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Research Article

Restoration Enhances Wetland Biodiversity and Ecosystem Service Supply, but Results Are Context-Dependent: A Meta-Analysis

* E-mail: [email protected]

Affiliations Natura y Ecosistemas Mexicanos A.C., México DF, México, Departamento de Ciencias de la Vida, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain

Affiliation Departamento de Ciencias de la Vida, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain

Affiliation Centro de Investigaciones en Ecosistemas, Universidad Nacional Autónoma de México, Morelia, Michoacán, México

• Paula Meli, 
• José María Rey Benayas, 
• Patricia Balvanera, 
• Miguel Martínez Ramos

• Published: April 17, 2014
• https://doi.org/10.1371/journal.pone.0093507
• Reader Comments

Wetlands are valuable ecosystems because they harbor a huge biodiversity and provide key services to societies. When natural or human factors degrade wetlands, ecological restoration is often carried out to recover biodiversity and ecosystem services (ES). Although such restorations are routinely performed, we lack systematic, evidence-based assessments of their effectiveness on the recovery of biodiversity and ES. Here we performed a meta-analysis of 70 experimental studies in order to assess the effectiveness of ecological restoration and identify what factors affect it. We compared selected ecosystem performance variables between degraded and restored wetlands and between restored and natural wetlands using response ratios and random-effects categorical modeling. We assessed how context factors such as ecosystem type, main agent of degradation, restoration action, experimental design, and restoration age influenced post-restoration biodiversity and ES. Biodiversity showed excellent recovery, though the precise recovery depended strongly on the type of organisms involved. Restored wetlands showed 36% higher levels of provisioning, regulating and supporting ES than did degraded wetlands. In fact, wetlands showed levels of provisioning and cultural ES similar to those of natural wetlands; however, their levels of supporting and regulating ES were, respectively, 16% and 22% lower than in natural wetlands. Recovery of biodiversity and of ES were positively correlated, indicating a win-win restoration outcome. The extent to which restoration increased biodiversity and ES in degraded wetlands depended primarily on the main agent of degradation, restoration actions, experimental design, and ecosystem type. In contrast, the choice of specific restoration actions alone explained most differences between restored and natural wetlands. These results highlight the importance of comprehensive, multi-factorial assessment to determine the ecological status of degraded, restored and natural wetlands and thereby evaluate the effectiveness of ecological restorations. Future research on wetland restoration should also seek to identify which restoration actions work best for specific habitats.

Citation: Meli P, Rey Benayas JM, Balvanera P, Martínez Ramos M (2014) Restoration Enhances Wetland Biodiversity and Ecosystem Service Supply, but Results Are Context-Dependent: A Meta-Analysis. PLoS ONE 9(4): e93507. https://doi.org/10.1371/journal.pone.0093507

Editor: Mehrdad Hajibabaei, University of Guelph, Canada

Received: October 28, 2013; Accepted: March 7, 2014; Published: April 17, 2014

Copyright: © 2014 Meli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This research was funded primarily by a Rufford Small Grant for Nature Conservation to P.M. (40.11.09) and by grants from Pemex and the WWF-FCS Alliance to Natura y Ecosistemas Mexicanos. J.M.R.B. acknowledges support from the Spanish Ministry of Science and Education (project CGL2010-18312) and the Autonomy of Madrid (S2009AMB-1783 REMEDINAL-2). M.M.R. received sabbatical support from CONACyT, and P.B. and M.M.R. also received support from PSPA-DGAPA-Universidad Nacional Autónoma de México. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: PEMEX is a commercial institution, it is a public-private partnership involving the Mexican government, who provided funding towards this study. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Introduction

Wetlands harbor significant biodiversity [1] and supply crucial ecosystem services (ES) [2] , [3] , which are defined as the benefits that people obtain from ecosystems [4] . ES provided by wetlands include regulating water purification, protecting the ecosystem from soil erosion and effects of flooding, and nursing the early growth of many species essential to oceanic fisheries ( Table 1 ). Although wetlands occupy less than 9% of the Earth's terrestrial surface, they contribute up to 40% of global annual renewable ES [5] . Despite their importance to human societies, wetlands are rapidly being degraded and destroyed [5] , threatening the ecosystem and biodiversity on which wetland ES depend.

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https://doi.org/10.1371/journal.pone.0093507.t001

To compensate for their extensive degradation, wetland restoration has become common practice around the world. Several studies have reported that restoration can recover much of the biodiversity and ES lost due to degradation [6] . On the other hand, studies have called into question the effectiveness of wetland restoration, suggesting that its positive impacts depend strongly on factors such as ecosystem type and restoration actions [5] . For example, some authors have suggested that current wetland restoration methods are too slow and incomplete to allow recovery of biological structure and biogeochemical function [7] . Therefore the effectiveness of wetland restoration remains controversial, and this is in part because different studies have applied different standards to evaluate outcomes [6] . At the same time, most studies evaluating wetland restoration, including a recent meta-analysis [7] , have not directly assessed ES recovery or how well restoration methods work for diverse types of organisms.

Recovering biodiversity and recovering ES can be regarded as distinct goals of wetland restoration, with a given restoration focusing on one or the other. However, assessing both types of recovery simultaneously is important for several reasons. Biodiversity and ES of restored ecosystems often do not reach pre-degradation levels or the levels of similar natural ecosystems, and recovery of biodiversity may correlate with recovery of ES [8] , [9] . Indeed, recovery of biodiversity may be a prerequisite for recovery of ES [7] ; for instance, increasing biodiversity enhances key ES such as primary productivity [10] and soil erosion control [11] . Thus, comparable recovery of biodiversity and ES may indicate a win-win outcome for ecosystem and society alike. Additionally, assessments of wetland restoration should consider the context in which the restoration occurs, since restoration effectiveness may strongly depend on the type of ecosystem being restored, its pre-restoration condition, and the factors responsible for its degradation. By analyzing wetland restoration simultaneously in terms of biodiversity and ES, we can identify factors that affect the recovery of either or both, allowing us to develop recommendations for researchers and practitioners.

To develop an evidence-based approach for planning and assessing wetland restoration, we conducted a meta-analysis of the peer-reviewed literature to address the following four questions: (1) how much biodiversity and (2) how much of ES levels can be recovered through wetland restoration, (3) whether biodiversity and ES recovery correlate, and (4) whether the effectiveness of biodiversity and ES recovery depends on context, including ecosystem type, cause of degradation, restoration action, experimental design, and restoration age. In examining what the literature says on these questions, we hope to inform and improve efforts to restore the biodiversity and ES of degraded wetlands.

Literature search

We systematically searched the research literature to identify quantitative studies of the effects of ecological restoration on biodiversity and ES of non-marine aquatic and semi-aquatic degraded wetlands. We searched the ISI Web of Knowledge database ( www.isiwebofknowledge.com ), as it provides access to peer-reviewed studies. We searched studies published between 1970 and 2010 using the following string of search terms: (riparian OR river* OR lake OR mangroves OR marsh OR stream OR wetland) AND (restor* OR re-creat* OR rehabilitat* OR forest* OR reforest* OR afforest* OR plant* OR recover*) AND ((ecosystem OR environment) AND (service OR function*)). Preliminary search results were filtered to include only the following ISI-defined subject areas: “agriculture”, “biodiversity and conservation”, “environmental sciences and ecology”, “fisheries”, “forestry”, “marine and freshwater biology”, “plant sciences”, “water resources”, and “zoology”. This resulted in a list of 1,931 references.

For inclusion in our meta-analysis, studies had to focus on at least one estuarine, lacustrine, palustrine, or riverine wetland, as defined by [1] , as well as report the following information:

• Quantitative assessment of passive restoration (i.e. natural regeneration) or active restoration in terms of variables related to biodiversity and/or to the supply of one or more wetland ES ( Table 1 ) consistent with the framework of the Millennium Ecosystem Assessment [4] , according to which biodiversity underpins all ES;
• Comparison of restored wetland with either degraded or natural wetland;
• Sample size of the reported data and at least a variance estimate of such data.

A total of 70 studies ( Supporting information S1 ) satisfied these criteria and were included in our meta-analysis. The number of observations included in each analysis is shown in the corresponding figures.

Database building and effect size estimation

We constructed a computer database in which rows were observations and columns were properties of those observations ( Supporting information S2 ; Table S1 ). For each study we extracted data on the variables used to measure the impacts of restoration (response variables). Separate databases were built for biodiversity and ES response variables. Whether we used one or the other database, or some combination of columns from both of them, depended on the specific question being addressed. Each measurement of restoration impact was recorded as a separate row in the database, even when the measurements came from the same study. Measurements were also recorded separately when the original study assumed spatially independent conditions within the same study site (e.g. measurements made near the shore vs. made on the open water of the same wetland).

We extracted data on type of wetland and ecosystem, the principal causes of degradation, specific restoration action(s) implemented, experimental design used to assess restoration outcomes, and the time elapsed since completion of the last restoration action (restoration age). All variables except restoration age were nominal and assigned to categories specifically created for our analyses ( Supporting information S3 ).

Since our meta-analysis included studies differing considerably in response variables and experimental designs, we assessed the effects of restoration on biodiversity and ES relative to a control using response ratios (RRs) as the effect size metric. As an indicator of the outcome of restoration, we calculated RRs of the restored wetlands relative to reference natural wetlands [ln(Rest/Ref)] and to degraded wetlands [ln(Rest/Deg)] for each measure of the biodiversity and ES extracted from the studies. Most response variables were expected to correlate positively with biodiversity or a particular ES; for example, greater biomass was predicted to mean a higher level of supporting or provisioning ES. However, some response variables were predicted to correlate negatively with biodiversity or ES; for example, a greater concentration of a water or soil contaminant or a greater abundance of non-native species were predicted to reduce, respectively, provisioning ES and biodiversity. In these cases we inverted the sign of the RR ( Supporting information S2 ).

We performed separate analyses to compare restored and degraded wetlands and to compare restored and natural wetlands [9] ( Supporting information S3 ). RR calculations and statistical analyses were performed using MetaWin v2.1 [12] .

Biodiversity recovery

All possible measures of biodiversity for which the included studies reported data were used to calculate RRs; these measures included (a) species, gender, taxon or family richness; and (b) indices of species abundance, diversity, similarity, and composition. Using biodiversity measures calculated for different taxonomic levels or by different formulas enabled us to screen for differences in responses to restoration at different levels of ecological complexity [9] , [13] . Each extracted datum was assigned to a single organism type. Data were analyzed using categorical, random-effects models because the data were most likely to satisfy the assumptions of these models [12] ; the categories in the model were organism types.

To evaluate possible pseudo-replication effects, we calculated the mean RR for each of the three largest categories: macroinvertebrates, aquatic invertebrates, and vascular plants, using only one randomly selected effect size from each study. These mean RRs were similar to the means obtained when all effect sizes from each study were included, and the bias-corrected 95% bootstrap confidence interval of the reduced dataset overlapped with that of the entire dataset ( Table S2 ). Therefore we retained all the data in our meta-analysis, similar to Rey Benayas et al. [9] and Vilà et al. [13] .

