The species composition—ecosystem function relationship: A global meta-analysis using data from intact and recovering ecosystems

Peter J Carrick; Katherine J Forsythe

doi:10.1371/journal.pone.0236550

. 2020 Jul 30;15(7):e0236550. doi: 10.1371/journal.pone.0236550

The species composition—ecosystem function relationship: A global meta-analysis using data from intact and recovering ecosystems

Peter J Carrick ^1,^‡,^*, Katherine J Forsythe ^2,^‡

Editor: Patrice Savadogo³

PMCID: PMC7392319 PMID: 32730290

Abstract

The idea that biodiversity is necessary in order for ecosystems to function properly has long been used as a basic argument for the conservation of species, and has led to an abundance of research exploring the relationships between species richness and ecosystem function. Here we present a meta-analysis of global ecosystems using the Bray-Curtis index to explore more complex changes in the species composition of natural ecosystems, and their relationship with ecosystem functions. By using data recorded, firstly in reference sites and secondly in recovering sites, captured in restoration ecology studies, we pose the following questions: Firstly, how much variation is there in species composition and in ecosystem function in an intact ecosystem? Secondly, once an ecosystem has become degraded, is there a general relationship between its recovery in species composition and its recovery in ecosystem function? Thirdly, is this relationship the same for all types of ecosystem functions? Data from 21 studies yielded 478 comparisons of mean values for ecosystems. On Average, sites within the same intact natural ecosystems shared only a 48% similarity in species composition but were 69% similar in ecosystem functioning. In recovering ecosystems the relationship between species composition and ecosystem function was weak and saturating (directly accounting for only 2% of the variation). Only two of the six types of ecosystem function examined, biomass and biotic structure, showed a significant relationship with species composition, and the three types that measured soil functions showed no significant relationship. To date, most biodiversity—ecosystem function (BEF) research has been conducted in simplified ecosystems using the simple species richness metric. This study encourages a broader examination of the drivers of ecosystem functions under realistic scenarios of biodiversity change, and highlights the need to properly account for the extensive natural variation.

Introduction

Concern over the effect of rapid biodiversity loss on an ecosystem’s ability to function, and in turn, on its ability to provide humans with valuable ecosystem services, gave rise to the extensive field of research on biodiversity and ecosystem functioning (BEF). Since the 1980s, considerable research, both experimental and correlational, has focused on determining whether the functioning of an ecosystem is influenced by the number of species within it (for reviews see e.g. [1–9]).

In the last few years, consolidation of the BEF field, has revealed wide support for the following generalities:

An overall positive relationship exists between species richness and individual ecosystem functions [4,5].
The positive relationship between species richness and ecosystem functions is overwhelmingly non-linear and saturating [1,7,8,10], but neither the direction or the shape of the relationship is consistent within or across all studies [1,8].
The functional traits of species, or particular combinations of species, are often important in determining species interactions (including species interactions between trophic levels) and ecosystem functioning, rather than number of species alone [3,6,7,11–15].
The impact of species richness on ecosystem multifunctionality (the integration of the impact on a number of functions) is greater than on an average single ecosystem function [10,16,17].
Increasing species richness confers stability to ecosystem functions, i.e. the variance of an average ecosystem function is decreased in measurements across time or space as biodiversity increases [3,4,7,11; but see 1].

Increasingly, studies support the notion that the types of species in the ecosystem have a greater impact on individual ecosystem functions than simply the number of species in the ecosystem [3,10,18,19]. In fact, species can have positive, negative or neutral impacts on ecosystem function, depending on the type of ecosystem function and the species involved [15,20]. Real-world studies have also shown that the abundance of individuals (or the simply the abundance common species) can be the most important driver of ecosystem function [21]. Thus, there is a critical need to explore relationships driven by species composition rather than simply by species richness.

In this paper we pose allied but fundamentally different questions to traditional BEF studies which have focussed on the relationship between the number of species in the ecosystem (i.e. species richness, or often more precisely, species density; [22]) and various measures of ecosystem function. Here, our focus is on the relationship between the species composition of an ecosystem (i.e. community composition or community assemblage) and ecosystem function.

Firstly, within intact natural ecosystems, what is the range of variation within species composition and within ecosystem function, and do the two differ? Secondly, among natural ecosystems that have become degraded by some means, does a general relationship exist between recovery in species composition and recovery in ecosystem function, and what is the strength and shape of this relationship? Thirdly, does the relationship between recovery in species composition and recovery in ecosystem function differ among types of functions and types of ecosystems?

We explore this relationship by conducting a meta-analysis using data from the rapidly expanding field of restoration ecology. A number of researchers have emphasised the need for future BEF research to work on more realistic scenarios, where human activities are modifying biodiversity, and to use more complex, real-world ecosystems already undergoing compositional shifts [7,23,24].

Considerable ecological research has been focussed on the restoration of natural ecosystems, and the data from these studies provide ideal opportunities for addressing large-scale BEF questions [15,25–27]. Ecological restoration studies are useful for such research because they increasingly use multiple reference sites [28, e.g. 29,30], which allows quantification of the inherent heterogeneity and the variety of states in which intact ecosystems naturally occur, both spatially and temporally [31,32]. Restoration studies tend to measure a broader range of ecological functions than most BEF studies [see 28], making them more representative of the range of functions that occur in natural ecosystems. Various measures of species identity and profusion (abundance, cover, biomass etc.) from restoration studies can usefully be integrated with similarity indices (e.g. the Bray-Curtis similarity index) for comparing the species composition of ecosystems. These indices render a more complete view of the ecosystems’ biota than species richness alone. Finally, restoration studies allow comparisons between species composition and ecosystem function to be made across the full spectrum of ecosystem conditions, from completely degraded, through recovering, to intact natural ecosystems [e.g. 33] and thus better reflect many of the states and complexities of the world’s ecosystems.

Data from restoration sites provide snapshots at arbitrary points in time, to meaningfully quantify the difference of a recovering site from their intact ecosystems in terms of species composition and ecosystem function. We were not interested in whether sites were moving on any trajectory towards degradation or towards recovery. Rather, species composition was compared with ecosystem function at whatever point it was measured in a restoration study, the pattern from all the available points applied to a best-fit relationship and the robustness of this relationship tested. To our knowledge this is the first global study to explicitly explore the relationship between species composition and ecosystem function across ecosystem and function types, and to do so using real-world ecosystems.

Materials and methods

Literature search

A literature search was conducted in Web of Knowledge (Thomson Reuters Web of Knowledge) using the terms (RESTOR* OR REHABILIT* OR REFOREST*) AND (ECOLOG* OR ECOSYSTEM OR ENVIRON*) AND (FUNCTION* OR PROCESS* OR SERVICE*) AND (COMPOSITION OR BIODIVERSITY OR DIVERSITY). The resulting studies were filtered by first examining titles, then abstracts for broad relevance, and finally the selected studies were read in full [34]. From these studies we selected only those that met the following two criteria: studies that measured species-level data at restoration sites, together with at least one measure of ecosystem function (in order to quantify both species composition and ecosystem function at the same point on the trajectory of restoration in each ecosystem); additionally species composition and ecosystem functions must also have been recorded in multiple reference sites (in order to quantify the range of natural or intact conditions for each ecosystem). Authors of these studies were then contacted for the raw data: abundance, cover or biomass of each individual species, and metrics for all measured ecosystem functions within each site.

