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Humerus
Left humeri display a non-stationary (i.e., spatially variable in intensity) distribution of tooth marks, with inhomogeneous intensity and with a clustering trend, especially on the proximal end. Tooth marks cluster on the proximal epiphyses, both on the tubercles as well as the articular surface area and proximal metadiaphyses. They also occur in the vicinity of the deltoid crest. Shafts present more abundant modifications on the caudal and medial sides. The cranial and lateral distal shafts show very few tooth marks in comparison. This distribution shows a connection between tooth mark occurrence and areas of muscle and ligament insertions. Tooth marks were probably created during defleshing and limb detachment from the trunk. They are most abundant on the neck junction between the articular head and the proximal metadiaphysis (Fig. 2).
Figure 2

Examples of three-dimensional tooth mark distribution from the lion-consumed carcass sample on each of the four long bones. Distribution of marks is shown on bilateral representation.

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Both, the K function and the pair-correlation function indicate an overall trend of clustering. This is nuanced by the other functions. The near-neighbour G function shows a slight clustering trend in short distances and a general asymptotic trend of dispersal in longer distances. The empty-space F function suggests a trend towards clustering within an overall CSR pattern (Fig. 3).
Figure 3

Three-dimensional plot of the distribution of tooth marks on the left humerus. (A) K-function plot. (B) G near-neighbour function plot. (C) F empty space function. (C) Pair-correlation function. The F function suggests a pattern non-differentiable from CSR. The other three functions suggest a mild clustering trend in short distances. Key to (A,B,D) Dotted red line shows the Poisson Complete Spatial Random (CSR) process and the gray band shows its confidence envelope. Black line shows the point process of the target sample (here, tooth marks on humerus). When above the CSR Poisson process, it indicates a clustering trend. When below, it indicates a regular scattering trend. The interpretation is reverse for (C) (F empty space function). Same interpretation applies to equivalent figures in the Supplementary Information.

