Increasing pressure on land resources necessitates landscape management strategies that simultaneously deliver multiple benefits to numerous stakeholder groups with competing interests. Accordingly, we developed an approach that combines ecological data on all types of ecosystem services with information describing the ecosystem service priorities of multiple stakeholder groups. We identified landscape scenarios that maximize the overall ecosystem service supply relative to demand (multifunctionality) for the whole stakeholder community, while maintaining equitable distribution of ecosystem benefits across groups. For rural Germany, we show that the current landscape composition is close to optimal, and that most scenarios that maximize one or a few services increase inequities. This indicates that most major land-use changes proposed for Europe (for example, large-scale tree planting or agricultural intensification) could lead to social conflicts and reduced multifunctionality. However, moderate gains in multifunctionality (4%) and equity (1%) can be achieved by expanding and diversifying forests and de-intensifying grasslands. More broadly, our approach provides a tool for quantifying the social impact of land-use changes and could be applied widely to identify sustainable land-use transformations.
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This work is based on data collected by several projects of the Biodiversity Exploratories programme (DFG Priority Program 1374). Most datasets are publicly available in the Biodiversity Exploratories Information System (https://doi.org/10.17616/R32P9Q). However, to give data owners and collectors time to perform their analyses, the Biodiversity Exploratories’ data and publication policy includes by default an embargo period of three years from the end of data collection/data assembly, which applies to the remaining datasets. These datasets will be made publicly available via the same data repository. All datasets and their current status (publicly available or not) are listed in Supplementary Table 1 and corresponding references. All correspondence and requests should be addressed to the corresponding author, or, when concerning a specific dataset, to the data owners (see the dataset references).
The full code to replicate the analyses can be found on GitHub (https://doi.org/10.5281/zenodo.7019909 or https://github.com/mneyret/landscape-equity).
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K. Wells, K. Reichel-Jung, S. Gockel, K. Wiesner, K. Lorenzen, A. Hemp and M. Gorke maintained the plot and project infrastructure (with S.R.); S. Pfeiffer, M. Gleisberg, C. Fischer, J. Mangels and V. Grießmeier provided administrative support; and J. Nieschulze, M. Owonibi and A. Ostrowski provided database management. E. Linsenmair, D. Hessenmöller, D. Prati, I. Schöning, E.-D. Schulze, W. W. Weisser and the late E. Kalko helped establish the Biodiversity Exploratories project (with F.B.). The administration of the Hainich National Park, the UNESCO Biosphere Reserves Swabian Alb and Schorfheide-Chorin and all landowners provided logistical support. G. Fraux provided the Rust code to run the landscape simulations. We acknowledge support from the German Research Foundation (DFG grants no. MA7144/1-1 and no. MA7144/1-2 (P.M.), no. Ka1241/19-1 (K.J. and S.C.R.), and project no. 493487387 (C.W.)). J.M.B. was funded by UKCEH project no. 06895. The work was partly funded by the DFG Priority Program 1374 ‘Infrastructure-Biodiversity-Exploratories’ and by Senckenberg Biodiversity and Climate Research Centre.
The authors declare no competing interests.
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Extended Data Fig. 1 Variation of multifunctionality (a) and equity (b) at the landscape level with increasing proportion of forests.
Each dot is one simulated landscape composition. The green line shows the fitted loess model. The dashed vertical line shows the current proportion of forests, while the solid line shows the optimal forest cover for the corresponding score (top row: multifunctionality; bottom row: equity). The analysis was completed only on landscapes with a crop cover equal to the baseline landscape composition, hence a maximum forest proportion of 60%.
Extended Data Fig. 2 Changes in landscape composition (a), service supply (b) and multifunctionality (c) when maximising carbon storage compared to the baseline landscape composition.
Data are presented as mean and 95% confidence intervals, calculated on n = 15 landscape compositions, each averaged across 200 replicated simulations.
Extended Data Fig. 3 Changes in landscape composition (a), service supply (b) and multifunctionality (c) in the ‘do-no-harm’ scenario (that is, when maintaining the supply of threatened services and preventing loss of multifunctionality by any stakeholder group) compared to the baseline landscape composition.
Data are presented as mean and 95% confidence intervals, calculated on n = 15 landscape compositions, each averaged across 200 replicated simulations.
Extended Data Fig. 4 Change in multifunctionality and (left) change in equity, (middle) number of stakeholder groups losing multifunctionality and (right) vulnerable service scores in multiple landscape compositions compared to the baseline landscape composition.
This figure shows the full range of landscape compositions, of which a subset is shown in the main figures Fig. 2c and 4. Large coloured dots show a few predefined scenarios while small black dots represent all the other scenarios that were simulated; they show the mean of all the 200 replicates for each given scenario.
Supplementary Figs. 1–31, Tables 1–9, methods, data and sensitivity analyses.
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Neyret, M., Peter, S., Le Provost, G. et al. Landscape management strategies for multifunctionality and social equity. Nat Sustain (2023). https://doi.org/10.1038/s41893-022-01045-w