The literature search produced 4072 studies. After examining these studies for title relevance, abstracts and then in full, 67 studies meeting our criteria remained and requests for the data were then made to the authors. Studies were excluded at this last stage because the data provided were not sufficient as they failed to either adequately measure species composition or to provide suitable reference sites. The authors of twenty studies were able to provide suitable raw data, and an additional five studies were included as sufficient data was provided within the publications themselves, or appendices and supplementary material (Fig 1). Twenty-one studies reported on the species composition of plants and four on the species composition of arthropods. After a preliminary analysis the four studies were excluded from the analysis in order that it focus on the single trophic level of plant species composition. Measures of ecosystem function were used exactly as reported by authors in their studies.

Fig 1 — Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram, modified from Moher *et al*. 2009 [35].

Studies took place in multiple countries, but were unevenly distributed among continents, with Australia, North America and Europe being well represented (ten, seven and five studies respectively) while only two studies were from Asia, one from South America and none from Africa (Table 1). The studies were also unevenly distributed among the earth’s climatic biomes, with tropical biomes being the most poorly represented, but were fairly evenly distributed among five ecosystem types (based on vegetation physiognomy): forest, woodland, shrubland, grassland and wetland. Measurements of ecosystem function were fairly evenly distributed into six broad categories, three measuring biological functions: biomass, biotic structure, biotic interactions, and three measuring soil functions: soil litter, soil nutrients, soil structure (Table 2).

Table 1. Data characteristics of all contributing studies used in the meta-analysis.

(Plant species composition was recorded in all sites.)

Study	Country	Number of Restoration Sites^*	Number of Reference Sites^*	Degradation Type	Ecosystem Type	Ecosystem Function Types^**
Andersen et al. [75]	Canada	8	3	Peat mining	Wetland	Soil litter(1), Biotic structure(1)
Brown et al. [76]	USA	5	2	Contaminated soils	Wetland	Biotic interactions(7), Soil nutrients(2)
Calviño-Cancela, Rubido-Bará & van Etten [77]	Spain	25	5	Clearing & plantations	Forest	Biomass(1)
Emery & Rudgers [78]	USA	18	18	Dune removal	Grassland	Biomass(2), Soil nutrients(4), Biotic interactions(2)
Forup & Memmott [79]	UK	2	2	Afforestation, agriculture	Shrubland	Biotic interactions(2)
Forup et al. [80]	UK	4	4	Afforestation, agriculture	Shrubland	Biotic interactions(2)
García-Palacios et al. [81]	Spain	(12,11)	(5,5)	Road Development	Grassland	Soil structure(1), Soil nutrients(3), Biotic Interactions(1)
Good et al. [82]	Australia	14	5	Clearing	Woodland	Biomass(3), Soil litter(1), Soil structure(1), Biotic structure(5), Soil nutrients(8)
Gould [83]	Australia	31	36	Mining	Woodland	Soil structure(1), Biotic structure(3), Soil nutrients(1)
Herath et al. [84]	Australia	4	3	Mining	Shrubland	Soil nutrients(8)
Jiao et al. [85]	China	(2,2)	(11,7)	Clearing	Shrubland	Soil structure(3), Biotic structure(1), Soil nutrients(6)
Luo, Sun & Xu [86]	China	3	3	Clearing	Wetland	Biomass(1)
Martin, Moloney & Wilsey [87]	USA	(1,1,1)	(3,3,3)	Agriculture	Grassland	Soil litter(2)
McLachlan & Bazely [88]	Canada	28	3	Clearing	Forest	Soil structure(1), Biotic structure(1)
Meers et al. [89]	Australia	3	4	Clearing & plantations	Woodland	Biotic interactions(3)
Miller et al. [90]	Australia	2	2	Mining	Shrubland	Soil nutrients(3)
Parrotta & Knowles [91]	Brazil	9	8	Mining	Forest	Soil litter(1), Biotic structure(1), Soil nutrients(1)
Polley, Wilsey & Derner [92]	USA	(5,5)	(5,5)	Agriculture	Grassland	Biomass(1)
Soini et al. [93]	Finland	1	10	Peat mining	Shrubland	Biotic structure(1)
Sonter et al. [94]	Australia	(1,1,1)	(5,5,5)	Clearing	Forest	Soil structure(1), Soil litter(1), Biotic structure(3)
Stefanik & Mitsch [95]	USA	5	3	Development	Wetland	Biomass(1)

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* Numbers in parentheses refer to the number of plots within distinct groups of restoration or references sites within a study (e.g. vegetation types, geographically separated areas, years etc.; treated as separate ecosystems).

** Numbers in parentheses refer to the number of ecosystem function measures in each ecosystem function type within a study.

Table 2. Descriptions of ecosystem function types used to group the various ecosystem function measures, from all contributing studies used in the meta-analyses.

Ecosystem Function Type	Explanation
Biomass	Measures of live plant biomass or primary productivity.
Biotic Structure	Structural characteristics of the plant community such as total plant cover, tree height and canopy cover/volume.
Biotic Interactions	Complex interaction between species, or between species and the environment. These interactions may relate to propagation of groups of species within the ecosystem (e.g. pollination, animal facilitated seed dispersal and seedbank composition/viability) or presence of important biota (e.g. soil invertebrates, bacteria and fungi) which fulfil multiple roles in the ecosystem (e.g. decomposition, soil aeration, mutualisms with plants).
Soil Litter	Measures of leaf litter and other dead plant material (e.g. dead wood) on the soil surface, but excludes measures of decomposition.
Soil Nutrients	Measures of nutrient pools in the soil, as well as indicators of nutrient cycling.
Soil Structure	Measures related to soil temperature, stability, texture, and water retention.

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For the analysis of the level of variation in intact ecosystems there were 28 different groups of reference sites. This produced 28 measures of species composition similarity (because some studies had reference sites in multiple ecosystems) and 55 measures of ecosystem function similarity (because some studies measured multiple ecosystem function types). For the analyses comparing restoration sites to intact ecosystems, the 21 studies yielded a total of 1850 unique measures, which allowed 478 comparisons of species composition and ecosystem function means (mean response ratios) among 205 restoration sites (again because most studies reported on multiple restoration sites and measured multiple ecosystem function types). (S1 Dataset) contains the full dataset, and (S1 Checklist) the PRISMA checklist [35].

Similarity metrics and response ratios

Similarity metrics were used to compare similarity in species composition among sites. The Bray Curtis metric has a number of numerical qualities which makes it is especially suitable for comparing species composition among ecological communities. For example, it can accommodate different measures of species profusion (e.g. counts or abundance, cover biomass and density) and ignores “joint absences” so does not consider samples similar just because they both lack a certain species [36]. Data for the abundance, cover, biomass etc. of each species were first squared root transformed to down weight the importance of overabundant species [36]. Similarity matrices were constructed and analysed using PRIMER v. 6 [37].

