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Benchmark regression resultsParallel trend testThe premise of using DID is that the treatment group and control group meet the assumptions of parallel trend, which means that before ETS is officially implemented, the evolution trend of ecologicalization efficiency of industry of the control group and the experimental group is consistent and does not show a systematic difference. This study uses a more rigorous empirical test in parallel trend test: if the interaction coefficient is not significant and is different from zero before the implementation of ETS; and if the interaction coefficient is significant and is different from zero after the implementation of ETS, it indicates that there is no significant difference in ecologicalization efficiency of industry between the control group and the experimental group before the implementation of ETS. Results are shown in Table 4: before ETS was officially implemented, the difference coefficient was not significant; after the official implementation of ETS in 2013, the difference coefficient was significant and not equal to 0, and the ecologicalization efficiency of industry was improved significantly, which met the parallel trend of the DID. Therefore, it is scientific and reasonable to evaluate the effectiveness of ETS with DID.Table 4 Parallel trend test.Full size tableDynamic effect analysisTo compare the conditions of the experimental group and the control group before and after the implementation of ETS, dynamic graphs are drawn in this study, as shown in Fig. 1, which shows the impact of ETS on the regional ecologicalization efficiency of industry. The vertical line represents a 95% confidence interval and the broken line shows the marginal effect of regional ecologicalization efficiency, which means that the confidence interval contains is 0 before ETS’s implementation, and the result is not significant. In contrast, after 2013, the effect of ETS became apparent, the marginal effect gradually increased and the results became significant, perhaps owing to the implementation of ETS.Figure 1Dynamic analysis diagram.Full size imageThe effect of ETS on ecologicalization efficiency of industryControlling time effect and fixed effect, this study collected the data of pilot and non-pilot provinces of ETS from 2007 to 2019 to analyze the impact of ETS on the regional ecologicalization efficiency of industry and regional heterogeneity. The results are shown in Table 5. According to the results in the first column, ETS has significantly promoted the regional ecologicalization efficiency of industry, and the national implementation of ETS has achieved remarkable results. Compared with the regions that are not ETS pilot areas, the ecologicalization efficiency of industry of pilot provinces and cities has increased by 35%. Results also show that ETS has different effects on the ecologicalization efficiency of industry in different regions. Specifically, ETS significantly promoted regional ecologicalization efficiency of industry in the eastern and central regions, and the efficiency in the eastern region was more significant than that of the central region. However, the impact of ETS on the regional ecologicalization efficiency of industry in the western region was negative which may result from the fact that compared to the central and western regions, the east region has better economic development, advanced technology, and lots of talents that can respond to the implementation of ETS, accelerate the upgrade of industries, and improve the utilization level of regional resources. There are many traditional industries in the central and western regions, and the development of scientific and technological levels as well as the resource utilization efficiency there are relatively slow. Besides, it is difficult for the central and western regions to adapt to ETS in a short-term of time leading to the failure of improving the regional ecologicalization efficiency of industry in a short time.Table 5 Influence of ETS on ecologicalization efficiency of industry.Full size tableRobustness testPropensity matching score—double difference method (PSM-DID)The assumption of homogeneity and randomness between the control group and the experimental group is the premise of using the DID model. However, due to the large economic and regional differences among provinces and cities, there may be systematic differences between the experimental group and the control group, which may cause deviations in the results. Therefore, the data after propensity score matching is used in this study, making the matched individuals have no other significant differences unless they have been treated or not. The dual difference is conducted again to avoid self-selection bias, and the robustness of the above results is verified according to the measurement results. Control variables were used to match characteristic variables, the experimental group was matched with the control group, and the Logit model was adopted to delete the samples that fail to meet the matching criteria. After the matching, there are 168 observation