Ecology

Overall simulation approach
We estimated net changes in soil organic, above-ground and below-ground living biomass carbon stocks due to mangrove-related LCC that occurred globally between 1996 and 2016, for the year 2016. We did not estimate carbon sequestration because while sequestration rates in mangrove forests are higher than in many other ecosystems, there is only limited capacity for this process to substantially impact global carbon fluxes due to the small area of mangrove forest7,32,39. In addition, global high-resolution maps of mangrove carbon sequestration rates are not available.
Uncertainty in estimates of the area of mangrove loss and gain, carbon stock density, date of deforestation or forestation, proportional carbon stock degradation due to deforestation, and proportional carbon stock accumulation due to forestation, was carried forward using a bootstrap simulation method, through which 1000 replications were used to generate median estimates and 95% confidence intervals. A summary of the sources of data, simulation parameters, and modelling of parameter variability is provided in Supplementary Table 1. For each bootstrap iteration, the area of mangrove gain and loss, carbon stock density, rt, and at values were simulated within each patch of mangrove gain or loss. The D, F, Drt, and Fat values for each patch were then calculated. The median value of the 1000 replicates was used as the estimate. The 2.5 and 97.5 percentiles of the 1000 simulation estimates of change in carbon stock were calculated, thus corresponding to bootstrap 95% confidence intervals for each of the estimated losses or gains in carbon stock within each patch. A comparable bootstrap method was applied to simulate carbon stocks in 1996 for each patch of mangrove present at that time.
In addition to the sources of uncertainty incorporated in the bootstrap simulation, we made several key methodological decisions that could be expected to impact the conclusions of the study. We conducted four sensitivity analyses to quantify the impacts of such decisions. The sensitivity analyses were conducted only for the region of Southeast Asia, because one sensitivity analysis required detailed and spatially explicit data on the replacement land uses following mangrove deforestation, which are only available with the necessary categorisation for Southeast Asia5. Methodological details and results of the sensitivity analyses are included in Supplementary Methods 1.
Mangrove areal extent
We used the Global Mangrove Watch (GMW) datasets of mangrove cover to quantify deforestation and forestation14,15,16,17. While other global mangrove map products exist for specific years42,43, GMW provides maps of extent from multiple years, allowing temporal comparison. The mangrove extent in 1996 and 2016 was mapped using the GMW data products, which are derived from ALOS PALSAR and Landsat satellite-borne sensor data14,15,16. Mangrove-related LCC was defined either as a conversion from mangrove to another form of land or water cover between the 1996 and 2016 datasets (deforestation), or vice versa (forestation). Any change in mangrove cover that was reversed before the end of the study period was therefore not captured. Areas of overlapping and nonoverlapping mangrove extent were compared between these dates to quantify mangrove present in 1996 that was not present in 2016, mangrove present in 2016 that was not previously present in 1996, and areas of no change in mangrove cover between 1996 and 2016. For each patch of mangrove cover in 1996, gain, and loss, the area was calculated under the Eckert VI equal-area projection. This projection was used to calculate the area of mangrove and mangrove change polygons only, and all other analyses were conducted using the World Geodetic System (WGS) 1984 projection17.
The GMW mapping of mangrove forest has an error rate15, leading to quantifiable uncertainty over the presence of mangroves at each location in 1996 and 2016. Accuracy statistics have not been published for each year in the GMW dataset17, so we assumed that all years had an identical accuracy to the best-documented year, which is 201015. Published error rates correspond to individual pixels in the original GMW dataset, so we modelled uncertainty at this spatial scale. For each pixel of recorded mangrove gain or loss, there is a probability that it is an erroneous, false positive example of gain or loss. For each pixel of mangrove or non-mangrove that is recorded in GMW as being the same in 2016 as in 1996, there is a similar probability of error—a false-negative case of gain or loss. While false positive gain and loss errors can be quantified fully based on the available information, we were not able to incorporate false negative errors in the simulation (Supplementary Methods 2). However, false-negative errors can be expected to impact estimates of forestation and deforestation area almost equally, while false-positive errors are biased toward a greater effect on estimates of deforestation (Supplementary Methods 2). For these reasons we incorporated only false positive classification uncertainty into the bootstrap simulation and generation of carbon stock change confidence intervals. Uncertainty in the areal extent of mangroves was not incorporated into the bootstrap simulation for the estimate of carbon stocks in 1996. As the uncertainty estimation for areal extent change does not include false negative classification errors, we do not report confidence intervals for area change statistics, and report only the median estimates from the bootstrap replicates.
For areas of mangrove loss and gain, we incorporated the probability of false positive recording of loss and gain into the simulation. For each pixel within each patch of mangrove gain, we simulated whether it was actually not mangrove in 1996 according to the misclassification error rate for non-mangrove (Supplementary Table 2), and whether it was truly mangrove in 2016 according to the misclassification error rate for mangrove (Supplementary Table 2). The simulated number of gain pixels in each patch was thus calculated as the number of pixels that were simulated to have been both not mangrove in 1996, and mangrove in 2016. Similarly, for each pixel recorded as mangrove deforestation, there is a probability that it was a false positive example of loss. For each pixel within each patch of mangrove loss, we simulated whether it was truly mangrove in 1996 according to the misclassification error rate for mangrove (Supplementary Table 2), and whether mangrove was truly absent in 2016 according to the misclassification error rate for non-mangrove (Supplementary Table 2). The simulated number of loss pixels in each mangrove patch was thus calculated as the number of pixels that were simulated to have been mangrove in 1996, and not mangrove in 2016.
Carbon stock density
Spatial patterns in mangrove carbon densities were quantified using previously published datasets of soil carbon to 1 m depth36, and above- and belowground tree biomass carbon37. Both datasets are derived from systematic reviews of the literature, so may be biased towards relatively high-quality mangrove forests, rather than those that have experienced some natural or anthropogenic disturbance37. The resulting maps of carbon stock density are therefore likely to represent an upper estimate for the potential carbon stock density at each location37. As the dates and resolutions of these mangrove carbon datasets differed from the GMW mangrove extent, per hectare carbon densities were extracted for each patch of mangrove extent in 1996, gain, and loss of mangrove. Where possible, we extracted mean carbon densities for the 0.05 degree grid cell (approximately 5 km) in which the centre of the mangrove patch coincided (Supplementary Table 3). Where data did not coincide at this resolution, 0.5° grid cells (approximately 50 km) were used (Supplementary Table 3). Any remaining data gaps were filled using the global mean carbon stock density (Supplementary Table 3).
Uncertainty in the estimate of carbon stock was modelled as a normally distributed random variable, with the mean value taken as the reported carbon stock density extracted from the published map layers36,37, and the standard deviation of the distribution taken as the reported root mean squared error (RMSE) between the model predictions and validation data36,37. The RMSE for soil carbon is reported in the study as 109 Mg per hectare36. The RMSE for aboveground biomass carbon was calculated as 104.1 Mg per hectare, based on a plot of observed versus predicted values digitised from the original study37 (Supplementary Fig. 2). The soil and biomass carbon stock densities for each patch of mangrove in 1996, patch of mangrove loss, and patch of mangrove gain were simulated, and values of less than zero were replaced with zero, to avoid negative carbon densities.
Loss and accumulation of mangrove carbon
After mangrove deforestation, carbon is lost gradually over a period of time, with biomass carbon typically depleting more rapidly than carbon stored in soils13. We modelled temporal losses of soil carbon stocks according to a previously-published meta-analysis of the proportion of the reference carbon stock (rt) lost over time13. For losses of biomass carbon stocks, we used a meta-analysis of temporal changes in the proportion of the reference tree diameter as a proxy for biomass carbon stock, because a meta-analysis of temporal changes in biomass carbon stock was not available13. The shape of these temporal rt relationships can be observed in Supplementary Fig. 3a, b. The approximate date of mangrove deforestation was quantified by cross-referencing several dates from the GMW dataset to establish the dates of presence and absence17. We cross-referenced the dates of 1996, 2007, 2010, and 2016 to identify the dates of mangrove presence and absence at each location.
Uncertainty in proportional losses of carbon due to mangrove deforestation was incorporated in two ways. First, there is uncertainty in the date of mangrove deforestation since the most recent observed date of presence. To model uncertainty in the date of deforestation we used a uniform distribution to select a date between the most recent date of observed mangrove presence and the oldest date of observed mangrove absence. Second, there is uncertainty in the relationship between the time since deforestation and proportion of mangrove carbon lost, quantified as the error present in the regression models (Supplementary Fig. 3a, b). We simulated the projected proportions of mangrove carbon remaining as a function of the length of time since deforestation (2016—date of deforestation), accounting for the error inherent in each linear model (Supplementary Methods 3).
As mangrove forests grow, they typically accumulate carbon in soil and tree biomass stocks, until reaching the value held by the reference community12,44. This process can be slow, taking from 20 to more than 50 years12,44. At a given point in time before the climax community is reached, the mangrove ecosystem contains a proportion of the value held in the climax community (at). The whole-ecosystem carbon accumulation curve for afforesting mangroves was estimated using data taken from a meta-analysis of blue carbon ecosystem restoration18, that we used to estimate the proportion of the reference ecosystem carbon accumulated following restoration (Supplementary Methods 4). The shape of the temporal relationship can be observed in Supplementary Fig. 3c. We used this general relationship describing restoration of all blue carbon ecosystems, because a mangrove forestation-specific meta-analysis is not currently available. To assess the impacts of this selection on the study findings, we also conducted a sensitivity analysis using data from two case studies of mangrove soil carbon and biomass accumulation in foresting mangroves (Supplementary Methods 2). The approximate date of mangrove forestation was quantified by cross-referencing several dates from the GMW dataset to establish the dates of presence and absence14,15,16. We cross-referenced the dates of 1996, 2007, 2010, and 2016 to identify the dates of mangrove presence and absence at each location.
Uncertainty in gains of carbon due to mangrove forestation was incorporated in two ways. First, there is uncertainty in the date of mangrove forestation since the most recent observed date of presence. To model uncertainty in the date of forestation we used a uniform distribution to select a date between the most recent date of observed mangrove absence and the oldest date of observed mangrove presence. Second, there is uncertainty in the relationship between the time since deforestation and proportion of mangrove carbon lost, quantified as the error present in the meta-analytic regression model (Supplementary Fig. 3c). We simulated the projected proportions of mangrove carbon remaining as a function of the length of time since deforestation (2016—date of deforestation), accounting for the error inherent in each linear model45 (Supplementary Methods 3).
We estimated D, F, rt, and at for each patch of mangrove gain and loss between 1996 and 2016. These data were then used to quantify four indicators of net change in mangrove carbon stocks, to evaluate the sensitivity of estimation to the inclusion or exclusion of afforestation and remnant carbon processes. The first indicator estimated the maximum carbon stock at risk of loss due to deforestation (D), following the approach used in the most recent global estimate of potential mangrove carbon emissions9. The second indicator estimated net loss of carbon assuming 100% carbon loss and gain rates (D − F). For the third indicator, we estimated the carbon stock loss due to deforestation but accounting for remnant carbon (Drt)8. Finally, the fourth indicator estimated net changes in mangrove carbon stock between 1996 and 2016 accounting for both forestation and proportional accumulation and loss rates of carbon following LCC (Drt − Fat). For mapping of spatial variability in net gains and losses of mangrove carbon stocks, we quantified the net change in mangrove carbon stock (Fat − Drt) by summarising all patches of mangrove gain and loss with their centroids located in cells across a global grid (Figs. 2 and 3). More

1.
Grassi, G. et al. The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Change 7, 220 (2017).
ADS  Google Scholar 
2.
Jia, G. et al. Land–Climate Interactions. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (Springer, Berlin, 2019).
Google Scholar 

3.
Harper, A. B. et al. Land-use emissions play a critical role in land-based mitigation for Paris climate targets. Nat. Commun. 9(1), 2938 (2018).
ADS  MathSciNet  PubMed  PubMed Central  Google Scholar 

4.
Bonan, G. B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320(5882), 1444–1449 (2008).
ADS  CAS  PubMed  Google Scholar 

5.
Davin, E. L. & de Noblet-Ducoudre, N. Climatic impact of global-scale deforestation: radiative versus nonradiative processes. J. Clim. 23(1), 97–112 (2010).
ADS  Google Scholar 

6.
Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. 6, 8 (2015).
Google Scholar 

7.
Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351(6273), 600–604 (2016).
ADS  CAS  PubMed  Google Scholar 

8.
Bright, R. M. et al. Local temperature response to land cover and management change driven by non-radiative processes. Nat. Clim. Change 7, 296 (2017).
ADS  Google Scholar 

9.
Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. 9(1), 679 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

10.
Augusto, L. et al. Influences of evergreen gymnosperm and deciduous angiosperm tree species on the functioning of temperate and boreal forests. Biol. Rev. 90(2), 444–466 (2015).
PubMed  Google Scholar 

11.
Naudts, K. et al. Europe’s forest management did not mitigate climate warming. Science 351(6273), 597–600 (2016).
ADS  CAS  PubMed  Google Scholar 

12.
Luyssaert, S. et al. Trade-offs in using European forests to meet climate objectives. Nature 562(7726), 259–262 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