Given that ecosystems always display some level of heterogeneity, variation will inevitably exist among samples from two or more reference sites within an ecosystem. By using only restoration studies which had multiple reference sites we were first able to compare the average variability among reference sites within a study ecosystem in terms of both species composition and ecosystem function. This metric (mean % similarity between reference sites) was then used as a baseline with which to gauge the similarity of restoration sites to the range of recorded states for the ‘intact ecosystem’. Thus creating a metric that allows us to examine the relationship between species composition and ecosystem function within restoration sites that is comparable across all the different ecosystems reported in the restoration studies.

To derive the mean % similarity for species composition among the reference sites for each study, we used measures of species profusion (e.g. abundance) to construct pairwise Bray-Curtis similarity matrices. First, using all pairwise values for reference sites, we calculated a mean % similarity for reference sites within each ecosystem. If a study included logical groupings (e.g. spatially segregated groups of reference and restoration sites) we treated these as separate ecosystems. Next, we compared the similarity of each restoration site to the range of the relevant reference sites. To do this we used the same pairwise Bray-Curtis metrics, with the comparison this time being between a restoration site and each of its reference sites. These comparisons were then used to derive a mean % similarity for each restoration site relative to the reference sites in an ecosystem.

Using both the mean % similarity within references sites and the mean % similarity between the restoration and reference sites, we were then able to calculate a response ratio which explicitly evaluates how close a restoration site is to the range of reference sites in a study. To do this we modified the traditional response ratio [38], ln(RR+1) = ln(REST+1/REF+1), where REST is the mean % similarity of restoration sites to reference sites and REF is the mean % similarity within reference sites. To account for zero values we added a value of one to both the numerator and denominator [e.g. 39].

The same response ratio was also calculated for ecosystem function. However, unlike species composition (where the Bray-Curtis metric was used to reduce multi-dimensional data to a single comparative metric), each site had only one value relating to an ecosystem function. Therefore, to calculate the pairwise similarities of ecosystem function, for both among reference sites and between restoration and reference sites, we simply used the ratio of the smaller to the larger measure. In cases where multiple measures of ecosystem function within the same ecosystem function type were provided, the mean response ratio across all those functions was used (rather than each measure separately) to avoid the analysis being unduly weighted by numerous measures from a few studies or from a few ecosystem function types.

Data analysis

First, we tested whether there was more similarity, within intact ecosystem sites, in species composition or in ecosystem function, using a general linear model. The response variable was the mean similarity within reference sites (as described above), and the explanatory variable was the type of measure (ecosystem function or species composition). Secondly, for ecosystem function we tested whether this similarity differed between different ecosystem function types, and for species composition we tested whether the similarity differed between ecosystem types. In this second model, the response variable was either the mean similarity within reference sites in terms of ecosystem function or species composition, and the explanatory variable was either the function type or the ecosystem type.

General linear mixed models were then used, with data from restoration sites, to assess the relationship between species composition and ecosystem function. The base model used the ecosystem function response ratio as the response variable and species composition response ratio as a fixed explanatory covariate. Two random terms, ‘study’ and ‘ecosystem function type’, were also included to account for multiple and differing numbers of restoration sites in each study and also to account for non-independence of multiple ecosystem function measures at some sites.

We explored whether ecosystem type or ecosystem function type influenced the relationship by including these factors and their interaction with species composition as fixed effects in the model, and ‘study’ retained as a random term. Backwards stepwise selection was used, starting with all predictor variables included in the model and removing factors and evaluating their influence on corrected Akaike Information Criterion (AICc). The Best-fit model chosen was the one with the lowest AICc value. If models provided comparable AICc values (within 2 units of the best model), then the one containing the fewest variables was chosen (S2 Dataset) contains the full list of AICc values. The models were fitted with the maximum likelihood (ML) criterion to allow comparison using AICc, but to obtain parameter estimates the models were refitted with restricted maximum likelihood (REML) criterion. The significance of the predictor variables in final models were examined using Type III F-tests. The Kenward-Roger approximation was used to estimate the denominator degrees of freedom and calculate p-values. Preliminary analyses tested additional factors in general linear mixed models (years since restoration commenced and active vs passive restoration). These factors did not increase resolution in the model, but reduced their power and scope as not all studies reported data for these factors (S3 Dataset) describes the models used in the final meta-analysis.

The nature of any interaction was explored by re-running models for a subset of data for each different group (i.e. each ecosystem function type). In addition to generating scatterplots of response ratios, values and model outputs were back-transformed to positive numbers 0–100% to be more intuitive to understand. Positive response ratios (values over 100%) were assigned the value of 100% (as they were within the range of intact ecosystems).

For all models, model fit was assessed visually using the residual and q-q norm plots to ensure model assumptions were not violated. To examine the proportion of the variance explained by the models we used the approach of Nakagawa & Schilzeth’s [40] to generate the marginal R² (fixed effects alone—considered analogous to the R² value used in simple linear models) and the conditional R² (fixed and random effects). All analyses were conducted in R [41].

Results

Heterogeneity within intact ecosystems

Among reference sites there was considerable range in the similarity of both plant species composition and ecosystem function (species composition ranged from 23 to 88%, ecosystem function ranged from 27 to 98%). Similarity was, however, greater for ecosystem functions than for species composition (F_1,81 = 26, p >0.001, Fig 2). Thus, these results indicate that intact ecosystems were more variable in terms of species composition than ecosystem function. The degree of similarity in ecosystem function did not differ between different ecosystem function types (F_5,49 = 0.76, p = 0.59, Fig 3) nor did the degree of similarity in species composition differ across different ecosystem types (F_4,23 = 1.7, p = 0.19, Fig 3).

Fig 2 — Similarity for species composition is the mean of pairwise Bray-Curtis similarity measures for each group of reference sites within a study, and similarity for ecosystem function is the mean of pairwise ratios for each ecosystem function type in each group of reference sites within a study. A significant difference (p < 0.05) between the mean similarity of species composition and the mean similarity of ecosystem function was found using a general linear model, and is denoted by different letters.

Fig 3 — (A) Mean similarity (±SD) in ecosystem function among different ecosystem function types within intact natural ecosystems globally (i.e. reference sites). (B) Mean similarity (±SD) in species composition (Bray-Curtis similarity) among different ecosystem types globally (i.e. reference sites). Similarity is the mean of pairwise ratios within each group of reference sites within a study, for each function or ecosystem type. There were no significant differences among function types or among ecosystem types.

The species composition—ecosystem function relationship

Overall we found a positive relationship between plant species composition and ecosystem function indicating that as a site’s species composition is restored (i.e. as it becomes more similar to references sites) so is its ecosystem function (Fig 4).

Fig 4 — For each measure, for species composition or ecosystem function, the response ratio is a measure of how similar the restoration sites are to their reference sites, and has the formula ln(similarity of restoration sites to reference sites +1/similarity within reference sites + 1). Values ≥ 0 on the x-axis or the y-axis can be considered to be restored to within the range of intact ecosystems (i.e. reference sites). The line is the output from a general linear mixed model with ecosystem function type and study as random factors. * Denotes a significant relationship.

