Biodiversity Monitoring in Constructed Wetlands: A Systematic Review of Assessment Methods and Ecosystem Functions

Abstract

Constructed wetlands (CWs) are widely implemented as nature-based solutions for delivering essential ecosystem services such as water purification, carbon sequestration, and habitat provision. However, biodiversity monitoring within CWs remains limited and unevenly integrated into performance evaluations. This scoping review analyzed 76 peer-reviewed studies to assess current methods for biodiversity monitoring, explore linkages to ecosystem functions, and examine the diversity indices most frequently applied. Results revealed a predominant focus on microbial communities, primarily assessed through high-throughput sequencing and general ecological indices such as the Shannon–Wiener Diversity Index and Chao1 Richness Estimator, with limited taxonomic depth or functional specificity. Plant and animal biodiversity were addressed less frequently and were rarely linked to treatment outcomes or ecosystem services beyond regulation. Vertical subsurface flow systems were the most studied configuration, particularly in lab-scale studies, while free water surface systems exhibited greater microbial phylum richness. These findings highlight a critical need for CW-specific biodiversity monitoring frameworks that integrate microbial, plant, and faunal assessments using functionally relevant phylogenetic indices such as Rao’s Quadratic Entropy and Faith’s Phylogenetic Diversity. Emphasis on standardization, trait-based analyses, and mechanistic approaches is essential for enhancing ecological interpretation and ensuring biodiversity is recognized as a central component of CW design, performance, and resilience.

1. Introduction

Biodiversity underpins the stability, resilience, and functionality of Earth’s ecosystems. It plays a vital role in ecological processes such as resource capture, biomass production, nutrient recycling, and the maintenance of ecosystem services that support human well-being [1,2]. The concept, derived from “biological diversity”, was introduced in 1985 and gained global recognition following the 1992 UN Earth Summit, which led to the establishment of international frameworks such as Agenda 21 and the Convention on Biological Diversity (CBD) [3]. In response to accelerating biodiversity loss, global frameworks including the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) have emphasized the importance of standardized biodiversity monitoring to support conservation and ecosystem management [4]. This international momentum was reinforced at the 16th Conference of the Parties (COP16), which promoted the Kumming-Montreal Global Biodiversity Framework and its target to protect 30% of ecosystems by 2030. These outcomes highlight the need to apply biodiversity indicators not only in natural but also in human-managed systems such as constructed wetlands (CWs). As engineered nature-based solutions (NBS), CWs simultaneously support conservation and ecosystem services such as water purification, carbon storage, and habitat provisioning [5], making them key candidates for advancing global biodiversity goals through integrative monitoring.

CWs have been widely adopted over the past four decades to manage wastewater, mitigate flooding, and restore ecological balance. Typically consisting of shallow vegetated basins populated with emergent macrophytes, CWs provide multiple ecological and biogeochemical services. Despite these multifunctional benefits, assessments of CW performance have traditionally focused on pollutant removal efficiencies [6,7]. As a result, biodiversity, a critical driver of ecosystem function and long-term system resilience, has remained underrepresented in the planning and management of CWs.

Higher levels of biodiversity are known to enhance carbon sequestration, support nutrient retention, improve water quality, and promote system stability [8]. However, the mechanisms linking biodiversity to these ecosystem services in CWs are not yet well understood. This gap is partly due to the use of biodiversity metrics that differ from those used to assess functional performance. Many commonly applied indices, such as the Shannon–Wiener Diversity Index (Shannon), Simpson’s Index (Simpson), and Pielou’s Evenness Index (Pielou’s Evenness), were originally developed for general ecological assessments and are sensitive to sample size [9,10]. Consequently, they may require standardization or adjustment to adequately capture the engineered complexity of CWs [11].

Inconsistencies in data reporting across spatial and temporal scales have been caused by the current diversity of assessment methodologies. This fragmentation has hindered the development of cohesive monitoring frameworks that can effectively link biodiversity patterns with functional outcomes. Without standardized, functionally relevant, and sample-size independent metrics, it remains challenging to assess biodiversity’s role in ecosystem services or compare findings across CW studies [12,13]. The urgent need for such standardization is compounded by the lack of indicators specifically developed for CWs and the continued reliance on legacy ecological approaches that do not reflect the unique design and performance goals of these engineered systems [14].