13.
Thom, D., Rammer, W. & Seidl, R. The impact of future forest dynamics on climate: interactive effects of changing vegetation and disturbance regimes. Ecol. Monogr. 87(4), 665–684 (2017).
PubMed  PubMed Central  Google Scholar 

14.
Hans Pretzsch, D. I. F. J. B. Mixed-Species Forests (Springer, Berlin Heidelberg, 2017).
Google Scholar 

15.
Astrup, R. et al. A sensible climate solution for the boreal forest. Nat. Clim. Change 8(1), 11–12 (2018).
ADS  Google Scholar 

16.
Pukkala, T. Effect of species composition on ecosystem services in European boreal forest. J. For. Res. 29(2), 261–272 (2018).
Google Scholar 

17.
Felton, A. et al. Replacing coniferous monocultures with mixed-species production stands: An assessment of the potential benefits for forest biodiversity in northern Europe. For. Ecol. Manage. 260(6), 939–947 (2010).
Google Scholar 

18.
Renaud, V. & Rebetez, M. Comparison between open-site and below-canopy climatic conditions in Switzerland during the exceptionally hot summer of 2003. Agric. For. Meteorol. 149(5), 873–880 (2009).
ADS  Google Scholar 

19.
Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437(7058), 529–533 (2005).
ADS  CAS  PubMed  Google Scholar 

20.
Robine, J.-M. et al. Death toll exceeded 70,000 in Europe during the summer of 2003. C.R. Biol. 331(2), 171–178 (2008).
PubMed  Google Scholar 

21.
Wood, S. N. Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC Texts in Statistical Science (CRC Press/Taylor & Francis Group, Boca Raton, 2017).
Google Scholar 

22.
Otto, J. et al. Forest summer albedo is sensitive to species and thinning: how should we account for this in Earth system models?. Biogeosciences 11(8), 2411–2427 (2014).
ADS  Google Scholar 

23.
Rydsaa, J. H., Stordal, F. & Tallaksen, L. M. Sensitivity of the regional European boreal climate to changes in surface properties resulting from structural vegetation perturbations. Biogeosciences 12(10), 3071–3087 (2015).
ADS  Google Scholar 

24.
Baldocchi, D. D. & Vogel, C. A. Energy and CO2 flux densities above and below a temperate broad-leaved forest and a boreal pine forest. Tree Physiol. 16(1–2), 5–16 (1996).
PubMed  Google Scholar 

25.
Carnicer, J. et al. Contrasting trait syndromes in angiosperms and conifers are associated with different responses of tree growth to temperature on a large scale. Front. Plant Sci. 4, 19 (2013).
Google Scholar 

26.
Davin, E. L. et al. Preferential cooling of hot extremes from cropland albedo management. Proc. Natl. Acad. Sci. USA 111(27), 9757–9761 (2014).
ADS  CAS  PubMed  Google Scholar 

27.
Baldocchi, D. D. & Xu, L. What limits evaporation from Mediterranean oak woodlands: the supply of moisture in the soil, physiological control by plants or the demand by the atmosphere?. Adv. Water Resour. 30(10), 2113–2122 (2007).
ADS  Google Scholar 

28.
Grossiord, C. et al. Influence of species interactions on transpiration of Mediterranean tree species during a summer drought. Eur. J. For. Res. 134(2), 365–376 (2015).
CAS  Google Scholar 

29.
Denissen, J. M. C. et al. Critical soil moisture derived from satellite observations over Europe. J. Geophys. Res. 125(6), e2019 (2020).
Google Scholar 

30.
Dezsi, Ş et al. High-resolution projections of evapotranspiration and water availability for Europe under climate change. Int. J. Climatol. 38(10), 3832–3841 (2018).
Google Scholar 

31.
Pita, G. et al. Carbon and water vapor fluxes over four forests in two contrasting climatic zones. Agric. For. Meteorol. 180, 211–224 (2013).
ADS  Google Scholar 

32.
EEA. Europe’s Biodiversity: Biogeographical Regions and Seas. Biogeographical Regions in Europe. The Atlantic Region—MILD and Green, Fragmented and Close to the Rising Sea (EEA, Copenhagen, 2003).
Google Scholar 

33.
Marshall, J. D. & Waring, R. H. Conifers and broadleaf species: stomatal sensitivity differs in western Oregon. Can. J. For. Res. 14(6), 905–908 (1984).
Google Scholar 

34.
EEA. Climate Change, Impacts and Vulnerability in Europe 2016: An Indicator-Based Report, EEA Report No 1/2017 (EEA, Copenhagen, 2017).
Google Scholar 

35.
Vilà-Cabrera, A. et al. Forest management for adaptation to climate change in the Mediterranean basin: a synthesis of evidence. For. Ecol. Manage. 407, 16–22 (2018).
Google Scholar 

36.
Cardil, A. et al. Temporal interactions among throughfall, type of canopy and thinning drive radial growth in an Iberian mixed pine-beech forest. Agric. For. Meteorol. 252, 62–74 (2018).
ADS  Google Scholar 

37.
Belyazid, S. & Giuliana, Z. Water limitation can negate the effect of higher temperatures on forest carbon sequestration. Eur. J. Forest Res. 138(2), 287–297 (2019).
Google Scholar 

38.
Schume, H., Jost, G. & Hager, H. Soil water depletion and recharge patterns in mixed and pure forest stands of European beech and Norway spruce. J. Hydrol. 289(1), 258–274 (2004).
ADS  Google Scholar 

39.
Teuling, A. J. et al. Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci. 3(10), 722–727 (2010).
ADS  CAS  Google Scholar 

40.
Gebhardt, T. et al. The more, the better? Water relations of Norway spruce stands after progressive thinning. Agric. For. Meteorol. 197, 235–243 (2014).
ADS  Google Scholar 

41.
Calev, A. et al. High-intensity thinning treatments in mature Pinus halepensis plantations experiencing prolonged drought. Eur. J. For. Res. 135(3), 551–563 (2016).
Google Scholar 

42.
Goisser, M. et al. Does belowground interaction with Fagus sylvatica increase drought susceptibility of photosynthesis and stem growth in Picea abies?. For. Ecol. Manage. 375, 268–278 (2016).
Google Scholar 

43.
Brinkmann, N. et al. Species-specific differences in water uptake depth of mature temperate trees vary with water availability in the soil. Plant Biol. 21(1), 71–81 (2019).
CAS  PubMed  Google Scholar 

44.
McGloin, R. et al. Available energy partitioning during drought at two Norway spruce forests and a European Beech forest in Central Europe. J. Geophys. Res. 124(7), 3726–3742 (2019).
Google Scholar 

45.
Schultz, N. M., Lawrence, P. J. & Lee, X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. Journal of Geophysical Research: Biogeosciences 122(4), 903–917 (2017).
ADS  Google Scholar 

46.
Peng, S.-S. et al. Afforestation in China cools local land surface temperature. Proc. Natl. Acad. Sci. USA 111(8), 2915–2919 (2014).
ADS  CAS  PubMed  Google Scholar 

47.
Zweifel, R., Rigling, A. & Dobbertin, M. Species-specific stomatal response of trees to drought: a link to vegetation dynamics?. J. Veg. Sci. 20(3), 442–454 (2009).
Google Scholar 

48.
Renaud, V. et al. Comparison between open-site and below-canopy climatic conditions in Switzerland for different types of forests over 10 years (1998–2007). Theoret. Appl. Climatol. 105(1), 119–127 (2011).
ADS  Google Scholar 

49.
Barr, A. G. et al. Intercomparison of BOREAS northern and southern study area surface fluxes in 1994. J. Geophys. Res. 106(D24), 33543–33550 (2001).
ADS  Google Scholar 

50.
Rebetez, M. et al. Meteorological data series from Swiss long-term forest ecosystem research plots since 1997. Ann. For. Sci. 75(2), 41 (2018).
Google Scholar 

51.
Good, E. J. An in situ-based analysis of the relationship between land surface “skin” and screen-level air temperatures. J. Geophys. Res. 121(15), 8801–8819 (2016).
Google Scholar 

52.
Mastrotheodoros, T. et al. More green and less blue water in the Alps during warmer summers. Nat. Clim. Change 10(2), 155–161 (2020).
ADS  Google Scholar 

53.
Zabret, K., Rakovec, J. & Šraj, M. Influence of meteorological variables on rainfall partitioning for deciduous and coniferous tree species in urban area. J. Hydrol. 558, 29–41 (2018).
ADS  Google Scholar 

54.
Li, X. et al. Process-based rainfall interception by small trees in Northern China: the effect of rainfall traits and crown structure characteristics. Agric. For. Meteorol. 218–219, 65–73 (2016).
ADS  Google Scholar 

55.
Xiao, Q. & McPherson, E. G. Surface water storage capacity of twenty tree species in Davis, California. J. Environ. Qual. 45(1), 188–198 (2016).
CAS  PubMed  Google Scholar 

56.
Leuschner, C. In Ecology of Central European Forests: Vegetation Ecology of Central Europe (ed. Ellenberg, H.) (Springer International Publishing, Cham, 2017).
Google Scholar 

57.
Knoke, T. et al. Admixing broadleaved to coniferous tree species: a review on yield, ecological stability and economics. Eur. J. For. Res. 127(2), 89–101 (2008).
Google Scholar 

58.
Ellenberg, H. & Strutt, G. K. Vegetation Ecology of Central Europe 4th edn. (Cambridge University Press, Cambridge, 2009).
Google Scholar 

59.
Schutz, J. P. et al. Vulnerability of spruce (Picea abies) and beech (Fagus sylvatica) forest stands to storms and consequences for silviculture. Eur. J. For. Res. 125(3), 291–302 (2006).
Google Scholar 

60.
Sousa-Silva, R. et al. Tree diversity mitigates defoliation after a drought-induced tipping point. Glob. Change Biol. 24(9), 4304–4315 (2018).
ADS  Google Scholar 

61.
Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Change 7, 395 (2017).
ADS  Google Scholar 

62.
IPCC Climate Change 2014: Synthesis Report. In Contribution of Working Groups I II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Pachauri, R. K. & Meyer, L. A.) 151 (IPCC, Geneva, 2014).
Google Scholar 

63.
Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl. Acad. Sci. USA 106(37), 15594–15598 (2009).
ADS  CAS  PubMed  Google Scholar 

64.
Anderson, G. B. & Bell, M. L. Heat waves in the United States: mortality risk during heat waves and effect modification by heat wave characteristics in 43 U.S. Communities. Environ Health Perspect 119(2), 210–218 (2011).
PubMed  Google Scholar 

65.
Zivin, J. G. & Neidell, M. Temperature and the allocation of time: implications for climate change. J. Labor Econ. 32(1), 1–26 (2014).
Google Scholar 

66.
Wan, Z. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ. 140, 36–45 (2014).
ADS  Google Scholar 

67.
Trigo, I. F. et al. Thermal land surface emissivity retrieved from SEVIRI/meteosat. IEEE Trans. Geosci. Remote Sens. 46(2), 307–315 (2008).
ADS  Google Scholar 

68.
Gottsche, F. M. et al. Long term validation of land surface temperature retrieved from MSG/SEVIRI with continuous in-situ measurements in Africa. Remote Sens. 8, 27 (2016).
Google Scholar 

69.
Freitas, S. C. et al. Quantifying the uncertainty of land surface temperature retrievals from SEVIRI/Meteosat. IEEE Trans. Geosci. Remote Sens. 48, 523–534 (2010).
ADS  Google Scholar 

70.
Langanke, T. Copernicus Land Monitoring Service: High Resolution Layer Forest: Product Specification Document (Copernicus team at EEA, European Environment Agency, Copenhagen, 2018).
Google Scholar 

71.
EEA, EU-DEM Statistical Validation (Copenhagen, Denmark, 2014).

72.
Kosztra, B. et al. Updated CLC Illustrated Nomenclature Guidelines (European Environment Agency, Vienna, 2017).
Google Scholar 

73.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2019).
Google Scholar 

74.
Wood, S. N., Goude, Y. & Shaw, S. Generalized additive models for large data sets. J. R. Stat. Soc. C 64(1), 139–155 (2015).
MathSciNet  Google Scholar 

75.
Wood, S. N., Pya, N. & Säfken, B. Smoothing parameter and model selection for general smooth models. J. Am. Stat. Assoc. 111(516), 1548–1563 (2016).
MathSciNet  CAS  Google Scholar 

76.
Götmark, F. et al. Broadleaved tree species in conifer-dominated forestry: Regeneration and limitation of saplings in southern Sweden. For. Ecol. Manage. 214(1), 142–157 (2005).
Google Scholar 

77.
von Arx, G. et al. Microclimate in forests with varying leaf area index and soil moisture: potential implications for seedling establishment in a changing climate. J. Ecol. 101(5), 1201–1213 (2013).
Google Scholar  More

Sampling rational and design
The EPA in conjunction with local authorities and other public bodies in Ireland has undertaken a substantial characterization of the physical water environment and the impact of human activities on waters17. Therefore, the monitored river water bodies in Ireland and the national river monitoring program are designed to obtain sufficiently representative information across river typologies and on significant pressures to assign a WFD status to each water body across our entire river network (Fig. 1).
Fig. 1

(a) Map of hydrometric areas (HA) in the dataset and (b) locations of all river biomonitoring stations 2007–2018 in Ireland. Both maps created using EPA data. See Table 5 for more details.