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Right humeri show a similar tendency of mark clustering around the neck under the articular surface with both tubercles impacted. Marks on the medial shaft are slightly more abundant than on the lateral shaft, while the latter shows higher concentrations around the deltoid crest. Interestingly, marks on the shaft cluster on the proximal and distal portions and the mid-shaft is mostly devoid of marks, regardless of orientation. The cranial side, especially the shaft, is again the least impacted by lions. All the functions show a moderate tendency to clustering; so much so in the K and pair-correlation functions because the latter is a modified version of the former (by using rings within the distance radius). The G function shows a very slight clustering trend in short distances, which in the F function is barely outside the CSR envelope (Fig. S4).
In sum, the slight clustering trends in both sides indicate a redundant pattern of tooth mark location. This shows that mark distribution in humeri is not random, since it is repeated across all the carcasses studied.
Femur
Left femora do not show a more widespread distribution of tooth marks than documented in both humeri. Most tooth marks also occur on the proximal half of the element. Most distal tooth marks appear concentrated on the epiphyses. They occur mostly on the medial condyle (on its medial facet) and on the medial portion of the trochlea. Marks on the proximal end occur on the trochanters and also on the spiral line of the neck. The lateral sides of the shaft are the least modified, followed by the caudal distal shaft. Tooth marks on the caudal shaft occur on both sides of the line aspera. As was the case with the humerus, a large portion of marks appear at or near muscle insertion areas. All the functions show a slight clustering trend in short distances and a CSR pattern in longer distances (Fig S5).
Right femora appear substantially more toothmarked than the left ones. Again, the proximal and distal ends exhibit the highest amount of marks. Both trochanters and the proximal metadiaphysis contain large numbers of modifications. Marks on the distal epiphysis occur both on the medial facet of the throclea and on both condyles. Marks on the caudal shaft, along the linea aspera, are more abundant than on the cranial shaft. All functions coincide in finding a moderate clustering trend, which indicates that BSM are not following a CSR pattern (Fig. S6).
As was the case for humeri, the non-random and moderately clustered pattern shows that there are locations, mostly coinciding with tendon and muscle insertions, that are more prone to be impacted by lions during carcass consumption than others.
Radius-ulna
Radii from carcasses consumed by lions are generally left unmodified12,26,27. Most of the damage concentrates on the olecranon of the ulna (Fig. S7). Only a few tooth marks have been documented scattered on the proximal metadiaphysis, some under the articular facet of the lateral epiphysis. The rest occur mostly in the form of isolated marks, without any specific preference for clustering or side. The left radius shows this distribution. Marks outside the ulna are very few and occur on the cranial and lateral sides of the proximal metadiaphysis, in proximity to the articular facet. Scattered marks can be observed on the distal end. In contrast with the stylopodials, the left radius-ulna shows more intense clustering of tooth marks, as denoted by the K,G, F and pair-correlation functions (Fig. S4). This may be the effect of the intense damage on the olecranon.
The right radius appears also very slightly toothmarked, despite the large number of carcasses involved. Most tooth marks concentrate on the ulnar olecranon, with very few scattered along the ulnar shaft and even less so on the radial shaft. The few tooth marks documented on the shaft appear on the uppermost cranial shaft and a couple on the lower caudal shaft. As was the case with the left radius, the second-order functions indicate a clear clustering of tooth marks in slightly longer distances than documented in the stylopods (Fig. S8).
In sum, marks in radii are few and mostly clustered on the ulna. Those on the radial shaft are scattered but also seem to be in connection with damage on the proximal end imparted during defleshing by lions.
Tibia
The left tibia shows a concentration of tooth marks on the proximal end, more specifically, on the epiphysis and, especially, on the crest. Marks on the shaft are not common and they cluster mostly on the lateral and medial sides and on the lateral portion of the caudal side. Marks in the lower half of the shaft are uncommon regardless of orientation (Fig. S9). This element exhibits the lowest frequency of marks of the whole long bone set. The second-order functions indicate a very minor clustering trend in short distances, probably caused by redundancy in damage in the proximal portion of the element, but most of the shaft, where the few scattered marks occur, seems very similar to a Poisson process. This suggests that damage to the tibia (with the exception of the crest and proximal end) is more stochastic than on the other elements.
Right tibiae are only slightly more toothmarked than the left tibiae. Given its overall greater length than other long bones, its low toothmarking frequencies renders them the least impacted elements in number of tooth marks. Most marks cluster on the proximal end, more specifically, on the tibial crest. The lateral side is more damaged than the other sides. In the whole collection, only one tooth mark was found in the distal half of the shaft (Fig. 2). Again, most of the damage on the caudal side was concentrated on the proximal lateral side, coinciding with the more intensive damage on the lateral portion of the cranial side. The second-order functions suggest also a very minor clustering trend, slightly more marked than on left tibiae, probably because all tooth marks documented concentrate on the proximal half of the element (Fig. S10).
In sum, tibiae show some of the least intense point processes resulting from toothmarking by lions on long bones. Marks occurring on the shaft are usually isolated and more random than on other elements, where they are more spatially recurrent.
Bilateral element comparison
Left and right humeri display a similar pattern in the location of most damage as the three-dimensional coordinates of the PCA show (Fig. 4). This is reinforced by the bivariate wavelet analysis, which shows that both sides of humeri show a strong correlation ( > 0.8) in the location of most tooth marks in specific locations (Fig. 5). Both humeri display high frequencies of tooth marks (remember, the lower the frequency, the higher the scale) and a clear clustering on the proximal epiphysis and proximal metadiaphysis, as well as on the distal shaft. Most of the mid-shaft shows almost no tooth marks and when they do, they occur in very low frequencies. High frequency marks have only been documented on the proximal epiphyseal portion (Fig. 5). The frequency distribution also shows that the medial and caudal sides bear more marks than the lateral and cranial sides. Most cranial marks are concentrated in the articular surface, tubercles and metadiaphyseal portion of the proximal end.
Figure 4

Principal component analysis (PCA) of each of the four long bones (humerus, femur, radius-ulna and tibia) according to side (left–right) showing point distribution according to components generated by compressing the three-dimensional coordinates. A 95% confidence ellipse per side shows variation and similarity of toothmark patterns in each of the bones. Percentages shown are for the first and second component respectively.

