Philippa Kaur

To explore how environmental gradients shape the distribution of cyanophages and picocyanobacteria, we conducted high-resolution surveys in surface waters along five oceanic transects on three cruises covering thousands of kilometres in the North Pacific Ocean in the spring or early summer of 2015, 2016 and 2017 (Fig. 1a–c). These cruises, two of which were out-and-back, passed through distinct regimes from warm, saline and nutrient-poor waters of the North Pacific Subtropical Gyre to cooler, less saline and nutrient-rich waters of higher latitudes influenced by the subpolar gyre (Fig. 1d–i)27. The shift between the two gyres was marked by abrupt changes in trophic indicators such as particulate carbon concentrations (Fig. 1g) and a chlorophyll front (defined as the 0.2 mg m−3 chlorophyll contour28; Fig. 1a–c). As such, the inter-gyre transition zone, defined by salinity and temperature thresholds29 (Fig. 1d), was distinct from both the subtropical and subpolar gyre ecosystems28.Fig. 1: Gradients in environmental conditions across the North Pacific gyres.a–c, Transects of three cruises overlaid on monthly averaged satellite-derived sea-surface chlorophyll in March 2015 (a), April 2016 (b) and June 2017 (c). d, Temperature–salinity diagram showing the boundaries of the subtropical and subpolar gyres (black dashed lines) based on the salinity thresholds reported by Roden29. e–i, Temperature (e), salinity (f) as well as the levels of particulate carbon (g), phosphate (h) and nitrate + nitrite (i) as a function of latitude. The coloured dashed lines show the position of the 0.2 mg m−3 chlorophyll contour. For environmental variables plotted against temperature, see Supplementary Fig. 3.Full size imageUnexpected Prochlorococcus declineProchlorococcus concentrations in the oligotrophic waters of the subtropical gyre were 1.5–3.0 × 105 cells ml−1, comprising an average of approximately 29% of the total bacteria (Extended Data Fig. 1) and numerically dominating the phytoplankton community in all three cruises (Extended Data Fig. 2). Prochlorococcus abundance remained high in the southern region of the transition zone in 2015 and 2016, decreasing precipitously to less than 2,000 cells ml−1 north of the chlorophyll front, generally constituting 80% of cyanophages measured, with the remainder consisting of T7-like clade A and TIM5-like cyanophages (Fig. 3 and Extended Data Fig. 4). Cyanophage abundances correlated positively with total picocyanobacteria in the subtropical gyre (Pearson’s coefficient of multiple correlation (r) = 0.54, P = 0.02, n = 26; Fig. 2d), suggesting that cyanophages were limited by the availability of susceptible hosts in this region and were not regulating picocyanobacterial populations. On average, less than 1% of the cyanobacterial populations were infected (Fig. 4), with higher infection rates by T4-like cyanophages than T7-like cyanophages (Extended Data Figs. 5 and 6). These instantaneous measurements of infection were used to estimate the daily rates of mortality39 (Methods and Supplementary Discussion), which suggests that 0.5–6% of picocyanobacterial populations were lysed by viruses each day (Extended Data Fig. 7). This implicates other factors, such as grazing45, as the major causes of cyanobacterial mortality in the North Pacific Subtropical Gyre.Fig. 3: Cyanophage community composition across the North Pacific gyres.a–c, Cyanophage abundance for the March 2015 (a), April 2016 (b) and June 2017 (c) transects. Insets: T7-like clade A and TIM5-like cyanophage abundances on an expanded scale (similar to the main images, the units for the vertical axes are ×105 viruses ml−1). The grey shaded regions show the position of the virus hotspot. See Extended Data Fig. 4 for the confidence intervals and out-and-back reproducibility and Supplementary Fig. 4 for cyanophage lineages plotted against latitude.Full size imageFig. 