A meta-analysis of drought effects on litter decomposition in streams

Vol.: (0123456789) 1 3 Hydrobiologia (2023) 850:1715–1735 https://doi.org/10.1007/s10750-023-05181-y REVIEW PAPER A meta‑analysis ofdrought effects onlitter decomposition instreams VerónicaFerreira · ManuelA.S.Graça· ArturoElosegi Received: 1 September 2022 / Revised: 1 February 2023 / Accepted: 16 February 2023 / Published online: 13 March 2023 © The Author(s) 2023 Abstract Droughts, or severe reductions of water flow, are expected to become more frequent and intense in rivers in many regions under the ongoing climate change scenario. It is therefore important to understand stream ecosystem functioning under drought conditions. We performed a meta-analysis of studies addressing drought effects on litter decomposition in streams (50 studies contributing 261 effect sizes) to quantify overall drought effects on this key ecosystem process and to identify the main moderators controlling these effects. Drought reduced litter decomposition by 43% overall, which can impact energy and matter fluxes along heterotrophic food webs. The magnitude of drought effects on litter decomposition depended on the type of drought (natural drought > human-induced drought), type of decomposer community (microbes + macroinvertebrates > microbes) under natural drought, climate (warm and humid > temperate and Mediterranean) under human-induced drought, and on litter identity. The magnitude of drought effects on litter decomposition also increased with the severity of the drought. The effects of ongoing climate change will likely be strongest in streams with abundant shredders undergoing natural drought, especially if the streams become temporary. The composition of the riparian vegetation may modulate the magnitude of drought effects on litter decomposition, which may have management applications. Keywords Ecosystem functioning· Heterotrophic pathway· Stream intermittency· Systematic review Introduction Litter decomposition is a key ecosystem process in forest streams, where it sustains aquatic food webs and is pivotal in the carbon and nutrient cycles (Wallace etal., 1997; Marks, 2019). Once in water, litter from the riparian vegetation is processed by microbial decomposers (mostly aquatic hyphomycetes, but also bacteria) and invertebrate shredders, which mediate the incorporation of litter carbon and nutrients into secondary production (Hieber & Gessner, 2002; González & Graça, 2003). Handling editor: Sally A. Entrekin Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1075002305181-y. V.Ferreira(*)· M.A.S.Graça MARE – Marine andEnvironmental Sciences Centre, ARNET – Aquatic Research Network, Department ofLife Sciences, University ofCoimbra, Calçada Martim de Freitas, 3000-456Coimbra, Portugal e-mail: v[email protected] A.Elosegi Faculty ofScience andTechnology, University oftheBasque Country (UPV/EHU), PO Box644, 48080Bilbao, Spain 1716 Hydrobiologia (2023) 850:1715–1735 1 3 Vol:. (1234567890) Aquatic decomposers (microbes and invertebrates) and litter decomposition are highly sensitive to environmental conditions and litter characteristics (Boyero etal., 2016; Yue etal., 2022). Drought, in particular, is a main factor structuring stream communities and processes (Rolls etal., 2012; Stubbington etal., 2017; Sabater etal., 2018). Drought periods result in severe reduction of surface flow and can even lead to the total drying in the so-called intermittent rivers. In fact, between 51 and 60% of global rivers length is intermittent (Messager etal., 2021) and the forecasted increases in air temperature, with the consequent increases in evapotranspiration and water abstraction, will exacerbate flow reduction in many areas, especially in arid regions (Asadieh & Krakauer, 2017). This reduction will likely be even stronger in regions subject to human-induced drought, e.g., where water is withdrawn from streams and rivers for irrigation agriculture (Meybeck, 2003; Milliman et al., 2008; Döll et al., 2009). Invertebrate shredders (mostly belonging to the orders Plecoptera and Trichoptera) are especially sensitive to the degradation of water quality (e.g., increases in temperature, dissolved nutrients and conductivity and decreases in dissolved oxygen) under drought conditions (Stubbington et al., 2017). Reduced flow velocity also decreases the activities of microbial decomposers due to less efficient diffusion of nutrients and oxygen at the water–biofilm interface (de Beer etal., 1996) and lack of physical stimulus for the release of spores by aquatic hyphomycetes (Ferreira & Graça, 2006; Bastias etal., 2020). Reduced flow and deterioration of water quality under drought, and consequent impacts on microbial decomposers and shredders, often reduce litter decomposition (Sabater et al., 2018), especially in isolated pools and on dry streambeds, where it is extremely slow (Langhans & Tockner, 2006; Corti et al., 2011; Abril et al., 2016). Litter decomposition is thus slower in intermittent than in