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Drivers of seedling establishment success in dryland restoration efforts

An Author Correction to this article was published on 28 July 2021

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Restoration of degraded drylands is urgently needed to mitigate climate change, reverse desertification and secure livelihoods for the two billion people who live in these areas. Bold global targets have been set for dryland restoration to restore millions of hectares of degraded land. These targets have been questioned as overly ambitious, but without a global evaluation of successes and failures it is impossible to gauge feasibility. Here we examine restoration seeding outcomes across 174 sites on six continents, encompassing 594,065 observations of 671 plant species. Our findings suggest reasons for optimism. Seeding had a positive impact on species presence: in almost a third of all treatments, 100% of species seeded were growing at first monitoring. However, dryland restoration is risky: 17% of projects failed, with no establishment of any seeded species, and consistent declines were found in seeded species as projects matured. Across projects, higher seeding rates and larger seed sizes resulted in a greater probability of recruitment, with further influences on species success including site aridity, taxonomic identity and species life form. Our findings suggest that investigations examining these predictive factors will yield more effective and informed restoration decision-making.

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Fig. 1: Restoration seeding in drylands globally showed limited but promising success.
Fig. 2: Individual species failed often, but seeding still resulted in significantly increased success for target species.
Fig. 3: There were clear predictors of seeding success.

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Data availability

Data housed in the GAZP database is a compilation of primary research data from active projects worldwide. The database is being launched as a publicly available tool, with some datasets requiring authorization by the individual contributor for full release due to internal data use agreements. To make it findable, accessible, interoperable and reusable (FAIR), the database requires extensive documentation and clear curation, which will be an ongoing effort as the project develops. The data used for this analysis that have been approved for release will be available, with clear metadata included, through github (, For the full subset of data used for this analysis, including the restricted data, please contact the corresponding author.

Code availability

Code for all statistical models and plots is available on github (, Note that the data housed publicly are not the full data set used in this analysis. To execute the code exactly as conducted here, please contact the corresponding author for the dataset used in the analysis.

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We would like to thank the supporters of the Global Arid Zone Project. The intellectual and energetic input of the network participants made this work possible. We also acknowledge the many employers and funding agencies that supported projects and the authors’ time in preparing this work and contributing data to the GAZP database. Please note that any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Author information

Authors and Affiliations



N. Shackelford conceived the work, conducted data collection, processing, analysis and writing. G.B.P. conducted processing, analysis, writing and compilation of supplementary material. D.E.W, K.L. Suding and T.E.E. conceived the work, assisted with data collection and guided the manuscript development and editing. E.A.L. and L.N.S. wrote, compiled and extensively edited the work. M.F.B., A.M.F. and P.A.H. assisted with data collection and edited the work. M.F.C., Q.G., A.K., D.J.L., K.Z.M., S.M.M., L.M.P., R.E.Q., P.T. and C.E.W. contributed to writing the work and providing feedback. A.A., M.A.B., E.A.B., N.B., O.W.B., C.B., M.E.L.B., C.S.B., C.M.B., P.J.B., E.C., P.J.C., A.C., C.D.C., K.W.D., B.D., J.D., S.D., J.E., E.E., H.L.F., S.E.F., M.G., E.G.R., P.J.G., P.A.G., B.H., P.M.H., J.J.J., J.J.-B., R.K., A.T.K., J.E.L., J.L., C.E.M., L.M.-M., T.M., S.J.M., T.A.M., A.M.M., J.A.N.-C., M.W.P., P.L.P., M.L.P., M.J.R., N. Saayman, M.C.S., T.P., E.W.S., K.L. Stuble, S.M.U., O.V., K.V., S.W., M.W. and Z.X. contributed data to the work.

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Correspondence to Nancy Shackelford.

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Peer review information Nature Ecology & Evolution thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Distribution of native and exotic richness of seeded species.

(a) Frequency distribution of species richness across treatments. (b) Richness of seeded native species across regions. (c) Relationship between richness of seeded species and aridity, and (d) Richness of seeded exotic species across regions.

Extended Data Fig. 2 Frequency distribution of response and predictors for initial recruitment.

(a) Frequency distribution of species success (response) and (b) seeded and unseeded treatments (predictor).

Extended Data Fig. 3 Frequency distribution of response and predictors for Q3 (trends in vegetation development).

(a) Frequency distribution of species success (response), and predictors: (b) life form, (c) weed control), (d) aridity, (e) seed rate, (f) weeks since restoration and (g) seed mass (g).

Extended Data Fig. 4 Predicted species presence (%) across biotic and abiotic drivers.

(a) seed rate, (b) site aridity, (c) weed control, (d) time since restoration, and (e) seed mass. Solid lines (a, b, d, f) and dots (c) indicate population-level predictions (that is marginal means) conditioned on the fixed effects. Confidence bands and error bars represent 95% confidence intervals. Continuous predictors (a, b, d, e) are shown after centering and standardization (z-score transformation).

Extended Data Fig. 5 Frequency distribution of response and predictors for Q3 (North America only, trends in vegetation development).

(a) Frequency distribution of species success (response), and predictors: (b) life form, (c) weed control), (d) aridity, (e) seed rate, (f) weeks since restoration and (g) seed mass (g). Data was cropped to studies performed in North America.

Extended Data Fig. 6 Predicted species presence (%) across biotic and abiotic drivers (North America only).

(a) seed rate, (b) site aridity, (c) weed control, (d) time since restoration, and (e) seed mass. Solid lines (a, b, d, f) and dots (c) indicate population-level predictions (that is marginal means) conditioned on the fixed effects. Confidence bands and error bars represent 95% confidence intervals. Continuous predictors (a, b, d, e) are shown after centering and standardization (z-score transformation). Data was cropped to studies performed in North America.

Extended Data Fig. 7 Distribution of species success.

Average probability of success (presence) ± standard error across 488 angiosperms seeded species (a). Species that occurred in less than 3 treatments were excluded from analysis. Each point represents a single species. The color gradient indicates the average probability of presence ranging from zero (blue) to one (red). The distribution of average probabilities is represented in the histogram on panel (b).

Extended Data Fig. 8 Distribution of genus and family’s success.

Average probability of success (presence) ± standard error for genus (a) and families. Only families and genera with more than 3 species are shown. Each point represents a single species.

Extended Data Fig. 9 Distribution of genus and family’s success (North America).

Average probability of success (presence) ± standard error for genus (a) and families. Only families and genera with more than 3 species are shown. Each point represents a single species. Data was cropped to studies performed in North America.

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Supplementary Notes 1–4, Tables 1–9 and Figs. 1–28.

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Shackelford, N., Paterno, G.B., Winkler, D.E. et al. Drivers of seedling establishment success in dryland restoration efforts. Nat Ecol Evol 5, 1283–1290 (2021).

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