values. The regression results of PSM-DID model show that, ETS has positive effects on the regional ecologicalization of industry (0.460***), which again proves that the conclusion that ETS improves regional ecologicalization of industry efficiency is reliable. The results are shown in Table 6.Table 6 The result of the PSM-DID.Full size tableCounterfactual testTo verify the robustness of the results again, six provinces and cities are randomly selected as experimental groups for multiple tests to construct new dummy variables of ETS, and the DID model was used again to verify the credibility of the above results. Four random samples were conducted in this study, and the results are shown in Table 7. It can be seen that the results are not significant, which also reversely proves that ETS improves the regional ecologicalization efficiency of industry.Table 7 Counterfactual test results.Full size tableActing pattern analysis of ETS on the regional ecologicalization efficiency of industryFirst, ETS may improve the regional ecologicalization efficiency of industry through industrial structure optimization and upgrading. Promoting upgrading of the industrial structure is one of the important approaches of social and economic development during the 14th Five-Year Plan formulation and is the only way to promote low-carbon and sustainable development of modern national industries. The upgrading of the industrial structure has been promoted to the national strategic level, contributing to the healthy development of the national economy system. ETS bring costs and benefits to enterprises, forcing them to transform and upgrade, increase investment in environmental protection and use clean energy, and accelerate the pace of energy conservation and emission reduction31. Second, ETS may improve the regional ecologicalization efficiency of industry through the coordinated agglomeration of resources. Marshall’s theory of scale economy, Krugman’s theory of new economic geography, Weber’s theory of agglomeration economy, Coase’s transaction cost theory, and so on reflect the importance of resource aggregation of economic activities through cost-saving, resource sharing, and other ways to improve industrial input–output efficiency, enhance industrial competitiveness, increase regional comprehensive strength and strengthen the competitive advantage of regional industrial clusters32. The benefits generated by resource aggregation far exceed the sum of benefits generated by various industries in the decentralized state. Under the pressure of ETS, enterprises may alleviate the mismatch between labor and capital through the collaborative aggregation of industrial resources, aiming to improve economic benefits and regional resource allocation efficiency and promote regional ecologicalization efficiency of industry. Third, ETS may improve the regional ecologicalization efficiency of industry by supporting ecological optimization. The sustainable development of the ecological environment is closely related to emission reduction policy. To alleviate the bad effects on the ecology, environmental protection is more and more brought to the attention of society and government. Policies for ecological protection have been introduced to reduce pollution20. All regions take effective and targeted measures to control environmental pollution and optimize the investment structure in light of their actual conditions. The purpose of ecological optimization is to improve the regional environment and strengthen pollution control which is one of the important parts of China’s fiscal spending. The government must guide the market to carry out ecological protection and environmental governance according to ETS. Studies have found that a low-carbon pilot policy helps to enhance the level of regional pollution control, promote the harmonious development of regional economy and environment, and then improve the regional ecologicalization efficiency of industry.To explore the transmission mechanism of ETS on the regional ecologicalization of industry efficiency, Baron and Kenny (1986)’s mediating effect model was referred to explore and verify whether there exists a structural optimization upgrade effect, resource synergistic agglomeration effect, ecological optimization support effect when ETC promotes regional ecologicalization efficiency of industry. Table 8 shows the regression results of the influence mechanism of ETS on the regional ecologicalization efficiency of industry. This study refers to the definition and research of industrial optimization and upgrading by Wang Qunwei, Huang Xianglan, and others, and the proportion of tertiary industry added value accounting for industrial added value is selected to measure the effectiveness of industrial optimization and upgrading. For resource synergistic agglomeration effect, this study refers to the calculation methods of Cui Shuhui, Chen Jianjun et al. and adopts the collaborative aggregation index of manufacturing and producer services to measure the collaborative aggregation effect of resources, which effectively avoids the scale difference between different regions. It can