Given the increasing recognition of biodiversity’s role in sustaining key ecosystem functions, there is a critical need for a standardized biodiversity monitoring framework specific to CWs. Thus, a scoping review was conducted to systematically map existing research, assess current methodologies, and identify gaps in biodiversity monitoring within CWs. This review addresses this need by pursuing three main objectives. First, to identify and describe the methods currently used to assess biodiversity in CWs, including field-based surveys, molecular tools, and bioindicator-based approaches. Second, to explore how different aspects of biodiversity specifically, microbial, plant, and faunal aspects, contribute to essential ecosystem functions such as carbon sequestration, water purification, and nutrient cycling. Third, to evaluate the biodiversity indices applied in CW research, with particular focus on their relevance, frequency of use, and applicability in capturing biodiversity’s role in supporting ecosystem services. By fulfilling these objectives, this study provides a basis for developing harmonized biodiversity monitoring strategies aimed at improving CW design, performance, and ecological resilience.

2. Materials and Methods

2.1. Review Protocol

This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines to ensure transparency and methodological rigor [15]. Additionally, the review protocol was pre-developed and refined iteratively to maintain consistency during the study selection and data extraction.

2.2. Eligibility Criteria

Studies were included if they met at least one of the following eligibility criteria: they employed assessment methods to measure biodiversity in CWs; examined the relationship between biodiversity and ecosystem functions such as nutrient removal, water purification, or carbon sequestration; or applied biodiversity indices to evaluate ecological patterns within CW systems.

The eligibility criteria were designed to capture a broad range of studies that reflect the multifaceted nature of biodiversity monitoring in CWs. This included research focusing on assessment methodologies, the functional roles of biodiversity in supporting ecosystem services, and the application of ecological indices to quantify biodiversity. These criteria were chosen to ensure comprehensive coverage of microbial, plant, and faunal components, and to highlight the diverse methodological approaches used to evaluate biodiversity within CW systems. A summary of the final inclusion and exclusion criteria is provided in

Table 1. Inclusion and exclusion criteria used to select studies for the scoping review on biodiversity monitoring in CWs.

Inclusion Criteria	Exclusion Criteria
Studies conducted in CWs or similar engineered/treatment wetlands.	Studies did not assess biodiversity and focused on wetland hydrology or treatment performance.
Studies assessed biodiversity using quantitative indices such as Shannon, Simpson, Abundance-based Coverage Estimator (ACE), and Chao1	Studies focused solely on physical or chemical treatment processes without biodiversity considerations.
Studies linked biodiversity to ecosystem functions.	Studies were opinion pieces, non-peer-reviewed, or lacked a clear methodology.
Studies were peer-reviewed articles.

.

2.3. Information Sources and Search Strategy

A systematic literature search was conducted in Scopus and Web of Science (WoS) between January and March 2025. The search strategy was developed iteratively over multiple days, during which various keyword combinations and Boolean operators (AND/OR) were tested and refined. It was ultimately structured into three thematic queries, designed to capture studies focused on biodiversity assessment methods, ecosystem functions, and the application of biodiversity indices within CWs. The search was limited to English-language publications that were either open-access or institutionally accessible. No publication year filter was applied to capture all relevant studies regardless of publication date, consistent with the scoping review objective of mapping the breadth and evolution of existing research. A summary of the refined search queries is presented in

Table 2. Search queries and themes used for the systematic literature review.

Database	Query Type	Optimized Search Query
Scopus, WOS	Biodiversity Assessment Methods in CWs	“Biodiversity assessment”, “species diversity”, “functional diversity”, “bioindicators”, “community structure”, “ecological indicators”, “constructed wetland”, “engineered wetland”, “assessment”, “survey”, “monitoring”, “evaluation”
Scopus, WOS	Biodiversity and Ecosystem Functions in CWs	“Biodiversity monitoring”, “species diversity”, “bioindicators”, “community structure”, “functional diversity”, “plant diversity”, “macroinvertebrate diversity”, “microbial diversity”, “vegetation diversity”, “faunal diversity”, “ecological diversity”, “trophic diversity”, “constructed wetland”, “engineered wetland”, “treatment wetland”, “surface flow wetland”, “subsurface flow wetland”, “hybrid constructed wetland”, “free water surface wetland”, “wetland treatment system”, “nature-based treatment system”, “wetland restoration”, “carbon sequestration”, “water purification”, “pollution removal”, “nutrient removal”, “nutrient retention”, “self-purification”, “biogeochemical cycling”, “ecosystem services”, “sediment retention”, “ecological function”, “primary productivity”
Scopus, WOS	Specific biodiversity indices in CWs	“Biodiversity index”, “species richness”, “diversity index”, “ACE”, “Shannon index”, “Simpson index”, “Phylogenetic diversity”, “constructed wetland”, “engineered wetland”, “treatment wetland”, “survey”, “monitoring”, “evaluation”, “analysis”

.