perennial streams, which has been attributed to legacy effects of past dry periods reducing shredder density when flow resumes (‘drying memory’; Datry etal., 2011). These legacy effects are likely less important for microbes, which can remain on litter even during emersed periods although becoming potentially less efficient when water returns after longer dry periods (Gonçalves etal., 2016, 2019; Arroita etal., 2018; Mora-Gómez etal., 2018). The effects of drought can, thus, depend on the severity of flow reduction, being especially pervasive when the streambed dries out (Langhans & Tockner, 2006; Foulquier etal., 2015; Abril etal., 2016). As shredders are responsible for stimulating litter decomposition globally by 74% overall (Yue et al., 2022), litter decomposition mediated by the combined activities of microbial decomposers and shredders is likely to be more responsive to drought than microbial-mediated litter decomposition (Riedl etal., 2013). Also, as shredders play a larger role on the decomposition of litter that is soft, nutrient-rich and has low concentrations of recalcitrant and defensive compounds than on the decomposition of more recalcitrant litter (Hieber & Gessner, 2002; Yue etal., 2022), it is expected that litter type (e.g., leaves vs. wood) and identity (genus) will determine its sensitivity to drought (Hill etal., 1988). Also, studies addressing drought effects on litter decomposition use a variety of methodological approaches and address varying drought magnitudes: studies with different drought severities, natural and cultural drought in real streams, mesocosm and experimental flume studies, litter bags incubated in and out of the water, seasonal comparisons in single streams versus comparisons between streams under contrasting drought regimes, before–after/control–impact studies of naturally occurring vs. experimentally induced drought, etc. All these can affect the magnitude of drought effects on litter decomposition and complicate between-study comparisons (Ferreira etal., 2015). We carried out a meta-analysis to assess the significance, magnitude, and direction of drought effects on litter decomposition in streams. We also aimed at determining the heterogeneity among studies and at assessing if drought effects on litter decomposition depended on type of drought, experimental approach, severity of drought, litter type, decomposer community, litter identity, and climate. The specific questions and hypotheses addressed are shown in Table1. This meta-analysis includes 50 studies that contribute 261 comparisons of litter decomposition between drought-stressed and reference (non-stressed) conditions. We considered the effects of all types of drought (i.e., natural and human-induced) and addressed how methodological approaches (i.e., type of drought, type of human-induced drought, type of experimental drought), severity of drought and characteristics of 1717 Hydrobiologia (2023) 850:1715–1735 1 3 Vol.: (0123456789) Table 1 Questions and hypotheses addressed, datasets used, and location of results Questions Hypotheses Dataset used Result Q1: Does drought affect litter decomposition in streams? H1: Drought reduces litter decomposition due to decreases in invertebrate shredder abundance and diversity and reduction of microbial decomposer activity All Table2, Fig.3 Q2: Does the response of litter decomposition to drought depend on the type of drought (natural vs. human-induced drought)? H2: Human-induced drought has stronger effects on litter decomposition than natural drought where stream communities are adapted to low flow All Table2, Fig.3 Q3: Does the response of litter decomposition to natural drought depend on the type of comparison (spatial vs. temporal)? H3: Natural drought has stronger effects when comparisons are made at spatial scales (i.e., comparison of perennial with intermittent reaches) than at temporal scales (comparison between before vs. after drought events) due to legacy effects (‘drying memory’) in the later comparison type (i.e., litter decomposition is already impaired in the before drought condition) Natural drought Table2, Fig.3 Q4: Does the response of litter decomposition to drought depend on the type of human-induced drought (cultural vs. experimental drought)? H4: Experimental drought has stronger effects on litter decomposition than cultural drought due to better control of confounding variables in the former than latter conditions Human-induced drought Table2, Fig.3 Q5: Does the response of litter decomposition to drought depend on the type of experimental drought (simulated water diversion vs. simulated desiccation vs. mesocosms)? H5: Simulated desiccation (i.e., comparison of immersed and emersed litter samples) has stronger effects on litter decomposition, followed by simulated water diversion (i.e., decreases on flow but generally still wet conditions) and mesocosms (i.e., generally decreases in flow) due to differences in drought severity among experimental approaches Experimental