Ecology

1.
MEA, M. E. A. Millennium ecosystem assessment. Ecosystems and Human Well-Being: Biodiversity Synthesis, Published by World Resources Institute, Washington, DC (2005).
2.
Cherlet, M. et al. World Atlas of Desertification: Rethinking Land Degradation and Sustainable Land Management. (Publications Office of the European Union, 2018).

3.
Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nature Climate Change 6, 166 (2016).
ADS  Article  Google Scholar 

4.
Gaitán, J. J. et al. Biotic and abiotic drivers of topsoil organic carbon concentration in drylands have similar effects at regional and global scales. Ecosystems 22, 1445–1456 (2019).
Article  Google Scholar 

5.
Middleton, N., Stringer, L., Goudie, A. & Thomas, D. The forgotten billion: MDG achievement in the drylands. (UNCCD Secretariat, 2011).

6.
Reynolds, J. F. et al. Global Desertification: Building a. Science for Dryland Development. Science 316, 847–851 (2007).
CAS  PubMed  Google Scholar 

7.
Bestelmeyer, B. T. et al. Land management in the American Southwest: A State-and-Transition approach to Ecosystem Complexity. Environmental Management 34, 38–51 (2004).
Article  Google Scholar 

8.
Maestre, F. T. et al. Structure and functioning of dryland ecosystems in a changing world. Annual review of ecology, evolution, and systematics 47, 215–237 (2016).
Article  Google Scholar 

9.
Maestre, F., Salguero-Gómez, R. & Quero, J. It’s getting hotter in here: determining and projecting the impacts of global change on dryland ecosystems and on the people living in them. Philosophical Transactions of the Royal Society B: Biological Sciences 367, 3062–3075 (2012).
Article  Google Scholar 

10.
Tongway, D. & Hindley, N. L. Landscape Function Analysis: Procedures for monitoring and assessing landscapes. With special reference to Minesites and Rangelands. Vol. 1 (CSIRO, 2004).

11.
Sankey, T. T., Leonard, J. M. & Moore, M. M. Unmanned aerial vehicle− Based rangeland monitoring: examining a century of vegetation changes. Rangeland Ecology & Management 72, 858–863 (2019).
Article  Google Scholar 

12.
Watson, I. W., Novelly, P. E. & Thomas, P. W. E. Monitoring changes in pastoral rangelands – the Western Australian Rangeland Monitoring System (WARMS). The Rangeland Journal 29, 191–205 (2007).
Article  Google Scholar 

13.
White, A. et al. AUSPLOTS rangelands survey protocols manual. (University of Adelaide Press, 2012).

14.
Guerin, G. R. et al. Opportunities for integrated ecological analysis across inland Australia with standardised data from Ausplots Rangelands. PloS one 12 (2017).

15.
Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).
ADS  CAS  Article  Google Scholar 

16.
Herrick, J. E., Van Zee, J. W., Havstad, K. M., Burkett, L. M. & Whitford, W. G. Monitoring Manual for Grassland, Shrubland and Savanna Ecosystems. Vol. I (USDA-ARS Jornada Experimental Range, 2005).

17.
Oliva, G. et al. Monitoring drylands: The MARAS system. Journal of Arid Environments 161, 55–63 (2019).
ADS  Article  Google Scholar 

18.
Oliva, G., Escobar, J., Siffredi, G., Salomone, J. & Buono, G. In Monitoring Patagonian Rangelands: The MARAS System. Monitoring Science and Technology Symposium. Denver CO. (eds C. Aguirre-Bravo, P. Pellicane, D. Burns, & S. Draggan) 188–193 (U.S. Dept. Agriculture, Forest Service, 2006).

19.
Oliva, G. et al. Manual para la instalación y lectura de monitores MARAS. Vol. 1 (PNUD, 2011).

20.
Bran, D. et al. Regiones Ecológicas Homogéneas de la Patagonia Argentina., (INTA, 2005).

21.
Oliva, G. et al. Installation Manual for MARAS Monitors: Environmental monitoring for arid and semiarid lands (PNUD, 2011).

22.
Hernández, F., Ríos, C. & Perotto-Baldivieso, H. L. Evolutionary history of herbivory in the Patagonian steppe: The role of climate, ancient megafauna, and guanaco. Quaternary Science Reviews 220, 279–290 (2019).
ADS  Article  Google Scholar 

23.
Borrelli, P. In Ganadería ovina sustentable en la Patagonia Austral Vol. Cap 5 (eds P Borrelli & G Oliva) 131-162 (INTA, 2001).

24.
Cornforth, I. S. & Sinclair, A. G. Fertiliser recommendations for pastures and crops in New Zealand. (New Zealand Ministry of Agriculture, 1984).

25.
Elissalde, N., Escobar, J. & Nakamatsu, V. Inventario y evaluación de pastizales naturales de la zona arida y semiarida de la Patagonia. (INTA Trelew, 2002).

26.
McLaren, C. A. Dry Sheep Equivalents for comparing different classes of livestock. 4 (Department of Primary Industries, State of Victoria, Victoria, 1997).

27.
INIA. Revisión y análisis de las bases históricas y científicas del uso de la equivalencia ovino:bovino “Hacia una nueva equivalencia para ser utilizada en Uruguay”. (INIA, 2012).

28.
SRM, G. R. S. C. A Glossary of terms used in range management: a definition of terms commonly used in range management. (Society for Range Management, 1989).

29.
Oliva, G. et al. The MARAS dataset, vegetation and soil characteristics of dryland rangelands across Patagonia. figshare https://doi.org/10.6084/m9.figshare.c.4789113 (2020).

30.
Tongway, D. Rangeland soil condition assessment manual. (CSIRO. Division of Wildlife and Ecology, 1994).

31.
Daget, P. & Poissonet, J. Une methode d’analyse phytologique des prairies. Ann Argr. France 22, 5–41 (1971).
Google Scholar 

32.
Halloy, S. & Barratt, B. I. P. Patterns of abundance and morphology as indicators of ecosystem status: A meta-analysis. Ecological Complexity 4, 128–147 (2007).
Article  Google Scholar 

33.
Zuloaga, F., Morrone, O. & Belgrano, M. Catálogo de las Plantas Vasculares del Cono Sur. Versión base de datos en sitio web. (Instituto Darwinion, 2009).

34.
Ludwig, J. A., Wilcox, B. P., Breshears, D. D., Tongway, D. J. & Imeson, A. C. Vegetation patches and runoff–erosion as interacting ecohydrological processes in semiarid landscapes. Ecology 86, 288–297 (2005).
Article  Google Scholar 

35.
Walkley, A. & Black, I. A. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil science 37, 29–38 (1934).
ADS  CAS  Article  Google Scholar 

36.
Schulte, E. & Hoskins, B. Recommended soil organic matter tests. Recommended Soil Testing Procedures for the North Eastern USA. Northeastern Regional Publication, 52-60 (1995).

37.
López, C., Rial, P., Elissalde, N., Llanos, E. & Behr, S. Grandes paisajes de la Patagonia Argentina. (INTA, 2005).

38.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International journal of climatology 37, 4302–4315 (2017).
ADS  Article  Google Scholar 

39.
Elzinga, C. L., Salzer, D. W. & Willoughby, J. W. Measuring & Monitoring Plant Populations. (BLM National Business Center, 1998).

40.
Magurran, A. E. Measuring Biological Diversity. (Blackwell Publishing, 2004).

41.
Oliva, G. et al. Estado de los Recursos Naturales de la Patagonia Sur 66 (INTA CRPATSU, Trelew, 2017).

42.
Gaitan, J. J. et al. Vegetation structure is as important as climate for explaining ecosystem function across Patagonian rangelands. Journal of Ecology 102, 1419–1428 (2014).
Article  Google Scholar 

43.
Gaitán, J. J. et al. Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes. Ecological Indicators 34, 181–191 (2013).
Article  Google Scholar 

44.
Gaitán, J. J. et al. Plant species richness and shrub cover attenuate drought effects on ecosystem functioning across Patagonian rangelands. Biology letters 10, 20140673 (2014).
Article  Google Scholar 

45.
Gaitán, J. J. et al. Aridity and Overgrazing Have Convergent Effects on Ecosystem Structure and Functioning in Patagonian Rangelands. Land Degradation & Development 29, 210–218 (2017).
Article  Google Scholar 

46.
Oliva, G., et al.) 1115-1117 (IRC 2016).

47.
Domínguez Díaz, E., Oliva, G. E., Báez Madariaga, J., Suárez Navarro, Á. & Pérez Castillo, C. Efectos del pastoreo holístico sobre la estructura y composición vegetal en praderas naturalizadas de uso ganadero, provincia de Última Esperanza, región de Magallanes, Chile. Anales del Instituto de la Patagonia 46, 17–28 (2018).
Article  Google Scholar 

48.
Borrelli, P. et al. Estándar para la regeneración y la sustentabilidad de los pastizales (GRASS). The Nature Conservancy, OVIS 21 (2013).

49.
T. exchange. RWS, Responsible Wool Standard. 73 (London, 2016).

50.
Cabrera, A. Fitogeografia de la República Argentina. Boletín de la Sociedad Argentina de Botánica 14, 1–42 (1971).
Google Scholar 

51.
León, R., Bran, D., Collantes, M., Paruelo, J. & Soriano, A. Grandes unidades de vegetación de la Patagonia extra andina. Ecologia Austral 8, 125–144 (1998).
Google Scholar 

52.
Luebert, F. & Pliscoff, P. Sinopsis bioclimática y vegetacional de Chile. (Santiago de Chile: Editorial Universitaria, 2006). More