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Figure 5

Bivariate wavelet coherence plot showing the correlation of most tooth mark damage on the proximal and distal sections of left and right humeri in low frequencies. Arrows indicate that in these two high-correlation areas, both humeral sides are in phase (i.e., the covary together in the same direction). In the distal area, the right humerus is leading (arrows pointing to the right-down or left-up) and in the proximal area, the left humerus leads (arrows pointing to the right-up or left-down). Binning of histograms is described in Table 3. (A) frequency of marks from distal end (left) to proximal (right) end; (B) frequency of marks from lateral (left) to medial (right), and, (C) frequency of marks on caudal (left) to cranial (right).

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Table 3 Binning of histograms according to bone length.
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Left and right femora also display a similar toothmarking pattern (Fig. 4). Both 95% confidence PCA ellipses overlap in most of their areas. The wavelet coherence analysis shows that both sides display a high correlation ( > 0.8) in toothmarking on proximal and distal ends as well as on the shaft when the frequency of marks is low or moderate. Most marks occur on the proximal portion of the element, with a higher impact on the cranial side and more medial for the left femur and more lateral for the right one (Fig. 6). Femoral mid-shafts, thus, appear more highly toothmarked than humeral shafts. Interestingly, the wavelet analysis also shows that when modifications are abundant, there is correspondence between left and right sides only at the distal end. This seems to respond to bone and muscle insertions and ways in which lions deflesh carcasses at this part of the limb. A moderate correlation ( > 0.6) between both sides of the element can be found at the level of the proximal articular neck (metadiaphysis) and surrounding the trochanter section (see yellow islands at the level of the 20th-23rd bins in Fig. 6).
Figure 6

Bivariate wavelet coherence plot showing the correlation of tooth mark damage on the proximal and distal sections of left and right femora in moderate frequencies. Arrows indicate that in these two high-correlation areas, both femoral sides are in phase (i.e., the covary together in the same direction). The right femur is leading (arrows pointing to the right-down or left-up). Binning of histograms is described in Table 3. (A) frequency of marks from distal end (left) to proximal (right) end; (B) frequency of marks from lateral (left) to medial (right), and, (C) frequency of marks on caudal (left) to cranial (right).

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As was the case of the upper limb bones, radii-ulnae also exhibit a localized tooth mark pattern. The 95% confidence PCA ellipses overlap for both sides is more intense even than with the stylopodials. The only points falling outside the confidence ellipse are those that appear in the form of single marks and are caused stochastically. The wavelet coherence analysis indicates a strong pattern between both sides, with marks clustering in the proximal epiphysis and strong correlation in the exhibition of low-impact modifications (i.e., few isolates marks) in most of the shaft (high scale = low frequency). There is a high frequency of modifications on the proximal end (see black line sloping upwards in Fig. 7), which decreases as we go down the shaft. The low frequency is maintained throughout the length of the shaft. Only because a few more marks have been documented on the distal and proximal ends, do we see a lower scale (i.e., higher frequency) at the beginning and end of the plot. The high correlation spread along the element shaft indicates that both the right and left radii-ulnae display virtually the same modification pattern.
Figure 7

Bivariate wavelet coherence plot showing the correlation of tooth mark damage on the proximal and distal sections of left and right radius-ulna in moderate to high frequencies. Arrows indicate that in these two high-correlation areas, both femoral sides are in phase (i.e., the covary together in the same direction). The right radius-ulna is leading (arrows pointing to the right-down or left-up). Binning of histograms is described in Table 3. (A) frequency of marks from distal end (left) to proximal (right) end; (B) frequency of marks from lateral (left) to medial (right), and, (C) frequency of marks on caudal(left) to cranial (right).

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Tibiae also show similar tooth-marking patterns when comparing right and left sides of the skeleton. A PCA shows that a 95% confidence ellipse of samples from both sides overlap in most of their areas (Fig. 4). However, it should be remarked that there is more coordinate variation (i.e., variation in distribution) of tooth marks in tibiae compared to the other long bones. The reason may be double. On the one hand, the tibia exhibits the longest length dimensions of the appendicular skeleton. On the other side, the occurrence of tooth marks outside the area surrounding the tibial crest is commonly in the form of isolated marks that are more prone to occur randomly during defleshing because no muscle insertions occur on the cranial aspect of the element. Only in the proximal caudal side are tooth marks more prone to cluster because of the muscle insertions on that side. A wavelet coherence analysis shows that tibiae show a low density of modifications, similar to radii-ulnae but over a more widespread area. This creates a situation of high correlation between the left and right sides in the location of the few scattered marks (Fig. 8). The correlation is also similar in the proximal and distal ends when modifications are more clustered. Overall, the lack of intensive (i.e., abundant clustering) modifications on the shaft, makes both tibial sides to lack a pattern, with the exception of the lateral and caudal proximal shafts. This moderate clustering there creates the small peninsula between bins 5 and 11 of Fig. 8.
Figure 8