drought Table2, Fig.3 Q6: Does the response of litter decomposition to drought depend on the severity of drought? H6: Drought effects on litter decomposition increase with drought severity since stronger reduction in flow or higher number of dry days have more severe impacts on aquatic communities (e.g., reduction in available habitat, desiccation) than milder reductions in flow of few dry days Natural drought Fig.4A Experimental desiccation Fig.4B Q7: Does the response of litter decomposition to drought depend on litter type (leaves vs. wood)? H7: Drought effects are stronger for the decomposition of leaves where invertebrates play a stronger role than of wood Natural drought Table2, Fig.5 Q8: Does the response of litter decomposition to drought depend on the decomposer community (microbial vs. total)? H8: Drought effects are stronger for total litter decomposition (i.e., mediated by the activities of both microbes and invertebrates) than for microbialmediated litter decomposition as invertebrates will be negatively affect by both the drought and the poor microbial conditioning of the litter Natural drought Table2, Fig.5 Human-induced drought Table2, Fig.6 1718 Hydrobiologia (2023) 850:1715–1735 1 3 Vol:. (1234567890) the decomposing litter (i.e., type, identity and decomposer community involved) affected the response of litter decomposition to drought (Table1). This metaanalysis is therefore complementary to a previous one assessing the effects of human-induced drought on streams (Sabater et al., 2018), including the effects on litter decomposition (7 studies, 41 comparisons), but addressing how drought effects are dependent on regional and stream characteristics (e.g., rainfall regime, season, stream order, nutrient status). Methods Literature search and study selection Primary studies (i.e., empirical studies, including published papers and gray literature such as Master or PhD dissertations) addressing the effects of drought on litter decomposition were searched on May 2nd, 2022. Studies in English, published between January 1st, 1970, and April 30th, 2022 (including online first), were located using Web of Science (WoS) (database: Core Collection; indices: Science Citation Index Expanded, Conference Proceedings Citation Index – Science and Book Citation Index – Science). We used the following search strings (applied to the field ‘Topic,’ which includes title, abstract, keywords (defined in the study) and keywords plus (keyworks chosen for indexing purposes)): (i) ‘((stream OR river) AND (drought OR intermitten* OR temporary OR ephemeral) AND (decomposition OR processing OR breakdown OR decay) NOT (facies OR model* OR microcosm*))’ to identify studies addressing effects of natural drought and (ii) ‘((stream OR river) AND (diversion OR abstraction OR scarc*) AND (decomposition OR processing OR breakdown OR decay) NOT (facies OR model* OR microcosm*))’ to identify the studies addressing effects of humaninduced drought (the ‘NOT’ component aimed at reducing the number of non-relevant studies). Search (i) identified 2855 records and search (ii) identified 2086 records; 4751 records remained after duplicates were removed (Fig. S1). Titles and abstracts were screened and studies were selected if they addressed the effects of drought (natural or human-induced) on benthic decomposition of litter derived from tree or macrophyte species (i.e., leaves or wood) and incubated in monocultures (i.e., Table 1 (continued) Questions Hypotheses Dataset used Result Q9: Does the response of litter decomposition to drought depend on litter identity? H9: Drought effects are stronger for the decomposition of palatable litter genera (e.g., soft, nutrient-rich, with low concentration of recalcitrant and secondary compounds) where invertebrate shredders play a relative larger role than for more recalcitrant litter Natural drought, Total decomposer community Table2, Fig.5 Human-induced drought Table2, Fig.6 Q10: Does the response of litter decomposition to drought depend on climate? H10: Drought effects are stronger for humid climates where drought is less common and aquatic communities may not be adapted to drought stress, than for dry climates where streams naturally face severe seasonal reductions in flow Natural drought, Total decomposer community Table2, Fig.5 Human-induced drought Table2, Fig.6 1719 Hydrobiologia (2023) 850:1715–1735 1 3 Vol.: (0123456789) not in litter mixture) on lotic systems (i.e., streams, rivers, outdoor artificial channels) by comparing at least one drought-stressed and one reference (nonstressed) condition. Studies addressing the effects of drought include those comparing perennial vs. intermittent (flowing, non-flowing or dry) streams (e.g., Datry etal., 2011; Abril et al., 2016), reaches with vs. without flow or dry in intermittent streams (e.g., Corti etal., 2011; Foulquier etal., 2015; Abril etal., 2016), reaches with flow in wet vs. dry years (Schlief & Mutz, 2011), upstream vs. downstream of dams that reduce discharge (Menéndez etal., 2012), water diversion (e.g., Dewson etal., 2007a, b; Death etal., 2009; Arroita etal., 2017), or simulated intermittency (e.g., Bruder et al., 2011; Foulquier et al., 2015). Therefore, drought conditions generally present discharge below normal baseflow. Studies that addressed the effects of hydrological changes (e.g., resulting from dams, seasonal flooding) but that did not provide evidence for drought stress (e.g., lower discharge in the affected location) were not considered. Also, studies comparing seasons, regions, or land uses that are expected to contrast in water availability but that did not address drought effects were not considered. After title and abstract screening, 47 records were kept (Fig. S1). The full text was screened and studies were selected for inclusion in the database if they reported a decomposition estimate, and associated variability measure (not mandatory for all studies as this can be imputed if missing values are few) and sample size, for both drought-stressed and reference conditions; missing information was requested from authors before a decision to impute data or to exclude the study was made. After accounting for double publication (i.e., when the same data are published in multiple studies), 44 unique studies were included. Additionally, 6 studies known to the authors and that met the inclusion criteria but were not identified in the WoS search were added to the database. The final database thus included 50 studies (Fig. S1, Tables S1 and S2). Data extraction Studies included in the database satisfied the inclusion criteria, but for several studies not all information pertaining to litter decomposition did and, therefore, not all data were extracted from these studies (e.g., litter decomposition in the hyporheic zone, litter decomposition in litter mixtures, litter decomposition in the period before drought in before–after control–impact designs, litter decomposition affected by other treatments, decomposition of cotton strips; TableS2). Litter decomposition estimates that complied with inclusion criteria (TableS1 and S2), variability measures, and sample size reported in the text and in tables were extracted directly, information in graphs was extracted with WebPlotDigitizer (https:// autom eris. io/ WebPl otDig itizer/), and missing information was requested from the authors. For studies that reported litter decomposition over time (e.g., Herbst & Reice, 1982; Boulton, 1991; Corti et al., 2011; Schlief & Mutz., 2011), only mass remaining or mass loss at the last sampling date was considered, and for those that reported both exponential and linear litter decomposition rates (e.g., Maamri et al., 1997), the former were considered. Variation measures were extracted as provided in the primary studies or by the authors (i.e., standard deviation (SD), standard error (SE), or 95% confidence interval (CI)). SD values were used directly for estimating the variance associated with the effect size, while SE and 95% CI were first converted into SD. For studies that did not report variation associated with litter decomposition (e.g., Richardson, 1990; Boulton, 1991; Bernal, 2010; Riedl etal., 2013; Huang et al., 2018), SD values were imputed from studies with similar experimental designs and that reported litter decomposition in the same unit (Lajeunesse, 2013; Appendix1). Values extracted from graphs or imputed may deviate from the real values, but not considering them would have limited the analysis. However, the potential bias introduced into the database by extracting data from graphs and by data imputation was assessed in sensitivity analyses. Effect size The effects of drought on litter decomposition were estimated as the response ratio R, given by the ratio between the estimate in the drought-stressed condition ( Xdrought ) to the estimate in the reference condition ( Xreference ); analyses were performed on lnR, i.e., ln( X drought ∕X reference) ; for litter decomposition expressed as mass remaining (which varies in the opposite direction to mass loss or decomposition 1720 Hydrobiologia (2023) 850:1715–1735 1 3 Vol:. (1234567890) rate), the numerator and denominator were switched for the calculation of lnR (Hedges et al., 1999; Appendix 1). R = 1 (lnR = 0) indicates no effect of drought on litter decomposition, R < 1 (lnR < 0) indicates reduction and R > 1 (lnR > 0) indicates stimulation under drought. R values can be converted into percentage change for ease interpretation of the magnitude of the effect (Appendix1). The variance associated with lnR (VlnR), needed to weigh each effect size in the analysis so that more precise effect sizes (i.e., with low variance) will be weighed more and contribute more to the overall estimate than less precise effect sizes, was calculated using the litter decomposition estimate, its SD and sample size (Borenstein et al., 2009; Appendix 1). The variance associated with R was also used to estimate the 95% CI associated with each effect size, so that R values with 95% CI that do not include 1 are significant(Appendix1). Individual litter decomposition studies contributed with multiple effect sizes to the database (2 – 36 per study) as a result from using coarseand fine-mesh litter bags, several litter species, streams, or drought treatments. Therefore, the 50 studies included in the database contributed with a total of 261 effect sizes (Table S1 and S2). Although considering multiple effect sizes per study might affect results if non-independence of effect sizes is a problem, not considering them would have resulted in a low number of effect sizes, which would have precluded the analysis. We have, nevertheless, carried out sensitivity analyses to assess the effects of non-independence of effect sizes on the results. Moderator variables Methodological choices and environmental factors may affect the magnitude and direction of the response of litter decomposition to drought and are termed ‘moderators’ in meta-analysis. Therefore, information on several potential moderators, according to our hypotheses (Table1), was recorded: type of drought (natural or human-induced), type of humaninduced drought (cultural or experimental), type of experimental drought (simulated water diversion or desiccation or mesocosms), type of comparison being made for natural drought (spatial or temporal), percentage of dry days during the litter incubation period (continuous) and percentage of flow reduction (continuous), litter type (leaves or wood), litter identity (several genera), decomposer community involved (microbial or total: microbes + macroinvertebrates), and climate (several) (TableS3). Information on other variables (e.g., water temperature, dissolved nutrients, current velocity, wet width, and depth) was also extracted, but sample size was too small to be used in analyses. Statistical analysis Overall effect size The studies differed in experimental conditions and, thus, the overall response of litter decomposition to drought, i.e., the grand mean effect size, was determined using the random-effects model of metaanalysis, which considers two sources of variance associated with effect sizes: within-study variance (VlnR) and between-study variance (estimated by the restricted maximum likelihood (REML) method) (Borenstein etal., 2009). Individual effect sizes were weighed by the inverse of their variance, and the grand mean effect size (R) was considered significant if its 95% CI did not include 1. The percentage of total variability that was due to between-study variation (I2) was also calculated (Borenstein etal., 2009). Moderator analyses The effects of categorical moderators on the magnitude and direction of the response of litter decomposition to drought were assessed for subsets of the database, considering available sample size (only moderator levels with at least three effect sizes were tested) and robustness to publication bias. Subgroup analysis was used to estimate mean effect sizes for moderator levels (subgroups), using the randomeffects model (with the REML method for betweenstudy variance) (Borenstein et al., 2009). Heterogeneity was compared between (QM) and within subgroups to assess the significance of each moderator and subgroup. Subgroups were significant if their 95% CI did not include 1, and two subgroups significantly differed if their 95% CI did not overlap. To avoid that other moderators confound the analysis of a given moderator, categorical moderators were tested hierarchically (Fig.1). 1721 Hydrobiologia (2023) 850:1715–1735 1 3 Vol.: (0123456789) The effects of continuous moderators on the response of litter decomposition to drought were assessed for subsets of the database by meta-regression, using the random-effects model (with the REML method for between-study variance) (Borenstein etal., 2009). Sensitivity analyses Effect sizes were coded as ‘provided’ when litter decomposition estimates and SD were provided in numerical format (i.e., shown directly in the text or in tables or provided by the authors) or as ‘estimated’ when values had to be extracted from graphs or imputed (Table S1 and S2). The potential bias introduced into the database by extracting data from Fig. 1 Hierarchical approach used in the subgroup analyses showing moderator levels with n ≥ 3 (moderator levels with n < 3 were not considered in specific analyses of that moderator and are not shown); comparison of moderator levels in a subgroup analysis was done for specific levels of the previous moderator in the hierarchical approach, except if there were no significant differences among levels in which case the subsequent analysis was made considering all levels of the previous moderator together. 