Ecology

RESEARCH BRIEFINGS
20 April 2022

Protected areas are a cornerstone of conservation policies, but many are not benefiting target species. These areas need to be managed better if they are to achieve conservation goals. More

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In this episode:00:46 What COP26 promises will do for climateAt COP26 countries made a host of promises and commitments to tackle global warming. Now, a new analysis suggests these pledges could limit warming to below 2˚C – if countries stick to them.BBC News: Climate change: COP26 promises will hold warming under 2C03:48 Efficiency boost for energy storage solutionStoring excess energy is a key obstacle preventing wider adoption of renewable power. One potential solution has been to store this energy as heat before converting it back into electricity, but to date this process has been inefficient. Last week, a team reported the development of a new type of ‘photothermovoltaic’ that increases the efficiency of converting stored heat back into electricity, potentially making the process economically viable.Science: ‘Thermal batteries’ could efficiently store wind and solar power in a renewable grid07:56 Leeches’ lunches help ecologists count wildlifeBlood ingested by leeches may be a way to track wildlife, suggests new research. Using DNA from the blood, researchers were able to detect 86 different species in China’s Ailaoshan Nature Reserve. Their results also suggest that biodiversity was highest in the high-altitude interior of the reserve, suggesting that human activity had pushed wildlife away from other areas.ScienceNews: Leeches expose wildlife’s whereabouts and may aid conservation efforts11:05 How communication evolved in underground cave fishResearch has revealed that Mexican tetra fish are very chatty, and capable of making six distinct sounds. They also showed that fish populations living in underground caves in north-eastern Mexico have distinct accents.New Scientist: Blind Mexican cave fish are developing cave-specific accents14:36 Declassified data hints at interstellar meteorite strikeIn 2014 a meteorite hit the Earth’s atmosphere that may have come from far outside the solar system, making it the first interstellar object to be detected. However, as some of the data needed to confirm this was classified by the US Government, the study was never published. Now the United States Space Command have confirmed the researchers’ findings, although the work has yet to be peer reviewed.LiveScience: An interstellar object exploded over Earth in 2014, declassified government data revealVice: Secret Government Info Confirms First Known Interstellar Object on Earth, Scientists SaySubscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed. More

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Coupling accuracy analysisPrecipitation simulation accuracyThe comparison between annual precipitation simulated by WRF-Hydro and measured precipitation is shown in the following Fig. 3a. From the Fig. 3a, we can get that the correlation between simulated precipitation and measured precipitation is 0.783, which is relatively high and the simulation is good. In addition, the simulated precipitation is less than the measured precipitation value in time. We guess that this error is caused by the precision and quality of precipitation products. WRF-Hydro can easily underestimate the duration of heavy rain when simulating precipitation, so the simulated precipitation is slightly smaller than the measured precipitation in long-term sequence, but the overall accuracy is good.Figure 3(a) Comparison between WRF-HYDRO simulation and measured annual precipitation in Daihai; (b) Comparison of runoff simulation and remote sensing estimation in Daihai Lake; (c) Modified runoff simulation and remote sensing estimation in Daihai Lake.Full size imageThe comparison between the simulated spatial distribution of annual precipitation and the verified products in the study area is shown in the Fig. 4. Generally speaking, the precipitation of interpolation products is slightly higher than the simulation value, which is consistent with the above analysis. In addition, the spatial distribution law of the two is consistent with each other, and the spatial variation law is basically the same. However, the transition of simulation results in areas with severe precipitation changes is relatively gentle, while the transition of interpolation products is more severe. The coverage of the maximum value in the simulation results is smaller than that of interpolation products. The guess is caused by the error of setting the precipitation boundary line. The boundary of interpolation products is China as a whole, and the boundary of simulation results is only Daihai Basin, which fundamentally determines that the precipitation simulation results will be slightly smaller than the interpolation products. Because the climate and hydrology mutual chamber is defined in the model setting from the surrounding grid points, the smaller the area causes some areas with mutual chamber cannot enter the boundary line, resulting in the precipitation simulation results less than the interpolation products. But in terms of the overall spatial differentiation law, the distribution of simulation results in interpolation products is not very different, which has good practical value.Figure 4Spatial comparison of WRF-HYDRO simulation and interpolation of annual precipitation in Daihai.Full size imageSimulation accuracy of runoff into LakeThe comparison between the WRF-Hydro simulation results and remote sensing estimation results of the runoff from Daihai Lake for many years is shown in the Fig. 3b. It can be seen from the figure that the correlation between simulation results and remote sensing estimation results is 0.629, which is better. But it is obvious that the simulation results are higher than those of remote sensing. The reason may be that the model does not set up the parameters of man-made water from the river entering the lake, including agricultural irrigation water and industrial water intake. So the simulation results are overestimated to the runoff into the lake. Therefore, the simulated runoff into the lake is modified in this study to reduce the water consumption ignored by the model.The comparison between the revised simulated runoff and remote sensing estimation is shown in the Fig. 3c. As can be seen from the figure, the correlation is increased to 0.650. Although not much improvement, the simulation results and remote sensing results are distributed evenly around the boundary.Analysis of coupling resultsPrecipitation analysisThe precipitation in Daihai Basin is relatively abundant. Except for some extreme drought years and humid years, the average annual precipitation is 300–600 mm (see Fig. 5a), and the average annual precipitation is about 400 mm. It can be seen from the figure that the minimum annual precipitation is less than 250 mm; The maximum annual diameter is higher than 750 mm. The difference between extreme dry year and extreme wet year is three times.Figure 5(a) Distribution curve of annual precipitation in Daihai Basin; (b) Distribution curve of annual mean monthly precipitation in Daihai Basin.Full size imageThe monthly average of precipitation in the Daihai Basin for many years is shown in the Fig. 5b. It can be seen from the figure that the precipitation in the Daihai Basin is unevenly distributed throughout the year, with the least in January at 1.73 mm and the most in July at 112.10 mm. The precipitation in July–August accounts for more than 50% of the total annual precipitation. In addition, it can be seen from the figure that the precipitation in the Daihai Basin is mainly concentrated in June to September, which is also the flood season in the Daihai Basin, accounting for more than 70% of the total annual precipitation.Combined with Table 3, overall, the average precipitation from 1980 to 1994 is 401.75 mm, with little fluctuation; During the period from 1995 to 2011, except for extreme precipitation in some years (more than 600 mm in both 1995 and 2003), the precipitation decrease, with an average value of 371.39 mm. There are several dry years and wet years, and the fluctuation range was sharp; From 2012 to 2020, the fluctuation range is small, and the average value rises to 451.75 mm.Table 3 Average precipitation (mm) in different periods in Dahai BasinFull size tableThe spatial distribution of annual precipitation in Daihai Basin is shown in the Fig. 6. It is obvious from the figure that the precipitation in 1990, 1995 and 2020 is abundant compared with other years. In addition, it is found that although the annual precipitation in Daihai Basin varies in size, its spatial distribution is basically the same.Figure 6Spatial distribution of annual precipitation in Daihai Basin.Full size imageThe spatial pattern of annual precipitation in Daihai Basin is as follows: the southeast of Liangcheng County and the north of Zuoyun County, the northwest of Liangcheng County and the northwest of Fengzhen county are the three precipitation centers, which gradually decrease outward. And the central