ES recovery

Response variables were related to a wide variety of ES, so multiple RR-ES combinations were included as separate rows in the database ( Table S1 ). The parallel assessment of these multiple associations allowed us to capture the simultaneous supply of several ES [14] , [15] . To avoid counting the same data more than once in a meta-analysis, we performed a separate meta-analysis for each ES using a random-effects model. We considered this approach suitable because we wanted to evaluate each ES separately, rather than the heterogeneity among different ES.

Correlation between biodiversity and ES recovery

We assessed the correlation between biodiversity recovery and ES recovery using the Spearman rank coefficient to quantify the correlation between the corresponding RRs. We used only RRs from studies that evaluated both biodiversity and ES, and we treated each of these studies as an independent sample. When the same study reported multiple measures of biodiversity or ES, the related RRs were averaged to generate an overall RR for biodiversity and an overall RR for ES for each study, thereby minimizing the risk of pseudo-replication. This approach led us to combine the four major ES types in order to ensure adequate sample size [9] .

Context dependence of biodiversity and ES recovery

We used linear mixed-effects models to evaluate whether the effects of restoration on biodiversity and ES varied with context. Context was parameterized using four nominal fixed factors (ecosystem type, main cause of degradation, restoration action, and experimental design) and the continuous fixed factor of restoration age, defined as the decimal logarithm of the number of months between completion of the last restoration action and evaluation. We added a fifth nominal fixed factor with two levels (biodiversity or ES) because we used RRs for both biodiversity and ES recovery in the analysis. Study site was the random-effect factor and RR was the dependent variable.

We also built a second model in which we reduced the degrees of freedom by including only factor categories containing at least 30 observations. Since this reduced the average sample size in each category, we discarded this model in favor of the first. Finally, we applied a backward elimination procedure in which non-significant terms ( p <0.05) were removed in order of decreasing p value. The selected final model contained main effects but no interactions. All model building and refinement was carried out using Data Desk v6 [16] .

The 70 studies analyzed here were distributed across 62 locations in 14 countries ( Supporting information S4 ). Riverine wetlands were the best-represented ecosystem type (38% of studies), followed by lacustrine wetlands (27%), and finally estuarine (18%) and palustrine wetlands (17%). Nearly all studies (68) were field-based comparisons, including three passive restoration studies (4%). The remaining two studies (3%) involved one field and one greenhouse experiment.

Restoring degraded wetlands enhanced biodiversity by 19% ( Fig. 1a ); and biodiversity in restored wetlands did not significantly differ from that in natural wetlands ( Fig. 1b ). Restoration significantly enhanced the diversity of vertebrates (+53%), vascular plants (+45%), and terrestrial (+17%) and aquatic (+15%) invertebrates, but it had no significant effect on macroinvertebrate diversity. Restored and natural wetlands showed similar diversity of vascular plants, aquatic invertebrates, macroinvertebrates and protists. In contrast, these two types of wetlands differed significantly in the diversity of non-native vascular plants, which was 44% lower in restored wetlands, and in vertebrate diversity, which was 37% higher in restored wetlands.

Numbers in parentheses indicate the sample size (number of comparisons) followed by the numbers of studies. Bars extending from the means indicate bias-corrected 95% bootstrap confidence intervals. A mean effect size is significantly different from zero if the 95% confidence interval does not overlap with it. In comparison (a), no data were available on non-native vascular plants and protists. In comparison (b), the confidence interval for terrestrial invertebrates is not visible because it is smaller than the mean marker.

https://doi.org/10.1371/journal.pone.0093507.g001

Overall ES supply was 43% higher in restored wetlands than in degraded ones ( Fig. 2a ), but 13% lower than in natural wetlands ( Fig. 2b ). Compared to degraded wetlands, restored wetlands showed much greater supply of provisioning ES (+80%), regulating ES (+47%) and supporting ES (+40%), while the two types of wetlands showed similar supply of cultural ES. Compared to natural wetlands, restored wetlands showed similar supply of provisioning and cultural ES, but lower supply of regulating (−22%) and supporting ES (−16%).

Bars extending from the means indicate bias-corrected 95% bootstrap confidence intervals. A mean effect size is significantly different from zero if the 95% confidence interval does not overlap with it. Numbers in parentheses indicate the sample size (number of comparisons) followed by the numbers of studies.

https://doi.org/10.1371/journal.pone.0093507.g002

Restoration increased most individual ES that we examined, although not to the same extent ( Fig. 2a ). Restoration increased the supply of supporting services, with increases ranging from 32% for biogeochemical cycling to 61% for biotic interactions. Increases in the supply of regulating services ranged from 31% for water quality to 176% for invasive species control. Restoration also increased both provisioning services examined in our meta-analysis: water supply (+108%) and the supply of food or raw materials of animal origin (+65%). For most individual ES that we examined, restored and natural wetlands tended to supply similar amounts ( Fig. 2b ). Exceptions, in decreasing order of difference between the two wetland types, were climate regulation, the supply of which was −30% lower in restored wetlands; provision of terrestrial habitat, −22%; regulation of fertility and soil erosion, −21%; and biogeochemical cycles, −14%.

Biodiversity and ES response ratios positively correlated in comparisons of restored and degraded wetlands ( Fig. 3a ) and in comparisons of restored and natural wetlands ( Fig. 3b ).

https://doi.org/10.1371/journal.pone.0093507.g003

Context dependence of biodiversity and ES recovery: restored vs. degraded wetlands

Comparison of restored and degraded wetlands showed that restoration effects depended on the following factors, listed in order of decreasing importance: main cause of degradation, restoration action, experimental design, and ecosystem type ( Table 2 ). In contrast, restoration age did not significantly affect restoration outcomes. These results were the same for the two outcomes of biodiversity recovery and ES recovery.

https://doi.org/10.1371/journal.pone.0093507.t002

Context variables explained relatively little variance (25.7%) in biodiversity and ES recovery. Nevertheless, the improvement in biodiversity and ES due to restoration varied substantially for different wetland types: salt marshes (+104%), freshwater marshes (+73%), rivers (+100%), lakes (+45%), mangroves (+33%), and streams (+9%; Fig. S1 ).

Restoration significantly ameliorated all causes of degradation that we examined, except for the presence of invasive species ( Fig. S2 ). Seven of the 10 restoration actions reported by the included studies showed significant effects on biodiversity and ES supply ( Fig. S3 ), with habitat creation leading to the greatest benefit (+119%), followed by soil amendment and revegetation (+91%), and passive restoration in third place (+57%). Of all restoration actions examined, exotic species removal was associated with the lowest effect size, which did not achieve statistical significance. Restoration showed significant positive effects on biodiversity and ES recovery for the three types of experimental designs in the included studies: paired experiments (+61%), before-after experiments (+33%) and control-impact experiments (+22%; Fig. S4 ).

Context dependence of biodiversity and ES recovery: restored vs. natural wetlands

Comparison of restored and natural wetlands showed that restoration significantly improved recovery of biodiversity and ES supply ( Table 2 ), although as before, the final model explained only a fraction of the variance (15.2%). All restoration actions led to full recovery of biodiversity and ES supply except for soil amendment and revegetation, which led to −124% lower levels of biodiversity and ES supply than in natural wetlands; passive restoration, which led to −31% lower levels; manipulation of structural heterogeneity, −15%; and hydrological dynamics, −21% ( Fig. S3 ).

Our global meta-analysis, including70 studies conducted in 14 countries, shows that wetland restoration increased biodiversity in degraded wetlands, consistent with another global meta-analysis of different ecosystem types [9] . In fact, restoration increased the biodiversity of native organisms to levels similar to those in natural wetlands. To be sure, restoration did not improve biodiversity of all organisms uniformly. Restoration increased vertebrate diversity to levels above those in natural wetlands, though this result may only be transient, since vertebrate richness can vary substantially over time [17] . Conversely, restoration led to levels of biodiversity of non-native vascular plants lower than levels in natural wetlands. Both of these outcomes may reflect the large, persistent effects of exotic plants on the habitat structure, biodiversity and functioning of wetlands [5] . In addition, wetlands dominated by exotic, invasive plants tend to support fewer native animal species and more invasive animals [5] .

Greater diversity by itself is insufficient to ensure high ecosystem functioning [18] . Potentially even more important are the identities and relative proportions of species involved in the restoration process, as well as their ecological and functional properties. Unfortunately, most studies in our meta-analysis reported aggregate measures of richness or diversity but not community composition ( Supporting information S1 ). Indeed a previous meta-analysis of how restoration affects major groups of organisms was restricted to calculating aggregate results for three general categories of vertebrates, macroinvertebrates, and plants [7] . Higher taxonomic and functional resolution is needed to explore the potentially quite different effects of restoration on organisms that can differ even within a class like vertebrates. Therefore, restoration studies dealing with species composition, community structure and functional ecology are urgently needed.

Our meta-analysis showed that restoration enhanced ES supply in degraded wetlands. The results also showed that it is more difficult to recover ES supply than to recover biodiversity; an alternative or complementary interpretation is that full recovery of ES supply takes longer than full recovery of biodiversity. Either interpretation is consistent with the meta-analysis by Rey Benayas et al. [9] , but inconsistent with the analysis of North American wetlands by Dodds et al. [8] .

Restoration did not enhance ES uniformly across all individual ES examined. We observed that restored wetlands provided, on average, 36% higher levels of provisioning, regulating and supporting ES than did degraded wetlands, but similar levels of cultural services. To be sure, we did not expect uniform recovery of all individual ES, given the heterogeneity of ES and wetland types included in the meta-analysis; wetlands types are known to differ in ecological dynamics, recovery rates and extents of recovery [7] .

Our finding that restoration increased supply of provisioning services more than the supply of other ES may reflect the fact that, among the included studies, the desired outcomes when restoring provisioning services (e.g. abundance of target species) were generally better defined and more homogeneous than were objectives for regulating, supporting, and cultural services. Effect sizes for these last three services showed wide confidence intervals in our study, suggesting higher intra-class heterogeneity than effect sizes for provisioning services [12] . Small sample size may explain our finding that restoration did not significantly affect cultural services. Compared to natural wetlands, restored wetlands showed similar supply of provisioning and cultural services but lower supply of regulating services (mainly climate regulation, soil fertility and erosion) and supporting services (mainly biogeochemical cycles and provision of terrestrial habitat). The lower levels of climate and soil regulation, biological structure and biogeochemical cycles may reflect the intrinsically slow recovery rates reported for these surrogate variables [7] . In contrast, faster recovery rates have been reported for the water regulation variables in our study, such as hydrological dynamics and water quality, and these latter variables indeed showed full recovery.