2.4. Selection of Sources of Evidence

A total of 182 records were initially identified across Scopus (n = 102) and WOS (n = 80), and 40 duplicate entries were removed, resulting in 142 records for screening, as shown in Figure 1. Manual screening of titles and abstracts was then conducted on these 142 records to assess relevance, leading to the exclusion of 38 records that were irrelevant, inaccessible, or outside the scope of the review. The remaining 104 full-text articles were assessed using the Rayyan platform, an online tool for managing systematic reviews [16]. Titles and abstracts were screened in the first phase to exclude irrelevant records. Full-text articles were evaluated against the eligibility criteria in the second phase. Studies were considered eligible if they addressed biodiversity assessment methods, examined biodiversity–function relationships, or applied biodiversity indices within the CW context. Studies were excluded if they lacked biodiversity data, did not assess ecosystem functions, or focused solely on chemical or hydrological treatment without biodiversity analysis.

Although the review focused on CWs, a few studies on natural or restored wetlands were included for their methodological relevance in informing biodiversity monitoring in engineered systems. In total, 76 studies met the inclusion criteria and were selected for synthesis. These studies encompassed a wide range of wetland types, assessment techniques, and functional linkages.

2.5. Data Extraction Processing and Visualization

All eligible studies were manually reviewed, and relevant data were extracted manually into structured Microsoft Excel spreadsheets. Data were categorized according to wetland type, biodiversity focus, assessment methods, and ecosystem functions. Information related to microbial sequencing techniques, taxonomic resolution, and diversity indices was also extracted and classified. Diversity indices were grouped into alpha diversity metrics and less commonly applied phylogenetic or trait-based indices. Visualizations such as the Sankey diagram, heatmap, and bar charts were generated using OriginPro to illustrate co-occurrences and trends across CW types, scales, and microbial methods. No bibliometric mapping was used in this study, as the focus was on content-based synthesis of biodiversity monitoring methods and functional linkages, rather than citation network analysis.

3. Results

3.1. Study Landscape and Institutional Support

Over the past two decades, research on biodiversity in CWs has expanded across countries and institutions. As shown in Figure 2, publication activity steadily increased after 2013, with notable peaks in 2020 and 2023. China has emerged as the most prolific contributor, with 48 publications since 2012. Other key contributors include the United States (14 publications) and Spain (4 publications). This growth in China, supported by national legislation such as the Provisions on Administration of Wetland Protection [17], laid the groundwork for wetland governance and ecological research priorities. This positions China to influence global practices in CW biodiversity assessment, particularly in advancing microbial profiling techniques and promoting biodiversity-integrated, multifunctional CW system design.

A total of 204 funding acknowledgments were identified across the reviewed studies. As summarized in

Table 3. Number of funding acknowledgments by type of institution across all reviewed studies.

Type of Institution/ Funding Body	Number of Entries
National Government/Program/Initiative	105
Regional or Local Authority/Program	47
University/Higher Education Institution	18
Others *	43

* Categories with fewer than 10 entries.

, the majority were attributed to national government programs (105), followed by regional and local authorities (47), universities (18), and international organizations (9). The National Natural Science Foundation of China (NSFC), cited in 28 studies, exemplifies a centralized and strategic approach to research funding. It supports international collaborations aligned with the Ramsar Convention’s goals on wetland conservation, and provides targeted funding for less developed regions, thereby promoting widespread participation in biodiversity monitoring across CWs [18].

3.2. Overview of Constructed Wetland Types and Research Scope

A total of 76 studies were included in this review, encompassing various CW types, operational scales, and geographic settings. As summarized in

Table 4. Number and percentage of CW types by operational scale among studies.

Wetland Type	Full Scale (n, %)	Lab-Scale/Mesocosm (n, %)	Total (n, %)
FWS	7 (16.7%)	7 (16.7%)	14 (33.33%)
HSSF	4 (9.5%)	5 (11.9%)	9 (21.4%)
VSSF	1 (2.4%)	15 (35.7%)	16 (38.1%)
Hybrid	2 (4.8%)	1 (2.4%)	3 (7.1%)
Total	14 (33.33%)	28 (66.7%)	42 (100%)

Abbreviations: FWS = Free Water Surface; HSSF = Horizontal Subsurface Flow; VSSF = Vertical Subsurface Flow.