Study area and period
Our study area is the tropics between 15°N–35°S6. We divided the study area into three continents and studied them separately: South America, Africa, and Australasia. Australasia includes Australia and southeast Asia, but excludes southern India. Our results are generated on 0.25° spatial resolution. We classify a cell as forest if it contained at least 50% tree cover (‘forest cover’ in this manuscript) in 1999 according to the dataset from ref. 41. The moisture recycling simulations were carried out for 2003–2014 (‘recent climate’), for which a consistent set of input data was available (see also ref. 11). ‘Late 21st century’ refers to 2071–2100.
Local-scale forest hysteresis
Previous research has shown that tropical forests may have local-scale tipping points at certain mean annual rainfall levels, but are also affected by the seasonality of that rainfall5,6,8. Local-scale tipping points for forest were determined using tree cover data following a method from ref. 6. Using potential analysis42, an empirical stability landscape (as in Fig. 1a) is constructed based on spatial distributions of tree cover against environmental variables such as mean annual rainfall for each continent separately. For each value of the environmental variable, the probability density of tree cover was determined using the MATLAB function ksdensity with a bandwidth of 5%. We applied Gaussian weights to the environmental variable with a standard deviation of 0.05 times the length of the axis of the environmental variable. Local maxima of the resulting probability density function are interpreted as stable states, where we ignored local maxima below a threshold value of 0.004. We used Landsat tree cover data for 2000 on 30 m resolution downloaded for every 0.01°43. We masked out human-used areas, water bodies, and bare ground using the ESA GlobCover land cover dataset for 2009 on 300 m resolution (values 11–30 and ≥190). From the resulting dataset we randomly sampled one million locations for each continent and used them to construct the stability landscapes6 against mean annual rainfall and average MCWD. MCWD is the cumulative difference between evapotranspiration and rainfall using monthly averages of those fluxes calculated for each calendar year44. It is set to zero when monthly rainfall exceeds monthly evapotranspiration and becomes more negative with an increasing water deficit. Following ref. 11, for both mean annual rainfall and MCWD, we took monthly data from the GLDAS 2.0 dataset45 for 1970–1999 so the 30-year period leading up to the land-cover sample (for the year 2000) was used.
Forest evapotranspiration
To estimate the fraction of evapotranspiration attributable to forest cover we used the large-scale hydrological model PCR-GLOBWB, run at 0.5° resolution46. Per grid cell, the model simulates evapotranspiration for a range of land-cover types. Here, we are specifically interested in the evapotranspiration of forests, or ‘tall natural vegetation’ in PCR-GLOBWB. Note that we here account for both forest transpiration and canopy interception evaporation instead of, as in ref. 11, only transpiration.
PCR-GLOBWB computes the water balance in two soil layers and a groundwater layer. Soil type, fractional area of saturated soil, and the spatiotemporal distribution of groundwater depth are accounted for (see refs. 46,47). It includes six land-cover types, with spatially varying parameters46: tall and short natural vegetation, pasture, rainfed crops, and paddy and non-paddy irrigated crops. The model was forced with WATCH Forcing Data ERA-Interim precipitation, temperature, and reference potential evapotranspiration for 1979–201448. We used monthly evapotranspiration output of PCR-GLOBWB, implying that we assume that forest component of evapotranspiration remains equal within each month. For detailed model descriptions and validation, we refer to earlier studies11,46,49,50.
Atmospheric moisture tracking
As an essential step in estimating the forest–rainfall feedback, we determined where the moisture from enhanced evapotranspiration precipitates again by using atmospheric moisture tracking. The method for atmospheric moisture tracking resembles that in ref. 11. Apart from the expansion of the study area to the entire tropics, a notable difference is that we here used ERA5 reanalysis data rather than ERA-Interim, meaning that the simulations were based on finer resolution input data (i.e. on 0.25° instead of 0.75° for spatial resolution, and 1 h instead of 3 h for temporal resolution). ERA5 has better performance than ERA-Interim regarding wind fields and rainfall, especially in the tropics51,52,53. Below we summarize the method (see also ref. 11).
We used a Lagrangian method of moisture tracking that is based on previous studies11,54,55,56 that track parcels of evaporated moisture forward through the atmosphere to their subsequent precipitation location. Moisture particles that enter the atmosphere are assigned a random location within the 0.25° grid cell and random starting height in the atmosphere scaled with the humidity profile, and their trajectories are then tracked through the atmosphere. The trajectories are forced with the three-dimensional ERA5 reanalysis estimates of wind speed and direction, which were linearly interpolated at every time step of 0.25 h. Water particles in the atmosphere have an equal probability of raining out, regardless of vertical position. Rainfall A (mm per time step) at location x,y and time t that has evaporated from any location of release in any cell is ref. 56

$$A_{x,y,t} = P_{x,y,t}frac{{W_{{mathrm{parcel}},t}E_{{mathrm{source}},t}}}{{{mathrm{TPW}}_{x,y,t}}},$$
(1)

where P is rainfall in mm per time step, Wparcel is the water in the tracked parcel in mm, Esource is its fraction of water that evapotranspired from the source, and TPW is the precipitable water in the atmospheric water column in mm. Every time step, the amount of water in the parcel is updated based on evapotranspiration ET into the parcel and rainfall P from it:

$$W_{{mathrm{parcel}},t} = W_{{mathrm{parcel}},t – 1} + ({mathrm{ET}}_{x,y,t} – P_{x,y,t})frac{{W_{{mathrm{parcel}},t – 1}}}{{{mathrm{TPW}}_{x,y,t}}}.$$
(2)

The fraction of water in the parcel that has evapotranspired from the source then becomes

$$E_{{mathrm{source}},t} = frac{{E_{{mathrm{source}},t – 1}W_{{mathrm{parcel}},t – 1} – A_{x,y,t}}}{{W_{{mathrm{parcel}},t}}}.$$
(3)

Thus, the amount of water that was tracked from the source location decreases with precipitation along its trajectory. Parcels were followed until either less than 5% of its original amount was left in the atmosphere, or the tracking time was 30 days. Any moisture remaining in the parcel when the trajectories end is assumed to rain out over non-land areas, thus not contributing to our analysis. We analysed each continent separately for reasons of computability. However, small moisture flows between forests in different continents can be expected, as has been simulated for flows from Africa to the Amazon57. Over all land points, ERA5 hourly evapotranspiration is linearly interpolated to every 0.25-h time step. The moisture flow mij in mm per month linking evapotranspiration in cell i to rainfall in cell j where (left[ {x,y} right] , {it{epsilon }} , j) over the course of a given month becomes

$$m_{ij} = mathop {sum}limits_{t = 0}^{{mathrm{month}}} {A_{j,t}} cdot frac{{{mathrm{ET}}_{i,t}}}{{W_{i,t}}},$$
(4)

where ETi,t is the evapotranspiration in mm per time step, and Wi,t is the tracked amount of water from source cell i at time step t.
At continental scales, evaporated moisture can rain down and re-evaporate multiple times. This also means that forest evapotranspiration can enhance rainfall multiple times. We accounted for this ‘cascading moisture recycling’ following refs. 11,14, in which the rainfall attributed to an upwind forest source is subsequently tracked upon re-evaporation. After six re-evapotranspiration cycles, cascading moisture recycling has decreased to practically zero11. Therefore, following ref. 11, seven iterations of cascading moisture recycling were performed.
Hysteresis experiments
We determined the hysteresis of tropical forests through a series of iterative runs; each one started either from a fully forested continent or from a fully deforested continent. We simulated the hypothetical mean annual rainfall levels across the (tropical part of the) continent given this initial condition, that is, rainfall without any forest evapotranspiration or rainfall including evapotranspiration from an entirely forested continent. Next, based on the empirical bifurcation diagrams (i.e. nonforest, bistable forests, and stable forests in each continent are determined based on the bistability ranges shown in Supplementary Figs. 1−3), we determined either the minimal distribution of tropical forest (in case of a no-forest initial condition, based on the higher end of the bistability range) or the maximal distribution (in case of a fully forested initial condition, based on the lower end of the bistability range) at these rainfall levels. Thus, in the simulations with an empty initial condition, only stable forests (green in Fig. 1) would regrow; in those with a full initial condition, both stable and bistable forests (green and yellow in Fig. 1) would disappear. Because the resulting new distribution of forest would generate different levels of rainfall, the procedure was repeated with the respective forest distribution as initial condition. This occurred until rainfall levels had (practically) stabilized between iterative runs (up to three iterations).
We assumed that no other vegetation type replaces the forest in order to show the theoretically possible distributions of tropical forests. This may lead to an overestimation of the actual effects of forest on rainfall, especially if forests would be replaced by highly transpiring crops58. Furthermore, land-cover changes will alter wind patterns and therefore the expected coupling between forests through evapotranspiration and rainfall59. Fossil fuel emissions not only alter the climate, but the emitted CO2 also fertilizes plants. This increases trees’ water-use efficiency, reducing their water demand, but it also increases biomass production60. The effects of this CO2 fertilization on the water cycle might be small61, but its net effects on tropical forest hysteresis remains uncertain.
For display of Fig. 2, the resolution of rainfall values was increased by a factor of 2, to 0.125° and smoothed using a two-dimensional Gaussian kernel with a standard deviation of 0.5°.
Climate-change scenario
As the estimate of late 21st-century rainfall conditions, we used the rainfall output from the SSP5-8.5 scenario simulations by seven available CMIP6 models62. These models are BCC-CSM2-MR, CanESM5, CNRM-CM6-1, CNRM-ESM2, IPSL-CM6A-LR, MRI-ESM2.0, and UKESM1.0-LL. We took the mean across the model outputs for the annual rainfall values for 2071–2100. The scenario is considered a severe climate-change scenario. Because the moisture tracking model is forced with atmospheric reanalysis data, we assumed that (forest-induced) moisture flows in the scenario are the same as in the period of our atmospheric simulations (2003–2014).
We assumed that a tipping point from a forested to a nonforested state occurs when mean annual rainfall in a forested cell (forest cover ≥ 50%) is currently (2003–2014) above the lower tipping point (Supplementary Figs. 1–3), but is reduced to below the lower tipping point in the climate-change scenario. Similarly, a tipping point from a nonforested to a forested state occurs when mean annual rainfall in a nonforested cell (forest cover  More