Bivariate wavelet coherence plot showing the correlation of tooth mark damage on the proximal and distal sections of left and right tibiae in moderate to high frequencies. Notice different location of proximal and distal ends compared to the other elements. Arrows indicate that in these two high-correlation areas, both tibial sides are in phase (i.e., the covary together in the same direction). Binning of histograms is described in Table 3. (A) frequency of marks from distal end (right) to proximal (left) end; (B) frequency of marks from lateral (right) to medial (left), and, (C) frequency of marks on caudal(left) and cranial (right).

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In summary, the humeri, femora and radii-ulnae exhibit strong patterning on how lions modify them after consumption, as reflected in tooth mark distribution on both sides of the same elements. The tibiae display a more variable pattern, which overall is reflected on fewer modifications, especially along the shaft. Given the commonly isolated nature of most marks created along the shaft, these respond more to stochastic processes and reflect higher variability than in the other elements. Exceptions to this observation are found in BSM observed on the tibial crest and proximal caudal-lateral portions of the shaft.
Multi-element comparison
The information contained in the three-dimensional coordinates of the toothmark pattern documented on each of the elements, when approached through the holistic consideration of the mean values of their global interrelation (as documented through the second-order functions), provides identity information (i.e., element-specific identification) for each of the bones analyzed. On a different scale, this could be applied to individual assemblages instead of individual elements as done here. In the comparison among the different elements and their sides, the way marks were distributed in each of their respective point processes (considering their intensity and distances per element) contained sufficient information to differentiate four different clusters corresponding to the four different elements (Table 2, Fig. 9). Within each element set, both sides were contained within the same node. This is of utmost interest, because in the variables used for this analysis, it is the patterns and not the raw coordinates of marks on each element that were used. This enabled the relativization of the actual location of marks on the different long bone elements and only the emergent properties of the mark assemblage in each of them (understood as individual point process) was considered. Thus, multi-element comparison was possible and different bones were successfully differentiated (Fig. 9).
Figure 9

Hierarchical clustering of the selected variables from the second-order functions, intensity, and nearest-neighbour distance. A phylogenetic dendrogram was used. Four groups were identified (different colors) corresponding to each of the four elements analyzed. Key: lHum (left humerus); rHum (right humerus); lFem (left femur); rFem (right femur); lRad (left radius-ulna); rRad (right radius-ulna); lTib (left tibia); rTib (right tibia).

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Author notes
These authors jointly supervised this work: Elizabeth A. Archie and José M. Montoya.

Affiliations

Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
Johannes R. Björk & Elizabeth A. Archie

Theoretical and Experimental Ecology Station, CNRS-University Paul Sabatier, Moulis, France
Johannes R. Björk & José M. Montoya

Natural History Museum, London, UK
Cristina Díez-Vives

School of Biological Sciences, University of Auckland, Auckland, New Zealand
Carmen Astudillo-García

Authors
Johannes R. Björk

Cristina Díez-Vives

Carmen Astudillo-García

Elizabeth A. Archie

José M. Montoya

Corresponding authors
Correspondence to Johannes R. Björk or Elizabeth A. Archie or José M. Montoya. More