1Data from Burrows etal. (2017) were not considered (total and microbial-mediated litter decomposition data are shown combined); 2Data on Castanea, Fraxinus, Nerium, Nothofagus and Ulmus litters were not considered (n < 3); 3Data on Acer litter was not considered (differs from most other litter genera in the previous analysis) and data on cold-dry climate was not considered (n < 3); 4Data on Fagus and Melicytus litters were not considered (n < 3); 5Data on Acer, Quercus and Salix litters were not considered (differ from most other litter genera in the previous analysis) and data on arid and boreal climates were not considered (n < 3) 1722 Hydrobiologia (2023) 850:1715–1735 1 3 Vol:. (1234567890) graphs and by data imputation was assessed by subgroup analysis comparing the grand mean effect sizes for ‘provided’ and ‘estimated’ subgroups (as described above for subgroup analysis). Bias would be a concern if the grand mean effect size (R) would be significantly lower (i.e., stronger effect) for the ‘estimated’ than for the ‘provided’ subgroup. Also, the previous subgroup analyses based on the entire database were repeated using the ‘provided’ data only and bias would be a concern if results interpretation differs when considering the entire database and when considering ‘provided’ data only. The potential effects of the non-independence of effect sizes, which results from each study contributing with multiple effect sizes to the database, on the results were assessed by repeating the analyses (to the extent possible) considering a single effect size per study (estimated in a subgroup analysis with ‘study code’ as the moderator and each study as a subgroup). Non-independence of effect sizes would be a problem if results interpretation based on independent effect sizes (i.e., one effect size per study) differ from those obtained using the full database (i.e., with multiple effects sizes per study). Publication bias Evidence of publication bias was assessed for the entire database by the funnel plot. This is a scatter plot that contrasts the effect sizes (lnR) with their precision (SE), with symmetrical distribution of effect sizes around the grand mean effect size indicating no publication bias. The impact of publication bias on the grand mean effect size was assessed by the Duval and Tweedie’s trim and fill method (Duval & Tweedie, 2000). This method estimates a new grand mean effect size considering the ‘missing’ effect sizes, which are imputed assuming that the funnel plot should be symmetric. Overlap between the 95% CIs of theoriginal and of the new grand mean effect size indicates that the original grand mean effect size is not strongly affected by publication bias. Evidence of publication bias in subsets of the database was assessed by the Rosenberg’s fail-safe number (Nfs). This value gives the number of missing effect sizes showing an insignificant effect that would be needed to nullify the mean effect size, with Nfs > 5 × n + 10 (n = number of effect sizes) indicating that the dataset can be considered robust to publication bias. Standard analytic methods were used (grand mean effect size, subgroup analyses, meta-regressions, and publication bias analyses; Borenstein et al., 2009). Analyses were performed using OpenMEE (Wallace etal., 2017), except for publication bias analyses that were performed using the metafor package (Viechtbauer, 2010) in RStudio (RStudio, 2012). Results Database The earliest study included in the database dates from 1982 (Herbst & Reice, 1982), and since then, the number of studies addressing drought effects on litter decomposition in streams has been accumulating exponentially reaching 50 in April 2022 (Fig. S2). There was an average of 0.3 studies/year before 2000, which increased to 0.8 in 2000 – 2009, 2.6 in 2010 – 2019, and 3.3 studies/year between 2020 and April 2022. Most studies were carried out in Europe (30), North America (8) and Oceania (7) (Fig.2). Out of 261 comparisons of litter decomposition between drought-stressed and non-stressed conditions contributed by the selected studies, 49% originated from studies addressing natural drought, 43% from studies addressing experimental drought, and 8% from studies addressing cultural drought (i.e., human-induced drought, not caused on purpose for the study) (Fig.1, Tables S1 and S3). Studies addressing effects of natural drought on litter decomposition more often performed spatial (e.g., perennial vs. intermittent stream; 87%) than temporal comparisons (before vs. after drought; 13%) (Fig. 1, Tables S1 and S3). Studies addressing effects of experimental drought on litter decomposition most often used desiccation (immersed vs. emersed litter bags; 64%), followed by experimental water diversion (27%) and mesocosm (9%) approaches (Fig. 1, Tables S1 and S3). Most comparisons (91%) derived from leaf litter and addressed litter decomposition by both microbes and invertebrates (80%) (Tables S1 and S3). Litter from 17 tree and macrophyte genera were used, with Populus (36%), Alnus (26%), and Phragmites (13%) leaf litter contributing most comparisons (Tables S1). 