Full size image

The data collected covers the range of ecological conditions found in Irish rivers to assist the assignment of an ecological status as required by the WFD. Ecological status is an assessment of the quality of the structure and functioning of surface water ecosystems and it highlights the influence of pressures (e.g. pollution and habitat degradation) on several identifiable quality elements. As part of the WFD, ecological status is determined for each of the surface water body categories (i.e. rivers, lakes, transitional waters and coastal waters) using intercalibrated (see Technical Validation for further details) biological quality elements (BQEs) and supported by physico-chemical and hydromorphological quality elements. Ecological status for surface water bodies is primarily driven by the BQEs, namely fish, aquatic flora, macroinvertebrates and phytoplankton. The overall ecological status classification for any water body is determined, according to the ‘one out, all out’ principle, by the element with the worst status out of all the biological and supporting quality elements. In Ireland, macroinvertebrates are the main BQE determining the ecological status in rivers3.
The WFD requires BQE scores to be expressed as an Ecological Quality Ratio (EQR) to standardize and provide a common scale of ecological quality across participatory Member States using differing national methods18. The EQR is determined by expressing the observed result over the expected result which calculates a ratio score (Table 1). The ‘expected’ or ‘reference’ condition (EQR close to 1) is the natural, undisturbed environment, i.e. the benchmark. The assessment of the scale of anthropogenic pollution in any water body is based on the extent of deviation from expected reference conditions and follows the definitions as outlined in the WFD (Table 1). For example, ‘High status’ is defined as the biological, chemical and morphological conditions associated with no or very low human pressure, and therefore, little or no deviation from reference, ‘Good status’ means ‘slight’ deviation, ‘Moderate status’ means ‘moderate’ deviation, and so on. EQRs provide a common scale to ensure comparability across different pressures, allowing water managers to easily recognise and characterize impact facilitating the development of mitigation measures to restore or preserve ecological status17. To assess the network of rivers in Ireland, monitoring stations cover all 37 hydrometric areas (HA) providing a full national coverage (Fig. 1).
Table 1 The Q-value, ecological quality ratio (EQR)*, and corresponding WFD status and pollution gradient resulting from anthropogenic pressures.
Full size table

Field sampling and data collection
River macroinvertebrates are collected from June to September each year, when flows are likely to be relatively low. Occasionally, for operational or weather-related reasons, surveys may occur outside of this period. Two approached are used. The first, and principal methodology used (96.7% of surveys in dataset), is by kick-sampling with a standard pond net (230 × 225 mm frame with 1 mm mesh). In this approach a semi-quantitative two-minute macroinvertebrate kick-sample is collected from the riverbed preferably from the faster flowing riffle habitats19. A further one-minute hand search is carried out to locate macroinvertebrates that remain attached to the underside of the cobbles19. Depending upon the proportion of various habitats (e.g. glides, margins, pools), time may also be spent sampling these habitats with operators moving location approximately every 4 to 5-seconds over a 50 m stretch. Similar studies in Ireland and elsewhere have found that this sampling approach is sufficient to achieve a suitable representation of taxa for bioassessment of lotic habitats20,21. Occasionally, when the substratum (e.g. bedrock) or flow conditions make kick-sampling difficult, or the abundance of macroinvertebrates collected is extremely low (e.g. toxic pollution, see Kelly-Quinn et al.7), it may have been necessary to spend a longer amount of time sampling the river to accumulate a sufficient diversity and abundance of macroinvertebrates. In fast flowing steep rivers, it may have been necessary to kick deeper into the riverbed to disturb the organisms and include more of the marginal areas to ensure taxa are recorded19. This sampling approach requires avoidance of obvious localized disturbance (e.g. cattle access points) which may adversely influence the sample taken.
If the river depth is too deep to wade, a separate approach is taken. In this scenario, a bankside extension net sampling approach for deep (non-wadable) rivers is used to collect macroinvertebrates. It must be noted that this methodology is used less frequently than the kick-sampling approach. If employed, the depth and number of extension poles attached to a modified hand net will vary on a site by site basis. The net (frame and mesh dimensions as above) is then pulled upstream along the riverbed, generally at a perpendicular angle to the bank to cover as much surface area as possible with operators moving location after every pull over a 20 to 50 m stretch. The net may also need to be emptied between pulls to ensure that macroinvertebrates already collected are not lost inadvertently during the next pull. The extension net is also used to sweep along the water surface and marginal vegetation. This approach is conducted for a minimum of five minutes or until a representative sample is obtained (see Technical Validation for more details).
Once a live sample is collected it is assessed on the riverbank and the EPA Q-value classification is assigned (see Toner et al.1 for more details). This involves recording the taxa present at a suitable and attainable (under field conditions) taxonomic resolution (Table 2) and their categorical relative abundance (Table 3), determined using approximate counts. Once all taxa and their relative abundance have been recorded, the sample is returned to the river. Potential users should note that actual numbers of taxa have not been recorded and are therefore unavailable within the dataset. Similarly, taxonomic resolution may vary from what is outlined in Table 2. Indeterminate specimens may be brought back to the laboratory for identification under a microscope. Taxa are also occasionally returned to the laboratory and identified by microscope as a quality control measure. A brief description of the Q-value ecological quality rating (EQR) is outlined in Table 1. The typology of each river station is described in Table 4, after Kelly-Quinn et al.22,23.
Table 2 The level of macroinvertebrate identification carried out in the field during WFD biomonitoring assessments.
Full size table

Table 3 Abundance categories for macroinvertebrates recorded in the field during WFD biomonitoring assessments.
Full size table

Table 4 Typologies of Irish rivers.
Full size table

Each hydrometric area (Table 5 and Fig. 1) is generally surveyed on a three-year cycle; however, full surveys of certain hydrometric areas may be spilt across two concurrent years (e.g. HA 25), and on occasion a subset of stations were surveyed/resurveyed outside of the main survey year to closely track any progress in status changes following the implementation of a program of measures (Table 6). Certain stations were sampled on a more frequent basis such as seriously polluted sites (i.e. Red dot sites – Fanning et al.24), WFD high status objective sites, priority areas for action identified in Ireland’s national river basin plan17 and occasional sites of interest to local authorities and the EPA Office of Environmental Enforcement.
Table 5 Hydrometric area (HA) codes and HA names on the island of Ireland.
Full size table

Table 6 The number of river biomonitoring stations assessed by year and hydrometric area (HA), held by the EPA* 2007 to 2018.
Full size table

Within each hydrometric area, water bodies may have one or more sampling stations along their continuum. The number of stations may also vary between survey years, although, unless health and safety, or other unforeseen circumstances arise, the EPA attempt to sample the same stations in each survey cycle. Similarly, the numbers of water bodies and stations sampled within each hydrometric area will reflect the geographical area and length of river network. More

1.
Ladyman, J., Lambert, J. & Wiesner, K. What is a complex system? Eur. J. Philos. Sci. 3, 33–67 (2013).
Google Scholar 
2.
Brevik, E. C. et al. The interdisciplinary nature of SOIL. Soil 1, 117–129 (2015).
Google Scholar 

3.
Blum, W. E. Functions of soil for society and the environment. Rev. Environ. Sci. Biotechnol. 4, 75–79 (2005).
Google Scholar 

4.
Baveye, P. C., Baveye, J. & Gowdy, J. Soil “ecosystem” services and natural capital: critical appraisal of research on uncertain ground. Front. Environ. Sci. 4, 41 (2016).
Google Scholar 

5.
Keith, A. M., Schmidt, O. & McMahon, B. J. Soil stewardship as a nexus between ecosystem services and one health. Ecosyst. Serv. 17, 40–42 (2016).
Google Scholar 

6.
Bünemann, E. K. et al. Soil quality–a critical review. Soil Biol. Biochem. 120, 105–125 (2018).
Google Scholar 

7.
Patzel, N., Sticher, H. & Karlen, D. L. Soil fertility – phenomenon and concept. J. Plant Nutr. Soil Sci. 163, 129–142 (2000).
Google Scholar 

8.
Doran, J. W. & Parkin, T. B. in Defining Soil Quality for a Sustainable Environment Vol. 32 (eds Doran, J. W., Coleman, D. C., Bezdicek, D. F. & Stewart, B. A.) 1–21 (Soil Science Society of America, 1994).

9.
Pankhurst, C. E., Doube, B. M. & Gupta, V. V. S. R. in Biological Indicators of Soil Health (eds Pankhurst, C., Doube, B. & Gupta, V.) 419–435 (CAB International, 1997).

10.
McBratney, A., Field, D. J. & Koch, A. The dimensions of soil security. Geoderma 213, 203–213 (2014).
Google Scholar 

11.
Koch, A. et al. Soil security: solving the global soil crisis. Glob. Policy 4, 434–441 (2013).
Google Scholar 

12.
Stankovics, P., Tóth, G. & Tóth, Z. Identifying gaps between the legislative tools of soil protection in the EU member states for a common European soil protection legislation. Sustainability 10, 2886 (2018).
Google Scholar 

13.
Montanarella, L. Agricultural policy: govern our soils. Nature 528, 32–33 (2015).
Google Scholar 

14.
Jian, J., Du, X. & Stewart, R. D. A database for global soil health assessment. Sci. Data 7, 16 (2020).
Google Scholar 

15.
Karlen, D. L., Veum, K. S., Sudduth, K. A., Obrycki, J. F. & Nunes, M. R. Soil health assessment: past accomplishments, current activities, and future opportunities. Soil Tillage Res. 195, 104365 (2019).
Google Scholar 

16.
Norris, C. E. & Congreves, K. A. Alternative management practices improve soil health indices in intensive vegetable cropping systems: a review. Front. Environ. Sci. 6, 50 (2018).
Google Scholar 

17.
O’Dell, R. E. & Claassen, V. P. Vertical distribution of organic amendment influences the rooting depth of revegetation species on barren, subgrade serpentine substrate. Plant Soil 285, 19–29 (2006).
Google Scholar 

18.
Congreves, K. A., Hayes, A., Verhallen, E. A. & Van Eerd, L. L. Long-term impact of tillage and crop rotation on soil health at four temperate agroecosystems. Soil Tillage Res. 152, 17–28 (2015).
Google Scholar 

19.
Hamza, M. A. & Anderson, W. K. Soil compaction in cropping systems: a review of the nature, causes and possible solutions. Soil Tillage Res. 82, 121–145 (2005).
Google Scholar 

20.
Jenkinson, D. S. The Rothamsted long-term experiments: Are they still of use? Agron. J. 83, 2–10 (1991).
Google Scholar 

21.
Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).
Google Scholar 

22.
Chaparro, J. M., Sheflin, A. M., Manter, D. K. & Vivanco, J. M. Manipulating the soil microbiome to increase soil health and plant fertility. Biol. Fertil. Soils 48, 489–499 (2012).
Google Scholar 

23.
Bonanomi, G., Lorito, M., Vinale, F. & Woo, S. L. Organic amendments, beneficial microbes, and soil microbiota: toward a unified framework for disease suppression. Annu. Rev. Phytopathol. 56, 1–20 (2018).
Google Scholar 

24.
Chen, X. D. et al. Soil biodiversity and biogeochemical function in managed ecosystems. Soil Res. 58, 1–20 (2020).
Google Scholar 

25.
Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).
Google Scholar 

26.
Ogle, S. M., Swan, A. & Paustian, K. No-till management impacts on crop productivity, carbon input and soil carbon sequestration. Agric. Ecosyst. Environ. 149, 37–49 (2012).
Google Scholar 

27.
Zimnicki, T. et al. On quantifying water quality benefits of healthy soils. BioScience 70, 343–352 (2020).
Google Scholar 

28.
Evans, A. E., Mateo-Sagasta, J., Qadir, M., Boelee, E. & Ippolito, A. Agricultural water pollution: key knowledge gaps and research needs. Curr. Opin. Environ. Sustain. 36, 20–27 (2019).
Google Scholar 

29.
Carpenter, S. R. et al. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl. 8, 559–568 (1998).
Google Scholar 

30.
Lamichhane, S., Krishna, K. B. & Sarukkalige, R. Polycyclic aromatic hydrocarbons (PAHs) removal by sorption: a review. Chemosphere 148, 336–353 (2016).
Google Scholar 

31.
Tournebize, J., Chaumont, C. & Mander, Ü. Implications for constructed wetlands to mitigate nitrate and pesticide pollution in agricultural drained watersheds. Ecol. Eng. 103, 415–425 (2017).
Google Scholar 

32.
Hanson, J. R., Macalady, J. L., Harris, D. & Scow, K. M. Linking toluene degradation with specific microbial populations in soil. Appl. Environ. Microbiol. 65, 5403–5408 (1999).
Google Scholar 

33.
Li, G., Sun, G. X., Ren, Y., Luo, X. S. & Zhu, Y. G. Urban soil and human health: a review. Eur. J. Soil Sci. 69, 196–215 (2018).
Google Scholar 

