. We identified tree species based on morphological characteristics using identification keys in the field and based on material deposited in the Herbarium of the National Institute of Amazonian Research (INPA). We took the root samples by removing individual roots from the plant's root systems close to the soil surface, and subsequently packaged them in paper envelopes. In the laboratory, the samples were cleaned and packaged in zipped bags with silica gel and kept in a freezer at an average temperature of −15°C. All sampling was done with legal permission (SISBIO license 66334‐1).

TABLE 2.

Summary of sampling effort and metabarcoding output by site and plot.

Site Plot Topography Plant species Individuals sampled (n) Reads (all mycorrhizae) ASVs (all mycorrhizae) Reads (AMF) ASVs (AMF) Reads (ECM) ASVs (ECM)
PARNA JAU JP1 Low Acosmium nitens 1 170 19 157 13 13 6
PARNA JAU JP1 Low Amanoa oblongifolia 2 309 36 181 16 127 19
PARNA JAU JP1 Low Eschweilera tenuifolia 2 5203 42 209 26 371 14
PARNA JAU JP1 Low Ormosia excelsa 1 18 7 9 5 5 1
PARNA JAU JP5 Mid Aldina latifolia 1 524 19 63 11 461 8
PARNA JAU JP5 Mid Elvasia sp. 1 261 31 216 24 45 7
PARNA JAU JP5 Mid Erythroxylum spruceanum 1 247 10 168 8 0 0
PARNA JAU JP5 Mid Pouteria elegans 2 2915 37 286 17 2629 20
PARNA JAU JP5 Mid Swartzia laevicarpa 1 414 23 119 13 295 10
PARNA JAU JP5 Mid Swartzia polyphylla 1 381 28 9 3 372 25
PARNA JAU JP5 Mid Tachigali sp. 2 1792 65 706 27 1065 36
PARNA JAU JP5 Mid Terminalia sp. 1 672 17 403 6 269 11
PARNA JAU JP5 Mid Tovomita spruceana 1 88 5 0 0 88 5
PARNA JAU JP6 Mid Amanoa oblongifolia 2 269 33 172 21 35 9
PARNA JAU JP6 Mid Burdachia sp. 2 896 30 613 7 283 23
PARNA JAU JP6 Mid Duroia velutina 1 105 16 1 1 104 15
PARNA JAU JP6 Mid Hydrochorea marginata 1 13 5 4 2 9 3
PARNA JAU JP6 Mid Maprounea amazonica 1 8011 20 828 11 7183 9
PARNA JAU JP6 Mid Micropholis humboldtiana 1 782 32 205 16 557 13
PARNA JAU JP7 High Alchorneopsis floribunda 1 138 27 79 12 55 14
PARNA JAU JP7 High Aldina latifolia 3 14,821 83 385 20 14,043 59
PARNA JAU JP7 High Campsiandra angustifolia 1 4084 63 5 2 4079 61
PARNA JAU JP7 High Dialium guianense 1 11,061 42 335 17 10,653 23
PARNA JAU JP7 High Eschweilera sp. 2 17,177 42 128 4 17,049 38
PARNA JAU JP7 High Eschweilera sp.3 1 94 10 9 4 85 6
PARNA JAU JP7 High Macrolobium acaciifolium 1 452 29 2 2 450 27
PARNA JAU JP7 High Miconia poeppigii 1 5442 41 63 7 5374 32
PARNA JAU JP7 High Swartzia laevicarpa 1 845 40 193 18 181 17
PARNA JAU JP7 High Vatairea guianensis 1 420 23 0 0 420 23
PARNA JAU JP9 High Aldina latifolia 1 854 28 243 15 611 13
PARNA JAU JP9 High Brosimum guianense 1 12 8 8 6 4 2
PARNA JAU JP9 High Elvasia quinqueloba 2 668 42 293 33 375 9
PARNA JAU JP9 High Licania adolphoduckei 1 190 29 123 23 67 6
PARNA JAU JP9 High Mouriri sp. 1 81 16 41 12 40 4
PARNA JAU JP9 High Pouteria elegans 1 480 24 202 12 278 12
PARNA JAU JP9 High Sacoglottis guianensis 1 516 7 18 3 498 4
PARNA JAU JP9 High Tachigali sp. 2 1346 36 747 21 599 15
RDS RIONEGRO CA1 High Abarema jupunba 1 219 12 73 9 146 3
RDS RIONEGRO CA1 High Acosmium nitens 4 5843 64 2450 49 3380 14
RDS RIONEGRO CA1 High Aspidosperma pachypterum 1 1284 12 870 6 413 5
RDS RIONEGRO CA1 High Cybianthus densiflorus 1 1240 15 771 7 469 8
RDS RIONEGRO CA1 High Elvasia calophyllea 1 2071 21 1514 14 557 7
RDS RIONEGRO CA1 High Licania apetala 1 890 28 678 21 212 7
RDS RIONEGRO CA1 High Micropholis humboldtiana 2 1328 19 564 16 764 3
RDS RIONEGRO CA1 High Mouriri sp. 2 2712 39 1560 23 1073 15
RDS RIONEGRO CA1 High Pouteria elegans 5 7674 69 4172 45 3502 24
RDS RIONEGRO CA1 High Swartzia acuminata 2 2931 26 385 10 2546 16
RDS RIONEGRO CA1 High Tachigali sp. 1 141 2 141 2 0 0
RDS RIONEGRO CA1 High Tovomita sp. 2 1613 19 780 14 833 5
RDS RIONEGRO CA2 Mid Calypthranthes sp. 1 914 27 697 20 207 6
RDS RIONEGRO CA2 Mid Micropholis humboldtiana 1 1061 23 1035 20 26 3
RDS RIONEGRO CA2 Mid Pouteria elegans 2 4023 27 3960 23 63 4
RDS RIONEGRO CA2 Mid Swartzia acuminata 1 7766 47 441 15 7307 31
RDS RIONEGRO CA2 Mid Tachigali oppositifolia 1 1241 43 682 26 559 17
RDS RIONEGRO CA3 Low Calypthranthes sp. 1 4955 45 623 16 1848 23
RDS RIONEGRO CA3 Low Duroia velutina 1 1327 22 213 9 285 10
RDS RIONEGRO CA3 Low Licania sp. 1 587 13 37 6 43 4
RDS RIONEGRO CA3 Low Micropholis humboldtiana 2 10,464 49 4573 34 439 13
RDS RIONEGRO CA3 Low Swartzia polyphylla 1 424 19 281 14 143 5
RDS RIONEGRO CA3 Low Swartzia sp. 1 258 10 43 4 10 4
RDS UATUMA A1A Low Campsiandra comosa 2 12,236 65 1190 46 11,043 18
RDS UATUMA A1A Low Caraipa richardiana 2 776 24 564 14 164 9
RDS UATUMA A1A Low Couratari tenuicarpa 2 497 19 86 5 410 13
RDS UATUMA A1A Low Crudia amazonica 2 1161 56 813 42 347 13
RDS UATUMA A1A Low Eperua duckeana 1 1413 42 807 11 606 31
RDS UATUMA A1A Low Eschweilera albiflora 3 3577 83 1564 51 963 27
RDS UATUMA A1A Low Eugenia lambertiana 3 2941 113 2285 86 622 24
RDS UATUMA A1A Low Macrolobium angustifolium 1 292 9 104 5 188 4
RDS UATUMA A1A Low Mouriri brevipes 1 2479 46 1419 41 1035 4
RDS UATUMA A1A Low Swartzia laevicarpa 1 338 49 236 38 101 10
RDS UATUMA A1A Low Swartzia polyphylla 2 675 49 343 32 323 15
RDS UATUMA A1A Low Zygia cataractae 4 5657 117 581 46 5076 71
RDS UATUMA A1B Low Amanoa oblongifolia 1 11,850 28 550 22 11,300 6
RDS UATUMA A1B Low Calophyllum brasiliense 1 667 30 378 15 288 14
RDS UATUMA A1B Low Campsiandra comosa 2 708 59 131 16 577 43
RDS UATUMA A1B Low Caraipa richardiana 4 2187 47 457 22 1361 22
RDS UATUMA A1B Low Couratari cf. tenuicarpa 1 531 37 156 22 147 12
RDS UATUMA A1B Low Couratari tenuicarpa 1 477 30 459 28 18 2
RDS UATUMA A1B Low Crudia amazonica 3 1937 73 990 43 885 28
RDS UATUMA A1B Low Dicorynia paraensis 1 2383 45 2215 27 136 17
RDS UATUMA A1B Low Elvasia calophyllea 1 1240 41 357 28 75 11
RDS UATUMA A1B Low Eschweilera albiflora 5 20,137 102 1515 68 18,431 33
RDS UATUMA A1B Low Eschweilera cf. albiflora 1 424 39 277 24 128 12
RDS UATUMA A1B Low Manilkara bidentata 2 1523 66 630 27 869 38
RDS UATUMA A1B Low Micropholis melinoniana 1 404 29 343 27 1 1
RDS UATUMA A1B Low Ocotea aciphylla 1 405 33 194 15 211 18
RDS UATUMA A1B Low Sacoglottis guianensis 2 855 39 129 21 577 15
RDS UATUMA A1B Low Swartzia laevicarpa 1 59,404 35 60 21 59,312 12
RDS UATUMA A1B Low Unonopsis guatterioides 1 13 9 10 7 3 2
RDS UATUMA A1B Low Zygia cataractae 3 487 57 60 14 426 42
RDS UATUMA A2A Mid Aspidosperma exelsum 1 497 22 309 17 152 4
RDS UATUMA A2A Mid Caraipa richardiana 1 108 6 26 2 82 4
RDS UATUMA A2A Mid Eperua rubiginosa 1 59 18 0 0 59 18
RDS UATUMA A2A Mid Humiriastrum cuspidatum 1 6394 49 1261 36 4294 8
RDS UATUMA A2A Mid Licania hypoleuca 1 518 19 451 13 67 6
RDS UATUMA A2B Mid Blepharocalyx eggersii 1 413 12 14 3 20 5
RDS UATUMA A2B Mid Campsiandra comosa 1 160 14 45 9 115 5
RDS UATUMA A2B Mid Licaria cannella 1 1047 61 549 31 498 30
RDS UATUMA A3A High Campsiandra comosa 2 11,920 63 2266 38 2079 17
RDS UATUMA A3A High Eschweilera albiflora 3 2851 45 542 28 322 11
RDS UATUMA A3A High Eschweilera tessmannii 1 2123 30 769 15 1352 14
RDS UATUMA A3A High Licania apetala 1 457 16 146 8 250 7
RDS UATUMA A3A High Unonopsis guatterioides 1 1105 22 335 14 770 8
RDS UATUMA A3A High Zygia cataractae 1 62 11 9 6 53 5
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Note: For each site × plot combination, we report the number of individuals for each tree species, total sequencing reads, and the number of detected ASVs for all mycorrhizae, arboreal mycorrhizae (AMF), and ectomycorrhizae (ECM).