effect of Fengzhen county is not obvious in some years. In addition, it is found that the area around Daihai Lake has the least precipitation in the whole Daihai Basin. This may be related to the terrain surrounding the Daihai Basin.In the whole study area, the annual precipitation in the north of Zuoyun County is larger than that in other regions. In some years, the annual precipitation reaches 800 mm, and the extension area is wide. In some years, it extends to the southeast of Liangcheng County. Therefore, it is speculated that mountain torrents, debris flows, rainstorms, snowstorms and other natural disasters are prone to occur here.In addition, combined with the topographic map, it is found that the southeast and northwest of Liangcheng County are the highest elevation in the study area, which coincides with the extreme precipitation. At the same time, it is found that the spatial consistency of precipitation distribution in the whole study area is higher than that of terrain distribution in the study area. Therefore, it is speculated that the precipitation in the study area is seriously affected by the terrain, in other words, the precipitation in the study area is mostly terrain rain or mountain convective rain.Runoff analysisThe Runoff Curve of Daihai Lake is shown in the Fig. 7a. It can be seen from the figure that the flow into the lake shows a downward trend from 1980 to 2020. Although it rebounded in 1996–1999 and 2005–2007, after 2010, the runoff into the lake decreased sharply below 8 × 106m3. From 1980 to 1990, the runoff into the lake decreased linearly with a larger slope and a faster speed; However, from 1990 to 2000, the runoff into the lake appeared the first vibration wave peak, and from 2000 to 2007, the second vibration wave peak. From 2008 to 2012, the decline rate was sharp, and the runoff into the lake had been reduced to 3.95 × 106m3 in 2012; Since 2013, the runoff into the lake tends to be flat, but it has not exceeded 10 × 106m3.Figure 7(a) Change of runoff in Daihai Lake over the years; (b) Changes of lake area in Daihai over the years; (c) Changes of lake water level in Daihai over the years; (d) Changes of volume water in Daihai Lake over the years.Full size imageThe change curve of Daihai Lake area is shown in the Fig. 7b. It can be seen from the figure that the area of Daihai Lake is declining in a straight line. In a short period of 40 years, the lake area has shrunk nearly 100 km2. In addition, we found that the shrinkage rate of Daihai Lake area slowed down from 1980 to 1985, but the lake area shrank sharply from 1995 to 2000. After 2005, the atrophy curve almost coincided with the fitting curve, and the overall fitting R2 was as high as 0.958.The water level variation curve of Daihai Lake is shown in the Fig. 7c. As can be seen from the figure, the variation trend of water level in Daihai Lake is very similar to that of lake area. However, the slope of lake water level change is less than the change rate of lake area. In the 40 years since 1975, the water level in Daihai has dropped by nearly 10 m. In addition, the water level rose slightly in 1995–1996 and 2003–2006. And after 2006, Daihai water level decline rate also accelerated. Since 2006, the water level of Daihai has dropped nearly 6 m, with a rate of 0.45 m/year.The trend of the volume water volume of the Daihai Lake is shown in the Fig. 7d. It can be clearly seen from the figure that the decline curve of the Daihai Lake water volume is close to a straight line, especially from 2005 to the present, the fitting degree is as high as 0.981. There should be some geometrical relationship among the lake area, water level and water volume, and this relationship should be related to the digital elevation model of the lake bottom. In addition, the changes of lake bottom topography are not linear, so there are still subtle differences between the three changes.The annual surface runoff of Daihai Basin is shown in the Fig. 8. It can be seen from the figure that the Gongba River, the Wuhao River, the Buliang River and the Tiancheng River in the south of Daihai Lake supply the Daihai Lake for a long time, and the Bantanzi River in the West also flows into the Dai sea in some years. Combined with the spatial distribution of annual precipitation, it can be concluded that surface runoff is seriously affected by precipitation. The annual distribution is uneven. The surface runoff from the southeast of Liangcheng County generally flows into Daihai Lake to the north, but in some drought years, it will be stopped and cannot flow into Daihai Lake. Bantanzi River in the west of Daihai Lake also supplies Daihai Lake in the year of more precipitation.Figure 8Spatial distribution of surface runoff in Daihai Basin.Full size imageTaking the surface runoff of Daihai Basin in January, April, July and October 2015 as an example, the distribution of surface runoff in different seasons of the year is analyzed, as shown in the Fig. 9. It can be seen from the figure that the rivers in Daihai Basin are seasonal rivers, which are prone to be cut off in autumn and winter. In winter (December–February), there will be different degrees of snowfall events in Daihai Basin, but due to the river freezing period and small snowfall, there will be no runoff. In spring (March to May), the precipitation in Daihai Basin began to increase, and the surface runoff also began to increase, mainly from the southeast and northwest of Liangcheng County. Gongba River, Wuhao River, buliang River, Tiancheng River and Bantanzi River in the south of Daihai Lake will supply Daihai Lake, but these rivers have small flow in spring, which is easy to break. Summer (June–August) is the main period of precipitation in Daihai Basin, and the surface runoff will also surge. In July 2015, the runoff in some areas reached 2000 mm, which was prone to flood disaster. The rivers in the west and south of Daihai Lake will supply it, but the runoff into Daihai Lake is not high, and most of the runoff is concentrated in the upper and middle reaches. In autumn (from September to November), the precipitation in Daihai Basin decreases. Before the freezing period, the precipitation may form runoff, but it is difficult to flow into Daihai Lake due to the small flow.Figure 9Spatial distribution of surface runoff in different seasons in Daihai Basin.Full size imageStatistical analysis of other factorsClimatic factors

(1)

Evaporation capacity

The variation curve of annual evaporation in Daihai is shown in the Fig. 10a. It can be seen from the figure that although the evaporation in Daihai Basin fluctuates, it shows an upward trend, with an upward slope of 8.855 and R2 of 0.560. From 1980 to 1986, the annual evaporation fluctuated around 1000 mm; From 1987 to 1992, the annual evaporation of Daihai Basin decreased sharply, but from 1993 to 2000, the annual evaporation increased sharply with a very high rate of increase; But after 2000, the annual evaporation fluctuated and remained at 1250 mm.

(2)

Average temperature

Figure 10Perennial (a) evaporation (b) annual average temperature (c) annual average wind speed change in Daihai Basin.Full size imageThe variation curve of annual average temperature in Daihai is shown in the Fig. 10b. It can be seen from the figure that the annual average temperature in Daihai Basin presents an obvious fluctuating upward trend, and the fitting upward slope is 0.040, R2 is 0.406. In addition, it can be observed that in a 10-year cycle, there will be two small fluctuations and one large fluctuation, and the fluctuation will rise.

(3)

Wind speed

The curve of annual average wind speed in Daihai is shown in the Fig. 10c. It can be seen from the figure that the annual average wind speed of Daihai Basin presents a fluctuating downward trend, and the fitting downward slope is 0.036, R2 is 0.368. In addition, it can be observed that the annual average wind speed fluctuated with a mean line of 6.2 from 1980 to 1987; In 1988 and 1990, it dropped sharply with a large slope; From 1990 to 2003, the fluctuation decreased. From 2003 to 2011, the fluctuation was stable at 4.5, and rose sharply in 2012. So far, the fluctuation has been stable at 5.2.Human factors

(1)

Cultivated land area

The change curve of cultivated land area in Daihai Basin is shown in the figure. It can be seen from the Fig. 11a that the annual average wind speed in Daihai Basin presents an upward trend, with the fitting rising rate of 0.017 and R2 of 0.970, almost in a straight line. In addition, it can be observed that from 1996 to 2005, the rising rate appeared a trough, that is, the rising rate first increased rapidly and then decreased. From 2000 to 2005, the rising rate was very slow and approached zero; But since 2006, it has returned to a straight-line rise.

(2)

Industrial water consumption

Figure 11Perennial (a) cultivated land area (b) industrial water consumption (c) total population change curve in Daihai Basin.Full size imageThe change curve of industrial water consumption in Daihai Basin is shown in the Fig. 11b. It can be seen from the figure that the industrial water consumption of Daihai Basin presents an upward trend, and the fitting rising rate is 0.433, R2 is 0.794. In addition, it can be observed that from 1975 to 1993, the industrial water consumption of Daihai Basin was below 3 × 106m3; From 1994 to 2005, except for the decrease in 1998–2000, it has been on the rise, and the rising speed is fast, which has increased five times in ten years; Since 2005, the industrial water consumption in Daihai Basin has been stable at about 15 × 106m3.

(3)

Total population

The change curve of total population in Daihai Basin is shown in the Fig. 11c. It can be seen from the figure that the total population of Daihai Basin presents an upward trend, and the fitting rising rate is 0.074, R2 is 0.864. In addition, it can be observed that the total population of Daihai Basin increased slowly from 1975 to 1985; From 1986 to 1990, the total population remained flat; It fluctuated from 1990 to 2000; Since 2000, the total population has risen sharply.Analysis of driving factors of hydrological informationIn this study, the average temperature, annual precipitation, annual evaporation, average wind speed in natural factors and cultivated land area, agricultural water consumption, industrial water consumption and population in human factors are considered as the influencing factors of runoff change in Daihai Lake. Therefore, the flow into the lake and the above elements constitute a variable sequence, and the correlation matrix is calculated. See the Table 4 for details.Table 4 Correlation matrix between lake inflow and influencing factors.Full size tableIt can be seen from the Table 4 that the cultivated land area has the highest correlation with the runoff into the lake, with a correlation of − 0.777, which is highly significant, followed by the wind speed, with a correlation of 0.690, which is highly significant; In addition, the total population, industrial water consumption, evaporation and average temperature were significantly correlated. Therefore, the discharge of Daihai Lake is influenced by both nature and human. It can be seen from the table that industrial water consumption, total population, cultivated land area, evaporation and annual average temperature have a negative impact on the flow into the lake, while wind speed has a positive impact.At the same time, the correlation between different factors can be obtained from the Table. For example, the correlation between industrial water consumption and population, cultivated land area and evaporation is as high as 0.8, which is highly significant; The correlation between population and cultivated land, cultivated land and wind speed and evaporation is also about 0.8, which is highly significant; In addition, the correlations between industrial water consumption and annual average temperature, population and annual average temperature, wind speed, evaporation, cultivated land, cultivated land and annual average temperature, evaporation and wind speed, wind speed and annual average temperature are all over 0.5.It can be clearly observed from the table that except for agricultural water consumption, precipitation and evaporation, the annual average temperature is significantly correlated with other factors, and the correlation is more than 0.5. The correlation between annual precipitation and other factors is small and not significant. Therefore, it can be determined that there is data redundancy between different elements. In order to eliminate the data redundancy and get the determinants of the discharge into the lake, the correlation analysis of the variable sequence is carried out, as shown in the table.It can be seen from the Table 5 that the cumulative variance of the first three principal components has reached 87.016%, and the eigenvalues of the first two principal components are greater than 1, which has met the standard. The variance contribution rate of the first principal component was 59.641%, and the order of load rate was cultivated land (0.967), industrial water (0.950), population (0.859), evaporation (0.856), wind speed (0.841), and the load rate was greater than 0.8; In the first principal component, the influence of human factors is greater than that of natural factors. In the second principal component, the variance contribution rate is 18.821%, in which the annual precipitation (− 0.875) and agricultural water consumption (0.736) have higher load rate, and the influence of natural factors is greater than that of human factors.Table 5 Component matrix of principal component analysis of different influencing factorsFull size tableFuture forecastAccording to the analysis in Sect. 3.4, we find that human factors have a huge impact on the lake inflow. In lake water balance, precipitation and evaporation are determined by climate. Now, the Inner Mongolian government has taken a series of measures to protect the Daihai Lake. Therefore, when we predict the future lake water volume, we consider two situations: (1) the future lake water volume in the natural state without any interference (protection or destruction) measures; (2) keeping the existing water volume unchanged future lake water volume in the case.Situation IFor the Situation I, we use two forecasting methods. Method I is to directly predict the future lake water volume by using the variation law of lake volume water volume with time. Method II is to use the lake water balance equation to estimate the change in lake water volume, and then estimate the future lake water volume. The results obtained by these two calculation methods are shown in the Table 6.Table 6 Future prediction of Daihai Lake in situation I.Full size tableWhen estimating the dry years of the Daihai Lake, the results obtained by using the time-varying laws of lake area, water volume and lake depth are inconsistent. Among them, the dry year of the Daihai Lake obtained by using the water volume is 2031, the lake area is 2047, and the water depth is 2096. The three are vastly different. The reason is the uncertainty of our modeling data. As Daihai Lake is a lake in an arid area, data is extremely scarce, and there is almost no continuous measurement of water level, depth, and water volume. The lake area is interpreted from remote sensing images and is an annual average, which results in neglect of inter-annual hydrological changes. Similarly, the water depth is also obtained by remote sensing. The resolution of the remote sensing image is 30 m. We use the interpolation method to control the accuracy to about 5 m. However, in the later stage of the prediction, when the lake depth is lower than 10 m, the results begin to become inaccurate. The modeling data of lake water volume were obtained from WRF-Hydro simulations, so the uncertainty of the data led to the inconsistency of the results. We choose the most recent year as the final result of method I, that is, the forecast result of water volume.From the Table 6, we can observe that the calculation results of the two methods are quite different. The reason is that in method I, we assume that the volume of water in the lake changes linearly, and there is only one variable; in method II, the number of variables increases and the uncertainty increases. However, the years when the Daihai Lake is predicted to dry up are basically the same. Method I predicts that the Daihai Lake will be depleted in 2031, and method II is 2033, which is not much different.Situation IIFor the situation II, we control the agricultural water consumption and industrial water consumption to remain unchanged, estimate the change of volume water at this time, and then estimate the future lake water volume. Among them, the change in water consumption is only evaporation, and the change in water replenishment is precipitation and runoff. The future lake inflow and lake water volume calculated by using the water balance equation are shown in the Table 7:Table 7 Future prediction of Daihai Lake in situation II.Full size tableFrom the Table 7, we can see that under human control, although the of lake inflow will continue to decline compared with no measures, the rate of decline will be significantly slower. And the lake inflow will drop to 0 in 2060. Similarly, the water volume in the Daihai Lake will decline. But the rate is significantly slower compared with situation I. And the water volume will drop to 0 in 2140, nearly 110 years later than 2032–3033 without any control. This shows that man-made protection of the Daihai Lake is extremely important. More

