Explain Why Wetlands Generally Have a High Level of Biodiversity

Explain Why Wetlands Generally Have a High Level of Biodiversity.

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


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 ofttimes 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 nosotros performed a meta-analysis of 70 experimental studies in gild to appraise the effectiveness of ecological restoration and place 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 fantabulous 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 like to those of natural wetlands; yet, their levels of supporting and regulating ES were, respectively, sixteen% and 22% lower than in natural wetlands. Recovery of biodiversity and of ES were positively correlated, indicating a win-win restoration upshot. The extent to which restoration increased biodiversity and ES in degraded wetlands depended primarily on the principal amanuensis of degradation, restoration actions, experimental pattern, and ecosystem blazon. In contrast, the pick of specific restoration actions lonely explained virtually differences betwixt 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 inquiry on wetland restoration should too seek to place which restoration deportment piece of work best for specific habitats.


Wetlands harbor significant biodiversity [1] and supply crucial ecosystem services (ES) [two], [3], which are defined every bit 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 xl% of global annual renewable ES [5]. Despite their importance to man societies, wetlands are quickly being degraded and destroyed [five], threatening the ecosystem and biodiversity on which wetland ES depend.

To compensate for their extensive degradation, wetland restoration has become mutual do around the world. Several studies have reported that restoration can recover much of the biodiversity and ES lost due to deposition [half-dozen]. On the other mitt, 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 [five]. For instance, some authors have suggested that current wetland restoration methods are also slow and incomplete to allow recovery of biological structure and biogeochemical role [7]. Therefore the effectiveness of wetland restoration remains controversial, and this is in role because different studies have practical different standards to evaluate outcomes [6]. At the same time, nigh studies evaluating wetland restoration, including a contempo meta-analysis [7], accept non directly assessed ES recovery or how well restoration methods work for various types of organisms.

Recovering biodiversity and recovering ES tin can exist 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 oftentimes 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 betoken a win-win consequence 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 blazon of ecosystem existence restored, its pre-restoration condition, and the factors responsible for its degradation. Past analyzing wetland restoration simultaneously in terms of biodiversity and ES, we can identify factors that touch the recovery of either or both, allowing us to develop recommendations for researchers and practitioners.

To develop an evidence-based arroyo for planning and assessing wetland restoration, we conducted a meta-analysis of the peer-reviewed literature to address the following four questions: (ane) how much biodiversity and (two) 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 activity, experimental design, and restoration age. In examining what the literature says on these questions, we promise to inform and improve efforts to restore the biodiversity and ES of degraded wetlands.


Literature search

Nosotros systematically searched the enquiry literature to identify quantitative studies of the effects of ecological restoration on biodiversity and ES of not-marine aquatic and semi-aquatic degraded wetlands. We searched the ISI Web of Noesis database (www.isiwebofknowledge.com), as it provides admission to peer-reviewed studies. We searched studies published betwixt 1970 and 2010 using the following cord of search terms: (riparian OR river* OR lake OR mangroves OR marsh OR stream OR wetland) AND (restor* OR re-creat* OR rehabilitat* OR woods* OR reforest* OR afforest* OR plant* OR recover*) AND ((ecosystem OR environment) AND (service OR function*)). Preliminary search results were filtered to include just the post-obit ISI-defined discipline areas: “agronomics”, “biodiversity and conservation”, “environmental sciences and environmental”, “fisheries”, “forestry”, “marine and freshwater biology”, “constitute sciences”, “water resource”, and “zoology”. This resulted in a listing of i,931 references.

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

  1. Quantitative assessment of passive restoration (i.e. natural regeneration) or agile restoration in terms of variables related to biodiversity and/or to the supply of 1 or more than wetland ES (Tabular array i) consistent with the framework of the Millennium Ecosystem Assessment [4], according to which biodiversity underpins all ES;
  2. Comparing of restored wetland with either degraded or natural wetland;
  3. Sample size of the reported data and at to the lowest degree a variance estimate of such data.

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

Database building and upshot size interpretation

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 congenital for biodiversity and ES response variables. Whether nosotros used one or the other database, or some combination of columns from both of them, depended on the specific question beingness addressed. Each measurement of restoration impact was recorded every bit a separate row in the database, fifty-fifty when the measurements came from the aforementioned report. Measurements were also recorded separately when the original study causeless spatially independent weather within the same study site (e.chiliad. measurements made near the shore vs. made on the open water of the same wetland).

