Identifying Biodiversity-Based Indicators for Regulating Ecosystem Services in Constructed Wetlands

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This paper presents biodiversity indicators that can be applied to evaluate the ecological performance of constructed wetlands. The indicators link microbial and plant diversity to key ecosystem functions, including pollutant removal, carbon storage, and microclimate control. Organized within the Biodiversity-based Ecosystem Service Index (BBESI) assessment framework, they provide a practical reference for wetland designers, researchers, and managers to guide monitoring design, assess treatment efficiency, and integrate biodiversity-based metrics into wetland planning and performance evaluation.

Abstract

Constructed wetlands (CWs) are nature-based solutions that integrate ecological processes for water purification, climate regulation, and biodiversity enhancement. However, biodiversity monitoring in CWs has often been underprioritized, limiting its recognition as a functional driver of ecosystem service performance. This study first developed the Biodiversity-based Ecosystem Service Index (BBESI), a hierarchical framework for evaluating biodiversity contributions to regulating services, and then systematically identified representative indicators from the literature to operationalize this framework. Following PRISMA 2020 guidelines, 39 studies spanning tropical, temperate, and arid climatic regions were reviewed across six ecosystem functions: pollutant removal, nutrient retention, biological uptake, carbon storage, greenhouse gas regulation, and microclimate control. Indicators were considered representative when they demonstrated clear functional relevance to CW ecosystem processes and were repeatedly supported across the reviewed studies. These included microbial diversity metrics, nutrient-cycling functional genes, plant–microbe functional complementarity, and vegetation structural attributes. Each indicator was mapped to the Essential Biodiversity Variables (EBV) framework, spanning Genetic Composition, Species Traits, Community Composition, Ecosystem Structure, and Ecosystem Function to provide a standardized basis for biodiversity assessment, using a rule-based assignment that prioritized the biological signal of each indicator rather than its functional category. Although all EBV classes were represented, this pattern reflects the available literature and is influenced by uneven reporting across microbial and plant indicators and across climatic regions, which limits broad generalization of indicator strength. The BBESI offers a transferable framework because its EBV-aligned structure and commonly measured indicators allow application across diverse CW designs and environmental contexts provided that multiple EBV co-signals are present rather than reliance on single-indicator measurements, with flexibility for future integration of various quantitative weighting approaches.

1. Introduction

Nature-based Solutions (NbS) harness ecological processes to address water pollution, climate change, and biodiversity loss while supporting human well-being [1,2,3]. Constructed wetlands (CWs) represent one of the most established NbS applications, engineered to mimic the structure and function of natural wetlands, through the combined action of substrates, vegetation, and microbial assemblages that enable water treatment and carbon storage [4,5,6]. Their adaptability supports implementation across diverse climatic and spatial contexts for managing wastewater, stormwater, and diffuse pollution [1,7,8]. Beyond pollutant removal, benefits such as biodiversity enhancement and recreation are provided by CWs [9,10]. Because these systems offer multifunctional benefits at costs comparable to gray infrastructure, they are increasingly regarded as cost-effective and sustainable alternatives [11].

Growing international initiatives increasingly recognize the role of CWs biodiversity conservation and ecological restoration. Their contribution to the United Nations Sustainable Development Goals (SDGs), specifically SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land) [12], reflects their demonstrated capacity to improve water quality and enhance climate resilience [13,14,15]. CWs are also recognized within the Kunming–Montreal Global Biodiversity Framework, for their contribution to restoring degraded inland water ecosystems [16]. Together, these frameworks underscore the ecological significance of CWs and situate them as practical, scalable mechanisms for delivering biodiversity-supporting environmental benefits.

The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) conceptual framework was developed to address the limitations of earlier ecosystem assessment models such as the Millennium Ecosystem Assessment (MEA) [17]. Within this framework, the term Nature’s Contributions to People (NCPs) was introduced as a broader typology of ecosystem benefits capturing both material and non-material pathways through which nature supports human well-being [18,19]. Although IPBES identifies 18 NCP categories, this study focuses on regulating categories to provide a clearer functional link to the biogeochemical processes operating in CWs. Specifically, NCP 4 (Regulation of Climate) and NCP 7 (Regulation of Water Quality) were selected because these services reflect core wetland ecosystem functions associated with carbon dynamics and water-quality improvement [5,20,21]. Cultural and provisioning services fall outside the scope of this study because the soil, water, and microbial indicators used for assessment do not capture these dimensions. Within the IPBES classification, regulating services are defined as ecosystem functions that maintain environmental conditions for human and ecological well-being, and these functions are widely recognized as key indicators of CW ecological performance across diverse environmental contexts.

In line with this framework, emphasis has been placed on the need to understand how biodiversity contributes to the ecological and treatment performance of CWs. A recent synthesis by the authors [22], which reviewed 76 CW studies that incorporated biodiversity assessment, showed that the central challenge is not merely limited monitoring but the uneven availability of measurable biodiversity indicators across studies, coupled with instances of over-correlation among commonly reported metrics. This mismatch highlights the need for clearer criteria to distinguish functionally meaningful indicators from those that are frequently measured but offer limited discriminatory value. The present study builds on that earlier observation by providing an independent, systematic assessment of how biodiversity has been evaluated in CW research and how it aligns with regulating ecosystem functions.

The need for integrative monitoring frameworks aligning biodiversity assessment with ecosystem service delivery was also emphasized. Standardized, cross-scalar protocols have been proposed to enhance data comparability across sites [23]. In support of this, the Essential Biodiversity Variables (EBVs) framework was developed as a globally recognized structure for organizing biodiversity indicators across ecological scales and functional dimensions [24], enabling more consistent linkage between biodiversity monitoring and ecosystem service outcomes in systems like CWs. The EBV framework was designed to harmonize and standardize biodiversity data for effective decision-making, highlighting its suitability as an operational tool for biodiversity assessment [25]. In this study, the EBV framework is used as the organizing structure for evaluating biodiversity indicators, as it provides standardized and scalable categories that align with the microbial, plant, and ecosystem-function variables commonly measured in CW research. The framework encompasses six core classes: Genetic Composition, Species Populations, Species Traits, Community Composition, Ecosystem Structure, and Ecosystem Function.

The functional performance of CWs is determined by three interrelated drivers: design and hydrology, biogeochemical processes, and biodiversity [26,27,28]. Design and hydrology include structural and operational features such as substrate type, flow configuration, and hydraulic connectivity, which control water movement, residence time, and pollutant removal [29]. Properly designed CWs are intended to emulate natural habitat conditions and enhance ecological connectivity [30]. Biogeochemical processes such as redox reactions, sorption, and decomposition regulate nutrient cycling and pollutant degradation, driven by spatial and temporal shifts between aerobic and anaerobic zones [31,32,33]. Biodiversity interacts with these physical and biogeochemical foundations by supplying the microbial and plant communities that mediate carbon and nutrient transformations and organic matter decomposition. By providing these biological communities, biodiversity underpins and sustains the hydrological and biogeochemical functions that govern CW performance over time [34].