Scopes and databases
The CEAP dataset comprises all the thermal power plants operating in China, totalling 2,714 plants (or 6,267 units), from 2014 to 2017 in 26 provinces and 4 municipalities (except Hong Kong, Macao, Taiwan and Tibet; Table 1). The thermal power plants produce electricity by combusting a variety of fossil energies, which fall into 4 categories: coal, gas plus oil, biomass and others (detailed in Table 2).
Table 1 China’s thermal power plants in CEAP.
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Table 2 Fuel type descriptions.
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The CEAP dataset integrates two databases, i.e., the CEMS data and unit-specific information. The CEMS data—the direct, real-time measurements of stack gas concentrations of PM, SO2 and NOX from China’s power plant stacks—are monitored by China’s CEMS network and reported to the China Ministry of Ecology and Environment (MEE; http://www.envsc.cn/). The CEMS data are recorded on a source and hourly basis. In total, the CEMS dataset covers 4,622 emission sources (i.e., power plant stacks) associated with 5,606 units (accounting for 98% of China’s thermal power capacity), 35,064 hours from 2014 to 2017, and 3 air pollutants (i.e., PM, SO2 and NOX) for each source-hour sample (Table 3). The MEE has also provided stack-specific information (regarding latitude and longitude, heights, temperature, diameter, etc.; http://permit.mee.gov.cn/).
Table 3 CEMS coverage of China’s thermal power plant units or stacks in CEAP.
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Unit-specific information is also derived from the MEE, involving activity levels (energy consumption and power generation), operating capacities, geographic allocations and pollution control equipment (particularly the types and removal efficiencies) at a yearly frequency. Due to data availability, the unit information is available only until 2016, and the activity levels for 2017 are projected following the overall trends in provincial thermal power generation between 2016 and 2017 (which are available in the China Energy Statistical Yearbooks26), under the assumption that new units constructed in 2017 have the same structures of installed capacities, energy uses and regions as those of the existing units in 2016.
With a combination of the two datasets, the CEAP dataset provides nationwide, plant-level, dynamic PM, SO2 and NOX emissions from China’s thermal power plants from 2014 to 2017. Relative to existing inventories, the CEAP dataset is innovative in that it incorporates comprehensive real CEMS-measured emission data, avoiding the use of average emission factors and the associated operational assumptions and uncertain parameters.
Pre-processing of CEMS data
We have been exclusively granted access to the data from China’s CEMS network. Generally, the CEMS consists of a sampling system (for filtering and sampling flue gas), an online analytical component (for monitoring flue gas parameters, particularly emission concentrations) and a data processing system (for collecting, processing and reporting monitoring data)27,28. According to the GB13223-2003 regulation29, the CEMS network should cover all power plant furnaces that burn coal (except stoker and spreader stoker) and oil and generate >65 tons of steam each hour, as well as those that burn pulverized coal and gas. Thus, some power plants have not yet been incorporated into the CEMS network (accounting for 3–4% of the total thermal power capacity from 2014 to 2017) because their furnaces did not meet the requirements necessary to install a CEMS. For the power plants outside the CEMS network, we assume their stack concentrations are similar to the averages of the units with similar fuel types and similar regions within the CEMS network.
To guarantee the reliability of CEMS data, China’s government has made great efforts in developing specific regulations and technical guidelines for power plants and local entities to follow and supervise, respectively24,28,30,31,32. These official documents elaborate on all the processes required to regulate the CEMS network, including not only CEMS installation, operation, inspection, maintenance and repair but also CEMS data collection, processing, reporting, analysis and storage28,32,33. Since 2014, all state-monitored companies have been mandated to report their CEMS data to the local governments through a series of online platforms for different provinces (listed in Supplementary Table 1). Local entities have random onsite inspections to check the truthfulness of the reported results on at least a quarterly basis23,24,28,32,34; this system enables a comparison of CEMS data across different firms to explore potential outliers and abnormalities and prevent data manipulation28,35. Then, the governments release the inspection results to the public through the same online platforms (listed in Supplementary Table 1)24,36,37. Severe financial penalties and criminal punishments can be imposed on firms that adopt data manipulation (in terms of deleting, distorting and forging CEMS data, for example)38,39.
The malfunction of CEMS monitors may also introduce large uncertainty to CEMS data during the processes of operation (indication errors, span drift, zero drift, etc.), maintenance (particularly the failure to perform calibration and reference tests) and data reporting (invalid data communication, data missing, etc.)24,28. Accordingly, each power plant is required to make at least one A-, B- and C-grade overhaul for 32–80, 14–50 and 9–30 days per 4–6, 2–3 and 1 year(s), respectively, as well as one D-grade overhaul (if needed) for 5–15 days per year, to check, maintain and upgrade its technologies, thereby reducing measurement uncertainty40. During these overhauls, CEMS operators conduct CEMS calibration (i.e., zero and span calibration), maintenance procedures (e.g., examining and cleaning major CEMS components and replacing or upgrading parts, if necessary, such as optical lens, filter and sampling meter) and a reference test (i.e., relative accuracy test audit). Furthermore, third-party operators examine CEMS operation and maintenance routines, to guarantee standardized CEMS operation and facilitate improvement in CEMS data accuracy27,28,31. All the related activities should be documented according to standardized requirement contents27,28. Even with the aforementioned efforts, there is still a small proportion of nulls and outliers in the CEMS database, which represent 1% and 0.1% of the total operating hours, respectively, from 2014 to 2017. We treat these samples seriously by following the relevant official documents, which have been released by China’s government. Table 4 provides the treatment methods for nulls or zeros, which can be divided into 3 types based on duration. On the one hand, we consider nulls and/or zeros that span at least 5 successive days as a downtime or overhaul and omit them in the estimation, according to the regulation27. On the other hand, missing data lasting  24 hours are assumed around the valid values near the time and set to the monthly averages27:

$${hat{C}}_{s,i,y,m,h}={bar{C}}_{s,i,y,m,bullet }$$
(1)

where ({C}_{s,i,y,m,h}) denotes the stack gas concentrations of pollutant s emitted by unit i for year y, month m and hour h (i.e., the actual measurements monitored by the CEMS network), defined as the amount of pollutants per unit of emitted stack gas (g m−3)41,42; ({widehat{C}}_{s,i,y,m,h}) is the imputation for the missing data ({C}_{s,i,y,m,h}); ({bar{C}}_{s,i,y,m,.}) is the mean of the hourly valid values for the same pollutant, unit, year and month as ({C}_{s,i,y,m,h}). In contrast, the missing data for 1–24 hour(s) are interpolated with the arithmetic averages of the two nearest valid points before and after them27,43:

$${widehat{C}}_{s,i,y,m,h}=frac{{C}_{s,i,y,m,h-l}+{C}_{s,i,y,m,h+q}}{2}$$
(2)

where ({C}_{s,i,y,m,h-l}) and ({C}_{s,i,y,m,h{rm{+}}q}) represent the nearest last known measurements (l hour(s) before) and next known measurements (q hour(s) after), respectively, for the missing data ({C}_{s,i,y,m,h}), namely, the series data ({C}_{s,i,y,m,h-l+1}),…, ({C}_{s,i,y,m,h}),…, ({C}_{s,i,y,m,h+q-1}) are all missing values. Furthermore, we treat the measurements that are out of the measurement ranges of CEMS instruments (outside of which the data are unreliable30,44; detailed in Supplementary Table 2) as abnormal data and process them in a similar way to nulls according to the official regulation27.
Table 4 Treatment methods for nulls and the relevant official documents.
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CEMS-based estimation of emission factors and absolute emissions
The introduction of real CEMS-monitored measurements provides a direct estimation for emission factors on a source and hourly basis, avoiding the use of average emission factors with many assumptions and uncertain parameters17,42,44.

$$E{F}_{s,i,y,m,h}={C}_{s,i,y,m,h}{V}_{i,y}$$
(3)

In Eq. (3), (E{F}_{s,i,y,m,h}) indicates the emission factor, defined as the amount of emissions per unit of fuel use (in g kg−1 for solid or liquid fuel and in g m−3 for gas fuel), and ({V}_{i,y}) is the theoretical flue gas rate, defined as the expected volume of flue gas per unit of fuel use under standard production conditions (m3 kg−1 for solid or liquid fuel and m3 m−3 for gas fuel)42, which was estimated by the China Pollution Source Census (2011)45 based on sufficient field measurements (detailed in Table 5). Based on Eq. (3), abated emission factors can be directly obtained even without the use of removal efficiencies and the relevant parameters, because CEMS monitors the gas concentrations at stacks after the effect of control equipment (if any).
Table 5 Theoretical flue gas rate.
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Notably, recent clean air policies (particularly different emissions standards) target stack concentrations, such that a large proportion of missing data exist regarding other measurements (particularly flue gas rates, with missing data accounting for 34.62%, 31.91%, 29.97% and 42.96% of the total samples in 2014, 2015, 2016 and 2017, respectively). Accordingly, we introduce theoretical flue gas rates into the estimation to avoid significant underestimation of the actual volume when there are too many missing data values46. In addition, the adoption of theoretical flue gas rates can address flue gas leakage, a common problem in power plants that greatly distorts the real flue gas volume46. The theoretical flue gas rates are derived from the China Pollution Source Census, with values varying across operating capacities, fuel types and boiler types42,45. Thus, the actual volume of flue gas is computed in terms of the theoretical flue gas rate times actual fuel consumption.
The absolute emissions of PM, SO2 and NOX from individual power plants can be estimated in terms of the emission factors times the activity levels21:

$${E}_{s,i,y,m}=E{F}_{s,i,y,m}{A}_{i,y,m}$$
(4)

where ({E}_{s,i,y,m}) represents the air pollution emissions (g); and ({A}_{i,y,m}) is the activity data, i.e., the amount of fuel use (kg for solid or liquid fuel and m3 for gas fuel). In the CEAP dataset, power plant emissions are estimated on a monthly basis (the smallest scale for activity data), in which the yearly unit-level activity data are allocated at the monthly scale using the monthly province-level thermal power generation as weights16:

$${A}_{i,y,m}=frac{{F}_{{p}_{i},y,m}}{{sum }_{m=1}^{12}{F}_{{p}_{i},y,m}}{A}_{i,y}$$
(5)

where ({F}_{{p}_{i},y,m}) denotes the thermal power generation by province Pi, which is obtained from the Chinese Energy Statistics Yearbook26, and ({p}_{i}) indicates the province where unit i is located. More

1.
Daws, M. I., Mullins, C. E., Burslem, D. F. R. P., Paton, S. R. & Dalling, J. W. Topographic position affects the water regime in a semideciduous tropical forest in Panamá. Plant Soil 238, 79–89. https://doi.org/10.1023/A:1014289930621 (2002).
CAS  Article  Google Scholar 
2.
Sundqvist, M. K., Sanders, N. J. & Wardle, D. A. Community and ecosystem responses to elevational gradients: processes, mechanisms, and insights for global change. Annu. Rev. Ecol. Evol. Syst. 44, 261–280. https://doi.org/10.1146/annurev-ecolsys-110512-135750 (2013).
Article  Google Scholar 

3.
Jucker, T. et al. Topography shapes the structure, composition and function of tropical forest landscapes. Ecol. Lett. 21, 989–1000. https://doi.org/10.1111/ele.12964 (2018).
Article  PubMed  PubMed Central  Google Scholar 

4.
Moeslund, J. E., Arge, L., Bøcher, P. K., Dalgaard, T. & Svenning, J.-C. Topography as a driver of local terrestrial vascular plant diversity patterns. Nord. J. Bot. 31, 129–144. https://doi.org/10.1111/j.1756-1051.2013.00082.x (2013).
Article  Google Scholar 

5.
Holland, P. G. & Steyn, D. G. Vegetational responses to latitudinal variations in slope angle and aspect. J. Biogeogr. 2, 179–183. https://doi.org/10.2307/3037989 (1975).
Article  Google Scholar 

6.
Yetemen, O., Istanbulluoglu, E. & Duvall, A. R. Solar radiation as a global driver of hillslope asymmetry: Insights from an ecogeomorphic landscape evolution model. Water Resour. Res. 51, 9843–9861. https://doi.org/10.1002/2015wr017103 (2015).
ADS  Article  Google Scholar 

7.
Bennie, J., Hill, M. O., Baxter, R. & Huntley, B. Influence of slope and aspect on long-term vegetation change in British chalk grasslands. J. Ecol. 94, 355–368. https://doi.org/10.1111/j.1365-2745.2006.01104.x (2006).
Article  Google Scholar 

8.
Cantlon, J. E. Vegetation and microclimates on north and south slopes of Cushetunk Mountain, New Jersey. Ecol. Monogr. 23, 241–270. https://doi.org/10.2307/1943593 (1953).
Article  Google Scholar 

9.
Warren, R. J. Mechanisms driving understory evergreen herb distributions across slope aspects: as derived from landscape position. Plant Ecol. 198, 297–308. https://doi.org/10.1007/s11258-008-9406-1 (2008).
Article  Google Scholar 

10.
Bennie, J., Huntley, B., Wiltshire, A., Hill, M. O. & Baxter, R. Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol. Model. 216, 47–59. https://doi.org/10.1016/j.ecolmodel.2008.04.010 (2008).
Article  Google Scholar 

11.
Burnett, B. N., Meyer, G. A. & McFadden, L. D. Aspect-related microclimatic influences on slope forms and processes, northeastern Arizona. J. Geophys. Res. Earth Surf. https://doi.org/10.1029/2007jf000789 (2008).
Article  Google Scholar 

12.
Geroy, I. J. et al. Aspect influences on soil water retention and storage. Hydrol. Process. 25, 3836–3842. https://doi.org/10.1002/hyp.8281 (2011).
ADS  Article  Google Scholar 

