Philippa Kaur

This study comprehensively evaluated the risk associations between cause-specific ambulance services, extreme temperatures, and mass concentrations of PM2.5 and its constituents. The significant cold effects on chest pain and headache/dizziness/vertigo/fainting/syncope and heat effects on coma and unconsciousness and lying at public were observed, while the risk of ambulance services of OHCA was elevated in both extreme heat and cold environments. Ambulance services of respiratory distress, lying at public, and OHCA increased as the PM2.5 concentration increased, and the risk was significant at the PM2.5 concentration of 20–60 60 μg/m3 for ambulance services of lying at public and higher than 60 μg/m3 for respiratory distress. After controlling for effects of daily average temperature and PM2.5 concentration, this study still identified the significant effects of sulfate and EC on ambulance services of lying at public and OC on headache/dizziness/vertigo/fainting/syncope as the concentrations of PM2.5 constituents were at 90th percentile.
Limited studies assessed associations between ambulance calls and ambient environment9,13,19,22,23,24,25,26,27. Studies in Emilia-Romagna in Italy23, Brisbane in Australia26, Taiwan19, and Huainan and Luoyang in China22,24, have indicated the numbers of ambulance calls associated with extreme heat; the risks generally increase as the daily temperature exceeds 27 °C19,23,26. However, no consistent finding for cold threshold was identified19,28. Kaohsiung City has a tropical climate (daily temperature ranging from 13.5 °C to 31.5 °C), but it is cooler than cities located near the equator, e.g., Singapore and Manila. Except for ambulance service of OHCA, we found that the significant risks associated with temperature were only identified in environments with extreme temperatures ( 90th percentiles; Fig. 3).
Fine particulate matter (PM2.5) are characterized with a small diameter ( More

Study area
The study area is a one-hectare (ha) subtropical mixed young forest in the age of 20 to 30 years of Hong Kong. It is located at Shek Kong (22.428774, 114.114968), in between the Kadoorie Farm and Botanic Garden (KFBG) and Kadoorie Institute, The University of Hong Kong (HKU) as shown in Fig. 1. This is a forest dynamic plot under ‘The Center for Tropical Forest Science–Forest Global Earth Observatory (CTFS-ForestGEO) (https://forestgeo.si.edu/). It acts as a research base on the forest dynamics, forest biodiversity, carbon sequestration and more, which also provide opportunity for public involvement in scientific research.
Figure 1

The study area: 1 hectare subtropical moist young forest plot, a full demonstration plot of the Forest Global Earth observatory (ForestGEO) project (https://forestgeo.si.edu/), located in Hong Kong in understanding the long-term forest dynamic. The map is generated by Authors using ArcMap version 10.5 (https://desktop.arcgis.com/en/arcmap/).

Full size image

The one-hectare forest plot was demarcated by 25 quadrats in 20 m × 20 m each, which were further sub-divided into sixteen 5 m × 5 m sub-quadrats; delineated with permanent marker poles by the professional surveying team. The forest survey was launched on January 11th, 2012 and completed on September 6th, 2012. A total of 63 species, 10,442 individual trees with 20,888 stems were recorded in the site. The stem locations (UTM WGS84 coordinate system) were recorded in nearest 5 cm, and DBH were measured at 1.3 m at breast height in nearest 1 mm. The dominant species are Litsea rotundifolia, Psychotria asiatica, Ilex asprella and Aporosa dioica, which all are native species and accounted for over 80% of stems with the study area. The forest condition of the study area is shown in Table 2.
Table 2 The forest condition and descriptive statistics.
Full size table

Methods
A three-stage methodological framework was outlined for this study as shown in Figs. 2 and 3. In the first stage, parameters including DBH, wood density and stem location were recorded for field-measured AGB computation. DBH was directly obtained from site, wood density (in g cm−3) was obtained from the global wood density database43,44 or World Agroforestry database45 (http://db.worldagroforestry.org//wd), up to species level. If the species was not recorded in the database, the value was replaced by its genus averaged wood density value. Tree height was derived from the LiDAR data with reference to the recorded x,y location of the stems. The second stage is LiDAR AGB model derivation. Five allometric models (Model 1 to Model 5) were used to estimate the AGB of individual trees based on field-measured parameters. The allometric model with the lowest model error was selected to compute the ‘Field-measured AGB’, which would be used as the dependent variable to develop the LiDAR-derived AGB model. The LiDAR plots metrics were generated in various plot-size (i.e., 10 m radius, 5 m radius and 2.5 m radius) within the study area. Stepwise linear regression was used to select the important and significant predictors in each regression model. Three different model forms were tested (Model I, II, III as discussed in “Stage 2: LiDAR AGB model derivation” section), The LiDAR derived AGB regression models would be evaluated by means of assumption tests and bootstrapping; as well as cross-validation (CV) in the last stage of model evaluation and validation.
Figure 2

The workflow of the study. Stage 1: allometric modeling to compute ground-truth AGB. Five allometric models were compared and the best one is selected to calibrate model in the next stage. Stage 2: LiDAR AGB Model Derivation, by comparing LiDAR plot metrics derived in different plot size (i.e. 10 m, 5 m, 2.5 m radius). Stage 3: Model Evaluation & Validation on the best model selected from Stage 2 (Source: Authors).

Full size image

Figure 3

The graphical abstract of the study (Source: Authors).

Full size image

Stage 1: allometric modeling
20,888 stems from 63 species, were planned to be used to develop the allometric models. Amongst, the 81% of the wood density value were measured up to species level (i.e., 51 species) and 19% were up to genus level (i.e., 12 species). However, as the tree height parameter was retrieved from the LiDAR 1 m Canopy Height Model (CHM), 263 stems found no value and thus the sample size reduced to 20,625 stems. The DBH of stems ranged from 1.0 cm to 57.1 cm, with mean of 2.55 cm and S.D. of 2.38 cm.
The selected five allometric models included two pantropical models by Chave et al.16; two subtropical models by Xu et al.46 and one local model by Nichol and Sarker47 were assessed. The inclusion of the subtropical models from Xu et al.46 is because of the similar forest type (i.e., subtropical moist forest from southern China) with our study area; whereas the pantropical models from Chave et al.16 is due to its large database across the pantropical regions yet not being explored adequately its applicability in subtropical forests.
The five allometric models were compared by One-way ANOVA and the Tukey HSD post-hoc analysis48. Model 1 (Eq. (3)) was the best model proposed by Chave et al.16, comprised of wood density (ρ), diameter-at-breast height (DBH) and height (H) as shown below (AIC = 3130):

$$AGB=0.0673times {left(rho {(DBH)}^{2}Hright)}^{0.976}$$
(3)

However, it is very unlikely to have a precise tree height data in a closed canopy49. Therefore, an alternative allometric equation without height was then proposed by Chave et al.16 and adopted as Model 2 (Eq. (4)) in our study (AIC =  − 4293):to represent the subtropical AGB

$$AGB=expleft[-1.803-0.976E+0.976text{ln}left(rho right)+2.673text{ln}left(DBHright)-0.0299left[{(text{ln}(DBH))}^{2}right]right]$$
(4)

The bioclimatic variable, “E”, which made up of three parameters to account for climatic variations: Temperature seasonality (TS), Long-term Maximum Climatological Water Deficit (CWD) and Precipitation Seasonality (PS). The formula of the bioclimatic variable, “E”, is shown below (Eq. (5)):

$$E={left(0.178 times TS-0.938 times CWD-6.61 times PSright)}^{{10}^{-3}}$$
(5)

The monthly mean temperature (℃), precipitation (mm) and evapotranspiration (mm) data were obtained from the Hong Kong Observatory50 in the period of 1981 to 2010 to compute the three variables. E was then computed as 0.261 and was input into Eq. (4) to derive the ground-truth AGB of individual stems.
The AGB models developed by Xu et al.46 were conducted in the subtropical mixed-species moist forest in southern part of China. Two allometric models from the study were selected to represent the subtropical AGB model. Model 3 (Eq. (6)) assumed tree height is available (by extracting from LiDAR):

$$AGB=text{exp}(-2.334+2.118text{ln}(DBH)+0.5436text{ln}(H)+0.5953text{ln}(rho ))$$
(6)

Meanwhile, an alternative allometric model would be used by assuming tree height was not available, which is the Model 4 (Eq. (7)) of this study:

$$AGB=text{exp}(-1.8226+2.4105text{ln}left(DBHright)+0.5781text{ln}(rho ))$$
(7)

Nichol and Sarker47 developed a local allometric model for Hong Kong by harvesting 75 trees from 15 dominant species of Hong Kong. DBH and H within 50 circular sample plots from a variety of tree stands were measured, which were then used to establish allometric model with field measured AGB. The best allometric model was a model using DBH as the sole parameter and thus selected as the Model 5 (Eq. (8)) of this study:

$$AGB=text{exp}(-1.8226+2.4105text{ln}left(DBHright)+0.5781text{ln}(rho ))$$
(8)

Stage 2: LiDAR AGB model derivation
The airborne LiDAR data used in this study was captured by Optech Gemini ALTM Airborne Laser Terrain Mapper and acquired by The Government of the Hong Kong Special Administrative Region from December 1st, 2010 to January 8th, 2011. A total of 5575 ground truth points were generated, with horizontal accuracy of 0.294 m (95% confidence interval (C.I.)). Vertical accuracy was also assessed against the orthometric heights of Hong Kong Principal Datum (HKPD), the average vertical accuracy was 0.1 m (95% C.I.). Multiple returns were recorded per pulse up to four range measurements (i.e., first, second third and last). The LiDAR acquisition parameters are shown in Table 3.
Table 3 The LiDAR acquisition parameters.
Full size table

Prior to generation of the plot metrics, the LiDAR data was pre-processed by creating the ground TIN by extracting only the ground returns. The extraction and processing were performed in ArcMap 10.5 with LAStools extension (https://rapidlasso.com/lastools/) and the FUSION 3.7 (http://forsys.cfr.washington.edu/fusion/fusionlatest.html). The canopy surface was defined using all non-ground returns with height above 2.0 m. The reason of choosing 2.0 m as the threshold was to avoid canopy returns to be mixed with ground returns51. Moreover, ‘all returns’ were used instead of the ‘first returns’, since the former provided more information on the lower canopies or understory51, while over 50% of returned points in our study area were classified as medium or low vegetation. The ground TIN was subtracted from the canopy surface to compute the normalized height value of each canopy point. The LiDAR metrics were then derived from the normalized height point cloud.
To be compatible with the LiDAR dataset and to facilitate the plot delineation procedure, the entire 1 ha study area was divided into circular plots with three plot sizes: (1) twenty-five 10 m radius plots (16,182 stems), (2) one-hundred 5 m radius plot (15,538 stems) and (3) four-hundred 2.5 m radius plots (15,100 stems) as shown in Fig. 4a–d. Circular plots were considered more favorable than rectangular or square plots, since the periphery-to-area ratio was the smallest and thus minimized the number of edge trees52.
Figure 4

The (a) 1 ha rectangular plot clipped into (b) twenty-five 10 m radius circular plots; (c) one-hundred 5 m radius plots; and (d) four-hundred 2.5 m radius plots respectively; for the LiDAR metrics derivation. The maps are generated by Authors using ArcMap version 10.5 (https://desktop.arcgis.com/en/arcmap/).