Analysis of the ES database, which included abundance data on both non-native plant and animal species, showed that restoration increased regulation of non-native species by reducing their abundance. This result is different than our finding that restoration increased the diversity of such species, though it should be noted that the biodiversity database contained data on non-native plants but not non-native animals. The abundance of non-native species may decrease rapidly during the restoration process because these species are directly eradicated. However, a reduction in abundance, which reduces the supply of ES, does not necessarily indicate a decrease in species diversity, such as when a habitat contains several rare species in low abundance. Thus, assessment of restoration should take into account both abundance and diversity indicators.

Correlation of biodiversity and ES recovery

The relationship between biodiversity and ES supply remains poorly understood [19] , yet it is crucial to work out because it has significant implications not only for restoration science but also for wider society, economics, and policy [20] , [21] . Our results showed that changes in biodiversity positively correlated with changes in ES supply in a variety of wetlands, ecosystem types and scales, which supports a functional role for biodiversity in the supply of ES [7] , [9] . This positive relationship is good news for restoration efforts, as it demonstrates the possibility of win-win scenarios for restoring biodiversity and ES. However, such win-win gains have not always proven feasible in practice, especially in restoration projects involving geographically dispersed areas [22] . Future research should explore how to optimize the synergy between biodiversity and ES supply in the design of management and conservation programs involving restoration.

The relationship between biodiversity and ES is also important because it has consequences beyond ecosystem restoration. For example, increasing plant diversity has been shown to enhance the provision of goods from plants and the regulation of erosion, invasive species and pathogens [23] ; thus, recovering plant diversity may contribute to the recovery of ES beyond the immediate effects of restoration activities. Future research is needed to disentangle direct and indirect effects of restoration on biodiversity and ES, as well as clarify how the two types of effects interact.

Context dependence

Our meta-analysis identified several context factors that significantly affected biodiversity and ES recovery in restored wetlands, including ecosystem type, main cause of degradation, restoration action taken, and experimental design used to assess the restoration. This highlights the need to take context into account when evaluating the effects of wetland restoration. Particularly, examining interaction effects may generate useful insights, but the risk of multiple interactions, including two or even three factors, is too high for the relatively low statistical power of our model.

Our results also showed that biodiversity and ES recovery did not depend on restoration age. Nevertheless, they may depend on how long the restoration process took, on how many times a restoration action was repeated and on the conditions of the degraded wetland prior to restoration. Unfortunately most of the studies included in our meta-analysis did not report such data. The type and duration of interventions required in restoration depend heavily on the type and extent of ecosystem damage [24] . Future research should examine these context factors in greater detail.

Our finding that restoration effects depended on ecosystem type is consistent with an earlier meta-analysis showing that wetlands with more hydrologic flow exchange recovered faster than those that did not receive external water flow [7] . We obtained different results showing that outcomes of restoration were unrelated to flow exchange, e.g. biodiversity and ES in rivers and streams were enhanced in very different amounts. Despite these differences, the available evidence strongly indicates that the effectiveness of restoration is habitat-specific, arguing for the need for more research into how to tailor restoration projects to particular environments and how to assess their outcomes accordingly [6] .

Our meta-analysis showed that only restoration action determined how close the biodiversity and ES supply of restored wetlands approached those of natural wetlands. This finding implies that unless the correct restoration action is chosen from the beginning, which is often impossible, the restored wetland may not come as close as possible to natural conditions. Applying a combination of restoration actions may therefore improve the likelihood of success.

Taken together, the results of our mixed models suggest that comparisons of degraded, restored, and reference conditions should be carried out to guide and evaluate restoration based on multiple indicators of both biodiversity and ES. These indicators should be consistent with the specific restoration goals [25] , which can vary greatly depending on the context and project [26] . Our models further suggest that restoration programs should involve multiple actions to improve the likelihood of success.

Implications for wetland restoration

Comparing degraded, restored and reference conditions to guide restoration may not be feasible in many cases because the irreversibility of much of man-made ecosystem damage makes it difficult to simulate the pre-degradation condition accurately [27] , and because movement of restored wetlands away from reference conditions makes it difficult to project desired outcomes [7] , but it should be advisable. This highlights the need for designing restoration programs with multiple, alternative goals in mind [27] , [28] . These goals should take into account the social context and human values associated with decisions about wetland management and restoration. The concept of ES can be a robust guide for wetland restoration decision-making because it identifies and quantifies valuable goods and describes the processes and components that provide essential services [29] . Since several ES are difficult to measure directly, surrogate measures of ecosystem function can be used instead [30] .

Accurately assessing the impact of restoration on biodiversity and ES supply requires identifying the particular ecosystem attributes in need of restoration. To capture potential differences in the restoration of individual ES, we linked the response variables to ES based on specific measures routinely included in ecological studies [31] . In addition, we evaluated the effects of response variables on multiple ES, since the variables may have indirect or unclear links to several ES that significantly affect restoration outcomes. For instance, although all plant species capture carbon, thereby increasing the supply of one ES, non-native species may have detrimental effects on other ES such as biotic interactions. A single restoration action may simultaneously affect various ES or act synergistically as a ‘cascade’ across trophic levels [14] . A restoration action may enhance the supply of one ES while precluding the supply of another [32] , or it may generate a disservice, such as the release of greenhouse gases. Therefore, analyses of restoration data should assess both the direction and magnitude of associations between response variables and individual ES [14] . Taking into account the multiple ES associated with a restoration action facilitates the identification of tradeoffs or compromises when planning wetland restoration in which the overriding goal is optimizing multiple ES [29] .

Cost plays an important role in restoration planning because it may limit the desired outcomes [33] , [34] . Surprisingly, the studies included in our meta-analysis did not address the issue of restoration costs. Costs are an important factor not only during restoration but also after: monitoring of wetlands following their restoration, mitigation or creation is often too brief because it is expensive to evaluate all the ecosystem functions involved.

These elements define a complex scenario for decision makers. Key to guiding decisions will be a systematic account of the relationships between wetland restoration variables and the supply of individual ES, for which the evidence base needs to be expanded. Indeed the low positive correlation between the recovery of biodiversity and ES suggests that reliable modeling of restoration outcomes will require incorporating multiple indicators that capture biodiversity, ES supply, and ecosystem processes. Such indicators should also include performance indicators that describe how much of available ES can be exploited [19] , since biodiversity-related ES, for example, vary over time and space and are species-dependent. This poses a challenge for model-building, since simple models for simultaneously maximizing biodiversity and ES are unrealistic or ambitious [35] , such that the two variables are not necessarily maximized in the same wetland [6] . The model that we have developed here may provide a basis for future studies that optimize biodiversity and ES supply for specific habitats and contexts.

Conclusions

Our meta-analysis strongly supports the idea that ecological restoration increases both biodiversity and ES supply in degraded wetlands, thereby benefiting the human communities that interact with and depend on them. The detailed effects of restoration depend heavily on context factors, emphasizing the need for habitat-specific planning and assessment of restorations [6] . Questions posed years ago remain largely unanswered today, such as “To what extent and over what time scale can ES be restored? [36] and “To what extent can mankind substitute for ES?” [37] . While restoration ecology is not obliged to answer these questions, exploring them may help improve the flows of ES and improve human well-being. Addressing these questions will require deepening our understanding of the links between restoration actions and changes in biophysical and ecological processes that generate ES [30] . While such research should inform and improve growing efforts to restore and mitigate loss of wetland area and loss of wetland ecosystem functions [35] , they should not take importance away from efforts to conserve natural wetlands and avoid environmental degradation in the first place [8] , [9] .

Supporting Information

Checklist s1..

https://doi.org/10.1371/journal.pone.0093507.s001

Supporting information S1.

Studies used in the meta-analysis.

https://doi.org/10.1371/journal.pone.0093507.s002

Supporting information S2.

Additional information on database building.

https://doi.org/10.1371/journal.pone.0093507.s003

Supporting information S3.

Methodological details of the meta-analysis.

https://doi.org/10.1371/journal.pone.0093507.s004

Supporting information S4.

Overview of studies included in the meta-analysis.

https://doi.org/10.1371/journal.pone.0093507.s005

Restoration effects by ecosystem type.

https://doi.org/10.1371/journal.pone.0093507.s006

Restoration effects by main degrading factor.

https://doi.org/10.1371/journal.pone.0093507.s007

Restoration effects by restoration action.

https://doi.org/10.1371/journal.pone.0093507.s008

Restoration effects by experimental design.

https://doi.org/10.1371/journal.pone.0093507.s009

PRISMA 2009 Flow Diagram.

https://doi.org/10.1371/journal.pone.0093507.s010

Linked databases used in the meta-analysis of biodiversity and ES.

https://doi.org/10.1371/journal.pone.0093507.s011

Comparison of biodiversity meta-analyses using a reduced or complete database.

https://doi.org/10.1371/journal.pone.0093507.s012

Acknowledgments

We thank all authors who provided their original data to develop this work. R. Aguilar and N. Mariano provided suggestions to improve the analyses. S. Quijas provided helped to improve the figures of this ms. We are indebted to A. Chapin Rodríguez, who greatly improved the presentation of a previous version of this manuscript.

Author Contributions

Conceived and designed the experiments: JMRB PM. Performed the experiments: PM. Analyzed the data: PM JMRB PB MMR. Contributed reagents/materials/analysis tools: PM JMRB PB MMR. Wrote the paper: PM JMRB PB MMR.

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REVIEW article

Effects of biodiversity on functional stability of freshwater wetlands: a systematic review.

• 1 State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
• 2 University of Chinese Academy of Sciences, Beijing, China

Freshwater wetlands are the wetland ecosystems surrounded by freshwater, which are at the interface of terrestrial and freshwater ecosystems, and are rich in ecological composition and function. Biodiversity in freshwater wetlands plays a key role in maintaining the stability of their habitat functions. Due to anthropogenic interference and global change, the biodiversity of freshwater wetlands decreases, which in turn destroys the habitat function of freshwater wetlands and leads to serious degradation of wetlands. An in-depth understanding of the effects of biodiversity on the stability of habitat function and its regulation in freshwater wetlands is crucial for wetland conservation. Therefore, this paper reviews the environmental drivers of habitat function stability in freshwater wetlands, explores the effects of plant diversity and microbial diversity on habitat function stability, reveals the impacts and mechanisms of habitat changes on biodiversity, and further proposes an outlook for freshwater wetland research. This paper provides an important reference for freshwater wetland conservation and its habitat function enhancement.

