Are plant traits drivers of endophytic communities in seasonally flooded tropical forests? Article Swipe
Marion Boisseaux
,
Valérie Troispoux
,
Alice Bordes
,
Jocelyn Cazal
,
Saint‐Omer Cazal
,
Sabrina Coste
,
Clément Stahl
,
Heidy Schimann
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/ajb2.16366
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/ajb2.16366
Premise In the Amazon basin, seasonally flooded (SF) forests offer varying water constraints, providing an excellent way to investigate the role of habitat selection on microbial communities within plants. However, variations in the microbial community among host plants cannot solely be attributed to environmental factors, and how plant traits contribute to microbial assemblages remains an open question. Methods We described leaf‐ and root‐associated microbial communities using ITS2 and 16 S high‐throughput sequencing and investigated the stochastic‐deterministic balance shaping these community assemblies using two null models. Plant ecophysiological functioning was evaluated by focusing on 10 leaf and root traits in 72 seedlings, belonging to seven tropical SF tree species in French Guiana. We then analyzed how root and leaf traits drove the assembly of endophytic communities. Results While both stochastic and deterministic processes governed the endophyte assembly in the leaves and roots, stochasticity prevailed. Discrepancies were found between fungi and bacteria, highlighting that these microorganisms have distinct ecological strategies within plants. Traits, especially leaf traits, host species and spatial predictors better explained diversity than composition, but they were modest predictors overall. Conclusions This study widens our knowledge about tree species in SF forests, a habitat sensitive to climate change, through the combined analyses of their associated microbial communities with functional traits. We emphasize the need to investigate other plant traits to better disentangle the drivers of the relationship between seedlings and their associated microbiomes, ultimately enhancing their adaptive capacities to climate change.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/ajb2.16366
- OA Status
- green
- Cited By
- 1
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400693603Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/ajb2.16366Digital Object Identifier
- Title
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Are plant traits drivers of endophytic communities in seasonally flooded tropical forests?Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-07-16Full publication date if available
- Authors
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Marion Boisseaux, Valérie Troispoux, Alice Bordes, Jocelyn Cazal, Saint‐Omer Cazal, Sabrina Coste, Clément Stahl, Heidy SchimannList of authors in order
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https://doi.org/10.1002/ajb2.16366Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://oskar-bordeaux.fr/handle/20.500.12278/201012Direct OA link when available
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Biology, Ecology, Habitat, Plant community, Temperate climate, Microbial population biology, Endophyte, Host (biology), Ecological succession, Botany, Bacteria, GeneticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.French | 110 |
| abstract_inverted_index.basin, | 5 |
| abstract_inverted_index.better | 170, 221 |
| abstract_inverted_index.cannot | 39 |
| abstract_inverted_index.leaves | 139 |
| abstract_inverted_index.modest | 178 |
| abstract_inverted_index.plants | 38 |
| abstract_inverted_index.roots, | 141 |
| abstract_inverted_index.solely | 40 |
| abstract_inverted_index.traits | 49, 98, 119, 219 |
| abstract_inverted_index.widens | 184 |
| abstract_inverted_index.within | 28, 159 |
| abstract_inverted_index.Guiana. | 111 |
| abstract_inverted_index.Methods | 58 |
| abstract_inverted_index.Premise | 1 |
| abstract_inverted_index.Results | 126 |
| abstract_inverted_index.Traits, | 161 |
| abstract_inverted_index.balance | 77 |
| abstract_inverted_index.between | 147, 228 |