Screening of solvent and adjuvant
The results of solvent screening are shown in Table 1. The original pesticide could not be completely dissolved using a single solvent. However, 5% N-methyl-2-pyrrolidone + 10% cyclohexanone could completely dissolve the original pesticide. There was no solid precipitation at room temperature, so the formulation could be used for the subsequent experiment. According to Table 2, a mixture of sulfonate adjuvants (70b) and fatty alcohol polyoxyethylene ether adjuvants (AEO-4, -5, -7, -9, 992) could stabilize the system in a single, transparent, homogeneous phase. Therefore, sulfonate adjuvant (70b) was selected and mixed with five adjuvants of the AEO series to prepare thidiazuron·diuron ultra-low-volume sprays, numbered 1–5 (as shown in Table 3).
Table 1 Selection of solvent type and dosage (%: mass fraction).
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Table 2 Selection of adjuvants type and dosage (%: mass fraction).
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Table 3 Ultra-low-volume formulations used in this study.
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Surface tension measurement
The critical surface tension of cotton leaves is 63.30–71.81 mN/m. Figure 1 shows that the surface tension of each sample was 31.67–33.37 mN/m, which was much lower than the critical surface tension of the leaf, indicating the agent was able to completely wet the leaf and be fully distributed on the leaf surface. The maximum surface tension of the reference product was 38.90 mN/m. Under the same dosage of adjuvant, sample 5 with adjuvant 992 had the smallest surface tension of 31.67 mN/m.
Figure 1

Surface tensions of different samples. Different letters (a–d) indicate significant differences between means. Means followed by the same letter are not significant at the 5% significance level by the LSD test (LSD = 0.05). Vertical bars indicate a standard deviation of the mean. The detailed data of the histogram is shown in Supplementary Table S1.

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Contact angle measurement
According to Young’s equation, the smaller the surface tension, the smaller the contact angle40,41. Figure 2 shows the contact angle of different samples on cotton leaves and the change in contact angle over time. The contact angles of oil agents containing the adjuvant 992, AEO-7 and AEO-9 were smaller than that of the reference product, and the spreading effect was superior to that of the reference product. In the surface tension test, sample 5 had the smallest surface tension of 31.67 mN/m; this sample showed the minimum initial contact angle (39°) and a static contact angle (22°). The surface tension of the reference product was 38.90 mN/m., with the maximum initial contact angle (65.5°). Therefore, the relationship between surface tension and contact angle conformed to Young’s equation.
Figure 2

Contact angles of different samples on cotton leaves in 0–10 s. The detailed data of drawing the contact Angle curve is shown in Supplementary Table S2.

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Volatilization rate measurement
As shown in Fig. 3, the volatilization rate of the oil agent was much lower than that of the reference product. The volatilization rate of the five treatments was 5.80–8.74%, while the volatilization rate of the reference product was 22.97%. The volatilization rate of the oil agent met the quality requirements of an ultra-low-volume spray (≤ 30%). A low volatilization rate helps with spraying defoliants in hot and dry areas such as Xinjiang, effectively preventing evaporation of the droplets and increasing deposition.
Figure 3

Volatilization of different samples on filter paper. Different letters (a–e) indicate significant differences between means. Means followed by the same letter are not significant at the 5% significance level by the LSD test (LSD = 0.05). Vertical bars indicate a standard deviation of the mean. The detailed data of the histogram is shown in Supplementary Table S3.

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Viscosity measurement
Viscosity is an important factor affecting the atomization performance of a formulation42. Figure 4 shows that the viscosity of the five oil agents ranged from 12.9 to 18.3 mPa s, meeting the quality requirements of an ultra-low-volume spray ( 20 V), the droplet size distribution tended to be stable. This coincided with data shown in Fig. 6, where the inflection point appeared when rotation speed was 9600 rpm (voltage = 20 V).
Figure 6

Relationship between the rotation speed of the centrifugal spray atomizer and droplet size. D10: 10% cumulative volume diameter, D50: 50% cumulative volume diameter, D90: 90% cumulative volume diameter. The detailed data of drawing the curve is shown in Supplementary Table S6.

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Figure 7

Relationship between the rotation speed of the centrifugal spray atomizer and the fog droplet spectrum. The detailed data of drawing the curve is shown in Supplementary Table S6.

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Therefore, we determined that the optimal working conditions for the rotary atomizer were achieved by setting the DC voltage stabilized power supply current to 1.00 A and voltage to 20 V, which were used for subsequent experiments.
Atomization performance
The relationship between viscosity and droplet spectrum are shown in Table 4 and Fig. 8. The cumulative volume diameter for the five treatments was less than 150 μm meeting the requirements of the ULV spray32. The cumulative volume diameter for the five treatments was larger than that for the reference product, the width of the droplet spectrum was narrower, and the droplet distribution was more uniform. Droplet size affects the drift of droplets43. The D10 of the reference product was 25.62 μm under these working conditions. This droplet size was highly susceptible to drift and deposition on non-target organisms. Water suspension was not suitable for this application at low dosage.
Table 4 Droplet size and droplet size distribution of different sample sprays.
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Figure 8

Relationship between formulation viscosity and droplet spectrum. The detailed data of drawing the figure is shown in Supplementary Table S7.