13.
Huang, Y.-M., Liu, D. & An, S.-S. Effects of slope aspect on soil nitrogen and microbial properties in the Chinese Loess region. CATENA 125, 135–145. https://doi.org/10.1016/j.catena.2014.09.010 (2015).
CAS  Article  Google Scholar 

14.
Lozano-García, B., Parras-Alcántara, L. & Brevik, E. C. Impact of topographic aspect and vegetation (native and reforested areas) on soil organic carbon and nitrogen budgets in Mediterranean natural areas. Sci. Total Environ. 544, 963–970. https://doi.org/10.1016/j.scitotenv.2015.12.022 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

15.
Rasmussen, C. & Tabor, N. J. Applying a quantitative pedogenic energy model across a range of environmental gradients. Soil Sci. Soc. Am. J. 71, 1719–1729. https://doi.org/10.2136/sssaj2007.0051 (2007).
ADS  CAS  Article  Google Scholar 

16.
Broxton, P. D., Troch, P. A. & Lyon, S. W. On the role of aspect to quantify water transit times in small mountainous catchments. Water Resour. Res. https://doi.org/10.1029/2008wr007438 (2009).
Article  Google Scholar 

17.
Casanova, M., Messing, I. & Joel, A. Influence of aspect and slope gradient on hydraulic conductivity measured by tension infiltrometer. Hydrol. Process. 14, 155–164. https://doi.org/10.1002/(sici)1099-1085(200001)14:1%3c155::aid-hyp917%3e3.0.co;2-j (2000).
ADS  Article  Google Scholar 

18.
Gutiérrez-Jurado, H. A., Vivoni, E. R., Istanbulluoglu, E. & Bras, R. L. Ecohydrological response to a geomorphically significant flood event in a semiarid catchment with contrasting ecosystems. Geophys. Res. Lett. https://doi.org/10.1029/2007gl030994 (2007).
Article  Google Scholar 

19.
Wang, L., Wei, S., Horton, R. & Shao, M. A. Effects of vegetation and slope aspect on water budget in the hill and gully region of the Loess Plateau of China. CATENA 87, 90–100. https://doi.org/10.1016/j.catena.2011.05.010 (2011).
Article  Google Scholar 

20.
Pook, E. & Moore, C. The influence of aspect on the composition and structure of dry sclerophyll forest on Black Mountain, Canberra. ACT. Aust. J. Bot. 14, 223–242. https://doi.org/10.1071/BT9660223 (1966).
Article  Google Scholar 

21.
Armesto, J. J. & Martίnez, J. A. Relations between vegetation structure and slope aspect in the Mediterranean region of Chile. J. Ecol. 66, 881–889. https://doi.org/10.2307/2259301 (1978).
Article  Google Scholar 

22.
Badano, E. I., Cavieres, L. A., Molina-Montenegro, M. A. & Quiroz, C. L. Slope aspect influences plant association patterns in the Mediterranean matorral of central Chile. J. Arid Environ. 62, 93–108. https://doi.org/10.1016/j.jaridenv.2004.10.012 (2005).
ADS  Article  Google Scholar 

23.
Zapata-Rios, X., Brooks, P. D., Troch, P. A., McIntosh, J. & Guo, Q. Influence of terrain aspect on water partitioning, vegetation structure and vegetation greening in high-elevation catchments in northern New Mexico. Ecohydrology 9, 782–795. https://doi.org/10.1002/eco.1674 (2016).
Article  Google Scholar 

24.
Poulos, H. M. & Camp, A. E. Topographic influences on vegetation mosaics and tree diversity in the Chihuahuan Desert Borderlands. Ecology 91, 1140–1151. https://doi.org/10.1890/08-1808.1 (2010).
Article  PubMed  Google Scholar 

25.
Kutiel, P. & Lavee, H. Efffect of slope aspect on soil and vegetation properties along an aridity transect. Isr. J. Plant Sci. 47, 169. https://doi.org/10.1080/07929978.1999.10676770 (1999).
Article  Google Scholar 

26.
Sternberg, M. & Shoshany, M. Influence of slope aspect on Mediterranean woody formations: comparison of a semiarid and an arid site in Israel. Ecol. Res. 16, 335–345. https://doi.org/10.1046/j.1440-1703.2001.00393.x (2001).
Article  Google Scholar 

27.
Méndez-Toribio, M., Meave, J. A., Zermeño-Hernández, I. & Ibarra-Manríquez, G. Effects of slope aspect and topographic position on environmental variables, disturbance regime and tree community attributes in a seasonal tropical dry forest. J. Veg. Sci. 27, 1094–1103. https://doi.org/10.1111/jvs.12455 (2016).
Article  Google Scholar 

28.
Gallardo-Cruz, J. A., Pérez-García, E. A. & Meave, J. A. β-Diversity and vegetation structure as influenced by slope aspect and altitude in a seasonally dry tropical landscape. Landsc. Ecol. 24, 473–482. https://doi.org/10.1007/s10980-009-9332-1 (2009).
Article  Google Scholar 

29.
Paudel, S. & Vetaas, O. R. Effects of topography and land use on woody plant species composition and beta diversity in an arid Trans-Himalayan landscape, Nepal. J. Mt. Sci. 11, 1112–1122. https://doi.org/10.1007/s11629-013-2858-3 (2014).
Article  Google Scholar 

30.
Zhang, R. The Dry Valley of the Hengduan Mountains Regions (Science Press, Beijing, 1992).
Google Scholar 

31.
Vegetation, E. B. O. S. Sichuan Vegetation (People’s Publishing House of Sichuan, Beijing, 1980).
Google Scholar 

32.
Guan, W. et al. Vegetation classification and the main types of vegetation of the dry valley of Minjiang River. J. Mt. Res. 22, 679–686 (2004).
Google Scholar 

33.
Ma, K.-M. et al. Multiple-scale soil moisture distribution and its implications for ecosystem restoration in an arid river valley, China. Land Degrad. Dev. 15, 75–85. https://doi.org/10.1002/ldr.584 (2004).
CAS  Article  Google Scholar 

34.
Lu, T., Ma, K. M., Zhang, W. H. & Fu, B. J. Differential responses of shrubs and herbs present at the Upper Minjiang River basin (Tibetan Plateau) to several soil variables. J. Arid Environ. 67, 373–390. https://doi.org/10.1016/j.jaridenv.2006.03.011 (2006).
ADS  Article  Google Scholar 

35.
Xu, X.-L., Ma, K.-M., Fu, B.-J., Song, C.-J. & Liu, W. Relationships between vegetation and soil and topography in a dry warm river valley, SW China. CATENA 75, 138–145. https://doi.org/10.1016/j.catena.2008.04.016 (2008).
Article  Google Scholar 

36.
Solon, J., Degórski, M. & Roo-Zielińska, E. Vegetation response to a topographical-soil gradient. CATENA 71, 309–320. https://doi.org/10.1016/j.catena.2007.01.006 (2007).
Article  Google Scholar 

37.
Birkeland, P. W. Soils and Geomorphology (Oxford University Press, Oxford, 1984).
Google Scholar 

38.
Loik, M. E., Breshears, D. D., Lauenroth, W. K. & Belnap, J. A multi-scale perspective of water pulses in dryland ecosystems: climatology and ecohydrology of the western USA. Oecologia 141, 269–281. https://doi.org/10.1007/s00442-004-1570-y (2004).
ADS  Article  PubMed  Google Scholar 

39.
Schwinning, S. & Sala, O. E. Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia 141, 211–220. https://doi.org/10.1007/s00442-004-1520-8 (2004).
ADS  Article  PubMed  Google Scholar 

40.
Fernandez-Going, B. M., Harrison, S., Anacker, B. & Safford, H. Climate interacts with soil to produce beta diversity in Californian plant communities. Ecology 94, 2007–2018. https://doi.org/10.1890/12-2011.1 (2013).
CAS  Article  PubMed  Google Scholar 

41.
Araya, Y. N., Gowing, D. J. & Dise, N. Does soil nitrogen availability mediate the response of grassland composition to water regime?. J. Veg. Sci. 24, 506–517. https://doi.org/10.1111/j.1654-1103.2012.01481.x (2013).
Article  Google Scholar 

42.
Araya, Y. N. et al. A fundamental, eco-hydrological basis for niche segregation in plant communities. New Phytol. 189, 253–258. https://doi.org/10.1111/j.1469-8137.2010.03475.x (2011).
Article  PubMed  Google Scholar 

43.
Li, Y. J., Bao, W. K. & Wu, N. Spatial patterns of the soil seed bank and extant vegetation across the dry Minjiang River valley in southwest China. J. Arid Environ. 75, 1083–1089. https://doi.org/10.1016/j.jaridenv.2011.05.012 (2011).
ADS  Article  Google Scholar 

44.
Li, F., Bao, W., Liu, J. & Wu, N. Eco-anatomical characteristics of Sophora davidii leaves along an elevation gradient in upper Minjiang River dry valley. Chin. J. Appl. Ecol. 17, 5–10 (2006).
Google Scholar 

45.
Li, F., Bao, W. & Zhu, L. Species diversity and spatial distribution of legumes in the dry valley of Minjiang River, SW China. J. Mt. Sci. 1, 76–84 (2010).
Google Scholar 

46.
Song, C. J. et al. Distribution patterns of shrubby N-fixers and non-N fixers in an arid valley in Southwest China: implications for ecological restoration. Ecol. Res. 25, 553–564. https://doi.org/10.1007/s11284-009-0685-3 (2010).
Article  Google Scholar 

47.
Liu, G. et al. Aboveground biomass of main shrubs in dry valley of Minjiang River. Acta Ecol. Sin. 23, 1757–1764. https://doi.org/10.1023/A:1022289509702 (2003).
Article  Google Scholar 

48.
Ellenberg, H. et al. Zeigerwerte von pflanzen in Mitteleuropa (1992).

49.
Shreve, F. Soil temperature as influenced by altitude and slope exposure. Ecology 5, 128–136. https://doi.org/10.2307/1929010 (1924).
Article  Google Scholar 

50.
Gutiérrez-Jurado, H. A. et al. On the observed ecohydrologic dynamics of a semiarid basin with aspect-delimited ecosystems. Water Resour. Res. 49, 8263–8284. https://doi.org/10.1002/2013wr014364 (2013).
Article  Google Scholar 

51.
Liu, G. et al. Distribution regulation of aboveground biomass of three main shrub types in the dry valley of Minjiang River. J. Mt. Sci. 21, 24–32 (2003).
Google Scholar 

52.
Pugnaire, F. I. & Luque, M. T. Changes in plant interactions along a gradient of environmental stress. Oikos 93, 42–49. https://doi.org/10.1034/j.1600-0706.2001.930104.x (2001).
Article  Google Scholar 

53.
Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity–biodiversity relationship. Nature 416, 427–430. https://doi.org/10.1038/416427a (2002).
ADS  CAS  Article  Google Scholar 

54.
Sarr, D. A., Hibbs, D. E. & Huston, M. A. A hierarchical perspective of plant diversity. Q. Rev. Biol. 80, 187–212. https://doi.org/10.1086/433058 (2005).
Article  PubMed  Google Scholar 