2.2. DNA Isolation and Quantification

Under the SisGen number AAB7D27, samples were sent to the AllGenetics & Biology SL (www.allgenetics.eu; Spain) for DNA extraction, amplification, and sequencing. Meta DNA was extracted from each sample using the DNeasy PowerSoil Pro kit (QIAGEN). Up to 30 mg of root tips tissue from each sample, cut into small pieces with flame‐sterilized scissors and tweezers, was placed in the PowerBead Pro tubes provided with the kit, along with a lysis buffer. The samples were then grounded in the TissueLyser LT (QIAGEN) at 50 Hz for 5 min. DNA extraction precisely followed the manufacturer's instructions. An extraction blank (Bex1 to Bex7) was included in each round to monitor cross‐contamination, and these blanks were processed like regular samples. DNA was resuspended in a final volume of 50 μL. DNA concentrations were quantified using the Qubit High Sensitivity dsDNA Assay (Thermo Fisher Scientific).

2.3. DNA Metabarcoding Amplification and Sequencing

To identify fungi in the roots, a 300 bp fragment of the ITS2 region was targeted for amplification using the forward primer ITS86F (5′ GTGAATCATCGAATCTTTGAA 3′) (Turenne et al. 1999) and the reverse primer ITS4R (5′ TCCTCCGCTTATTGATATGC 3′) (White et al. 1990). This ITS2 fragment is widely used for fungal identification at the species level (White et al. 1990; Schoch et al. 2012; Kõljalg et al. 2013). Primer sequences for Illumina sequencing were attached to the 5′ ends of these primers. Amplification was carried out in a 12.5 μL reaction volume, containing 1.25 μL of DNA template, 0.5 μM of each primer, 3.13 μL of Supreme NZYTaq 2× Green Master Mix (NZYTech), and ultrapure water up to 12.5 μL. The PCR conditions included an initial denaturation at 95°C for 5 min, followed by 30 cycles of 95°C for 30 s, 47°C for 45 s, 72°C for 45 s, and a final extension at 72°C for 7 min.

Oligonucleotide indices required for multiplexing libraries were attached in a second amplification step under identical conditions but with only 5 cycles and a 60°C annealing temperature. A negative control without DNA (BPCR) was included in each PCR round to check for contamination. Library size was verified using 2% agarose gels stained with GreenSafe (NZYTech) and visualized under UV light. Libraries were purified using Mag‐Bind RXNPure Plus magnetic beads (Omega Biotek), following the manufacturer's instructions. Final libraries were pooled in equimolar amounts based on Qubit dsDNA HS Assay (Thermo Fisher Scientific) quantification and sequenced on a NovaSeq PE250 flow cell (Illumina), targeting a total output of 9 gigabases.

2.4. Sequence Analyses and Taxonomic Assessment

We produced amplicon sequence variants (ASVs) using the DADA2 v.1.16 package (Callahan et al. 2016) in R v. 4.3.0 (R Core Team 2024) to process the raw data. This included primer removal, quality filtering, sequence merging, chimera removal, ASV inference, and taxonomic assignment. Reads containing ambiguous bases (maxN = 0) were excluded. Quality filtering was based on the error rates of the forward and reverse sequences, allowing a maximum of three errors for forward reads and five errors for reverse reads (maxEE = c(3,5), truncQ = 2). ASVs were inferred using run‐specific error rates for each sample. Paired‐end reads were merged with a minimum overlap of 12 base pairs, and sequences with mismatches in the overlapping region were discarded. Taxonomic assignment of ASVs was performed using the UNITE database v.16.10.2022 (Abarenkov et al. 2010). Additionally, functional guilds were assigned to ASVs identified as fungi using the FungalTraits database v.0.0.3 (Põlme et al. 2020), with manual verification of the assignments to ensure accuracy.

2.5. Statistical Analysis

We conducted all analyses in the R environment. We used the tidyverse package v.1.3.0 (Wickham et al. 2019) for data curation and ggplot2 v. 3.3.2 (Wickham 2016), ggfortify v. 0.4.11 (Tang et al. 2016), and gridExtra v. 2.3 (Auguie and Antonov 2017), for data visualization (scripts in Appendix S1).

For all analyses, we only retained ASV identified as belonging to mycorrhizal fungi in the FungalTraits database. We calculated the ASV richness (the number of ASV per tree), Shannon's index, and inverse Simpson's index for alpha diversity using the phyloseq v.1.34.0 (McMurdie and Holmes 2013) R package. To evaluate whether mycorrhizal alpha diversity varied across localities and flood levels, we applied Kruskal–Wallis test, followed by post hoc Dunn tests with Bonferroni correction for pairwise comparisons using the dunn.test V.1.3.6 R package (Dinno and Dinno 2017). All analyses were conducted separately for the total observed mycorrhizal fungi and for the subset of ECM.