, a total of 42 studies explicitly reported the flow configuration. Vertical Subsurface Flow (VSSF) wetlands were the most studied, appearing in 16 studies, 15 of which were lab-scale, likely due to their structural simplicity, compact design, and compatibility with controlled experiments [19]. Lab-scale VSSF wetlands have enabled detailed microbial biodiversity analysis, particularly through high-throughput sequencing (HTS). Enhanced aeration and subsurface flow may also create unique microhabitats that influence microbial community structure and functional diversity.

FWS systems appeared in 14 studies, evenly divided between lab- and full-scale systems. These wetlands offer strong biodiversity support and deliver ecosystem functions such as wildlife habitat provisioning and aesthetic value [20]. However, their open-water design also makes them more sensitive to seasonal temperature fluctuations. In contrast, Horizontal Subsurface Flow (HSSF) wetlands, reported in nine studies, are typically implemented in rural or expansive areas due to their greater land requirements and more complex hydraulic infrastructure [21]. HSSF systems offer advantages in phosphorus and pathogen removal, making them suitable for pollutant-focused treatment goals [20,22]. Hybrid systems, such as Integrated Constructed Wetlands (ICW), were the least represented, appearing in only three studies. Additionally, 34 studies did not specify a flow configuration, limiting comparative evaluation of design-related biodiversity and functional outcomes. System size was inconsistently reported across studies and thus not analyzed as a factor, though it may significantly influence biodiversity–function relationships.

Among the 76 studies, 60 addressed all three components of the review: biodiversity, ecosystem function, and assessment method. In contrast, only 13 studies explored ecosystem functions and monitoring methods without explicitly incorporating biodiversity data, and 1 study focused solely on biodiversity assessment, indicating a gap, as biodiversity is rarely studied as an independent focus in CW research, often treated as a supporting or indirect variable rather than a primary outcome [6] (see Figure 3).

3.3. Microbial Sequencing Strategies and Diversity Metrics Across Constructed Wetlands

A Sankey diagram illustrating the connections between CW flow types, study scales, microbial analysis methods, and the biodiversity indices applied is presented in Figure 4. The Sankey diagram is a synthesis-based visualization constructed from manually extracted categorical data across the reviewed studies. It illustrates the co-occurrence frequency of flow types, study scales, microbial analysis methods, and diversity indices, rather than representing continuous or quantitative flow values. In the diagram, each colored block represents a category and the lines connecting them represent the number of studies that link one category to another. Thicker lines indicate stronger connections, reflecting higher frequencies of co-occurrence across the reviewed studies. This visualization highlights key trends in how microbial communities have been studied in CWs.

Microbial communities in CWs have been analyzed using various molecular techniques, including HTS, Sanger sequencing, qPCR, and metagenomics. HTS has become the predominant method due to its scalability and sensitivity to rare or low-abundance taxa. When paired with bioinformatics tools, HTS supports ecological interpretation across complex microbial communities [23]. Commonly applied diversity indices for microbial community analysis in CWs include Shannon, Simpson, Chao1 Richness Estimator (Chao1), and Abundance-based Coverage Estimator (ACE). These indices were most often applied in lab-scale studies where controlled conditions allow for detailed microbial analysis. In contrast, indices such as Faith’s Phylogenetic Diversity (Faith’s PD), Pielou’s Evenness Index (Pielou’s Evenness), and Good’s Coverage Estimator (Good’s Coverage) were used in fewer than 5% of reported cases.

The predominance of HTS reflects a broader shift in CW research toward high-resolution, data-rich techniques in CW microbial research. These methods enable the profiling of both culturable and unculturable taxa [24]. Given the central role of microbes in nutrient removal, organic degradation, and biogeochemical cycling [25], microbial profiling is key to optimizing CW performance. However, HTS using 16S rRNA amplicon sequencing is often limited to the genus-level identification, reducing the resolution of functional interpretations, especially when microbial traits vary within genera. As demonstrated by Lemos et al. [26], datasets with low sequence coverage yield insufficient taxonomic resolution and underestimate diversity, limiting the detection of rare or functionally critically taxa. Commonly used indices like Shannon and Chao1 emphasize richness and diversity but lack ecological depth. In contrast, less commonly used metrics like Faith’s PD and Pielou’s Evenness offer greater ecological relevance by incorporating evolutionary relationships and community balance [27]. Comparative studies suggest that these alternative indices may better detect ecological shifts or treatment impacts [28]. The absence of standardized protocols and baseline references hinders cross-study synthesis, highlighting the need for harmonized microbial monitoring frameworks.

Recent studies further highlight the limitations of standard alpha diversity metrics in linking microbial diversity to functional performance in CWs. For instance, Cakin et al. [29] demonstrated that on HSSF CWs treating distillery wastewater, Shannon and richness indices captured general trends but failed to reflect the functional roles of key taxa such as Acidobacteriia, Planctomycetacia, and Rozellomycota in driving COD, TON, and NH₄⁺ removal. Similarly, Ibekwe et al. [30], noted strong correlations between microbial community composition and treatment efficiency, yet relied primarily on richness-based indices that lacked functional specificity.

This over-reliance on generalized indices may obscure functionally important dynamics within microbial communities, especially in systems where treatment performance depends on narrow or specialized microbial guilds. Despite their value, richness and evenness metrics often fall short in detecting ecological shifts that influence CW function, as noted in comparative assessments by Jost [27] and Feranchuk et al. [28]. By contrast, phylogenetic and trait-based indices, such as Faith’s PD and Rao’s Quadratic Entropy, have shown greater sensitivity to functional shifts in microbial communities and may be more appropriate for engineered ecosystems with complex pollutant loads.

To address this gap, future CW research should expand beyond taxonomic profiling and integrate multi-dimensional biodiversity indices that reflect both evolutionary history and ecological function. Developing CW-specific index frameworks and linking them directly to pollutant removal performance could significantly enhance the ecological relevance and management utility of microbial diversity assessments. Broader adoption of functional diversity indices, phylogenetic metrics, and trait-based indices tailored to CW processes is recommended. These metrics offer greater sensitivity to ecological shifts and may better capture the functional contributions of microbial communities to ecosystem services such as nutrient cycling, organic matter degradation, and carbon sequestration. Establishing standardized protocols that include these indices would improve comparability across studies and support the development of more predictive and performance-linked biodiversity monitoring frameworks. To advance the ecological relevance of microbial monitoring, future research should standardize sequencing protocols, adopt functionally relevant indices and expand the use of metagenomic tools capable of resolving microbial traits. Cross-validation between molecular findings and functional performance metrics will be key in optimizing CW design and management.

3.4. Microbial Phyla and Taxonomic Resolution in Constructed Wetlands

Lab-scale studies commonly featured more detailed microbial analyses and biodiversity metrics, whereas full-scale studies were less likely to report species-level resolution. This gap highlights the operational difficulty of biodiversity monitoring in real-world systems, particularly at microbial and invertebrate scales. Lab-scale systems have enabled finer assessments of microbial stratification and enzymatic activity under controlled conditions, whereas full-scale applications often face constraints related to sampling complexity, taxonomic resolution, and resource limitations [25]. Greater precision in reporting CW flow types and operational scales is needed to improve cross-study comparisons. Standardizing typologies would enhance meta-analyses and support global knowledge exchange on biodiversity–function relationships.

Microbial taxonomic resolution varied across CW types. This analysis focused on phylum-, genus-, and species-level classifications, which were the most consistently reported across the reviewed studies. Intermediate ranks such as class, order, and family were excluded due to inconsistent reporting and limited functional interpretation. Studies that focused on VSSF showed the lowest species-level identification (5.4%) and the highest genus-level classification (74.5%), while HSSF systems demonstrated a more balanced distribution across taxonomic ranks. FWS systems reported the highest proportion of phylum-level identifications (55.5%) and the greatest overall phylum richness, with 26 distinct groups. These patterns are illustrated in Figure 5, which compares taxonomic resolution across CW configurations.

The predominance of phylum and genus-level identifications reflects both methodological and technical constraints in microbial monitoring. Most studies employed 16S rRNA gene amplicon sequencing, which is effective for broad community profiling but typically limited to genus-level resolution. Even with more targeted approaches like clone libraries and phylogenetic reconstruction, as noted by Arroyo et al. [31], many sequences could only be assigned to the family or order level, with some remaining unclassified due to reference database gaps. These challenges were particularly evident in high-stress environments like swine wastewater systems, where microbial diversity was low and communities were dominated by beta-proteobacteria. Silveira et al. [32] similarly observed that microbial richness and structure varied spatially within horizontal flow CWs, yet even HTS failed to resolve finer community differences, highlighting the need for improved sampling strategies and deeper sequencing. These taxonomic limitations are not due to HTS itself, but rather the reliance on marker genes with restricted resolution. Although advanced methods like shotgun metagenomics offer better taxonomic and functional detail, their high cost and technical complexity have limited routine use in CW studies.

Following the resolution analysis, microbial composition at the phylum level displayed clear differences across wetland types. As shown in Figure 6, Proteobacteria were dominant in all systems, comprising 32.1% in HSSF, 32.4% in FWS, and 60.5% in VSSF wetlands. This group plays vital roles in treatment processes such as ammonification, nitrification, denitrification, and degradation of organic pollutants via biosorption and catalytic transformation [33]. Other phyla varied more widely, Euryarchaeota and Bacteroidetes were more prevalent in HSSF and VSSF systems, while Firmicutes and Actinobacteria appeared consistently across all types but at a lower relative abundance.

In terms of richness, FWS systems exhibited the greatest phylum diversity (26), followed by VSSF (24) and HSSF (22). This richness in FWS wetlands likely results from their open structure and dense vegetation, which create heterogeneous microhabitats and oxygen gradients that favor diverse microbial communities [30]. In contrast, the more compact and oxygen-limited environments of HSSF and VSSF tended to support fewer but more specialized phyla. Some phyla were enriched or uniquely observed within specific systems, Ascomycota and Rozellomycota in HSSF, Caldiserica, Chloroflexi, and Chlamydiae in VSSF, and rare groups such as Nitrospirae, Synergistetes, and Verrucomicrobia in FWS. However, because most identifications were at the phylum level, finer-scale diversity may be underrepresented, reinforcing the need for higher-resolution methods and more robust taxonomic frameworks [24]. Moving forward, microbial community studies in CWs should incorporate deeper sequencing platforms such as shotgun metagenomics or full-length 16S approaches.

3.5. Plant and Animal Biodiversity in Constructed Wetlands

3.5.1. Types of Macrophytes and Floating Plants Used

A total of 13 macrophyte genera were reported in lab-scale studies, with Phragmites, Typha, Canna, Acorus, Iris, and Thalia being the most frequently used. In contrast, full-scale systems reported a broader diversity of 33 genera. The most cited were Phragmites, Typha, Canna, Carex, Cyperus, Iris, and Scirpus, along with other genera such as Eleocharis, Juncus, Mentha, Sparganium, and several others cited only once. This difference in diversity reflects the broader ecological and functional goals of full-scale systems compared to the more standardized species selection in lab-scale experiments. Mixed culture systems have been shown to consistently achieve higher nitrogen and phosphorus removal efficiencies. These improvements are linked to increased oxygen and organic carbon release in the rhizosphere, healthier root structures, and enhanced microbial communities that support nutrient cycling. Diverse plant configurations play a functional role in improving microbial nitrification and denitrification processes in constructed wetlands [34]. Figure 7 illustrates notable differences in macrophyte species usage between lab-scale/microcosm and full-scale CWs.

This disparity in macrophyte diversity reflects both methodological constraints in lab-scale experiments and broader ecological and functional goals in full-scale systems. Lab-scale studies often rely on a core group of robust, fast-growing species known for pollutant uptake efficiency and root oxygen release, such as Phragmites, Typha, and Canna [35]. These selections enable replicability and control in small-scale systems. In contrast, full-scale CWs integrate a much wider range of macrophytes to address landscape heterogeneity, site-specific hydrology, habitat provisioning, and long-term stability.

While dominant species often drive core treatment functions in CWs, multispecies assemblages have been shown to enhance system resilience and deliver additional co-benefits. These include improved nutrient removal efficiency, greater stability under fluctuating environmental conditions, and increased plant productivity. For instance, Brisson et al. [36] conducted a meta-analysis of 28 wetland studies and found that increased plant species richness modestly but significantly improved nutrient and organic pollutant removal, particularly for total nitrogen (TN) and chemical oxygen demand (COD). Similarly, Luo et al. [37] demonstrated that both species richness and plant growth forms significantly improved nitrogen and phosphorus removal in horizontal subsurface flow mesocosms, with mixed growth types outperforming single-form plantings. These findings suggest that lab-scale CWs would benefit from incorporating greater macrophyte diversity to more accurately reflect the complexity and multifunctionality of full-scale systems.