Full size image

The 59 plot metrics, under the descriptive, height, intensity and canopy cover categories, were derived by the ‘cloudmetrics’ function in FUSION version 3.7. The LiDAR metrics, and its log-transformed metrics were input into stepwise regression model as independent predictors of AGB. Significant predictors were selected (F  1.0 were removed) into the regression model. Three sets of regression models were generated (Table 4) and the allometric models tended to be linear and normal after logarithmic transformation18,19.
Table 4 The input variables for the three regression models.
Full size table

Observing the normal Q–Q plot (Fig. 5a) of the dependent variable (i.e., AGB) and the Kolmogorov–Smirnov statistic (d = 0.216, p  0.200) indicated a normal distribution (Fig. 5b) and which became the Model II. The further log-transformation into Model III did not indicate significant improvement and thus Model II in various plot sizes were to be further explored.
Figure 5

The normal Q–Q plot, in 10 m radius plot size, of (a) raw AGB and (b) log-AGB. After logarithmic transformation, the AGB (dependent variable) was normalized.

Full size image

Assumption tests including test on normality, homoscedasticity and absence of multi-collinearity were conducted. The Normal P–P plot, scatter plots on residuals, ‘Tolerance’ index and Variance Inflation Factor (VIF) were applied to detect the violation of assumptions. If the ‘Tolerance’ index is smaller than 0.1, or VIF is greater than 10, it indicates a significance chance of collinearity53.
Stage 3: model evaluation and validation
AGB regression models were to be evaluated in this stage. The Model R2, Adjusted R2, Mean-absolute-deviation (MAE) and Root-mean-squared-error (RMSE) were reported to indicate the explanatory power of the model. R2 showed the amount of explained variance by the model, MAE was the average magnitude of error without considering the direction (Eq. (9)). RMSE (Eq. (10)) was the square root of the averaged squared residual and being sensitive to outliers (i.e., larger error) as the errors are squared. The RMSE would be reported in the unit of kg/ha.

$$MAE =frac{sum_{i=1}^{n}left|(widehat{y}-y)right|}{n}$$
(9)

$$RMSE= sqrt[]{frac{{{sum }_{i=1}^{n}(widehat{y}-y)}^{2}}{n}}$$
(10)

(widehat{text{y}}) is the ith estimate for AGB of each plot, y is the ith observation of that plot, divided by the (n), denoting the sample size.
However, as the regression model was built with limited sample plots, it might subject to model overfitting. Bootstrapping was adopted to assess statistical accuracy in terms of the confidence intervals54. Ultimately, it provides a robust estimation of the standard errors, confidence intervals for estimates, including the model regression coefficient, mean and correlation coefficients. The model uncertainty of this study was assessed by 1000 runs of bootstrapping. The 95% “Bias-corrected and accelerated (BCa) confidence interval (C.I.)” on the beta coefficient of model predictors was reported.
Leave-one-out cross validation (LOOCV) was utilized for model validation. The model would be trained by all data points except the one being left out for validation55. The Predicted Residual Error Sum of Squares, PRESS, was the sum of the ‘squared deleted residuals’ (SSDR) of the n−1 observation. Predicted R2 was computed by dividing PRESS by the total sum of square residual (SSTO) expressed in Eq. (11). The RMSE of the CV model was also reported as Eq. (12). The RMSE of the CV model without overfitting shall be approximate to that of the original model.

$${R}_{pred}^{2}=1-frac{PRESS}{SSTO}$$
(11)

$$C{V}_{RMSE}= sqrt[]{frac{PRESS}{d.f.}},$$
(12)

d.f. stands for degree of freedom (i.e., d.f. = 21). These model calibration and validation work were conducted in IBM SPSS Statistics version 24. More

1.
Devendra, C. & Thomas, D. Smallholder farming systems in Asia. Agric. Syst. 71, 17–25 (2002).
Article  Google Scholar 
2.
Herrero, M. et al. Smart investments in sustainable food production: revisiting mixed crop-livestock systems. Science 327, 822–825 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Wright, I. A. et al. Integrating crops and livestock in subtropical agricultural systems. J. Sci. Food Agric. 92, 1010–1015 (2011).
PubMed  Article  CAS  Google Scholar 

4.
Halstead, P. Pastoralism or household herding? Problems of scale and specialization in early Greek animal husbandry. World Archaeol. 28, 20–42 (1996).
Article  Google Scholar 

5.
Bogaard, A. et al. Crop manuring and intensive land management by Europe’s first farmers. Proc. Natl. Acad. Sci. 110, 12589–12594 (2013).
ADS  CAS  PubMed  Article  Google Scholar 

6.
Altieri, M. A., Nicholls, C. I., Henao, A. & Lana, M. A. Agroecology and the design of climate change-resilient farming systems. Agron. Sustain. Dev. 35, 869–890 (2015).
Article  Google Scholar 

7.
Garrett, R. D. et al. Drivers of decoupling and recoupling of crop and livestock systems at farm and territorial scales. Ecol. Soc. 25, 24 (2020).
Article  Google Scholar 

8.
Verhoeven, J. T. A., Arheimer, B., Yin, C. & Hefting, M. M. Regional and global concerns over wetlands and water quality. Trends Ecol. Evol. 21, 96–103 (2006).
PubMed  Article  Google Scholar 

9.
Liu, J. et al. A high-resolution assessment on global nitrogen flows in cropland. Proc. Natl. Acad. Sci. 107, 8035–8040 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

10.
Macdonald, J. M., & Mcbride, W. D. The transformation of U.S. livestock agriculture: scale, efficiency, and risks (2009).

11.
Gerber, P. J. et al. Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (2013).

12.
Lin, B. B. Resilience in agriculture through crop diversification: adaptive management for environmental change. Bioscience 61, 183–193 (2011).
Article  Google Scholar 

13.
Gaudin, A. C. M. et al. Increasing crop diversity mitigates weather variations and improves yield stability. PLoS ONE 10, e0113261 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

14.
Peterson, C. A., Eviner, V. T. & Gaudin, A. C. M. Ways forward for resilience research in agroecosystems. Agric. Syst. 162, 19–27 (2018).
Article  Google Scholar 

15.
Bowles, T. M. et al. Long-term evidence shows that crop-rotation diversification increases agricultural resilience to adverse growing conditions in North America. One Earth 2, 284–293 (2020).
Article  Google Scholar 

16.
Lobell, D. B. & Field, C. B. Global scale climate-crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2, 014002 (2007).
ADS  Article  Google Scholar 

17.
Gornall, J. et al. Implications of climate change for agricultural productivity in the early twenty-first century. Philos. Trans. R. Soc. B 365, 2973–2989 (2010).
Article  Google Scholar 

18.
Osborne, T. M. & Wheeler, T. R. Evidence for a climate signal in trends of global crop yield variability over the past 50 years. Environ. Res. Lett. 8, 024001 (2013).
ADS  Article  Google Scholar 

19.
United Nations. Population division of the department of economic and social affairs of the United Nations: world population prospects. https://population.un.org/wpp/DataQuery/ (2019).

20.
Schmidhuber, J. & Tubiello, F. N. Global food security under climate change. Proc. Natl. Acad. Sci. 104, 19703–19708 (2007).
ADS  CAS  PubMed  Article  Google Scholar 

21.
Bullock, J. M. et al. Resilience and food security: rethinking an ecological concept. J. Ecol. 105, 880–884 (2017).
Article  Google Scholar 

22.
Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 1–9 (2018).
CAS  Article  Google Scholar 

23.
de Moraes, A. et al. Integrated crop-livestock systems in the Brazilian subtropics. Eur. J. Agron. 57, 4–9 (2014).
Article  Google Scholar 

24.
Niles, M. T., Garrett, R. D. & Walsh, D. Ecological and economic benefits of integrating sheep into viticulture production. Agron. Sustain. Dev. 38, 1–11 (2018).
Article  Google Scholar 

25.
Companhia Nacional de Abastecimento (CONAB). Safra brasileira de grãos: Tabela de levantamento. https://www.conab.gov.br/info-agro/safras/graos (2020).

26.
Empresa Brasileira de Pesquisa Agropecuária (Embrapa). ILPF em números. https://ainfo.cnptia.embrapa.br/digital/bitstream/item/158636/1/2016-cpamt-ilpf-em-numeros.pdf (2016).

27.
Garrett, R. D. et al. Social and ecological analysis of commercial integrated crop livestock systems: current knowledge and remaining uncertainty. Agric. Syst. 155, 136–146 (2017).
Article  Google Scholar 

28.
Bell, L. W. & Moore, A. D. Integrated crop-livestock systems in Australian agriculture: trends, drivers and implications. Agric. Syst. 111, 1–12 (2012).
Article  Google Scholar 

29.
Sulc, R. M. & Franzluebbers, A. J. Exploring integrated crop-livestock systems in different ecoregions of the United States. Eur. J. Agron. 57, 21–30 (2014).
Article  Google Scholar 

30.
Carvalho, P. C. F. et al. Animal production and soil characteristics from integrated crop-livestock systems: toward sustainable intensification. J. Anim. Sci. 96, 3513–3525 (2018).
PubMed  PubMed Central  Article  Google Scholar 

31.
Russelle, M. P., Entz, M. H. & Franzluebbers, A. J. Reconsidering integrated crop-livestock systems in North America. Agron. J. 99, 325–334 (2007).
Article  Google Scholar 

32.
Carvalho, P. C. F. et al. Managing grazing animals to achieve nutrient cycling and soil improvement in no-till integrated systems. Nutr. Cycl. Agroecosyst. 88, 259–273 (2010).
Article  Google Scholar 

33.
Oliveira, C. A. O. et al. Comparison of an integrated crop-livestock system with soybean only: economic and production responses in southern Brazil. Renew. Agric. Food Syst. 29, 230–238 (2013).
Article  Google Scholar 

34.
Ryschawy, J., Choisis, N., Choisis, J. P., Joannon, A. & Gibon, A. Mixed crop-livestock systems: an economic and environmental-friendly way of farming?. Animal 6, 1722–1730 (2012).
CAS  PubMed  Article  Google Scholar 

35.
Peterson, C. A., Bell, L. W., Carvalho, P. C. F. & Gaudin, A. C. M. Resilience of an integrated crop–livestock system to climate change: a simulation analysis of cover crop grazing in southern Brazil. Front. Sustain. Food Syst. 4, 604099 (2020).
Article  Google Scholar 

36.
Chávez, L. F. et al. Diversidade metabólica e atividade microbiana no solo em sistema de integração lavoura-pecuária sob intensidades de pastejo. Pesq. Agropec. Bras. 46, 1254–1261 (2011).
Article  Google Scholar 

37.
Peterson, C. A. et al. Winter grazing does not affect soybean yield despite lower soil water content in a subtropical crop-livestock system. Agron. Sustain. Dev. 39, 26 (2019).
CAS  Article  Google Scholar 

38.
Assmann, J. M. et al. Soil carbon and nitrogen stocks and fractions in a long-term integrated crop-livestock system under no-tillage in southern Brazil. Agric. Ecosyst. Environ. 190, 52–59 (2014).
CAS  Article  Google Scholar 

39.
Peyraud, J. L. & Peeters, A. The role of grassland based production system in the protein security. Grassland Science in Europe – The multiple roles of grassland in the European bioeconomy 21, 29–43 (2016).
Google Scholar 