We explored beta diversity patterns using non‐metric multidimensional scaling (NMDS) ordinations. NMDS was performed both on relative abundance data using Bray–Curtis distances and on presence/absence data using Jaccard distances with the phyloseq package. To test the significance of the difference in community composition for flood level and localities, we applied PERMANOVA with 999 permutations on both Bray–Curtis and Jaccard distances for relative abundance and presence and absence matrix, respectively, with the vegan v.2.6.10 R package (Oksanen et al. 2013). Finally, we tested for homogeneity of multivariate dispersions across groups using the betadisper function of the vegan package, ensuring that PERMANOVA results could be interpreted in terms of compositional differences rather than unequal dispersions.

3. Results

We found a total of 11,043 ASVs of which 7760 were assigned as fungi. Of those, we identified 568 as mycorrhizal fungi: 238 as arbuscular mycorrhizal fungi (from the division Glomeromycota; AMF), 307 as ectomycorrhizal fungi from various taxa (ECM), three as ericoid mycorrhizal fungi, and 20 as mycorrhizal fungi of unidentified type and taxon. The Number of ASVs per tree is available in Table 2.

Across flood levels, the total mycorrhizal fungi ASV richness per sample varied significantly (Kruskal–Wallis test: χ 2 = 7.36, p = 0.02; Figure 2). The post hoc Dunn tests revealed that the total mycorrhizal fungi ASV richness was significantly higher in low versus high igapós (p = 0.02), but no significant difference between low versus medium (p = 0.07) and medium versus high igapós (p = 1; Figure 2, Figures S1 and S2). In contrast, neither the inverse Simpson diversity (Kruskal–Wallis test: χ 2 = 3.14, p = 0.2) nor the Shannon diversity (Kruskal–Wallis test: χ 2 = 5.23, p = 0.07) varied significantly across flood levels for total mycorrhizal fungi ASVs.

FIGURE 2.

FIGURE 2

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Alpha diversity of mycorrhizal amplicon sequence variants (ASVs), including arbuscular (AMF), ectomycorrhizal (ECM), ericoid, and unidentified mycorrhizal fungi, from roots across flood levels (High, Mid, Low) in three Amazonian igapó sites: PARNA Jaú, RDS Rio Negro, and RDS Uatumã. Each point represents the number of different mycorrhizal ASVs observed per sample plot. Larger circles indicate the mean and the bars represent the standard deviation per flood level within each site. For Shannon and inverse Simpson diversity indexes, figures are available at Supporting Information (Figures S1 and S2).

Across localities, the total mycorrhizal fungi ASVs richness also varied, with 416 ASVs recorded in RDS Uatumã, 221 in RDS Rio Negro, and 321 in PARNA Jaú. This difference was significant (χ 2 = 8.42, p = 0.01; Figure 2). The post hoc Dunn tests indicated that RDS Uatumã had significantly higher total mycorrhizal richness than RDS Rio Negro (p = 0.007), while no significant differences were detected between PARNA Jaú and the other sites. The Shannon (Kruskal–Wallis test: χ 2 = 8.91, p = 0.01, Figure 2) and inverse Simpson (Kruskal–Wallis test: χ 2 = 6.36, p = 0.04) diversity also indicated a significant difference.

Focussing on the ECM, ASVs richness did not vary significantly across flood levels (Kruskal–Wallis test: χ 2 = 1.28, p = 0.52, Figure 3) but varied significantly across location, with 416 ASVs recorded in RDS Uatumã, 224 in RDS Rio Negro, and 321 in PARNA Jaú (Kruskal–Wallis test: χ 2 = 14.5, p < 0.001; Figure 3). The Dunn test showed that RDS Rio Negro is significantly different from PARNA Jaú (p < 0.001) and RDS Uatumã (p = 0.003).

FIGURE 3.

FIGURE 3

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Alpha diversity of ectomycorrhizal ASVs from roots across flood levels (High, Mid, Low) in three Amazonian igapó sites: PARNA Jaú, RDS Rio Negro, and RDS Uatumã. Each point represents the richness of mycorrhizal amplicon sequence variants (ASVs) observed per plot. Larger circles and bars indicate the mean, and the bars represent the standard deviation per flood level within each site.

Concerning community composition, the non‐metric multidimensional scaling (NMDS) based on Bray–Curtis and Jaccard dissimilarities showed a substantial overlap among samples from different localities and flood levels (Figure 4). Consistently, the PERMANOVA results indicate only small (albeit significant) effects of both topography and locality on community composition for relative abundance (Flood level: R 2 = 0.025, F = 2.08, p = 0.001; Area: R 2 = 0.050, F = 4.13, p = 0.001) and for presence/absence (Flood level: R 2 = 0.022, F = 1.82, p = 0.001; Area: R 2 = 0.052, F = 4.35, p = 0.001). The tests for homogeneity of dispersions indicated no significant differences across topographies (relative abundance: p = 0.87; presence/absence: p = 0.45), but significant differences across localities (relative abundance: p < 0.001; presence/absence: p = 0.06). These results suggest that the PERMANOVA effects for locality should be interpreted with caution, as they may be partly driven by differences in multivariate dispersion.

FIGURE 4.

FIGURE 4

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Non‐metric multidimensional scaling (NMDS) of mycorrhizal ASV communities across flood levels and localities. (A) Relative‐abundance (Bray–Curtis; stress ≈0.25) and (B) presence/absence (Jaccard; stress ≈0.23) ordinations. Colors indicate flood level (High, Mid, Low) and shapes indicate locality (circle = PARNA Jaú; triangle = RDS Rio Negro; square = RDS Uatumã). Ordinations show broad overlap among groups with subtle shifts along NMDS1/NMDS2.

The presence and relative abundance of different taxonomic groups varied between localities (Figure 5). In PARNA Jaú, Archaeorhizomycetes, Entolomataceae (ECM), and Glomeraceae (AMF) were the dominant taxa, with other ECM families such as Hymenogastraceae, Russulaceae, Sclerodermataceae, and Thelephoraceae present at lower abundances. A similar taxonomic composition was observed in RDS Rio Negro, although relative abundances varied. Conversely, RDS Uatumã showed a distinct pattern, with a higher abundance of unidentified fungal lineages and a reversal in ECM‐AMF dominance: Entolomataceae (ECM) were more abundant than Glomeraceae (AMF). For taxonomic groups, for site and topography see Appendix Figures S1–S3.

FIGURE 5.

FIGURE 5

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Proportion of mycorrhizal amplicon sequence variants (ASVs) assigned to major fungal taxa at different taxonomic ranks (phylum, order, family, genus), pooled across all root samples from the three igapó sites. The 10 most ASV‐rich taxa at each rank are named. Taxa with fewer ASVs are grouped as “Other,” and ASVs that could not be assigned at a given taxonomic rank are shown as separate “Unidentified” categories.

4. Discussion

Our study revealed a significant variability in the alpha and beta diversity of mycorrhizal fungi across different igapó sites and flood levels. In contrast to our expectations (H1), mycorrhizal fungi ASV richness was highest in low‐level igapós, which experience longer flooding and are dominated by few tree species. However, this pattern was dependent on functional group and locality: for the ECM, richness was highest in high and mid‐level igapós at PARNA Jaú and RDS Rio Negro, whereas at RDS Uatumã the diversity was highest in low‐level igapós. It is important to note that richness metrics tend to overemphasize rare taxa; accordingly, for topographic effects, Shannon and inverse Simpson indices were not significant. These indices down‐weight rare taxa and are less sensitive to uneven sampling and in metabarcoding contexts with uneven sequencing depth (Tedersoo, Bahram, et al. 2022; Tedersoo, Mikryukov, et al. 2022). Taken together, our results suggest that flood‐level differences may occur primarily in the rare tail of the mycorrhizal community in certain contexts, while overall dominance structure and evenness were not consistently altered across flood levels when considering the full mycorrhizal assemblage. Mycorrhizal communities were modestly structured by flood‐level topography and locality, stressing that despite a superficial physiognomic homogeneity, central Amazonian igapós are diverse in mycorrhizal fungi composition at different sites (Householder et al. 2024). Correspondingly, the occurrence of fungal families and orders varied across topographic levels, reflecting the influence of flooding and host tree species.