The base model’s positive relationship between species composition and ecosystem function was significant, but only explained a small amount of the variance in the dataset. The marginal R² (R_M²) was only 2%, indicating that the fixed effect alone (species composition) explains very little of the variance in ecosystem function (Table 3). When back-transformed and plotted on a 0–100% scale, the relationship was curvilinear, and although weak, indicated a positive saturating curve, that even at full species composition does not attain the ecosystem function levels of intact ecosystems (Fig 5).

Table 3. Output of the general linear mixed models for the relationship between species composition and ecosystem function, in 21 studies around the globe.

Analysis includes the base model which controls for the random effects of study and ecosystem function type and the best-fitting model which includes ecosystem function type and the interaction between species composition and ecosystem function type as fixed factors, and study as a random factor. SC = Species Composition; EFtype = Ecosystem Function Type (for descriptions see Table 2).

Model and Component	F-value	^*df	^*p-value	Deviance explained (%)	^**R_M² (%)	^**R_C² (%)
Base Model:
SC + (study + EFtype)					1.98	35.5
SC	4.6	1, 245	0.033	0.7
Best-fitting Model:
SC +EFtype+ SC^*EFtype + (study)					21.0	35.1
SC	14.3	1, 266	<0.001	0.7
EFtype	2.9	5, 402	0.01	11.7
SC^*EFtype	4.7	5, 398	<0.001	3.3

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* The Kenward-Roger approximation was used to estimate the denominator degrees of freedom (numerator, approximated denominator) and calculate p-values.

** Estimated variance is explained by marginal R² values (R_M² = fixed factors only) and conditional R² values (R_C² = both fixed and random factors).

Fig 5 — Data have been back-transformed into positive values by taking the exponent of both the species composition and ecosystem function response ratios. The black line is the model output and the grey area represents 95% confidence intervals around the model. * Denotes a significant relationship.

Our best fitting model included species composition, ecosystem function type and their interaction, but excluded ecosystem type. This indicates that the inclusion of ecosystem type had little impact on the relationship, and that the relationship between species composition and ecosystem function was not consistent among different ecosystem function types. The best-fit model had far greater explanatory power than that of the base model (R_M² = 21%; Table 3). A preliminary analysis using the same model, but including data for from four arthropod studies together with the 21 plant species composition studies, had almost identical results (R_M² = 21%; R_C² = 33%).

The interaction between ecosystem function type and species composition was explored by examining the model outputs for each ecosystem function separately (Fig 6). Species composition was only significantly associated with two ecosystem function types (Table 4, Fig 6). For the functions biomass and biotic structure, species composition explained a sizable amount of variance in the data (R_M² = 31% and R_M² = 17% respectively, Table 4), and when back-transformed to a 0–100% scale these two ecosystem functions exhibited strong saturating relationships (Fig 7). For the other four ecosystem functions types (biotic interactions, soil litter, soil nutrients and soil structure), the relationship between species composition and function were not significant, most of the points sitting at or close to the level of functions in intact ecosystems regardless of species composition, indicating that the identity of species in the ecosystem was immaterial to those ecosystem functions.

Fig 6 — For species composition or ecosystem function, the response ratio is a measure of how similar the restoration sites are to their reference sites, and has the formula ln(similarity of restoration sites to reference sites +1/similarity within reference sites + 1). Values ≥ 0 on the x-axis or the y-axis can be considered to be restored to within the range of intact ecosystems (i.e. reference sites), and lines are the output of separate general linear mixed models. * Denotes a significant relationship.

Table 4. Output of general linear models for each of the six ecosystem function types separately.

Each model contains species composition as a fixed factor and study as a random factor. (For descriptions of ecosystem function types see Table 2.)

Ecosystem Function Type	F-value	^*df	^*p-value	Deviance Explained (%)	^**R_M² (%)	^**R_C² (%)
Biomass	7.260	1, 65	0.009	6.5	31.4	33.2
Biotic Structure	7.250	1, 40	0.010	5.7	17.2	49.4
Biotic Interaction	0.190	1, 23	0.666	0.4	0.7	20.0
Soil Litter	0.060	1, 3	0.823	0.3	0.4	6.2
Soil Nutrients	0.790	1, 63	0.377	0.7	1.4	38.4
Soil Structure	<0.001	1, 11	0.984	<0.1	<0.1	8.8

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* The Kenward-Roger approximation was used to estimate the denominator degrees of freedom (numerator, approximated denominator) and calculate p-values.

** Estimated variance is explained by marginal R² values (R_M² = fixed factors only) and conditional R² values (R_C² = both fixed and random factors).

Fig 7 — Data have been back-transformed into positive values by taking the exponent of the response ratio. The black line is the model output and the grey area represents 95% confidence intervals around the model. * Denotes a significant relationship.

Discussion

The species composition—ecosystem function relationship

In this study our interest was not simply in whether adding or removing species to an ecosystem allows us to detect changes in one or other ecosystem function [e.g. 42]. Instead, we are interested in concomitant changes in the levels of both species composition and ecosystem function relative to their intact natural condition. BEF research has often failed to incorporate natural levels of diversity and heterogeneity, and consequently studies have frequently been conducted in artificially simplified ecosystems [7,24]. Even when these studies use natural ecosystems, they tend to be conducted in ecosystems with inherently low levels of diversity or those with relatively simple structure, particularly grasslands [12,42,43]. The concentration of studies on low diversity ecosystems makes extrapolating to more complex ecosystems problematic [6,44,45]. Our approach overcame many of these inherent problems and allowed us to test the generalisable nature of these relationships across different ecosystems, regardless of their inherent level of biodiversity (i.e. species rich or species poor ecosystems). In doing so we ensured that these relationships were directly relevant to real-world ecosystems.

Our global meta-analysis of ecosystem function in relation to species composition across a range of degraded, recovering and intact ecosystems revealed the following generalities (contrast with the generalities in BEF studies outlined in the Introduction):

The overall relationship between species composition and ecosystem function is positive. This demonstrates that the types, and abundance, of species present in an ecosystem can influence how an ecosystem functions.
The relationship between species composition and ecosystem function is non-linear and saturating, but it was not consistent across all the ecosystem functions that were explored.
Different ecosystem functions exhibited different relationships with species composition, and for some functions we found no relationship at all. Consequently, the weak relationship in the base model was strengthened by an order of magnitude when the type of ecosystem function is taken into account.
We did not explore the relationship between species composition and ecosystem multifunctionality. Although analytically complex, this may be a rewarding avenue for future studies utilising the rapidly expanding data available from restoration ecology and similar fields.
We did not test the stability of ecosystem function relative to species composition, and consider it unfeasible using this type of data (as it entails holding species composition constant but lower than intact ecosystems, across time or space, in order to generate the replicate measures needed to generate reliable stability metrics across a range of species compositions).

Unlike the differences in the type of ecosystem function, accounting for differences in the type of ecosystem did not affect the relationship between species composition and ecosystem function in our models, implying that the relationship may well be generalizable across global ecosystems. The number of studies across the different ecosystem types in our meta-analysis was small and thus our ability to identify differences in the relationship among ecosystem types was fairly weak. Aquatic and terrestrial ecosystems have been found to have similar species richness–ecosystem function relationships in a number of studies [4–6,15,17]. Our meta-analysis, similarly, did not differentiate wetland from the other four terrestrial ecosystem types. Few BEF meta-analyses compare species richness–ecosystem function relationships among terrestrial ecosystem types, but in the comprehensive study of Cardinale et al. [6] results were also fairly consistent across ecosystem types, with the only difference being a suggestion that forest ecosystems responded differently in terms of biomass functions. In the smaller Balvanera etal. study [4] the relationships were actually weaker in ecosystem types with more studies (grassland, forest, aquatic and marine), and stronger in ecosystem types for which there was fewer data (ruderal, crop, salt marsh, bacterial and soil).