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Pollution gradients, soil series, and glasshouse conditions for the empirical studySoils used for this experiment were collected from a wood preservation site (6 ha). In this site, the use of creosote, and various Cu-based salts has resulted in soil Cu-contamination over the whole site and large patches of PAH in smaller areas59. Former studies have shown the ecotoxic impact of this contamination on vegetation biomass, cover, richness and diversity60 and the reduction of soil enzymatic activity61. In February 2016, 65 kg of soil were collected from two areas of the site, one known for its contamination in Cu, and the other previously identified for its contamination in PAH59. An additional control soil was collected from the grassland next to the site. The control corresponds to an alluvial sandy soil (Fluviosol – Eutric Gleysols, World Reference Base for soil resources) and the contaminated soils were developed on this Fluviosol. Soils were transferred to a glasshouse nearby and spread out thinly on a tarpaulin for 15 days to ensure complete air drying. Ten samples of each soil were analysed for their PAH, Cu, C, N, and P concentrations. N concentration was higher in the control soils, while polluted soils showed higher P-availability (Supplementary Table 1). Regarding PAH, the 16 regulatory PAH were quantified. The range of soil properties and contamination values (889 ± 10 mg Cu.kgsoil−1 and 657 ± 331 mg PAH.kgsoil−1 for the first contaminated soil, 4276 ± 209 mg Cu.kg−1 and 3142 ± 419 mg PAH.kgsoil−1 for the second contaminated soil) showed Cu and PAH contamination in both cases, with higher contamination of the second soil. In this study, these soils are referred to as Cu-PAH soil and HIGH-Cu-PAH soil, respectively.To create the soil series, both soils were mixed by combining one third and two-thirds of air-dried contaminated soils with the control soil (March 2016) giving seven soil treatments: Control, 1/3 Cu-PAH, 2/3 Cu-PAH, Cu-PAH, 1/3 HIGH-Cu-PAH, 2/3 HIGH-Cu-PAH, and HIGH-Cu-PAH. Each of these seven soils was divided into 25 pots (10 × 10 × 15 cm) containing 800 mg of soil, giving a total of 175 pots. In order to inoculate all potted soils with similar micro-organism populations (especially Rhizobium populations), 1 g of control soil was added to the pots with undiluted polluted soils and vice versa. All pots were watered and weighed to determine their water holding capacity and left for 2 weeks to enable micro-organisms populations to react.To ensure that the environment was as homogeneous as possible during the whole experiment, a whitened glasshouse was used to favour diffuse and homogeneous solar radiation, and to limit differences in temperature and Vapour Pressure Deficit (VPD). In the case of a temperature increase above 25 °C, the glasshouse was also cooled by automatic ventilation and misting was used to avoid an increase of VPD above 1 kPa. An air temperature and humidity probe (U23 Prov V2 ®Hobo) was used to monitor VPD variations (kPa) during the whole course of the experiment.Plant cultivation and monitoring of plant developmentThe dwarf bean (Phaseolus vulgaris, cv. Oxinel, ® Vilmorin) was chosen as a model plant species because of its known plasticity of biomass allocation, both for wild and selected genotypes29. This plasticity has been detected in response to soil resources29 and also light regimes62. In addition, it is a species commonly used as bio-assays in ecotoxicology due to its sensitivity to soil pollution (see ref. 26 using soils from the same site as this study). Seeds of similar weight [0.22; 0.30 g] were selected to avoid large differences in seed reserves. After soaking for 4 h in tap water, three seeds were sown in each of the pots on March 21. Germination took 11–16.2 days depending on the soil treatment and this time increased with soil contamination. As a large majority of the seeds germinated, 1 seedling per pot was selected randomly and kept for the experiment. For each soil, we planned to harvest five plants at five different development stages (stage 1: end of cotyledon opening, stage 2: first trifoliate leaf, 3: second trifoliate leaf, 4: 3–4 trifoliate leaves, 5: 5–6 trifoliate leaves) giving 25 plants for each soil. Pots were watered every 2 or 3 days and weighed to maintain the water holding capacity (WHC) of soil at 60%63. Plants were harvested for analysis when they reached the desired development stage. In most phytotoxic soils, plants did not reach the fourth or fifth stage by the end of the experiment, thus they were harvested and classified into their real development stage at harvest (see for instance Table 1 shows that most plants of the High-Cu-PAH soil did not grow and were classified in development stage 1).Biomass partitioning, root and shoot (specific) areasOn the day of harvest, plant parts were separated (stem, leaves, and roots). Roots were washed gently with water and nodule numbers were counted. All organs were scanned and analysed to determine their area (software Winfolia for leaves and stems, WinRhizo for roots, Regents Instruments, Quebec, Canada). Then all plant samples were dried and weighed. The whole process determined the dry biomass of plant parts, their area, as well as Specific Leaf Area (SLA, cm2.g−1) and Specific Root Area (SRA, cm2.g−1). Analysis of SLA and SRA is important because: (i) they may also be involved in plant response along resource gradients to maintain a functional equilibrium. For instance, SLA can increase strength in the shade to maintain light capture area13, and SRA can increase to maintain water uptake during water stress64; and (ii) they may be impacted by soil pollution. A decrease in SRA is part of the root syndrome in phytotoxic soils because of decreasing root elongation and root thickening43.Indicators of resource acquisitionTo estimate light capture and potential acquisition of photo-chemical energy, we assessed chlorophyll a, b and another carotenoid synthesis by determining their leaf concentrations (See Supplementary Information for more details regarding corresponding methods).Water uptake and transpiration: To limit water evaporation, the soil in each pot was covered with a small