4.1. Alpha Diversity

The data presented in this study enrich existing information on the mycorrhizal communities of Amazonia: a recent review of global fungi data had revealed that all of the 1362 available metabarcoding samples from the Neotropics combined only documented 406 ECM fungal lineages (Corrales et al. 2022). In contrast, our study of the central Amazonian igapós, a previously undersampled ecosystem, alone identified 307 unique ASVs of ECM fungi, underscoring the remarkable diversity, potential endemism, and need for further research of these communities (Tedersoo et al. 2014, 2024).

The documented richness patterns of overall mycorrhizal fungi (including AMF, ECM, and ericoid mycorrhiza) among the studied rivers are striking. The study trees at PARNA Jaú emerge as a hotspot for mycorrhizal diversity, particularly in high‐level igapós where all mycorrhizal types co‐occur at their highest richness. This pattern contrasts sharply with the Abacate River (RDS Uatumã) system, where maximum diversity occurs in low‐lying, frequently flooded areas. The Cuieiras River (RDS Rio Negro) presents an intermediate scenario, with peak diversity concentrated in mid‐level igapós. Notably, Glomeraceae (AMF) reaches its highest abundance in the Cuieiras River and the Jaú River. Intermediate flood levels fall between these two extremes. The reasons for these differences among rivers need verification on a larger geographic scale with a larger sample size from a comparative sampling scheme, but may be related to river structure and origin. The Jaú drains weathered, nutrient‐poor soils, with high concentrations of dissolved organic carbon derived from hydromorphic soils (Forsberg et al. 2001). The Uatumã River flows through the Guiana Shield and the central Amazon sedimentary basin, where clayey and sandy soils predominate (Ríos‐Villamizar et al. 2020).

4.2. Beta Diversity

Our study reveals that mycorrhizal fungal assemblages in Amazonian igapó forests are structured primarily by locality, with flood‐level topography acting as a secondary, context‐dependent filter. Although overall community overlap was high across samples, multivariate analyses consistently detected significant effects of both basin and flood level on community composition. These effects were modest in magnitude, yet robust across abundance‐weighted (Bray–Curtis) and occurrence‐based (Jaccard) dissimilarities, indicating that differences among sites are expressed both in shifts in dominant lineages and in the distribution of rarer taxa.

The relatively small effect sizes observed for both locality and topography are ecologically expected in igapó systems, which share broad physiognomic similarity but differ subtly in geomorphology, hydrological regime, and floristic composition (Montero et al. 2012; Montero and Latrubesse 2013; Scudeller and Villarrúbia 2018). Tests of multivariate dispersion further indicate that locality effects may partly reflect differences in within‐site heterogeneity, suggesting that basins differ not only in the central tendency of their fungal communities but also in the breadth of assemblages that can establish locally. Taken together, these results support a view of igapó forests as a mosaic of belowground communities shaped by basin‐scale environmental context and filtered by flood‐related gradients.

4.3. Taxonomic Composition

Taxonomic summaries across phylum, order, family, and genus levels provide biologically meaningful context for these compositional patterns. Across all sites and flood levels, mycorrhizal communities shared a consistent high‐level structure dominated by Basidiomycota (including ECM and orchid mycorrhizae taxa), Glomeromycota (AMF), and Ascomycota (ECM and ericoid mycorrhizae), reflecting the widespread presence of ECM and AMF fungi in igapó root systems. This stability at higher taxonomic ranks contrasts with clearer differentiation at finer levels, where basin‐specific assemblages become apparent. Although AMF diversity is well represented by Glomeromycota, ECM taxa appear to predominate overall when considering the combined richness of all families performing this symbiotic mode.

At the order and family levels, Glomerales and Glomeraceae consistently represented a major component of the communities, underscoring the pervasive role of AMF across igapó forests. Within the Agaricales, Entolomataceae (ECM) ranked as the second most ASV‐rich family, with Entoloma as the most prominent genera. The order Trechisporales also contributed substantially to ECM diversity, with richness concentrated in Hydnodontaceae and Trechispora as the most representative genus that inhabits the soil, but taxonomic information and DNA sequences of root and basidiomata fungi are still poorly reported in Brazilian biomes (Vanégas‐Léon et al. 2019).

Likely, the fungal community composition reflects the distribution of host trees and the influence of environmental filters (Junk et al. 1989; Scudeller and Villarrúbia 2018). In general, the floristic variation in igapós along the Rio Negro basin is high, although with most trees concentrated in a few families, such as Fabaceae, Lecythidaceae, and Sapotaceae, with some abundant species, such as Tachigali sp., Pouteria elegans and Eschweilera albiflora (Ferreira and Stohlgren 1999). Flood duration impacts plant diversity and distribution patterns, resulting in unique floristic compositions between low, medium, and high igapós. Comparisons between the igapós of the Jaú/Tarumã and Uatumã/Abacate Rivers show different tree assemblages at distinct topographic levels (Ferreira 2000; Ferreira and Stohlgren 1999; Wittmann et al. 2010; Lopes et al. 2014; Lobo et al. 2019). Floristic differences are attributed to environmental filters such as soil and flood regimes (Scudeller and Villarrúbia 2018; Lopes et al. 2014). Altogether, these patterns suggest that mycorrhizal assemblages in Amazonian igapós are structured by the interplay of flood regime, host identity, and basin‐specific environmental filters. Either way, our results suggest that Amazonian igapós maintain exceptional mycorrhizal diversity, particularly of ECM fungi, surpassing most other Neotropical ecosystems (Vasco‐Palacios et al. 2020; Corrales et al. 2022). This may include forests flooded by nutrient‐rich white water (várzea), which exhibit lower ECM fungi diversity, possibly due to greater nutrient availability (Bredin et al. 2021). These patterns underscore how flood regimes interact with local environmental filters and topography to shape complex mycorrhizal assemblages in these unique floodplain forests.

Flood‐level topography explained a smaller fraction of compositional variance than locality, yet its effects were consistent and ecologically interpretable. Flood levels integrate multiple environmental constraints relevant to fungi, including oxygen availability, redox conditions, and host turnover along the inundation gradient (Wittmann and Parolin 2005; Meyer et al. 2010; Householder et al. 2021, 2024). The taxonomic summaries suggest that flood regime modulates community composition within basins by shifting the relative representation of taxa already present in the regional species pool, rather than by generating entirely distinct assemblages. This supports a two‐tier assembly framework in which basin‐scale environmental context defines the available fungal pool, while flood‐level topography acts as a filter shaping relative abundances within that pool.

Differences in the balance between ECM‐ and AMF‐associated lineages across basins further reinforce this composition‐centered interpretation. While AMF lineages remained prominent across all sites, ECM‐associated taxa formed a substantial and sometimes dominant component of igapó root communities. The relative prominence of ECM‐associated families in some basins, particularly RDS Uatumã, indicates that ectomycorrhizal fungi are not marginal in these blackwater flooded forests, challenging earlier assumptions that ECM are rare or functionally unimportant in tropical floodplain systems. Rather than responding uniformly to flooding, mycorrhizal symbiotic modes appear to be differentially favored depending on basin‐specific host composition and environmental conditions.

These multivariate patterns demonstrate that igapó mycorrhizal communities are structured by meaningful site‐level differentiation expressed through shifts in dominance, compositional turnover at finer taxonomic ranks, and variation in community heterogeneity among basins. Although samples broadly overlap, these patterns indicate that Amazonian blackwater flooded forests host distinct belowground symbiotic assemblages shaped by hydrology, basin connectivity, and host tree composition.