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As presented in Table 4, droplet size increased with increasing viscosity, which influenced the droplet spectrum. The results in Fig. 8 show that the span of droplet size decreased with the increase of viscosity, indicating that droplets with more uniform distribution could be obtained by increasing the viscosity of the formulation41.
Droplet deposition effect
We tested the efficacy of the ULV spray formulation by spraying cotton plants using an UAV. The test results in Table 5 indicate that increasing the dosage of application would increase droplet size, coverage, and deposition density. At the same application dosage, the droplet size of the ultra-low-volume spray was slightly larger than that of the reference product, and the coverage and deposition density were greater than those of the reference product. The droplet spectral width (Rs) of the five treatments was less than 1, and the coefficient of variation was less than 7%, indicating that the droplet distribution was relatively uniform. Among treatments, T2 had the narrowest Rs and coefficient of variation (CV), where the droplet size distribution was the most uniform. For the ultra-low-volume spray, at the application dosage of 4.5–9.0 L/ha, the droplet coverage gradually increased from 0.85 to 4.15%; the droplet deposition densities were 15.63, 17.24, 28.45, and 42.57 pcs/cm2, which were larger than requirements suggested in the literature. The droplet coverage of the reference product (T5) was 0.73%, and the deposition density was only 11.32 pcs/cm2.
Table 5 Droplet size, coverage, deposition density, spectral width and variation coefficient for each treatment.
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Efficacy trials
The efficacy of cotton defoliant is reflected in the defoliation rate and boll opening rate of cotton after application. Therefore, we surveyed the defoliation rate and boll opening rate of cotton in the test area 3–15 days after application. The results are shown in Figs. 9 and 10.
Figure 9

Defoliation rate 3–15 days after treatment. The detailed data of drawing the curve is shown in Supplementary Table S8.

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Figure 10

Boll opening rate 3–15 days after treatment. The detailed data of drawing the curve is shown in Supplementary Table S9.

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Figure 9 indicates that the defoliation rates of the five treatments 15 days after the pesticide treatment were 59.82%, 63.96%, 71.40%, 77.84%, and 54.58%, respectively. The defoliation rates of T1, T2, and T5 were less than 70%.
Application of the ultra-low-volume spray at 4.50 L/ha or 6.00 L/ha and the reference product at 6.00 L/ha had a poor defoliation effect. T4 (9.00 L/ha) was superior to the others, and the defoliation rate reached 77.84% 15 days after application. As shown in Fig. 10, the boll opening rates of the five treatments were 58.54%, 67.74%, 95.35%, 100%, and 44.68% 15 days after application. Similarly, the boll opening rates of T1, T2, and T5 were poor, with the boll opening rate of the control T5 only 44.68%. We analyzed significant differences between the defoliation rates and boll opening rates of the five treatments. The results showed that the defoliation rate and boll opening rate associated with the thidiazuron·diuron ultra-low-volume spray on cotton plants were significantly different from those of the reference product.
Overall, the defoliation rate and boll opening rate produced by the ultra-low-volume spray were superior to those produced by the reference product. This result was consistent with data shown in Table 5. The higher the droplet coverage rate, the higher the droplet deposition density and the higher the defoliation rate and boll opening rate. T1, T2 and T5 had poor deposition effect on cotton plants, and the effective pesticide utilization rate was low, resulting in dissatisfactory defoliation rates and boll opening rates. Both the droplet coverage rate and the droplet deposition density of T3 and T4 were large. Therefore, droplets of pesticide solution could deposit more easily and uniformly on cotton leaves, allowing the plants to defoliate and open their bolls easily. More

Indian prime minister Indira Gandhi meets Maurice Strong, who chaired the 1972 Stockholm Conference on the Human Environment. Gandhi saw UNEP’s potential at a time when other countries doubted its value.Credit: Yutaka Nagata/UN Photo