55.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853. https://doi.org/10.1038/35002501 (2000).
ADS  CAS  Article  PubMed  Google Scholar 

56.
Ricklefs, R. E. Community diversity: relative roles of local and regional processes. Science 235, 167–171. https://doi.org/10.1126/science.235.4785.167 (1987).
ADS  CAS  Article  PubMed  Google Scholar 

57.
Pang, X. Y., Bao, W. K. & Ning, W. U. Reasons of dry valley climate characteristic and its formation reason in upstream of Minjiang River. Resour. Environ. Yangtze Basin 17, 46–53 (2008).
Google Scholar 

58.
Minchin, P. R. An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio 69, 89–107. https://doi.org/10.1007/BF00038690 (1987).
Article  Google Scholar 

59.
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143. https://doi.org/10.1111/j.1442-9993.1993.tb00438.x (1993).
Article  Google Scholar 

60.
Oksanen, J. et al.vegan: Community Ecology Package. R package version 2.5-2. 2018 (2018).

61.
Wang, Y. J., Huang, C. D., Zhang, J., Yang, W. Q. & Wang, X. S. Species Diversity, biomass and their relationship of shrubberies in an arid valley of the Minjiang River. Arid Zone Res. 27, 567–572. https://doi.org/10.3724/SP.J.1077.2010.01263 (2010).
CAS  Article  Google Scholar 

62.
Gotelli, N. J. & Colwell, R. K. Estimating species richness. In: Biological Diversity: Frontiers in Measurement and Assessment, Vol. 12 (eds Magurran, A. & McGill, B.) 39–54 (Oxford University Press, Oxford, 2011).
Google Scholar 

63.
Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Philos. Trans. R. Soc. Lond. B Biol. Sci. 345, 101–118 (1994).
ADS  CAS  Article  Google Scholar 

64.
Smith, E. P. & van Belle, G. Nonparametric estimation of species richness. Biometrics 40, 119–129 (1984).
Article  Google Scholar 

65.
Maurer, B. A. & McGill, B. J. In Biological Diversity: Frontiers in Measurement and Assessment (eds Magurran, A. E. & McGill, B. J.) 55–65 (Oxford University Press, Oxford, 2011).

66.
Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42–58 (1943).
Article  Google Scholar 

67.
Sørensen, T. A. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. K. Dan. Vidensk. Selsk. Biol. Skr. 5, 1–34 (1948).
Google Scholar 

68.
Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence-absence data. J. Anim. Ecol. 72, 367–382. https://doi.org/10.1046/j.1365-2656.2003.00710.x (2003).
Article  Google Scholar 

69.
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253. https://doi.org/10.1111/j.1541-0420.2005.00440.x (2006).
MathSciNet  Article  PubMed  MATH  Google Scholar 

70.
Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693. https://doi.org/10.1111/j.1461-0248.2006.00926.x (2006).
Article  PubMed  Google Scholar 

71.
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2018). More

1.
Reynolds, M. P. (eds) Climate Change and Crop Production (CABI Publishing, 2010).
2.
Morales-Castilla, I. et al. Diversity buffers winegrowing regions from climate change losses. Proc. Natl Acad. Sci. USA 117, 2864–2869 (2020).
CAS  Article  Google Scholar 

3.
Nicotra, A. B. et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 15, 684–692 (2010).
CAS  Article  Google Scholar 

4.
Bellon, M. R., Hodson, D. & Hellin, J. Assessing the vulnerability of traditional maize seed systems in Mexico to climate change. Proc. Natl Acad. Sci. USA 108, 13432–13437 (2011).
CAS  Article  Google Scholar 

5.
Driedonks, N., Rieu, I. & Vriezen, W. H. Breeding for plant heat tolerance at vegetative and reproductive stages. Plant Reprod. 29, 67–79 (2016).
CAS  Article  Google Scholar 

6.
Mercer, K. L. & Perales, H. Evolutionary response of landraces to climate change in centers of crop diversity. Evol. Appl. 3, 480–493 (2010).
Article  Google Scholar 

7.
FAOSTAT (FAO, 2020); http://www.fao.org/faostat/en/#home

8.
Gibson, R., Mwanga, R. O. M., Namanda, S., Jeremiah, S. C. & Barker, I. Review of Sweetpotato Seed Systems in East and Southern Africa Working Paper 2009-1 (International Potato Center, 2009).

9.
Laurie, R. N., Laurie, S. M., Du Plooy, C. P., Finnie, J. F. & Van Staden, J. Yield of drought-stressed sweet potato in relation to canopy cover, stem length and stomatal conductance. J. Agric. Sci. 7, 201–214 (2015).
Google Scholar 

10.
Yang et al. High-throughput deep sequencing reveals the important role that microRNAs play in the salt response in sweet potato (Ipomoea batatas L.). BMC Genomics 21, 164 (2020).
CAS  Article  Google Scholar 

11.
Warren, J. F. Typhoons and droughts: food shortages and famine in the Philippines since the seventeenth century. Int. Rev. Environ. Hist. 4, 27–44 (2018).
Article  Google Scholar 

12.
Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).
Article  Google Scholar 

13.
Roullier, C. et al. Disentangling the origins of cultivated sweet potato (Ipomoea batatas (L.) Lam.). PLoS ONE 8, e62707 (2013).
CAS  Article  Google Scholar 

14.
Kassali, R. Economics of sweet potato production. Int. J. Veg. Sci. 17, 313–321 (2011).
Article  Google Scholar 

15.
Omotobora, B. O., Adebola, P. O., Modise, D. M., Laurie, S. M. & Gerrano, A. S. Greenhouse and field evaluation of selected sweetpotato (Ipomoea batatas (L.) Lam.) accessions for drought tolerance in South Africa. Am. J. Plant Sci. 5, 3328–3339 (2014).
Article  Google Scholar 

16.
Woolfe, J. A. Sweetpotato: An Untapped Food Resource (Cambridge Univ. Press, 1992).

17.
Jayne, T. S., Villareal, M., Pingali, P. & Hemrich, G. Interactions between the Agricultural Sector and the HIV/AIDS Pandemic: Implications for Agricultural Policy ESA Working Paper No. 04-46 (FAO, 2004).

18.
Lebot, V. in Root and Tuber Crops (ed. Bradshaw, J. E.) 97–125 (Springer, 2010).

19.
Zhao et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).
CAS  Article  Google Scholar 

20.
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).
CAS  Article  Google Scholar 

21.
Nangombe et al. Record-breaking climate extremes in Africa under stabilized 1.5 °C and 2 °C global warming scenarios. Nat. Clim. Change 8, 375–380 (2018).
Article  Google Scholar 

22.
Seneviratne, S. I. et al. in Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (Cambridge Univ. Press, 2012).

23.
O’sullivan et al. Thermal limits of leaf metabolism across biomes. Glob. Change Biol. 23, 209–223 (2017).
Article  Google Scholar 

24.
Tack, J., Lingenfelser, J. & Jagadish, S. K. Disaggregating sorghum yield reductions under warming scenarios exposes narrow genetic diversity in US breeding programs. Proc. Natl Acad. Sci. USA 114, 9296–9301 (2017).
CAS  Article  Google Scholar 

25.
Araújo et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
Article  Google Scholar 

26.
Singh, K. D. & Mandal, R. C. Performance of coleus and sweet potato in relation to seasonal variations, time of planting. J. Root Crops 2, 17–22 (1976).
Google Scholar 

27.
Gajanayake, B. R., Reddy, K., Shankle, M. W., Arancibia, R. A. & Villordon, A. Quantifying storage root initiation, growth, and developmental responses of sweetpotato to early season temperature. Agr. J. 106, 1795–1804 (2014).
Article  Google Scholar 

28.
Boeck, H. J. D., Velde, H. V. D., Groote, T. D. & Nijs, I. Ideas and perspectives: heat stress: more than hot air. Biogeosciences 13, 5821–5825 (2016).
Article  Google Scholar 

29.
Wahid, A., Gelani, S., Ashraf, M. & Foolad, M. Heat tolerance in plants: an overview. Environ. Exp. Bot. 61, 199–223 (2007).
Article  Google Scholar 

30.
Bita, C. & Gerats, T. Plant tolerance to high temperature in a changing environment: scientific fundamentals and production of heat stress-tolerant crops. Front. Plant Sci. 4, 273 (2013).
Article  Google Scholar 

31.
Jones, H. G. Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology (Cambridge Univ. Press, 2013).

32.
Dong, N., Prentice, I. C., Harrison, S. P., Song, Q. H. & Zhang, Y. P. Biophysical homoeostasis of leaf temperature: a neglected process for vegetation and land-surface modelling. Glob. Ecol. Biogeogr. 26, 998–1007 (2017).
Article  Google Scholar 

33.
Reynolds, M. P. et al. Evaluating physiological traits to complement empirical selection for wheat in warm environments. Euphytica 100, 84–95 (1998).
Article  Google Scholar 

34.
Park, S. et al. Orange protein has a role in phytoene synthase stabilization in sweetpotato. Sci. Rep. 6, 33563 (2016).
CAS  Article  Google Scholar 

35.
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Change 9, 758–763 (2019).
Article  Google Scholar 

36.
Herrera, J. M. et al. Lessons from 20 years of studies of wheat genotypes in multiple environments and under contrasting production systems. Front. Plant Sci. 10, 1745 (2020).
Article  Google Scholar 

37.
Khoury, C. K. et al. Distributions, ex situ conservation priorities, and genetic resource potential of crop wild relatives of sweetpotato [Ipomoea batatas (L.) Lam., I. series Batatas]. Front. Plant Sci. 6, 251 (2015).
Article  Google Scholar 

38.
Alwang, J. et al. Pathways from research on improved staple crop germplasm to poverty reduction for smallholder farmers. Agric. Sys. 172, 16–27 (2019).
Article  Google Scholar 

39.
Pilling, D., Bélanger, J. & Hoffmann, I. Declining biodiversity for food and agriculture needs urgent global action. Nat. Food 1, 144–147 (2020).
Article  Google Scholar 

40.
Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).
CAS  Article  Google Scholar 

41.
Beck, H. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).
Article  Google Scholar 

42.
Patterson, H. D. & Williams, E. R. A new class of resolvable incomplete block designs. Biometrika 63, 83–92 (1976).
Article  Google Scholar 

43.
Kumar, A. et al. Improving the efficiency of wheat breeding experiments using alpha lattice design over randomized complete block design. Cereal Res. Commun. 48, 95–101 (2020).
Article  Google Scholar 

44.
Khan, M. et al. Comparative efficiency of alpha lattice design and complete randomized block design in wheat, maize and potato field trials. J. Res. Dev. Manag. 11, 115–118 (2015).
Google Scholar 

45.
Faye, E., Rebaudo, F., Yánez-Cajo, D., Cauvy-Fraunié, S. & Dangles, O. A toolbox for studying thermal heterogeneity across spatial scales: from unmanned aerial vehicle imagery to landscape metrics. Methods Ecol. Evol. 7, 437–446 (2016).
Article  Google Scholar 

46.
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 

47.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Clim. 37, 4302–4315 (2017).
Article  Google Scholar 