1 Introduction

Freshwater wetlands (FWs) are ecosystems formed by the interaction between freshwater rivers, lakes and land, mainly including riverine wetlands, lakes, marshes and floodplains. FWs not only provide suitable habitats for many plants and animals ( McKown et al., 2021 ), but also play an important role in nutrient cycling, water purification and biodiversity maintenance ( Li C. et al., 2022 ; Yu et al., 2023 ; Li et al., 2024 ). FWs have four the ecological services categories: provisioning, regulating, cultural and supporting services ( Keddy et al., 2009 ). However, FWs have been severely damaged due to the increase in global population and economic development, resulting in a decrease in the global wetland area ( Davidson, 2014 ), and a consequent severe destruction of wetland functions and biodiversity ( Herbert et al., 2015 ; Ndehedehe et al., 2020 ).

Biodiversity is a complex system formed by the interaction between organisms and the external environment, expressing in genetic diversity, species diversity, and ecosystem diversity ( Song, 2017 ; Liang et al., 2023 ). Habitat function refers to the specific functions and conditions providing for organisms, and many studies have shown that biodiversity plays a crucial role in habitat function and its stability ( Weisser et al., 2017 ; Yao et al., 2017 ). FWs are complex ecosystems composed of special environmental conditions and organisms, and their functional stability is affected by many factors ( Rideout et al., 2022 ). In FWs, high biodiversity can enhance the stability of wetland functions, such as nutrient cycling, water purification, and biodiversity maintenance ( Thomaz, 2023 ). Rich diversity can alleviate competitive pressures among organisms by providing more ecological niches through complementary effects, allowing different species in FWs to fully utilize resources such as water, nutrients and sunlight ( Steudel et al., 2011 ). In addition, biodiversity can also improve the stability and disturbance resistance of food chains, mitigating external disturbances in wetlands by building complex foodweb structures ( Peel et al., 2019 ; Hatton et al., 2024 ).

Although many studies showed that the biodiversity of FWs has an important impact on the functional stability of the habitats in which they exist, few literatures have been reviewed and summarized. Therefore, the objectives of this study are to (1) analyze the effects of biodiversity on the functional stability of freshwater wetland habitats; (2) illuminate the impacts and mechanisms of habitat change on biodiversity; and (3) propose future research directions and perspectives. This paper synthesizes the environmental drivers of functional stability in FWs, the effects of plant and microbial diversity on the functional stability of FWs, and further discusses the effects and mechanisms of habitat change on biodiversity.

2 Environmental drivers of functional stability in freshwater wetlands

Freshwater wetlands provide numerous functions such as biodiversity maintenance, freshwater supply, carbon storage, etc., and at the same time they are one of the most fragile ecosystems ( Zedler and Kercher, 2005 ). Changes in environmental drivers such as hydrological factors, climatic factors, water quality, and soil physicochemical properties have led to serious functional degradation of some wetlands ( Xue et al., 2018 ; Xiu et al., 2019 ). Therefore, understanding the effects of these environmental drivers on freshwater wetland ecosystems ( Table 1 ) is important for improving the functional stability of FWs and optimizing wetland management options.

Table 1 . Effects of environmental drivers on freshwater wetland ecosystems.

2.1 Hydrology

Water plays a crucial role in the formation, development, succession, and extinction of wetlands, directly affecting their structure, function, and ecosystem stability ( Wang et al., 2015 ). Human activities and climate change cause changes in precipitation, evapotranspiration, and temperature, which lead to changes in hydrological conditions such as water-holding capacity, water level, and inundation duration of wetlands ( Karim et al., 2015 ). Changes in these hydrological characteristics in turn affect the structure, distribution ( Todd et al., 2010 ; Maietta et al., 2020a ) and biogeochemical cycling ( Chen et al., 2013 ) of biological communities in FWs, leading to degradation of wetland ecosystem functions.

An increase in water loss from FWs leads to hydrological conditions variation and a decrease in available water resources, which can disrupt their freshwater supply ( Zhao and Liu, 2016 ). Hydrological changes can also affect the structure, distribution and biogeochemical cycling of freshwater wetland biological communities, which in turn can degrade wetland ecosystems ( Chen et al., 2013 ; Maietta et al., 2020a ). Large fluctuations in water level can affect the structure and diversity of biological communities ( Luo, 2009 ). During periods of low water levels in the Paraná River delta, the beta diversity and individual biomass of zooplankton decreases, leading to a simplification of the functional diversity ( Gutierrez et al., 2022 ) and a degradation of the wetland environment that sustains aquatic vegetation in Lake Michigan-Lake Huron ( DeVries-Zimmerman et al., 2021 ), whereas high water levels have led to a decrease in vegetation cover in Lake Ontario ( Smith et al., 2021 ), resulting in habitat loss and the frustration of the supply functions of FWs. Overall, water level with too low or high is not conducive to wetland ecosystems. Soil water content, aeration conditions and redox potential also change with fluctuations in wetland water level, affecting the ecological processes and metabolic activities of microbial communities ( Ma et al., 2018 ). Therefore, the relative stability of water level plays an important role in maintaining the functional stability of FWs.

2.2 Water quality and soil properties

Humans production and life discharge heavy metals ( Li et al., 2021 ), pesticides and nutrient salts ( Sremacki et al., 2020 ; Ding et al., 2021 ) into freshwater wetland ecosystems, directly and indirectly leading to changes in water quality and soil physicochemical properties of wetlands, which in turn cause wetland degradation ( Wei et al., 2019 ). Relevant studies have shown that increased loading of nutrients such as nitrogen and phosphorus in water will deteriorate water quality, and cause eutrophication of the water body, leading to significant changes in the structure and function of wetland ecosystems ( Khan and Ansari, 2005 ; Bano et al., 2022 ). It has been found that increased loading of nitrogen and phosphorus in FWs may affect the rates of nitrification, denitrification, and methane production, which in turn affects the nutrient cycling ( Herbert et al., 2020 ). Soil physicochemical properties are key factors in shaping microbial community structure, composition, and metabolic activity ( Ou et al., 2019 ). Changes in soil physicochemical properties caused by human disturbances and natural processes likewise have serious impacts on freshwater wetland biological communities ( Lai, 2010 ).

2.3 Temperature

Temperature is recognized as one of the key climatic factors influencing the functional stability of FWs ( Bano et al., 2022 ). Changes in temperature can have pervasive effects on the structure and function of freshwater wetland ecosystems ( Hamilton, 2010 ). Wetland plant growth and photosynthesis efficiency increase with increasing temperatures within a certain range, increasing nutrient uptake and conversion ( Zou et al., 2014 ). However, excessively high temperatures may reduce the germination of plant seeds and incubation of animals, which can have serious effects on wetland plant and microbial communities, disrupting wetland biodiversity ( Nielsen et al., 2015 ). Temperature changes can also have an impact on microbial metabolism, for example, the role of iron-reducing bacteria in inhibiting methane production may diminish as the global average temperature increases, thus affecting greenhouse gas emissions from FWs. In addition, temperature changes may also lead to species migration and range shifts ( Chen X. et al., 2023 ).

The hydrological conditions of wetlands are closely related to temperature changes, and global warming will lead to changes in evaporation and precipitation, which may alter the hydrological cycle of wetlands and thus indirectly affect the functional stability of wetlands ( Luo, 2009 ; You et al., 2015 ). A previous study showed that a 10% decrease in rainfall will lead to changes in the redox conditions of the soil in the Everglades, thus affecting its biogeochemical processes; whereas the elemental load of the wetland ecosystem may increase when rainfall increases by 10%, which helps to maintain suitable redox conditions and promotes biogeochemical elemental cycling ( Orem et al., 2015 ).

3 Impact of plant diversity on functional stability of freshwater wetlands

Freshwater wetlands are rich in plant species, which play multiple roles in wetland ecosystems ( Figure 1A ). Different types of wetlands have different dominant vegetation, and diverse plants play an important role in maintaining the stability of wetland habitat functions (e.g., water purification, carbon storage, biodiversity maintenance, etc.) ( Zhang et al., 2014 ).

Figure 1 . Role (A) and pollutant removal (B) of wetland plants.

3.1 Water purification

Removal of pollutants by wetlands plants is one of the main ways of water quality purification, mainly through two main pathways involving in direct pollutants removal and microbial processes mediating ( Figure 1B ; Stottmeister et al., 2003 ). The uptake of nutrients and heavy metals varies among different plant species ( Adhikari et al., 2011 ; Abbasi et al., 2018 ). The study showed that the nitrogen uptake and fixation capacity of Rhododendron ilfescens Siberianum was higher, and the remediation of nitrogen pollution in wetlands was more effective ( Weragoda et al., 2012 ). In addition, the dissolved oxygen in the water were affected by the abundance of submerged plant species ( Qian, 2019 ), and different plants had different inter-roots, physiological processes, and growth modes, which might affect the community structure and activity of microorganisms, and further affect water quality purification ( Zhang et al., 2010 ; Pang et al., 2016 ). Resource complementarity between plant species may also play a positive role in nutrient uptake and water purification ( Choudhury et al., 2018 ). Therefore, maintaining high plant diversity can help to improve pollutants removal from water ( Brisson et al., 2020 ).

3.2 Carbon storage

Freshwater wetlands are one of the valuable carbon storage sites, covering about 6% of the land area, and contain more than 30% of the soil carbon pool ( Stewart et al., 2024 ). Plants play an important role in wetland carbon storage ( Sheng et al., 2021 ). Wetland plants can convert atmospheric carbon dioxide into biomass through photosynthesis, and plant residues and leaves are deposited at wetland after death, which is one of the main mechanisms of carbon storage in wetlands ( Adhikari et al., 2009 ). Previous studies have shown that the plants vary in nutrient and light utilization ( Abbasi et al., 2018 ). Plant diversity has an important effect on freshwater wetland productivity ( Isbell et al., 2013 ; Chaturvedi and Raghubanshi, 2015 ). Means et al. (2016) found a positive correlation between plant diversity and productivity in freshwater artificial wetlands. Cardinale et al. (2011) found that high diversity plant communities can use more ecological niches and increase the efficiency of nutrient utilization, which in turn increases primary productivity. An increase in wetland productivity can increase the capacity and total amount of carbon input from plants to the soil, which in turn increases carbon storage ( Zhang et al., 2022 ).

In addition, the decomposition mode (humification and mineralization) and rate of plant apoplasts are particularly important for wetland carbon storage ( Prescott and Vesterdal, 2021 ). Litter from different types of plants has different chemical compositions ( Yan et al., 2018 ) and decomposition rates ( Xi et al., 2023 ). It has been shown that the litter of freshwater wetland vegetation has the ability to alter the nutrient content of soil nitrogen and carbon, thus leading to the construction of different dominant microorganisms ( Bonetti et al., 2021 ). Some plant litter leads to the production of microbial communities of humification, while others lead to the construction of microbial communities of carbon dioxide or methane production ( Lin et al., 2015 ). Increased plant diversity can provide a wider variety of little, and this little can lead to the construction of more stable and resilient microbial communities, affecting the carbon storage capacity of the wetland ( Maietta et al., 2020b ).