3.5.2. Animal Biodiversity and Functional Roles

CWs support a wide range of faunal biodiversity across multiple taxonomic groups (

Table 5. Representative faunal taxa reported in constructed wetlands, categorized by major taxonomic groups.

Taxonomic Group	Observed Taxa
Birds (Avifauna)	Anas platyrhynchos, Fulica atra, Ardea alba, Charadrius dubius, Vanellus vanellus, etc.
Reptiles and Amphibians	Trachemys scripta, Chelydra serpentina, Emydoidea blandingii, Sternotherus odoratus
Fish	Anguilla reinhardtii, Macrobrachium novaehollandiae, Misgurnus anguillicaudatus, Terapon jarbua
Crustaceans	Gammarus pulex, Neosarmatium meinerti, Uca spp.
Mollusks	Potamopyrgus antipodarum, Radix balthica, Terebralia palustris, Saccostrea cucullata
Insects and Arthropods	Cloeon dipterum, Enallagma cyathigerum, Chaoborus crystallinus, Orthetrum spp., Coleoptera, Plecoptera
Zooplankton and Protozoa	Brachionus calyciflorus, Daphnia spp., Cyclops spp., Lecane spp., Polyarthra vulgaris
Pollinators	Apis mellifera, Bombus spp., Lasioglossum spp., Eristalis spp., Toxomerus spp.
Annelids and Worms	Eisenia foetida, Monopylephorus sp., Tubulanus, Micrura
Other Invertebrates	Capitella sp., Harpacticoid copepoda, Manayunkia aestuarina, Nereidae, Turbellaria

). Birds such as Anas platyrhynchos, Fulica atra, and Ardea alba were among the most frequently reported avifauna. These species were typically monitored using fixed-radius point counts, with observers recording all birds seen or heard within a 200 m radius during early morning surveys under favorable weather conditions [38]. Insect pollinators, including wild Hymenoptera and Diptera, were sampled using vane traps, pan traps, and targeted netting along 25 m floral transects, followed by species identification from frozen specimens in the lab [39]. Other faunal groups, such as fish, amphibians, mollusks, and macroinvertebrates, were generally documented through field observations or incidental reports, though their sampling was less consistently described.

To quantify biodiversity, studies commonly employed metrics such as species richness, the Shannon–Wiener Diversity Index, and the Simpson Index, particularly for bird and insect assemblages [38,39]. Some analyses also included Hill’s diversity numbers (N1 and N2) to assess evenness and dominance in pollinator communities. While these indices offer valuable insights into faunal composition, most studies focus on single taxonomic groups in isolation [35], often without linking biodiversity data to wetland treatment performance, hydrological dynamics, or vegetation structure.

Faunal communities play a direct role in supporting ecosystem functioning CWs. Birds facilitate seed dispersal and nutrient redistribution, macroinvertebrates contribute to organic matter decomposition and nutrient cycling, and pollinators enhance plant reproductive success, promoting long-term vegetation diversity and system resilience. This was also emphasized by Zhu et al. [35], who found that diverse macrophyte compositions enhanced pollinator visitation and functional support, illustrating how biotic interactions can influence vegetation dynamics and treatment resilience in CW systems. Despite these contributions, faunal indicators remain underutilized in wetland monitoring and design frameworks. Very few studies explicitly connect animal diversity with treatment outcomes or use it as a feedback mechanism for adaptive management. A more integrated approach to faunal monitoring would support holistic evaluation of CW performance and strengthen their role as multifunctional NBS. Biodiversity monitoring in CWs should be expanded to systematically include faunal groups such as macroinvertebrates and birds, linking their ecological roles directly to treatment outcomes and landscape resilience. In addition, lab-scale experiments would benefit from the inclusion of greater plant species richness and functional traits to better reflect real-world complexity and ecological function.