We extracted information on type of wetland and ecosystem, the chief 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 data S3).

Since our meta-assay 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) equally the effect size metric. Equally an indicator of the effect of restoration, we calculated RRs of the restored wetlands relative to reference natural wetlands [ln(Rest/Ref)] and to degraded wetlands [ln(Remainder/Deg)] for each measure of the biodiversity and ES extracted from the studies. Well-nigh response variables were expected to correlate positively with biodiversity or a particular ES; for instance, 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 instance, a greater concentration of a h2o 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 split analyses to compare restored and degraded wetlands and to compare restored and natural wetlands [ix] (Supporting information S3). RR calculations and statistical analyses were performed using MetaWin v2.i [12].

Biodiversity recovery

All possible measures of biodiversity for which the included studies reported information were used to calculate RRs; these measures included (a) species, gender, taxon or family richness; and (b) indices of species affluence, diversity, similarity, and composition. Using biodiversity measures calculated for different taxonomic levels or past dissimilar formulas enabled the states to screen for differences in responses to restoration at different levels of ecological complication [ix], [xiii]. Each extracted datum was assigned to a single organism blazon. 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 written report. These hateful RRs were similar to the means obtained when all effect sizes from each study were included, and the bias-corrected 95% bootstrap conviction interval of the reduced dataset overlapped with that of the entire dataset (Tabular array S2). Therefore we retained all the information in our meta-analysis, similar to Rey Benayas
et al.
[9] and Vilà
et al.

ES recovery

Response variables were related to a wide variety of ES, so multiple RR-ES combinations were included as divide rows in the database (Table S1). The parallel assessment of these multiple associations allowed the states to capture the simultaneous supply of several ES [fourteen], [15]. To avoid counting the same information more than in one case in a meta-assay, we performed a carve up meta-analysis for each ES using a random-effects model. We considered this arroyo suitable because nosotros wanted to evaluate each ES separately, rather than the heterogeneity amidst 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. Nosotros used just RRs from studies that evaluated both biodiversity and ES, and we treated each of these studies equally an independent sample. When the aforementioned report 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 iv major ES types in social club to ensure adequate sample size [9].

Context dependence of biodiversity and ES recovery

Nosotros used linear mixed-effects models to evaluate whether the furnishings of restoration on biodiversity and ES varied with context. Context was parameterized using four nominal stock-still factors (ecosystem type, main cause of deposition, restoration action, and experimental pattern) and the continuous fixed gene of restoration age, defined every bit the decimal logarithm of the number of months between completion of the concluding restoration activeness and evaluation. We added a fifth nominal fixed gene with ii levels (biodiversity or ES) considering we used RRs for both biodiversity and ES recovery in the analysis. Study site was the random-effect cistron and RR was the dependent variable.

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


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

Biodiversity recovery

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 diverseness of vertebrates (+53%), vascular plants (+45%), and terrestrial (+17%) and aquatic (+fifteen%) invertebrates, merely it had no significant effect on macroinvertebrate diversity. Restored and natural wetlands showed like diversity of vascular plants, aquatic invertebrates, macroinvertebrates and protists. In dissimilarity, 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.


Figure 1.

Mean effect size (response ratio) of ecological restoration on overall biodiversity and biodiversity of specific types of organisms in restored wetlands with respect to (a) degraded wetlands or (b) natural wetlands.

Numbers in parentheses signal the sample size (number of comparisons) followed by the numbers of studies. Bars extending from the means indicate bias-corrected 95% bootstrap conviction intervals. A mean upshot size is significantly dissimilar from nada if the 95% confidence interval does not overlap with information technology. In comparison (a), no information were available on not-native vascular plants and protists. In comparison (b), the confidence interval for terrestrial invertebrates is not visible considering it is smaller than the mean marking.


ES recovery

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 (+twoscore%), while the 2 types of wetlands showed like supply of cultural ES. Compared to natural wetlands, restored wetlands showed similar supply of provisioning and cultural ES, just lower supply of regulating (−22%) and supporting ES (−sixteen%).


Figure ii.

Mean issue size (response ratio) of ecological restoration on four major ES types defined past the MEA (2005) and on 13 individual ES (see details in
Table i) in restored wetlands with respect to (a) degraded wetlands or (b) natural wetlands.