48.
Knox, J., Hess, T., Daccache, A. & Wheeler, T. Climate change impacts on crop productivity in Africa and South Asia. Environ. Res. Lett. 7, 034032 (2012).
Article  Google Scholar 

49.
Adoption of the Paris Agreement FCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015). More

1.
Klicka, J. & Zink, R. M. Pleistocene effects on North American songbird evolution. Proc. R. Soc. B Biol. Sci. 266, 695–700 (1999).
Article  Google Scholar 
2.
Johnson, N. K. & Cicero, C. New mitochondrial DNA data affirm the importance of pleistocene speciation in North American birds. Evolution 58, 1122–1130 (2004).
PubMed  Article  Google Scholar 

3.
Avise, J. C. & Walker, D. Pleistocene phylogeographic effects on avian populations and the speciation process. Proc. R. Soc. B Biol. Sci. 265, 457–463 (1998).
CAS  Article  Google Scholar 

4.
April, J., Hanner, R. H., Dion-Côté, A.-M. & Bernatchez, L. Glacial cycles as an allopatric speciation pump in north-eastern American freshwater fishes. Mol. Ecol. 22, 409–422 (2013).
CAS  PubMed  Article  Google Scholar 

5.
Ribera, I. & Vogler, A. P. Speciation of Iberian diving beetles in Pleistocene refugia (Coleoptera, Dytiscidae). Mol. Ecol. 13, 179–193 (2004).
CAS  PubMed  Article  Google Scholar 

6.
Hewitt, G. Some genetic consequences of ice ages, and their role in divergence and speciation. Biol. J. Linn. Soc. 58, 247–276 (1996).
Article  Google Scholar 

7.
Hewitt, G. M. Genetic consequences of climatic oscillations in the Quaternary. . Philos. Trans. R. Soc. Lond., B Biol. Sci. 359, 183–195 (2004).
CAS  Article  Google Scholar 

8.
Hewitt, G. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. 68, 87–112 (1999).
Article  Google Scholar 

9.
Skrzecz, I. & Bulka, M. Insect assemblages in Norway spruce (Picea abies (L.) Karst.) stumps in the Eastern Sudetes. Folia For. Pol. Ser. A. 52, 98–107 (2010).
Google Scholar 

10.
Röder, J. et al. Arthropod species richness in the Norway Spruce (Picea abies (L.) Karst.) canopy along an elevation gradient. For. Ecol. Manage. 259, 1513–1521 (2010).
Article  Google Scholar 

11.
Tollefsrud, M. M. et al. Genetic consequences of glacial survival and postglacial colonization in Norway spruce: combined analysis of mitochondrial DNA and fossil pollen. Mol. Ecol. 17, 4134–4150 (2008).
CAS  PubMed  Article  Google Scholar 

12.
Latałowa, M. & van der Knaap, W. O. Late quaternary expansion of Norway spruce Picea abies (L.) Karst. in Europe according to pollen data. Quat. Sci. Rev. 25, 2780–2805 (2006).
ADS  Article  Google Scholar 

13.
Gałka, M. & Tobolski, K. Macrofossil evidence of early Holocene presence of Picea abies (Norway spruce) in NE Poland. Ann. Bot. Fenn. 3847, 129–141 (2013).
Article  Google Scholar 

14.
Dering, M. & Lewandowski, A. Finding the meeting zone: Where have the northern and southern ranges of Norway spruce overlapped?. For. Ecol. Manage. 259, 229–235 (2009).
Article  Google Scholar 

15.
Giesecke, T. & Bennett, K. D. The Holocene spread of Picea abies (L.) Karst. in Fennoscandia and adjacent areas. J. Biogeogr. 31, 1523–1548 (2004).
Article  Google Scholar 

16.
Taberlet, P., Fumagalli, L., Wust-Saucy, A. G. & Cosson, J. F. Comparative phylogeography and postglacial colonization routes in Europe. Mol. Ecol. 7, 453–464 (1998).
CAS  PubMed  Article  Google Scholar 

17.
Zając, A. & Zając, M. Atlas rozmieszczenia roślin naczyniowych w Polsce. (ed. Zając, A. & Zając, M) (Pracownia Chorologii Komputerowej Instytutu Botaniki Uniwersytetu Jagiellońskiego, 2001).

18.
Sallé, A., Arthofer, W., Lieutier, F., Stauffer, C. & Kerdelhué, C. Phylogeography of a host-specific insect: Genetic structure of Ips typographus in Europe does not reflect past fragmentation of its host. Biol. J. Linn. Soc. 90, 239–246 (2007).
Article  Google Scholar 

19.
Stauffer, C., Lakatos, F. & Hewitt, G. M. Phylogeography and postglacial colonization routes of Ips typographus L. (Coleoptera, Scolytidae). Mol. Ecol. 8, 763–773 (1999).
Article  Google Scholar 

20.
Bertheau, C. et al. Divergent evolutionary histories of two sympatric spruce bark beetle species. Mol. Ecol. 22, 3318–3332 (2013).
CAS  PubMed  Article  Google Scholar 

21.
Mayer, F. et al. Comparative multilocus phylogeography of two Palaearctic spruce bark beetles: influence of contrasting ecological strategies on genetic variation. Mol. Ecol. 24, 1292–1310 (2015).
PubMed  Article  Google Scholar 

22.
Schebeck, M. et al. Pleistocene climate cycling and host plant association shaped the demographic history of the bark beetle Pityogenes chalcographus. Sci. Rep. 8, 14207 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Forsse, E. & Solbreck, C. Migration in the bark beetle Ips typographus L: duration, timing and height of flight. Zeitschrift für Angewandte Entomologie 100, 47–57 (1985).
Article  Google Scholar 

24.
Putz, J., Vorwagner, E. M. & Hoch, G. Flight performance of Monochamus sartor and Monochamus sutor, potential vectors of the pine wood nematode. For. J. 62, 195–201 (2016).
Google Scholar 

25.
Kawai, M. et al. Genetic Structure of Pine Sawyer Monochamus alternatus (Coleoptera: Cerambycidae) Populations in Northeast Asia: Consequences of the Spread of Pine Wilt Disease. Environ. Entomol. 35, 569–579 (2006).
Article  Google Scholar 

26.
Shoda-Kagaya, E. Genetic differentiation of the pine wilt disease vector Monochamus alternatus (Coleoptera: Cerambycidae) over a mountain range – revealed from microsatellite DNA markers. Bull. Entomol. Res. 97, 167–174 (2007).
CAS  PubMed  Article  Google Scholar 

27.
Haran, J., Rousselet, J., Tellez, D., Roques, A. & Roux, G. Phylogeography of Monochamus galloprovincialis, the European vector of the pinewood nematode. J. Pest Sci. 2004(91), 247–257 (2018).
Article  Google Scholar 

28.
Koutroumpa, F. A., Rougon, D., Bertheau, C., Lieutier, F. & Roux-Morabito, G. Evolutionary relationships within European Monochamus (Coleoptera: Cerambycidae) highlight the role of altitude in species delineation. Biol. J. Linn. Soc. 109, 354–376 (2013).
Article  Google Scholar 

29.
Plewa, R. et al. Morphology, genetics and Wolbachia endosymbionts support distinctiveness of Monochamus sartor sartor and M. s. urussovii (Coleoptera: Cerambycidae). Arthropod Syst. Phylogeny 76, 123–135 (2018).
Google Scholar 

30.
Ballard, J. W. O. & Whitlock, M. C. The incomplete natural history of mitochondria. Mol. Ecol. 13, 729–744 (2004).
PubMed  Article  Google Scholar 

31.
Toews, D. P. L. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).
CAS  PubMed  Article  Google Scholar 

32.
Bazin, E. Population size does not influence mitochondrial genetic diversity in animals. Science 312, 570–572 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

33.
Plavil’shhikov, N. Zhuki-drovoseki, chast’3. Podsemejstvo Lamiinae, ch. 1. (ed. Plavil’shhikov, N.) 510–514 (Fauna SSSR. Zhestkokrylye, 1958).

34.
Sama, G. Atlas of the Cerambycidae of Europe and the Mediterranean Area (ed. Sama, G.) 98–99 (Vit Kabourek, 2002).

35.
Sama, G. & Löbl, L. Cerambycidae, Western Palaearctic taxa.In:Catalogue of Palaearctic Coleoptera. Volume 6. Chrysomeloidea (ed. Löbl, I. & Smetana, A.) 281–283 (Apollo Books, 2010).

36.
Wallin, H., Schroeder, M. & Kvamme, T. A review of the European species of Monochamus Dejean, 1821 (Coleoptera, Cerambycidae) -with a description of the genitalia characters. Nor. J. Entomol. 60, 11–38 (2013).
Google Scholar 

37.
Rossa, R., Goczał, J. & Tofilski, A. Within and between-species variation of wing venation in genus Monochamus (Coleoptera: Cerambycidae). J. Insect Sci. 16, 5 (2016).
PubMed  PubMed Central  Article  Google Scholar 

38.
Ravazzi, C. Late quaternary history of spruce in southern Europe. Rev. Palaeobot. Palynol. 120, 131–177 (2002).
Article  Google Scholar 

39.
Terhürne-Berson, R. Changing distribution patterns of selected conifers in the Quaternary of Europe caused by climatic variations.https://d-nb.info/975944576/34 (2005).

40.
Ralska-Jasiewiczowa, M Latałowa, M. et al. Late Glacial and Holocene history of vegetation in Poland based on isopollen maps(ed. Ralska-Jasiewiczowa, M Latałowa, M. et al.) 147–159 (W. Szafer Institute of Botany, Polish Academy of Sciences, 2004).

41.
Adams, D. C., Rohlf, F. J. & Slice, D. E. Geometric morphometrics: ten years of progress following the ‘revolution’. Ital. J. Zool. 71, 5–16 (2004).
Article  Google Scholar 

42.
Zelditch, M. L., Swiderski, D. L., Sheets, H. D. & Fink, W. L. Geometric Morphometrics for Biologist. A Primer(ed. Zelditch, M. L., Swiderski, D. L., Sheets, H. D. & Fink, W. L.). 263–490 (2004).

43.
Oleksa, A. & Tofilski, A. Wing geometric morphometrics and microsatellite analysis provide similar discrimination of honey bee subspecies. Apidologie 46, 49–60 (2015).
Article  Google Scholar 

44.
Hurtado-Burillo, M. et al. A geometric morphometric and microsatellite analyses of Scaptotrigona mexicana and S. pectoralis (Apidae: Meliponini) sheds light on the biodiversity of Mesoamerican stingless bees. J. Insect Conserv. 5, 753–763 (2016).
Article  Google Scholar 

45.
Haack, R. A. Exotic bark- and wood-boring Coleoptera in the United States: recent establishments and interceptions. Can. J. For. Res. 36, 269–288 (2006).
Article  Google Scholar 

46.
Shoda, E., Kubota, K. & Makihara, H. Geographical structuring of mitochondrial DNA in Semanotus japonicus (Coleoptera: Cerambycidae). Appl. Entomol. Zool. 38, 339–345 (2003).
CAS  Article  Google Scholar 

47.
Shoda, E., Kubota, K. & Makihara, H. Geographical structure of morphological characters in Semanotus japonicus (Coleoptera: Cerambycidae) in Japan. Appl. Entomol. Zool. 38, 369–377 (2003).
Article  Google Scholar 

48.
Rossa, R., Goczał, J., Pawliczek, B. & Ohbayashi, N. Hind wing variation in Leptura annularis complex among European and Asiatic populations (Coleoptera, Cerambycidae). Zookeys 724, 31–42 (2017).
Article  Google Scholar 

49.
Kolk, A. & Starzyk, J. Atlas szkodliwych owadów lesnych (The atlas of harmful forest insects). 268–488 (Multico,1996).