34.
Laurenson, G., Laurenson, S., Bolan, N., Beecham, S. & Clark, I. The role of bioretention systems in the treatment of stormwater. Adv. Agron. 120, 223–274 (2013).
Google Scholar 

35.
Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).
Google Scholar 

36.
Kadam, A. M., Oza, G. H., Nemade, P. D. & Shankar, H. S. Pathogen removal from municipal wastewater in constructed soil filter. Ecol. Eng. 33, 37–44 (2008).
Google Scholar 

37.
Welch, R. M. & Graham, R. D. Breeding for micronutrients in staple food crops from a human nutrition perspective. J. Exp. Bot. 55, 353–364 (2004).
Google Scholar 

38.
Barrett, C. B. & Bevis, L. E. The self-reinforcing feedback between low soil fertility and chronic poverty. Nat. Geosci. 8, 907–912 (2015).
Google Scholar 

39.
Wood, S. A., Tirfessa, D. & Baudron, F. Soil organic matter underlies crop nutritional quality and productivity in smallholder agriculture. Agric. Ecosyst. Environ. 266, 100–108 (2018).
Google Scholar 

40.
Wall, D. H., Nielsen, U. N. & Six, J. Soil biodiversity and human health. Nature 528, 69–76 (2015).
Google Scholar 

41.
Jacoby, R., Peukert, M., Succurro, A., Koprivova, A. & Kopriva, S. The role of soil microorganisms in plant mineral nutrition—current knowledge and future directions. Front. Plant Sci. 8, 1617 (2017).
Google Scholar 

42.
Schlatter, D., Kinkel, L., Thomashow, L., Weller, D. & Paulitz, T. Disease suppressive soils: new insights from the soil microbiome. Phytopathology 107, 1284–1297 (2017).
Google Scholar 

43.
Rillig, M. C., Lehmann, A., Lehmann, J., Camenzind, T. & Rauh, C. Soil biodiversity effects from field to fork. Trends Plant Sci. 23, 17–24 (2018).
Google Scholar 

44.
Oliver, M. A. & Gregory, P. J. Soil, food security and human health: a review. Eur. J. Soil Sci. 66, 257–276 (2015).
Google Scholar 

45.
Hussein, H. S. & Brasel, J. M. Toxicity, metabolism, and impact of mycotoxins on humans and animals. Toxicology 167, 101–134 (2001).
Google Scholar 

46.
Bethony, J. et al. Soil-transmitted helminth infections: ascariasis, trichuriasis, and hookworm. Lancet 367, 1521–1532 (2006).
Google Scholar 

47.
Schatz, A., Bugle, E. & Waksman, S. A. Streptomycin, a substance exhibiting antibiotic activity against gram-positive and gram-negative bacteria. Proc. Soc. Exp. Biol. Med. 55, 66–69 (1944).
Google Scholar 

48.
Ling, L. L. et al. A new antibiotic kills pathogens without detectable resistance. Nature 517, 455–459 (2015).
Google Scholar 

49.
Veresoglou, S. D., Halley, J. M. & Rillig, M. C. Extinction risk of soil biota. Nat. Commun. 6, 8862 (2015).
Google Scholar 

50.
Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).
Google Scholar 

51.
Paustian, K. et al. Climate-smart soils. Nature 532, 49–57 (2016).
Google Scholar 

52.
Denef, K. & Six, J. Clay mineralogy determines the importance of biological versus abiotic processes for macroaggregate formation and stabilization. Eur. J. Soil Sci. 56, 469–479 (2005).
Google Scholar 

53.
Rinot, O., Levy, G. J., Steinberger, Y., Svoray, T. & Eshel, G. Soil health assessment: A critical review of current methodologies and a proposed new approach. Sci. Total. Environ. 648, 1484–1491 (2019).
Google Scholar 

54.
Van Wesemael, B. et al. An indicator for organic matter dynamics in temperate agricultural soils. Agric. Ecosyst. Environ. 274, 62–75 (2019).
Google Scholar 

55.
Bouma, J. et al. in Global Soil Security (eds Field, D. J., Morgan, C. L. S. & McBratney, A. B.) 27–44 (Springer, 2017).

56.
Schoenholtz, S. H., Van Miegroet, H. & Burger, J. A. A review of chemical and physical properties as indicators of forest soil quality: challenges and opportunities. For. Ecol. Manag. 138, 335–356 (2000).
Google Scholar 

57.
Andrews, S. S. & Carroll, C. R. Designing a soil quality assessment tool for sustainable agroecosystem management. Ecol. Appl. 11, 1573–1585 (2001).
Google Scholar 

58.
Lilburne, L. R., Hewitt, A. E., Sparling, G. P. & Selvarajah, N. Soil quality in New Zealand: policy and the science response. J. Environ. Qual. 31, 1768–1773 (2002).
Google Scholar 

59.
Idowu, O. J. et al. Use of an integrative soil health test for evaluation of soil management impacts. Renew. Agric. Food Syst. 24, 214–224 (2009).
Google Scholar 

60.
Cherubin, M. R. et al. A Soil Management Assessment Framework (SMAF) evaluation of Brazilian sugarcane expansion on soil quality. Soil Sci. Soc. Am. J. 80, 215–226 (2016).
Google Scholar 

61.
E.U. Mission Board Soil Health and Food. Caring for Soil is Caring for Life. The Publications Office of the European Union https://op.europa.eu/en/web/eu-law-and-publications/publication-detail/-/publication/32d5d312-b689-11ea-bb7a-01aa75ed71a1 (European Commission, 2020).

62.
Nunes, M. R., Karlen, D. L., Veum, K. S., Moorman, T. B. & Cambardella, C. A. Biological soil health indicators respond to tillage intensity: a US meta-analysis. Geoderma 369, 114335 (2020).
Google Scholar 

63.
Kaiser, E. A. et al. Nitrous oxide release from arable soil: importance of N-fertilization, crops and temporal variation. Soil Biol. Biochem. 30, 1553–1563 (1998).
Google Scholar 

64.
Baldock, J. A., Beare, M. H., Curtin, D. & Hawke, B. Stocks, composition and vulnerability to loss of soil organic carbon predicted using mid-infrared spectroscopy. Soil Res. 56, 468–480 (2018).
Google Scholar 

65.
Rossel, R. V. et al. Continental-scale soil carbon composition and vulnerability modulated by regional environmental controls. Nat. Geosci. 12, 547–552 (2019).
Google Scholar 

66.
Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).
Google Scholar 

67.
Pietrelli, A., Bavasso, I., Lovecchio, N., Ferrara, V. & Allard, B. in 8th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI) 302–306 (IEEE, 2019).

68.
Shaikh, F. K. & Zeadally, S. Energy harvesting in wireless sensor networks: a comprehensive review. Renew. Sustain. Energy Rev. 55, 1041–1054 (2016).
Google Scholar 

69.
Tan, X., Sun, Z., Wang, P. & Sun, Y. Environment-aware localization for wireless sensor networks using magnetic induction. Ad Hoc Netw. 98, 102030 (2020).
Google Scholar 

70.
Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534 (2020).
Google Scholar 

71.
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1078 (2014).
Google Scholar 

72.
Van Den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).
Google Scholar 

73.
Rillig, M. C., Bonneval, K. & Lehmann, J. Sounds of soil: a new world of interactions under our feet? Soil Syst. 3, 45 (2019).
Google Scholar 

74.
Smolka, M. et al. A mobile lab-on-a-chip device for on-site soil nutrient analysis. Precision Agric. 18, 152–168 (2017).
Google Scholar 

75.
Rossel, R. A. V. & Bouma, J. Soil sensing: a new paradigm for agriculture. Agric. Syst. 148, 71–74 (2016).
Google Scholar 

76.
Ali, M. A., Dong, L., Dhau, J., Khosla, A. & Kaushik, A. Perspective — electrochemical sensors for soil quality assessment. J. Electrochem. Soc. 167, 037550 (2020).
Google Scholar 

77.
Enell, A. et al. Combining leaching and passive sampling to measure the mobility and distribution between porewater, DOC, and colloids of native oxy-PAHs, N-PACs, and PAHs in historically contaminated soil. Environ. Sci. Technol. 50, 11797–11805 (2016).
Google Scholar 

78.
Sismaet, H. J. & Goluch, E. D. Electrochemical probes of microbial community behaviour. Annu. Rev. Anal. Chem. 11, 441–461 (2018).
Google Scholar 

79.
Chabrillat, S. et al. Imaging spectroscopy for soil mapping and monitoring. Surv. Geophys. 40, 361–399 (2019).
Google Scholar 

80.
Mohanty, B. P., Cosh, M. H., Lakshmi, V. & Montzka, C. Soil moisture remote sensing: state-of-the-science. Vadose Zone J. 16, 1–9 (2017).
Google Scholar 

81.
Paustian, K. et al. Quantifying carbon for agricultural soil management: from the current status toward a global soil information system. Carbon Manage. 10, 567–587 (2019).
Google Scholar 

82.
Duckett, T. et al. Agricultural robotics: the future of robotic agriculture. Preprint arXiv https://arxiv.org/abs/1806.06762 (2018).

83.
Hussain, I., Olson, K. R., Wander, M. M. & Karlen, D. L. Adaptation of soil quality indices and application to three tillage systems in southern Illinois. Soil Tillage Res. 50, 237–249 (1999).
Google Scholar 

84.
Fine, A. K., van Es, H. M. & Schindelbeck, R. R. Statistics, scoring functions, and regional analysis of a comprehensive soil health database. Soil Sci. Soc. Am. J. 81, 589–601 (2017).
Google Scholar 

85.
Svoray, T., Hassid, I., Atkinson, P. M., Moebius-Clune, B. N. & van Es, H. M. Mapping soil health over large agriculturally important areas. Soil Sci. Soc. Am. J. 79, 1420–1434 (2015).
Google Scholar 

86.
Moebius-Clune, B. N. et al. Comprehensive Assessment of Soil Health – The Cornell Framework, Edition 3.1 (Cornell Univ. Press, 2016).

87.
Wiesmeier, M. et al. Soil organic carbon storage as a key function of soils-a review of drivers and indicators at various scales. Geoderma 333, 149–162 (2019).
Google Scholar 

88.
Lima, A. C. R., Brussaard, L., Totola, M. R., Hoogmoed, W. B. & de Goede, R. G. M. A functional evaluation of three indicator sets for assessing soil quality. Appl. Soil Ecol. 64, 194–200 (2013).
Google Scholar 

89.
Verheijen, F. G., Bellamy, P. H., Kibblewhite, M. G. & Gaunt, J. L. Organic carbon ranges in arable soils of England and Wales. Soil Use Manage. 21, 2–9 (2005).
Google Scholar 

90.
Bucka, F. B., Kölbl, A., Uteau, D., Peth, S. & Kögel-Knabner, I. Organic matter input determines structure development and aggregate formation in artificial soils. Geoderma 354, 113881 (2019).
Google Scholar 

91.
Kaiser, K. et al. Driving forces of soil bacterial community structure, diversity, and function in temperate grasslands and forests. Sci. Rep. 6, 33696 (2016).
Google Scholar 

92.
Jordan-Meille, L. et al. An overview of fertilizer-P recommendations in Europe: soil testing, calibration and fertilizer recommendations. Soil Use Manage. 28, 419–435 (2012).
Google Scholar 

93.
McLellan, E. L. et al. The nitrogen balancing act: tracking the environmental performance of food production. Bioscience 68, 194–203 (2018).
Google Scholar 

94.
Brevik, E. C. & Sauer, T. J. The past, present, and future of soils and human health studies. Soil 1, 35–46 (2015).
Google Scholar 

95.
Pereira, P., Bogunovic, I., Muñoz-Rojas, M. & Brevik, E. C. Soil ecosystem services, sustainability, valuation and management. Curr. Opin. Environ. Sci. Health 5, 7–13 (2018).
Google Scholar 

96.
Bampa, F. et al. Harvesting European knowledge on soil functions and land management using multi-criteria decision analysis. Soil Use Manage. 35, 6–20 (2019).
Google Scholar 

97.
Schulte, R. P. et al. Demands on land: mapping competing societal expectations for the functionality of agricultural soils in Europe. Environ. Sci. Policy 100, 113–125 (2019).
Google Scholar 

98.
Ward, M. O., Grinstein, G. & Keim, D. Interactive Data Visualization: Foundations, Techniques, and Applications (AK Peters/CRC Press, 2015).