4: Viral infection patterns of picocyanobacteria in the North Pacific Ocean.a–f, Viral infection levels (black) of Prochlorococcus (a,c,e) and Synechococcus (b,d,f) plotted against temperature for the March 2015 (a,b), April 2016 (c,d) and June 2017 (e,f) transects. Insets: infection levels on an expanded scale. The solid lines show infection (red), Prochlorococcus (green) and Synechococcus (pink) averaged and plotted for every 0.5 °C. The dashed lines and shaded regions show the position of the chlorophyll front and the virus hotspot, respectively. For plots by latitude and the upper and lower bounds of infection, see Extended Data Figs. 5 and 6.Full size imageWithin the transition zone we observed a steep latitudinal increase in the abundance of cyanophages for every transect, which we define as a cyanophage hotspot (Fig. 2c and Extended Data Figs. 2 and 4). The cyanophage abundances in this hotspot were between three- and tenfold greater than in the subtropical gyre (Fig. 2c). Notably, cyanophages were approximately 25% more abundant (an increase of approximately 5 × 105 viruses ml−1) in the hotspot on the 2017 cruise relative to the other two cruises, reaching a maximum of 2 × 106 viruses ml−1. The hotspot peaked at temperatures of 15–16 °C on all transects, regardless of the geographical location, season or the exact pattern of the Prochlorococcus and Synechococcus distributions (Fig. 2c). Notably, the numbers of T7-like clade B cyanophages increased sharply in the transition zone to become the most abundant lineage, whereas T4-like cyanophages increased more modestly (Fig. 3 and Extended Data Fig. 4). The change in the cyanophage community structure was particularly pronounced in June 2017, when T7-like cyanophages were up to 2.3-fold more abundant than T4-like cyanophages (Fig. 3c). The switch in the relative abundance of T4-like and T7-like clade B cyanophages was diagnostic of the cyanophage hotspot compared with patterns in the subtropical and subpolar gyres.To begin assessing whether cyanophages negatively affected cyanobacterial populations in the hotspot, we tested the relationship between the abundance of cyanophages and total cyanobacteria. This showed a significant negative correlation between cyanophage and cyanobacterial abundances across all three cruises (Pearson’s r = −0.56, two-sided P = 0.0005, n = 34). This relationship was particularly distinct in 2017, when cyanobacteria were at their overall lowest abundances and cyanophages at their highest (Pearson’s r = −0.65, two-sided P = 0.004, n = 18). This suggests that viruses are one of the key regulators of picocyanobacteria in the region of the hotspot. However, no significant correlation was found across all regimes and all years (Pearson’s r = −0.008, two-sided P = 0.9, n = 87; Fig. 2d), indicating that factors other than viruses are likely to be more important in regulating the abundances of cyanobacteria in other regimes.Our single-cell infection measurements allowed us to directly evaluate active viral infection and its impact on picocyanobacteria in the transition zone. Viral infection spiked in this region each year with infection levels that were an average of two- to ninefold higher than those in the subtropical gyre (Fig. 4 and Extended Data Figs. 5,6 and 8). Infection peaked within the temperature range of 12–18 °C and was associated with a concomitant dip in Prochlorococcus abundances in all three cruises (Fig. 4 and Extended Data Fig. 5). These findings provide independent support for the strong negative correlation between cell and virus abundances (Fig. 2d) being the result of virus-induced mortality.Lineage-specific infection was also distinct in the transition zone relative to the subtropical gyre. Infection by T7-like clade B cyanophages generally increased to reach (2015 and 2016) or exceed (2017) those of T4-like cyanophages (Extended Data Figs. 5 and 6). In addition, the ratio of the abundances of T7-like clade B cyanophages to the number of cells they infected was 2.6-fold greater in the hotspot than the subtropics, whereas this ratio was similar in both regions for T4-like cyanophages. Together, these results indicate that, within the hotspot, the T4-like cyanophages displayed increased levels of infection, whereas the T7-like cyanophages displayed both increased levels of infection and produced more viruses per infection, suggesting that T7-like clade B cyanophages are better adapted to conditions in the transition zone (see below).Of the three cruises, the highest levels of viral infection were observed in June 2017, with up to 9.5% and 8.9% of Prochlorococcus and Synechococcus infected, respectively (Fig. 4e,f). This dramatic increase in infection mirrored the massive decline in Prochlorococcus abundances (Fig. 4e and Extended Data Fig. 5i). We estimate that viruses killed 