Bars extending from the means signal bias-corrected 95% bootstrap conviction intervals. A hateful upshot size is significantly different from zero if the 95% conviction interval does non overlap with information technology. Numbers in parentheses indicate the sample size (number of comparisons) followed by the numbers of studies.


Restoration increased most individual ES that nosotros examined, although non 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 h2o quality to 176% for invasive species command. Restoration also increased both provisioning services examined in our meta-analysis: water supply (+108%) and the supply of food or raw materials of fauna origin (+65%). For most individual ES that we examined, restored and natural wetlands tended to supply similar amounts (Fig. 2b). Exceptions, in decreasing gild of difference betwixt the two wetland types, were climate regulation, the supply of which was −xxx% lower in restored wetlands; provision of terrestrial habitat, −22%; regulation of fertility and soil erosion, −21%; and biogeochemical cycles, −14%.

Correlation between biodiversity and ES recovery

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).

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

Comparing of restored and degraded wetlands showed that restoration effects depended on the post-obit factors, listed in order of decreasing importance: main crusade of degradation, restoration activity, experimental design, and ecosystem type (Table two). In dissimilarity, restoration age did not significantly impact restoration outcomes. These results were the same for the two outcomes of biodiversity recovery and ES recovery.

Context variables explained relatively little variance (25.vii%) in biodiversity and ES recovery. Notwithstanding, the improvement in biodiversity and ES due to restoration varied substantially for unlike wetland types: common 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 past 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 subpoena and revegetation (+91%), and passive restoration in third identify (+57%). Of all restoration actions examined, exotic species removal was associated with the everyman effect size, which did not reach statistical significance. Restoration showed significant positive effects on biodiversity and ES recovery for the 3 types of experimental designs in the included studies: paired experiments (+61%), before-later on experiments (+33%) and control-affect 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 (Tabular array 2), although every bit before, the last model explained just a fraction of the variance (15.2%). All restoration actions led to total 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).


Biodiversity recovery

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 [ix]. In fact, restoration increased the biodiversity of native organisms to levels like to those in natural wetlands. To exist sure, restoration did non 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 construction, biodiversity and functioning of wetlands [five]. In add-on, wetlands dominated past exotic, invasive plants tend to support fewer native animate being species and more than invasive animals [5].

Greater diversity by itself is insufficient to ensure high ecosystem functioning [18]. Potentially even more of import are the identities and relative proportions of species involved in the restoration procedure, as well as their ecological and functional backdrop. Unfortunately, most studies in our meta-analysis reported aggregate measures of richness or diversity simply not community limerick (Supporting data 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 fifty-fifty within a form like vertebrates. Therefore, restoration studies dealing with species composition, community construction and functional ecology are urgently needed.

ES recovery

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 total recovery of ES supply takes longer than full recovery of biodiversity. Either interpretation is consistent with the meta-assay by Rey Benayas
et al.
[9], simply inconsistent with the analysis of N American wetlands by Dodds
et al.

Restoration did not raise ES uniformly across all individual ES examined. Nosotros observed that restored wetlands provided, on average, 36% higher levels of provisioning, regulating and supporting ES than did degraded wetlands, simply similar levels of cultural services. To be sure, we did not expect compatible recovery of all individual ES, given the heterogeneity of ES and wetland types included in the meta-assay; 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 reverberate 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 terminal three services showed broad confidence intervals in our report, suggesting higher intra-class heterogeneity than effect sizes for provisioning services [12]. Small sample size may explain our finding that restoration did non 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 reverberate the intrinsically slow recovery rates reported for these surrogate variables [seven]. 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 total recovery.

Assay of the ES database, which included abundance data on both not-native plant and animal species, showed that restoration increased regulation of non-native species by reducing their abundance. This outcome is different than our finding that restoration increased the diverseness of such species, though it should exist noted that the biodiversity database contained data on non-native plants simply not not-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 non necessarily point a decrease in species diversity, such as when a habitat contains several rare species in low abundance. Thus, assessment of restoration should take into business relationship both abundance and diversity indicators.