40.
Harrison, G. W. Stability under environmental stress: resistance, resilience, persistence, and variability. Am. Nat. 113, 659–669 (1979).
MathSciNet  Article  Google Scholar 

41.
Lehman, C. L. & Tilman, D. Biodiversity, stability, and productivity in competitive communities. Am. Nat. 156, 534–552 (2000).
PubMed  Article  Google Scholar 

42.
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

43.
Lightfoot, C. W. F., Dear, K. B. G. & Mead, R. Intercropping sorghum with cowpea in dryland farming systems in Botswana. II. Comparative stability of alternative cropping systems. Exp. Agric. 23, 435–442 (1987).
Article  Google Scholar 

44.
Li, M., Peterson, C. A., Tautges, N. E., Scow, K. M. & Gaudin, A. C. M. Yields and resilience outcomes of organic, cover crop, and conventional practices in a Mediterranean climate. Sci. Rep. 9, 12283 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

45.
Nielsen, D. C. & Vigil, M. F. Wheat yield and yield stability of eight dryland crop rotations. Agron. J. 110, 594–601 (2018).
Article  Google Scholar 

46.
Temesgen, T., Keneni, G., Sefera, T. & Jarso, M. Yield stability and relationships among stability parameters in faba bean (Vicia faba L.) genotypes. Crop J. 3, 258–268 (2015).
Article  Google Scholar 

47.
Finlay, K. W. & Wilkinson, G. N. The analysis of adaptation in a plant-breeding programe. Aust. J. Agric. Res. 14, 742–754 (1963).
Article  Google Scholar 

48.
Raun, W. R., Barreto, H. J. & Westerman, R. L. Use of stability analysis for long-term soil fertility experiments. Agron. J. 85, 159–167 (1993).
Article  Google Scholar 

49.
Williams, A. et al. Soil water holding capacity mitigates downside risk and volatility in US rainfed maize: time to invest in soil organic matter?. PLoS ONE 11, e0160974 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Williams, A. et al. A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability. Sci. Rep. 8, 8467 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

51.
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

52.
Craven, D. et al. Multiple facets of biodiversity drive the diversity-stability relationship. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-018-0647-7 (2018).
Article  PubMed  Google Scholar 

53.
Bennett, J. A. et al. Resistance of soil biota and plant growth to disturbance increases with plant diversity. Ecol. Lett. https://doi.org/10.1111/ele.13408 (2019).
Article  PubMed  Google Scholar 

54.
Instituto Nacional de Meteorologia (INMET). Normais climatológicas do Brasil. https://portal.inmet.gov.br/normais (2020).

55.
Soil Survey Staff. Soil Taxonomy: a basic system of soil classification for making and interpreting soil surveys (USDA Natural Resources Conservation Service, 1999).

56.
Comissão de Química e Fertilidade do Solo – RS/SC (CQFS RS/SC). Manual de adubação e calagem para os Estados do Rio Grande do Sul e Santa Catarina (Sociedade Brasileira de Ciência do Solo, 2004).

57.
Barthram, G. T. (1985). Experimental techniques: The HFRO sward stick. In Alcok, M. M. The Hill farming research organization Biennial report 1984/1985, pp. 29–30 (1985).

58.
Mott, G. O., & Lucas, H. L. The design, conduct, and interpretation of grazing trials on cultivated and improved pastures. In Proceedings of the international grassland congress, pp. 1380–1386 (1952).

59.
Klingman, D. L., Miles, S. R. & Mott, G. O. The Cage Method for determining consumption and yield of pasture herbage. Agron. J. 35, 739–746 (1943).
Article  Google Scholar 

60.
Nunes, P. A. A. et al. Grazing intensity determines pasture spatial heterogeneity and productivity in an integrated crop-livestock system. Grassl. Sci. 65, 49–59 (2019).
Article  Google Scholar 

61.
van Zanten, H. H. E., Mollenhorst, H., Klootwijk, C. W., van Middelaar, C. E. & Boer, I. J. M. Global food supply: land use efficiency of livestock systems. Int. J. Life Cycle Assess. 21, 747–758 (2016).
Article  CAS  Google Scholar 

62.
Gil, J. D. B. et al. Tradeoffs in the quest for climate smart agricultural intensification in Mato Grosso, Brazil. Environ. Res. Lett. 13, 064025 (2018).
ADS  Article  Google Scholar 

63.
National Research Council. Growth and Body Reserves. In: Nutrient Requirements of Beef Cattle, pp. 22–39 (NRC, 2016).

64.
USDA. Agricultural research service of the United States Department of Agriculture: FoodData Central. https://fdc.nal.usda.gov/fdc-app.html#/food-details/174270/nutrients (2019).

65.
Banco Central do Brasil. Correção de valores pela caderneta de poupança. https://www.bcb.gov.br (2020).

66.
Agrolink. Cotações dos produtos agropecuários: Bovinos. https://www.agrolink.com.br/cotacoes/historico/rs/boi-gordo-kg-vivo-1kg (2019).

67.
Agrolink. Cotações dos produtos agropecuários: Soja. https://www.agrolink.com.br/cotacoes/historico/rs/soja-em-grao-sc-60kg (2019).

68.
Banco Central do Brasil. Correção de valores pelo Índice Geral de Preços do Mercado (IGP-M/FGV). https://www3.bcb.gov.br/CALCIDADAO/publico/corrigirPorIndice.do?method=corrigirPorIndice (2020).

69.
International Monetary Fund. Exchange rate archives by month. https://www.imf.org/external/np/fin/data/param_rms_mth.aspx (2019).

70.
Companhia Nacional de Abastecimento (CONAB). Planilhas de custos de produção – Séries históricas. https://www.conab.gov.br/info-agro/custos-de-producao/planilhas-de-custo-de-producao/itemlist/category/414-planilhas-de-custos-de-producao-series-historicas (2019).

71.
R Core Team. R: a language and environment for statistical computing (2018).

72.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

73.
Lenth, R. emmeans: estimated marginal means, aka least-squares means. R package version 1.3.1. (2018).

74.
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Article  Google Scholar 

75.
Fox, J. & Weisberg, S. An {R} companion to applied regression (2011).

76.
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometrical J. 50, 346–363 (2008).
MathSciNet  MATH  Article  Google Scholar 

77.
de Mendiburu, F. agricolae: statistical procedures for agricultural research. R package version 1.2–8. (2017).

78.
Kunrath, T. R., Carvalho, P. C. F., Cadenazzi, M., Bredemeier, C. & Anghinoni, I. Grazing management in an integrated crop-livestock system: soybean development and grain yield. Rev. Ciência Agronômica 46, 645–653 (2015).
Google Scholar 

79.
Peterson, C. A., Deiss, L. & Gaudin, C. M. Commercial integrated crop-livestock systems achieve comparable crop yields to specialized production systems: a meta-analysis. PLoS ONE 15, e0231840 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

80.
Franzluebbers, A. J. & Stuedemann, J. A. Soil physical responses to cattle grazing cover crops under conventional and no tillage in the Southern Piedmont USA. Soil Tillage Res. 100, 141–153 (2008).
Article  Google Scholar 

81.
Tracy, B. F. & Zhang, Y. Soil compaction, corn yield response, and soil nutrient pool dynamics within an integrated crop-livestock system in Illinois. Crop Sci. 48, 1211–1218 (2008).
CAS  Article  Google Scholar 

82.
Schmitt, J. Nematoides fitoparasitas e de vida livre como bioindicadores de qualidade do solo de um sistema de integração lavoura-pecuária (Universidade Federal de Santa Maria, 2019).

83.
Bronick, C. J. & Lal, R. Soil structure and management: a review. Geoderma 124, 3–22 (2005).
ADS  CAS  Article  Google Scholar 

84.
Ingram, L. J. et al. Grazing impacts on soil carbon and microbial communities in a mixed-grass ecosystem. Soil Sci. Soc. Am. J. 72, 939–948 (2008).
ADS  CAS  Article  Google Scholar 

85.
Rosenfeld, J. S. Functional redundancy in ecology and conservation. Oikos 98, 156–162 (2002).
Article  Google Scholar 

86.
Oliver, T. H. et al. Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684 (2015).
PubMed  Article  Google Scholar 

87.
Noy-Meir, I. et al. Stability of grazing systems: an application of predator-prey graphs. J. Ecol. 63, 459–481 (1975).
Article  Google Scholar 

88.
Franzluebbers, A. J. et al. Well-managed grazing systems: a forgotten hero of conservation. J. Soil Water Conserv. 67, 100A-104A (2012).
Article  Google Scholar 

89.
Schuster, M. Z. et al. Grazing intensities affect weed seedling emergence and the seed bank in an integrated crop-livestock system. Agric. Ecosyst. Environ. 232, 232–239 (2016).
Article  Google Scholar 

90.
Kunrath, T. R. et al. Sward height determines pasture production and animal performance in a long-term soybean-beef cattle integrated system. Agric. Syst. 177, 102716 (2020).
Article  Google Scholar 

91.
Mott, G. O. Grazing pressure and the measurement of pasture production. In: Proceedings of the International Grassland Congress, pp. 606–611 (1960).

92.
Maraschin, G. E. et al. Native pasture, forage on offer and animal response. In: Proceedings of the international grassland congress, pp. 26–27 (1997).

93.
de Souza Filho, W. et al. Mitigation of enteric methane emissions through pasture management in integrated crop-livestock systems: trade-offs between animal performance and environmental impacts. J. Clean. Prod. 213, 968–975 (2019).

94.
Soussana, J.-F. & Lemaire, G. Coupling carbon and nitrogen cycles for environmentally sustainable intensification of grasslands and crop-livestock systems. Agric. Ecosyst. Environ. 190, 9–17 (2014).
CAS  Article  Google Scholar 

95.
Food and Agriculture Organization of the United Nations (FAO). Trade and markets: the FAO meat price index. http://www.fao.org/economic/est/est-commodities/meat/en/ (2020).

96.
Center for Advanced Studies on Applied Economics (CEPEA). Agricultural prices: Soybean. https://www.cepea.esalq.usp.br/en/indicator/soybean.aspx (2020).

97.
Center for Advanced Studies on Applied Economics (CEPEA). Agricultural prices: Cattle. https://www.cepea.esalq.usp.br/en/indicator/cattle.aspx (2020). More

1.
Aitken, S. N., Yeaman, S., Holliday, J. A., Wang, T. & Curtis-McLane, S. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1, 95–111 (2008).
Article  Google Scholar 
2.
Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to Quaternary climate change. Science 292, 673–679 (2001).
CAS  Article  Google Scholar 

3.
Ikeda, D. H. et al. Genetically informed ecological niche models improve climate change predictions. Glob. Change Biol. 23, 164–176 (2017).
Article  Google Scholar 

4.
Maguire, K. C., Shinneman, D. J., Potter, K. M. & Hipkins, V. D. Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change. Syst. Biol. https://doi.org/10.1093/sysbio/syy017 (2018).