Consistent with river‐network connectivity (Vannote et al. 1980), the Rio Negro links the Jaú and Cuieiras basins, exhibiting broadly similar assemblages, characterized by strong representation of AMF lineages, particularly Glomeraceae, alongside consistently present ECM‐associated families such as Entolomataceae and additional ECM taxa at lower relative abundances. In contrast, the more isolated Abacate River (RDS Uatumã) forms a distinct assemblage characterized by higher abundances of unidentified ECM lineages and a shift in the relative balance between ECM‐ and AMF‐associated taxa, with Entolomataceae exceeding Glomeraceae in relative abundance. This pattern suggests that basin context influences not only which fungal taxa occur but also their relative dominance within communities. The elevated proportion of unidentified lineages at RDS Uatumã likely reflects both genuine compositional distinctness and limitations of current reference databases for Amazonian root‐associated fungi, emphasizing the need for improved taxonomic coverage in the region.

4.4. ECM Diversity

The observed high diversity of ECM fungi and their compositional variability across topographies and rivers, in particular compared to AMF, challenges the long‐held assumption that ECM are rare in tropical forests and largely restricted to a small set of tree families. Instead, our data add to recent evidence of unexpectedly high ECM diversity in the Neotropics, including novel associations with families not previously recognized as ECM hosts (e.g., Lecythidaceae, Sapotaceae, Humiriaceae). Moreover, our results show that dominant trees are not always the main ECM hosts; in contrast, species of lower abundance often harbored disproportionately high ECM diversity.

In contrast to Singer and Araújo (1979) who suggested that low‐level igapós do not favor ECM due to oxygen limitation, our data indicate that the opposite may occur: in the Abacate River (RDS Uatumã), the highest ECM diversity occurs in low‐level igapós, which remain flooded for longer periods. An intermediate pattern may also occur as we found in the Cuieiras River. The reasons remain elusive, but may be related to plant adaptations such as well‐developed root aerenchyma that ensure minimum oxygen concentration required by the ECM fungi. Additionally, fungi adaptations, such as membrane proteins such as aquaporins may aid in the suberization of the root sheath in ectomycorrhizas in waterlogged soils (Khan 1993; Haase and Rätsch 2010; Meyer et al. 2010; Dietz et al. 2011; Johnson 2018). Alternatively, the mycelium range of fungal individuals may reach out of the flooded areas to the more oxygen‐rich soil of the igapó margin or local terra‐firme spots, assuring the oxygen supply. In any case, these results reinforce that the seasonal flood of igapós does not constitute a principal barrier for the fungi survival in these soils, particularly the mycorrhizae among the rhizospheres.

ECM‐associated orders such as Agaricales and Trechisporales, and families including Entolomataceae, Russulaceae, Thelephoraceae, and Hydnodontaceae contributed substantially to compositional differences among sites. These patterns were maintained at the genus level, where a small number of abundant genera accounted for a large fraction of ASVs, accompanied by a long tail of low‐abundance lineages grouped as “Other” or “Unidentified,” a characteristic feature of metabarcoding datasets in understudied tropical systems.

5. Conclusion

Our study demonstrates that Amazonian igapó tree roots harbor highly diverse mycorrhizal communities whose composition is shaped by the interplay between flood regime, topography, locality, and host tree composition. Despite the strong environmental constraints imposed by prolonged flooding, igapó forests sustain unexpectedly high mycorrhizal diversity, particularly of ectomycorrhizal fungi, potentially exceeding that reported for many other Neotropical ecosystems. Fungal diversity peaks differed among basins, occurring in low‐level igapós of the Abacate River, high‐level igapós of the Jaú River, and mid‐level igapós of the Cuieiras River, underscoring the dominant role of basin‐scale environmental context over topography alone. Community differentiation was expressed primarily through shifts in taxonomic composition and relative dominance rather than complete species turnover, indicating that flood‐level topography acts as a secondary filter on basin‐specific species pools. The high richness and prevalence of ECM fungi challenge the long‐standing view that ectomycorrhizal symbioses are rare or marginal in tropical floodplain forests. With 307 ECM ASVs detected, comparable to the 406 previously documented ECM lineages across the entire Neotropics, our results identify igapó forests as previously unrecognized hotspots of mycorrhizal specialization. These findings highlight the resilience and adaptability of plant–fungal symbioses under seasonal flooding and emphasize the importance of conserving igapó ecosystems in the face of ongoing hydrological alteration and climate change.

Author Contributions

Maihyra Marina Pombo: conceptualization (equal), data curation (equal), investigation (equal), methodology (equal), writing – original draft (equal), writing – review and editing (equal). Camila Duarte Ritter: formal analysis (equal), visualization (equal), writing – review and editing (equal). Florian Wittmann: conceptualization (equal), project administration (equal), supervision (equal). Jadson José Souza de Oliveira: conceptualization (equal), data curation (equal), supervision (equal), writing – review and editing (equal). Maria Teresa Fernandez Piedade: funding acquisition (equal). Jochen Schongart: funding acquisition (equal). Alexander Zizka: funding acquisition (equal), visualization (equal), writing – review and editing (equal).

Funding

This study was supported by the Long‐Term Ecological Research Program PELD‐MAUA (Phases 3 and 4), funded by CNPq and FAPEAM, under the project titled “Demonstration sites of pristine and impacted oligotrophic wetland ecosystems in Central Amazonia: identifying trends and filling knowledge gaps” (CNPq Grant No. 441811/2020‐5, 446044/2024‐5; FAPEAM Grant No. 01.02.016301.02630/2022‐76), within the call CNPq/MCTI/CONFAP‐FAPs/PELD No. 21/2020. Long‐Term Ecological Research Program PELD‐MAUA (Phase 2), funded by CNPq and FAPEAM, under the project titled “Ecology and monitoring of vegetation in oligotrophic wetlands in Central Amazonia: anthropogenic impacts and implications for conservation in protected areas in the Negro and Uatumã river basins” within the call PELD/CNPq/CAPES/FAPs/BC/NEWTON FUND No. 441590/2016‐0, FAPEAM Grant No 015/2016. M.M.P. also acknowledges CAPES for a research fellowship.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Tree species sampled in Amazonian igapó plots across three study areas (RDS Uatumã, PARANA Jaú, and RDS Rio Negro), showing their relative abundance (Rel Abu.), calculated plot density (ind/m2), corresponding topographic position (Top), and the distribution status: dominant (D), rare (R) or intermediate (I). Density values were estimated by standardizing relative abundance by plot area.

ECE3-16-e73373-s002.xlsx (16.2KB, xlsx)

Figure S1: Plot‐level variation in dominant families by locality and flood level.

Figure S2: Taxonomic composition across flood‐level topography.

Figure S3: Taxonomic composition across localities (river basins).

ECE3-16-e73373-s003.docx (426.6KB, docx)

Data S1: ece373373‐sup‐0003‐Supinfo.zip.

ECE3-16-e73373-s001.zip (84.5KB, zip)

Acknowledgments

This study was supported by the Long‐Term Ecological Research Program PELD‐MAUA (Phases 3 and 4), funded by CNPq and FAPEAM, under the project titled “Demonstration sites of pristine and impacted oligotrophic wetland ecosystems in Central Amazonia: identifying trends and filling knowledge gaps” (CNPq Grant No. 441811/2020‐5, 446044/2024‐5; FAPEAM Grant No. 01.02.016301.02630/2022‐76), within the call CNPq/MCTI/CONFAP‐FAPs/PELD No. 21/2020. Long‐Term Ecological Research Program PELD‐MAUA (Phase 2), funded by CNPq and FAPEAM, under the project titled “Ecology and monitoring of vegetation in oligotrophic wetlands in Central Amazonia: anthropogenic impacts and implications for conservation in protected areas in the Negro and Uatumã river basins” within the call PELD/CNPq/CAPES/FAPs/BC/NEWTON FUND No. 441590/2016‐0, FAPEAM Grant No 015/2016. M.M.P. also acknowledges CAPES for a research fellowship. Juli S. Cardoso and Célia C. B. Soares (for sharing basidioma collections in the various flooded forests of the Amazon), Mike Hopkins (curator of the INPA Herbarium), MAUA group (logistics and fieldwork), José Ramos (for tree identification), Kleuto Moraes and Thiago Mouzinho for their field support on the Cuieiras River, Charles Zartman and PPGBot‐INPA. Open Access funding enabled and organized by Projekt DEAL.