| abstract_inverted_index.change, | 198 |
| abstract_inverted_index.change. | 241 |
| abstract_inverted_index.climate | 197, 240 |
| abstract_inverted_index.drivers | 224 |
| abstract_inverted_index.flooded | 7 |
| abstract_inverted_index.forests | 9 |
| abstract_inverted_index.habitat | 23, 194 |
| abstract_inverted_index.leaf‐ | 61 |
| abstract_inverted_index.models. | 85 |
| abstract_inverted_index.plants. | 29, 160 |
| abstract_inverted_index.remains | 54 |
| abstract_inverted_index.shaping | 78 |
| abstract_inverted_index.spatial | 168 |
| abstract_inverted_index.species | 108, 166, 189 |
| abstract_inverted_index.through | 199 |
| abstract_inverted_index.traits, | 164 |
| abstract_inverted_index.traits. | 210 |
| abstract_inverted_index.varying | 11 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.adaptive | 237 |
| abstract_inverted_index.analyses | 202 |
| abstract_inverted_index.analyzed | 114 |
| abstract_inverted_index.assembly | 122, 136 |
| abstract_inverted_index.combined | 201 |
| abstract_inverted_index.distinct | 156 |
| abstract_inverted_index.factors, | 45 |
| abstract_inverted_index.focusing | 92 |
| abstract_inverted_index.forests, | 192 |
| abstract_inverted_index.governed | 133 |
| abstract_inverted_index.overall. | 180 |
| abstract_inverted_index.tropical | 105 |
| abstract_inverted_index.bacteria, | 150 |
| abstract_inverted_index.belonging | 102 |
| abstract_inverted_index.community | 35, 80 |
| abstract_inverted_index.described | 60 |
| abstract_inverted_index.diversity | 172 |
| abstract_inverted_index.emphasize | 212 |
| abstract_inverted_index.endophyte | 135 |
| abstract_inverted_index.enhancing | 235 |
| abstract_inverted_index.evaluated | 90 |
| abstract_inverted_index.excellent | 16 |
| abstract_inverted_index.explained | 171 |
| abstract_inverted_index.knowledge | 186 |
| abstract_inverted_index.microbial | 26, 34, 52, 64, 206 |
| abstract_inverted_index.processes | 132 |
| abstract_inverted_index.providing | 14 |
| abstract_inverted_index.question. | 57 |
| abstract_inverted_index.seedlings | 229 |
| abstract_inverted_index.selection | 24 |
| abstract_inverted_index.sensitive | 195 |
| abstract_inverted_index.assemblies | 81 |
| abstract_inverted_index.associated | 205, 232 |
| abstract_inverted_index.attributed | 42 |
| abstract_inverted_index.capacities | 238 |
| abstract_inverted_index.contribute | 50 |
| abstract_inverted_index.ecological | 157 |
| abstract_inverted_index.endophytic | 124 |
| abstract_inverted_index.especially | 162 |
| abstract_inverted_index.functional | 209 |
| abstract_inverted_index.predictors | 169, 179 |
| abstract_inverted_index.prevailed. | 143 |
| abstract_inverted_index.seasonally | 6 |
| abstract_inverted_index.seedlings, | 101 |
| abstract_inverted_index.sequencing | 72 |
| abstract_inverted_index.stochastic | 129 |
| abstract_inverted_index.strategies | 158 |
| abstract_inverted_index.ultimately | 234 |
| abstract_inverted_index.variations | 31 |
| abstract_inverted_index.Conclusions | 181 |
| abstract_inverted_index.assemblages | 53 |
| abstract_inverted_index.communities | 27, 65, 207 |
| abstract_inverted_index.disentangle | 222 |
| abstract_inverted_index.functioning | 88 |
| abstract_inverted_index.investigate | 19, 216 |
| abstract_inverted_index.communities. | 125 |
| abstract_inverted_index.composition, | 174 |
| abstract_inverted_index.constraints, | 13 |
| abstract_inverted_index.highlighting | 151 |
| abstract_inverted_index.investigated | 74 |
| abstract_inverted_index.microbiomes, | 233 |
| abstract_inverted_index.relationship | 227 |
| abstract_inverted_index.Discrepancies | 144 |
| abstract_inverted_index.deterministic | 131 |
| abstract_inverted_index.environmental | 44 |
| abstract_inverted_index.stochasticity | 142 |
| abstract_inverted_index.microorganisms | 154 |
| abstract_inverted_index.ecophysiological | 87 |
| abstract_inverted_index.high‐throughput | 71 |
| abstract_inverted_index.root‐associated | 63 |
| abstract_inverted_index.stochastic‐deterministic | 76 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.60821265 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |