Ecology

Spatial map of Chinese protected areas
The database of protected areas (PA) distribution in China and a digitized spatial map thereof were compiled from Zhao et al.40 and Zhang et al.41. In total, we obtained the information of 2622 protected areas in China, which also included marine reserves. In order to evaluate the representation of terrestrial protected areas, we excluded marine reserves from our analyses. We also excluded Taiwan because we did not have the spatial distribution data for nature reserves in Taiwan. Finally, we had the boundary information of 2572 protected areas covering about 15.2% land area in China.
Species’ range maps
Range maps of threatened vertebrates (birds, mammals, amphibians and reptiles) were obtained from the IUCN’s Red List42. Distribution data of threatened plants were compiled from Flora of China, Atlas of woody plants in China, provincial and local floras, checklists of nature reserves, various inventory reports across China and peer-reviewed papers. We obtained the information for critically endangered (CR), endangered (EN) and vulnerable (VU) species. The conservation status of vertebrates was obtained from IUCN Red List42, while that of plants was obtained from Qin et al.43. In total, we obtained the distribution information of 103 birds, 86 mammals, 134 amphibians, 50 reptiles, and 2983 plants in China (see Supplementary Data 1). We estimated the number of species in each PA by overlaying the map of PA with the species’ range maps in ArcGIS 10.2 (ESRI, Redlands, CA). In order to validate the distribution of species, we further verified the presence of species in respective PA by checking their inventory reports.
Human footprint data
In order to measure the extent of human pressure on the protected areas, we obtained the most comprehensive global map of human pressure i.e., human footprint (HFP) from https://wcshumanfootprint.org. The human footprint measures the cumulative impact of direct pressures on environment from human activities and is based on data from built environments, agricultural lands, pasture lands, human population density, night-time lights, railways, roads and navigable waterways44. It is one of the most complete and finest terrestrial datasets on cumulative human pressure on the environment. The human footprint maps of two time periods (1993 and 2009) are available at present. We downloaded the maps of both time periods at the spatial resolution of 1 km × 1 km to quantify the change in human pressure within Chinese protected areas over a 16-year period. It should, however, be noted that any point estimate of the change in HFP might include errors due to the resolution and reliability of the component layers. For example, one of the components of HFP is the night-time lights, which changed over time from incandescent to mercury vapor to light emitting diode. This means that the change in night light is due to more than development. As a result, the systemic bias in regional economy could likely cause low HFP in wealthy as compared to rural areas. While this issue does not invalidate our analyses, such comparisons should be applied with caution.
Climate data
In order to represent the climatic conditions of the past and the present, we obtained 20 years climate data comprising monthly minimum temperature, monthly maximum temperature and monthly precipitation for two time periods (past: 1961–1970 and present: 2010–2019). We used 10 years window for each time period to capture the variability in climatic conditions in order to prevent over- or underestimation of the past and present climate. In total, we obtained 12 months × 10 years × 3 variables × 2 time periods = 720 global raster layers from the Climate Research Unit (CRU TS v. 4.04) database (http://www.cru.uea.ac.uk/data) at the spatial resolution of 0.5° × 0.5°45. We then calculated the monthly mean values for the three variables for each time period separately. From these monthly mean values, we estimated mean annual temperature (MAT), mean temperature of warmest quarter (MTWQ), mean temperature of coldest quarter (MTCQ), mean annual precipitation (MAP), precipitation of driest quarter (PDQ) and precipitation of wettest quarter (PWQ) for each time period using biovars function in the R package ‘dismo’46. We then estimated the average value of climate variables in each PA using zonal.stats function in the R package ‘spatialEco’47. In order to reduce dimensionality and collinearity of the 6 climate variables, we performed principal component analysis (PCA) using prcomp function in R v4.0.248. Following Carroll et al.37, we used climate data for both past and present based on the first 2 PCA axes, which explained 89.4–89.8 % of the variance (Supplementary Tables 1-2). Additionally, we also estimated the change in mean annual temperature to identify PAs that have experienced climate warming higher than the Paris Agreement threshold of 1.5 °C. All the analyses were performed in R version 4.0.248.
Vulnerability mapping
We calculated three indices of vulnerability within each protected area: (i) species vulnerability, (ii) anthropogenic vulnerability, and (iii) climate vulnerability. In order to measure species vulnerability, we first assigned numerical value to each IUCN threat category using a geometric progression49,50. We gave scores of 2, 4, and 8 to species belonging to categories VU, EN, and CR, respectively. We, then, summed the score of all species in each PA and standardized the value to the range of 0–1 using minimum–maximum normalization. We performed these steps separately for birds, mammals, amphibians, reptiles and plants to calculate the vulnerability score of each group. We also calculated the cumulative score by combining the total scores of all five groups. Values close to 0 indicated low species vulnerability and values close to 1 indicated high species vulnerability. Although species diversity is highly correlated with the species vulnerability metric used herein (Pearson’s correlation coefficient = 0.94 − 0.99, p  More

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Climate change has increased water temperature and altered wind-driven water movements in aquatic systems1,2. This applies not only to the mean conditions3,4, but also to the frequency of extreme events (i.e., near the upper ends of the range of observed values5,  > 80th percentile). For example, high air temperature or powerful winds5,6,7,8,9 has affected the behaviour of surface gravity waves10. Understanding the changes in wind and wave climate provides insight into the prediction and management of climate change impacts related to coastal dynamics, such as coastal erosion and sediment budgets, water motions, and biological responses6,11,12. Several studies on the impacts of climate change on oceanic waves12,13,14,15 have been undertaken, including a recent study16 that shows a 0.41% annual increase in global wave power (WP; the transport of energy by waves, which represents the temporal variations of energy transferred from the atmosphere to the ocean surface motion over cumulative periods of time16,17 (Eq. 2) due to stronger winds caused by increases in sea surface temperature. The oceanic wave climate also responds to global atmospheric phenomena (e.g., El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation), in which sea surface temperature modifies wind patterns and storm cyclogenesis12,18,19,20. A systematic long-term assessment of climate warming impacts on waves in lakes remains to be undertaken, but should include winds, which are one of the principal sources of mechanical energy for lake circulation and interbasin coupling (e.g., exchange)21,22,23,24.
The Laurentian Great Lakes, which consist of lakes Superior, Michigan, Huron, Erie, and Ontario (Fig. 1a), are the largest group of freshwater lakes on Earth; they contain 21% of the world’s volume of fresh surface water. These lakes have been affected by climate change in several ways including increased surface water temperature, longer summer stratification related hypoxia (i.e., dissolved oxygen [DO] concentrations  0.05); all the black bars are significant (i.e., p  8 m s−1) from the south and southwest that are the common wind directions over the Great Lakes23, tilt the thermocline upward in the western and northern part of the central basin due to Ekman transport of surface water southward22,38,42,43,44,45. As this hypolimnetic water upwells into shallower depths it can be transported counter clockwise by the alongshore surface currents moving to the west32. If there is a calm period following the high winds, the upwelled water in the northwestern part of the central basin will flow southward because of the pressure gradient and also in a clockwise direction (to the west) because of the Coriolis effect, and so will intrude into the western basin (i.e., a geostrophic flow) opposite to the hydraulic flow from the Detroit River (Fig. 1c)22,32,46. This causes the rapid (on the order of hours) formation of a thermocline within the northeastern portion of the western basin (Pelee Passage) due to the intrusion of low temperature bottom water22,42, which can also be hypoxic22 or anoxic (i.e., DO (approx) 0) at the sediment surface22 and contain high soluble reactive phosphorus concentrations (SRP; 0.02–0.05 mg L−1)47,48,49. Low values of sediment oxygen uptake are observed during these events in the western basin due to stratification and weak bottom shear and turbulence, which results in thicker diffusive sublayer22.