In common with BEF studies, our results are not drawn evenly from continents and biomes around the world. Temperate grasslands dominate species richness–ecosystem function studies [4,6,8,46], and large meta-analyses have repeatedly found that tropical biomes and the continents of South America and Africa are underrepresented [4,6,8,46,47]. Our study reflects a similar bias in restoration research across the globe.

Nature of species composition and other biodiversity relationships

BEF research has been one of the most prominent areas of ecological research over the past three decades [7]. Even in highly simplified and microcosm experiments, however, results have not always been consistent [4–6,8] and the explanatory power of fitted relationships between species richness and ecosystem functions has ranged widely (e.g. R² = 71% [5]; R² = 29–73% [6]). Attempts to generalise across real ecosystems have produced significant relationships with ecosystem function, but also found large variance [46,48,49]. For example, Maestre et al. [49] explored the relationship between species richness and productivity/nutrient functions in multiple drylands across the globe. They found that species richness was ranked amongst the best predictor variables for ecosystem function, although on its own it accounted for very little of the variation (highest R² value = 3.2%). In two recent global meta-analyses, using data from naturally assembled communities, abiotic factors and functional composition were found to be stronger drivers of ecosystem function than species richness [46,50]. In our study, the weak relationship between species composition and ecosystem function suggests factors other than species composition may control the recovery of ecosystem function.

Being able to reliably predict the point of biodiversity change where large or irreversible damage to ecosystem function occurs also remains elusive [6,7]. Expert opinion originally estimated that 50% of species would be required to maintain ecosystem functions at 75% of their maximum [51]. More recent analyses have suggested this may be an underestimate [6]. Perhaps a more pertinent question, and one that we have attempted to address here, is what proportion of the species composition is required to maintain ecosystem function at similar levels to those of intact ecosystems? Our results suggest that on average, species composition would need to be 40–50% similar in order to have functions at 75% of reference site means. The major increases in ecosystem function, as a whole, occurred within the first c. 20% of similarity in species composition. These estimates provide a starting point for further exploration of species composition relationships. However, the high levels of variation and inconsistency of the relationships among the different ecosystem function types do limit the generality of these predictions.

These studies suggests that a few species may be responsible for the majority of functioning within an ecosystem, with additional species providing limited further benefits. If this is consistently true, the implications for biodiversity are substantial, but we cannot on this basis, claim that dramatically reduced species composition would be adequate in providing ecosystem functions in most ecosystems, under most conditions or most of the time. In fact this suggested redundancy may be the primary mechanism that confers stability and resilience to ecosystems, ensuring that ecological functioning is maintained despite the decline or extinction of particular species or the change in conditions within an ecosystem, and this has been termed the insurance effect [2,14]. This insurance effect is especially relevant when larger space and time scales are considered [1,52,53]. Isbell et al. [53] have comprehensively debunked a simplistic view of redundancy, showing clearly that the proportion of species in an ecosystem providing an ecosystem function, increases with the number of years, places and environmental changes considered, and that these increases are further compounded by interactions between these factors, supportive of a general complementarity rather than simple redundancy.

There is overwhelming evidence that species richness and species composition play a role in determining ecosystem function [4–8], but if this role only accounts for a small proportion of the variance in most real-world ecosystems, then the emphasis given to this relationship should be re-evaluated. There is a need to examine other factors that play a role in ecosystem function, if we intend to ensure their sustainability [6].

Heterogeneity within intact ecosystems

The consistently low levels of similarity in species composition that was found within intact ecosystems, highlights that there are many naturally occurring combinations of species occupying any one ecosystem. Despite the resurgent attention on alternate stable states in ecology [54,55] the levels of species heterogeneity inherent in natural ecosystems has typically been underestimated. This is implicitly demonstrated by the fact that many studies that report on compositional change in ecosystems, ascribe the change to an external impact (e.g. changes in climate, fire, herbivory etc.), rather than imagining compositional drift to be an inherent dynamic.

The levels of similarity in species composition among intact ecosystems reported here were low (a mean of 48% in Bray-Curtis similarity) but were similar to other studies with comparable statistics, measured through time rather than space (e.g. fynbos heathlands in South Africa [56,57], jarrah forests in Australia [58], upland grasslands in Wales [59] and semi-arid succulent karoo shrublands in South Africa [60]). Across a broad range of ecosystem types therefore, without external impacts beyond natural disturbances, it is not unusual for half the species composition to change within a single site, over about 30 years.

Differences in the relationship among ecosystem function types

Surprisingly only two of the six ecosystem function types exhibited strong and significant relationships with species composition, namely, biomass and biotic structure. Measures of biomass and productivity are ecosystem functions most commonly used in BEF research, and are invariably found to have among the strongest relationships with species richness in meta-analyses [1,4,6,7,9,10,61,62]. For biomass functions, however, even when species composition was fully recovered, ecosystem function remained lower than that of intact sites, indicating that full species composition alone was not sufficient to attain full ecosystem functioning. These functions are often heavily influenced by large, slow-growing plant species, which need to reach a certain size before full levels of these functions are achieved [63]. Even when not governed by specific large species, some functions may only fully return with the passing of time [e.g. 45,64–66].

More that half of the biotic interactions concerned soil-based interactions (e.g. bacteria, fungi and biological crusts) and the flat species composition relationship of this function is consistent with those of the other soil-based ecosystem functions in our meta-analysis. While measures of soil structure are not widely reported in the BEF literature, measures of soil nutrient pools, mineralisation and decomposition are, and meta-analyses frequently find these soil nutrient and soil litter functions to have a weak but significant relationship with species richness [1,4,6,7,9,10,62]. Potentially then species richness and species composition relationships may differ for these functions The inference being that vastly different, but diverse, species compositions may all support the development of a certain level of nutrient cycling and availability.

The ecosystem function concept is broadly defined, with some ecosystem functions apparently not driven by either species richness or composition [4,6,7,45,50]. The field as a whole would benefit from a greater refinement of the concept.

Implications for restoration ecology

The high level of variation found within intact reference ecosystems in our study emphasises the importance of including multiple reference sites against which to compare any altered ecosystem. The field of restoration ecology has recognised both that vegetation may occur naturally in a range of species compositions, or states [67,68], and the corollary that there is a need to use multiple reference sites in restoration projects [28,30,31]. Without a baseline which captures the inherent heterogeneity of the broader target ecosystem it is very difficult to accurately assess whether a site should be considered intact, degraded or on a path of recovery between the two. The end goal for restoration projects should not be a specific reference point but rather any point within a cloud of reference conditions or states. The reference conditions can be defined both in terms of species composition and in terms of ecosystem function, with our results suggesting more inherent heterogeneity expected in species composition measures than in ecosystem function measures.