Contributor Information

Camila Duarte Ritter, Email: kmicaduarte@gmail.com.

Alexander Zizka, Email: alexander.zizka@biologie.uni-marburg.de.

Data Availability Statement

Genbank SUB16094505; All the required data are uploaded as Supporting Information.

References

  1. Abarenkov, K. , Nilsson R. H., Larsson K. H., et al. 2010. “The UNITE Database for Molecular Identification of Fungi—Recent Updates and Future Perspectives.” New Phytologist 186, no. 2: 281–285. [DOI] [PubMed] [Google Scholar]
  2. Alexopoulos, C. J. , Mims C. W., and Blackwell M.. 1996. Introductory Mycology. 4th ed, 869. John Wiley and Sons, Inc. [Google Scholar]
  3. Auguie, B. , and Antonov A.. 2017. “Grid Extra: Miscellaneous Functions for Grid Plots.” R Package Version 2.3. https://CRAN.R‐project.org/package=gridExtra.
  4. Bredin, Y. K. , Hess L. L., Scabin A. B., et al. 2021. “Above‐and Below‐Ground Biodiversity Responses to the Prolonged Flood Pulse in Central‐Western Amazonia, Brazil.” Environmental DNA 00: 1–16. [Google Scholar]
  5. Brundrett, M. C. , and Tedersoo L.. 2018. “Evolutionary History of Mycorrhizal Symbioses and Global Host Plant Diversity.” New Phytologist 220: 1108–1115. [DOI] [PubMed] [Google Scholar]
  6. Callahan, B. , McMurdie P., Rosen M., et al. 2016. “DADA2: High‐Resolution Sample Inference From Illumina Amplicon Data.” Nature Methods 13: 581–583. 10.1038/nmeth.3869. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cho, Y. , Yoo S., Park M. S., Kim J. S., Kim C. S., and Lim Y. W.. 2021. “Ectomycorrhizal Fungi Associated With Pinus densiflora Seedlings Under Flooding Stress.” Sustainability 13, no. 8: 4367. 10.3390/su13084367. [DOI] [Google Scholar]
  8. Corrales, A. , Henkel T. W., and Smith M. E.. 2018. “Ectomycorrhizal Associations in the Tropics—Biogeography, Diversity Patterns and Ecosystem Roles.” New Phytologist 220: 1076–1091. [DOI] [PubMed] [Google Scholar]
  9. Corrales, A. , Koch R. A., Vasco‐Palacios A. M., Smith M. E., Ge Z. W., and Henkel T. W.. 2022. “Diversidade e distribuição de fungos ectomicorrízicos tropicais.” Mycology 114, no. 6: 919–933. [DOI] [PubMed] [Google Scholar]
  10. Courty, P. E. , Buée M., Diedhiou A. G., et al. 2010. “The Role of Ectomycorrhizal Communities in Forest Ecosystem Processes: New Perspectives and Emerging Concepts.” Soil Biology and Biochemistry 42, no. 5: 679–698. [Google Scholar]
  11. Das, A. K. , Lee D. S., Woo Y. J., Sultana S., Mahmud A., and Yun B. W.. 2025. “The Impact of Flooding on Soil Microbial Communities and Their Functions: A Review.” Stress 5, no. 2: 30. [Google Scholar]
  12. Dial, R. , and Roughgarden J.. 1998. “Theory of Marine Communities: The Intermediate Disturbance Hypothesis.” Ecology 79, no. 4: 1412–1424. 10.2307/176752. [DOI] [Google Scholar]
  13. Dietz, S. , Bulow J., Beitz E., and Nehls U.. 2011. “The Aquaporin Gene Family of the Ectomycorrhizal Fungus Laccaria Bicolor: Lessons for Symbiotic Functions.” New Phytologist 190: 927–940. [DOI] [PubMed] [Google Scholar]
  14. Dinno, A. , and Dinno M. A.. 2017. “Package ‘dunn.test’.” CRAN Repos 10: 1–7. [Google Scholar]
  15. Ferreira, L. V. 2000. “Effects of Flooding Duration on Species Richness, Floristic Composition and Forest Structure in River Margin Habitat in Amazonian Blackwater Floodplain Forests: Implications for Future Design of Protected Areas.” Biodiversity and Conservation 9: 1–14. [Google Scholar]
  16. Ferreira, L. V. , and Stohlgren T. J.. 1999. “Effects of River Level Fluctuation on Plant Species Richness, Diversity, and Distribution in a Floodplain Forest in Central Amazonia.” Oecologia 120: 582–587. [DOI] [PubMed] [Google Scholar]
  17. Forsberg, B. R. , Castro J. G. D., Cargnin‐Ferreira E., and Rosenqvist A.. 2001. “The Structure and Function of the Negro River Ecosystem: Insights From the Jau Project.” In Conservation and Management of Ornamental Fish Resources of the Rio Negro Basin, edited by Cgão N. L., Petry P., Prang P., Sonneschein L., and Tlusty M., vol. 1. Piaba Project. [Google Scholar]
  18. Furch, K. 1997. “Chemistry of Várzea and Igapó Soils and Nutrient Inventory of Their Floodplain Forests.” In The Central Amazon Floodplain. Ecological Studies (Analysis and Synthesis), edited by Junk W. J., vol. 126. Springer. [Google Scholar]
  19. Haase, K. , and Rätsch G.. 2010. “Chapter 7: The Morphology and Anatomy of Tree Roots and Their Aeration Strategies.” In Amazonian Floodplain Forests. Ecophysiology, Biodiversity and Sustainable Management. Ecological Studies, edited by Junk W. J., Piedade M. T. F., Wittmann F., Schöngart J., and Parolin P.. Springer. [Google Scholar]
  20. Halling, R. E. 2001. “Ectomycorrhizae: Co‐Evolution, Significance, and Biogeography.” Annals of the Missouri Botanical Garden 88: 5–13. [Google Scholar]
  21. Henkel, T. W. , Terborgh J., and Vilgalys R. J.. 2002. “Ectomycorrhizal Fungi and Their Leguminous Hosts in the Pakaraima Mountains of Guyana.” Mycological Research 106, no. 5: 515–531. [Google Scholar]
  22. Hess, L. L. , Melack J. M., Affonso A. G., Barbosa C., Gastil‐Buhl M., and Novo E. M. L. M.. 2015. “Wetlands of the Lowland Amazon Basin: Extent, Vegetative Cover, and Dual‐Season Inundated Area as Mapped With JERS‐1 Synthetic Aperture Radar.” Wetlands 35: 745–756. 10.1007/s13157-015-0666-y. [DOI] [Google Scholar]
  23. Householder, J. E. , Schöngart J., Piedade M. T. F., et al. 2021. “Modeling the Ecological Responses of Tree Species to the Flood Pulse of the Amazon Negro River Floodplains.” Frontiers in Ecology and Evolution 9: 628606. 10.3389/fevo.2021.628606. [DOI] [Google Scholar]
  24. Householder, J. E. , Wittmann F., Schöngart J., et al. 2024. “One Sixth of Amazonian Tree Diversity Is Dependent on River Floodplains.” Nature Ecology & Evolution 8: 901–911. 10.1038/s41559-024-02364-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Johnson, D. 2018. “Water, Water Everywhere…But How Does It Affect the Functional Diversity of Ectomycorrhizal Fungi?” New Phytologist 220: 1171–1184. [DOI] [PubMed] [Google Scholar]
  26. Junk, W. J. , Bayley P. B., and Sparks R. E.. 1989. “The Flood Pulse Concept in River Floodplain Systems.” In Proceedings of the International Large River Symposium, edited by Dodge D. P., vol. 106, 110–127. Canadian Journal of Fisheries and Aquatic Sciences. [Google Scholar]
  27. Junk, W. J. , Piedade M. T. F., Lourival R., et al. 2014. “Brazilian Wetlands: Their Definition, Delineation, and Classification for Research, Sustainable Management, and Protection.” Aquatic Conservation: Marine and Freshwater Ecosystems 24: 5–22. 10.1002/aqc.2386. [DOI] [Google Scholar]