be seen from the table that the implementation of ETS has significantly influenced the three effects proposed by this study: the optimization and upgrading effect of industrial structure, the synergistic aggregation effect of resources, and the support effect of ecological optimization. In addition, ETS has a positive and significant impact on the regional ecologicalization efficiency of industry. The results in Columns 3, 5, and 7 of the table show the industrial optimization and upgrading effect, resource synergistic aggregation effect, structural upgrading effect, and resource allocation effect generated in the process of low-carbon pilot policy operation can significantly promote regional ecologicalization efficiency of industry and have an obvious intermediary effect. The mediating effect produced by industrial structure optimization and upgrading is about 0.042, the mediating effect produced by resource synergy agglomeration is about 0.148, and the mediating effect produced by ecological optimization support is about 0.166. According to the Sobal test results, all of them have passed the test, indicating that the above results are reliable.Table 8 Mediating effect test results.Full size table More

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Climate driversTo explore the impact of climate on forest resilience (see the following sections), we used monthly averaged total precipitation, 2-m air temperature, evapotranspiration deficit and surface solar radiation downwards acquired from the ERA5-Land reanalysis product at 0.1° spatial resolution for the 2000–2020 period (https://cds.climate.copernicus.eu/cdsapp#!/home). Evapotranspiration deficit was quantified as the total precipitation minus evapotranspiration. In this study, we referred to climate regions as defined by the Köppen–Geiger world map of climate classification51 (http://koeppen-geiger.vu-wien.ac.at/present.htm). The original 31 climatic zones were merged into major zones and only those characterized by vegetation cover were included in our study (tropical, arid, temperate and boreal; Extended Data Fig. 8).Vegetation dynamicsNDVI data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra satellite was used to derive changes in global vegetation for the period 2000–2020. We used cloud-free spatial composites provided at 16-day temporal resolution and 0.05° spatial resolution (MOD13C1 Version 6; https://lpdaac.usgs.gov/products/mod13c1v006/) and retained only pixels with good and marginal overall quality. The MODIS-derived NDVI dataset represents a state-of-the-art product of vegetation state whose retrieval algorithm is constantly improved52, and being derived from a unique platform and sensor, it is temporally and spatially consistent. Vegetation dynamics were analysed in terms of kNDVI, a nonlinear generalization of the NDVI based on ref. 22 and derived as follows:$$text{kNDVI=}tanh left({text{NDVI}}^{2}right)$$
(1)
kNDVI has recently been proposed as a strong proxy for ecosystem productivity that shows high correlations with both plot level measurements of primary productivity and satellite retrievals of sun-induced fluorescence22. In addition, kNDVI has been documented to be more closely related to primary productivity, to be resistant to saturation, bias and complex phenological cycles, and to show enhanced robustness to noise and stability across spatial and temporal scales compared to alternative products (for example, NDVI and near-infrared reflectance of vegetation). For these reasons, it has been retained in this study as the preferred metric to describe the state of the forest ecosystem.To obtain an accurate estimate of resilience indicators, vegetation time series need to be stationary without seasonal periodic patterns or long-term trends53. To this aim, vegetation anomalies were obtained from kNDVI data by first subtracting the multi-year 16-day sample mean and then removing linear trends from the resulting time series. Missing data, due for instance to snow cover affecting the retrieval of reflectance properties, have been gap-filled by climatological kNDVI values. The time series of kNDVI-based vegetation anomalies was used to derive resilience indicators and assess their spatial and temporal variations (see next sections).Interannual changes in vegetation were assessed in terms of growing-season-averaged kNDVI. To this end, a climatological growing season that spanned months with at least 75% of days in the greenness phase was derived from the Vegetation Index and Phenology satellite-based product54 (https://vip.arizona.edu/) and acquired for the 2000–2016 period at 0.05° spatial resolution. In addition, forest cover (FC) fraction was derived from the annual land-cover maps of the European Space Agency’s Climate Change Initiative (https://www.esa-landcover-cci.org/)55 over the 2000–2018 period at 300-m spatial resolution. FC was retrieved by summing the fraction of broadleaved deciduous, broadleaved evergreen, needle leaf deciduous and needle leaf evergreen forest. FC was resampled to 0.05° to match the kNDVI spatial resolution.Spatial patterns of slowness and its dependence on environmental