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How conservation initiatives go to scale

Abstract

Although a major portion of the planet’s land and sea is managed to conserve biodiversity, little is known about the extent, speed and patterns of adoption of conservation initiatives. We undertook a quantitative exploration of how area-based conservation initiatives go to scale by analysing the adoption of 22 widely recognized and diverse initiatives from across the globe. We use a standardized approach to compare the potential of different initiatives to reach scale. While our study is not exhaustive, our analyses reveal consistent patterns across a variety of initiatives: adoption of most initiatives (82% of our case studies) started slowly before rapidly going to scale. Consistent with diffusion of innovation theory, most initiatives exhibit slow–fast–slow (that is, sigmoidal) dynamics driven by interactions between existing and potential adopters. However, uptake rates and saturation points vary among the initiatives and across localities. Our models suggest that the uptake of most of our case studies is limited; over half of the initiatives will be taken up by <30% of their potential adopters. We also provide a methodology for quantitatively understanding the process of scaling. Our findings inform us how initiatives scale up to widespread adoption, which will facilitate forecasts of the future level of adoption of initiatives, and benchmark their extent and speed of adoption against those of our case studies.

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Fig. 1: Location of the area-based biodiversity conservation initiatives analysed.
Fig. 2: The cumulative percentage of adopters of conservation initiatives.
Fig. 3: Slow–fast–slow models describing adoption of marine reserves by municipalities in the Philippines.
Fig. 4: Trade-off between the proportion of potential adopters predicted to adopt an initiative and the uptake rate for conservation initiatives that follow the slow–fast–slow model.

Data availability

Supplementary Table 1 lists all sources of the data used to estimate the total number of potential adopters and number of adoptions for each intervention per year. The data that support the findings of this study are available from the corresponding author on request. Correspondence and requests for materials should be addressed to M.M.

Code availability

All code for the modelling is available on GitHub (https://github.com/MikeBode/Diffusion_of_innovation_fitting).

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Acknowledgements

The Centre of Excellence for Environmental Decisions funded the workshop at the International Congress for Conservation Biology 2015 in Montpellier, France. We thank the Fiji LMMA Network for Fiji data, the CTI Atlas for Solomon Islands data and the Philippines Marine Protected Area Support Network for permission to use their MPA Database. We thank T. Blomley for permission to use his data on joint and community-based forest management, and S. Pailler for data on wildlife management areas. M.M. thanks J. Muntifering and B. Morkel for input. S.G. thanks Conicyt FB0002 and FONDECYT 1190109. R.W. thanks the Australian Research Council Centre of Excellence for Coral Reef Studies. M.M., M.B.M. and L.G. thank the Margaret A. Cargill Foundation.

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Authors

Contributions

M.M., M.B., M.B.M. and H.P.P. conceived the idea and led the study design. M.M., R.W., S.G., N.D., H.G., C.L.A., C.R., L.G. and R.N. collected and collated quantitative data. M.M., M.B. and M.H. conducted the analysis. All authors drafted, reviewed and edited the paper.

Corresponding author

Correspondence to Morena Mills.

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The authors declare no competing interests.

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Supplementary Information

Supplementary Information

Supplementary Figs. 1–6, Table 3 and references.

Supplementary Dataset 1.

Supplementary Tables 1, 2, 4 and 5.

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Mills, M., Bode, M., Mascia, M.B. et al. How conservation initiatives go to scale. Nat Sustain 2, 935–940 (2019). https://doi.org/10.1038/s41893-019-0384-1

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