99.
Villamil, M. B., Miguez, F. E. & Bollero, G. A. Multivariate analysis and visualization of soil quality data for no-till systems. J. Environ. Qual. 37, 2063–2069 (2008).
Google Scholar 

100.
Börner, K., Bueckle, A. & Ginda, M. Data visualization literacy: definitions, conceptual frameworks, exercises, and assessments. Proc. Natl Acad. Sci. USA 116, 1857–1864 (2019).
Google Scholar 

101.
Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).
Google Scholar 

102.
Tian, R. & Wu, J. Groundwater quality appraisal by improved set pair analysis with game theory weightage and health risk estimation of contaminants for Xuecha drinking water source in a loess area in Northwest China. Hum. Ecol. Risk Assess. Int. J. 25, 132–157 (2019).
Google Scholar 

103.
Finger, R., Swinton, S. M., Benni, N. E. & Walter, A. Precision farming at the nexus of agricultural production and the environment. Annu. Rev. Resour. Econ. 11, 313–335 (2019).
Google Scholar 

104.
van Joolingen, W. R., de Jong, T., Lazonder, A. W., Savelsbergh, E. R. & Manlove, S. Co-Lab: research and development of an online learning environment for collaborative scientific discovery learning. Comput. Hum. Behav. 21, 671–688 (2005).
Google Scholar 

105.
Stott, D. E. Recommended Soil Health Indicators and Associated Laboratory Procedures. Soil Health Technical Note No. 450-03 (U.S. Department of Agriculture, Natural Resources Conservation Service, 2019).

106.
Haberern, J. A soil health index. J. Soil Water Conserv. 47, 6 (1992).
Google Scholar 

107.
Pankhurst, C. E. et al. Evaluation of soil biological properties as potential bioindicators of soil health. Austr. J. Exp. Agric. 35, 1015–1028 (1995).
Google Scholar 

108.
Doran, J. W. & Zeiss, M. R. Soil health and sustainability: managing the biotic component of soil quality. Appl. Soil Ecol. 15, 3–11 (2000).
Google Scholar 

109.
Winiwarter, V. & Blum, W. E. in Footprints in the Soil. People and Ideas in Soil History (ed. Warkentin, B.) 107–122 (Elsevier, 2006).

110.
Capra, G. F., Ganga, A. & Moore, A. F. Songs for our soils. How soil themes have been represented in popular song. Soil Sci. Plant Nutr. 63, 517–525 (2017).
Google Scholar 

111.
Jenny, H. in Study Week on Organic Matter and Soil Fertility. Pontificiae Academiae Scientarium Scripta, Varia 32. 947–979 (North Holland Publ. Co and Wiley Interscience Division, 1968).

112.
Feller, C., Landa, E. R., Toland, A. & Wessolek, G. Case studies of soil in art. Soil 1, 543–559 (2015).
Google Scholar 

113.
Brevik, E. C. & Hartemink, A. E. Early soil knowledge and the birth and development of soil science. Catena 83, 23–33 (2010).
Google Scholar 

114.
Carson, R. Silent Spring (Houghton Mifflin, 1962).

115.
Lovelock, J. E. Gaia, a New Look at Life on Earth (Oxford Univ. Press, 1979).

116.
Keesstra, S. D. et al. The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals. Soil 2, 111–128 (2016).
Google Scholar 

117.
Mausel, P. W. Soil quality in Illinois — an example of a soils geography resource analysis. Prof. Geogr. 23, 127–136 (1971).
Google Scholar 

118.
Sojka, R. E. & Upchurch, D. R. Reservations regarding the soil quality concept. Soil Sci. Soc. Am. J. 63, 1039–1054 (1999).
Google Scholar 

119.
Rumpel, C. et al. Put more carbon in soils to meet Paris climate pledges. Nature 564, 32–34 (2018).
Google Scholar 

120.
Freidberg, S. Assembled but unrehearsed: corporate food power and the ‘dance’ of supply chain sustainability. J. Peasant Stud. 47, 383–400 (2020).
Google Scholar 

121.
Chabbi, A. et al. Aligning agriculture and climate policy. Nat. Clim. Change 7, 307–309 (2017).
Google Scholar 

122.
Puig de la Bellacasa, M. Re-animating soils: transforming human–soil affections through science, culture and community. Sociol. Rev. 67, 391–407 (2019).
Google Scholar  More

1.
De Roy, K., Marzorati, M., Van den Abbeele, P., Van de Wiele, T. & Boon, N. Synthetic microbial ecosystems: an exciting tool to understand and apply microbial communities. Environ. Microbiol. 16, 1472–1481 (2014).
PubMed  Google Scholar 
2.
Mee, M. T. & Wang, H. H. Engineering ecosystems and synthetic ecologies. Mol. Biosyst. 8, 2470 (2012).
PubMed  PubMed Central  CAS  Google Scholar 

3.
Hendrickx, L. et al. Microbial ecology of the closed artificial ecosystem MELiSSA (micro-ecological life support system alternative): reinventing and compartmentalizing the Earth’s food and oxygen regeneration system for long-haul space exploration missions. Res. Microbiol. 157, 77–86 (2006).
PubMed  Google Scholar 

4.
Chen, Y. Development and application of co-culture for ethanol production by co-fermentation of glucose and xylose: a systematic review. J. Ind. Microbiol. Biotechnol. 38, 581–597 (2011).
PubMed  CAS  Google Scholar 

5.
Spus, M. et al. Strain diversity and phage resistance in complex dairy starter cultures. J. Dairy Sci. 98, 5173–5182 (2015).
PubMed  CAS  Google Scholar 

6.
Ma, Q. et al. Integrated proteomic and metabolomic analysis of an artificial microbial community for two-step production of vitamin C. PLoS ONE 6, e26108 (2011).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

7.
Petrof, E. O. et al. Stool substitute transplant therapy for the eradication of Clostridium difficile infection: ‘RePOOPulating’ the gut. Microbiome 1, 3 (2013).
PubMed  PubMed Central  Google Scholar 

8.
Bolhuis, H. & Stal, L. J. Analysis of bacterial and archaeal diversity in coastal microbial mats using massive parallel 16S rRNA gene tag sequencing. ISME J. 5, 1701–1712 (2011).
PubMed  PubMed Central  CAS  Google Scholar 

9.
van Gemerden, H. Microbial mats: a joint venture. Mar. Geol. 113, 3–25 (1993).
ADS  Google Scholar 

10.
Tolker-Nielsen, T. & Molin, S. Spatial organization of microbial biofilm communities. Microb. Ecol. 40, 75–84 (2000).
PubMed  CAS  Google Scholar 

11.
Bolhuis, H., Cretoiu, M. S. & Stal, L. J. Molecular ecology of microbial mats. FEMS Microbiol. Ecol. 90, 335–350 (2014).
PubMed  CAS  Google Scholar 

12.
Cohen, S. E. & Golden, S. S. Circadian rhythms in cyanobacteria. Microbiol. Mol. Biol. Rev. 79, 373–385 (2015).
PubMed  PubMed Central  CAS  Google Scholar 

13.
Nakajima, M. et al. Reconstitution of circadian oscillation of cyanobacterial KaiC phosphorylation in vitro. Science 308, 414–415 (2005).
ADS  PubMed  CAS  Google Scholar 

14.
Murayama, Y. et al. Low temperature nullifies the circadian clock in cyanobacteria through Hopf bifurcation. Proc. Natl. Acad. Sci. USA. 114, 5641–5646 (2017).
PubMed  CAS  Google Scholar 

15.
Johnson, C. H., Zhao, C., Xu, Y. & Mori, T. Timing the day: what makes bacterial clocks tick?. Nat. Rev. Microbiol. 15, 232–242 (2017).
PubMed  PubMed Central  CAS  Google Scholar 

16.
Woelfle, M. A., Ouyang, Y., Phanvijhitsiri, K. & Johnson, C. H. The adaptive value of circadian clocks: an experimental assessment in cyanobacteria. Curr. Biol. 14, 1481–1486 (2004).
PubMed  CAS  Google Scholar 

17.
Welkie, D. G. et al. A hard day’s night: cyanobacteria in diel cycles. Trends Microbiol. 27, 231–242 (2019).
PubMed  CAS  Google Scholar 

18.
Ivleva, N. B., Gao, T., LiWang, A. C. & Golden, S. S. Quinone sensing by the circadian input kinase of the cyanobacterial circadian clock. Proc. Natl. Acad. Sci. USA. 103, 17468–17473 (2006).
ADS  PubMed  CAS  Google Scholar 

19.
Gutu, A. & O’Shea, E. K. Two antagonistic clock-regulated histidine kinases time the activation of circadian gene expression. Mol. Cell 50, 288–294 (2013).
PubMed  PubMed Central  CAS  Google Scholar 

20.
Rust, M. J., Golden, S. S. & O’Shea, E. K. Light-driven changes in energy metabolism directly entrain the cyanobacterial circadian oscillator. Science 331, 220–223 (2011).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

21.
Pattanayak, G. & Rust, M. J. The cyanobacterial clock and metabolism. Curr. Opin. Microbiol. 18, 90–95 (2014).
PubMed  PubMed Central  CAS  Google Scholar 

22.
Mackey, S. R., Golden, S. S. & Ditty, J. L. The itty-bitty time machine genetics of the cyanobacterial circadian clock. Adv. Genet. 74, 13–53 (2011).
PubMed  PubMed Central  CAS  Google Scholar 

23.
Resch, A., Rosenstein, R., Nerz, C. & Götz, F. Differential gene expression profiling of Staphylococcus aureus cultivated under biofilm and planktonic conditions. Appl. Environ. Microbiol. 71, 2663–2676 (2005).
PubMed  PubMed Central  CAS  Google Scholar 

24.
Moorthy, S. & Watnick, P. I. Identification of novel stage-specific genetic requirements through whole genome transcription profiling of Vibrio cholerae biofilm development. Mol. Microbiol. 57, 1623–1635 (2005).
PubMed  PubMed Central  CAS  Google Scholar 

25.
Edgar, R. S. et al. Peroxiredoxins are conserved markers of circadian rhythms. Nature 485, 459–464 (2012).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

26.
Pinto, F., Pacheco, C. C., Ferreira, D., Moradas-Ferreira, P. & Tamagnini, P. Selection of suitable reference genes for RT-qPCR analyses in cyanobacteria. PLoS ONE 7, 1–9 (2012).
Google Scholar 

27.
Pfaffl, M. W., Tichopad, A., Prgomet, C. & Neuvians, T. P. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: Bestkeeper–Excel-based tool using pair-wise correlations. Biotechnol. Lett. 26, 509–515 (2004).
PubMed  CAS  Google Scholar 

28.
Oksanen, J. et al. Package ‘vegan’ Title Community Ecology Package. Community Ecol. Packag. 2, (2019).

29.
Wu, G., Anafi, R. C., Hughes, M. E., Kornacker, K. & Hogenesch, J. B. MetaCycle: an integrated R package to evaluate periodicity in large scale data. Bioinformatics 32, 3351–3353 (2016).
PubMed  PubMed Central  CAS  Google Scholar 

30.
Hörnlein, C., Confurius-Guns, V., Stal, L. J. & Bolhuis, H. Daily rhythmicity in coastal microbial mats. npj Biofilms Microbiomes 4, 11 (2018).
PubMed  PubMed Central  Google Scholar 

31.
Kondo, T. et al. Circadian rhythms in prokaryotes: luciferase as a reporter of circadian gene expression in cyanobacteria. Proc. Natl. Acad. Sci. USA. 90, 5672–5676 (1993).
ADS  PubMed  CAS  Google Scholar 

32.
Tomita, J. No transcription-translation feedback in circadian rhythm of KaiC phosphorylation. Science 307, 251–254 (2005).
ADS  PubMed  CAS  Google Scholar 

33.
Whale, G. F. & Walsby, A. E. Motility of the cyanobacterium Microcoleus chthonoplastes in mud. Br. Phycol. J. 19, 117–123 (1984).
Google Scholar 

34.
Urmeneta, J., Navarrete, A., Huete, J. & Guerrero, R. Isolation and characterization of cyanobacteria from microbial mats of the Ebro Delta Spain. Curr. Microbiol. 46, 199–204 (2003).
PubMed  CAS  Google Scholar 

35.
Kothari, A., Vaughn, M. & Garcia-Pichel, F. Comparative genomic analyses of the cyanobacterium, Lyngbya aestuarii BL J, a powerful hydrogen producer. Front. Microbiol. 4, 363 (2013).
PubMed  PubMed Central  Google Scholar 

36.
Rath, J. & Adhikary, S. P. Response of the estuarine cyanobacterium Lyngbya aestuarii to UV-B radiation. J. Appl. Phycol. 19, 529–536 (2007).
CAS  Google Scholar 

37.
Holtzendorff, J. et al. Genome streamlining results in loss of robustness of the circadian clock in the marine cyanobacterium Prochlorococcus marinus PCC 9511. J. Biol. Rhythms 23, 187–199 (2008).
PubMed  CAS  Google Scholar 

38.
O’Neill, J. & Reddy, A. Circadian clocks in human red blood cells. Nature 469, 498–503 (2011).
ADS  PubMed  PubMed Central  Google Scholar 

39.
Shi, T., Ilikchyan, I., Rabouille, S. & Zehr, J. P. Genome-wide analysis of diel gene expression in the unicellular N 2-fixing cyanobacterium Crocosphaera watsonii WH 8501. ISME J. 4, 621–632 (2010).
PubMed  CAS  Google Scholar 

40.
Červený, J., Sinetova, M. A., Valledor, L., Sherman, L. A. & Nedbal, L. Ultradian metabolic rhythm in the diazotrophic cyanobacterium Cyanothece sp. ATCC 51142. Proc. Natl. Acad. Sci. U. S. A. 110, 13210–5 (2013).
ADS  PubMed  PubMed Central  Google Scholar 