Building on previous findings, biodiversity has been inconsistently assessed in CWs and is rarely incorporated into standardized performance evaluation frameworks. Existing multimeric indices primarily emphasize pollutant removal or hydraulic efficiency and generally lack an explicit structure for integrating biodiversity indicators into ecosystem function assessment. To address this gap, the Biodiversity-Based Ecosystem Service Index (BBESI) is proposed as a conceptual framework aligned with the IPBES classification of NCPs, specifically targeting Water Quality Regulation and Climate Regulation. Through a structured review of published CWs studies, biodiversity indicators mapped to EBV classes are identified to ensure consistency with global biodiversity monitoring standards. The BBESI framework and its selected indicators provide a transferable approach for evaluating ecosystem service performance across diverse CW contexts. Accordingly, this study investigates which biodiversity indicators are associated with water quality and climate regulation in CWs, how these indicators align with the EBV framework, and what gaps remain in current biodiversity monitoring approaches.

2. Methods

2.1. Conceptual Framework and Hierarchical Structure

To evaluate the contribution of biodiversity to NCP delivery in CWs, this study developed a hierarchical framework for a BBESI. The structure adopts a multi-level design commonly used in decision-support frameworks, including the Analytical Hierarchy Process (AHP), where the goal, criteria, and sub-criteria are defined allowing integration of weighting or stakeholder prioritization [35]. In this study, the AHP-style hierarchy structure is used conceptually to organize these levels, but no weighting analysis was applied. At the first level, the framework sets the goal of developing a biodiversity-based index tailored to CWs. The second level identifies two regulating NCPs under the IPBES classification: Water Quality Regulation and Climate Regulation. These services were selected for their alignment with the IPBES framework as key regulating contributions of ecosystems and for their relevance to the core functional roles of CWs [36]. This hierarchical structure aligns with existing approaches such as those used in multi-criteria analysis frameworks, allowing for future integration of weighting or stakeholder prioritization [37].

At the third level, the framework defines sub-criteria as ecosystem functions that support the delivery of each regulating service. According to the IPBES framework, the regulating contributions under NCP 7 and NCP 4 encompass a wide range of pathways, including filtration, pathogen removal, nutrient cycling, chemical regulation, carbon sequestration, greenhouse-gas fluxes, evapotranspiration, and other biophysical-atmospheric feedback. However, in this study we selected only the pathways that are mediated by microbial and plant communities within CW systems. Faunal groups were not included because they are infrequently reported in CW studies and are rarely linked to the biogeochemical pathways underlying water-quality and climate-regulation services, consistent with findings from our earlier systematic synthesis [22]. For Water Quality Regulation (NCP 7), processes such as pollutant removal, nutrient retention, and biological uptake correspond to the principal biogeochemical mechanisms identified in CWs [7]. For Climate Regulation (NCP 4), functions related to carbon storage, greenhouse-gas regulation, and microclimate control align with evidence that wetlands act as long-term carbon sinks, modulate methane and carbon dioxide fluxes, and influence local climate through evapotranspiration and surface-energy effects [38]. These CW-relevant pathways therefore form the sub-criteria used in the framework, reflecting biodiversity-driven mechanisms most applicable to regulating-service delivery in CWs. This structured framework forms the foundation for identifying measurable biodiversity indicators that support service-based CW monitoring. These sub-criteria are visually organized in Figure 1, which presents the hierarchical structure linking CW regulating services to microbial- and plant-mediated ecosystem functions.

2.2. Systematic Review Methodology

To identify biodiversity-relevant indicators for each ecosystem function in the BBESI framework, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The use of PRISMA enhances transparency for a wide range of stakeholders by ensuring that methods are reported in sufficient detail, allowing readers to assess their appropriateness and the reliability of the results [39]. To inform the development of the service-specific search strings, an initial Scopus scoping search was performed using a general biodiversity-CW query with key terms representing CWs, biodiversity descriptors, and ecological functions. This search retrieved 3535 documents. The full search string is provided in the Supplementary Materials. The indexed keywords from these records were analyzed using VOSviewer 1.6.20 to identify dominant terminology associated with biodiversity, water-quality regulation, and climate -regulation services. VOSviewer is a bibliometric mapping software designed for constructing and visualizing co-occurrence networks, making it suitable for identifying dominant terminology patterns in large literature datasets [40]. The resulting keyword co-occurrence map is presented in Figure 2. Based on these insights, service and function-specific search queries were developed using combinations of keywords related to biodiversity, CWs, and ecological processes. Studies were screened based on predefined inclusion and exclusion criteria, emphasizing peer-reviewed articles that reported measurable biodiversity indicators linked to the selected functions. This structured review ensured consistency, transparency, and relevance in selecting indicators for integration into the BBESI framework.

The extracted indicators were evaluated and mapped against the EBV framework using established EBV definitions and operationalization principles. The EBV classes were interpreted following Pereira et al. (2013) [24] who conceptualized EBVs as an intermediate layer that groups biodiversity measurements according to shared ecological attributes. Mapping criteria were further guided by Kissling et al. (2018) [41], who emphasized assigning variables to the EBV class that reflects their primary ecological dimension and note that such classification relies on expert interpretation and consensus. Accordingly, the authors independently mapped each indicator and resolved any discrepancies through discussion. For indicators that plausibly aligned with multiple EBV classes, classification followed the EBV class that best represented the indicator’s dominant biological signal within CW functioning. To ensure balanced ecological representation, indicators were organized into community-level, functional-level, and taxonomic-level metrics [42].

This categorization follows established biodiversity-indicator groupings used in ecological assessment literature and was applied here as a methodological organizing step rather than a newly proposed classification [43]. The overall methodological process is summarized in Figure 3, which outlines the sequential steps from literature retrieval to EBV-based indicator mapping. The systematic review process consisted of three main stages: (1) development of service-specific search queries, (2) study selection and eligibility screening, and (3) indicator categorization and EBV mapping.

2.2.1. Development of Service-Specific Search Queries

Scopus was selected as the primary database for literature retrieval because of its extensive coverage of peer-reviewed environmental and CW research, as well as its established use in prior CW-focused bibliometric analyses [44]. To identify indicators explicitly associated with each regulating service, two refined search strings were developed by grouping functional keywords according to the corresponding ecosystem services recognized by the IPBES, specifically Water Quality Regulation and Climate Regulation. These functional terms were combined with biodiversity-related keywords using Boolean operators (AND, OR) to ensure that studies reporting biodiversity measures in relation to specific ecological functions were retrieved.

Although Scopus was considered adequate for capturing the breadth of CW biodiversity literature, relying on a single database may introduce publication bias, which represents a limitation of this review. The complete search strings for each regulating service are provided in the Supplementary Materials. The difference in resulting study counts for Water Quality (31 studies) and Climate Regulation (27 studies) appears to reflect the historical emphasis of CW research on pollutant removal and water-quality functions [45] rather than an inadequacy of the climate-related search terms, which were drawn directly from the dominant terminology of the 3535-document scoping search.

To ensure comprehensive coverage of CWs applied in wastewater and stormwater treatment contexts, the first part of each search string included commonly used terminology such as “constructed wetland”, “treatment wetland”, “engineered wetland *”, “HSSF” (horizontal subsurface flow), “VSSF” (vertical subsurface flow), and “FWS” (free water surface). These terms were selected based on their widespread use in the literature to describe engineered wetland systems designed for pollutant removal, hydrological control, and climate-related processes [7]. Pilot tests were conducted by running separate Scopus searches using acronym-only terms (HSSF, VSSF, FWS) and their corresponding full terminology (horizontal subsurface flow, vertical subsurface flow, free water surface). Both searches returned identical sets of documents, indicating that CW system types are typically reported in both formats within titles, abstracts, or indexed keywords. As a result, including acronyms did not reduce retrieval completeness. However, retrieval completeness should not be interpreted as biological completeness; the presence of system-type terminology in search results does not guarantee balanced representation of biodiversity indicators across studies. Although these terms represent globally recognized CW categories, their usage varies across regions due to differences in wetland design development which may introduce minor regional bias and is acknowledged in this review.

The biodiversity-metric terms and ecosystem-function terms used in the second and third components of the search strings were derived directly from the dominant keyword patterns identified in the initial 3535-document co-occurrence map (Figure 2). This approach ensured that the final queries reflected terminology widely used in CW biodiversity literature rather than terminology selected solely by the researchers. Potentially relevant but less frequently used biodiversity descriptors such as “beta diversity”, “phylogenetic diversity”, and “functional redundancy” did not appear as prominent or high co-occurrence terms in the keyword analysis but were still included in the final search strings to ensure comprehensive biodiversity coverage. All searches were restricted to peer-reviewed, English-language journal articles published between 2001 and 2025, reflecting the publication year filters applied in both search strings. The complete list of keyword combinations used in the service-specific search queries is provided in

Table 1. Keywords used in Scopus search queries for Water Quality Regulation and Climate Regulation NCPs.

NCP (Ecosystem Service)	Search Query (Scopus Advanced Search Format)	Results (n)
Water Quality Regulation	Constructed wetland terms (constructed wetland *, treatment wetland *, engineered wetland *, HSSF, VSSF, FWS).	31
	Biodiversity metrics (biodiverse *, species richness, Shannon, Simpson, community composition, microbial diversity);
	Water quality indicators (TN, TP, NH4, NO3, COD, BOD, TSS, turbidity, removal efficiency, micropollutants, HRT, MFC
Climate Regulation	Constructed wetland terms (constructed wetland *, treatment wetland *, engineered wetland *, HSSF, VSSF, FWS).	27
	Biodiversity metrics (biodiverse *, species richness, diversity index, Shannon, Simpson, community composition, microbial diversity);
	Climate regulation indicators (carbon sequestration, carbon storage, SOC, biomass, greenhouse gases, carbon flux, methane, CO2, N2O, respiration, evapotranspiration).

* The wildcard (*) in Scopus represents zero or more characters and is used to capture multiple word variants and derivations.

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2.2.2. Inclusion Criteria and Study Selection Process

Studies were selected through a multi-stage screening process conducted in accordance with the PRISMA 2020 guidelines. Title and abstract screening was first performed to eliminate duplicate entries and records that were irrelevant. Full-text articles were then reviewed using predefined eligibility criteria. Studies were included if they focused on constructed, engineered, managed or treatment wetlands, defined as man-made systems intentionally designed to emphasize wetland characteristics that enhance treatment performance [29], and if they demonstrated a linkage between biodiversity and an assessed ecosystem function. Other wetland-like systems such as agricultural paddy fields, may provide ecosystem functions including water purification and local climate regulation, but these services arise as secondary outcomes of these systems rather than primary design objectives [46]; such systems were therefore excluded from this review. This linkage was defined as cases where a study measured at least one biodiversity metric and assessed an ecosystem function, and where a direct relationship between the two was presented through statistical analysis, experimental comparison, or a stated mechanistic explanation relevant to water quality or climate regulation. All three evidence types were treated as equally valid for inclusion because this review focused on identifying whether studies articulated any explicit biodiversity–function linkage, rather than evaluating the relative strength of the underlying evidence. The specific inclusion and exclusion criteria applied during the screening process are summarized in

Table 2. Inclusion and exclusion criteria applied for selecting studies in the review.

Inclusion Criteria	Exclusion Criteria
Studies conducted in constructed, engineered, managed, or treatment wetlands designed for water quality improvement or ecosystem enhancement.	Studies lacking biodiversity data or focusing exclusively on physicochemical parameters without biological assessment.
Empirical studies reporting microbial, plant, or faunal biodiversity indicators within CWs.	Research focusing on unmanaged natural wetlands, ponds, rice fields, or unrelated aquatic systems not functioning as treatment or restoration wetlands.
Studies demonstrating an explicit linkage between biodiversity and ecosystem functions, such as nutrient removal, carbon storage, or greenhouse gas regulation.	Non–peer-reviewed, methodologically incomplete, or review articles without original empirical data.

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The overall selection process is illustrated in the PRISMA 2020 flow diagram (Figure 4). A total of 58 records were retrieved from the two service-specific search queries. After removing 7 duplicates, 51 records remained for title and abstract screening. Of these, 3 records could not be retrieved or were inaccessible to the authors, which presents as a limitation of this study. The remaining 48 full text articles were assessed for eligibility, and 9 studies were excluded (6 that did not report biodiversity metrics, 2 that examined non-CW systems, and 1 did not present primary data). Ultimately, 39 studies were retained for inclusion in the review. This structured approach ensured transparency, reproducibility, and alignment with recognized standards for systematic reviews. Based on study locations, approximately 75–82% of the included studies originated from temperate or warm-temperate regions, 5–10% from tropical or subtropical regions, and 5–8% from arid or semi-arid regions, with the remaining 5–10% situated in high-altitude or transitional climates. The final dataset included studies from a range of climatic regions, including tropical, temperate, and arid environments; however, the distribution of studies was not uniform across zones, which should be considered when interpreting generalizability.

2.2.3. Indicator Categorization and EBV Mapping

Biodiversity is more complex to monitor than climate, encompassing multiple levels from genes to ecosystems and requiring extensive, high-quality data. The EBV framework addresses this challenge by standardizing biodiversity data to enable integrated monitoring and informed decision-making [47]. As biodiversity measurements often span multiple ecological dimensions, individual indicators or data sources may correspond to one EBV class, and in some cases can act as proxies for multiple EBVs simultaneously [48,49], highlighting the need for analytical rules when assigning indicators to the EBV hierarchy to ensure that each metric is mapped to its most relevant ecological dimension.

In this study, EBV mapping was used as a conceptual validation step rather than an attempt to quantify EBVs directly. The purpose of this step was to confirm that the extracted indicators collectively represented the biological dimensions necessary for a coherent biodiversity monitoring framework and to ensure that the BBESI indicator set could be meaningfully integrated into a multicriteria assessment structure. Accordingly, each indicator was assigned to the EBV class that best reflected its underlying biological signal, regardless of the ecosystem function to which it was linked in individual studies. For instance, indicators such as the Shannon Index were classified according to their intrinsic biological meaning. In this case, the Shannon Index was assigned to the Community Composition EBV because it reflects species diversity patterns, rather than the ecosystem functions it may correlate with in applied contexts. Any disagreements in indicator assignment were resolved through majority agreement among the authors, with further discussion used to reach consensus when needed.

To ensure transparent and ecologically grounded classification, indicators were assigned to EBV classes based on the dominant biological mechanism they measure, the ecological or measurement scale at which they operate, and their primary ecological interpretation within CWs. Indicators that plausibly aligned with more than one EBV class were independently evaluated by the authors and assigned to the class that captured their most central ecological meaning. When indicators spanned multiple EBV domains, tie-breaking followed three decision rules: (i) prioritizing the biological scale at which the indicator is measured, (ii) considering the primary measurement domain used to generate the indicator, and (iii) evaluating the proximal ecological pathway that the indicator most directly reflects.

This mapping served as a structural validity check to confirm that the selected indicators represented recognized biodiversity dimensions and could be consistently applied within the BBESI framework. The EBV classification guided the selection of ecological, measurable, and comparable indicators for consistent interpretation [43]. EBV alignment was therefore used solely to support indicator selection and ensure biological coherence within the multicriteria assessment structure. A statistical meta-analysis was not feasible because the included studies varied substantially in CW type and study goals, which resulted in insufficient comparable quantitative outputs. These inconsistencies prevented the extraction of standardized effect sizes or common statistical endpoints, limiting the review to qualitative indicator synthesis rather than quantitative aggregation.

3. Results and Discussion

3.1. Biodiversity Indicators for Water Quality Regulation

Water quality regulation in CWs is primarily driven by microbial processes responsible for the transformation and removal of pollutants. These systems function as biogeochemical reactors, together with plant-mediated oxygenation and substrate interactions enables pollutant degradation and nutrient cycling [50,51,52]. Microbial diversity supports functional redundancy and adaptability under fluctuating redox conditions, enabling stable pollutant transformation within rhizosphere zones [8]. Because vegetation and substrate shape the habitat environment, microbial assemblage structure strongly influences treatment efficiency. As a result, microbial biodiversity indicators serve as key proxies for water quality regulation capacity in CWs [53]. In this review, microbial diversity, taxonomic composition, and functional gene abundance are treated as indicators because they represent observable biological signals of pollutant transformation processes. Across the 39 retained studies, these indicators appeared with varying frequency: pollutant-degrading taxa, nutrient-cycling genera, and functional gene diversity were each reported in approximately 5–10% of studies, while rhizosphere-enriched genera, functional complementarity, and root-zone oxygenation taxa were reported less frequently, in roughly 3–8% of studies. Although these microbial groups perform distinct functions, they collectively enhance treatment stability by maintaining activity under shifting redox conditions, partitioning metabolic roles across pollutant- and nutrient-transformation pathways, and occupying complementary niches within root, substrate, and oxygenated microsites. This functional diversity allows biodegradation to persist even as environmental conditions fluctuate. However, variations in sampling depth, sequencing method, and wetland design introduce cross-study limitations that affect direct comparability of indicator strength. The following subsections present these indicators according to their corresponding ecosystem functions: pollutant removal, nutrient retention, and biological uptake, that collectively support Water Quality Regulation in CWs. A summary of the identified indicators and supporting references is presented in

Table 3. Biodiversity indicators supporting water quality regulation in CWs.

Ecosystem Function	Biodiversity Indicator	Reference
Pollutant Removal	Microbial Alpha Diversity (Shannon Index)	[54]
	Taxonomic Composition (Phylum Level)	[55]
	Functional Microbial Abundance (Pollutant-Degrading Taxa)	[56,57]
Nutrient Retention	Microbial Alpha Diversity (Shannon Index)	[58]
	Taxonomic Composition (Nutrient-Cycling Genera)	[57,59]
	Functional Gene Diversity (Nutrient Cycling)	[59,60]
Biological Uptake	Microbial Diversity in Vegetated Sediments	[61]
	Relative Abundance of Rhizosphere-Enriched Genera	[62]
	Plant–Microbe Functional Complementarity	[58]
	Root-Zone Oxygenation Indicator Taxa	[63]

1. Pollutant-Degrading Taxa: microbes known to metabolize specific contaminants. 2. Nutrient-Cycling Genera: taxa associated with nitrification, denitrification, or phosphorus transformations. 3. Functional Gene Diversity: diversity of genes regulating nitrogen or phosphorus cycling. 4. Rhizosphere-Enriched Genera: microbes preferentially inhabiting plant root zones. 5. Functional Complementarity: plant–microbe interactions that enhance nutrient uptake or pollutant removal. 6. Root-Zone Oxygenation Taxa: aerobic or oxygen-tolerant microbes indicating plant-mediated oxygen release.

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3.1.1. Pollutant Removal

Pollutant removal in CWs is driven by microbial biodiversity, where community diversity and composition regulate contaminant degradation efficiency. Microbial alpha diversity, phylum-level composition, and pollutant-degrading genera are used as indicators because they reflect the community’s metabolic capacity to oxidize, transform, or mineralize contaminants. As an indicator, microbial diversity reflects functional redundancy and the system’s ability to sustain pollutant transformation under fluctuating redox conditions. The Shannon index, which incorporates both species richness and evenness, is commonly applied to quantify microbial diversity in CWs [64]. Seth et al. (2024) [54] demonstrated this relationship by showing that rhizospheric sediment within an integrated CW system exhibited slightly higher Shannon diversity (7.70) than stabilization-pond sludge (7.52), attributable to oxygen and nutrient inputs from plant roots. Although numerically similar, Shannon values are logarithmic and therefore may present substantial changes in species abundance distributions particularly within high-diversity microbial communities, where notable compositional shifts may occur even when diversity values appear similar [65]. The integrated CW achieved removal efficiencies ranging from 59.9% to 95.8% for key water quality parameters, including total suspended solids (TSS), total dissolved solids (TDS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH₃–N), and Phosphate phosphorus (PO₄–P), suggesting that rich microbial diversity in rhizospheric zones is associated with effective contaminant transformation.

At finer taxonomic scales, shifts in microbial community composition at the phylum level reveal adaptive responses to pollutant stress and the capacity to degrade diverse contaminants. Dominant phyla including Proteobacteria, Firmicutes, Bacteroidetes, Chloroflexi, and Actinobacteria are characteristic of high-performing wetlands [56]. Nowrotek et al. (2015) [55] reported strong correlations between microbial diversity and the removal of micropollutants, with correlation coefficients of r = 0.9 for diclofenac and r = 0.7 for sulfamethoxazole in planted CW columns. The study focused on ammonia-oxidizing bacteria, identifying Nitrosomonas and Nitrosospira, both belonging to Proteobacteria, as dominant taxa associated with nitrification processes under pharmaceutical exposure. However, phylum-level patterns should be interpreted cautiously because phyla are too broad to reliably indicate specific microbial functions. The abundance of pollutant-degrading genera highlights the enrichment of metabolically active microbes responsible for contaminant breakdown.

In CWs enhanced with microbial fuel cell (MFC) technology and iron–carbon substrates, electrochemical stimulation promotes the enrichment of electroactive genera such as Shewanella, Geobacter, Pseudomonas. Li et al. (2025) [56] achieved over 99% removal of ofloxacin and more than 93% removal of COD, ammonium nitrogen (NH₄⁺–N), and total phosphorus (TP), while Wang et al. (2024) [57] reported up to 92% NH₄⁺–N and 84% total nitrogen (TN) removal. Because these studies evaluated MFC-augmented wetland systems rather than passive CWs, their high removal efficiencies reflect system-specific electrochemical enhancement. Accordingly, the results are most informative for their microbial enrichment patterns rather than for their absolute treatment performance.

A key limitation is that high removal efficiencies in MFC-enhanced or substrate-amended CWs may not represent typical passive CW performance, which restricts direct generalization of diversity–performance relationships. These patterns indicate that microbial diversity and the presence of pollutant-degrading or nitrifying taxa serve as reliable indicators of pollutant removal capacity in CWs. Their enrichment signals active biogeochemical transformation pathways and supports the use of microbial biodiversity metrics in evaluating water quality regulation.

3.1.2. Nutrient Retention

Nutrient retention in CWs reflects the metabolic versatility of microbial communities that mediate nitrogen and phosphorus turnover under dynamic redox conditions. Here, nutrient-cycling genera, functional nitrogen/phosphorus genes, and alpha diversity are considered indicators because they represent the microbial pathways responsible for nitrification, denitrification, ammonification, and phosphorus turnover. Across CW ecosystems, microbial diversity in nutrient-enriched zones, commonly quantified using the Shannon index, indicates functional redundancy and ecological resilience. Zhang et al. (2016) [58] observed that Shannon index values were positively correlated with nitrate concentration, suggesting that nutrient availability enhances microbial diversity and promotes active nitrogen cycling.

Contrasting evidence by Bi et al. (2024) [60], however, indicates that nutrient retention does not always scale with higher microbial richness. In their comparison of five CW systems with different substrate amendments, the iron-ore based configuration achieved the highest TN removal efficiency but exhibited the lowest Shannon diversity. This contrast arises because the two studies evaluate different ecological dimensions. Zhang et al. [58] assessed diversity patterns across nutrient gradients whereas Bi et al. [60] assessed nitrogen removal performance across engineered CW configurations, where strong substrate-driven selection enriched specialized nitrifying, denitrifying, nitrate-ammonifying fermentative organisms (NAFO), and Feammox taxa. These substrate-driven enrichments highlight that effective nutrient retention may emerge through functional specialization rather than broad taxonomic diversity.

This pattern is further supported by Roy et al. (2022) [59], who observed that in CW microcosms subjected to high pollutant loadings, microbial Shannon diversity declined over time while the relative abundance of pollutant-resistant and metabolically versatile taxa increased. These shifts underscore that selective pressures can restructure communities toward taxa capable of maintaining nitrogen cycling under stress.

Taxonomic composition of microbial communities in CWs also offers insight into the dominant functional groups responsible for nutrient transformations. Wang et al. (2024) [57] found that in CW–MFC systems, microbial communities on activated carbon anodes were dominated by Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes, with nutrient-cycling genera such as Geobacter, a metal-reducing and extracellular electron-transferring bacterium; Pseudomonas, a widely recognized denitrifying genus; and Citrobacter, a heterotrophic organic-matter degrader, comprising 15–17% of the microbial community. These genera are known to facilitate denitrification and organic matter degradation. Their enrichment coincided with increased TN removal, suggesting that shifts in community composition toward metabolically versatile nutrient-cycling taxa enhance nitrogen transformation under redox-stratified conditions.

However, differences in substrate type, hydraulic regime, and pollutant loading among studies introduce variability that limits the direct comparability of nutrient-related biodiversity indicators across CW systems. These patterns indicate that microbial diversity and the enrichment of nitrogen-transforming taxa provide reliable indicators of nutrient retention capacity in CWs, reflecting active nitrification–denitrification pathways and sustained nitrogen cycling under variable redox conditions.

3.1.3. Biological Uptake

Plant–microbe interactions in the rhizosphere act as a key zone for nutrient uptake, redox regulation, and pollutant transformation in CWs. Microbial diversity in vegetated sediments, rhizosphere-enriched taxa, and root-zone oxygenation indicators function as biological uptake indicators because they reflect plant-driven habitat modification and the microbial processes that support nutrient assimilation and redox regulation.

Microbial diversity within vegetated sediments functions as an indicator of rhizosphere stability and treatment potential. Xiang et al. (2020) [61] reported Chao1 values rising from 93.10 to 161.07 and Shannon diversity increasing from 2.04 to 3.69 with seasonal root development of Acorus Calamus. Chao1 is a richness estimator that infers total species numbers by accounting for undetected low abundance taxa and is widely used across ecological studies to correct for sampling limitations [66]. These values are lower than those reported for whole-system microbial communities because Xiang et al. [61] analyzed substrate-associated rhizosphere samples using a clone-library-based approach under petroleum-contaminated conditions, resulting in shallower sequencing depth and narrower taxonomic coverage. Such methodological and ecological differences account for the apparent discrepancy with higher Shannon values observed in high-throughput studies such as that reported by Seth et al. [54]

This observed increase in microbial diversity, attributed to seasonal root growth that provided stable oxygenated niches, was strongly correlated with hydrocarbon removal, showing correlation coefficients between 0.83 and 0.85. Similarly, Nguyen et al. (2024) [62] reported that CWs planted with Ipomoea aquatica exhibited greater microbial richness and substrate utilization in vegetated zones due to the influence of root exudates. These findings indicate that higher microbial alpha diversity in vegetated sediments reflects enhanced colonization and rhizosphere functioning.

Nevertheless, indicator strength varies with sequencing method, plant species identity, and depth of rhizosphere sampling, which affects consistency across studies. At the community level, specific microbial genera enriched in vegetated zones provide insight into rhizosphere-driven microbial structuring. Nguyen et al. (2024) [62] reported distinct bacterial community structures between planted and unplanted systems. In planted wetlands with Ipomoea aquatica, sediment samples showed higher relative abundances of Acinetobacter, Comamonas, and Pseudomonas, while Nitrospira, a nitrite-oxidizing bacterium, was detected only in the planted system. These patterns suggest that vegetation may promote the enrichment of specific bacterial taxa in rhizosphere-associated sediments. Under metal stress conditions, Roy et al. (2022) [59] found that rhizosphere biofilms retained key genera such as Fusibacter and Aeromonas despite declines in overall diversity, highlighting their role in ecological resilience. These findings indicate that the relative abundance of rhizosphere-enriched taxa may reflect plant-mediated selection of functionally important microbes. These indicators collectively demonstrate how plant-driven microbial structuring supports nutrient assimilation and redox stabilization, highlighting their value for assessing biological uptake processes in CWs.

Functionally, plant-mediated oxygen release establishes redox gradients that guide microbial community assembly. Zhang et al. (2016) [67] demonstrated that submerged macrophytes supported denitrifiers and electroactive bacteria within biofilms which sustained microbial activity under nitrate-enriched conditions. Wang et al. (2020) [63] observed 77–89% TN removal in tidal-flow wetlands planted with Typha latifolia. This was associated with the abundance of oxygen-tolerant nitrifiers such as Nitrosomonas and Nitrosopumilus, which showed inverse correlations (r = −0.62 to −0.69) with ammonium concentrations. These nitrifiers indicate oxygenated microzones formed by root aerenchyma, highlighting how macrophyte-driven aeration shapes microbial communities. Their prevalence underscores the role of root-zone oxygenation in sustaining nitrogen cycling and broader microbial functions that support nutrient uptake and biogeochemical stability in CWs.

3.2. Biodiversity Indicators for Climate Regulation

CWs serve as carbon sinks and local climate stabilizers by storing carbon through plant biomass accumulation, soil organic matter buildup, and microbial retention of organic carbon in sediments [68,69,70]. The effectiveness of these processes depends on plant species identity and microbial activity, which together shape long-term carbon sequestration in CWs. Carbon transformation in CWs is controlled by the balance among methanogenesis, methane oxidation, sulfate reduction, and denitrification. Carbon-related biodiversity indicators, including plant biomass, peat-forming species, microbial guilds, and functional carbon-cycling genes, are included because they represent the biological drivers of carbon accumulation, methane regulation, and surface energy exchange. Despite representing different biological roles, these indicator groups collectively stabilize climate-regulation processes by combining redox tolerance, complementary carbon- and GHG-transformation pathways, and fine-scale niche partitioning across root, substrate, and oxygenated microsites, enabling carbon retention and gas flux regulation under fluctuating environmental conditions. This balance is shaped by redox-sensitive microbial guilds and plant-mediated oxygenation, determining whether a CW acts as a carbon sink or a greenhouse gas source. Vegetation also regulates microclimate through canopy structure, plant functional traits, and seasonal productivity, which influence shading, evapotranspiration, and surface energy exchange.

However, climate-regulation indicators are sensitive to site-specific hydrology, climatic conditions, and vegetation composition, which introduces uncertainty when comparing results across different CW settings. The following subsections present biodiversity indicators associated with three principal ecosystem functions: carbon storage, greenhouse-gas regulation, and microclimate control. These functions represent the role of biodiversity in sustaining climate-related ecosystem services under the Climate Regulation domain (NCP 4) in CWs. A summary of the corresponding indicators is provided in

Table 4. Biodiversity indicators supporting climate regulation in CWs.

Ecosystem Function	Biodiversity Indicator	Reference
Carbon Storage	Plant Biomass and Allocation	[71,72,73]
	Dominant High-Biomass or Peat-Forming Species	[74]
	Root-Zone Microbial Activity (Carbon Stabilization)	[75]
Greenhouse Gas Regulation	Microbial Community Complexity (Community Structure)	[76]
	Redox-Regulating Microbial Guilds	[76,77,78]
	Functional Gene Diversity (Carbon and GHG Cycling)	[79]
Microclimate Control	Canopy Structure	[74,80]
	Plant Functional Diversity	[72]
	Seasonal Biomass (Productivity)	[71,81]

1. Functional Diversity: variation in plant traits that influence ecosystem processes. 2. Functional Gene Diversity: diversity of metabolic genes involved in carbon or greenhouse gas cycling. 3. Functional guilds: microbial groups performing similar redox or biogeochemical functions as represented in this table by indicators such as redox-regulating microbial guilds.

, and each group of indicators is discussed in detail in the following section.

3.2.1. Carbon Storage

Plant biomass allocation and the presence of peat-forming vegetation are used as indicators because they determine the quantity and quality of organic material entering long-term soil carbon pools. Meerburg et al. (2010) [71] reported that Phragmites australis in a surface-flow CW in the Netherlands produced on average 903 g m⁻² yr⁻¹ of aboveground biomass one year after planting, with maximum values approaching 1900 g m⁻² yr⁻¹ in the most productive sections. However, these biomass values do not directly represent carbon sequestration; only the fraction of plant material that is transferred to soils through litter deposition, root turnover, or release of organic exudates contributes to long-term carbon storage. Similarly, Han et al. (2016) [72] found that plant species richness significantly enhanced both above- and below-ground biomass in CWs, with mixed-species assemblages producing substantially more total biomass than monocultures. However, biomass production and carbon storage are not equivalent; vegetation supports carbon storage only when plant-derived materials such as litter, root turnover, or exudates enter the soil and become incorporated into the organic matter pool. Soil carbon accumulation therefore depends not on biomass itself but on the transfer and stabilization of this plant-derived material in wetland soils [82]. Direct comparison with microbial indicators is also limited because biomass reflects potential carbon inputs rather than the efficiency of carbon stabilization, while microbial indicators capture the underlying transformation processes. In addition, microbial guilds respond rapidly to hydrological and redox fluctuations, whereas biomass integrates longer-term vegetation dynamics, producing different temporal sensitivities. Evidence linking biodiversity to carbon outcomes is also generally stronger for microbial processes than for plant-driven sequestration, creating inherent differences in indicator strength.

Although both studies focused on biomass productivity rather than carbon storage, their findings show that vegetation growth and allocation influence soil conditions that favor carbon stabilization. In CWs, much of the carbon that accumulates in soils originate from plant-derived material, as productive vegetation contributes aboveground litter and root inputs that settle onto the soil surface, decompose, and gradually from organic matter. This pattern is evident in the higher carbon levels found in surface soils compared to deeper layers, highlighting that most of the stored carbon originates from plant material settling and breaking down at the soil surface [83]. This interpretation aligns with Yang et al. (2020) [73], who demonstrated that increases in soil organic carbon in CWs were primarily mediated through vegetation-induced shifts in soil temperature, oxygen, and microbial structure rather than through direct correlations with plant biomass.