factorsIn this study, we quantified the resilience of forest ecosystems—their ability to recover from external perturbations—by the use of the 1-lag TAC (refs. 3,4,5). Such an indicator was initially computed on the whole time series of vegetation anomalies (2000–2020) for forest pixels with less than 50% missing data in the original NDVI and FC greater than 0.05 and referred to in the text as long-term TAC. This analysis was used to assess the spatial patterns of the forest slowness mediated by environmental factors that affect plant growth rates and capacity to recover from perturbations. The long-term TAC was explored both in the geographic and climate space (Extended Data Fig. 1). In the climate space, long-term TAC was binned in a 50 × 50 grid as a function of average annual precipitation and temperature, both computed over the 2000–2020 period, using the average as an aggregation metric weighted by the areal extents of each record. We retained only bins with at least 50 records.To explore the potential drivers of long-term TAC, we developed an RF regression model23 and predicted the observed long-term TAC (response variable) based on a set of environmental features (predictors). The use of machine learning in general and of RF in particular, being nonparametric and nonlinear data-driven methods, does not require a priori assumptions about the functional form relating the key drivers and the response functions. The environmental variables include vegetation properties (FC and growing-season-averaged kNDVI) and climate variables (total precipitation, 2-m air temperature, evapotranspiration deficit and surface solar radiation downwards). Each of the climate variables was expressed in terms of average, coefficient of variation and 1-lag autocorrelation and resampled to 0.05° spatial resolution to match the spatial resolution of kNDVI. All environmental variables were computed annually and then averaged over time, except the autocorrelation that was computed directly for the whole period, analogously to the long-term TAC. This resulted in a set of 14 predictors representing the forest density, the background climate, the climate variability and its TAC in the observational period (Extended Data Table 1). The RF model was developed by splitting the observed long-term TAC into two separate samples: 60% of records were used for model calibration, and the remaining 40% were used to validate model performances in terms of coefficient of determination (R2), mean squared error and percentage bias (PBIAS). Each record refers to a 0.05° pixel. The RF implemented here uses 100 regression trees, whose depth and number of predictors to sample at each node were identified using Bayesian optimization. The general model formulation is as follows:$$text{TAC},=,fleft(Xright)+{varepsilon }_{{rm{f}}}$$
(2)
in which f is the RF regression model, X are the environmental predictors and εf are the residuals. We found that the model explains 87% of the spatial variance (R2) of the observed long-term TAC with a mean squared error of 0.007 and an average overestimation of 0.058 (PBIAS; Extended Data Fig. 2a). By definition, machine learning methods are not based on the mechanistic representation of the phenomena and therefore cannot provide direct information on the underlying processes influencing the system response to drivers. However, some model-agnostic methods can be applied to gain insights into the outputs of RF models. Here we used variable importance metrics to quantify and rank how individual environmental factors influence TAC (Extended Data Fig. 2b). Furthermore, using partial dependence plots derived from the machine learning algorithm RF, we explored the ecosystem response function (TAC) across gradients of vegetation and climate features (Supplementary Discussion 1 and Extended Data Fig. 2c–f).CSD indicatorsTo explore the temporal variation in forest resilience, we used CSD indicators, here quantified in terms of temporal changes in TAC retrieved for two consecutive and independent periods ranging from 2000 to 2010 and from 2011 to 2020, and assessed the significance of the change in the sampled mean aggregated for different climate regions through a two-sided t-test (Fig. 1c). This analysis was complemented by the computation of TAC on the annual scale over a 2-year lagged temporal window (3-year window size) to track the temporal changes in CSD. This resulted in a time series of TAC with an annual time step.We point out that temporal dynamics of annual TAC are driven by two processes: the changes in the resilience of the system that affect the velocity of the recovery from external perturbations and the confounding effects of the changes in autocorrelation of the climate drivers (Xac) that directly affect the autocorrelation of NDVI. Given the specific goals of this study, we factored out the second process from the total TAC signal to avoid that an increasing autocorrelation in the drivers would affect our analysis and conclusions about the resilience and the potential increase in instability56. For this purpose, we disentangled the temporal changes in TAC due to variations in autocorrelation in the climate drivers (({rm{TAC}}| {X}_{{rm{ac}}})) by adopting the space-for-time analogy and applied the RF model (f) at an annual time step (t) in a set of factorial simulations as follows:$${text{TAC}}^{t},{rm{| }},{X}_{{rm{ac}}}=fleft({X}^{t}right)-fleft({X}_{-{rm{ac}}}^{t},{X}_{{rm{ac}}}^{2000}right)$$
(3)
The first term on the right side of equation (3) is the RF model simulation obtained by accounting for the dynamics of all predictors, and the second term is the RF model simulation generated by considering all predictors dynamic except the factors of autocorrelation in climate that are kept constant to their first-year value (year 2000). For such runs, we used predictors computed on an annual scale over a 2-year lagged temporal window, consistently to the TAC time series. We found that the direct effects of autocorrelation in climate have led to a positive trend of TAC in dry zones (due to the increasing autocorrelation of the drivers in these regions) and to an opposite effect in temperate humid forests (Supplementary Fig. 3). To remove these confounding effects, the estimated term ({{rm{TAC}}}^{t}| {X}_{{rm{ac}}}) is factored out from the TACt by subtraction to derive an enhanced estimate of annual resilience that is independent of autocorrelation in climate (Extended Data Fig. 3).Long-term linear trends computed on the resulting enhanced TAC time series (δTAC) represent our reference CSD indicator used in this study to explore the changes in forest resilience. δTAC was quantified for each grid cell (Fig. 1a) and represented in the climate space following the methodology previously described (Fig. 1b). We then assessed the significance of the trends at bin level by applying a two-sided t-test for the sampled trend distributions within each bin. This significance test is independent from the structural temporal dependencies originating from the use of a 2-year lagged temporal window to compute the TAC time series.Following an analogous approach described in equation (3), we disentangled the effect of the variation in forest density, background climate and climate variability on temporal changes in TAC (Fig. 1d,e). We recognize that other environmental factors not explicitly accounted for in our RF model could play a role in modulating the temporal variations in TAC. However, given the comprehensiveness of the suite of predictors used in equation (2) (Extended Data Table 1), it seems plausible that residuals mostly reflect the intrinsic forest resilience, the component intimately connected to the short-term responses of forests to perturbations, which is not directly related to climate variability. Forest ecosystem evolutionary processes could also play a role, but longer time series would be required to reliably capture these dynamics. Furthermore, abrupt declines (ADs) in the vegetation state and following recoveries, similarly to those potentially originating from forest disturbances (for example, wildfires and insect outbreaks), could influence the TAC changes. However, such occurrences, being distributed across the globe throughout the whole period, are expected to only marginally affect the resulting trend in TAC time series.Sensitivity analysisTo assess the robustness of our results with respect to the modelling choices described above, we performed a series of sensitivity analyses for the difference in TAC retrieved for the two independent periods (2000–2010 and 2011–2020). To this aim, we tested their dependence on: the quality flag of the NDVI data used for the analyses (good, good and marginal); the gap-filling procedure tested on different periods (year and growing season); the inclusion or exclusion of forest areas affected by ADs; the threshold on the maximum percentage of missing NDVI data allowed at the pixel level (20%, 50% and 80%); the threshold on the minimum percentage of FC allowed at the pixel level (5%, 50% and 90%); and the pixel spatial resolution used for the analyses (0.05°, 0.25° and 1°). In addition, we tested the sensitivity of the trend in total TAC signal on the moving temporal window length used to calculate autocorrelation at lag 1. Results obtained for the different configurations were compared in terms of frequency distributions, separately for climate regions (Extended Data Fig. 4), and further explored in the climate space (Extended Data Figs. 5 and 6). Outcomes of the sensitivity analysis are discussed in Supplementary Discussion 2.Interplay between GPP and CSDResilience and GPP interact with each other through mutual causal links. On one hand, a reduction in forest resilience makes the system more sensitive to perturbations with potential consequent losses in GPP (ref. 26). On the other hand, a reduction in GPP may lead to a decline in resilience according to the carbon starvation hypothesis, and may be associated with increasing hydraulic failure46. To explore the link between forest resilience and primary productivity, we quantified the correlation between TAC and GPP. Estimates of GPP were derived from the FluxCom Model Tree Ensemble for the 2001–2019 period at 8-daily temporal resolution and 0.0833° spatial