5.
Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H.-H. & Warren, D. Niche estimation above and below the species level. Trends Ecol. Evol. 34, 260–273 (2019).
Article  Google Scholar 

6.
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).
CAS  Article  Google Scholar 

7.
Wang, T., O’Neill, G. A. & Aitken, S. N. Integrating environmental and genetic effects to predict responses of tree populations to climate. Ecol. Appl. 20, 153–163 (2010).
CAS  Article  Google Scholar 

8.
Radeloff, V. C. et al. The rise of novelty in ecosystems. Ecol. Appl. 25, 2051–2068 (2015).
Article  Google Scholar 

9.
Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).
Article  Google Scholar 

10.
Aitken, S. N. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388 (2013).
Article  Google Scholar 

11.
Vitt, P., Havens, K., Kramer, A. T., Sollenberger, D. & Yates, E. Assisted migration of plants: changes in latitudes, changes in attitudes. Biol. Conserv. 143, 18–27 (2010).
Article  Google Scholar 

12.
Williams, M. I. & Dumroese, R. K. Preparing for climate change: forestry and assisted migration. J. For. 111, 287–297 (2013).
Google Scholar 

13.
Keller, S. R., Levsen, N., Olson, M. S. & Tiffin, P. Local adaptation in the flowering-time gene network of balsam poplar, Populus balsamifera L. Mol. Biol. Evol. 29, 3143–3152 (2012).
CAS  Article  Google Scholar 

14.
Keller, S. R., Chhatre, V. E. & Fitzpatrick, M. C. Influence of range position on locally adaptive gene–environment associations in Populus flowering time genes. J. Hered. 109, 47–58 (2018).
CAS  Article  Google Scholar 

15.
Chuine, I. Why does phenology drive species distribution? Phil. Trans. R. Soc. B 365, 3149–3160 (2010).
Article  Google Scholar 

16.
Morin, X., Viner, D. & Chuine, I. Tree species range shifts at a continental scale: new predictive insights from a process-based model. J. Ecol. 96, 784–794 (2008).
Article  Google Scholar 

17.
Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib. 13, 252–264 (2007).
Article  Google Scholar 

18.
Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).
Article  Google Scholar 

19.
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).
Article  Google Scholar 

20.
Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).
Article  Google Scholar 

21.
VanDerWal, J. et al. Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nat. Clim. Change 3, 239–243 (2013).
Article  Google Scholar 

22.
Shaw, R. G. From the past to the future: considering the value and limits of evolutionary prediction. Am. Nat. 193, 1–10 (2018).
Article  Google Scholar 

23.
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecol. Lett. 8, 461–467 (2005).
Article  Google Scholar 

24.
Yun, J. et al. Influence of winter precipitation on spring phenology in boreal forests. Glob. Change Biol. 24, 5176–5187 (2018).
Article  Google Scholar 

25.
Fu, Y. H. et al. Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes. Glob. Change Biol. 20, 3743–3755 (2014).
Article  Google Scholar 

26.
Peterson, M. L., Doak, D. F. & Morris, W. F. Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Glob. Change Biol. 25, 775–793 (2019).
Article  Google Scholar 

27.
Atkins, K. E. & Travis, J. M. J. Local adaptation and the evolution of species’ ranges under climate change. J. Theor. Biol. 266, 449–457 (2010).
CAS  Article  Google Scholar 

28.
Chen, I.-C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).
CAS  Article  Google Scholar 

29.
Groom, Q. J. Some poleward movement of British native vascular plants is occurring, but the fingerprint of climate change is not evident. PeerJ 1, e77 (2013).
Article  Google Scholar 

30.
Olson, M. S. et al. The adaptive potential of Populus balsamifera L. to phenology requirements in a warmer global climate. Mol. Ecol. 22, 1214–1230 (2013).
CAS  Article  Google Scholar 

31.
Fitzpatrick, M., Chhatre, V., Soolanayakanahally, R. & Keller, S. Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests. Preprint at https://doi.org/10.22541/au.159863198.86187354 (2020).

32.
Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).
CAS  Article  Google Scholar 

33.
Keller, S. R. et al. Climate-driven local adaptation of ecophysiology and phenology in balsam poplar, Populus balsamifera L. (Salicaceae). Am. J. Bot. 98, 99–108 (2011).
Article  Google Scholar 

34.
Alberto, F. J. et al. Potential for evolutionary responses to climate change—evidence from tree populations. Glob. Change Biol. 19, 1645–1661 (2013).
Article  Google Scholar 

35.
Little, E. L. Atlas of United States Trees (US Dept of Agriculture, Forest Service, 1971).

36.
Romero-Lankao, P. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects (eds Barros, V. R. et al.) 1439–1498 (Cambridge Univ. Press, 2014).

37.
Fetter, K. C., Gugger, P. F. & Keller, S. R. in Comparative and Evolutionary Genomics of Angiosperm Trees (eds Groover, A. & Cronk, Q.) 303–333 (Springer International, 2017); https://doi.org/10.1007/7397_2016_19

38.
Soolanayakanahally, R. Y., Guy, R. D., Silim, S. N., Drewes, E. C. & Schroeder, W. R. Enhanced assimilation rate and water use efficiency with latitude through increased photosynthetic capacity and internal conductance in balsam poplar (Populus balsamifera L.). Plant Cell Environ. 32, 1821–1832 (2009).
CAS  Article  Google Scholar 

39.
Chhatre, V. E. et al. Climatic niche predicts the landscape structure of locally adaptive standing genetic variation. Preprint at https://doi.org/10.1101/817411 (2019).

40.
Günther, T. & Coop, G. Robust identification of local adaptation from allele frequencies. Genetics 195, 205–220 (2013).
Article  Google Scholar 

41.
Frichot, E., Schoville, S. D., Bouchard, G. & François, O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol. 30, 1687–1699 (2013).
CAS  Article  Google Scholar 

42.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Article  Google Scholar 

43.
Frichot, E. & François, O. LEA: an R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).
Article  Google Scholar 

44.
Goudet, J. & Jombart, T. hierfstat: Estimation and tests of hierarchical F-statistics. R package version 0.04-22 (2015).

45.
Manion, G., Lisk, M., Nieto-Lugilde, D., Mokany, K. & Fitzpatrick, M. gdm: Generalized dissimilarity modeling. R package version 1.3.11 (2018).

46.
Hijmans, R. J. geosphere: Spherical trigonometry. R package version 1.5-10 (2019).

47.
Gougherty, A. V., Chhatre, V. E., Keller, S. R. & Fitzpatrick, M. C. Contemporary range position predicts the range-wide pattern of genetic diversity in balsam poplar (Populus balsamifera L.). J. Biogeogr. 47, 1246–1257 (2020).
Article  Google Scholar 

48.
Vallejos, R., Osorio, F. & Bevilacqua, M. Spatial Relationships Between Two Georeferenced Variables: With Applications in R (Springer, 2018). More

Experimental design and data collection
The data came from a highly replicated field trial (The Farm Scale Evaluations, FSE) designed to test whether the adoption of genetically modified, herbicide-tolerant (GMHT) crops would have led to significant changes in UK farmland biodiversity by comparison with the conventional crops and herbicide management then used23. The FSE sampled the biodiversity in and around 187 spring sown crop fields across the UK using a standard methodology (Supplementary Fig. S5). Post-hoc analyses demonstrated that the trial design had more than sufficient statistical power to test the null hypothesis of no change in biodiversity31 and indicated that the species nodes were fully sampled32,33. Full details of the experimental design and protocols for data collection can be found in Champion et al. 34 and Bohan et al. 35, which we briefly detail here.
The count data for the carabids, weed seedbank and seed rain, and gastropod molluscs came from 66 spring-sown beet, 55 spring maize and 66 spring oilseed rape fields. The fields were distributed across the UK (Supplementary Fig. S5) and each field was sampled for one cropping year23 between 2000 and 2004. The great majority of fields were below 15 Ha in size, and typically varied between 2 and 10 Ha depending upon the crop34,35. The trial used a half-field design, and each field was divided in two with one half sown with a conventional crop and the other a GMHT variety of the same crop. Data from both treatments are used for the analyses presented in this study, giving a total of 374 half-fields.
The pitfall trapping of soil-surface-active invertebrates employed the method described by Brooks et al. 36. Pitfall traps, spaced at 2, 8 and 32 m points along 4 transects running from the field-edge into the cropped area, were opened in the spring (April ⁄ May) and summer (June ⁄ July) and in late summer (August). The weed seedbank was measured by taking 8 soil samples in each field, at 2 and 32 m points on four transects, prior to the crops being sown and after harvest37. The soil samples in each half-field were bulked, placed in germination trays in a greenhouse and the seeds they contained allowed to germinate over the course of 18 weeks37,38. The viable weed seeds available to the carabids were measured as the seed rain onto the soil surface from the weed plants in the field. Eight seed rain traps, placed at 2 and 32 m along 4 of the transects, sampled the rain of weed seed throughout the growing season37. Gastropods were sampled using baited refuge traps at the same positions used for the pitfall trapping in late April and in early August for spring oilseed rape, and in May and August for maize and beet36. All carabids, molluscs and weed seeds sampled were identified as species and counted. For the carabids and molluscs, counts were then pooled, by summation, to give a year-total estimate for each species in each half-field, from which a relative abundance of each species was calculated by dividing the count of that species by the total count for the group of carabids and molluscs, respectively. Total, monocotyledon and dicotyledon weed species counts were pooled, by summation, to give an estimate of these weed seedbanks before (t0) and after (t1) the crop was sown and harvested, respectively.
Network construction
The species sample data were supplemented with carabid dietary information recovered from the literature. We assumed that where a carabid species, A, was noted to consume a resource species, B, in the literature and both these species were present in a half-field, then this trophic interaction was realised6,39,40,41. To standardise the (trophic interaction) sampling effort across all carabid species and reflect the generalist nature of carabid consumers it was assumed, following Honek et al. 42, that each carabid species would consume the same resources as other carabid species within the same genus39,41. A similar generalisation was made at the resource level: where a particular species of carabid was recorded to feed upon one species of gastropod or weed in the literature, we assumed that this carabid would also consume other resource species of the same genus43. This assumption of generalisation was done to reduce the numbers of artefactually isolated species within each network, and to avoid the bias towards more highly-studied species44,45.
The interaction frequency for each realised link between consumer and resource was calculated as the multiplicative product of the consumer and resource relative abundances, under the assumption that species abundance is a predictor of the strength of interaction between species46. Applying a frequency weighting to the links in this manner integrates a quantitative estimate of the interaction strength between species in each network, enriching the simple binary network structure built from presence/absence data. Future consideration of carabid interaction strength prey may include traits, such as body size and mandible type47. Following construction of the carabid-weed seed (herbivore) and carabid-gastropod (carnivore) layers, each carabid species was assigned to an empirical trophic group based upon their role in each replicate multilayer network. Carabid nodes linked only to gastropods were assigned to the ‘carnivore’ grouping, while those consuming only weeds were ‘herbivores’, and ‘omnivores’ were generalist species linked to both gastropods and weeds. Thus, a particular carabid species might be a carnivore, an herbivore or an omnivore in different replicate multilayer networks. Some species may therefore appear in different classifications across the collection of networks; a particular carabid species may be a weed consuming herbivore in some networks, a gastropod carnivore in others and an omnivore in yet other networks.
Regulation of the weeds in the seedbank
Regulation was calculated from the change in total, monocotyledon and dicotyledon seedbank counts between t0 and t1, as:

$${rm{regulation}} = {mathrm{ln}}left( {frac{{t_1 + 0.5}}{{t_0 + 0.5}}} right)$$
(1)