plastic sheet (10 × 10 cm). At each watering, the mass of water added to maintain the pot at 60% of SWHC was recorded as the amount of water taken up and transpired since the last watering. The last 10 days before harvest were considered for analysis of plant transpiration. Independently of soil treatments, the amount of water transpired could be impacted by the leaf area (and the number of stomata), and by the variation of VPD occurring in the glasshouse despite cooling and water misting. Therefore, the weight of water transpired per leaf area, per day and per kPa of VPD (({{{{{{rm{mg}}}}}}}_{{{{{{{rm{H}}}}}}}_{2}0}).cm−2.day−1. kPaVPD−1) was calculated.Nitrogen acquisition and Symbiotic Nitrogen Fixation (SNF): To estimate N acquisition by plants, their leaf N concentration and an indicator of their SNF were determined. After drying and grinding (Retsch PM4 planetary grinder, Retsch, Haan, Germany), leaf N concentration was measured by an elemental analyser (NA 1500 NCS, Carlo Erba, Milan, Italy) for a subset of 112 samples encompassing all soil treatments and a wide range of plant size. Regarding SNF, most plants in this experiment did not initiate nodulation (because of toxicity and their small size). It was planned to use 15N soil labelling and the isotopic dilution method to estimate the efficiency of the SNF65, but it was not applicable in our experiment because of the low nodulation and the small amount of N derived from the atmosphere. Instead, at harvest, roots were cleaned gently and the number of nodules was determined.Statistics and reproducibilityAll pots were placed randomly in the glasshouse at the beginning of the experiment. They were moved randomly every 15 days to avoid any spatial dependency between sample units. All statistical analyses were performed with R software (R Core Team, 2016). Regarding the biomass of plant parts, stems and leaves were considered together in a single shoot compartment when analysing the results. Bean stems are also photosynthetically active, and separate analyses for leaves gave consistent results.All measured plant traits (SRA, SLA, water transpiration, nodule number, leaf N and chlorophyll concentration) can vary with plant ontogeny and plant size. Thus, variation of these traits (dependent variables) was analysed considering both plant size and soil treatments (explanatory variables). Shoot biomass was used as a surrogate for size for aboveground traits (SLA, water transpiration, leaf N, and chlorophyll concentration). Root biomass was used for belowground traits (root nodule number). Water transpiration was analysed by ANCOVA (shoot biomass as a regressor, soil treatment as a covariate). Leaf nitrogen and chlorophyll concentrations were first analysed by segmented linear modelling (segmented package) because of a radical change in the relationship between shoot biomass and these leaf traits at some size threshold. Then soil treatment effect on these traits was analysed on the residuals of the segmented relationships by ANOVA. Note that similar responses were observed for the different kinds of pigments, and only the results for chlorophylla+b concentrations are reported in this study. Similarly, we used a segmented linear model (segmented package) for the relationship between root nodule number and root biomass, and the soil treatment effect was analysed on this first model residuals. As to SLA and SRA, plants were grouped according to their shoot and root biomass tertile respectively. Then for each tertile, ANOVA was used to test the difference between SLA and SRA with soil treatment. When performing ANCOVAs, in the case of significant effects of soil treatment and interaction with plant size, post-hoc pairwise comparisons were used to test the difference of intercept or slopes between soil treatments (emmeans package). When performing ANOVAs, Post-hoc Tukey HSD pairwise comparisons were performed in case of significant effect of soil treatment. All variables were log-transformed when necessary to respect the condition of application of linear modelling.When investigating allometric relationships between root and shoot biomass, or root and shoot areas, the interest is related to the analysis of how root biomass (or area) scales against shoot biomass (or area), rather than predicting the value of one variable from another. Standard Major Axis (SMA) regression (smatr package) on log-transformed variables was used accordingly to study this allometric scaling and its changes with soil treatments66. When changes in α scaling exponent with soil treatment are significant, estimation of differences in β (proportionality coefficient) between treatments is not enabled by SMA regression66. In that case, after estimating α with SMA regression, we estimated β value for each soil treatment using non-linear least square modelling (see Supplementary Table 2) because changes in β values have a biological meaning in our context (delay in early root development).Meta-analysis of literature dealing with plant biomass partitioning and modelling of changes in root: shoot ratioCollection of published studies and case studies: we used the ISI Web of Science database to locate published studies on the effect of soil pollution on plant biomass partitioning. We entered a general query made using the combination of two phrases, one regarding biomass partitioning, and the other regarding soil pollution. We used several equivalent phrases regarding both terms, leading to the following query: (“biomass partitioning” OR “biomass partition” OR “biomass allocation” OR “root: shoot”) AND (“pollution” OR “contamination” OR “heavy metals” OR “PAH” OR “phytoremediation” OR “phytomanagement”). Some additional studies were picked out from the reference list found in the studies collected from our query. From the first selection of 53 potential studies (from their title and summary), the final collection made after careful reading of the entire studies comprised only 15 references (Table 2). From these studies, we identified 25 case studies suitable for the meta-analysis that was conducted in a large variety of geographical locations and climates. Studies and case studies were excluded from our database when MR: MS could not be