50.
Cherepanov, A. Usachi Severnoj Azii (Lamiinae) (ed. Cherepanov, A.) 97–102 (Nauka Publishers, 1983).

51.
Danilevsky, M. L. A check list of the longicorn beetles (Cerambycoidea) of Russia.https://www.zin.ru/Animalia/Coleoptera/doc/List_of_Russia.doc (2015).

52.
Haran, J. & Roux-Morabito, G. Development of 12 microsatellites loci for the longhorn beetle Monochamus galloprovincialis (Coleoptera Cerambycidae), vector of the Pine Wood Nematode in Europe. Conserv. Genet. Res. 6, 975–977 (2014).
Article  Google Scholar 

53.
Chybicki, I. J. & Burczyk, J. Simultaneous estimation of null alleles and inbreeding coefficients. J. Hered. 100, 106–113 (2009).
CAS  PubMed  Article  Google Scholar 

54.
Guo, S. W. & Thompson, E. A. Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 1, 361–372 (1992).
MATH  Article  Google Scholar 

55.
Paradis, E. Pegas: An R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419–420 (2010).
CAS  PubMed  Article  Google Scholar 

56.
Agapow, P. M. & Burt, A. Indices of multilocus linkage disequilibrium. Mol. Ecol. Notes 1, 101–102 (2001).
CAS  Article  Google Scholar 

57.
Kamvar, Z. N., Tabima, J. F. & Gr̈unwald, N. J. Poppr, ,. An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ e281, 1–14 (2014).
Google Scholar 

58.
Storey, J. D., Bass, A. J., Dabney, A. & Robinson, D. qvalue: Q-value estimation for false discovery rate control. R package version 2.14.1. https://qvalue.princeton.edu (2019).

59.
Goudet, J. & Jombart, T. hierfstat: estimation and tests of hierarchical F-statistics. https://github.com/jgx65/hierfstat (2015).

60.
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: iNterpolation and EXTrapolation for species diversity. R package version 2.0.19. https://chao.stat.nthu.edu.tw/blog/software-download/ (2019).

61.
Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).
Article  Google Scholar 

62.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Jombart, T. et al. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).
PubMed  PubMed Central  Article  Google Scholar 

64.
Jombart, T. Adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).
CAS  PubMed  Article  Google Scholar 

65.
Pastore, M. Overlapping: a R package for estimating overlapping in empirical distributions. J. Open Source Softw. 3, 1023 (2018).
ADS  Article  Google Scholar 

66.
Cornuet, J.-M. et al. DIYABC v20: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30, 1187–1189 (2014).
CAS  PubMed  Article  Google Scholar 

67.
Goczał, J., Rossa, R. & Tofilski, A. Intersexual and intrasexual patterns of horn size and shape variation in the European rhinoceros beetle: quantifying the shape of weapons. Biol. J. Linn. Soc. 127, 34–43 (2019).
Article  Google Scholar 

68.
Rohlf, F. & Slice, D. Extensions of the procrustes method for the optimal superimposition of landmarks. Syst. Zool. 39, 40–59 (1990).
Article  Google Scholar 

69.
Cattell, R. B. The scree test for the number of factors. Multivariate Behav. Res. 1, 245–276 (1966).
CAS  PubMed  Article  Google Scholar 

70.
TIBCO Software Inc. Statistica (data analysis software system) version 13. https://www.statsoft.pl/statistica-i-tibco-software/ (2017).

71.
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

72.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org/ (2015). More

1.
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Raposo, D., Kaufman, M. T. & Churchland, A. K. A category-free neural population supports evolving demands during decision-making. Nat. Neurosci. 17, 1784–1792 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Roy, J. E., Buschman, T. J. & Miller, E. K. PFC neurons reflect categorical decisions about ambiguous stimuli. J. Cogn. Neurosci. 26, 1283–1291 (2014).
PubMed  PubMed Central  Article  Google Scholar 

4.
Siegel, M., Buschman, T. J. & Miller, E. K. Cortical information flow during flexible sensorimotor decisions. Science 348, 1352–1355 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

5.
Katz, L. N., Yates, J. L., Pillow, J. W. & Huk, A. C. Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535, 285–288 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Erlich, J. C., Brunton, B. W., Duan, C. A., Hanks, T. D. & Brody, C. D. Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat. eLife 4, e05457 (2015).
PubMed Central  Article  PubMed  Google Scholar 

8.
Goard, M. J., Pho, G. N., Woodson, J. & Sur, M. Distinct roles of visual, parietal, and frontal motor cortices in memory-guided sensorimotor decisions. eLife 5, e13764 (2016).
PubMed  PubMed Central  Article  Google Scholar 

9.
Bruce, C. J. & Goldberg, M. E. Primate frontal eye fields. I. Single neurons discharging before saccades. J. Neurophysiol. 53, 603–635 (1985).
CAS  PubMed  Article  Google Scholar 

10.
Kim, J. N. & Shadlen, M. N. Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nat. Neurosci. 2, 176–185 (1999).
PubMed  Article  Google Scholar 

11.
Ding, L. & Gold, J. I. Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field. Cereb. Cortex 22, 1052–1067 (2011).
PubMed  PubMed Central  Article  Google Scholar 

12.
Stokes, M. G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364–375 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Purcell, B. A. et al. Neurally constrained modeling of perceptual decision making. Psychol. Rev. 117, 1113–1143 (2010).
PubMed Central  Article  PubMed  Google Scholar 

15.
Heitz, R. P. & Schall, J. D. Neural mechanisms of speed-accuracy tradeoff. Neuron 76, 616–628 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Parthasarathy, A. et al. Mixed selectivity morphs population codes in prefrontal cortex. Nat. Neurosci. 20, 1770–1779 (2017).
CAS  PubMed  Article  Google Scholar 

17.
Beck, J. M. et al. Probabilistic population codes for Bayesian decision making. Neuron 60, 1142–1152 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Churchland, M. M. et al. Neural population dynamics during reaching. Nature 487, 51–56 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
Kaufman, M. T., Churchland, M. M., Ryu, S. I. & Shenoy, K. V. Cortical activity in the null space: permitting preparation without movement. Nat. Neurosci. 17, 440–448 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Lara, A. H., Cunningham, J. P. & Churchland, M. M. Different population dynamics in the supplementary motor area and motor cortex during reaching. Nat. Commun. 9, 2754 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Akaike, H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974).
Article  Google Scholar 

22.
Aoi, M. & Pillow, J. W. Model-based targeted dimensionality reduction for neuronal population data. Adv. Neural Inform. Process. Syst. 31, 6690–6699 (2018).
Google Scholar 

23.
Elsayed, G. F. & Cunningham, J. P. Structure in neural population recordings: an expected byproduct of simpler phenomena? Nat. Neurosci. 20, 1310–1318 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Rossi-Pool, R. et al. Decoding a decision process in the neuronal population of dorsal premotor cortex. Neuron 96, 1432–1446 (2017).
CAS  PubMed  Article  Google Scholar 

25.
Williamson, R. C. et al. Scaling properties of dimensionality reduction for neural populations and network models. PLoS Comput. Biol. 12, e1005141 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Cunningham, J. P. & Byron, M. Y. Dimensionality reduction for large-scale neural recordings. Nature Neurosci. 17, 1500–1509 (2014).
CAS  PubMed  Article  Google Scholar 

27.
Kobak, D. et al. Demixed principal component analysis of neural population data. eLife 5, e10989 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

28.
Machens, C. K. Demixing population activity in higher cortical areas. Front. Comput. Neurosci. 4, 126 (2010).
PubMed  PubMed Central  Article  Google Scholar 

29.
Machens, C. K., Romo, R. & Brody, C. D. Functional, but not anatomical, separation of ‘what’ and ‘when’ in prefrontal cortex. J. Neurosci. 30, 350–360 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Romo, R., Brody, C. D., Hernández, A. & Lemus, L. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399, 470–473 (1999).
CAS  PubMed  Article  Google Scholar 

31.
Hernández, A., Zainos, A. & Romo, R. Temporal evolution of a decision-making process in medial premotor cortex. Neuron 33, 959–972 (2002).
PubMed  Article  Google Scholar 

32.
Romo, R., Hernández, A., Zainos, A., Lemus, L. & Brody, C. D. Neuronal correlates of decision-making in secondary somatosensory cortex. Nat. Neurosci. 5, 1217–1225 (2002).
CAS  PubMed  Article  Google Scholar 

33.
Romo, R., Hernández, A. & Zainos, A. Neuronal correlates of a perceptual decision in ventral premotor cortex. Neuron 41, 165–173 (2004).
CAS  PubMed  Article  Google Scholar 

34.
Elsayed, G. F., Lara, A. H., Kaufman, M. T., Churchland, M. M. & Cunningham, J. P. Reorganization between preparatory and movement population responses in motor cortex. Nat. Commun. 7, 13239 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Gold, J. I. & Shadlen, M. N. The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. J. Neurosci. 23, 632–651 (2003).
CAS  PubMed  PubMed Central  Article  Google Scholar 

36.
Kiani, R., Hanks, T. D. & Shadlen, M. N. Bounded integration in parietal cortex underlies decisions even when viewing duration is dictated by the environment. J. Neurosci. 28, 3017–3029 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Meister, M. L. R., Hennig, J. A. & Huk, A. C. Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making. J. Neurosci. 33, 2254–2267 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Latimer, K. W., Yates, J. L., Meister, M. L. R., Huk, A. C. & Pillow, J. W. Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science 349, 184–187 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W. & Huk, A. C. Functional dissection of signal and noise in mt and lip during decision-making. Nat. Neurosci. 20, 1285–1292 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

40.
Vernet, M., Quentin, R., Chanes, L., Mitsumasu, A. & Valero-Cabré, A. Frontal eye field, where art thou? anatomy, function, and non-invasive manipulation of frontal regions involved in eye movements and associated cognitive operations. Front. Integr. Neurosci. 8, 1–24 (2014).
Google Scholar 

41.
Bruce, C. J., Goldberg, M. E., Bushnell, M. C. & Stanton, G. B. Primate frontal eye fields. II. Physiological and anatomical correlates of electrically evoked eye movements. J. Neurophysiol. 54, 714–734 (1985).
CAS  PubMed  Article  Google Scholar 

42.
Rizzolatti, G., Riggio, L., Dascola, I. & Umiltá, C. Reorienting attention across the horizontal and vertical meridians: evidence in favor of a premotor theory of attention. Neuropsychologia 25, 31–40 (1987).
CAS  PubMed  Article  Google Scholar 