Our results also suggest that restoring species composition cannot be taken as a proxy for restoring ecosystem function, or vice versa. Restoring ecosystems for function alone, can lead to the assembly of novel ecosystems which do not resemble the reference sites’ species composition [69]. This may be misaligned with conservation goals, especially if novel ecosystems contain exotic or invasive species [69,70]. While we would not consider an ecosystem containing significant invasive species restored, it is necessary to accept that, accelerated by multi-faceted human-induced global change, it may no longer be possible to reinstate specific assemblages if, for instance, component species have shifted range, have been extirpated [71] or the underlying substrate, rock structure or hydrology has been altered. Developing goals for recovering critical levels of ecosystem function should concern restoration ecologists as much as recovering critical levels of ecosystem composition.

Some ecosystem functions may be more easily restored than others. Not all ecosystem functions are equal measures of ecosystem recovery, and in some cases, are not indicative of recovery at all. Therefore understanding the role and sequence that functions play in the trajectory of restoration is critical [72,73]. For example, monitoring soil nutrients, soil structure, and potentially soil biotic interactions, may be critical in the early stages of restoration, as their recovery may be an obligate condition for the restoration of the ecosystem as a whole, but the emphasis may shift to biological functions at later stages of restoration [63–68]. There may be a variety of goals and priorities for each restoration project, but a project should never focus solely on any single component of the ecosystem. A true test of restoration efficacy would be to target the functions that are hardest, rather than easiest, to return [74].

Supporting information

S1 Checklist. PRISMA (preferred reporting items for systematic reviews and meta-analyses) checklist to assist in the critical appraisal of the meta-analysis and systematic reviews.

(DOC)

Click here for additional data file.^{(60.5KB, doc)}

S1 Dataset. Data and similarity metrics for each restoration site (n = 205), including each measure of ecosystem function.

(XLSX)

Click here for additional data file.^{(120.8KB, xlsx)}

S2 Dataset. Corrected Akaike Information Criterion (AICc) for each general linear mixed model.

(XLSX)

Click here for additional data file.^{(9.1KB, xlsx)}

S3 Dataset. Descriptions of the models run.

(XLSX)

Click here for additional data file.^{(13.7KB, xlsx)}

Acknowledgments

The authors would like to thank Arjun Amar for statistical advice and comments on earlier versions of this paper. The authors would also like to thank Jasper Slingsby and a number of anonymous reviewers for comments that greatly improved the paper. We are particularly grateful to all the authors of the studies used for providing their raw data, without which we could not have undertaken this study.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0236550.r001

Decision Letter 0

Patrice Savadogo

14 Feb 2020

PONE-D-19-31206

The species composition - ecosystem function relationship: a global meta-analysis

using data from intact and recovering ecosystems

PLOS ONE

Dear Dr. Carrick,

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General comments

Review of PONE-D-19-31206: The species composition - ecosystem function relationship: a global meta-analysis using data from intact and recovering ecosystems

This manuscript meta-analytically reviews literature on biodiversity and ecosystem function (BEF) that involved the outcomes of 25 individual restoration studies. Focusing on the methodological features of the study, the authors appropriately follow the PRISMA guidelines in reporting the findings of their meta analysis and the methods of the literature search and statistical analyses generally appear to be appropriate. There were some areas, however, where it would be beneficial to be more explicit about various methodological details of the meta-analysis. Specifically: (i) The eligibility criteria for studies being chosen for the review: the authors have considered a wide-variety of degradation types in their paper selection that is a great benefit but it is a bit questionable that 4 out of 25 studies were related to insects. Also a strong effort to find all the studies from major biomes would make the study a valuable resource for future studies in this field. One of the reviewer has pointed out that “tropical systems have severely weak represented”. (ii) The author should clarify whether all the studies included in the pooled meta-analysis have a comparable diagnostic cut-off point.

Based on the above and from the referees' comments, this paper require is a Major revisions. Please use the comments to make a thorough and careful revision.

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Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have conducted a meta-analysis of the relationship between species diversity (via the Bray-Curtis index) and a variety of ecosystem function metrics. The topic is timely. A willingness to re-examine long-held relationships in the field is essential to continued growth, and this paper could make a valuable contribution in that sense. However, I have concerns that the analysis, as presented, lacks sufficient documentation and breadth to support the authors’ conclusions. My largest issues are:

1. Representation of study scope. The study is repeatedly presented as a wide-reaching and “global” meta-analysis, but only 25 studies were analyzed, 21 of those were plant studies (the other four were insects), and tropical systems have severely weak represented. The authors should make such limitations in taxa, region, and study size more explicit in scope of their paper and conclusions.

2. Lack of documentation on study comparability. One of the greatest challenges of a meta analysis is ensuring studies are suitably comparable in their response metric, as well as the attributes of the study systems themselves. Currently, it is difficult to evaluate how the authors addressed this challenge. Though the authors have created a standardized summary statistic, this does not address underlying qualitative differences (e.g. species, region, study methods) that might create unaccounted variation (i.e. the apples to oranges problem). This unaccounted variation could contribute to the weak correlation reported by the authors. Additionally, the authors’ summary statistic is a simple mean, and so does not account for within-study variance in the response variable. Koricheva et al.’s Handbook of meta-analysis in ecology and evolution is a useful resource for addressing these issues.

3. Focus on a single similarity metric. A central premise of the paper is that species composition has greater impact than species richness. Why not, then, calculate both composition and richness, instead of composition alone? To me, the most powerful approach would be to compare these two measures within a common system to see if/how the results truly differ. Analyzing results from multiple metrics would make the conclusions more robust and would make better use of the power of meta analysis.

L77-78: I would appreciate more detail on what specific components/definitions of ecosystem functioning (e.g. nutrient cycling, primary productivity, food production, etc.) the authors are referring to here and in other parts of the introduction. This would help define the scope of the paper and be more illustrative to readers not as familiar with the field.

L80-81: What breadth of regions/taxa do these studies represent?

L108: How are “intact natural ecosystems” defined? Are the systems under consideration similar in region/biome/size?

L119-120: While I agree it is important to consider how ecosystems respond to real-world change driven by factors such as “degradation, restoration, fragmentation or climate change,” these processes likely have very different trajectories/outcomes in terms of species composition and ecosystem function. The authors might consider clarifying this, as well as what scope of altered landscapes are under consideration in this paper.

L140-143: On what evidence is this claim based? How far is “sufficiently far”?

L171-172: Some info on studies is available in Table 2, but more summary information would be helpful. How many studies were available in each habitat category? What species/regions are represented, and what are their distributions? What types of restoration projects do the studies represent? What spatial/temporal scales do the studies cover? One of the most challenging aspects of a meta analysis is ensuring the selected studies are sufficiently comparable, so providing more summary information on the studies is essential.

L172: What is the difference between forest and woodland?

L196: What are some examples of an “intact ecosystem” across the different studies considered? Are they comparable?

L214: How does this equation incorporate variance in the measurements? As far as I can see, only the mean values are being used. A more informative metric might consider how much the two distributions overlap, not just how far the two means are. Otherwise, you could have two mean values that are far apart, but only because the two distributions have wide variance.