resolution and generated using ecosystem GPP fluxes from the FLUXNET network and MODIS remote sensing data as predictor variables36 (http://www.fluxcom.org/). Annual maps of GPP were quantified and resampled to 0.05° to match the temporal and spatial resolution of TAC time series. The Spearman rank correlation (ρ) was then computed between annual GPP and TAC over a 1° spatial moving window to better sample the empirical distribution of the two variables (Fig. 2d). The significance of ρ(GPP,TAC) was assessed over the climate space separately for each bin (Fig. 2e), similarly to the approach used to test the significance of δTAC. Furthermore, we explored the relationships between the trend in GPP (δGPP) and the trend in TAC (δTAC) by clustering the globe according to the directions of the long-term trajectories of the above-mentioned variables (Fig. 2f).Disentangling the impact of forest managementTo characterize TAC on different forest types and disentangle the potential effects originating from forest management, results were separately analysed for intact forests and managed forests. Intact forests were considered those forest pixels constituting the Intact Forest Landscapes57 dataset (https://intactforests.org/). Intact Forest Landscapes identifies the forest extents with no sign of significant human activity over the period 2000–2016 based on Landsat time series. The remaining forests pixels—not labelled as intact—were considered as managed forests (Extended Data Fig. 8). The resulting forest type map is consistent with those used for United Nations Framework Convention on Climate Change reporting58, although with more conservative estimates of intact forests in the boreal zone due to the masking based on FC and percentage of missing data applied in this study.We analysed the differences in long-term TAC (computed for the whole 2000–2020 period) between managed and intact forests by masking out the potential effect of climate background. To this aim, we compared the climate spaces generated separately for managed and intact forests by extracting only those bins that are covered by both forest classes. The resulting distributions—one for each forest class—have the same sample size, and each pair of elements shares the same climate background. Potential confounding environmental effects on average recovery rates are, therefore, minimized. We then applied a two-sided t-test for analysing the significance of the difference in the sampled means (Fig. 2a). An analogous approach was used to test the differences in δTAC and ρ(GPP,TAC) between managed and intact forests (Fig. 2b,c).Early-warning signals of abrupt forest declinesWhen forest ecosystems are subject to an extended and progressive degradation, the loss of resilience can lead to an AD (refs. 3,4,5). Such abrupt changes can trigger a regime shift (tipping point) depending on the capacity of the system to recover from the perturbations (Supplementary Methods 1 and 2). We investigated the potential of changes in TAC as early-warning signals of ADs in intact forests over the 2010–2020 period. To this aim, we quantified at the pixel level ADs as the events occurring on a certain year when the corresponding growing-season average kNDVI was more than n-times local standard deviation below the local mean. Local mean and standard deviation (σ) were computed over the 10-year antecedent temporal window (undisturbed) period and n varies between 1 and 6 with higher values reflecting more severe changes in the state of the system. For each pixel and for each fixed n value, we recorded only the first AD occurrence, thus imposing a univocal record for each abrupt change in the state of the system.We then explored whether the retrieved ADs were statistically associated with antecedent high values of δTAC. To avoid confusion with the attribution of causality, for each AD that occurred at time t (over the 2010–2020 period), we derived the δTAC over the temporal window 2000 − (t − 1). The resulting trend in δTAC is therefore antecedent and independent of the changes in vegetation associated with the AD. Then, for each pixel with an AD at time t, we also extracted randomly one of the undisturbed (with no AD) adjacent pixels and retrieved δTAC over the same temporal window. This analysis produced two distributions of δTAC associated with pixels with and without ADs (AD and no AD, respectively). The two distributions have the same size and each pair of elements shares similar background climate. We calculated the probability of occurrence of AD conditional on the trend in δTAC (({rm{AD}}| delta {rm{TAC}})) as the frequency of ADs for which (delta {rm{TAC}}left(mathrm{AD}right)| > delta {rm{TAC}}left(mathrm{no; AD}right)), and the significance of the difference in the two sampled means (AD and no AD) was evaluated through a two-sided t-test. Probability and significance were assessed for different climate regions and severity of ADs (Fig. 3a). High statistically significant probabilities suggest that the AD is following the drifting towards a critical resilience threshold plausibly associated with changes in environmental drivers.We complemented the