3.3 Biodiversity maintenance

Plants can create unique microhabitat structures and provide suitable conditions for many animals and microorganisms ( Choi et al., 2014 ; Weilhoefer et al., 2017 ). Freshwater wetland plants serve as the basis of the food chain in this ecosystem, and rich wetland plant communities provide a more complex and stable food web that supports the nutrient needs of many animals and microorganisms, thus contributing to the maintenance of biodiversity ( Peel et al., 2019 ). In addition, higher plant diversity improves the resistance of wetland ecosystems to invasive alien species and better defends against invasive alien species, thus maintaining the stability of other organisms within the wetland ( Peter and Burdick, 2010 ). Therefore, the protection and maintenance of plant diversity in FWs is essential for maintaining wetland biodiversity.

4 Impact of microbial diversity on functional stability of freshwater wetlands

Microorganisms in FWs are rich and diverse, with some differences in microbial composition among different wetland types, which can be mainly categorized into bacteria, archaea, fungi and protozoa ( Cao et al., 2017 ). Microorganisms play an irreplaceable role in maintaining the stability of freshwater wetland habitat functions (e.g., water purification and biogeochemical cycles, etc.) ( De Mandal et al., 2020 ; Chen M. et al., 2023 ; Qiao et al., 2023 ; Chen et al., 2024 ).

4.1 Water purification

Microorganisms can participate in various water purification processes through a series of metabolic and interaction processes, especially some functional microorganisms play a crucial role in wetland water purification ( Wang et al., 2022 ). For example, some inter-root microorganisms such as Pseudomonas and Flavobacterium can effectively remove micropollutants ( Brunhoferova et al., 2022 ). Fusobacterium , Rhizobium and Erythrobacterium have significant removal effects on organic pollutants such as petroleum in wetlands, and their removal rates are positively correlated with the abundance of bacterial species ( Xiang et al., 2020 ). Burkholderia , Hydrophilus , and Thiobacillus play important roles in the remediation of arsenic and antimony pollution in wetlands ( Deng et al., 2022 ).

The areas riched in wetland microbial diversity usually have higher degradation capacity of organic pollutants, and different microbial communities can co-operate together to decompose complex organic matter and convert it into harmless products ( Berrier et al., 2022 ). Studies have shown that hydrocarbon-degrading microorganisms (e.g., Pseudomonas , Rhodococcus , and Nocardia ) in FWs can form microbial aggregates, improving the removal efficiency of n-alkanes and polycyclic aromatic hydrocarbons (PAHs) ( Liu et al., 2021 ). Anaerobic ammonia-oxidizing bacteria in wetlands can cooperate with certain archaea (e.g., nitrate archaea and sulfate-dependent archaea) to complete the denitrification process in wetlands ( Wang et al., 2019 ). In addition, some microorganisms can remove multiple pollutants simultaneously. For example, Flavobacterium and Chryseobacterium can simultaneously degrade nitrogen and organic matter in wetlands ( Shen et al., 2018 ). Sulfate-reducing bacteria, such as Desulfovibrio , Desulfobacter , and Desulfobulbus , also play dual roles in wetland restoration: (1) participating in the sulfate reduction process, producing hydrogen sulfide; (2) hydrogen sulfide reacts with heavy metals to form precipitation, which promotes the passivation of heavy metals ( Chen et al., 2021 ).

4.2 Biogeochemical cycles

Wetland microorganisms are involved in the process of storage, transformation and release of C, N and other elements, and are the dominant driver of the biogeochemical cycle in FWs ( Hussain et al., 2023 ).

The biogeochemical cycle of carbon in FWs has received much attention ( Zou et al., 2022 ; Bao et al., 2023 ; Qian et al., 2023 ), and microorganisms are mainly involved in the carbon cycle through the processes of respiration, methane production and conversion, and decomposition of organic matter ( Bardgett et al., 2008 ). Microorganisms play an important role in methane production and transformation of FWs ( Figure 2 ). It is now widely accepted that methanogenic bacteria are distributed in seven orders of the phylum Euryarchaeota (Methanopyrales, Methanococcales, Methanobacteriales, Methanomicrobiales, Methanomassiliicoccales, Methanosarcinales, and Methanocellales) ( Dean et al., 2018 ). Among them, Methanomicrobiales, Methanosarcinales, Methanomassiliicoccales, and Methanobacteriaceae methanogenic bacteria have widely found in wetland ecosystems ( Horn Marcus et al., 2003 ; Zhang et al., 2008 ; Söllinger et al., 2016 ). There are three main pathways of freshwater wetland methanogens involved in methanogenesis: acetate fermentation, hydrogenotrophic and methylotrophic methanogenesis ( Narrowe et al., 2019 ), whereas wetland methane oxidation is of two types: aerobic and anaerobic oxidation. The diverse microorganisms can adapt to the different environmental conditions and can better maintain the balance of wetland methane production and conversion. It was found that the microbial community can change the methanogenic pathway by adjusting the composition and activity of the microbial community under the fluctuation of nutrients, and then maintaining the stability of carbon cycle ( Holmes et al., 2014 ). In addition, the richness of microbial diversity in FWs is closely related to the rate of mineralization of organic matter, and an active microbial community can increase organic matter degradation and mineralization ( Li et al., 2015 ).

Figure 2 . Methane production and transformation by wetland microorganisms.

Microorganisms in FWs are also critical for maintaining the relative stability of the nitrogen cycle, and diverse microorganisms are an important player in driving nitrogen conversion and its cycling processes ( Mellado and Vera, 2021 ; Sheng et al., 2023 ). Microorganisms such as nitrogen-fixing bacteria and cyanobacteria can convert atmospheric N 2 into bioavailable forms such as ammonia and nitrate, supplying the wetland ecosystem with available nitrogen ( Bae et al., 2018 ). It has been found that the efficiency and rate of nitrogen fixation are usually positively correlated with the number and diversity of microorganisms such as nitrogen-fixing bacteria ( Li H. et al., 2022 ). On the other hand, some microorganisms (e.g., anaerobic ammonia-oxidizing bacteria, ammonia-oxidizing archaea, and denitrifying anaerobic methane-oxidizing bacteria) are also present in FWs, involved in key nitrogen transformation processes such as ammonia oxidation, nitrification and denitrification ( Chen et al., 2020 ). These microorganisms differ in their tolerance and sensitivity to environmental factors, and a high diversity of microorganisms can provide different kinds of microbial functional groups, improving the adaptability and stability of FWs to environmental changes and maintaining the relative stability of the nitrogen cycle ( Hu et al., 2017 ).

5 Impacts and mechanisms of habitat change on biodiversity

Wetlands provide habitat for nearly 20% of the world’s species and are one of the most biodiversity-rich systems, however, they are under great pressure from human activities and climate change ( Fang et al., 2006 ). This is causing a large degree of degradation of FWs and affecting the biodiversity of ecosystems ( Al-Obaid et al., 2017 ). Habitat changes have important effects on wetlands ( Figure 3 ). Among these, habitat changes and alterations in food chains and interspecific relationships are the two main factors ( Ohba et al., 2019 ; Wang et al., 2021 ).

Figure 3 . Impacts of habitat change on biodiversity in FWs.

Habitat loss and fragmentation can result in the reduction and fragmentation of freshwater wetland areas, weakening the available area and connectivity of habitats for species, and these can directly lead to the reduction of the number and distribution range of some species, and consequently the decline of biodiversity ( Jamin et al., 2020 ). For example, the size and connectivity of wetlands in Xin Jiang Wan Town, Shanghai, decreased with the accelerated urbanization of the area, leading to habitat loss and diversity reduction of wetland birds ( Xu et al., 2018 ). Vascular plants in the wetlands of the canton of Zurich in eastern Switzerland became extinct as a result of the reduction of wetland connectivity and patch size under human activities ( Jamin et al., 2020 ). In addition, the movement and migration of amphibians are limited when wetlands are fragmented, which may lead to the delayed extinction of these species ( Gimmi et al., 2011 ).

Habitat change also affects wetland biodiversity by altering wetland food chains and interspecific relationships ( Araújo et al., 2014 ). Previous studies have found that species richness of insectivorous birds in the Lampertheimer Altrhein area has decreased, due to the reducing food resources for insectivorous birds under agricultural intensification ( Schrauth and Wink, 2018 ). The reduction in species richness and cover of plant communities during the degradation of the Ruoerge wetland has led to changes in the trophic structure of omnivores and algae, which in turn had a serious impact on the diversity of nematode communities ( Wu et al., 2017 ). In addition, biological invasions are recognized as one of the main drivers of biodiversity loss ( Mazor et al., 2018 ). Habitat changes can promote the invasion and spread of non-native species (e.g., Spartina alterniflora ), and these invasive species can disrupt the original food chains and interspecific relationships of ecosystems, thus leading to biodiversity reduction ( Wang et al., 2021 ).

In addition, changes in environmental factors such as wetland water level and pollution have significant impacts on biodiversity. For example, during the degradation of wet marshes to meadows in the Sanjiang Plain, changes in wetland water level alter the living conditions of organisms, which in turn affects the diversity and community composition of plants and microorganisms ( Sui et al., 2017 ; Liping et al., 2020 ). The overuse of herbicides and pesticides in agricultural production activities has caused severe pollution of the Infranz wetlands in north-west Ethiopia, adversely affecting their biodiversity ( Eneyew and Assefa, 2021 ).

6 Future prospects

Freshwater wetlands with high biodiversity play an extremely important role in maintaining the functional stability of wetland habitats. Many environmental drivers such as water level, water quality, soil properties, temperature, and biological drivers (e.g., plant/microbial diversity) have important impacts on the functional stability of freshwater wetland ecosystems, but many in-depth studies are needed in the following aspects in the future:

1. Changes in biodiversity can directly or indirectly regulate ecosystem processes, and biodiversity is the main determinant of maintaining ecosystem functional stability. Therefore, it is of great significance to investigate the relationship between biodiversity and functional stability. Nowadays, most studies on the functional stability and biodiversity of freshwater wetland have focused on small-scale scales and homogeneous habitats, ignoring the effects of spatial and temporal scales and environmental heterogeneity. Therefore, the study on the multi-scale integration and relationship between biodiversity and functional stability at different scales is important. This will help maintain the stability of freshwater ecosystems and provide theoretical support for the conservation of FWs.

2. Many studies are about the response of habitat function to environmental and biological elements in the context of global change. Most studies agreed that high levels of biodiversity can better maintain the stability of habitat function. In addition, changes in environmental factors can indirectly affect ecosystem habitat function through biodiversity. Therefore, future research needs to focus on the mechanisms by which environmental and biological factors drive habitat function enhancement through community composition, species diversity, environmental heterogeneity and biological interactions.