  28. Junk, W. J. , Piedade M. T. F., Schöngart J., Cohn‐Haft M., Adeney J. M., and Wittmann F.. 2011. “A Classification of Major Naturally‐Occurring Amazonian Lowland Wetlands.” Wetlands 31: 623–640. [Google Scholar]
  29. Junk, W. , Schöngart J., Wittmann F., and Piedade M. T. F.. 2024. “As áreas úmidas ao longo dos grandes rios Amazônicos: Várzeas e igapós.” In Inventário das Áreas Ùmidas Brasileiras: Distribuição, ecologia, manejo, ameaças e lacunas de conhecimento, 168–199. Carlini & Caniato Editorial (Editora TantaTinta Ltda.). [Google Scholar]
  30. Kasper, D. , Forsberg B. R., Amaral J. H. F., et al. 2014. “Reservoir Stratification Affects Methylmercury Levels in River Water, Plankton, and Fish Downstream From Balbina Hydroelectric Dam, Amazonas, Brazil.” Environmental Science & Technology 48: 1032–1040. [DOI] [PubMed] [Google Scholar]
  31. Khan, A. G. 1993. “Occurrence and Importance of Mycorrhizae in Aquatic Trees of New South Wales, Australia.” Mycorrhiza 3: 31–38. [Google Scholar]
  32. Kõljalg, U. , Nilsson R. H., Abarenkov K., et al. 2013. “Towards a Unified Paradigm for Sequence‐Based Identification of Fungi.” Molecular Ecology 22, no. 21: 5271–5277. [DOI] [PubMed] [Google Scholar]
  33. Kubitzki, K. 1989. “The Ecogeographical Differentiation of Amazon Inundation Forests.” Plant Systematics and Evolution 162: 285–304. [Google Scholar]
  34. Lobo, G. S. , Wittmann F., and Piedade M. T. F.. 2019. “Response of Black‐Water Floodplain (igapó) Forests to Flood Pulse Regulation in a Dammed Amazonian River.” Forest Ecology and Management 434: 110–118. [Google Scholar]
  35. Lopes, A. , Wittmann F., Schöngart J., and Piedade M. T. F.. 2014. “Herbáceas aquáticas em seis igapós na Amazônia Central: composição e diversidade de gêneros.” Rev. Geogr. Acadêmica 8: 5–17. [Google Scholar]
  36. Lutzoni, F. , Nowak M. D., Alfaro M. E., et al. 2018. “Contemporary Radiations of Fungi and Plants Linked to Symbiosis.” Nature Communications 9: 5451–5462. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Marengo, J. , Tomasella J., Alves L., Soares W., and Rodriguez D.. 2011. “The Drought of 2010 in the Context of Historical Droughts in the Amazon Region.” Geophysical Research Letters 38: 1–5. [Google Scholar]
  38. Martínez‐Arias, C. , Witzell J., Solla A., Martin J. A., and Rodríguez‐Calcerrada J.. 2022. “Beneficial and Pathogenic Plant‐Microbe Interactions During Flooding Stress.” Plant, Cell & Environment 45, no. 10: 2875–2897. 10.1111/pce.14403. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. McMurdie, P. J. , and Holmes S.. 2013. “Phyloseq: An R Package for Reproducible Interactive Analysis and Graphing of Microbiome Census Data.” PLoS One 8, no. 4: e61217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Meyer, U. , Junk W. J., and Linck C.. 2010. “Chapter 8: Fine Root Systems and Mycorrhizal Associations in Two Central Amazonian Inundation Forests: Igapó and Várzea.” In Amazonian Floodplain Forests. Ecophysiology, Biodiversity and Sustainable Management. Ecological Studies, edited by Junk W. J., Piedade M. T. F., Wittmann F., Schöngart J., and Parolin P.. Springer. [Google Scholar]
  41. Montero, J. C. , and Latrubesse E. M.. 2013. “The igapó of the Negro River in Central Amazonia: Linking Late Successional Inundation Forest With Fluvial Geomorphology.” Journal of South American Earth Sciences 46: 137–149. [Google Scholar]
  42. Montero, J. C. , Piedade M. T. F., and Wittmann F.. 2012. “Floristic Variation Across 600 km of Inundation Forests (Igapó) Along the Negro River, Central Amazônia.” Hydrobiologia 729: 1–19. [Google Scholar]
  43. Oksanen, J. , Blanchet F. G., Kindt R., et al. 2013. “Package ‘vegan’.” Community Ecology Package, Version 2, no. 9: 1–295. [Google Scholar]
  44. Põlme, S. , Abarenkov K., Henrik Nilsson R., et al. 2020. “FungalTraits: A User‐Friendly Traits Database of Fungi and Fungus‐Like Stramenopiles.” Fungal Diversity 105, no. 1: 1–16. 10.1007/s13225-020-00466-2. [DOI] [Google Scholar]
  45. R Core Team . 2024. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R‐project.org/. [Google Scholar]
  46. Rinaldi, A. , Comandini O., and Kuyper T.. 2008. “Ectomycorrhizal Fungal Diversity: Separating the Wheat From the Chaff.” Fungal Diversity 33: 1–45. [Google Scholar]
  47. Ríos‐Villamizar, E. A. , Adeney J. M., Piedade M. T. F., and Junk W. J.. 2020. “New Insights on the Classification of Major Amazonian River Water Types.” Sustainable Water Resources Management 6, no. 5: 83. 10.1007/s40899-020-00440-5. [DOI] [Google Scholar]
  48. Ritter, C. D. , Dunthorn M., Anslan S., et al. 2020. “Advancing Biodiversity Assessments With Environmental DNA: Long‐Read Technologies Help Reveal the Drivers of Amazonian Fungal Diversity.” Ecology and Evolution 10: 1–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Roy, M. , Schimann H., Braga‐Neto R., et al. 2016. “Diversity and Distribution of Ectomycorrhizal Fungi From Amazonian Lowland White‐Sand Forests in Brazil and French Guiana.” Biotropica 48, no. 1: 90–100. [Google Scholar]
  50. Schoch, C. L. , Seifert K. A., Huhndorf S., et al. 2012. “Nuclear Ribosomal Internal Transcribed Spacer (ITS) Region as a Universal DNA Barcode Marker for Fungi.” Proceedings of the National Academy of Sciences 109, no. 16: 6241–6246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Scudeller, V. V. , and Villarrúbia T. V.. 2018. “Distribution and β‐Diversity of Tree Species in igapó Forests (Negro River Basin, Brazilian Amazon).” Journal of Vegetal Science 29, no. 6: 1052–1064. [Google Scholar]
  52. Semenov, M. 2021. “Metabarcoding and Metagenomics in Soil Ecology Research: Achievements, Challenges, and Prospects.” Biology Bulletin Reviews 11: 403–417. 10.1134/S2079086421010084. [DOI] [Google Scholar]
  53. Singer, R. , and Aguiar I. A.. 1986. “Litter Decomposing and Ectomycorrhizal Basidiomycetes in an Igapó Forest.” Plant Systematics and Evolution 153: 107–117. [Google Scholar]
  54. Singer, R. , and Araújo I. J. S.. 1979. “Litter Decomposition and Ectomycorrhiza in Amazonian Forests.” Acta Amazonica 9, no. 1: 25–41. [Google Scholar]
  55. Smith, M. E. , Henkel T. W., Aime M. C., Fremier A. K., and Vilgalys R.. 2011. “Ectomycorrhizal Fungal Diversity and Community Structure on Three Co‐Occurring Leguminous Canopy Tree Species in a Neotropical Rainforest.” New Phytologist 192: 699–712. [DOI] [PubMed] [Google Scholar]
  56. Steidinger, B. S. , Crowther T. W., Liang J., et al. 2019. “Climatic Controls of Decomposition Drive the Global Biogeography of Forest‐Tree Symbioses.” Nature 569: 404–408. [DOI] [PubMed] [Google Scholar]