1723 Hydrobiologia (2023) 850:1715–1735 1 3 Vol.: (0123456789) Overall effects of drought on litter decomposition The majority (85%) of individual effect sizes lnR were negative, with a large number being strongly negative (Table S1), which contributed to a grand mean effect size lnR of – 0.57 (95% CI: – 0.66 to – 0.48) (Fig. S3). This translated into a grand mean effect size R of 0.57 (95% CI: 0.52 – 0.62), indicating a significant reduction of litter decomposition by 43% under drought conditions (p < 0.001) (Fig.3). The funnel plot was, however, asymmetric, with 39 effect sizes ‘missing’ to the left of the grand mean effect size (Fig. S4A), which suggests publication bias. The new grand mean effect size R estimated by the trim and fill method (after imputing the ‘missing’ effect sizes) was 0.49 (95% CI: 0.45 – 0.54), which suggests a reduction of litter decomposition under drought by 51%. The original and the new grand mean effects sizes were, however, not significantly different (their 95% CIs overlapped), indicating that the grand mean effect size based on the database was not strongly affected by publications bias. In fact, the Rosenberg’s fail-safe number was well above the threshold for considering the database robust to publication bias (Table 2). The percentage of total variability that is due to between-study variation was high (I2 > 99%), suggesting that the response of litter decomposition to drought is affected by methodological choices and environmental factors. Effects of moderators on the response of litter decomposition to drought The effect of drought on litter decomposition significantly depended on the type of drought with stronger reduction under natural than under human-induced drought (51% vs. 35% reduction), although significant in both cases (Table2, Fig.3). However, drought effects on litter decomposition did not depend on the type of comparison for natural drought (spatial or temporal), type of human-induced drought (cultural or experimental), or type of experimental drought (water diversion, desiccation, or mesocosm studies) (Table 2, Fig. 3). Drought effects on litter decomposition depended on the severity of the drought, with effects becoming stronger as the percentage flow reduction increased in studies addressing natural drought (Fig. 4A) and as the percentage number of dry days during the litter incubation period increased in studies addressing experimental desiccation (Fig.4B). In the case of human-induced drought, no significant relationship was found between the Fig. 2 Global distribution of the studies included in the database (n = 50) 1730 Hydrobiologia (2023) 850:1715–1735 1 3 Vol:. (1234567890) decomposition, likely because ecological flows are maintained. Although there is little information on other regions, we suspect that where human activities lead to total stream desiccation, its effects on litter decomposition will be strong. Effects of drought are especially strong for total litter decomposition, suggesting that streams where shredders are abundant will undergo a stronger reduction of litter decomposition under warming than streams where litter decomposition is mostly mediated by microbial decomposers. Also, reduction of litter decomposition with drought depends on litter identity, which can have management implications as the effects of drought may be exacerbated or mitigated by changes in the composition of the riparian vegetation. Acknowledgements We thank the many authors who provided information that was not easily accessible in the primary studies and the reviewers for their insightful comments. This study was financed by the Portuguese Foundation for Science and Technology (FCT) through the research project STREAMECO (SFRH/BD/140761/2018), projects UIDP/04292/2020 and UIDB/04292/2020 granted to MARE, and project LA/P/0069/2020 granted to the Associate Laboratory ARNET, and by the Basque Government (IT147122). Financial support granted by the FCT to VF (CEECIND/02484/2018) is also acknowledged. Author contributions VF contributed to conceptualization, preparation of the literature search, study selection and data extraction protocols, literature search, study selection, data extraction, data analysis, and writing. MASG contributed to conceptualization, contribution to the protocols, revision of the manuscript, and approval of its final version. AE contributed to the protocols, revision of multiple versions of the manuscript, and approval of its final version. Funding Open access funding provided by FCT|FCCN (b-on). This study was financed by the Portuguese Foundation for Science and Technology (FCT) through the research project STREAMECO (SFRH/BD/140761/2018) and the strategic projects UIDP/04292/2020 and UIDB/04292/2020 granted to MARE and project LA/P/0069/2020 granted to the Associate Laboratory ARNET, and by the Basque Government (IT1471-22). VF was financially supported by the FCT (CEECIND/02484/2018). Data Availability Data used in the analyses are provided in Supplementary Material. Code availability Not applicable. Declarations Conflict of interest Authors have no conflicting or competing interests. 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