10–30% of Prochlorococcus and Synechococcus cells daily at these high instantaneous levels of infection (Extended Data Fig. 6) based on the expected number of infection cycles cyanophages were able to complete at the light and temperature conditions in the transition zone (Methods and Supplementary Discussion). Given that Prochlorococcus is estimated to double every 2.8 ± 0.8 d at the low temperatures in this region12, we estimate that 21–51% of the population was infected and killed in the interval before cell division. Synechococcus is expected to have faster growth rates at these temperatures, doubling every 1.1 ± 0.2 d (refs. 12,46). Thus, we estimate that less of the Synechococcus population (9–31%) was killed before division.Under quasi-steady state conditions, abiotic controls on the growth rate of Prochlorococcus are balanced by mortality due to viral lysis, grazing and other mortality agents39,45,47. Based on the high levels of virus-mediated mortality, the parallel pattern between Prochlorococcus’ death and viral infection, and the negative correlation between cyanophage and picocyanobacterial abundances in the transition zone, we propose that enhanced viral infection in 2017 disrupted this balance, leading to the unexpected decline in Prochlorococcus populations. Grazing and other mortality agents not investigated here could also have contributed to additional mortality beyond the steady state, resulting in further losses of Prochlorococcus. In contrast to Prochlorococcus, Synechococcus maintained large populations despite high levels of infection (Fig. 4f), presumably due to their faster growth rates enabling them to maintain a positive net growth despite enhanced mortality. These findings suggest that virus-mediated mortality in 2017 was an important factor in limiting the geographic range of Prochlorococcus that resulted in a massive loss of habitat of approximately 550 km.Cyanophage abundances and infection levels dropped sharply in the higher-latitude waters north of the hotspot (Figs. 2c, 4 and Extended Data Figs. 1d,h and 2). The abundances of both T7-like clade B and T4-like cyanophages declined precipitously, yet T4-like cyanophages were the dominant cyanophage lineage (Fig. 3). T7-like clade A cyanophages generally increased locally at the northern border of the hotspot and became the dominant T7-like lineage in two samples between 38 and 39.2° N in 2017 (Fig. 3c and Extended Data Fig. 4). In contrast to all other cyanophages, the abundances of TIM5-like cyanophages increased in waters north of the hotspot (Fig. 3 and Extended Data Fig. 4d,i,m) but remained a minor component of the cyanophage community. No relationship was found between cyanophage and cyanobacterial abundances (Fig. 2d), and less than 1.5% of picocyanobacteria were infected by all cyanophage lineages in these waters (Fig. 4).The cyanophage hotspot in the transition zone is a ridge of high virus activity that separates the subtropical and subpolar gyres. The reproducibility of our observations, which were separated by days to weeks within each cruise (2016 and 2017) and by years among the three cruises (Extended Data Fig. 4), indicates that this virus hotspot is a recurrent feature at the boundary of these two major gyres in the North Pacific Ocean. This suggests that the hotspot forms due to the distinctive environment of the inter-gyre transition zone creating conditions that enhance infection of picocyanobacteria and proliferation of cyanophages. Prochlorococcus in the transition zone may be prone to stress due to being close to the limits of their temperature growth range5,6, which has the potential to increase susceptibility to viral infection. Alternatively, there may be temperature-dependent trade-offs between virus decay and production that lead to replication optima within a narrow temperature range48. Cyanophage infectivity has been observed to decay more slowly at colder temperatures49, which may allow for the accumulation of infective viruses, leading to increased infection. In addition, cyanophage infections may be more productive due to enhanced nutrient supply in the transition zone27 (Fig. 1h,i) relative to the subtropics, given that the cyanophages replicate in hosts with presumably greater intracellular nutrient quota and obtain more extracellular nutrients, both of which may increase progeny production9,10. The