L222-224: How similar were measures of ecosystem function across studies? (in terms of the specific metrics used, not the categories the authors assigned). This information is important to evaluate whether the measurements are truly comparable, and even when standardizing them into ratios.

L233: I am not sure that a linear model is the most appropriate way to analyze these data. My concern is that the explanatory variables, ecosystem function and species composition, are not wholly independent — both measurements are calculated for each group of reference sites. I imagine there is large variation among different groups of reference sites, and I don’t see how the model is currently accounting for this. Would it not be better to use something like a paired T-test, where the two measures are directly compared within the context of their site?

L243-246: The authors should consider including additional variables in the model that might explain variation among sites, such as study species, region, study size, etc. These factors could create variation among studies that will mask overall trends.

Table 2: Looking at the table, I am concerned about the representativeness and comparability of the data. First, the studies included in analysis are primarily plants. I believe the authors should make this clearer upfront, and structure the scope of their questions/conclusions accordingly. Second, how comparable are ants to vegetation? And what kinds of vegetation — grasses, trees? Finally, the study is described multiple places as “global,” but this is a bit of a micharacterization, as there are 10 studies from Australia, 7 from North America, 5 from Europe, 2 from Asia, 1 from South America, and 0 from Africa. This regional bias toward temperate systems should be at the very least addressed, preferably incorporated more directly in analysis, especially given the high richness of species and ecosystem services in tropical areas that is currently not being represented.

L316: I believe the reference figure should be Fig 3, not Fig 2.

Table 3: Authors should report a full list of models run (not just the best-fitting model), as well as the AICc values. This would be fine as a supplementary table.

Fig 4/5: Would it be possible to report both raw data and the modeled response in the same figure? This would make interpretation easier and take up less figure space. Similar thoughts for fig 6/7.

L426-429: For a relationship like this, I would expect time to be an important factor. Among the studies included, what amount of time had the sites been degraded? How long had restoration been taking place? Can these factors be accounted for in the model?

L48: The authors use the Bray-Curtis index and find limited evidence for a relationship with ecosystem functioning. Would this result hold true with other composition/richness indices? How does choice of measurement influence results? Conclusions would be more robust if multiple indices were considered.

L441: But this study did not analyze species richness, despite the seeming ease of doing so. Why not report results from both Bray-Curtis and richness, so as to compare them directly within a common analytical framework? This would make for a more robust result and take better advantage of the meta analysis capabilities.

L507-509: What sizes and separation distances are represented by these sites? How many of the cited examples are plants? Are there any contrary examples that might be illustrative? I ask because (1) I would expect studies with smaller plots or greater distance between plots to have higher species turnover, and (2) I would expect plants to have higher turnover due to localized growth patterns of some species, as compared to more mobile vertebrate taxa.

L521-525: What range of species richnesses is represented by the studies? I do not recall seeing this reported.

Reviewer #2: Comments to the Corresponding author

The manuscript “The species composition - ecosystem function relationship: a global meta-analysis using data from intact and recovering ecosystems ” presents a well written study on the relationship between species composition and ecosystem function. The authors used formal meta-analysis to synthesize the outcomes of 25 individual restoration studies. At the surface, this study is about the species composition - ecosystem function relationship, but deep down, it asks a more fundamental question: Is there scientific evidence that the results from constructed BEF experiments can be transferred to real world ecosystems - as is often implied by BEF studies? Thereby, the manuscript addresses an long-standing topic and open question in ecology which makes it an immensely valuable contribution to the scientific literature around BEF studies. The manuscript underpins these questions with a sound methodological synthesis approach which is the true strength of formal meta-analyses.

The methodology is sound, the analysis is sophisticated and the conclusions drawn from the results of the study are valid. However, the results are not clearly presented and the discussion section requires much more focus. I’ll try to list some points for improvement below:

Abstract

- Overall, I find the abstract to be the weakest part of the manuscript, but it can be improved easily. For example, as someone new to the topic of restoration research I was confused how “reference sites in restoration studies” (l50) differ from “degraded but restored sites” (l55)? After going through the manuscript I think I now know what is meant here, but since PLOS One caters to a very wide and interdisciplinary audience the abstract should be more easy to access. Please rephrase accordingly.

- l56-l57 Please rephrase

- l58 The six types of EF mentioned here come somewhat out of the blue. Either mention which these six are or remove.

- l62-65 I don’t see how these conclusions can been drawn from what has been written so far in the abstract. Please change to a concluding sentence that allows the abstract to stand on its own.

- Please mention how many studies and measures were used in the meta analyses in the abstract

Introduction

- l75ff When reflecting on the BEF field, consider swapping out the term biodiversity for species richness. Many of the the cited studies actually focus on SR.

- the literature cited in the entire paper seems a bit outdated with the most recent reference being from 2017. There have been many, also topically suitable studies been published in the last couple of years. Please bring the references up to date. Here are some recent meta-analyses which could fit in the manuscript, but there are many, many more available:

Beckmann, M., Gerstner, K., Akin‐Fajiye, M., Ceaușu, S., Kambach, S., Kinlock, N. L., ... & Newbold, T. (2019). Conventional land‐use intensification reduces species richness and increases production: A global meta‐analysis. Global change biology, 25(6), 1941-1956.

Forbes, E. S., Cushman, J. H., Burkepile, D. E., Young, T. P., Klope, M., & Young, H. S. (2019). Synthesizing the effects of large, wild herbivore exclusion on ecosystem function. Functional Ecology, 33(9), 1597-1610.

Taylor, N., Grillas, P., Fennessy, M., Goodyer, E., Graham, L., Karofeld, E., ... & Ross, S. (2019). A synthesis of evidence for the effects of interventions to conserve peatland vegetation: overview and critical discussion. Mires and Peat, 24.

Methods:

- The first section on the “literature search” reads a bit like a textbook description of how one would conduct any meta analysis and does refer very little to the study presented here. I suggest to either move Figure 1 (PRISMA diagram) here and refer to it in the text or put some numbers in the text and move the PRISMA diagram to the appendix (I would prefer the second option). Let the reader know e.g. how many abstracts were screened, how many authors were contacted and how many studies were coded in the end, the text lacks connection to the study otherwise. I am also sure (judging from the numbers in Figure 1) you were not at able to code most studies directly from published sources – a common problem encountered when conducting meta analyses. Consider reflecting on this issue somewhere in your manuscript, either here in the methods or in the discussion section. These sources might be of help:

Gerstner, K., Moreno‐Mateos, D., Gurevitch, J., Beckmann, M., Kambach, S., Jones, H. P., & Seppelt, R. (2017). Will your paper be used in a meta‐analysis? Make the reach of your research broader and longer lasting. Methods in Ecology and Evolution, 8(6), 777-784.

Spake, R., & Doncaster, C. P. (2017). Use of meta-analysis in forest biodiversity research: key challenges and considerations. Forest Ecology and Management, 400, 429-437.

Andivia, E., Villar-Salvador, P., Oliet, J. A., Puértolas, J., & Dumroese, R. K. (2019). How can my research paper be useful for future meta-analyses on forest restoration plantations?. New Forests, 50(2), 255-266.