7 Conclusion

Freshwater wetlands are one of the most biodiverse ecosystems, and abundant species has a significant impact on the habitat function of FWs. Many environmental factors are changing under global change and human activities, and these changes can either directly affect the stability of wetland habitat functions or indirectly affect habitat functions by altering the biodiversity of FWs. Our study analyzes the roles of environmental drivers maintaining the stability of wetland habitat functions, such as hydrology, temperature, and water quality, discusses the impacts of plant and microbial diversity on the functional stability of FWs, and further reveals the impacts and mechanisms of habitat changes on biodiversity. In general, biodiversity can promote the stability of habitat functions in FWs. However, most studies focus on small-scale scales and homogeneous habitats. Therefore, future studies on biodiversity and stability of habitat functions in FWs at large scales and non-homogeneous habitats still need to be further explored.

Author contributions

AS: Writing – original draft. SL: Data curation, Software, Validation, Writing – review & editing. HL: Conceptualization, Project administration, Supervision, Writing – review & editing. BY: Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was financially supported by the National Key R&D Program of China (no. 2022YFF1300901), National Natural Science Foundation of China (no. 42077353), and Natural Science Foundation of Jilin Province (no. 20230101100JC).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: biodiversity, habitat functional stability, freshwater wetlands, habitat change, impact mechanisms

Citation: Song A, Liang S, Li H and Yan B (2024) Effects of biodiversity on functional stability of freshwater wetlands: a systematic review. Front. Microbiol . 15:1397683. doi: 10.3389/fmicb.2024.1397683

Received: 08 March 2024; Accepted: 27 March 2024; Published: 08 April 2024.

Reviewed by:

Copyright © 2024 Song, Liang, Li and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Huai Li, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 06 June 2019

Bird diversity and waterbird habitat preferences in relation to wetland restoration at Dianchi Lake, south-west China

• Kang Luo 1 , 2 , 3 , 4 ,
• Zhaolu Wu   ORCID: orcid.org/0000-0002-8182-2086 2 ,
• Haotian Bai 2 &
• Zijiang Wang 2  

Avian Research volume  10 , Article number:  21 ( 2019 ) Cite this article

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Restoration projects have been implemented worldwide to mitigate the adverse effects of the loss and degradation of wetland habitats. Much research has been carried out on the impacts on birds of wetland restoration and management projects in China. Studies have mainly investigated central or coastal wetlands, while inland wetlands in remote areas have been much less studied. We focused on examining the response of wild birds to wetland restoration in Dianchi Lake, south-west China.

The line transect method was performed at 26 sampling plots. Three of these were in the city, and to acquire all wild bird data 23 plots were located every 2–8 km along the shore of Dianchi Lake, between December 2011 and November 2013. We collected all related bird records by searching the available literature, articles, newspapers and records of birdwatchers to compare species variation before and after implementation of wetland restoration. To measure the relationships between waterbird assemblages and habitat structures, we used canonical correspondence analysis (CCA) to pair the main matrix of bird assemblages with a second matrix of habitat variables.

We recorded 182 bird species belonging to 51 families and 17 orders. Of the species, 42 were new records for Kunming City and 20 were new records for Yunnan Province. Ten waterbird species were found to have disappeared from the shore of Dianchi Lake. CCA results indicated that waterbirds could be divided into four categories based on their habitat preference: synanthropic (wintering gulls), special habitat (shorebirds), semi-natural (wintering coots and ducks) and disturbance-tolerant (resident) species.

Conclusions

Our study is the first to consider the entire wild bird community throughout the year and discuss the species variation before and after wetland restoration projects launched for Dianchi Lake. Distinct habitat requirements of different waterbird groups were detected in our study, suggesting different types of restoration and management should be implemented.

Wetlands harbor highly diverse biological communities and provide extensive ecosystem services such as water purification, flood abatement and climate regulation (Zedler and Kercher 2005 ). However, they are frequently degraded and destroyed. It was estimated that over 50% of total wetland surface was lost during the last century (Mitsch and Gosselink 2007 ). Consequently, declines in wetland-dependent species have been some of the greatest recorded (Sievers et al. 2018 ), and wetland birds in particular are sensitive indicators of wetland conditions. Wetlands in urban settings fulfill additional environmental and social needs, which include storm-water retention of runoff from impervious surfaces, as well as removing pollutants and waste from water. Urban wetlands also provide extended recreational opportunities and visual aesthetics. The economic benefits include potentially reducing infrastructure costs, due to their ability to act as storm-water retention areas (Asomani-Boateng 2019 ). Unlike rural wetlands, urban wetlands are subject to urban development pressures, resulting in profound and extensive damage, loss, and degradation (Zedler and Leach 1998 ; Ehrenfeld 2000 ). The effects of urbanisation on bird diversity may be mitigated by the presence of wetlands, which may provide enhanced habitat and increase resource availability. Nevertheless, urbanization is one of the main driving factors in the degradation of natural wetlands (Russi et al. 2013 ). Wetland restoration projects are believed to be beneficial to storm water treatment or public amenities, but are also expected to compensate for bird habitat loss as natural wetlands decline (Zedler 2000 ; Mao et al. 2019 ). Restoration projects have been implemented worldwide to mitigate adverse effects resulting from the loss and degradation of wetland habitats (Pethick 2002 ; Nakamura et al. 2006 ). It is important, therefore, to understand the habitat requirements of birds, and to assess the suitability of habitats for birds, when restoring wetlands (Ma et al. 2010 ; Terörde and Turpie 2013 ).

In China, there has been serious wetland degradation due to urbanization and other anthropogenic threats during the last six decades. Fortunately, large numbers of wetland reserves have been established, and a wide range of management and restoration projects have been implemented in both inland and coastal areas since 1980 (Xu et al. 1999 ; An et al. 2007 ). Nevertheless, most useful research on the impacts of wetland restoration or management projects on birds in wetland reserves has considered affluent and developed areas, namely eastern or coastal China (such as Chongming Island, Ma et al. 2002 ; Yellow River Delta, Li et al. 2011 and Hua et al. 2012 ; Dongting River, Yuan et al. 2014 ; Mai Po, Wei et al. 2018 ). While coastal wetlands have been identified as important habitat for more than 230 species of waterbirds (Cao and Fox 2009 ), inland wetlands located in remote areas are much less studied (Wang et al. 2018 ). Many of these do not even appear in the Ramsar List of Wetlands of International Importance.

Wetlands and lakes in Yunnan Plateau, south-west China, are important habitats for waterbirds (Chen 1998 ; Cui et al. 2014 ). Most studies have a seasonal focus on wintering waterbirds in Lashihai (Quan et al. 2002 ), Napahai and Bitahai wetlands (Han et al. 2009 ; Li and Sun 2014 ), Luguhu Lake (Li and Yang 2015 ), Qionghai Lake (Hu et al. 2015 ) and Dianchi Lake (Wang et al. 2006 ; Wu et al. 2008 , 2009 ; Han et al. 2012 ). Few studies have documented diversity of all wild birds throughout the year (Han et al. 2014 ).

Dianchi Lake, a vital shallow lake next to Kunming City, the political and cultural center of Yunnan Province, has been greatly affected by human disturbance and has undergone severe degradation since the 1980 s. The implementation of an ambitious large-scale ecological restoration project, the “Kunming Urban Master Plan (2008‒2020)”, began along the shore of Dianchi Lake in 2008. Most researches about Dian Lake focus on the water quality recovery, while there is little information about how birds are responding to the restoration of Dianchi Lake wetlands (but see Wang et al. 2016 ). The aims of the present study were to: (1) record the entire wild bird composition; (2) identify differences in species occurrences between historical and current data; and (3) analyze waterbird habitat preference with respect to wetland restoration. Finally, we discuss waterbird response, in a general sense, to wetland restoration under conditions of urbanization.

Dianchi Lake (24°40′‒25°20′N, 102°36′‒102°47′E) is an ancient tectonic lake located in the Yunnan-Guizhou Plateau in the Yangtze River Basin (Fig.  1 a, b; Xiang 2014 ; Ma and Wang 2015 ). With an area of 308.6 km 2 , it is the sixth largest freshwater lake in China and the largest on the plateau. It is separated into two parts by a semi-artificial dam: the northern part, Caohai, has a water area of 10.7 km 2 and a mean water depth of 2.5 m; the southern part, Waihai, has a water area of 297.9 km 2 and a mean water depth of 4.3 m. The regional climate is a subtropical humid monsoon type, with a mean temperature of 14.4 °C, mean annual precipitation of 1036.1 mm and 227 frost-free days per year (Wang and Dou 1998 ). More than 20 main streams comprise the Dianchi Lake watershed, and the lake’s stable water level of 1886.9 m above sea level is maintained by an artificial floodgate in the only natural outlet south-west of Waihai. The lake is nearly semicircular: its length is 40.4 km and it has a mean width of 7.0 km. The shoreline is 150 km (Yang et al. 2010 ) in extent. Located in the southern suburbs of Kunming City, Dianchi Lake plays important roles in industrial and agricultural water supply, water storage regulation, flood control and tourism.

Location of the Dian Lake ( a , b ) and the sampling sites along the Dianchi Lake ( c )

It was known for its crystal-clear water and was once dubbed “the pearl of the Yunnan-Guizhou Plateau”. However, the lakeside was over-reclaimed in the 1970s and the water has become heavily eutrophic. The ability of the lake to self-purify has not been able to keep up with the massive discharge of municipal and industrial sewage into the water. Along with Taihu and Chaohu lakes, Dianchi is listed as one of the three most polluted lakes in China (Liu and Qiu 2007 ).

In 1988, Kunming Municipality enacted the Dianchi Protection Regulations to carry out a comprehensive ecological restoration plan. An ambitious large-scale ecological restoration project, the “Kunming urban master plan (2008–2020)”, has been implemented along the lakeside of Dianchi Lake since 2008. This is an attempt to build a green wetland belt around the lakeside, reconstructed from farmland, fishponds and residential areas, and aims to recover the wetland ecosystem function. Reed, cattail, water hyacinth, duckweed and trees were planted in the restored artificial wetlands, as these are the most conspicuous landscapes in the lakeside ecological belt.

Sampling plots

We surveyed birds at 26 sampling plots: 3 plots in Kunming City and 23 plots every 2‒8 km along Dianchi lakeside (Fig.  1 c). The plots covered multiple types of environmental gradients (urban and suburban areas, low and high human density, semi-natural wild areas and areas under construction), as well as habitats (forested, grassy, cultivated and built areas, open water, and mosaics of land and water). Land covers of sampling plots were classified into 12 types based on Google Earth images and field observation (Table  1 ). We obtained the satellite images from Google Earth for each plot and measured the amount of obvious land cover. This included areas of water, mud flood (MF), building area (BA), small pond (SP), cultivated land (CL) and wasteland (WL), as identified by Arcgis. We estimated the percentage of open water area (OA) and macrophyte cover (MC) of the water area during field surveys in April 2013. We also estimated plant cover variables: forest cover [Dry forest cover (DF) and wetland forest cover (WF)] was estimated in five randomly selected quadrats (20 m × 20 m) along each transect. Within each forest cover estimation quadrat, we randomly selected four quadrats (5 m × 5 m) to estimate shrub (SB), high grassland (HG) and low grassland (LG) coverage. A matrix of habitat variables consisting of the coverage percentages of 12 types of land cover was produced for further analysis (see details in Additional file 1 : Table S1).