Interbasin exchange has been observed in lakes with multiple basins elsewhere (e.g., Lake Geneva50, Nechako Reservoir51) as well as in the Great Lakes region (e.g., Muskegon Bay52, Green Bay53, Kempenfelt Bay54, Pere Marquette River55). In Lake Michigan, for example, high winds can lead to coastal upwelling into Muskegon Lake causing episodic hypoxia52. In case of Lake Erie, interbasin exchange was identified as the dominant cause (63%) of hypoxia in the northeastern portion of the western basin during biweekly fishing trawls in August over the past 30 years22. However, there are no long-term continuous water quality observations to assess the occurrence and historic trends in these hypoxic events. Extreme winds prevailing from upwelling favourable directions (i.e., from the south and southwest) can generate strong surface waves and water currents through momentum flux at the air–water interface. Therefore, WP can be used as an indicator or proxy (but not the cause) of interbasin exchange. Here, we examine the historical trends in water temperature, winds and resultant waves in the context of climate change in the summer in the Great Lakes (Fig. 1a) with an emphasis on the western basin of Lake Erie (Fig. 1b). We examine data for August, which is the month when hypoxia is most likely to occur in dimictic north temperate lakes before the fall turnover, and when large HAB have been observed in the western basin of Lake Erie. August is also the time when the spatial extent of hypoxia in the central basin is the largest and when the aforementioned upwelling into the western basin is likely to occur22,40,56. The data examined are from buoys with the longest historical records (Fig. 1a and Table S1). We examine winds from the south and southwest directions, which are the common wind directions over the Great Lakes during August, and which are favourable for upwelling into the western basin of Lake Erie. The results show that the WP in Great Lakes has increased in the past 40 years. A pattern in WP (a proxy for hypoxic upwelling events into the western basin of Lake Erie) has also increased in frequency over this time, which has implications for the water quality (e.g., dissolved oxygen and total phosphorus) of the lake. The increased frequency of interbasin upwelling was confirmed using historical records of lake bottom water temperature (LBT), as well as dissolved oxygen and total phosphorus concentrations. This is the first time that WP has been identified as an indicator of climate change-driven biogeochemical responses in lakes.
Long-term trends in WP and LST in the Great Lakes
First, we investigate the historical trends in average lake surface temperature (LST), wind, and waves in the Great Lakes during August. Results show that LST and LSTw (hereinafter subscript ”w” is used to denote the variables measured during upwelling favourable winds from 180° to 270°, clockwise from north) have both increased significantly (p  0.2% year−1) since 1980, although lower trends were observed in lake Erie and Michigan (Figs. S1–S5 ((a) and (b)) and Table S1). These changes in the LST correspond to a warming trend in air temperature (Tair); the average Tair over the Great Lakes increased significantly by ~ 0.4 (pm) 0.2 ((pm) standard error) oC decade−1 since 1980 (Fig. S6a,b). There was an associated significant increase in wind speed (W) over the Great Lakes during August (Ww) of ~ 0.4 (pm) 0.1 m s−1 decade−1 for winds from the south and southwest (Figs. S1–S5 ((c) and (d)) and Table S1). Consequently, the wind stress associated with wind from the south and southwest over the water surface of the Great Lakes (({tau }_{w}=0.0012{rho }_{air}{W}_{w}^{2}), where ({rho }_{air})=1.22 kg m−3 is the density of air57, and the wind speed is measured 10 m above the water) increased significantly by 0.006 (pm) 0.002 Pa decade−1 during August (3.0 (pm) 0.9% year−1; Figs. S1–S5 ((e) and (f)) and Table S1).