Correlation of biodiversity and ES recovery

The relationship betwixt biodiversity and ES supply remains poorly understood [19], yet information technology is crucial to work out because it has significant implications non just for restoration scientific discipline but also for wider order, economics, and policy [twenty], [21]. Our results showed that changes in biodiversity positively correlated with changes in ES supply in a diversity of wetlands, ecosystem types and scales, which supports a functional function for biodiversity in the supply of ES [7], [9]. This positive relationship is good news for restoration efforts, every bit it demonstrates the possibility of win-win scenarios for restoring biodiversity and ES. However, such win-win gains have not always proven viable in practice, especially in restoration projects involving geographically dispersed areas [22]. Future enquiry should explore how to optimize the synergy betwixt biodiversity and ES supply in the pattern of management and conservation programs involving restoration.

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

Context dependence

Our meta-analysis identified several context factors that significantly afflicted biodiversity and ES recovery in restored wetlands, including ecosystem type, main cause of degradation, restoration activeness 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. Peculiarly, examining interaction furnishings may generate useful insights, but the risk of multiple interactions, including two or even three factors, is besides high for the relatively low statistical power of our model.

Our results likewise 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 activeness was repeated and on the conditions of the degraded wetland prior to restoration. Unfortunately well-nigh of the studies included in our meta-analysis did not report such data. The type and elapsing 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 non receive external h2o flow [7]. We obtained different results showing that outcomes of restoration were unrelated to flow exchange, due east.g. biodiversity and ES in rivers and streams were enhanced in very different amounts. Despite these differences, the bachelor 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 appraise their outcomes accordingly [half-dozen].

Our meta-assay 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 get-go, which is often impossible, the restored wetland may not come up as close as possible to natural weather. 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 atmospheric condition 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 amend the likelihood of success.

Implications for wetland restoration

Comparison 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 status accurately [27], and because motion of restored wetlands away from reference weather condition makes it hard 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 business relationship the social context and human being values associated with decisions about wetland management and restoration. The concept of ES can be a robust guide for wetland restoration decision-making because information technology identifies and quantifies valuable goods and describes the processes and components that provide essential services [29]. Since several ES are hard to mensurate direct, surrogate measures of ecosystem function can be used instead [30].

Accurately assessing the touch 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 add-on, we evaluated the effects of response variables on multiple ES, since the variables may have indirect or unclear links to several ES that significantly bear on restoration outcomes. For instance, although all plant species capture carbon, thereby increasing the supply of one ES, not-native species may have detrimental effects on other ES such as biotic interactions. A single restoration action may simultaneously affect various ES or deed synergistically every bit 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 every bit the release of greenhouse gases. Therefore, analyses of restoration information should assess both the direction and magnitude of associations between response variables and individual ES [14]. Taking into business relationship 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].

Toll plays an important office in restoration planning considering it may limit the desired outcomes [33], [34]. Surprisingly, the studies included in our meta-analysis did not accost the effect of restoration costs. Costs are an important factor non merely during restoration but also afterward: monitoring of wetlands post-obit their restoration, mitigation or creation is oftentimes too cursory because it is expensive to evaluate all the ecosystem functions involved.

These elements define a circuitous 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 testify 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 besides include functioning indicators that describe how much of available ES can be exploited [19], since biodiversity-related ES, for example, vary over fourth dimension and space and are species-dependent. This poses a challenge for model-building, since elementary models for simultaneously maximizing biodiversity and ES are unrealistic or ambitious [35], such that the 2 variables are not necessarily maximized in the aforementioned 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.


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 cess of restorations [six]. Questions posed years ago remain largely unanswered today, such as “To what extent and over what fourth dimension scale can ES be restored? [36] and “To what extent tin can mankind substitute for ES?” [37]. While restoration ecology is non obliged to answer these questions, exploring them may help better the flows of ES and improve man well-existence. Addressing these questions will crave deepening our understanding of the links between restoration actions and changes in biophysical and ecological processes that generate ES [thirty]. While such enquiry should inform and meliorate 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 avert environmental degradation in the outset place [8], [9].

Supporting Information


Nosotros thank all authors who provided their original data to develop this work. R. Aguilar and Due north. Mariano provided suggestions to meliorate 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 Lead MMR. Contributed reagents/materials/analysis tools: PM JMRB Atomic number 82 MMR. Wrote the newspaper: PM JMRB Atomic number 82 MMR.


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Explain Why Wetlands Generally Have a High Level of Biodiversity

Source: https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0093507

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