Bird surveys

Field bird surveys were carried out continuously for 24 months from December 2011 to November 2013 to obtain basic bird composition data. Surveys were conducted in the mid-part or late in each month for 3‒4 days from 08:00‒12:00 to 13:30‒18:30 each day. The transect method was used to survey wild birds. As the lakeshore micro-habitats usually included open areas and areas densely filled with plants, three types of transect were used: type 1 for open habitat (300 m × 200 m), type 2 for dense habitat (300 m × 50 m) and type 3 for habitat containing a dense area next to an open area (300 m × 125 m). Between one and three transects were carried out in each plot according to plot size (details in Additional file 1 : Table S1). It usually took 15 min to count the birds in each transect (Ntongani and Andrew 2013 ). All birds heard, seen in or hovering over the plots were counted. Birds that flew over the plots quickly (usually taking less than 10 s) were not recorded. All individuals were counted, and group-counting was used in the case of large flocks (more than 500 individuals) (Bibby et al. 2000 ; Yuan et al. 2014 ). Birds were observed using telescopes (Celestron 20‒60 × 80 monocular and Sharks 8 × 42 binocular) and photographed with digital cameras (Canon 650D; EF 400 mm f/4–5.6L IS USM). Bird species were identified according to MacKinnon et al. 2000 and the IOC Checklist (v 4.4) (Gill and Donsker 2014 ). We used the maximum number of individuals counted in a single month as the species abundance for each species.

Information on bird breeding divisions and migration status was obtained from Yang and Yang ( 2004 ) and Zhao ( 2001 ). Birds were classified into waterbird (two ecological types: Grallatore and Natatores) and non-waterbird (four ecological types: Passeres, Terrestores, Raptors and Scansores) (Zhao 2001 ; Zheng 2012 ). Waterbirds were further divided into 7 sub-groups: gulls (SG1), wintering coots and ducks (SG2), shorebirds (SG3), egrets (SG4), breeding rails (SG5, dominated by the resident species Gallinula chloropus ), grebes (SG6, dominated by the resident species Tachybapus ruficollis ) and others (SG7, only containing a total of 6 individuals belonging to 5 species, excluded from further analysis) for further habitat preference analysis (Cardoni et al. 2008 ; Zhang et al. 2011 ; Yuan et al. 2014 ; Wei et al. 2018 ).

New bird records and absent species

In this paper, we define new records of birds in Kunming and Yunnan as species recorded along Dianchi Lake between 2008 and 2016, as the restoration projects mainly began in 2008.

In addition to our own field observations, new bird records were obtained by searching published papers using “new bird record”, “bird”, “Yunnan”, “Dianchi” and “Kunming” as keywords at CNKI and CQVIP, two databases of Chinese journals, and the Google Scholar for Chinese website. We also checked the references listed in the publications we collected for any potential literature we had overlooked. We also searched all articles and newspapers relating to birds in Dianchi Lake, using Baidu Search. Finally, we communicated with birdwatchers (Yunnan Bird Association) who had investigated Dianchi Lake to record birds. After acquiring the records, we first confirmed that the data from scientific publications were accurate and reliable, and reviewed the few debated records with other researchers. The related records from Baidu Search and birdwatchers were adopted only when robust evidence (e.g. photos) was available. The earliest literature related to Dianchi Lake we could reach was published in 1960 (Kuang 1960 ), which conducted field surveys during 1958 and 1959. Therefore, we categorized the records obtained from searching data collected during 1958 to 2008 as historical, and those from between 2008 and 2016 as new.

Because there was no reliable and special representative bird checklist for Dianchi Lake before 2008, we compared our current bird records (including our own field observation data and those of others) with the bird checklists of Kunming City and Yunnan Province to produce the new bird record for Kunming (hereafter NKB) and for Yunnan (NYB). We used the appendix of Wang et al. ( 2015 ) as the bird checklist of Kunming. As the appendix provided dynamic historical information for some waterbird species that were explicitly recorded along Dianchi Lake, we also detected that some species had vanished from the shore of Dianchi Lake. The bird checklist of Yunnan mainly refers to Yang and Yang ( 2004 ).

Data analysis

To test whether our sampling effort was sufficient to represent the bird species richness of Dianchi Lake, we first performed rarefaction analyses based on a Monte Carlo simulation procedure implemented with EcoSim7.0 (Gotelli and Entsminger 2006 ).

Canonical correspondence analysis (CCA) was performed to reveal the relationships between waterbird assemblages and habitat variables (Yuan et al. 2014 ; Wei et al. 2018 ). In CCA, habitat data were considered as explanatory variables and abundances of waterbirds were taken as response variables. For both the habitat and species data, no data transformation was applied. We used all 12 variables to perform the CCA analysis and produce the CCA bi-plot. We also performed a Pearson two-tailed test to determine the correlation coefficients between different habitat variables. Forward selection procedures were then applied to test the habitat variables with significant influences. Partial CCA was executed to determine the independent influence of each variable; when a significant variable was used as a definitive one, the others were used as covariables. The proportion of explained variation (net effect) was measured by using the ratio of particular canonical eigenvalues to the sum of all eigenvalues in partial CCA procedures (Lososová et al. 2004). We performed a partial CCA and a Monte Carlo permutation test with 999 permutations to evaluate the significance of variables separately. The statistical significance of each species responding to the five major environmental variables was tested by producing t -value bi-plots based on the CCA procedure. All the analyses were carried out in CANOCO 4.5 (Lepš and Šmilauer 2003 ).

Sampling adequacy and species composition

The results of sample-based rarefaction curves illustrated the completeness of survey inventories and the sufficiency of sampling efforts, because of their rapid approach to an asymptote (Fig.  2 ). In total, 25,102 records for birds belonging to 182 species, 51 families and 17 orders were recorded during the 24 continuous months from December 2011 to November 2013. Of these, 67 species were waterbirds and 115 were non-waterbirds. Many of the birds recorded are protected nationally or internationally. Of the 182 species, nine were found to be “second-class protected species” in China and 144 species appeared in the Lists of state-protected terrestrial wildlife with beneficial or important economic or scientific value in China. We found that 74 species were bi-protected between China and Japan and 38 species were bi-protected between China and Australia (see details in Additional file 2 : Table S2).

Sample-based rarefaction curves. The X -axis has been scaled to show numbers of individuals

The greatest number of registered species were from the order Passeriformes (95 species, accounting for 52.20% of total registered species), while the Charadriiformes was the order with highest number of records (14,703 records, 58.57% of total records) owing to the dominance of wintering Black-headed Gulls ( Chroicocephalus ridibundus ) (14,285 records; see details in Additional file 2 : Table S2). Of the five different categories related to migration status, residents accounted for the highest number of species, while winter visitors accounted for the largest number of individual records. With respect to the six ecological types, the suborder Passeres contained the highest number of registered species, and the Natatores accounted for the largest number of counted individuals. Birds breeding in the study site were mainly comprised by species of the Oriental realm and by widespread species (Table  2 ; see details in Additional file 2 : Table S2).

Species changes before and after 2008

Through comparisons with historical data (1958‒2008), 42 bird species recorded along the shore of Dianchi Lake were new records for Kunming City. Of these, 34 species were waterbirds, including 24 shorebird species. We found that 20 species were new records for Yunnan Province; of these, 18 were waterbirds (including 15 shorebird species) (see details in Additional file 3 : Table S3). This suggests that waterbirds, and particularly shorebirds, accounted for most new records for Dianchi Lake since 2008. The appendix of Wang et al. ( 2015 ) suggests that 10 waterbird species have disappeared from the shore of Dianchi Lake. These are Ciconia nigra , Platalea leucorodia , Anas formosa , Mergus albellus , Charadrius hiaticula , Larus crassirostris , L. canus , Sterna aurantia , S. caspia and Grus grus . Particularly, G. grus was once the dominant species along Dianchi Lake.

Waterbird habitat preference

Sixty-three waterbird species (94.30% of total registered waterbird species) representing 18,913 records (99.85% of total waterbird records) were recorded in 26 plots throughout the 24-month field investigation. All habitat variables were used to examine the relationship with the abundance of waterbirds by CCA analysis. High collinearity between habitat variables is shown in Fig.  3 , except for OA, MF and WL (see Pearson correlation of habitat variables in Additional file 1 : Table S1).

CCA ordination diagram of species distribution and environmental factors in Dianchi Lake. Black arrows represented the significant affected land-cover variables ( p MF  = 0.025; p DF  = 0.006; p LG  = 0.031; p WL  = 0.007; p SB  = 0.058), while dotted arrows were the insignificant ones. Sample sits were represented by open circles, whereas species were meant by black diamonds. See abbreviation of land-cover variables in Table  1 and species code in Additional file 2 : Table S2

The results of the CCA are shown in Table  3 . The eigenvalues of the first two canonical axes were much higher than those for the other two axes. All canonical axes explain 80.3% of the variance in species data and 99.8% of the variance in species–environment relationships. The cumulative explanation of the first two axes reached 73.2% of species data and 90.9% of species–environment relationship. Monte Carlo permutation tests for the first and all canonical axes were highly significant ( p  = 0.002, p Y  = 0.001, respectively). For species–environment relationships, approximately 41.5% and 29.3% of the variations were explained by axis 1 and axis 2, respectively. Overall, the first two canonical axes were able to explain quite well the relationship between species and the environmental variables. We found that seven habitat variables (OA, CL, WF, LG, SB, WL and MF) were positively correlated with axis 1 and the remaining five were negatively correlated with axis 1. Five habitat variables (PC, SP, HG, MF and WL) and the remaining seven variables were negatively correlated with axis 2. The bi-plot of the overall species distribution and habitat explanatory variables is shown in Fig.  3 .

Forward selection of environmental variables suggested that the effects of five habitat variables (including a marginal one)—MF, DF, LG, WL and SB—( p  = 0.058) significantly ( p  < 0.05) affected waterbird distribution (Fig.  3 ). Partial CCA suggested that, of the five main habitat variables, MF alone accounted for 40% ( p Y  = 0.001) of the variation in the bird data, and DF solely explained 40.1% ( p Y  = 0.01) of the variation. LG, WL and SB did not reach a significant level.