so that for each half-field three metrics of regulation for the total, dicotyledon and monocotyledon seedbanks were calculated. Negative values of this metric indicate a decline in the size of the weed seedbank from t0 to t1. Following Bohan et al. 14, negative relationships between the metric of regulation and carabid counts are indicative of seedbank regulation by these beetles.
Statistics and reproducibility
All analysis was done in R (R Core Team 2019) using the cheddar48, bipartite49 and vegan50 packages. Network plots were created with the HiveR package51. Interaction frequencies were calculated separately for herbivores, carnivores and omnivores in each network.
Species and link turnover across the collection of multilayer networks were measured using Bray-Curtis dissimilarity in the vegan package50. Each network layer is a realisation of the interactions drawn from the composite network52, with the interactions only being contingent on local species composition and abundances. To assess how species and link turnover changes across the herbivore/carnivore gradient, we used the number of herbivores and carnivores within each network as factor levels with which to categorise the networks (i.e. networks with 1 herbivore, 2 herbivores, 3 herbivores, etc.). We ensured that no single network appeared in more than one group by randomly assigning networks to either their herbivore or carnivore grouping, and then calculated the Bray-Curtis dissimilarity between the herbivore and carnivore groups.
The regression modelling between species and link variables was done using Generalised Linear Mixed-effects Models (GLMM) with Gaussian errors. Each field was treated as a replicate as there was no repeat sampling from any one site23. All analyses were appropriately subjected to tests of normality and behaviour of the model residuals53.
Species richness
GLMMs were used to assess the effect that increasing numbers of gastropod species has on the number of weed species, and the number of herbivorous carabids implicated in each multilayer network. Field identity was nested within crop type (spring-sown beet, spring maize or spring oilseed rape), which was nested within management type (conventional or GMHT) and included as a random effect. The species richness of all groups was log(x+0.5) transformed to conform to normality. The relationship between the number of carabid species acting as herbivores and carnivores in each network was assessed using the same model structure.
Link richness
The relationship between the number of links to plant resources and links to gastropod resources for the omnivorous carabid nodes was assessed using GLMMs, with field identity nested within crop type (spring-sown beet, spring maize or spring oilseed rape), which was nested within management type (conventional or GMHT) and included as a random effect.
Link frequency
Due to the extreme distribution pattern of the carnivore and herbivore predation interaction frequencies, the correlation between these two variables was examined on the log(x + 0.5) scale using LOESS smoothing53.
Weed seed regulation
The relationship between weed seed regulation and the count of herbivores or herbivory interaction frequency was modelled with GLMMs. The count of herbivores and herbivore interaction frequency were log(x+0.5) transformed to attain normality. Field identity was nested within crop type (spring-sown beet, spring maize or spring oilseed rape), which was nested within management type (conventional or GMHT) and included as a random effect.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.
Wesche, K. et al. The Palaearctic steppe biome: a new synthesis. Biodivers. Conserv. 25, 2197–2231 (2016).
Article  Google Scholar 
2.
Pfeiffer, M., Dulamsuren, C., Jäschke, Y. & Wesche, K. Grasslands of China and Mongolia: spatial extent, land use and conservation. In Grasslands of the World (eds Squires, V. R. et al.) 168–196 (CRC Press, Boca Raton, 2018).
Google Scholar 

3.
Squires, V. & Limin, H. North-West China’s rangelands and peoples: facts, figures, challenges and responses. In Towards Sustainable Use of Rangelands in North-West China (eds Squires, V. et al.) 3–18 (Springer, Dordrecht, 2010).
Google Scholar 

4.
Tsvegemed, M., Shabier, A., Schlecht, E., Jordan, G. & Wiehle, M. Evolution of rural livelihood strategies in a remote Sino-Mongolian border area: a cross-country analysis. Sustainability 10, 1011 (2018).
Article  Google Scholar 

5.
Gruschke, A. & Breuer, I. Tibetan Pastoralists and Development: Negotiating the Future of Grassland Livelihoods (Dr. Ludwig Reichert Verlag, Wiesbaden , 2017).
Google Scholar 

6.
Zhao, Y., Liu, Z. & Wu, J. Grassland ecosystem services: a systematic review of research advances and future directions. Landsape Ecol. 35, 793–814 (2020).
Article  Google Scholar 

7.
Han, J. G. et al. Rangeland degradation and restoration management in China. Rangeland J. 30, 233–239 (2008).
Article  Google Scholar 

8.
Harris, R. B. Rangeland degradation on the Qinghai-Tibetan plateau: A review of the evidence of its magnitude and causes. J. Arid Environ. 74, 1–12 (2010).
ADS  CAS  Article  Google Scholar 

9.
Jin, G. & Zhu, J. Case study 8: Northern Xinjiang. In Rangeland Degradation and Recovery in China’s Pastoral Lands (ed. Squires, V. R.) 197–215 (CABI, Wallingford, 2009).
Google Scholar 

10.
Rae, A. China’s agriculture, smallholders and trade: driven by the livestock revolution?. Aust. J. Agr. Resour. Econ. 52, 283–302 (2008).
Article  Google Scholar 

11.
Waldron, S., Brown, C. & Longworth, J. An assessment of China’s approach to grassland degradation and livelihood problems in the pastoral region. In Proceedings of the 5th Annual Conference of the Consortium for Western China Development Studies, Xi’an, China, July 22–24, 2008 (2008).

12.
Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Saccá, M. L., Caracciolo, A. B., Di Lenola, M. & Grenni, P. Ecosystem services provided by soil microorganisms. In Soil Biological Communities and Ecosystem Resilience (eds Lukac, M. et al.) 9–24 (Springer, Cham, 2017).
Google Scholar 

14.
van Eekeren, N., Murray, P. & Smeding, F. Soil biota in Grassland, its ecosystems and the impact of management. In Permanent and Temporary Grassland Plant, Environment and Economy, Proceedings of the 14th International Symposium of the European Grassland Federation, Ghent, Belgium, September 3–5, 2007 (eds. de Vliegher, A. & Carlier, L.) 247–258 (2007).

15.
Brussaard, L. Ecosystem services provided by the soil biota. In Soil Ecology and Ecosystem Services (ed. Wall, D. H.) 45–58 (Oxford University Press, Oxford, 2012).
Google Scholar 

16.
Schnitzer, S. A. et al. Soil microbes drive the classic plant diversity-productivity pattern. Ecology 92, 296–303 (2011).
PubMed  Article  Google Scholar 

17.
van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).
PubMed  Article  Google Scholar 

18.
Chen, C., Chen, H. Y. H., Chen, X. & Huang, Z. Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration. Nat. Commun. 10, 1332 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

19.
Lange, M. et al. Plant diversity drives soil carbon storage by increased soil microbial activity. Nat. Commun. 6, 6707 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Eisenhauer, N. et al. Root biomass and exudates link plant diversity with soil bacterial and fungal biomass. Sci. Rep. 7, 44641 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Taboada, M. A., Rubio, G., Chaneton, E. J., Hatfield, J. L. & Sauer, T. J. Grazing impacts on soil physical, chemical, and ecological properties in forage production systems. In Soil Management: Building a Stable Base for Agriculture (eds Hatfield, J. L. & Sauer, T. J.) 301–320 (American Society of Agronomy, Soil Science Society of America, Madison, WI, 2011).
Google Scholar 

22.
Yan, L., Zhou, G. & Zhang, F. Effects of different grazing intensities on grassland production in China: a meta-analysis. PLoS ONE 8, e81466b (2013).
ADS  Article  CAS  Google Scholar 

23.
Hao, Y. & He, Z. Effects of grazing patterns on grassland biomass and soil environments in China: a meta-analysis. PLoS ONE 14, e0215223 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Bardgett, R. D. & Wardle, D. A. Herbivore-mediated linkages between aboveground and belowground communities. Ecology 84, 2258–2268 (2003).
Article  Google Scholar 

25.
Liu, N., Zhang, Y., Chang, S., Kan, H. & Lin, L. Impact of grazing on soil carbon and microbial biomass in typical steppe and desert steppe of Inner Mongolia. PLoS ONE 7, e36434 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Qi, S. et al. Effects of livestock grazing intensity on soil biota in a semiarid steppe of Inner Mongolia. Plant Soil 340, 117–126 (2011).
MathSciNet  CAS  Article  Google Scholar 

27.
Bardgett, R. D., Keiller, S., Cook, R. & Gilburn, A. S. Dynamic interactions between soil animals and microorganisms in upland grassland soils amended with sheep dung: a microcosm experiment. Soil Biol. Biochem. 30, 531–539 (1998).
CAS  Article  Google Scholar 

28.
Hiltbrunner, D., Schulze, S., Hagedorn, F., Schmidt, M. W. I. & Zimmmermann, S. Cattle trampling alters soil properties and changes soil microbial communities in a Swiss sub-alpine pasture. Geoderma 170, 369–377 (2012).
ADS  CAS  Article  Google Scholar 

29.
Xun, W. et al. Grazing-induced microbiome alterations drive soil organic carbon turnover and productivity in meadow steppe. Microbiome 6, 170 (2018).
PubMed  PubMed Central  Article  Google Scholar 

30.
Zornoza, R. et al. Identification of sensitive indicators to assess the interrelationship between soil quality, management practices and human health. Soil 1, 173–185 (2015).
Article  Google Scholar 

31.
Lv, C. et al. Vegetation responses to fixed stocking densities in highly variable montane pastures in the Chinese Altay. Range Ecol. Manag. 72, 812–821 (2019).
Article  Google Scholar 

32.
Fu, Q., Li, B., Yang, L., Wu, Z. & Zhang, X. Ecosystem services evaluation and its spatial characteristics in Central Asia’s arid regions: a case study in Altay prefecture, China. Sustainability 7, 8335–8353 (2015).
Article  Google Scholar 

33.
Jordan, G. et al. Spatio-temporal patterns of herbage availability and livestock movements: a cross-border analysis in the Chinese–Mongolian Altay. Pastoralism 6, 1–17 (2016).
Article  Google Scholar 

34.
Gee, G. W. & Or, D. Particle-size analysis. In Methods of Soil Analysis (eds Dane, J. H. & Topp, C. G.) 255–294 (Soil Science Society of America, SSSA Book Series, Madison, WI, 2002).
Google Scholar 

35.
Blume, H.-P., Stahr, K. & Leinweber, P. Bodenkundliches Praktikum (Spektrum Akademischer Verlag, Heidelberg, 2011).
Google Scholar 

36.
Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).
CAS  Article  Google Scholar 

37.
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).
CAS  Article  Google Scholar 

38.
Mueller, T., Joergensen, R. G. & Meyer, B. Estimation of soil microbial biomass C in the presence of living roots by fumigation-extraction. Soil Biol. Biochem. 24, 179–181 (1992).
Article  Google Scholar 

39.
Wu, J., Joergensen, R. G., Pommerening, B., Chaussod, R. & Brookes, P. C. Measurement of soil microbial biomass C by fumigation-extraction—an automated procedure. Soil Biol. Biochem. 22, 1167–1169 (1990).
CAS  Article  Google Scholar 

40.
Djajakirana, G., Joergensen, R. G. & Meyer, B. Ergosterol and microbial biomass relationship in soil. Biol. Fertil. Soils. 22, 299–304 (1996).
CAS  Article  Google Scholar 

41.
Wang, Y. & Wesche, K. Vegetation and soil responses to livestock grazing in Central Asian grasslands: a review of Chinese literature. Biodivers. Conserv. 25, 2401–2420 (2016).
Article  Google Scholar 