43.
Thompson, K. G., Biscoe, K. L. & Sato, T. R. Neuronal basis of covert spatial attention in the frontal eye field. J. Neurosci. 25, 9479–9487 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Schall, J. D. On the role of frontal eye field in guiding attention and saccades. Vision Res. 44, 1453–1467 (2004).
PubMed  Article  Google Scholar 

45.
Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. Series B Stat. Methodol. 64, 479–498 (2002).
Article  Google Scholar 

46.
Brody, C. D., Hernández, A., Zainos, A. & Romo, R. Timing and neural encoding of somatosensory parametric working memory in macaque prefrontal cortex. Cereb. Cortex 13, 1196–1207 (2003).
PubMed  Article  Google Scholar 

47.
Roitman, J. D. & Shadlen, M. N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002).
PubMed Central  Article  PubMed  Google Scholar 

48.
Lawrence, N. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. Mach. Learn. Res. 6, 1783–1816 (2005).
Google Scholar 

49.
Cunningham, J. P. & Ghahramani, Z. Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. 16, 2859–2900 (2015).

50.
Mackevicius, E. L. et al. Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience. eLife 8, e38471 (2019).
PubMed  PubMed Central  Article  Google Scholar  More

1.
Ferrier, S. et al. IPBES Summary for Policymakers of the Methodological Assessment of Scenarios and Models of Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2019).
2.
Ripple, W. J., Wolf, C., Newsome, T. M., Barnard, P. & Moomaw, W. R. World scientists’ warning of a climate emergency. BioScience 70, 8–12 (2020).
Google Scholar 

3.
IPCC in Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) Summary for Policymakers (WMO, 2018).

4.
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
Google Scholar 

5.
Fazey, I. et al. Ten essentials for action-oriented and second order energy transitions, transformations and climate change research. Energy Res. Soc. Sci. 40, 54–70 (2018).
Google Scholar 

6.
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).
Google Scholar 

7.
Turnhout, E., Metze, T., Wyborn, C., Klenk, N. & Louder, E. The politics of co-production: participation, power, and transformation. Curr. Opin. Environ. Sustain. 42, 15–21 (2020).
Google Scholar 

8.
Wolfram, M. Conceptualizing urban transformative capacity: a framework for research and policy. Cities 51, 121–130 (2016).
Google Scholar 

9.
Etzion, D. Management for sustainability. Nat. Sustain. 1, 744–749 (2018).
Google Scholar 

10.
Ostrom, E. A diagnostic approach for going beyond panaceas. Proc. Natl Acad. Sci. USA 104, 15181–15187 (2007).
CAS  Google Scholar 

11.
Jerneck, A. et al. Structuring sustainability science. Sustain. Sci. 6, 69–82 (2011).
Google Scholar 

12.
Kates, R. W. What kind of a science is sustainability science? Proc. Natl Acad. Sci. USA 108, 19449–19450 (2011).
CAS  Google Scholar 

13.
Cash, D. W. et al. Knowledge systems for sustainable development. Proc. Natl Acad. Sci. USA 100, 8086–8091 (2003).
CAS  Google Scholar 

14.
Cumming, G. S., Olsson, P., Chapin, F. S. & Holling, C. S. Resilience, experimentation, and scale mismatches in social-ecological landscapes. Landsc. Ecol. 28, 1139–1150 (2013).
Google Scholar 

15.
Mitchell, S. D. Unsimple Truths: Science, Complexity, and Policy (Univ. Chicago Press, 2009).

16.
Davidson, D. Actions, reasons, and causes. J. Philos. 60, 685–700 (1963).
Google Scholar 

17.
Anscombe, G. E. M. Intention (Harvard Univ. Press, 1957).

18.
Dewey, J. The Quest for Certainty: A Study of the Relation of Knowledge and Action (Minton, Balch, 1929).

19.
Lubchenco, J. Entering the century of the environment: a new social contract for science. 279, 491–497 (1998).

20.
Nowotny, H., Scott, P., Gibbons, M. & Scott, P. B. Re-thinking Science: Knowledge and the Public in an Age of Uncertainty (SciELO Argentina, 2001).

21.
Ravetz, J. R. What is post-normal science. Futures 31, 647–653 (1999).
Google Scholar 

22.
König, A. Sustainability Science: Key Issues (Routledge, 2018).

23.
Wyborn, C. et al. Co-producing sustainability: reordering the governance of science, policy, and practice. Annu. Rev. Environ. Resour. 44, 319–346 (2019).
Google Scholar 

24.
Lang, D. J. et al. Transdisciplinary research in sustainability science: practice, principles, and challenges. Sustain. Sci. 7, 25–43 (2012).
Google Scholar 

25.
Umpleby, S. A. Second-order cybernetics as a fundamental revolution in science. Constr. Found. 11, 455–465 (2016).
Google Scholar 

26.
Irwin, E. G. et al. Bridging barriers to advance global sustainability. Nat. Sustain. 1, 324–326 (2018).
Google Scholar 

27.
Norström, A. V. et al. Principles for knowledge co-production in sustainability research. Nat. Sustain. 3, 182–190 (2020).
Google Scholar 

28.
Scoones, I. et al. Transformations to sustainability: combining structural, systemic and enabling approaches. Curr. Opin. Environ. Sustain. 42, 65–75 (2020).
Google Scholar 

29.
Clark, W. C., van Kerkhoff, L., Lebel, L. & Gallopin, G. Crafting usable knowledge for sustainable development. Proc. Natl Acad. Sci. USA 113, 4570–4578 (2016).
CAS  Google Scholar 

30.
Norton, B. G. Sustainability: A Philosophy of Adaptive Ecosystem Management (Univ. Chicago Press, 2005).

31.
Popa, F., Guillermin, M. & Dedeurwaerdere, T. A pragmatist approach to transdisciplinarity in sustainability research: from complex systems theory to reflexive science. Futures 65, 45–56 (2015).
Google Scholar 

32.
Ansell, C. K. & Bartenberger, M. Varieties of experimentalism. Ecol. Econ. 130, 64–73 (2016).
Google Scholar 

33.
Caniglia, G. et al. Experiments and evidence in sustainability science: a typology. J. Clean. Prod. 169, 39–47 (2017).
Google Scholar 

34.
Wiek, A., Ness, B., Schweizer-Ries, P., Brand, F. S. & Farioli, F. From complex systems analysis to transformational change: a comparative appraisal of sustainability science projects. Sustain. Sci. 7, 5–24 (2012).
Google Scholar 

35.
Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems: organizing principles for advancing research methods and approaches. Ecol. Soc. 23, 46 (2018).
Google Scholar 

36.
Grunwald, A. Working towards sustainable development in the face of uncertainty and incomplete knowledge. J. Environ. Policy Plan. 9, 245–262 (2007).
Google Scholar 

37.
Bratman, M. Intention, Plans, and Practical Reason (Harvard Univ. Press, 1987).

38.
Midgley, G. in Systemic Intervention 113–133 (Springer, 2000).

39.
Tengö, M., Brondizio, E. S., Elmqvist, T., Malmer, P. & Spierenburg, M. Connecting diverse knowledge systems for enhanced ecosystem governance: the multiple evidence base approach. Ambio 43, 579–591 (2014).
Google Scholar 

40.
Sperling, G. B., Winthrop, R., Kwauk, C. & Yousafzai, M. What Works in Girls’ Education (The Brookings Institution, 2016).

41.
Jones, T. M. Ethical decision making by individuals in organizations: an issue-contingent model. Acad. Manag. Rev. 16, 366–395 (1991).
Google Scholar 

42.
Pelenc, J., Bazile, D. & Ceruti, C. Collective capability and collective agency for sustainability: a case study. Ecol. Econ. 118, 226–239 (2015).
Google Scholar 

43.
Farjoun, M., Ansell, C. & Boin, A. PERSPECTIVE—Pragmatism in organization studies: meeting the challenges of a dynamic and complex world. Organ. Sci. 26, 1787–1804 (2015).
Google Scholar 

44.
Djoudi, H. et al. Beyond dichotomies: gender and intersecting inequalities in climate change studies. Ambio 45, 248–262 (2016).
Google Scholar 

45.
Schlüter, M. et al. Capturing emergent phenomena in social-ecological systems: an analytical framework. Ecol. Soc. 24, 11 (2019).
Google Scholar 

46.
von Wirth, T., Fuenfschilling, L., Frantzeskaki, N. & Coenen, L. Impacts of urban living labs on sustainability transitions: mechanisms and strategies for systemic change through experimentation. Eur. Plan. Stud. 27, 229–257 (2019).
Google Scholar 

47.
Luederitz, C. et al. Learning through evaluation – a tentative evaluative scheme for sustainability transition experiments. J. Clean. Prod. 169, 61–76 (2017).
Google Scholar 

48.
Berkhout, F. et al. Sustainability experiments in Asia: innovations shaping alternative development pathways? Environ. Sci. Policy 13, 261–271 (2010).
Google Scholar 

49.
Freeth, R. & Caniglia, G. Learning to collaborate while collaborating: advancing interdisciplinary sustainability research. Sustain. Sci. 15, 247–261 (2020).
Google Scholar 

50.
Polanyi, M. The Tacit Dimension (Univ. Chicago Press, 2009).

51.
Lansing, J. S. Priests and Programmers: Technologies of Power in the Engineered Landscape of Bali (Princeton Univ. Press, 2009).

52.
Nonaka, I. & Takeuchi, H. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation (Oxford Univ. Press, 1995).

53.
Lam, D. P. M. et al. Indigenous and local knowledge in sustainability transformations research: a literature review. Ecol. Soc. 25, 3 (2020).
Google Scholar 

54.
Ryle, G. Knowing how and knowing that: the presidential address. Proc. Aristot. Soc. 46, 1–16 (1945).
Google Scholar 

55.
Sahni, U. From Learning Outcomes to Life Outcomes: What Can You Do and Who Can You Be? A Case Study in Girls’ Education in India (Center for Universal Education at Brookings, 2012).

56.
Luederitz, C., Abson, D. J., Audet, R. & Lang, D. J. Many pathways toward sustainability: not conflict but co-learning between transition narratives. Sustain. Sci. 12, 393–407 (2017).
Google Scholar 

57.
Messerli, P. et al. Expansion of sustainability science needed for the SDGs. Nat. Sustain. 2, 892–894 (2019).
Google Scholar 

58.
Balvanera, P. et al. Key features for more successful place-based sustainability research on social-ecological systems: a programme on ecosystem change and society (PECS) perspective. Ecol. Soc. 22, 14 (2017).
Google Scholar 

59.
Schäpke, N. et al. Jointly experimenting for transformation? Shaping real-world laboratories by comparing them. GAIA 27, 85–96 (2018).
Google Scholar 

60.
Brundiers, K., Wiek, A. & Redman, C. L. Real-world learning opportunities in sustainability: from classroom into the real world. Int. J. Sustain. High. Educ. 11, 308–324 (2010).
Google Scholar 

61.
Ploum, L., Blok, V., Lans, T. & Omta, O. Toward a validated competence framework for sustainable entrepreneurship. Organ. Environ. 31, 113–132 (2018).
Google Scholar 

62.
Blok, V., Gremmen, B. & Wesselink, R. Dealing with the wicked problem of sustainability in advance. Bus. Prof. Ethics J. https://doi.org/10.5840/bpej201621737 (2016). More