calculated, when they dealt with air pollution (not our subject), when no phytotoxic effects were shown (no decrease in plant growth), when no statistical analyses or tests had been done for the reported results regarding MR: MS and root and shoot parts. When plant growth was reported both in hydroponic and for growth in soil substrates, we assumed that results from soil substrates where more suitable for analysing the biomass partitioning response. When other treatments were used (for instance mycorrhizae inoculation), we averaged the response to these treatments at each level of soil pollution. Finally, we considered one case study as being the unique combination of one team of researchers, one studied plant species, and one contaminant at stake. In one study, we made an exception and considered two case studies for two populations of the same plant species being exposed to the same contaminant, the two populations being reported as being metallicolous and non-metallicolous and which showed contrasting responses.The statistics and information recorded: we aimed to answer three questions: Is there any general pattern (increase or decrease) of the MR: MS ratio reported in the literature? Do changes in MR: MS depend on some explicative factors (for instance the contaminant type)? and Do MR: MS variations depend on pollution effect on plant size? This last question is important in our study which aimed to distinguish changes due to simple allometric effects rather than plant response. For each case study, the main indicator of biomass partitioning available was the MR: MS ratio, either provided directly, or calculated from root and shoot biomasses. Total plant dry biomasses were also recorded. Then, we calculated two statistics to enable the comparison of studies that were not originally designed to be compared. Firstly, we calculated the effect size metric (referred to as relative response in this study) to estimate the effect of pollution on the MR: MS ratio as follows:$${{{Relative}}},{M}_{R}:{M}_{S}={{{{{rm{log }}}}}}big({M}_{R}:{M}_{S{{_}}{{{polluted}}}}/{M}_{R}:{M}_{S{{_}}{{{control}}}}big)$$
(3)
Values close to 0 are associated with a negligible effect of the treatment, while negative and positive values indicate negative and positive effects of the treatment, respectively. The relative response is a reliable approach to quantify the effects of treatment compared to control and is regularly used in plant science (e.g. ref. 67). Secondly, we calculated a phytotoxic effect by normalisation of the effect of pollution on plant growth as follows:$$Phytotoxicity,(biomass,loss)=1-big({biomass}_{polluted}/{biomass}_{control}big)$$
(4)
Values close to 0 are associated with a negligible effect on plant growth, while values close to 1 indicate a strong decrease in plant growth. Additionally, we report relevant information to analyse its potential influence on MR: MS results. We reported the plant species involved, its functional group (monocotyledonous grass, dicotyledonous forb, and woody species), its life cycle (annual, perennial), the experiment duration (and if several measurements were made at different times) and the kind of contaminant at stake (see Table 2).The meta-analysis: regarding the general pattern of MR: MS changes, they were classified on the basis of the statistical results reported in the different studies as follows: (i) stable: no change of the MR: MS value was reported; (ii) variable: increase or decrease of the MR: MS ratio was reported for a pollution treatment compared to the control, and other treatments with a higher level of pollution showed no or opposite effects; (iii) increase: increase of the MR: MS ratio was reported for pollution treatment, and other treatments with higher levels of pollution also showed an increase compared to the control; (iv): decrease: an opposite situation to increase described above. Additionally, we tested the effect of the contaminant type, plant functional type, and plant life cycle on the relative MR: MS by ANOVA. Finally, we tested the dependence of the relative MR: MS with biomass loss for case studies showing an increase or decrease in this ratio by using linear modelling. This was done to compare results among case studies (one averaged value per case study). For results within a case study (when several soil pollution levels were available per case study), relative MR: MS and biomass loss compared to the control was calculated for each pollution level. Rate of relative MR: MS changes (Δ MR: MS /Δ biomass loss) was calculated and compared to 0.Modelling of changes in root: shoot ratio with exposure to pollution stress: we modelled the changes in the root: shoot ratio compared to a control situation without exposure to excess contaminants. We considered the change of allometric relationships (Eq. 1.) between root and shoot parts by the three potential drivers related to soil effect and plant response, and we followed the following steps. First, we calculated the growth of shoot parts as follows:$$S={gr}, .,left(1-{{gr}}_{{{{{{{mathrm{decrease}}}}}}}}right),.,d$$
(5)
gr represents plant growth rate (it can concern shoot biomass or area) per day; d is the duration of the growing period (in days); grdecrease is the phytotoxic effect on plant growth (interval [0,0.8] is considered here); S is the number of shoot parts produced after the corresponding duration d.Second, we calculated corresponding root parts as follows$$R={{upbeta }},.,left(1-{{{upbeta }}}_{{{{decrease}}}}right),.,{S}^{{{upalpha }}.(1+{{upalpha }}_{{{{increase}}}})}$$
(6)
With β and α the parameters of the allometric relationship of a given plant species in a control soil; βdecrease (the interval [0;0.5] is considered) is the effect of pollution stress on the early root development; αincrease (the interval [0;0.5] is considered) corresponds to plant response with increasing biomass partitioning in favour of roots; and R is the number of root parts produced.Finally, changes in root: shoot ratios were calculated by dividing the root: shoot ratio obtained on polluted soils by the root: shoot ratio obtained in a control situation (grdecrease; βdecrease; and αincrease set to 0).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More