42.
Sun, J. et al. Verification of the biomass transfer hypothesis under moderate grazing across the Tibetan plateau: a meta-analysis. Plant Soil 20, 634 (2019).
Google Scholar 

43.
Zhou, G. et al. Grazing intensity significantly affects belowground carbon and nitrogen cycling in grassland ecosystems: a meta-analysis. Glob. Chang. Biol. 23, 1167–1179 (2017).
ADS  PubMed  Article  PubMed Central  Google Scholar 

44.
Sun, G. et al. Responses of root exudation and nutrient cycling to grazing intensities and recovery practices in an alpine meadow: an implication for pasture management. Plant Soil 416, 515–525 (2017).
CAS  Article  Google Scholar 

45.
Joergensen, R. G. & Wichern, F. Alive and kicking: why dormant soil microorganisms matter. Soil Biol. Biochem. 116, 419–430 (2018).
CAS  Article  Google Scholar 

46.
Anderson, T.-H. & Domsch, K. H. Application of eco-physiological quotients (qCO2 and qD) on microbial biomasses from soils of different cropping histories. Soil Biol. Biochem. 22, 251–255 (1990).
Article  Google Scholar 

47.
Anderson, T.-H. & Domsch, K. H. Determination of ecophysiological maintenance carbon requirements of soil microorganisms in a dormant state. Biol. Fertil. Soils. 1, 81–89 (1985).
CAS  Article  Google Scholar 

48.
Ingram, L. J. et al. Grazing impacts on soil carbon and microbial communities in a mixed-grass ecosystem. Soil Sci. Soc. Am. J. 72, 939–948 (2008).
ADS  CAS  Article  Google Scholar 

49.
Wu, J. Change in soil microbial biomass and regulating factors in an alpine meadow site on the Qinghai–Tibetan Plateau. Soil Sci. Plant Nutr. 66, 177–194 (2020).
CAS  Article  Google Scholar 

50.
Goenster-Jordan, S., Jannoura, R., Jordan, G., Buerkert, A. & Joergensen, R. G. Spatial variability of soil properties in the floodplain of a river oasis in the Mongolian Altay Mountains. Geoderma 330, 99–106 (2018).
ADS  CAS  Article  Google Scholar 

51.
Aarons, S. R., O’Connor, C. R., Hosseini, H. M. & Gourley, C. J. P. Dung pads increase pasture production, soil nutrients and microbial biomass carbon in grazed dairy systems. Nutr. Cycl. Agroecosyst. 84, 81–92 (2009).
CAS  Article  Google Scholar 

52.
Wachendorf, C. & Joergensen, R. G. Mid-term tracing of 15N derived from urine and dung in soil microbial biomass. Biol. Fertil. Soils. 47, 147–155 (2011).
CAS  Article  Google Scholar 

53.
Zhao, Y. et al. Spatial variability of soil properties affected by grazing intensity in Inner Mongolia grassland. Ecol. Model. 205, 241–254 (2007).
Article  Google Scholar 

54.
Goenster, S., Gründler, C., Buerkert, A. & Joergensen, R. G. Soil microbial indicators across land use types in the river oasis Bulgan sum center, Western Mongolia. Ecol. Indic. 76, 111–118 (2017).
CAS  Article  Google Scholar 

55.
Hamilton, E. W., Frank, D. A., Hinchey, P. M. & Murray, T. R. Defoliation induces root exudation and triggers positive rhizospheric feedbacks in a temperate grassland. Soil Biol. Biochem. 40, 2865–2873 (2008).
CAS  Article  Google Scholar 

56.
Hupe, A. et al. Get on your boots: estimating root biomass and rhizodeposition of peas under field conditions reveals the necessity of field experiments. Plant Soil 443, 449–462 (2019).
CAS  Article  Google Scholar  More

1.
Sardain, A., Sardain, E. & Leung, B. Global forecasts of shipping traffic and biological invasions to 2050. Nat. Sustain. 2, 274–282 (2019).
Article  Google Scholar 
2.
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
PubMed  Article  CAS  Google Scholar 

3.
Pöysä, H. et al. Changes in species richness and composition of boreal waterbird communities: a comparison between two time periods 25 years apart. Sci. Rep. 9, 1725 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Underwood, G. J. C. et al. Organic matter from Arctic sea-ice loss alters bacterial community structure and function. Nat. Clim. Change 9, 170–176 (2019).
Article  Google Scholar 

5.
Kubicek, A., Breckling, B., Hoegh-Guldberg, O. & Reuter, H. Climate change drives trait-shifts in coral reef communities. Sci. Rep. 9, 3721 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

6.
Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).
Article  Google Scholar 

7.
Hastings, A. Timescales, dynamics, and ecological understanding. Ecology 91, 3471–3480 (2010).
PubMed  Article  Google Scholar 

8.
Hastings, A. Timescales and the management of ecological systems. Proc. Natl Acad. Sci. USA 113, 14568–14573 (2016).
CAS  PubMed  Article  Google Scholar 

9.
Hastings, A. Transients: the key to long-term ecological understanding? Trends Ecol. Evol. 19, 39–45 (2004).
PubMed  Article  Google Scholar 

10.
Hastings, A. & Higgins, K. Persistence of transients in spatially structured ecological models. Science 263, 1133–1136 (1994).
CAS  PubMed  Article  Google Scholar 

11.
Hastings, A. Transient dynamics and persistence of ecological systems. Ecol. Lett. 4, 215–220 (2001).
Article  Google Scholar 

12.
Likens, G. E. (ed.) Long-Term Studies in Ecology: Approaches and Alternatives (Springer, 1989).

13.
Franklin, J. F., Bledsoe, C. S. & Callahan, J. T. Contributions of the Long-term Ecological Research program. Bioscience 40, 509–523 (1990).
Article  Google Scholar 

14.
Ratajczak, Z. et al. The interactive effects of press/pulse intensity and duration on regime shifts at multiple scales. Ecol. Monogr. 87, 198–218 (2017).
Article  Google Scholar 

15.
Hastings, A. et al. Transient phenomena in ecology. Science 361, eaat6412 (2018).
PubMed  Article  CAS  Google Scholar 

16.
Morozov, A. et al. Long transients in ecology: theory and applications. Phys. Life Rev. 32, 1–40 (2020).
PubMed  Article  Google Scholar 

17.
Holling, C. S. Adaptive Environmental Assessment and Management (International Institute for Applied Systems Analysis, 1978).

18.
Walters, C. Adaptive Management of Renewable Resources (Macmillan, 1986).

19.
Lee, K. N. Appraising adaptive management. Conserv. Ecol. 3, 3 (1999).
Article  Google Scholar 

20.
Gunderson, L. & Light, S. S. Adaptive management and adaptive governance in the Everglades ecosystem. Policy Sci. 39, 323–334 (2006).
Article  Google Scholar 

21.
Franklin, J. Biological legacies: a critical management concept from Mount St. Helens. In Trans. 55th North American Wildlife and Natural Resources Conference (1990).

22.
Funk, J. L. et al. Keys to enhancing the value of invasion ecology research for management. Biol. Invasions https://doi.org/10.1007/s10530-020-02267-9 (2020).

23.
Beaury, E. M. et al. Incorporating climate change into invasive species management: insights from managers. Biol. Invasions 22, 233–252 (2020).
Article  Google Scholar 

24.
Cuddington, K. et al. Process-based models are required to manage ecological systems in a changing world. Ecosphere https://doi.org/10.1890/ES12-00178.1 (2013).

25.
White, J. W., Botsford, L. W., Hastings, A., Baskett, M. L. & Kaplan, D. M. Transient responses of fished populations to marine reserve establishment. Conserv. Lett. 6, 180–191 (2013).
Article  Google Scholar 

26.
Kaplan, K. A. et al. Setting expected timelines of fished population recovery for the adaptive management of a marine protected area network. Ecol. Appl. https://doi.org/10.1002/eap.1949 (2019).

27.
Hopf, J. K., Jones, G. P., Williamson, D. H. & Connolly, S. R. Marine reserves stabilize fish populations and fisheries yields in disturbed coral reef systems. Ecol. Appl. 29, e01905 (2019).
PubMed  Article  Google Scholar 

28.
Caselle, J. E., Davis, K. & Marks, L. M. Marine management affects the invasion success of a non-native species in a temperate reef system in California, USA. Ecol. Lett. 21, 43–53 (2018).
PubMed  Article  Google Scholar 

29.
Mahmood, A. H. et al. Comparison of techniques to control the aggressive environmental invasive species Galenia pubescens in a degraded grassland reserve, Victoria, Australia. PLoS ONE 13, 1–16 (2018).
Google Scholar 

30.
Liebhold, A. M. et al. Eradication of invading insect populations: from concepts to applications. Annu. Rev. Entemol. 61, 335–352 (2016).
CAS  Article  Google Scholar 

31.
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  Article  PubMed Central  Google Scholar 

32.
Clark, C. M. & Tilman, D. Recovery of plant diversity following N cessation: effects of recruitment, litter, and elevated N cycling. Ecology 91, 3620–3630 (2010).
PubMed  Article  PubMed Central  Google Scholar 

33.
Storkey, J. et al. Grassland biodiversity bounces back from long-term nitrogen addition. Nature 528, 401–404 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Brettin, A. Ecological Management Practices Informed by Flow–Kick Dynamics. PhD thesis, Univ. Minnesota (2019).

35.
Meyer, K. et al. Quantifying resilience to recurrent ecosystem disturbances using flow–kick dynamics. Nat. Sustain. 1, 671–678 (2018).
Article  Google Scholar 

36.
Schindler, D. W. The dilemma of controlling cultural eutrophication of lakes. Proc. R. Soc. B 279, 4322–4333 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Schindler, D. W., Carpenter, S. R., Chapra, S. C., Hecky, R. E. & Orihel, D. M. Reducing phosphorus to curb lake eutrophication is a success. Environ. Sci. Technol. 50, 8923–8929 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Scheffer, M., Carpenter, S. R., Foley, J. E., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Hopf, J. K., Jones, G. P., Williamson, D. H. & Connolly, S. R. Fishery consequences of marine reserves: short-term pain for longer-term gain. Ecol. Appl. 26, 818–829 (2016).
PubMed  Article  Google Scholar 

40.
Hobbs, W. O. et al. A 200-year perspective on alternative stable state theory and lake management from a biomanipulated shallow lake. Ecol. Appl. 22, 1483–1496 (2012).
PubMed  Article  Google Scholar 

41.
Fastner, J. et al. Combating cyanobacterial proliferation by avoiding or treating inflows with high P load-experiences from eight case studies. Aquat. Ecol. 50, 367–383 (2016).
CAS  Article  Google Scholar 

42.
Vollenweider, R. A. Input-output models with special reference to the phosphorus loading concept in limnology. Schweiz. Z. Hydrol. 37, 53–84 (1975).
CAS  Google Scholar 

43.
Cullen, P. & Forsberg, C. Experiences with reducing point sources of phosphorus to lakes. Hydrobiologia 170, 321–336 (1988).
CAS  Article  Google Scholar 

44.
Jeppesen, E. et al. Lake responses to reduced nutrient loading – an analysis of contemporary long-term data from 35 case studies. Freshwat. Biol. 50, 1747–1771 (2005).
CAS  Article  Google Scholar 

45.
Carpenter, S. R. & Brock, W. A. Spatial complexity, resilience, and policy diversity: fishing on lake-rich landscapes. Ecol. Soc. 9, 8 (2004).
Article  Google Scholar 

46.
Walters, C. & Kitchell, J. F. Cultivation/depensation effects on juvenile survival and recruitment: implications for the theory of fishing. Can. J. Fish. Aquat. Sci. 58, 39–50 (2001).
Article  Google Scholar 

47.
Carpenter, S. R. Ecological futures: building an ecology of the long now. Ecology 83, 2069–2083 (2002).
Google Scholar 

48.
Carpenter, S. R. Regime Shifts in Lake Ecosystems: Pattern and Variation (Ecology Institute, 2003).

49.
Francis, T. B. & Schindler, D. E. Degradation of littoral habitats by residential development: woody debris in lakes of the Pacific Northwest and Midwest, United States. Ambio 35, 274–280 (2006).

