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<p>Floodplain forests are threatened by hydrological alterations that modify flood frequency, duration, and water table depths due to climate change and human activities. Understanding species-specific responses to water table variations in this ecosystem is essential for effective restoration strategies. We developed a quantitative framework for classifying tree species based on groundwater level adaptability in an alluvial forest in southern Brazil and implemented it as an open-source interactive web application to facilitate its use by researchers and practitioners. Using abundance data from the ten most abundant species in the first inventory (2013) across 48 permanent plots monitored over nine years (2013–2022) and water table depth measurements obtained bimonthly over one year (2013), we calculated hydrophilic affinity (species preference for shallow water tables) and hydrological amplitude (tolerance range for water table variations). These metrics were integrated into categorical classifications and a continuous Hydrological Niche Index (HNI). The framework discriminated the ten species into three functional groups: High Affinity Generalists (7 species; hydrophilic affinity 0.64–1.00, hydrological amplitude 0.70–1.00; HNI 6.93–9.20), Low-Affinity Generalists (1 species; hydrophilic affinity 0.32, hydrological amplitude 0.56; HNI 4.40), and Low-Affinity Specialists (2 species; hydrophilic affinity 0.00–0.28, hydrological amplitude 0.00–0.31; HNI 0.00–2.94). Demographic validation showed that <i>Casearia decandra</i> (High-Affinity Generalist) had positive population growth (recruitment 5.44% year⁻<sup>1</sup>, mortality 2.82% year⁻<sup>1</sup>) while <i>Prunus myrtifolia</i> (Low-Affinity Specialist) declined (recruitment 1.04% year⁻<sup>1</sup>, mortality 4.09% year⁻<sup>1</sup>). This initial validation provides proof-of-concept for the framework’s potential in predicting species responses and supporting restoration planning in flood-prone environments, though further testing across diverse sites is needed to fully assess its operational applicability.</p>

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Groundwater-based classification of floodplain trees: integrating water table preferences and tolerance ranges

  • 期刊论文

  • 发表日期：

    2025年10月26日

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  • Karla Juliana Silva da Costa,
  • Maria Julia Carvalho Cruz,
  • Victória Oliveira Cabral Hassan,
  • Clenio Vieira Oliveira,
  • Danilo dos Santos Alves,
  • Guilherme Fortkamp,
  • Welinton Felipe da Silva,
  • Ranúbia Figueiredo dos Santos,
  • Ana Carolina da Silva,
  • Pedro Higuchi

摘要

Floodplain forests are threatened by hydrological alterations that modify flood frequency, duration, and water table depths due to climate change and human activities. Understanding species-specific responses to water table variations in this ecosystem is essential for effective restoration strategies. We developed a quantitative framework for classifying tree species based on groundwater level adaptability in an alluvial forest in southern Brazil and implemented it as an open-source interactive web application to facilitate its use by researchers and practitioners. Using abundance data from the ten most abundant species in the first inventory (2013) across 48 permanent plots monitored over nine years (2013–2022) and water table depth measurements obtained bimonthly over one year (2013), we calculated hydrophilic affinity (species preference for shallow water tables) and hydrological amplitude (tolerance range for water table variations). These metrics were integrated into categorical classifications and a continuous Hydrological Niche Index (HNI). The framework discriminated the ten species into three functional groups: High Affinity Generalists (7 species; hydrophilic affinity 0.64–1.00, hydrological amplitude 0.70–1.00; HNI 6.93–9.20), Low-Affinity Generalists (1 species; hydrophilic affinity 0.32, hydrological amplitude 0.56; HNI 4.40), and Low-Affinity Specialists (2 species; hydrophilic affinity 0.00–0.28, hydrological amplitude 0.00–0.31; HNI 0.00–2.94). Demographic validation showed that Casearia decandra (High-Affinity Generalist) had positive population growth (recruitment 5.44% year⁻1, mortality 2.82% year⁻1) while Prunus myrtifolia (Low-Affinity Specialist) declined (recruitment 1.04% year⁻1, mortality 4.09% year⁻1). This initial validation provides proof-of-concept for the framework’s potential in predicting species responses and supporting restoration planning in flood-prone environments, though further testing across diverse sites is needed to fully assess its operational applicability.