41.
Johnson, C. H., Stewart, P. L. & Egli, M. The cyanobacterial circadian system: from biophysics to bioevolution. Annu. Rev. Biophys. 40, 143–167 (2011).
PubMed  PubMed Central  CAS  Google Scholar 

42.
Mitsui, A. et al. Strategy by which nitrogen-fixing unicellular cyanobacteria grow photoautotrophically. Nature 323, 720–722 (1986).
ADS  CAS  Google Scholar 

43.
Berman-Frank, I. et al. Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium Trichodesmium. Science 294, 1534–1537 (2001).
ADS  PubMed  CAS  Google Scholar 

44.
Besharova, O., Suchanek, V. M., Hartmann, R., Drescher, K. & Sourjik, V. Diversification of gene expression during formation of static submerged biofilms by Escherichia coli. Front. Microbiol. 7, 1568 (2016).
PubMed  PubMed Central  Google Scholar 

45.
Nishiyama, Y. et al. Oxidative stress inhibits the repair of photodamage to the photosynthetic machinery. EMBO J. 20, 5587–5594 (2001).
PubMed  PubMed Central  CAS  Google Scholar 

46.
Nishiyama, Y., Allakhverdiev, S. I., Yamamoto, H., Hayashi, H. & Murata, N. Singlet oxygen inhibits the repair of photosystem II by suppressing the translation elongation of the D1 protein in Synechocystis sp. PCC 6803. Biochemistry 43, 11321–11330 (2004).
PubMed  CAS  Google Scholar 

47.
Los, D. A. & Zinchenko, V. V. in Lipids Photosynth. Essent. Regul. Funct. (eds. Wada, H. & Murata, N.) 329–348 (Springer Netherlands, 2010). doi:10.1007/978–90–481–2863–1_15

48.
Zehr, J. P., Crumbliss, L. L., Church, M. J., Omoregie, E. O. & Jenkins, B. D. Nitrogenase genes in PCR and RT-PCR reagents: implications for studies of diversity of functional genes. Biotechniques 35, 996–1005 (2003).
PubMed  CAS  Google Scholar 

49.
Stal, L. J., Bolhuis, H. & Cretoiu, M. S. Phototrophic marine benthic microbiomes: the ecophysiology of these biological entities. Environ. Microbiol. 21, 1529-1551/////// (2019).
PubMed  Google Scholar 

50.
Bolhuis, H., Severin, I., Confurius-Guns, V., Wollenzien, U. I. a & Stal, L. J. Horizontal transfer of the nitrogen fixation gene cluster in the cyanobacterium Microcoleus chthonoplastes. ISME J. 4, 121–30 (2010).

51.
Lee, S. H., Pulakat, L., Parker, K. C. & Gavini, N. Genetic analysis on the NifW by utilizing the yeast two-hybrid system revealed that the NifW of Azotobacter vinelandii interacts with the NifZ to form higher-order complexes. Biochem. Biophys. Res. Commun. 244, 498–504 (1998).
PubMed  CAS  Google Scholar 

52.
Severin, I. & Stal, L. J. NifH expression by five groups of phototrophs compared with nitrogenase activity in coastal microbial mats. FEMS Microbiol. Ecol. 73, 55–67 (2010).
PubMed  CAS  Google Scholar 

53.
Ottesen, E. a et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc. Natl. Acad. Sci. U. S. A. 110, E488–97 (2013).

54.
Stal, L. J., Gemerden, H. & Krumbein, W. E. Structure and development of a benthic marine microbial mat. FEMS Microbiol. Lett. 31, 111–125 (1985).
CAS  Google Scholar 

55.
Villbrandt, M. & Stal, L. J. The effect of sulfide on nitrogen fixation in heterocystous and non-heterocystous cyanobacterial mat communities. Algol. Stud. für Hydrobiol. Suppl. 83, 549–563 (1996).
Google Scholar 

56.
Paerl, H. W., Pinckney, J. L. & Steppe, T. F. Cyanobacterial-bacterial mat consortia: Examining the functional unit of microbial survival and growth in extreme environments. Environ. Microbiol. 2, 11–26 (2000).
PubMed  CAS  Google Scholar 

57.
Fourçans, A. et al. Characterization of functional bacterial groups in a hypersaline microbial mat community (Salins-de-Giraud, Camargue, France). FEMS Microbiol. Ecol. 51, 55–70 (2004).
PubMed  Google Scholar 

58.
Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
PubMed  PubMed Central  Google Scholar 

59.
Pabinger, S., Rödiger, S., Kriegner, A., Vierlinger, K. & Weinhäusel, A. A survey of tools for the analysis of quantitative PCR (qPCR) data. Biomol. Detect. Quantif. 1, 23–33 (2014).
PubMed  PubMed Central  Google Scholar  More

1.
Bastin, J. F. et al. The global tree restoration potential. Science 364, 76–79 (2019).
Google Scholar 
2.
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Regenerate natural forests to store carbon. Nature 568, 25–28 (2019).
CAS  PubMed  Google Scholar 

3.
Chazdon, R. L. & Brancalion, P. Restoring forests as a means to many ends. Science 365, 24–25 (2019).
CAS  PubMed  Google Scholar 

4.
Busch, J. et al. Potential for low-cost carbon dioxide removal through tropical reforestation. Nat. Clim. Change 9, 463–466 (2019).
CAS  Google Scholar 

5.
Brancalion, P. H. S. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).
PubMed  PubMed Central  Google Scholar 

6.
Strassburg, B. B. N. et al. Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 3, 62–70 (2019).
PubMed  Google Scholar 

7.
Friedlingstein, P., Allen, M., Canadell, J. G., Peters, G. P. & Seneviratne, S. I. Comment on “The global tree restoration potential”. Science 366, eaay8060 (2019).
PubMed  Google Scholar 

8.
Lewis, S. L. et al. Comment on “The global tree restoration potential”. Science 366, eaaz0388 (2019).
CAS  PubMed  Google Scholar 

9.
Veldman, J. W. et al. Comment on “The global tree restoration potential”. Science 366, eaay7976 (2019).
PubMed  Google Scholar 

10.
Anderson, C. M. et al. Natural climate solutions are not enough. Science 363, 933–934 (2019).
CAS  PubMed  Google Scholar 

11.
Maron, M. et al. Global no net loss of natural ecosystems. Nat. Ecol. Evol. 4, 46–49 (2019).
PubMed  Google Scholar 

12.
Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).
Google Scholar 

13.
Mappin, B. et al. Restoration priorities to achieve the global protected area target. Conserv. Lett. 12, e12646 (2019).
Google Scholar 

14.
Pritchard, R. & Brockington, D. Forests: regrow with locals’ participation. Nature 569, 630 (2019).
CAS  PubMed  Google Scholar 

15.
Potapov, P., Laestadius, L. & Minnemeyer, S. Global map of forest landscape restoration opportunities. World Resources Institute https://www.wri.org/applications/maps/flr-atlas/#&init=y (2011).

16.
Oldekop, J. A., Holmes, G., Harris, W. E. & Evans, K. L. A global assessment of the social and conservation outcomes of protected areas. Conserv. Biol. 30, 133–141 (2016).
CAS  PubMed  Google Scholar 

17.
Agrawal, A. & Redford, K. Conservation and displacement: an overview. Conservat. Soc. 7, 1–10 (2009).
Google Scholar 

18.
Chazdon, R. L. Protecting intact forests requires holistic approaches. Nat. Ecol. Evol. 2, 915 (2018).
PubMed  Google Scholar 

19.
IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

20.
Loft, L. et al. Risks to REDD+: potential pitfalls for policy design and implementation. Environ. Conserv. 44, 44–55 (2017).
Google Scholar 

21.
Brancalion, P. H. S. & Chazdon, R. L. Beyond hectares: four principles to guide reforestation in the context of tropical forest and landscape restoration. Restor. Ecol. 25, 491–496 (2017).
Google Scholar 

22.
Mansourian, S. in Forest Restoration in Landscapes: Beyond Planting Trees (eds Mansourian, S., Vallauri, D. & Dudley, N.) 8–13 (Springer, 2005).

23.
Sabogal, C., Besacier, C. & McGuire, D. Forest and landscape restoration: concepts, approaches and challenges for implementation. Unasylva 66, 3–10 (2015).
Google Scholar 

24.
Stanturf, J. A. et al. Implementing forest landscape restoration under the Bonn Challenge: a systematic approach. Ann. For. Sci. 76, 50 (2019).
Google Scholar 

25.
Sayer, J. et al. Ten principles for a landscape approach to reconciling agriculture, conservation, and other competing land uses. Proc. Natl Acad. Sci. USA 110, 8349–8356 (2013).
CAS  PubMed  Google Scholar 

26.
S, Mansourian. In the eye of the beholder: reconciling interpretations of forest landscape restoration. Land. Degrad. Dev. 29, 2888–2898 (2018).
Google Scholar 

27.
Fagan, M. E., Reid, J. L., Holland, M. B., Drew, J. G. & Zahawi, R. A. How feasible are global forest restoration commitments? Conserv. Lett. 13, e12700 (2020).
Google Scholar 

28.
Mansourian, S., Stanturf, J. A., Derkyi, M. A. A. & Engel, V. L. Forest landscape restoration: increasing the positive impacts of forest restoration or simply the area under tree cover? Restor. Ecol. 25, 178–183 (2017).
Google Scholar 

29.
Mansourian, S. et al. Putting the pieces together: integration for forest landscape restoration implementation. Land. Degrad. Dev. 31, 419–429 (2020).
Google Scholar 

30.
Erbaugh, J. T. & Oldekop, J. A. Forest landscape restoration for livelihoods and well-being. Curr. Opin. Environ. Sustain. 32, 76–83 (2018).
Google Scholar 

31.
Mansourian, S. & Parrotta, J. Forest landscape restoration: integrated approaches to support effective implementation (Routledge, 2018).

32.
Adams, C., Rodrigues, S. T., Calmon, M. & Kumar, C. Impacts of large-scale forest restoration on socioeconomic status and local livelihoods: what we know and do not know. Biotropica 48, 731–744 (2016).
Google Scholar 

33.
Fox, H. & Cundill, G. Towards increased community-engaged ecological restoration: a review of current practice and future directions. Ecol. Restor. 36, 208–218 (2018).
Google Scholar 

34.
Persha, L., Agrawal, A. & Chhatre, A. Social and ecological synergy: local rulemaking, forest livelihoods, and biodiversity conservation. Science 331, 1606–1608 (2011).
CAS  PubMed  Google Scholar 

35.
Brooks, J. S., Waylen, K. A. & Mulder, M. B. How national context, project design, and local community characteristics influence success in community-based conservation projects. Proc. Natl Acad. Sci. USA 109, 21265–21270 (2012).
CAS  PubMed  Google Scholar 

36.
Boedhihartono, A. K. & Sayer, J. in Forest Landscape Restoration Vol. 15 (eds Stanturf, J. et al.) 309–323 (Springer, 2012).

37.
Chazdon, R. L. et al. A policy-driven knowledge agenda for global forest and landscape restoration. Conserv. Lett. 10, 125–132 (2017).
Google Scholar 

38.
Bennett, M. M. & Smith, L. C. Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sens. Environ. 192, 176–197 (2017).
Google Scholar 

39.
Proville, J., Zavala-Araiza, D. & Wagner, G. Night-time lights: a global, long term look at links to socio-economic trends. PLoS ONE 12, e0174610 (2017).
PubMed  PubMed Central  Google Scholar 

40.
Kyba, C. C. M. et al. Artificially lit surface of Earth at night increasing in radiance and extent. Sci. Adv. 3, e1701528 (2017).
PubMed  PubMed Central  Google Scholar 

41.
Soares-Filho, B. S. et al. Modelling conservation in the Amazon basin. Nature 440, 520–523 (2006).
CAS  PubMed  Google Scholar 

42.
Oldekop, J. A., Sims, K. R. E., Karna, B. K., Whittingham, M. J. & Agrawal, A. Reductions in deforestation and poverty from decentralized forest management in Nepal. Nat. Sustain. 2, 421–428 (2019).
Google Scholar 

43.
At a Crossroads. Trends in Recognition of Community-Based Forest Tenure from 2002–2017 (Rights and Resources Initiative, 2018).

44.
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).
CAS  PubMed  Google Scholar 

45.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
CAS  PubMed  Google Scholar 

46.
Veldman, J. W. et al. Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience 65, 1011–1018 (2015).
Google Scholar 

47.
Veldman, J. W. et al. Toward an old-growth concept for grasslands, savannas, and woodlands. Front. Ecol. Environ. 13, 154–162 (2015).
Google Scholar 

48.
Gridded Population of the World, version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals, Revision 11 (CIESIN, 2018).