50.
Christensen, D. L., Herwig, B. R., Schindler, D. E. & Carpenter, S. R. Impacts of lakeshore residential development on coarse woody debris in north temperate lakes. Ecol. Appl. 6, 1143–1149 (1996).
Article  Google Scholar 

51.
Grebogi, C., Ott, E. & Yorke, J. A. Crises, sudden changes in chaotic attractors and chaotic transients. Phys. D 7, 181–200 (1983).
Article  Google Scholar 

52.
Tél, T. in Directions in Chaos (3): Experimental Study and Characterization of Chaos (ed. Hao, B.-L.) 149–211 (World Scientific, 1990).

53.
Lai, Y.-C. & Tél, T. Transient Chaos: Complex Dynamics on Finite-Time Scales (Springer, 2011).

54.
McCann, K. S. & Yodzis, P. Nonlinear dynamics and population disappearances. Am. Nat. 144, 873–879 (1994).
Article  Google Scholar 

55.
Schiff, S. J. et al. Controlling chaos in the brain. Nature 370, 615–620 (1994).
CAS  PubMed  Article  Google Scholar 

56.
Dhamala, M. & Lai, Y.-C. Controlling transient chaos in deterministic flows with applications to electrical power systems and ecology. Phys. Rev. E 59, 1646–1655 (1999).
CAS  Article  Google Scholar 

57.
Hilker, F. M. & Westerhoff, F. H. Preventing extinction and outbreaks in chaotic populations. Am. Nat. 170, 232–241 (2007).
PubMed  Article  Google Scholar 

58.
Park, M.-G., Park, S.-A., Cho, K. & Jang, B. Controlling transient of species in food chain. Proc. Korean Ind. Appl. Math. Assoc. 6, 249–253 (2011).
Google Scholar 

59.
Tel, T. Controlling transient chaos. J. Phys. A 24, L1359–L1368 (1991).
Article  Google Scholar 

60.
Lai, Y.-C. & Grebogi, C. Converting transient chaos into sustained chaos by feedback control. Phys. Rev. E 49, 1094–1098 (1994).
CAS  Article  Google Scholar 

61.
Schwartz, I. B. & Triandaf, I. Sustaining chaos by using basin boundary saddles. Phys. Rev. Lett. 77, 4740–4743 (1996).
CAS  PubMed  Article  Google Scholar 

62.
Ott, E., Grebogi, C. & Yorke, J. A. Controlling chaos. Phys. Rev. Lett. 64, 1196–1199 (1990).
CAS  PubMed  Article  Google Scholar 

63.
Folke, C. et al. Resilience thinking: integrating resilience, adaptability and transformability. Ecol. Soc. 15, 20 (2010).
Article  Google Scholar 

64.
Walters, C. J. & Holling, C. S. Large-scale management experiments and learning by doing. Ecology 71, 2060–2068 (1990).
Article  Google Scholar 

65.
Bulman, C. R. et al. Minimum viable metapopulation size, extinction debt, and the conservation of a declining species. Ecol. Appl. 17, 1460–1473 (2007).
PubMed  Article  Google Scholar 

66.
Mcdonald, J. L., Stott, I., Townley, S. & Hodgson, D. J. Transients drive the demographic dynamics of plant populations in variable environments. J. Ecol. 104, 306–314 (2016).
PubMed  PubMed Central  Article  Google Scholar 

67.
Carpenter, S. R. & Gunderson, L. H. Coping with collapse: ecological and social dynamics in ecosystem management. Bioscience 51, 451–457 (2001).
Article  Google Scholar 

68.
Fulton, E. A. et al. A multi-model approach to engaging stakeholder and modellers in complex environmental problems. Environ. Sci. Policy 48, 44–56 (2015).
Article  Google Scholar 

69.
Plagányi, É. E. et al. Multispecies fisheries management and conservation: tactical applications using models of intermediate complexity. Fish Fish. 15, 1–22 (2014).
Article  Google Scholar 

70.
Collie, J. S. et al. Ecosystem models for fisheries management: finding the sweet spot. Fish Fish. 17, 101–125 (2016).
Article  Google Scholar 

71.
Rowland, J. A. et al. Selecting and applying indicators of ecosystem collapse for risk assessments. Conserv. Biol. 32, 1233–1245 (2018).
PubMed  Article  Google Scholar 

72.
Silvertown, J. et al. The Park Grass Experiment 1856–2006: its contribution to ecology. J. Ecol. 94, 801–814 (2006).
CAS  Article  Google Scholar 

73.
Pace, M. L., Carpenter, S. R. & Wilkinson, G. M. Long-term studies and reproducibility: lessons from whole-lake experiments. Limnol. Oceanogr. 64, S22–S33 (2019).
CAS  Article  Google Scholar 

74.
McGlathery, K. J. et al. Nonlinear dynamics and alternative stable states in shallow coastal systems. Oceanography 26, 220–231 (2013).
Article  Google Scholar 

75.
Van Cleve, K. & Martin, S. (eds) Long-Term Ecological Research in the United States: A Network of Research Sites 6th edn (Long Term Ecological Research Office, 1991).

76.
Bestelmeyer, B. T. et al. Analysis of abrupt transitions in ecological systems. Ecosphere https://doi.org/10.1890/ES11-00216.1 (2011).

77.
Reed-Andersen, T., Carpenter, S. R. & Lathrop, R. C. Phosphorus flow in a watershed-lake ecosystem. Ecosystems 3, 561–573 (2000).
CAS  Article  Google Scholar 

78.
Bell, D. M. et al. Long-term ecological research and evolving frameworks of disturbance ecology. BioScience 70, 141–156 (2020).
Article  Google Scholar 

79.
Pahl-Wostl, C. A conceptual framework for analysing adaptive capacity and multi-level learning processes in resource governance regimes. Glob. Environ. Change 19, 354–365 (2009).
Article  Google Scholar 

80.
White, J. W. et al. Transient responses of fished populations to marine reserve establishment. Conserv. Lett. 6, 180–191 (2013).
Article  Google Scholar 

81.
Chadès, I. et al. Optimization methods to solve adaptive management problems. Theor. Ecol. 10, 1–20 (2017).
Article  Google Scholar 

82.
Kot, M. Elements of Mathematical Ecology (Cambridge Univ. Press, 2001).

83.
Strogatz, S. H. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (Addison-Wesley, 1994). More

1.
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 
2.
Blenkinsop, S. & Fowler, H. J. Changes in drought frequency, severity and duration for the British Isles projected by the PRUDENCE regional climate models. J. Hydrol. 342, 50–71 (2007).
Article  ADS  Google Scholar 

3.
Guardiola-Claramonte, M. et al. Decreased streamflow in semi-arid basins following drought-induced tree die-off: a counter-intuitive and indirect climate impact on hydrology. J. Hydrol. 406, 225–233 (2011).
Article  ADS  Google Scholar 

4.
Hartmann, H. et al. Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol. 218, 15–28 (2018).
PubMed  Article  PubMed Central  Google Scholar 

5.
Chaturvedi, R. K., Raghubanshi, A. S., Tomlinson, K. & Singh, J. S. Impacts of human disturbance in tropical dry forests increase with soil moisture stress. J. Veg. Sci. 28, 997–1007 (2017).
Article  Google Scholar 

6.
Sjöman, H., Hirons, A. D. & Bassuk, N. L. Improving confidence in tree species selection for challenging urban sites: a role for leaf turgor loss. Urban Ecosyst. 21, 1171–1188 (2018).
Article  Google Scholar 

7.
Esperon-Rodriguez, M. et al. Assessing the vulnerability of Australia’s urban forests to climate extremes. Plants People Planet. 1, 387–397 (2019).
Article  Google Scholar 

8.
Thaiutsa, B., Puangchit, L., Kjelgren, R. & Arunpraparut, W. Urban green space, street tree and heritage large tree assessment in Bangkok, Thailand. Urban For. Urban Green. 7, 219–229 (2008).
Article  Google Scholar 

9.
Chaturvedi, R.K., Tripathi, A., Raghubanshi, A.S. & Singh, J.S. Functional traits indicate a continuum of treee drought strategies across a soil water availability gradient in a tropical dry forest. For. Ecol. Manag. 2020 (In press).

10.
Chaturvedi, R. K., Raghubanshi, A. S. & Singh, J. S. Growth of tree seedlings in a tropical dry forest in relation to soil moisture and leaf traits. J. Plant Ecol. 6, 158–170 (2013).
Article  Google Scholar 

11.
Krauss, K. W., Twilley, R. R., Doyle, T. W. & Gardiner, E. S. Leaf gas exchange characteristics of three neotropical mangrove species in response to varying hydroperiod. Tree Physiol. 26, 959–968 (2006).
PubMed  Article  PubMed Central  Google Scholar 

12.
Yan, M.-J., Yamanaka, N., Yamamoto, F. & Du, S. Responses of leaf gas exchange, water relations, and water consumption in seedlings of four semiarid tree species to soil drying. Acta Physiol. Plant. 32, 183–189 (2010).
Article  Google Scholar 

13.
Yan, W., Zheng, S., Zhong, Y. & Shangguan, Z. Contrasting dynamics of leaf potential and gas exchange during progressive drought cycles and recovery in Amorpha fruticosa and Robinia pseudoacacia. Sci. Rep. 7, 4470 (2017).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

14.
Medlyn, B. E. et al. Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: a synthesis. New Phytol. 149, 247–264 (2001).
Article  Google Scholar 

15.
Wullschleger, S. D., Gunderson, C. A., Hanson, P. J., Wilson, K. B. & Norby, R. J. Sensitivity of stomatal and canopy conductance to elevated CO2 concentration: interacting variables and perspectives of scale. New Phytol. 153, 485–496 (2002).
CAS  Article  Google Scholar 

16.
Ainsworth, E. A. & Rogers, A. The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions. Plant. Cell Environ. 30, 258–270. https://doi.org/10.1111/j.1365-3040.2007.01641.x (2007).
CAS  Article  PubMed  Google Scholar 

17.
Tor-ngern, P. et al. Increases in atmospheric CO2 have little influence on transpiration of a temperate forest canopy. New Phytol. 205, 518–525 (2015).
CAS  PubMed  Article  Google Scholar 

18.
Schäfer, K. V. R. et al. Exposure to an enriched CO2 atmosphere alters carbon assimilation and allocation in a pine forest ecosystem. Glob. Chang. Biol. 9, 1378–1400 (2003).
Article  ADS  Google Scholar 