  57. Tang, Y. , Horikoshi M., and Li W.. 2016. “Ggfortify: Unified Interface to Visualize Statistical Results of Popular R Packages.” R Journal 8, no. 2: 474–485. [Google Scholar]
  58. Tedersoo, L. , Bahram M., Põlme S., et al. 2014. “Global Diversity and Geography of Soil Fungi.” In Science, vol. 346. Springer. [DOI] [PubMed] [Google Scholar]
  59. Tedersoo, L. , Bahram M., Puusepp R., Nilsson R. H., and James T. Y.. 2017. “Novel Soil‐Inhabiting Clades Fill Gaps in the Fungal Tree of Life.” Microbiome 5, no. 1: 1–10. 10.1186/s40168-017-0259-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Tedersoo, L. , Bahram M., Zinger L., et al. 2022. “Best Practices in Metabarcoding of Fungi: From Experimental Design to Results.” Molecular Ecology 31, no. 10: 2769–2795. [DOI] [PubMed] [Google Scholar]
  61. Tedersoo, L. , and Brundrett M. C.. 2017. “Chapter 19: Evolution of Ectomycorrhizal Symbiosis in Plants.” In Evolution of Ectomycorrhizal Symbiosis in Plants. Ecological Studies, edited by Tedersoo L. and Brundrett M., vol. 230, 407–467. Springer. [Google Scholar]
  62. Tedersoo, L. , Drenkhan R., Abarenkov K., Anslan S., Bahram M., and Bitenieks K.. 2024. “The Influence of Tree Genus, Phylogeny, and Richness on the Specificity, Rarity, and Diversity of Ectomycorrhizal Fungi. Global Patterns in Endemicity and Vulnerability of Soil Fungi.” Global Change Biology 28: 6696–6710. [Google Scholar]
  63. Tedersoo, L. , May T. W., and Smith M. E.. 2010. “Ectomycorrhizal Lifestyle in Fungi: Global Diversity, Distribution, and Evolution of Phylogenetic Lineages.” Mycorrhiza 20, no. 4: 217–263. [DOI] [PubMed] [Google Scholar]
  64. Tedersoo, L. , Mikryukov V., Zizka A., et al. 2022. “Global Patterns in Endemicity and Vulnerability of Soil Fungi.” Global Change Biology 28: 6696–6710. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Tedersoo, L. , Sadam A., Zambrano M., Valencia R., and Bahram M.. 2010. “Low Diversity and High Host Preference of Ectomycorrhizal Fungi in Western Amazonia, a Neotropical Biodiversity Hotspot.” ISME Journal 4, no. 4: 465–471. [DOI] [PubMed] [Google Scholar]
  66. Thomas, P. W. 2021. “Ectomycorrhiza Resilience and Recovery to Extreme Flood Events in Tuber Aestivum and Quercus robur .” Mycorrhiza 31: 511–517. 10.1007/s00572-021-01035-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Turenne, C. Y. , Sanche S. E., Hoban D. J., Karlowsky J. A., and Kabani A. M.. 1999. “Rapid Identification of Fungi by Using the ITS2 Genetic Region and an Automated Fluorescent Capillary Electrophoresis System.” Journal of Clinical Microbiology 37, no. 6: 1846–1851. 10.1128/jcm.37.6.1846-1851.1999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Vanégas‐Léon, M. L. , Sulzbacher M. A., Rinaldi A. C., Roy M., Selosse M. A., and Neves M. A.. 2019. “Are Trechisporales Ectomycorrhizal or Non‐Mycorrhizal Root Endophytes?” Mycological Progress 18: 1231–1240. [Google Scholar]
  69. Vannote, R. L. , Minshall G. W., Cummins K. W., Sedell J. R., and Kuehn C. E.. 1980. “The River Continuum Concept.” Canadian Journal of Fisheries and Aquatic Sciences 37, no. 11: 1923–1924. [Google Scholar]
  70. Vasco‐Palacios, A. M. , Bahram M., Boekhout T., and Tedersoo L.. 2020. “Carbon Content and pH as Important Drivers of Fungal Community Structure in Three Amazon Forests.” Plant and Soil 450: 111–131. [Google Scholar]
  71. Voller, F. , Ardanuy A., Taylor A. F. S., and Johnson D.. 2023. “Maintenance of Host Specialisation Gradients in Ectomycorrhizal Symbionts.” New Phytologist 242, no. 4: 1426–1435. 10.1111/nph.19395. [DOI] [PubMed] [Google Scholar]
  72. Walker, I. , Miyai R., and Melo M. D. A.. 1999. “Observations on Aquatic Macrophyte Dynamics in the Reservoir of the Balbina Hydroelectric Powerplant, Amazonas State.” Acta Amazonica 29, no. 2: 243–265. [Google Scholar]
  73. Wang, F. , and Rengel Z.. 2024. “Disentangling the Contributions of Arbuscular Mycorrhizal Fungi to Soil Multifunctionality.” Pedosphere 34, no. 2: 269–278. [Google Scholar]
  74. Watkinson, S. , Boddy L., and Money N.. 2016. The Fungi. 3th ed. Academic Press. [Google Scholar]
  75. White, T. J. , Bruns T., Lee S., and Taylor J.. 1990. “Amplification and Direct Sequencing of Fungal Ribosomal RNA Genes for Phylogenetics.” PCR Protocols: A Guide to Methods and Applications 18, no. 1: 315–322. [Google Scholar]
  76. Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer‐Verlag. ISBN 978‐3‐319‐24277‐4. https://ggplot2.tidyverse.org. [Google Scholar]
  77. Wickham, H. , Averick M., Bryan J., et al. 2019. “Welcome to the Tidyverse.” Journal of Open Source Software 4, no. 43: 1686. 10.21105/joss.01686. [DOI] [Google Scholar]
  78. Wittmann, F. , Householder J. E., Piedade M. T. F., et al. 2022. “A Review of the Ecological and Biogeographic Differences of Amazonian Floodplain Forests.” Water 14, no. 21: 3360. 10.3390/w14213360. [DOI] [Google Scholar]
  79. Wittmann, F. , and Parolin P.. 2005. “Above Ground Roots in Amazonian Floodplains Trees.” Biotropica 37, no. 4: 609–619. [Google Scholar]
  80. Wittmann, F. , Schöngart J., and Junk W. J.. 2010. “Chapter 4: Phytogeography, Species Diversity, Community Structure and Dynamics of Central Amazonian Floodplain Forests.” In Amazonian Floodplain Forests. Ecophysiology, Biodiversity and Sustainable Management. Ecological Studies, edited by Junk W. J., Piedade M. T. F., Wittmann F., Schöngart J., and Parolin P.. Springer. [Google Scholar]
  81. Zhang, X. , Johnston E. R., Barberán A., Ren Y., Wang Z., and Han X.. 2018. “Effect of Intermediate Disturbance on Soil Microbial Functional Diversity Depends on the Amount of Effective Resources.” Environmental Microbiology 20, no. 10: 3862–3875. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1: Tree species sampled in Amazonian igapó plots across three study areas (RDS Uatumã, PARANA Jaú, and RDS Rio Negro), showing their relative abundance (Rel Abu.), calculated plot density (ind/m2), corresponding topographic position (Top), and the distribution status: dominant (D), rare (R) or intermediate (I). Density values were estimated by standardizing relative abundance by plot area.

ECE3-16-e73373-s002.xlsx (16.2KB, xlsx)

Figure S1: Plot‐level variation in dominant families by locality and flood level.

Figure S2: Taxonomic composition across flood‐level topography.

Figure S3: Taxonomic composition across localities (river basins).

ECE3-16-e73373-s003.docx (426.6KB, docx)

Data S1: ece373373‐sup‐0003‐Supinfo.zip.

ECE3-16-e73373-s001.zip (84.5KB, zip)

Data Availability Statement

Genbank SUB16094505; All the required data are uploaded as Supporting Information.

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