49.
Version 1 VIIRS Day/Night Band Nighttime Lights (NOAA, 2019).

50.
World Development Indicators (The World Bank, 2019).

51.
Schlager, E. & Ostrom, E. Property-rights regimes and natural resources: a conceptual analysis. Land Econ. 68, 249–262 (1992).
Google Scholar 

52.
Sikor, T., He, J. U. N. & Lestrelin, G. Property rights regimes and natural resources: a conceptual analysis revisited. World Dev. 93, 337–349 (2017).
Google Scholar 

53.
Who Owns the World’s Land? A Global Baseline of Formally Recognized Indigenous and Community Land Rights (Rights and Resources Initiative, 2015). More

1.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
CAS  PubMed  PubMed Central  Google Scholar 
2.
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Google Scholar 

3.
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

4.
van der Plas, F. Biodiversity and ecosystem functioning in naturally assembled communities. Biol. Rev. 94, 1220–1245 (2019).
PubMed  Google Scholar 

5.
Schulze, E.-D. & Mooney, H. Biodiversity and Ecosystem Functioning (Springer, 1993).

6.
Naeem, S., Thompson, L. J., Lawler, S. P., Lawton, J. H. & Woodfin, R. M. Declining biodiversity can alter the performance of ecosystems. Nature 368, 734–737 (1994).
Google Scholar 

7.
Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).
PubMed  Google Scholar 

8.
Hines, J. et al. Mapping change in biodiversity and ecosystem function research: food webs foster integration of experiments and science policy. Adv. Ecol. Res. 61, 297–322 (2019).
Google Scholar 

9.
Tilman, D., Wedin, D. & Knops, J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720 (1996).
CAS  Google Scholar 

10.
Roscher, C., Schumacher, J. & Baade, J. The role of biodiversity for element cycling and trophic interactions: an experimental approach in a grassland community. Basic Appl. Ecol. 121, 107–121 (2004).
Google Scholar 

11.
Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).
CAS  PubMed  Google Scholar 

12.
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 

13.
Cardinale, B. J. et al. The functional role of producer diversity in ecosystems. Am. J. Bot. 98, 572–592 (2011).
PubMed  Google Scholar 

14.
O’Connor, M. I. et al. A general biodiversity–function relationship is mediated by trophic level. Oikos 126, 18–31 (2017).
Google Scholar 

15.
Loreau, M. et al. Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294, 804–808 (2001).
CAS  PubMed  Google Scholar 

16.
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).
CAS  PubMed  Google Scholar 

17.
Huston, M. A. Hidden treatments in ecological experiments: re-evaluating the ecosystem function of biodiversity. Oecologia 110, 449–460 (1997).
PubMed  Google Scholar 

18.
Grime, J. P. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).
Google Scholar 

19.
Wardle, D. A. et al. Biodiversity and ecosystem function: an issue in ecology. Bull. Ecol. Soc. Am. 81, 235–239 (2000).
Google Scholar 

20.
Leps, J. What do the biodiversity experiments tell us about consequences of plant species loss in the real world? Basic Appl. Ecol. 5, 529–534 (2004).
Google Scholar 

21.
Srivastava, D. S. & Vellend, M. Biodiversity–ecosystem function research: is it relevant to conservation? Annu. Rev. Ecol. Evol. Syst. 36, 267–294 (2005).
Google Scholar 

22.
Duffy, J. E. Why biodiversity is important to the functioning of real-world ecosystems. Front. Ecol. Environ. 7, 437–444 (2008).
Google Scholar 

23.
Duffy, J. E. Biodiversity effects: trends and exceptions—a reply to Wardle and Jonsson. Front. Ecol. Environ. 8, 11–12 (2010).
Google Scholar 

24.
Wardle, D. A. & Jonsson, M. Biodiversity effects in real ecosystems—a response to Duffy. Front. Ecol. Environ. 8, 10–11 (2010).
Google Scholar 

25.
Wardle, D. A. Do experiments exploring plant diversity–ecosystem functioning relationships inform how biodiversity loss impacts natural ecosystems? J. Veg. Sci. 27, 646–653 (2016).
Google Scholar 

26.
Manning, P. et al. Transferring biodiversity-ecosystem function research to the management of ‘real-world’ ecosystems. Adv. Ecol. Res. 61, 323–356 (2019).
Google Scholar 

27.
Wilsey, B. J. & Potvin, C. Biodiversity and ecosystem functioning: importance of species evenness in an old field. Ecology 81, 887–892 (2000).
Google Scholar 

28.
Wilsey, B. J. & Polley, H. W. Realistically low species evenness does not alter grassland species-richness–productivity relationships. Ecology 85, 2693–2700 (2004).
Google Scholar 

29.
Hillebrand, H., Bennett, D. & Cadotte, M. Consequences of dominance: a review of evenness effects on local and regional ecosystem processes. Ecology 89, 1510–1520 (2008).
PubMed  Google Scholar 

30.
Schmitz, M. et al. Consistent effects of biodiversity on ecosystem functioning under varying density and evenness. Folia Geobot. 48, 335–353 (2013).
Google Scholar 

31.
Finn, J. A. et al. Ecosystem function enhanced by combining four functional types of plant species in intensively managed grassland mixtures: a 3-year continental-scale field experiment. J. Appl. Ecol. 50, 365–375 (2013).
Google Scholar 

32.
Weisser, W. W. et al. Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: patterns, mechanisms, and open questions. Basic Appl. Ecol. 23, 1–73 (2017).
Google Scholar 

33.
Schmid, B. & Hector, A. The value of biodiversity experiments. Basic Appl. Ecol. 5, 535–542 (2004).
Google Scholar 

34.
Eisenhauer, N. et al. Biodiversity–ecosystem function experiments reveal the mechanisms underlying the consequences of biodiversity change in real world ecosystems. J. Veg. Sci. 27, 1061–1070 (2016).
Google Scholar 

35.
Isbell, F. et al. Nutrient enrichment, biodiversity loss, and consequent declines in ecosystem productivity. Proc. Natl Acad. Sci. USA 110, 11911–11916 (2013).
CAS  PubMed  Google Scholar 

36.
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).
CAS  PubMed  Google Scholar 

37.
Buchmann, T. et al. Connecting experimental biodiversity research to real-world grasslands. Perspect. Plant Ecol. Evol. Syst. 33, 78–88 (2018).
Google Scholar 

38.
Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).
CAS  Google Scholar 

39.
Tilman, D., Reich, P. B. & Isbell, F. Biodiversity impacts ecosystem productivity as much as resources, disturbance, or herbivory. Proc. Natl Acad. Sci. USA 109, 10394–10397 (2012).
CAS  PubMed  Google Scholar 

40.
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).
CAS  PubMed  Google Scholar 

41.
Fischer, M. et al. Implementing large-scale and long-term functional biodiversity research: the biodiversity exploratories. Basic Appl. Ecol. 11, 473–485 (2010).
Google Scholar 

42.
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).
CAS  Google Scholar 

43.
Tilman, D. Secondary succession and the pattern of plant dominance along experimental nitrogen gradients. Ecol. Monogr. 57, 189–214 (1987).
Google Scholar 

44.
Clark, C. M. & Tilman, D. Loss of plant species after chronic low-level nitrogen deposition to prairie grasslands. Nature 451, 712–715 (2008).
CAS  PubMed  Google Scholar 

45.
Inouye, R. et al. Old-field succession on a Minnesota sand plain. Ecology 68, 12–26 (1987).
Google Scholar 

46.
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2015).
PubMed  Google Scholar 

47.
Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).
PubMed  Google Scholar 

48.
Nakamura, G., Gonçalves, L. O. & da Silva Duarte, L. Revisiting the dimensionality of biological diversity. Ecography (Cop.) 43, 539–548 (2020).
Google Scholar 

49.
Stevens, R. D. & Tello, J. S. On the measurement of dimensionality of biodiversity. Glob. Ecol. Biogeogr. 23, 1115–1125 (2014).
Google Scholar 

50.
Manning, P. et al. Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks. J. Appl. Ecol. 52, 1188–1196 (2015).
CAS  Google Scholar 

51.
Adler, D. & Kelly, T. vioplot: Violin plot. R package version 0.3.0 (2018).

52.
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

53.
Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).
PubMed  PubMed Central  Google Scholar 

54.
Le Bagousse-Pinguet, Y. et al. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 116, 8419–8424 (2019).
PubMed  Google Scholar 

55.
Venail, P. et al. Species richness, but not phylogenetic diversity, influences community biomass production and temporal stability in a re-examination of 16 grassland biodiversity studies. Funct. Ecol. 29, 615–626 (2015).
Google Scholar 

56.
Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: consolidation and progress in functional biodiversity research. Ecol. Lett. 12, 1405–1419 (2009).
PubMed  Google Scholar 

57.
Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).
CAS  PubMed  Google Scholar 

58.
Liang, J. et al. Positive biodiversity–productivity relationship predominant in global forests. Science 354, aaf8957 (2016).
PubMed  Google Scholar 

59.
Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl Acad. Sci. USA 114, 10160–10165 (2017).
CAS  PubMed  Google Scholar 

60.
Díaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl Acad. Sci. USA 104, 20684–20689 (2007).
PubMed  Google Scholar 

61.
Lavorel, S. et al. Using plant functional traits to understand the landscape distribution of multiple ecosystem services. J. Ecol. 99, 135–147 (2011).
Google Scholar 

62.
Schmid, B. The species richness–productivity controversy. Trends Ecol. Evol. 17, 113–114 (2002).
Google Scholar 

63.
Loreau, M. Biodiversity and ecosystem functioning: a mechanistic model. Proc. Natl Acad. Sci. USA 95, 5632–5636 (1998).
CAS  PubMed  Google Scholar 

64.
Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

65.
van der Plas, F. et al. Jack-of-all-trades effects drive biodiversity–ecosystem multifunctionality relationships in European forests. Nat. Commun. 7, 11109 (2016).
PubMed  PubMed Central  Google Scholar 

66.
Socher, S. A. et al. Direct and productivity-mediated indirect effects of fertilization, mowing and grazing on grassland species richness. J. Ecol. 100, 1391–1399 (2012).
Google Scholar 

67.
Hobbs, R. J., Higgs, E. & Harris, J. A. Novel ecosystems: implications for conservation and restoration. Trends Ecol. Evol. 24, 599–605 (2009).
PubMed  Google Scholar 

68.
Klaus, V. H. et al. Do biodiversity–ecosystem functioning experiments inform stakeholders how to simultaneously conserve biodiversity and increase ecosystem service provisioning in grasslands? Biol. Conserv. 245, 108552 (2020).
Google Scholar 

69.
Roscher, C. et al. Convergent high diversity in naturally colonized experimental grasslands is not related to increased productivity. Perspect. Plant Ecol. Evol. Syst. 20, 32–45 (2016).
Google Scholar 

70.
Ellenberg, H. & Leuschner, C. Vegetation Mitteleuropas mit den Alpen: In Ökologischer, Dynamischer und Historischer Sicht (UTB, 2010).

71.
Blüthgen, N. et al. A quantitative index of land-use intensity in grasslands: integrating mowing, grazing and fertilization. Basic Appl. Ecol. 13, 207–220 (2012).
Google Scholar 

72.
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).
CAS  PubMed  PubMed Central  Google Scholar 

73.
Tilman, D. Community invasibility, recruitment limitation, and grassland biodiversity. Ecology 78, 81–92 (1997).
Google Scholar 

74.
Catford, J. A. et al. Traits linked with species invasiveness and community invasibility vary with time, stage and indicator of invasion in a long-term grassland experiment. Ecol. Lett. 22, 593–604 (2019).
PubMed  Google Scholar 

75.
Fargione, J. et al. From selection to complementarity: shifts in the causes of biodiversity–productivity relationships in a long-term biodiversity experiment. Proc. R. Soc. B 274, 871–876 (2007).
PubMed  Google Scholar 

76.
Londo, G. The decimal scale for releves of permanent quadrats. Vegetatio 33, 61–64 (1976).
Google Scholar 

77.
Roscher, C. et al. What happens to the sown species if a biodiversity experiment is not weeded? Basic Appl. Ecol. 14, 187–198 (2013).
Google Scholar 

78.
Kattge, J. et al. TRY—a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).
Google Scholar 

79.
Cayuela, L., Stein, A. & Oksanen, J. Taxonstand: Taxonomic standardization of plant species names. R package version 2.1 (2017).

80.
The Plant List version 1.1 (2013); http://www.theplantlist.org/

81.
Qian, H. & Jin, Y. An updated megaphylogeny of plants, a tool for generating plant phylogenies and an analysis of phylogenetic community structure. J. Plant Ecol. 9, 233–239 (2016).
Google Scholar 

82.
Martins, W. S., Carmo, W. C., Longo, H. J., Rosa, T. C. & Rangel, T. F. SUNPLIN: simulation with uncertainty for phylogenetic investigations. BMC Bioinform. 14, 324 (2013).
Google Scholar 

83.
Rangel, T. F. et al. Phylogenetic uncertainty revisited: implications for ecological analyses. Evolution 69, 1301–1312 (2015).
PubMed  Google Scholar 

84.
Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).
Google Scholar 

85.
Goolsby, E. W., Bruggeman, J. & Ane, C. Rphylopars: Phylogenetic comparative tools for missing data and within-species variation. R package version 0.2.9 (2016).