19.
Williams, M. et al. Modelling the soil-plant-atmosphere continuum in a Quercus-Acer stand at Harvard Forest: the regulation of stomatal conductance by light, nitrogen and soil/plant hydraulic properties. Plant. Cell Environ. 19, 911–927 (1996).
Article  Google Scholar 

20.
Granier, A. Une nouvelle méthode pour la mesure du flux de sève brute dans le tronc des arbres. Ann. For. Sci. 42, 193–200 (1985).
Article  Google Scholar 

21.
Granier, A. Evaluation of transpiration in a Douglas-fir stand by means of sap flow measurements. Tree Physiol. 3, 309–320 (1987).
CAS  PubMed  Article  Google Scholar 

22.
Green, S., Clothier, B. & Jardine, B. Theory and practical application of heat pulse to measure sap flow. Agron. J. 95, 1371–1379 (2003).
Article  Google Scholar 

23.
Burgess, S. S. O. et al. An improved heat pulse method to measure low and reverse rates of sap flow in woody plants. Tree Physiol. 21, 589–598 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Sakuratani, T. A Heat balance method for measuring water flux in the stem of intact plants. J. Agric. Meteorol. 37, 9–17 (1981).
Article  Google Scholar 

25.
Chang, X., Zhao, W., Zhang, Z. & Su, Y. Sap flow and tree conductance of shelter-belt in arid region of China. Agric. For. Meteorol. 138, 132–141 (2006).
Article  ADS  Google Scholar 

26.
Ewers, B. E., Oren, R., Phillips, N., Strömgren, M. & Linder, S. Mean canopy stomatal conductance responses to water and nutrient availabilities in Picea abies and Pinus taeda. Tree Physiol. 21, 841–850 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Pataki, D. E., Oren, R. & Phillips, N. Responses of sap flux and stomatal conductance of Pinus taeda L. trees to stepwise reductions in leaf area. J. Exp. Bot. 49, 871–878 (1998).
CAS  Article  Google Scholar 

28.
Ryan, M. G. et al. Transpiration and whole-tree conductance in ponderosa pine trees of different heights. Oecologia 124, 553–560 (2000).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

29.
Oishi, A. C., Oren, R., Novick, K. A., Palmroth, S. & Katul, G. G. Interannual invariability of forest evapotranspiration and its consequence to water flow downstream. Ecosystems 13, 421–436 (2010).
Article  Google Scholar 

30.
Oishi, A. C., Oren, R. & Stoy, P. C. Estimating components of forest evapotranspiration: a footprint approach for scaling sap flux measurements. Agric. For. Meteorol. 148, 1719–1732 (2008).
Article  ADS  Google Scholar 

31.
Bell, D. M. et al. A state-space modeling approach to estimating canopy conductance and associated uncertainties from sap flux density data. Tree Physiol. 35, 792–802 (2015).
PubMed  Article  PubMed Central  Google Scholar 

32.
Kim, H.-S., Oren, R. & Hinckley, T. M. Actual and potential transpiration and carbon assimilation in an irrigated poplar plantation. Tree Physiol. 28, 559–577 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Meinzer, F. C., James, S. A. & Goldstein, G. Dynamics of transpiration, sap flow and use of stored water in tropical forest canopy trees. Tree Physiol. 24, 901–909 (2004).
PubMed  Article  PubMed Central  Google Scholar 

34.
Phillips, N., Nagchaudhuri, A., Oren, R. & Katul, G. Time constant for water transport in loblolly pine trees estimated from time series of evaporative demand and stem sapflow. Trees 11, 412–419 (1997).
Article  Google Scholar 

35.
Tor-ngern, P. et al. Ecophysiological variation of transpiration of pine forests: synthesis of new and published results. Ecol. Appl. 27, 118–133 (2017).
PubMed  Article  PubMed Central  Google Scholar 

36.
Clark, J. S. et al. Inferential ecosystem models, from network data to prediction. Ecol. Appl. 21, 1523–1536 (2011).
PubMed  Article  PubMed Central  Google Scholar 

37.
Lu, P., Urban, L. & Zhao, P. Granier’s thermal dissipation probe (TDP) method for measuring sap flow in trees: theory and practice. Acta Bot. Sin. 46, 631–646 (2004).
Google Scholar 

38.
Ewers, B. & Oren, R. Analyses of assumptions and errors in the calculation of stomatal conductance from sap flux measurements. Tree Physiol. 20, 579–589 (2000).
PubMed  Article  PubMed Central  Google Scholar 

39.
Ward, E. J., Oren, R., Sigurdsson, B. D., Jarvis, P. G. & Linder, S. Fertilization effects on mean stomatal conductance are mediated through changes in the hydraulic attributes of mature Norway spruce trees. Tree Physiol. 28, 579–596 (2008).
PubMed  Article  PubMed Central  Google Scholar 

40.
Addington, R. N., Mitchell, R. J., Oren, R. & Donovan, L. A. Stomatal sensitivity to vapor pressure deficit and its relationship to hydraulic conductance in Pinus palustris. Tree Physiol. 24, 561–569 (2004).
PubMed  Article  Google Scholar 

41.
Leuning, R. A critical appraisal of a combined stomatal-photosynthesis model for C3 plants. Plant. Cell Environ. 18, 339–355 (1995).
CAS  Article  Google Scholar 

42.
Meinzer, F. C., Hinckley, T. M. & Ceulemans, R. Apparent responses of stomata to transpiration and humidity in a hybrid poplar canopy. Plant. Cell Environ. 20, 1301–1308 (1997).
Article  Google Scholar 

43.
Monteith, J. L. A reinterpretation of stomatal responses to humidity. Plant. Cell Environ. 18, 357–364 (1995).
Article  Google Scholar 

44.
Loustau, D. et al. Transpiration of a 64-year-old maritime pine stand in Portugal. Oecologia 107, 33–42 (1996).
CAS  PubMed  Article  ADS  Google Scholar 

45.
Domec, J.-C. & Gartner, B. L. Cavitation and water storage capacity in bole xylem segments of mature and young Douglas-fir trees. Trees 15, 204–214 (2001).
Article  Google Scholar 

46.
Moore, G. W., Bond, B. J., Jones, J. A., Phillips, N. & Meinzer, F. C. Structural and compositional controls on transpiration in 40- and 450-year-old riparian forests in western Oregon, USA. Tree Physiol. 24, 481–491 (2004).
PubMed  Article  Google Scholar 

47.
Leigh, A., Sevanto, S., Close, J. D. & Nicotra, A. B. The influence of leaf size and shape on leaf thermal dynamics: does theory hold up under natural conditions?. Plant. Cell Environ. 40, 237–248 (2017).
CAS  PubMed  Article  Google Scholar 

48.
Brodribb, T. J., Holbrook, N. M., Edwards, E. J. & Gutiérrez, M. V. Relations between stomatal closure, leaf turgor and xylem vulnerability in eight tropical dry forest trees. Plant. Cell Environ. 26, 443–450 (2003).
Article  Google Scholar 

49.
Choat, B., Ball, M., Luly, J., Donnelly, C. & Holtum, J. Seasonal patterns of leaf gas exchange and water relations in dry rain forest trees of contrasting leaf phenology. Tree Physiol. 26, 657–664 (2006).
PubMed  Article  PubMed Central  Google Scholar 

50.
Tor-ngern, P. & Puangchit, L. Effects of varying soil and atmospheric water deficit on water use characteristics of tropical street tree species. Urban For. Urban Green. 36, 76–83 (2018).
Article  Google Scholar 

51.
Jarvis, P. G. Transpiration and assimilation of tree and agricultural crops: the omega factor. In Attributes of Trees as Crop Plants (eds Cannel, M. G. R. & Jackson, J. E.) 460–480 (Institute of Terrestrial Ecology Huntingdon, UK, 1985).
Google Scholar 

52.
Marchin, R. M., Broadhead, A. A., Bostic, L. E., Dunn, R. R. & Hoffmann, W. A. Stomatal acclimation to vapour pressure deficit doubles transpiration of small tree seedlings with warming. Plant. Cell Environ. 39, 2221–2234 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Mahan, J. R. & Upchurch, D. R. Maintenance of constant leaf temperature by plants—I Hypothesis-limited homeothermy. Environ. Exp. Bot. 28, 351–357 (1988).
Article  Google Scholar 

54.
Schultz, H. R. Differences in hydraulic architecture account for near-isohydric and anisohydric behaviour of two field-grown Vitis vinifera L. cultivars during drought. Plant. Cell Environ. 26, 1393–1405 (2003).
Article  Google Scholar 

55.
Harris, P. P., Huntingford, C., Cox, P. M., Gash, J. H. C. & Malhi, Y. Effect of soil moisture on canopy conductance of Amazonian rainforest. Agric. For. Meteorol. 122, 215–227 (2004).
Article  ADS  Google Scholar 

56.
Kjelgren, R., Joyce, D. & Doley, D. Subtropical-tropical urban tree water relations and drought stress response strategies. Arboric. Urban For. 39, 125–131 (2013).
Google Scholar 

57.
West, A. G., Hultine, K. R., Jackson, T. L. & Ehleringer, J. R. Differential summer water use by Pinus edulis and Juniperus osteosperma reflects contrasting hydraulic characteristics. Tree Physiol. 27, 1711–1720 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Hatfield, J. L. & Prueger, J. H. Temperature extremes: effect on plant growth and development. Weather Clim. Extrem. 10, 4–10 (2015).
Article  Google Scholar 

59.
Suralta, R. R. & Yamauchi, A. Root growth, aerenchyma development, and oxygen transport in rice genotypes subjected to drought and waterlogging. Environ. Exp. Bot. 64, 75–82 (2008).
CAS  Article  Google Scholar 

60.
Lawler, J. J. et al. The scope and treatment of threats in endangered species recovery plans. Ecol. Appl. 12, 663–667 (2002).
Article  Google Scholar 

61.
Liu, H., Lin, J., Zhang, M., Lin, Z. & Wen, T. Extinction of poorest competitors and temporal heterogeneity of habitat destruction. Ecol. Modell. 219, 30–38 (2008).
Article  Google Scholar 

62.
Baltzer, J. L., Grégoire, D. M., Bunyavejchewin, S., Noor, N. S. M. & Davies, S. J. Coordination of foliar and wood anatomical traits contributes to tropical tree distributions and productivity along the Malay-Thai Peninsula. Am. J. Bot. 96, 2214–2223 (2009).
PubMed  Article  Google Scholar 

63.
Kursar, T. A. et al. Tolerance to low leaf water status of tropical tree seedlings is related to drought performance and distribution. Funct. Ecol. 23, 93–102 (2009).
Article  Google Scholar 

64.
Monteith, J. L. & Unsworth, M. H. Principles of Environmental Physics 287 (Butterworth-Heinemann, Oxford, 1990).
Google Scholar 

65.
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods. 9(7), 671–675 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Oishi, A. C., Hawthorne, D. A. & Oren, R. Baseliner: an open-source, interactive tool for processing sap flux data from thermal dissipation probes. Software X. 5, 139–143 (2016).
ADS  Google Scholar 

67.
Ball, J., Woodrow, I. & Berry, J. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. Prog. Photosynth. Res. 4, 221–224 (1987).
Google Scholar 

68.
Jarvis, P. G. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos. Trans. R. Soc. London. B Biol. Sci. 273, 593–610 (1976).
CAS  Article  ADS  Google Scholar  More