The United Nations Environment Programme (UNEP) will be 50 next year. But the globe’s green watchdog, which helped to create the Intergovernmental Panel on Climate Change (IPCC), very nearly didn’t exist.
During talks hosted by Sweden in 1972, low- and middle-income countries were concerned that such a body would inhibit their industrial development. Some high-income countries also questioned its creation. UK representative Solly Zuckerman, a former chief scientific adviser to prime ministers including Winston Churchill, said the science did not justify warnings that human activities could have irreversible consequences for the planet. The view in London was that, on balance, environmental pollution was for individual nations to solve — not the UN.
But the idea of UNEP had powerful supporters, too. India’s prime minister, Indira Gandhi, foresaw its potential in enabling industry to become cleaner and more humane. And the host nation made a wise choice in picking Canadian industrialist Maurice Strong to steer the often fractious talks to success. He would become UNEP’s first executive director. Two decades later, Strong re-emerged to chair the 1992 Earth Summit in Rio de Janeiro, Brazil, which created three landmark international agreements: to protect biodiversity, safeguard the climate and combat desertification.
UNEP has chalked up some impressive achievements in science and legislation. In 1988, working with the World Meteorological Organization, it co-founded the IPCC, whose scientific assessments have been pivotal to global climate action. It also responded to scientists’ warnings about the hole in the ozone layer, leading to the creation of the 1987 Montreal Protocol, an international law to phase out ozone-depleting chemicals.
Strong’s successors would go on to identify emerging green-policy issues and nudge them into the mainstream. UNEP has pushed the world of finance to think about how to stop funding polluting industries. It has also advocated working with China to green its rapid industrial growth — including the Belt and Road Initiative to develop global infrastructure. It is essential that this work continues.
UNEP also accelerated the creation of environment ministries around the world. Their ministers sit on the programme’s governing council; at their annual meeting last week, they reflected on what UNEP must do to tackle the environmental crisis. Although the environment is a rising priority for governments, businesses and civil society, progress on the UN’s flagship Sustainable Development Goals — in biodiversity, climate, land degradation, pollution, finance and more — is next to non-existent. Moreover, the degradation of nature is putting hard-won gains at risk, argues a report that UNEP commissioned as part of its half-century commemorations.
The report, Making Peace with Nature, assesses much of the same literature as would a climate- or land-degradation assessment, but its key strength is in how it brings together researchers from across environmental science. In doing so, UNEP is helping to accelerate a mode of working that should be standard. If, for example, there is to be an assessment of how climate change affects biodiversity, it makes much more sense for this to be carried out by a joint team from the IPCC and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) than by researchers from just one of these organizations.
The UNEP report’s authors stop short of recommending such changes to the architecture of the UN’s scientific advisory bodies. That is a missed opportunity. Also missing is a discussion and recommendations on how to make countries more accountable for their environmental pledges.
Both these actions are sorely needed if the world is to take more meaningful steps to battle climate change and biodiversity loss. Countries have become expert in capturing data and reporting them to UN organizations. But there is no mechanism that holds nations to account. For example, there is no system to ensure compliance with targets for the Sustainable Development Goals.
Last week, the UN produced a report in which countries published their progress towards commitments under the 2015 Paris climate agreement, known as nationally determined contributions. The agreement includes almost 200 countries, but just 75 reported their data. There are few incentives for success and no penalties for failure. Without such measures, it is hard to see how meaningful change could ever happen.
In the past, researchers have proposed that UNEP’s member states upgrade its powers so it becomes more of a compliance body — a World Environment Organization that, like the World Trade Organization, has the power to censure countries for failing to keep to agreements. But this has been resisted as too radical a step, which would upend the autonomy of the UN biodiversity and climate organizations that UNEP itself helped to bring into being.
Twenty years ago, there might have been some justification for such a view, but now, with the world on a path to extreme climate change, any action will need to be radical, including considering how to give UNEP more teeth.
UNEP helped to lay the foundations for a scientific consensus on environmental decline, and it should be proud of the body of law that has been enacted globally. Alas, such measures risk being too little, too late. As it embarks on a year of reflection ahead of its anniversary, member states must consider what more they need to do to empower UNEP to tackle the planetary emergency. More