The statistical significance of each species’ response to the five major environmental variables was tested by producing t -value bi-plots based on the CCA procedure. The relationship between single species and a particular environmental variable is shown in Fig.  4 . DF had a significant positive correlation with gulls (SG1) and a significant negative correlation with wintering coots and ducks (SG2) (Fig.  4 a). SG2 were significantly positively related with WL, while SG1 were significantly negatively correlated with WL (Fig.  4 b). Shorebirds (SG3) were significantly positively correlated with MF (Fig.  4 c) while other habitat variables showed little relevance to this sub-group. Overall, SG4, SG5 and SG6 showed similar responses, and presented similar correlation trends to the habitat variables, but none was statistically significantly affected. SG1 responses to habitat variables were opposite those of SG4/SG5/SG6 except for response to MF (Fig.  4 a‒e).

t -value biplots of the five main environmental variables. a DF, b WL, c MF, d LG and e SB. The arrows indicated each sub species groups, and empty boxes represented environmental variables. The circles filled with gray color represented negative correlation while the transparent ones represented positive correlation

In our two-year survey, 182 species belonging to 49 families and 15 orders of birds were detected, suggesting that Dianchi Lake could provide a suitable habitat for wild birds and not just for waterbirds. Species of different migration status, ecological types and breeding fauna of bird species and individuals recorded here suggest that Dianchi Lake is an important wild bird breeding, stopover and wintering site (Table  2 ). This implies that lake restoration management should take into account the requirements of different wild birds, and especially waterbirds (breeders, migrants and winter visitors).

Species responses to wetland restoration in urbanized area

We found many new bird records for Kunming City and Yunnan Province during our fieldwork and in the observations of others (see details in Additional file 3 : Table S3). This suggests that more frequent bird surveys (that is, greater sampling effort) could lead to additional new bird records (Liu et al. 2013 ). Most of the new records are of waterbirds (34 of all 42 NKB and 18 of all 20 NYB), and this may indicate that wetland restoration projects in urban settings benefit birds, and especially waterbirds (Mander et al. 2007 ; McKinney et al. 2011 ). We also found that numerous new bird records were of shorebirds (24 NKB and 15 NYB). Most of these species were recorded in mud-flooded wetlands, showing the high dependence on this habitat by shorebirds (Murray and Fuller 2015 ). In addition, more than eight studies reported shorebirds among the new bird records for Yunnan Province (Luo 2014 ). The use of inland mud-flooded wetlands by shorebirds may also be a result of coastal wetland degradation, driving some of them to seek new habitats in inland areas (Ma et al. 2002 ). If mud-flooded habitats were to form during migration seasons in western or central China, we believe that more (new) shorebird species would be found in these regions by more researchers and birdwatchers.

Although there is no reliable and representative bird checklist of Dianchi Lake before 2008, we still found the ten of the most historically recorded waterbird species are now absent from Dianchi Lake. Little information was available on the absent species, but the Common Crane ( G. grus )—a dominant wintering visitor around Dianchi Lake in the 1960s (Kuang 1960 )—is today absent from the lake (Wang et al. 2015 ). Conspicuous and rapid increases in numbers of wintering Black-headed Gulls were apparent, from about 3000–30,000 individuals during 1985–2000 (Wang et al. 2006 ). One reason for this may be related to the growing tourism activity of feeding them in Kunming City parks (Guan et al. 2008 ). We suggest that the intensified urbanization and reclamation of the last few decades (Tan et al. 2010 ) has driven away sensitive species, while synanthropic species have increased rapidly (Blair 1996 ; Maciusik et al. 2010 ; Donaldson et al. 2016 ). An alternative reason for species disappearance may be that the historical records were of vagrant visitors or rare species in Dianchi Lake. During re-checking of specimens in 2005, a specimen collected in 1981 was confirmed to be a Caspian Tern ( Sterna caspia ). As this was the first record of this species in Yunnan (Yang 2005 ), it suggests that it was a rare species or vagrant visitor to Dianchi Lake in 1981.

The relationships between the distribution of most waterbirds and habitat characteristics, as revealed by CCA, were in agreement with the birds’ ecological requirements. For instance, the shorebirds (SG3) concentrated significantly in the mudflat (MF) wetlands (Bellio and Kingsford 2013 ; Aarif et al. 2014 ; Clemens et al. 2014 ; Murray and Fuller 2015 ). The wintering ducks and coots (SG2) clearly preferred the water area next to the waste lands cover (WL) and avoided dry forest cover (DF) (Paracuellos 2006 ; Cardoni et al. 2008 ; Ma et al. 2010 ). Along Dianchi Lake, trees have always been planted in the relatively well-managed parks for their scenic value, attracting many visitors who toss large quantities of food to gulls during the winter season (unpublished observation). The higher DF along Dianchi Lake usually suggested more human recreation activities. The habitat preference of gulls (SG1) was the complete opposite of SG2, which significantly avoided the WL and concentrated in DF (Andersson et al. 1981 ; Guan et al. 2008 ; Liordos 2010 ; Maciusik et al. 2010 ). The distributions of egrets (SG4), breeding rails (SG5) and grebes (SG6) were negatively correlated with DF and shrub cover (SB), while positively correlating with other variables, but none of these relationships was statistically significant. This situation seemed to reflect wider habitat use by resident species, which can move among patches during different seasons, searching for suitable resources, and which are more tolerant of variation in the local habitats (Chen et al. 2000 ; Rendón et al. 2008 ; Donaldson et al. 2016 ).

The partial CCA also indicated that DF and MF were the only two independent explanatory variables that can significantly explain the variation in the bird data alone, 40.1% and 40%, respectively, in our study. This may suggest the vulnerability of the bird community along Dianchi Lake, because these two variables are highly dependent on human activities. The wintering gulls concentrated significantly on DF, which means they foraged in the well-managed parks, depending on the food supply from tourists (Wu et al. 2008 ). The concentration of gulls increases their vulnerability to disease, reduces their wariness of people and favors their domestication (Ma et al. 2009 ). After avian influenza broke out in China in the early 2000s, tourists did not dare to feed the gulls, and numbers of gulls foraging in urban parks reduced by 9000 (Kunming Bird Association 2006). Most shorebirds were counted in the mudflat wetlands formed temporarily by construction-work yards. Once the construction is finished the mudflat wetlands also disappear. In the biggest mudflat wetlands with the highest shorebird numbers of our sampling sites, many fewer shorebirds were encountered after construction stopped in 2014, as the area became bare ground without any water (H. Bai, personal communication 2015). No significant correlations between most variables and species were found in our study. This does not imply, however, that those variables were of no importance in determining species community composition. We can only conclude that the correlation did not reach a significant level in this study. Hence, further work is needed to determine the effects of the variables.

Management implications

Wetland restoration projects can benefit wetland birds (Mander et al. 2007 ; Murray et al. 2013 ). Studies on managed realignment sites in the UK have shown that birds colonize and adapt quickly to new habitats (Mander et al. 2007 ). We suggest that, as the food resource of wintering gulls in our study is largely dependent on humans, food is provided appropriately to keep them wild and that attention is paid to avian influenza (Poland et al. 2007 ; Wu et al. 2008 ) and potential water contamination (Jones and Reynolds 2008 ). Several more well-managed parks should be included along the suburban lakeshore to dilute the high density of foraging gulls in the urban area (Maciusik et al. 2010 ). The use of food-supply platforms for gulls should be considered when designing the parks, rather than depending on volunteers to throw food for them during sensitive periods, such as during outbreaks of avian influenza. Our results testified that a certain number of shorebirds also use available habitats in inland China, although most of them were encountered in the mudflat wetlands formed temporarily by construction-work yards. These habitats disappear when the construction projects finish, resulting in a decrease in shorebird diversity (Ma et al. 2014 ). We strongly recommend that mudflat habitats be designed and managed for migrating shorebirds on the lakeshore to allow a more comprehensive restoration of Dianchi wetland ecosystem functions (Francesco et al. 2013 ; Clemens et al. 2014 ; Murray and Fuller 2015 ; Wang et al. 2016 ). In our study, wintering coots and ducks usually appeared in large numbers in the water area next to WL, suggesting a lower human presence there (Paracuellos 2006 ; Cardoni et al. 2008 ; Ma et al. 2010 ). This indicated to us that, when the wetlands for the entire lakeshore are designed, a certain degree of semi-natural habitat with low or no human recreation access must be reserved for these species (Cardoni et al. 2008 ; Ma et al. 2010 ). The recording of mostly resident species of waterbird in all 26 sites may be explained by the long history of adaptation to local fragmentation and disturbance (Rendón et al. 2008 ; Donaldson et al. 2016 ). Nevertheless, dense aquatic plants, excess MC and human disturbance have had a negative effect on their habitat use (Cardoni et al. 2008 ; Ma et al. 2010 and our observation). Incorporating appropriate plant density and a buffer zone (lowering human disturbance) should be taken into consideration when implementing restoration, to meet the habitat utilization requirements of resident waterbirds.

In summary, the presence of numerous migratory and resident birds recorded in our study shows that Dianchi Lake is an important habitat for wild birds, which could use it as a breeding, stopover and wintering site. We suggest that intensified urbanization and reclamation during the last few decades has driven away sensitive species, while synanthropic species have increased rapidly. Wetland restoration projects have benefited many bird species, especially waterbirds. Distribution of different waterbird species is highly dependent on human activities. Different types of restoration management should be implemented, to take into account the varied habitat requirements of different waterbird groups, and allow a more comprehensive restoration of Dianchi Lake wetland ecosystem functions.

Availability of data and materials

The datasets used in the present study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank Doyle McKey from Université de Montpellier, Bo Wang from Xishuangbanna Tropical Botanical Garden, Christos Mammides from Guangxi University and Donglai Li from Liaoning University for their generous help in language editing and insightful comments about the manuscript. We thank senior schoolmates Jianyun Gao and Dongdong Su for their enormous help in this study during the pre-surveys, PhD senior schoolmate Longyuan He for his kind help in mapping, Prof. Zhiming Zhang for his great help in the data analyses, faculty of the Kunming Bird Association for their help in the field investigation and memberships in the QQ groups of Students union of Birdwatching, and Yunnan Wild Bird Association and Young Ornithologists for their help in identifying some species.

The National Natural Science Foundation of China (41471149 and 31060079) financially supported this study.

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Kang Luo, Zhaolu Wu, Haotian Bai & Zijiang Wang

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KL and Z. Wu designed the experiments. KL and HB implemented the field surveys and collected the data. KL finished the data analysis and wrote the first draft. KL, Z. Wu and Z. Wang supervised the research and provided multiple revisions in the early stages of writing. All authors read and approved the final manuscript.

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Additional files

Additional file 1: table s1..

Percentages of different land-covers of the 26 sampling sites around Dian Lake and the Pearson correlation of the land-cover variables.

Additional file 2: Table S2.

Bird checklist of Dianchi Lake.

Additional file 3: Table S3.

New bird records found along Dianchi Lake during 2008‒2016.

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Luo, K., Wu, Z., Bai, H. et al. Bird diversity and waterbird habitat preferences in relation to wetland restoration at Dianchi Lake, south-west China. Avian Res 10 , 21 (2019). https://doi.org/10.1186/s40657-019-0162-9

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