The effects of increased wind stress can also be seen in wave power, which is a function of the square of significant wave height (the mean value of the largest third of the wave heights during typically 1 h, SWH) and the wave period (({T}_{p}); i.e., (WP propto {{T}_{p} times SWH}^{2})); and changes in wind are reflected in wave power ((WP propto {W}^{2.4}) and (propto {W}^{5}) for developing and fully developed waves, respectively; see “Materials and methods”). The average SWH and SWHw in the Great Lakes during August have increased significantly by 0.03 (pm) 0.02 and 0.04 (pm) 0.03 m decade−1, respectively (i.e., ~ 1.0 (pm) 0.8% and ~ 1.7 (pm) 1.5% year−1, respectively), and this is largely driven by the increase in the frequency of extreme surface winds58 (Figs. S1–S5g and h; WP responds to changes in mean values, but it is more sensitive to extreme events because WP (propto { SWH}^{2})16). Consequently, the average WP and WPw in the Great Lakes during August have increased by ~ 0.04 (pm) 0.02 and ~ 0.06 (pm) 0.03 kW m−1 decade−1, respectively (i.e., ~ 1.0 (pm) 0.6% and ~ 2.0 (pm) 0.9% year−1, respectively; Fig. 2). In Lake Erie, WPw during August increased significantly by 0.02 (pm) 0.01 kW m−1 decade−1 (1.4 (pm) 0.2% year−1; Fig. 2 and Table S1; the increasing trend in WP = 0.02 (pm) 0.02 or 0.5 (pm) 0.1% was not statistically significant). It is relevant to note that these results are based on observations from a single buoy per lake; the one with the longest available data records (Fig. 1a and Table S2). However, the wind records and historical wave trends between buoys Sta. NDBC 45005 and Port Stanley in Lake Erie (Fig. 1a), which are ~ 130 km apart, are consistent based on the available records. Specifically, wind speed and direction in 2018 have Pearson correlation coefficients, r  > 0.6 (Fig. S7a,b, respectively); Ww and WPw are also correlated with r = 0.51 and 0.67, respectively, during August of 1990–2018 and the buoys show similar temporal increases in WPw (~ 0.025 (pm) 0.02 and 0.02 ± 0.01 kW m−1 decade−1 in Port Stanley and Sta. NDBC 45005, respectively). The trends in historical LSTw and WPw are related statistically (i.e., higher mutual information; Fig. S8) similar to the relationship described for global sea surface temperature and oceanic WP used as an indicator of climate change16.
Figure 2

Historical patterns in wave power in Great Lakes. 10 year moving average of wave power (WP) during the August (a) and during August with the wind from south and southwest and (WPw; b). The dashed lines show the linear regression (statistical results provided in Table S1).

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The long-term variations in WP and LST may be related to the global atmospheric phenomena. The LSTw anomaly in all the lakes show an increasing trend beginning in 1995 (Fig. S9a), which corresponds to the switch from the negative mode of the Atlantic Multidecadal Oscillation (AMO) to the positive mode (associated with increased tropical cyclone activity and stronger westerly winds) between the 1980s and the early 2000s (Fig. S9b)16. Both the WPw and LSTw anomaly are positively correlated with the AMO (r ~ 0.50 and ~ 0.55, respectively, since 1990). Similar to global oceanic wave power16, peaks in WPw in the Great Lakes are associated with strong El Niño years (i.e., Multivariate El Niño/Southern Oscillation (MEI) greater than 1.5; Fig. S9c,d), which can contribute to the enhanced wind energy due to increased cyclonic events16. MEI and WPw in Great Lakes are generally correlated by r  > 0.45 since 1990, however, the impacts of global atmospheric events on temperature and water dynamics of Great Lakes requires further study.
Episodic hypoxic upwelling events in the western basin of Lake Erie
We used historical records (Table S2) of long-term near-bottom water temperature (1998–2018) and dissolved oxygen (2007–2018) in the northeastern portion of the western basin of Lake Erie as well as wave observations in the western portion of the central basin (1980–2018 in Sta. NDBC 45005, Fig. 1) in August to determine the frequency of hypoxic upwelling events and the impacts of these events on the total phosphorus concentration in the northeast portion of the western basin. These analyses do not include the local hypoxia due to periods of calm and warm atmospheric conditions that may occur annually31 and, which are different than episodic upwelling events. Intrusion of cold hypoxic hypolimnetic water from the central basin into the western basin, following high winds from upwelling favourable directions, can cause a sudden drop (on the order of hours) in LBT and dissolved oxygen (DO) when the hypolimnetic water in the central basin is hypoxic22. The LBT time series in the western basin from 2017 to 2018 show that LBT decreased more than 3 °C in less than 12 h during upwelling events; e.g., 9–16, 18–22 and 26–31 August 2018 at Sta E (Fig. 3b) and 24–29 August 2017 at Leamington and Sta E (Fig. S10b). The records of LBT measured by the Ontario Ministry of Natural Resources and Forestry (MNRF) in August in Leamington Ontario between 1998 and 2018 detected 23 events of intrusion of cold water, which are consistent with upwelling (the blue symbols in Fig. 4a).