86.
Penone, C. et al. Imputation of missing data in life-history trait datasets: which approach performs the best? Methods Ecol. Evol. 5, 961–970 (2014).
Google Scholar 

87.
Oksanen, J. et al. Vegan: Community ecology package. R package version 2.3-4 (2016).

88.
Hill, M. Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427–432 (1973).
Google Scholar 

89.
Smith, B. & Wilson, J. B. A consumer’s guide to evenness indices. Oikos 76, 70–82 (1996).
Google Scholar 

90.
Magurran, A. Measuring Biological Diversity (Blackwell, 2004).

91.
Morris, E. K. et al. Choosing and using diversity indices: insights for ecological applications from the German biodiversity exploratories. Ecol. Evol. 4, 3514–3524 (2014).
PubMed  PubMed Central  Google Scholar 

92.
Tucker, C. M. et al. A guide to phylogenetic metrics for conservation, community ecology and macroecology. Biol. Rev. 92, 698–715 (2017).
PubMed  Google Scholar 

93.
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).
CAS  PubMed  Google Scholar 

94.
Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).
PubMed  Google Scholar 

95.
Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).
PubMed  Google Scholar 

96.
Mouchet, M. A., Villéger, S., Mason, N. W. H. & Mouillot, D. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules. Funct. Ecol. 24, 867–876 (2010).
Google Scholar 

97.
Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0-12 (2014).

98.
R: A Language and Environment for Statistical Computing v.3.4.2 (R Core Team, 2019); https://doi.org/10.1007/978-3-540-74686-7

99.
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).
Google Scholar 

100.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Google Scholar 

101.
Jochum, M. et al. R-code and aggregated data from: The results of biodiversity-ecosystem functioning experiments are realistic. iDiv Data Repository https://doi.org/10.25829/idiv.1869-11-3082 (2020).

102.
Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE, 2011).

103.
Pebesma, E. & Bivand, R. Classes and methods for spatial data in R. R News 5, 9–13 (2005).
Google Scholar 

104.
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R (Springer, 2013).

105.
Habel, K., Grasman, R., Gramacy, R. B., Stahel, A. & Sterratt, D. C. geometry: Mesh generation and surface tessellation. R package version 0.4.1 (2019).

106.
Blonder, B. & Harris, D. hypervolume: High dimensional geometry and set operations using kernel density estimation, support vector machines, and convex hulls. R package version 2.0.11 (2018).

107.
Meyer, S. T. et al. Effects of biodiversity strengthen over time as ecosystem functioning declines at low and increases at high biodiversity. Ecosphere 7, e01619 (2016).
Google Scholar 

108.
Brownrigg, R. mapdata: Extra map databases. R package version 2.3.0 (2018). More

1.
Bell, M. A., Baumgartner, J. V. & Olson, E. C. Patterns of temporal change in single morphological characters of a Miocene stickleback fish. Paleobiology 11, 258–271 (1985).
Google Scholar 
2.
Bell, M. A. Implications of a fossil stickleback assemblage for Darwinian gradualism. J. Fish. Biol. 75, 1977–1999 (2009).
CAS  PubMed  Google Scholar 

3.
Bell, M. A., Travis, M. P. & Blouw, D. M. Inferring natural selection in a fossil threespine stickleback. Paleobiology 32, 562–577 (2006).
Google Scholar 

4.
Hunt, G., Bell, M. A. & Travis, M. P. Evolution toward a new adaptive optimum: phenotypic evolution in a fossil stickleback lineage. Evolution 62, 700–710 (2008).
PubMed  Google Scholar 

5.
Klepaker, T., Østbye, K. & Bell, M. A. Regressive evolution of the pelvic complex in stickleback fishes: a study of convergent evolution. Evol. Ecol. Res. 15, 413–435 (2013).
Google Scholar 

6.
Bell, M. A., Ortí, G., Walker, J. A. & Koenings, J. P. Evolution of pelvic reduction in threespine stickleback fish: a test of competing hypotheses. Evolution 47, 906–914 (1993).
PubMed  Google Scholar 

7.
Chan, Y. F. et al. Adaptive evolution of pelvic reduction in sticklebacks by recurrent deletion of a Pitx1 enhancer. Science 327, 302–305 (2010).
CAS  PubMed  Google Scholar 

8.
Cole, N. J., Tanaka, M., Prescott, A. & Tickle, C. Expression of limb initiation genes and clues to the morphological diversification of threespine stickleback. Curr. Biol. 13, R951–R952 (2003).
CAS  PubMed  Google Scholar 

9.
Shapiro, M. D. et al. Genetic and developmental basis of evolutionary pelvic reduction in threespine sticklebacks. Nature 428, 717–723 (2004).
CAS  PubMed  Google Scholar 

10.
Cresko, W. A. et al. Parallel genetic basis for repeated evolution of armor loss in Alaskan threespine stickleback populations. Proc. Natl Acad. Sci. USA 101, 6050–6055 (2004).
CAS  PubMed  Google Scholar 

11.
Coyle, S. M., Huntingford, F. A. & Peichel, C. L. Parallel evolution of Pitx1 underlies pelvic reduction in Scottish threespine stickleback (Gasterosteus aculeatus). J. Hered. 98, 581–586 (2007).
CAS  PubMed  Google Scholar 

12.
Xie, K. T. et al. DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science 363, 81–84 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

13.
Bell, M. A., Khalef, V. & Travis, M. P. Directional asymmetry of pelvic vestiges in threespine stickleback. J. Exp. Zool. B Mol. Dev. Evol. 308B, 189–199 (2007).
Google Scholar 

14.
Palmer, A. R. Symmetry breaking and the evolution of development. Science 306, 828–833 (2004).
CAS  PubMed  Google Scholar 

15.
Marcil, A., Dumontier, É., Chamberland, M., Camper, S. A. & Drouin, J. Pitx1 and Pitx2 are required for development of hindlimb buds. Development 130, 45–55 (2003).
CAS  PubMed  Google Scholar 

16.
Shapiro, M. D., Bell, M. A. & Kingsley, D. M. Parallel genetic origins of pelvic reduction in vertebrates. Proc. Natl Acad. Sci. USA 103, 13753–13758 (2006).
CAS  PubMed  Google Scholar 

17.
Bell, M. A. in The Evolutionary Biology of the Threespine Stickleback (eds Bell, M. A. & Foster, S. A.) 438–471 (Oxford Univ. Press, 1994).

18.
Peichel, C. L. et al. The genetic architecture of divergence between threespine stickleback species. Nature 414, 901–905 (2001).
CAS  PubMed  Google Scholar 

19.
Rollins, J. L., Lohman, B. K. & Bell, M. A. Does ion limitation select for pelvic reduction in threespine stickleback (Gasterosteus aculeatus)? Evol. Ecol. Res. 16, 101–120 (2014).
Google Scholar 

20.
Reimchen, T. E. Spine deficiency and polymorphism in a population of Gasterosteus aculeatus: an adaptation to predators? Can. J. Zool. 58, 1232–1244 (1980).
Google Scholar 

21.
Reimchen, T. E. in The Evolutionary Biology of the Threespine Stickleback (eds Bell, M. A. & Foster, S. A.) 240–276 (Oxford Univ. Press, 1994).

22.
Hoogland, R., Morris, D. & Tinbergen, N. The spines of sticklebacks (Gasterosteus and Pygosteus) as a means of defense against predators (Perca and Esox). Behaviour 10, 205–236 (1956).
Google Scholar 

23.
Baumgartner, J. V. A new fossil ictalurid catfish from the Miocene middle member of the Truckee Formation, Nevada. Copeia 1982, 38–46 (1982).
Google Scholar 

24.
Stearley, R. F. & Smith, G. R. Fishes of the Mio-Pliocene western snake river plain and vicinity. Misc. Publ. Mus. Zool. Univ. Mich. 204, 1–43 (2016).
Google Scholar 

25.
Baker, J. A. in The Evolutionary Biology of the Threespine Stickleback (eds Bell, M. A. & Foster, S. A.) 144–187 (Oxford Univ. Press, 1994).

26.
Bell, M. A. Interacting evolutionary constraints in pelvic reduction of threespine sticklebacks, Gasterosteus aculeatus (Pisces, Gasterosteidae). Biol. J. Linn. Soc. Lond. 31, 347–382 (1987).
Google Scholar 

27.
Bell, M. A. & Harris, E. I. Developmental osteology of the pelvic complex of Gasterosteus aculeatus. Copeia 1985, 789–792 (1985).
Google Scholar 

28.
Schmid, L. & Sánchez-Villagra, M. R. Potential genetic bases of morphological evolution in the Triassic fish Saurichthys. J. Exp. Zool. B Mol. Dev. Evol. 314B, 519–526 (2010).
Google Scholar 

29.
Meredith, R. W., Gatesy, J., Murphy, W. J., Ryder, O. A. & Springer, M. S. Molecular decay of the tooth gene Enamelin (ENAM) mirrors the loss of enamel in the fossil record of placental mammals. PLoS Genet. 5, e1000634 (2009).
PubMed  PubMed Central  Google Scholar 

30.
Qu, Q., Haitina, T., Zhu, M. & Ahlberg, P. E. New genomic and fossil data illuminate the origin of enamel. Nature 526, 108–111 (2015).
CAS  PubMed  Google Scholar 

31.
Zhu, M. et al. A Silurian placoderm with osteichthyan-like marginal jaw bones. Nature 502, 188–193 (2013).
CAS  PubMed  Google Scholar 

32.
Zhu, M. Bone gain and loss: insights from genomes and fossils. Natl Sci. Rev. 1, 490–492 (2014).
CAS  Google Scholar 

33.
Hunt, G. Evolutionary divergence in directions of high phenotypic variance in the ostracode genus Poseidonamicus. Evolution 61, 1560–1576 (2007).
PubMed  Google Scholar 

34.
Thompson, J. R. et al. Reorganization of sea urchin gene regulatory networks at least 268 million years ago as revealed by oldest fossil cidaroid echinoid. Sci. Rep. 5, 15541 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Organ, C. L., Janes, D. E., Meade, A. & Pagel, M. Genotypic sex determination enabled adaptive radiations of extinct marine reptiles. Nature 461, 389–392 (2009).
CAS  Google Scholar 

36.
Organ, C. L., Shedlock, A. M., Meade, A., Pagel, M. & Edwards, S. V. Origin of avian genome size and structure in non-avian dinosaurs. Nature 446, 180–184 (2007).
CAS  PubMed  Google Scholar 

37.
Organ, C. L. & Shedlock, A. M. Palaeogenomics of pterosaurs and the evolution of small genome size in flying vertebrates. Biol. Lett. 5, 47–50 (2009).
PubMed  Google Scholar 

38.
Conte, G. L., Arnegard, M. E., Peichel, C. L. & Schluter, D. The probability of genetic parallelism and convergence in natural populations. Proc. Biol. Sci. 279, 5039–5047 (2012).
PubMed  PubMed Central  Google Scholar 

39.
Rennison, D. J., Stuart, Y. E., Bolnick, D. I. & Peichel, C. L. Ecological factors and morphological traits are associated with repeated genomic differentiation between lake and stream stickleback. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 374, 20180241 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

40.
Szeto, D. P. et al. Role of the Bicoid-related homeodomain factor Pitx1 in specifying hindlimb morphogenesis and pituitary development. Genes Dev. 13, 484–494 (1999).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Thompson, A. C. et al. A novel enhancer near the Pitx1 gene influences development and evolution of pelvic appendages in vertebrates. eLife 7, e38555 (2018).
PubMed  PubMed Central  Google Scholar 

42.
Bell, M. A., Stewart, J. D. & Park, P. J. The world’s oldest fossil threespine stickleback fish. Copeia 2009, 256–265 (2009).
Google Scholar 

43.
Rawlinson, S. E. & Bell, M. A. A stickleback fish (Pungitius) from the Neogene Sterling Formation, Kenai Peninsula, Alaska. J. Paleontol. 56, 583–588 (1982).
Google Scholar 

44.
Rohlf, F. J. tpsDIG v.2.10 (2006).

45.
Bowne, P. S. in The Evolutionary Biology of the Threespine Stickleback (eds Bell, M. A. & Foster, S. A.) 28–60 (Oxford Univ. Press, 1994).

46.
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

47.
Lleonart, J., Salat, J. & Torres, G. J. Removing allometric effects of body size in morphological analysis. J. Theor. Biol. 205, 85–93 (2000).
CAS  PubMed  Google Scholar 

48.
Oke, K. B. et al. Does plasticity enhance or dampen phenotypic parallelism? A test with three lake-stream stickleback pairs. J. Evol. Biol. 29, 126–143 (2016).
CAS  PubMed  Google Scholar 

49.
Stuart, Y. E. et al. Contrasting effects of environment and genetics generate a continuum of parallel evolution. Nat. Ecol. Evol. 1, 0158 (2017).
Google Scholar 

50.
Hartigan, J. A. & Hartigan, P. M. The dip test of unimodality. Ann. Stat. 13, 70–84 (1985).
Google Scholar 

51.
Maechler, M. diptest: Hartigan’s dip statistic for unimodality—corrected v.0.75-7 (2016); https://rdrr.io/cran/diptest/ More