aforementioned analyses by retrieving the tolerance and proximity to AD, hereafter determined for a 3σ severity. We first quantified the TAC that proceeded the occurrence of an AD and followed a progressive loss of resilience as captured by positive δTAC. This value, hereafter referred to as abrupt decline temporal autocorrelation (TACAD), reflects the TAC threshold over which we observed an abrupt change in the forest state (Fig. 3b). The tolerance to AD was quantified as the difference between the local TACAD and the TAC value averaged over the 2000–2009 period to characterize the pre-disturbance conditions. The tolerance metric was explored across a gradient of aridity index59 (Fig. 3c).TACAD can be directly retrieved only on those forest pixels that have already experienced an AD. As a considerable fraction of undisturbed forests could potentially be close to their critical TAC threshold, or even have already passed it, it is important to determine their TACAD. To this aim, we developed an RF regression model that expresses the TACAD as a function of the set X of environmental variables used in model f (equation (2)) but excluding the autocorrelation in climate drivers (Xreduced) already disentangled in the TAC signal. The general formulation is as follows:$${{rm{TAC}}}_{{rm{AD}}}=gleft({X}_{text{reduced}}right)+{varepsilon }_{{rm{g}}}$$
(4)
in which g is the RF regression model, Xreduced are the environmental predictors and εg are the residuals. Implementation, calibration and validation of g follow the same rationale described before for the f model. We found that the RF model explains 50% of the variance (R2) of the observed TACAD, with a mean squared error of 0.019 and an average underestimation of 0.86 (PBIAS).The RF model was then used to predict the TACAD over the whole domain of intact forests and served as input to quantify the proximity to AD of undisturbed forest pixels at the end of the observational period (year 2020). Here we defined the proximity metric as the difference between the value of TAC in 2020 and TACAD. Proximity takes negative or zero values when TACAD has already been reached (({{{rm{TAC}}}^{2020}ge {rm{TAC}}}_{{rm{AD}}})) and positive values when there are still margins before reaching the critical threshold (({{{rm{TAC}}}^{2020} < {rm{TAC}}}_{{rm{AD}}})). Together (delta {rm{TAC}} > 0) and ({{{rm{TAC}}}^{2020}ge {rm{TAC}}}_{{rm{AD}}}) therefore represent the most critical conditions, as they indicate that the critical resilience threshold for AD has already been reached and the ecosystem is continuing to lose its capacity to respond to external perturbations. We finally quantified the amount of GPP potentially exposed to such critical conditions by linearly extrapolating the GPP for the year 2020 (available GPP data stop in 2019) and overlaying it on the map of critical conditions (proximity to ({rm{AD}} < 0) and (delta {rm{TAC}} > 0)).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this paper. More

The Sargasso Sea was identified as the spawning area of the European eel (Anguilla anguilla) 100 years ago, and numerous subsequent surveys have verified that eel larvae just a week old are regularly recorded there. However, no adult eels or eel eggs have ever been found, leaving room for alternative hypotheses on the reproduction biology of this enigmatic species. Chang et al.1 theorize about an area along the Mid-Atlantic Ridge as a potential spawning ground. The main argument for this hypothesis was that the chemical signature found in eel otoliths would indicate that early stage larvae had been exposed to a volcanic environment, such as the one present along the Mid-Atlantic Ridge. Since this correlation was solely based on a mis-interpretation of cited literature data, no new, conclusive information to pinpoint the Mid-Atlantic Ridge as an additional or even alternative spawning area was presented by Chang et al.For more than 100 years, the life history of Atlantic eels remains a matter of scientific debate. In a recent paper by Chang and colleagues, published in Scientific Reports (Sci Rep 10, 15981 (2020)), it is hypothesized that the spawning areas of the European eel (Anguilla anguilla) and the American eel (A. rostrata) are located along the Mid-Atlantic Ridge at longitudes between 50° W and 40° W1. This area lies outside the Sargasso Sea, which has so far been widely assumed to be the spawning region of both species since the beginning of the twentieth century2. The Danish researcher Johannes Schmidt collected eel leptocephali 30 mm long or less, some as short as 9 mm, all south of 30° N and west of 50° W3,4. Since then, Schmidt’s assumption was supported by a number of investigations that found recently hatched European eel larvae ( More

The whiskers of seals are known to function as vibration receptors. Earlier experiments with blindfolded harbour seals in captivity have for example revealed that the animals can detect small water movements, and follow the hydrodynamic trails created by passing objects. But it is unclear if seals in the wild actively use this ability to find prey.
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