- PLOS One requires authors to make all data underlying the findings described in their manuscript fully available without restriction. I assume S1 will include that data but I do not have access to it and I wonder if it includes the raw data or the summaries (effect sizes). In case the original authors were asked if they approve publication of the raw data I suggest to do exactly that. If the original authors were not asked, please publish at least the summarized data (is this S1?). Also, please publish the R-code used for the analysis (e.g. on gitHub). Only this way your study becomes fully reproducible. Refer to PLOS One author guidelines which provide further information on where to deposit data and code, the supplementary material might not be the best location.

- The Data Analysis section could be improved by summarizing the performed models in a table and reducing the text accordingly.

Results

- The Literature Search section should be merged with the Methods section, see comments above.

- While informative, Table 2 is also quite wasteful regarding space and it is not very readable. Try reducing the number of rows used by introducing abbreviations and reducing repetition (e.g. only 4 out of 25 studies use something else than vegetation abundance as a species composition measure). Also, please include here the number of species composition measures and EF similarity measures per study.

- L298-306 should be integrated in the methods section as well

- General comment regarding all figures: they are currently of terrible quality and very difficult to read. In fact, Figs 2 and 3 are almost completely black and indecipherable. This might be caused by the submission system but should be improved for re-submission.

- Caption of Figure 4: include the number of studies (25) as well. This way the reader gets the full context. As the text is now it suggests a much larger scope.

- Table 3: Please include results from all tested models not just the base and the best fitting one.

Discussion

- Overall, with 9 pages, the discussion is rather long and should be reduced by at least two and a half pages. I also suggest a restructuring of the discussion to help condensing this section: move subsection “Nature of species composition and other biodiversity relationships” to beginning. Get rid of the contrasting list and shorten the remaining subsections by half each. See below for details.

- The contrasting list is a nice idea but does not really work. Rather write where you confirm the outcomes from expected from BEF studies and where the real world differs from BEF studies (which is actually done a bit later and would be the start of the discussion if you follow my suggestion above).

- Tighten text in l487-509

- l479 - 485 I am not sure what the point is here, one could argue that the 25 studies is a low number itself. I suggest to remove this part.

- l565-575 this largely repeats what has been already said in introduction and earlier in discussion

- l611-613 this does not add much and could be removed

- section in l592-630 can be shortend by half

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PLoS One. 2020 Jul 30;15(7):e0236550. doi: 10.1371/journal.pone.0236550.r002

Author response to Decision Letter 0

24 May 2020

As requested by the editor I have drafter a rebuttal letter that responds to each point raised by the academic editor and each reviewer. This letter was uploaded as separate file and labeled 'Response to Reviewers'. All the editor's and reviewer's comments have been comprehensively addressed as you will see from the attached revised manuscript, figures and supporting information.

The Response to Reviewers rebuttal letter is also comprehensive and the nature of our revision of the manuscript is set out for every point. It is also lengthy and so have not copied the entire document into this space. It is far more readable as the uploaded Word document.

Attachment

Submitted filename: renamed_a5887.docx

Click here for additional data file.^{(103KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0236550.r003

Decision Letter 1

Patrice Savadogo

10 Jul 2020

The species composition - ecosystem function relationship: a global meta-analysis using data from intact and recovering ecosystems

PONE-D-19-31206R1

Dear Dr. Carrick,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Patrice Savadogo, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear Authors

Your revised manuscript, PONE-D-19-31206R1 has been subjected to a double-blind 2nd-round review process conducted by one of the original referees who reviewed your earlier manuscript and a new one. They both came to the conclusion that you have addressed all comments and suggestions and improved the manuscript accordingly. I agree with their assessment. The manuscript is a good contribution and deserves being published in its present form.

Patrice Savadogo, PhD (Academic Editor)

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Reviewer #2: The authors have addressed all of my comments in a satisfactory manner and I therefore recommend acceptance of the manuscript.

Reviewer #3: Authors have addressed most of reviewer's suggestions and improved the manuscript accordingly. When authors did not agree with comments of reviewer 1, they have explained their reasons convincingly. The manuscript is a good contribution and deserves being published in its present form.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #2: No

Reviewer #3: No

PLoS One. doi: 10.1371/journal.pone.0236550.r004

Acceptance letter

Patrice Savadogo

17 Jul 2020

PONE-D-19-31206R1

The species composition - ecosystem function relationship: a global meta-analysis using data from intact and recovering ecosystems

Dear Dr. Carrick:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Patrice Savadogo

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. PRISMA (preferred reporting items for systematic reviews and meta-analyses) checklist to assist in the critical appraisal of the meta-analysis and systematic reviews.

(DOC)

Click here for additional data file.^{(60.5KB, doc)}

S1 Dataset. Data and similarity metrics for each restoration site (n = 205), including each measure of ecosystem function.

(XLSX)

Click here for additional data file.^{(120.8KB, xlsx)}

S2 Dataset. Corrected Akaike Information Criterion (AICc) for each general linear mixed model.

(XLSX)

Click here for additional data file.^{(9.1KB, xlsx)}

S3 Dataset. Descriptions of the models run.

(XLSX)

Click here for additional data file.^{(13.7KB, xlsx)}

Attachment

Submitted filename: renamed_a5887.docx

Click here for additional data file.^{(103KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

The species composition—ecosystem function relationship: A global meta-analysis using data from intact and recovering ecosystems

Peter J Carrick

Katherine J Forsythe

Roles

Abstract

Introduction

Materials and methods

Literature search

Fig 1. A PRISMA flow diagram, depicting the process of searching for studies, filtering of studies and the inclusion of data used in this meta-analysis.

Table 1. Data characteristics of all contributing studies used in the meta-analysis.

Table 2. Descriptions of ecosystem function types used to group the various ecosystem function measures, from all contributing studies used in the meta-analyses.

Similarity metrics and response ratios

Data analysis

Results

Heterogeneity within intact ecosystems

Fig 2. Mean similarity (±SD) in species composition and ecosystem function within intact natural ecosystems globally (i.e. reference sites: n = 28 for species composition, n = 55 for ecosystem function).

Fig 3.

The species composition—ecosystem function relationship

Fig 4. Relationship between species composition and ecosystem function in 478 mean measures from 205 restoration sites, in 21 studies around the globe.

Table 3. Output of the general linear mixed models for the relationship between species composition and ecosystem function, in 21 studies around the globe.

Fig 5. Modelled relationship between species composition and ecosystem function, in ecosystems recovering towards intact conditions.

Fig 6. Relationship between species composition and ecosystem function for each of the six ecosystem function types, for 478 mean measures from 205 restoration sites, in 21 studies around the globe.

Table 4. Output of general linear models for each of the six ecosystem function types separately.

Fig 7. Modelled relationship between species composition and ecosystem function for each of the six ecosystem functions types, in ecosystems recovering towards intact conditions.

Discussion

The species composition—ecosystem function relationship

Nature of species composition and other biodiversity relationships

Heterogeneity within intact ecosystems

Differences in the relationship among ecosystem function types

Implications for restoration ecology

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Patrice Savadogo

Roles

Author response to Decision Letter 0

Decision Letter 1

Patrice Savadogo

Roles

Acceptance letter

Patrice Savadogo

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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