3.6. Linking Biodiversity Types to Ecosystem Service Functions in Constructed Wetlands

Figure 8 presents a heatmap linking biodiversity types with key ecosystem service categories. Each cell value represents the raw number of studies that reported a link between a specific biodiversity type and an ecosystem service. The color gradient shown in the legend on the right side of the figure is not a correlation scale, but a visual representation of the number of supporting studies, ranging from 0 (dark blue) to 37 (deep red). Warmer colors indicate stronger associations, while cooler tones reflect weaker or absent linkages. This visual scale allows for rapid identification of well-studied connections and underexplored areas within the biodiversity–ecosystem service framework. To present these linkages more clearly, the ecosystem services were categorized following the Millennium Ecosystem Assessment framework into four types: regulating, supporting, cultural, and provisioning [40]. The heatmap was developed by systematically categorizing and counting studies that reported explicit links between biodiversity types and ecosystem services.

As reflected in the heatmap, biodiversity monitoring in CWs was most frequently associated with regulating services such as water purification and nutrient cycling. Microbial and plant diversity were strongly linked with pollutant attenuation, carbon sequestration, and hydrological regulation. For instance, Brisson et al. [36] demonstrated that plant species richness positively influenced nitrogen and COD removal, suggesting that vegetation diversity enhances the regulating functions of CWs. Similarly, studies on microbial communities have shown how root-associated bacteria contribute to pollutant breakdown and carbon transformations, further supporting the microbial role in biogeochemical regulation [35,41].

Supporting services, including habitat provision and ecological connectivity, were often assessed through faunal and plant biodiversity metrics. Macroinvertebrate assemblages were shown by Silveira et al. [32] to reflect both wetland ecological integrity and biodiversity value, even in artificial pond systems. In addition, Biervliet et al. [42] found that integrated CWs not only improved nutrient removal but also supported high bird species richness, highlighting the contribution of animal diversity to both supporting and cultural services. Cultural values were occasionally addressed, particularly in relation to avian diversity and landscape aesthetics, though often as secondary observations rather than core study objectives. Moreover, Almeida et al. [43] emphasized the multifunctional role of CWs by comparing bird and fish community composition across natural and CWs, showing that well-designed systems can deliver biodiversity benefits comparable to natural analogues. Their findings suggest that CWs can potentially provide regulating, supporting, and cultural services when designed with multiple taxonomic groups in mind. Notably, provisioning services such as biomass production or water supply were rarely addressed, revealing a consistent research gap. This heatmap highlights the current focus of biodiversity–function research and underscores the need for more integrated assessments that capture the full spectrum of ecosystem services delivered by CWs.

While this review identified numerous studies linking biodiversity with ecosystem functions such as nutrient removal and carbon sequestration, most findings remain correlational rather than mechanistic. This reflects a broader limitation in current CW research, where functional pathways connecting specific taxa or community traits to ecosystem outcomes are rarely explored in depth. Future studies should aim to incorporate functional trait analysis, experimental manipulations, or metagenomic profiling to better establish causal relationships between biodiversity and ecosystem service delivery. Future CW research should integrate ecosystem service frameworks more explicitly, using multi-taxa biodiversity data to assess regulating, supporting, provisioning, and cultural service outcomes. Functional trait analysis and process-based modeling should be prioritized to uncover the mechanistic links between biodiversity and service delivery across different CW types and contexts.

4. Conclusions

This scoping review examined 76 studies to assess current practices in biodiversity monitoring within CWs, with a focus on methods, functional linkages, and the application of biodiversity indices. The review highlighted the growing yet uneven integration of biodiversity across microbial, plant, and faunal domains. While microbial diversity was the most frequently assessed, evaluations often lacked functional depth and relied on generalized indices not tailored to engineered ecosystems. Plant and faunal biodiversity remained underrepresented, particularly in relation to supporting and cultural ecosystem services. Most studies continued to treat biodiversity as a secondary outcome, prioritizing treatment performance. Overreliance on indices such as Shannon and Chao1 limited cross-study comparability and reduced ecological interpretability. Consequently, biodiversity’s role in sustaining ecosystem services was often acknowledged but rarely demonstrated with functional specificity. These findings underscore the need for more targeted, CW-specific biodiversity evaluation strategies.

Future research should prioritize integrative monitoring frameworks that combine microbial, plant, and animal assessments aligned with ecosystem service delivery. Functional and phylogenetic indices could improve sensitivity to ecological change, while standardized and cross-scalar protocols would support data integration. Greater adoption of trait-based approaches and experimental designs can help move beyond correlations to identify causal biodiversity–function relationships. Addressing these gaps would reinforce biodiversity as a core metric in CW performance assessments and help position CWs more effectively as multifunctional, climate-resilient NBS. In doing so, biodiversity monitoring can shift from a supporting role to a central pillar in sustainable wetland management.