Figure 3

Wave power and bottom water temperature during August 2018 in the western basin of Lake Erie. (a) Time series of wave power (WP; black line), wave period (Tp; magenta), and significant wave height (SWH; blue) recorded at Sta. NDBC 45005. (b) Time series of dissolved oxygen (DO; red) and water temperature (LBT; blue dashed-line) in Sta. E at 1 m above the bed and bottom water temperature in Leamington (blue solid-line) in August 2018. The red triangles represent the observed hypoxic events in the western basin of Lake Erie. The wave power of the waves from south and southwest (i.e., favourable for upwelling) are positive preceding upwelling.

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Figure 4

Number of hypoxic upwelling events in the western basin. (a) The number of hypoxic upwelling events based on patterns in wave power at Sta. NDBC 45005 (dark grey: average WPw  > 0.44 kW m−1, light grey: 0.37  8 m s−1 from similar directions, which corresponds to the ~ 80th percentile of wind speeds and is greater than the sum of the average and standard deviation of the wind speed (~ 6 and 2 m s−1, respectively). This wind threshold is consistent with Rao et al.’s44 wind speed that led to upwelling, which resulted in a fish kill along the north shore of the central basin in 2012.
We used a least-square method to find a wave pattern (i.e., wave direction, duration, and power) that could be applied to predict the number of upwelling events that could be hypoxic between 1998 and 2018 based on LBT observations. A rapid decrease in the LBT at both Sta E and Leamington (12 km vs. 20 km from the Pelee Passage, respectively) occurred during events in which the average WP was  > 0.44 kW m−1 (i.e., 22–24 August 2017; Fig. S10a,b). The model predicted 25 upwelling events at Leamington (dark bars in Fig. 4a) of which 23 were observed (as stated above; no data were available for 2012; blue circles in Fig. 4a) for waves from south and southwest that lasted for at least 15 h with an average wave power greater than 0.37 kW m−1. Of the 23 observed events, the model predicted 21 events providing a root mean square error [RMSE] of 0.20 events. We validated the model predictions using the biweekly DO measurements from MNRF cruises between 2007 and 2018, which happened to sample 17 of the 23 observed events of low LBT. We note, however, that two hypoxic upwelling events were also recorded outside the study period, i.e., early September; this supports the study’s focus on August. Hypoxic conditions (DO  1.6 events year−1 in 2018 based on a 10-year moving average. Specifically, 21 of 49 (~ 43%) upwelling events in the last four decades have occurred in the past 10 years. Thirty-two of these were strong events with WP  > 0.44 kW m−1, 15 of which (~ 47%) occurred after 2009. Interestingly, this pattern in wave power (i.e., waves from south and southwest that last for  > 15 h with an average WP  > 0.37 kW m−1 from the historical data) was also observed in August 1980 (Fig. 4a), when the LBT dropped following rapid formation of a thermocline, which at the time was attributed to the upwelling of hypolimnetic water from the central basin40,42. These results indicate that an increase in extreme winds from south and southwest during August, over the last four decades, has resulted in more frequent upwelling from the central basin into the western basin and consequently a greater number of episodic hypoxic events in that part of Lake Erie.
The effect of upwelling on phosphorus concentrations was examined through an analysis of the water column-average total phosphorus (TP) observations from biweekly cruises conducted by the MNRF at station W5 (Fig. 1b). We examined the available data recorded between 15 July and 15 September from 2000 to 2018 (3–5 records year−1; 66 observations in total), which is a period in which linear patterns in TP vs. sampling date were not evident (p  >   > 0.05). The z-score (standard deviate) was determined for the data within a given year (({mathrm{Z}}_{mathrm{TP}}=left(mathrm{TP}-{mathrm{TP}}_{mathrm{mean}}right)/mathrm{SD}), where ({mathrm{TP}}_{mathrm{mean}}) is the annual average of TP and SD is the standard deviation). Positive ({mathrm{Z}}_{mathrm{TP}}) values (i.e., (mathrm{TP} >{mathrm{TP}}_{mathrm{mean}})) were observed in 11 cases in which the sampling occurred  1) observed during 5 August–8 September sampling (black solid circles in Fig. 4b). Statistical comparison revealed that the average ({mathrm{Z}}_{mathrm{TP}}) was significantly higher during upwelling vs. non-upwelling samples (i.e., 0.95 ± 0.18, n = 11 vs. − 0.26 ± 0.12, n = 25; ANOVA F1,34 = 29.64, p  More