Monge Salazar et al. (2022) [74] found that plant community composition was closely related to carbon storage. Areas dominated by peat-forming vegetation, including Distichia muscoides, Phylloscirpus acaulis, and Ranunculus flagelliformis, corresponded to sites with the deepest peat layers where organic matter had accumulated over time. The dense, water-retaining structure of these species creates saturated and oxygen-limited conditions that slow decomposition and promote long-term carbon preservation. However, the study by Monge-Salazar et al. (2022) [74] was conducted in high-altitude Andean environments, and the peat-forming species they examined may not represent the vegetation commonly found in lowland CWs. However, Overbeek et al. (2020) [84] showed that peat-forming processes can begin even without true peat-forming species, as high-biomass helophytes such as Typha spp. were able to initiate early peat accumulation under saturated and nutrient-rich conditions in a temperate lowland CW. Even so, the underlying process by which dense, water-retaining plant communities create saturated and oxygen-limited conditions that enhance carbon retention is relevant across many wetland systems.

A limitation of current evidence is that carbon accumulation processes differ markedly between high-altitude, temperate, and lowland CWs, making cross-system comparisons less straightforward. These findings show that the dominance of high-biomass, peat-forming macrophytes is a key driver of carbon accumulation in wetland ecosystems. At the functional level, microbes are the main drivers of carbon storage, as the stabilization and turnover of organic carbon depend on their metabolic activity. Elhaj Baddar et al. (2022) [75] reported that total sediment carbon was positively correlated with Geobacter and sulfate-reducing bacteria (r = 0.53 and 0.49), while methane-oxidizing and methanogenic groups showed negative correlations (r = −0.83 and −0.41). However, the study did not report the sample size or statistical significance associated with these correlation coefficients, which limits the extent to which their strength and reliability can be interpreted. As a result, the values should be viewed as indicative patterns rather than definitive quantitative relationships. These findings suggest that anaerobic microbial processes, particularly those involved in metal and sulfate reduction, promote organic carbon stabilization within sediments. Microbial activity in the root zone therefore serves as a functional indicator of carbon retention in CWs and forms the basis for redox-driven greenhouse gas regulation discussed in the following section.

3.2.2. Greenhouse Gas Regulation

Building on the microbial pathways that underlie carbon stabilization, greenhouse-gas regulation in CWs depends on the balance between methanogenesis, methane oxidation, sulfate reduction, and denitrification, processes driven by the composition and functional diversity of microbial assemblages. Redox-regulating microbial guilds and functional gene diversity serve as indicators because they directly mediate methane production, methane oxidation, sulfate reduction, and denitrification, which are the core pathways that control GHG fluxes in CWs. Redox-regulating microbial guilds capture the joint influence of methanotrophs, methanogens, sulfate-reducing bacteria, and oxygen-releasing macrophytes in controlling methane emissions. In planted FWS wetlands, Elhaj Baddar et al. (2024) [76] analyzed bacterial and archaeal groups associated with methane cycling. These microbial communities, through their response to environmental changes, act as biological indicators of CW performance.

This was demonstrated using alpha diversity indices to assess species richness, evenness, and dominance. Results showed high gene abundances of Desulfuromonas (5.37 × 10⁶ g⁻¹ dry sediment), methane-oxidizing bacteria (6.92 × 10⁶ g⁻¹ dry sediment), and methanogenic microorganisms (3.02 × 10⁵ g⁻¹ dry sediment) during cool months, whereas warm months were marked by the dominance of sulfate-reducing bacteria (3.31 × 10⁶ g⁻¹ dry sediment). This seasonal shift reflects established anaerobic respiratory pathways: sulfate-reducing bacteria gain a thermodynamic advantage over methanogens when sulfate is available because sulfate reduction yields more energy, and warmer temperatures increase fermentation rates and the production of substrates such as acetate and hydrogen that fuel sulfate reduction. Philben et al. (2020) [85] also showed that moderately reducing conditions favor sulfate reduction, whereas methanogenesis becomes dominant only under more strongly reduced conditions, explaining why sulfate reducers prevail during warm periods characterized by lower redox potential. These seasonal shifts among microbial guilds illustrate how redox dynamics regulate the balance between methane production and oxidation in CWs.

Oberholzer et al. (2022) [77] reported that sulfate-reducing taxa such as Clostridia and Deltaproteobacteria dominated under high-sulfate, low-redox conditions in CWs treating acid mine drainage. Their activity was associated with increased pH and reduced sulfate concentrations, demonstrating the regulatory influence of redox-sensitive microbial guilds on wetland biogeochemistry. Sulfate reduction also suppresses methane production through competitive mechanisms, as sulfate-reducing bacteria and methanogens compete for organic substrates such as hydrogen and acetate; the more energetically favorable sulfate reduction pathway therefore inhibits methanogenesis under sulfate-rich, moderately reducing conditions [86].

Yet, differences in redox gradients, sulfate availability, and temperature regimes across studies limit the extent to which indicator–GHG relationships can be generalized. Similarly, Cakin et al. (2025) [78] found that dominant microbial guilds such as Planctomycetacia, Acidobacteriia, and Bacilli were strongly correlated with redox-mediated removal of nitrogen, organic carbon, and phosphorus in HSSF CWs. Functional gene diversity and microbial community complexity further characterize the metabolic capacity and resilience of carbon-transforming assemblages. Zhou et al. (2022) [79] analyzed nosZ clade I genes in a multi-stage surface-flow CW and found that denitrifying community richness and diversity were higher in the final stage compared with earlier stages. Zhou et al. (2022) [79] Noted that the relative abundance of taxa such as Bradyrhizobium and Mesorhizobium varied along gradients of sediment nutrients and pH, although specific correlation coefficients or significance levels were not reported, limiting quantitative interpretation of these associations. These results suggest that functional gene diversity linked to N₂O reduction supports greenhouse-gas mitigation through enhanced denitrification efficiency in CWs.

3.2.3. Microclimate Control

Canopy structure governs the capacity of vegetation to regulate temperature, humidity, and carbon dioxide (CO₂) exchange in CWs. Canopy structure, plant functional diversity, and seasonal biomass are treated as microclimate-control indicators because they influence shading, evapotranspiration, and surface energy exchange, which stabilize local temperature and humidity. Liu et al. (2023) [80] demonstrated that optimizing vegetation community structure and expanding green coverage within CWs significantly enhances overall carbon retention and reduces life-cycle greenhouse gas emissions. They noted that variations in vegetation layering, planting density, and surface topography influence plant growth and local microenvironmental conditions, thereby enhancing the system’s overall carbon storage capacity. This suggests that well-developed canopy structures contribute not only to carbon regulation but also to the stabilization of temperature and humidity within CWs. Shading provided by dense vegetation lowers soil temperature and slows microbial decomposition, thereby reducing CO₂ release and enhancing carbon retention [87]. In addition to shading, wetlands exhibit elevated evapotranspiration rates that cool the surrounding microclimate by increasing latent heat flux, producing lower summer air temperatures and higher humidity relative to adjacent areas [88]. This evapotranspiration-mediated cooling reduces evaporative demand and helps maintain moist, mild soil conditions that slow aerobic decomposition, thereby supporting greater carbon retention. Monge-Salazar et al. (2022) [74] demonstrated that vegetation community composition was positively associated with soil moisture and organic carbon accumulation, indicating that structural attributes of plant assemblages influence microenvironmental stability within wetland ecosystems. However, microclimate-related indicators are strongly influenced by regional climate, planting density, and wetland design, which may limit their consistency across CW types and climatic contexts.

Functional and temporal indicators complement these structural drivers by linking plant traits and growth dynamics to sustained carbon regulation. Han et al. (2016) [72] showed that functionally diverse plant assemblages with contrasting morphological and physiological traits improved overall productivity in CWs, suggesting that species complementarity enhances resource use and canopy development. Seasonal biomass dynamics further integrate this response functioning as an operational indicator of vegetation-driven carbon uptake and microclimate moderation. Meerburg et al. (2020) [71] reported that Phragmites australis biomass increased from 221 to 903 g m⁻² within a single growing season, a shift that reflects measurable changes in aboveground biomass that directly represent CO₂ assimilation capacity and evapotranspirative cooling potential. Yang et al. (2024) [81] similarly found that maintaining high planting density and incorporating cold-tolerant species sustained vegetation activity during winter, thereby moderating temperature fluctuations and preserving local microclimate stability. Additional evidence shows that microclimate buffering and carbon-regulating capacity can be expressed through measurable structural and functional indicators. Microclimate control is governed by leaf area index, canopy cover, and canopy height, which regulate understory temperature, humidity, and vapor pressure conditions [89]. Canopy productivity, resource-use efficiency, and seasonal vegetation dynamics are captured through FAPAR (fraction of absorbed photosynthetically active radiation), NDVI (normalized difference vegetation index), aboveground biomass, plant height, and multispectral indicators such as GNDVI (green NDVI), OSAVI (optimized soil-adjusted vegetation index), NDRE (normalized difference red-edge index), and LCI (leaf chlorophyll index) [90,91]. Together, these findings indicate that functional diversity and continuous plant growth cycles collectively stabilize microclimatic conditions in CWs.

3.3. EBV-Based Synthesis of Biodiversity Indicators

The biodiversity indicators identified across the Water Quality and Climate Regulation domains were synthesized using the EBV framework to standardize their integration within the BBESI framework. The EBV framework includes six hierarchical dimensions: Genetic Composition, Species Populations, Species Traits, Community Composition, Ecosystem Structure, and Ecosystem Function. Mapping the selected indicators onto these EBV classes showed that the BBESI indicator set consisted of 3 indicators in Genetic Composition, 3 in Species Populations, 4 in Species Traits, 4 in Community Composition, 3 in Ecosystem Structure, and 1 in Ecosystem Function (Figure 5). This distribution indicates that most EBV dimensions within the BBESI are represented by three to four indicators, with only Ecosystem Function containing a single indicator due to the limited functional biodiversity metrics commonly reported in CW studies.

Rather than interpreting this distribution as evidence of comprehensiveness, it is more appropriate to note that balanced representation across EBV classes was a priori design objective of the BBESI. This was intended to avoid the structural biases that characterize many national biodiversity monitoring frameworks, where population- and structure-based indicators dominate while community-level and functional metrics remain underrepresented [92]. The resulting EBV alignment therefore shows that the BBESI’s design criterion of proportional EBV representation was successfully achieved, ensuring a systematic and ecologically grounded integration of biodiversity indicators across genetic, species, community, and ecosystem dimensions.

This multi-scale synthesis clarifies how genetic and community-level diversity sustains structural and functional outcomes in CWs. The EBV framework enables these indicators to be operationalized across biological scales through measurable and repeatable datasets, bridging molecular, organismal, and ecosystem dimensions of biodiversity [41]. For instance, microbial EBVs can be quantified through 16S rRNA or functional-gene sequencing, eDNA profiling, and biofilm biomass assays, while plant and structural EBVs can be derived from in situ vegetation surveys, biomass harvesting, and remote-sensing metrics [93]. The application of these widely accessible molecular and remote-sensing methods enhances the transferability of the framework across different CW types, hydrological regimes, and climatic contexts. Integrating these datasets within the BBESI allows indicator normalization and weighting that reflect both ecological function and data reliability.

Because the EBV framework was originally developed for biodiversity monitoring rather than ecosystem-service assessment, its direct application to service-focused evaluations such as water quality and climate regulation functions require careful interpretation. This use of EBVs also aligns conceptually with broader global indicator systems under the Convention on Biological Diversity (CBD) and GEO BON, which similarly organize biodiversity metrics across hierarchical biological levels. Within the BBESI, the EBV hierarchy provides a standardized organizational structure, supporting universal comparison across systems and offering a transparent basis for quantitative weighting through the AHP so that biodiversity indicators are prioritized according to their functional significance. The relevance and applicability of the EBV framework are further supported by its adoption in several regional and national monitoring initiatives, including the EuropaBON project in Europe and national-scale biodiversity observation programs in Japan and Finland, which have used EBVs to strengthen terrestrial ecosystem assessment and reporting [94,95].

4. Conclusions

This study synthesized 18 biodiversity indicators across the ecosystem service domains of pollutant removal, nutrient retention, biological uptake, carbon storage, greenhouse gas regulation, and microclimate control, and proposed the BBESI as a standardized framework for evaluating ecosystem service delivery in CWs. By organizing microbial and plant-based indicators within the EBV hierarchy, the BBESI establishes a consistent structure for assessing how biodiversity supports ecological performance across genetic, species, community, and ecosystem levels. Evidence from the reviewed literature suggests that functional attributes expressed through microbial diversity, plant traits, and their interactions reflect the processes that sustain these services. Structuring indicators within the EBV hierarchy provides a shared biological reference system that can facilitate comparison across studies by aligning heterogeneous datasets to common genetic, species, community, and ecosystem dimensions.

Several limitations should be acknowledged. Indicator availability across the literature is uneven, with microbial metrics more frequently reported than plant or faunal indicators, which may introduce selection bias into the BBESI. In addition, the framework has not yet been empirically validated across multiple CW sites, and its practical applicability will depend on data availability, methodological consistency, and the monitoring capacity of wetland management programs. Because the EBV framework was originally developed for biodiversity monitoring rather than for ecosystem-service evaluation, its application to service-oriented assessments such as water quality and climate regulation functions require careful interpretation.

The BBESI nevertheless provides a structured foundation for evaluating CW performance and informing design and management decisions, and future development will require a transparent approach to indicator prioritization. In this process, stakeholder involvement should inform the identification of valued ecosystem functions, while decision authority over final weighting or prioritization rests with designated management bodies or regulatory institutions to ensure consistency and accountability. Future extensions of the framework may incorporate subjective weighting approaches such as AHP, objective methods such as entropy weighting, and integrated approaches that combine expert judgment with data-driven criteria. Weighting decisions will require coordinated input from microbial ecologists, plant ecologists, wetland biogeochemists, remote-sensing specialists, CW engineers, wetland managers, and environmental policymakers, as different expert groups may emphasize different biodiversity components. Future research should operationalize these weighting processes to develop a composite index and complement the BBESI with design-based and biogeochemical indicators to strengthen its predictive capacity for ecosystem-service performance and resilience.