environmental factors influencing the production and removal of viruses probably vary in intensity at different times, leading to variability in cyanophage abundance and infection levels. Thus, the putative cyanophage replication optimum in the hotspot may reflect the combined effects of temperature and nutrient conditions that are intrinsically linked to the oceanographic forces that shape the transition zone itself.Changes in the cyanophage community structure over environmental gradients are likely to reflect differences in host range, infection properties and genomic potential to remodel host metabolism9. Our data, together with previous measurements in the North Pacific Subtropical Gyre38,39, indicate that the T4-like cyanophages are the lineage best adapted to the low-nutrient waters of the subtropics (Fig. 2d–f). As these waters are inhabited by hundreds of genomically diverse subpopulations of Prochlorococcus50, the broad host range of many T4-like cyanophages18,19,22,51 may be advantageous for finding a suitable host. T4-like cyanophages also have a large and diverse repertoire of host-derived genes21,51—such as nutrient acquisition, photosynthesis and carbon-metabolism genes—that augment host metabolism52 and may increase fitness in nutrient-poor conditions in the subtropics51. In contrast, T7-like clade A and B cyanophages seem to be better adapted to conditions in the transition zone (Fig. 3). T7-like cyanophages have narrow host ranges19,22,40, with smaller genomes and fewer genes to manipulate the host metabolism23, which may allow them to replicate and produce more progeny in regions with elevated nutrient concentrations relative to subtropical conditions. The maximal abundances of TIM5-like cyanophages were found in the most productive waters at the northern end of the transects where the cyanobacterial abundances were lowest and Synechococcus was the dominant picocyanobacterium. This may be partially due to the narrow host range of TIM5-like cyanophages and their specificity for Synechococcus40,44. Our findings of reproducible lineage-specific responses to changing ocean regimes indicate that cyanophage lineages occupy distinct ecological niches.Temperature and nutrient changes occurring in the transition zone are expected to result in shifts in picocyanobacterial diversity at the sub-genus level (Supplementary Discussion), which we speculate may affect community susceptibility to viral infection. One mechanism for this may be that the picocyanobacteria that thrive in the transition zone are intrinsically more susceptible to viral infection. Another scenario may be related to trade-offs associated with the evolution of resistance to viral infection. The horizontal advection of nutrient-rich waters to the transition zone28 may select for rapidly growing cells adapted for efficient resource utilization. Viral resistance in picocyanobacteria often incurs the cost of reduced growth rates53,54. Thus, competition for nutrients in this region may favour cells with faster growth rates but increased susceptibility to viral infection. Thus, it is probable that the cyanophage distributions do not always follow the cyanobacterial patterns (Extended Data Fig. 2) because of complex interactions between lineage-specific cyanophage traits, host community structure and environmental variables, which may vary seasonally or annually as a result of interannual variability in environmental conditions (see below).Despite consistent features in cyanophage distributions across the North Pacific Ocean, cyanophage infection was higher (Fig. 4 and Extended Data Fig. 7), whereas Prochlorococcus abundances were consistently lower (Fig. 2a), across the June 2017 transects relative to the March 2015 and April 2016 transects. Seasonality and/or climate variability could explain this interannual variability, although the data currently available to assess this are sparse. Viral infection of picocyanobacteria in the subtropical gyre increased from early spring to summer, suggesting a potential seasonal pattern that may extend across the transect (Extended Data Fig. 9a). In addition, the June 2017 transect occurred during a neutral-to-negative El Niño phase with lower sea-surface temperatures relative to the 2015 and 2016 transects, which were in years of a record marine heatwave, followed by a strong El Niño55 (Extended Data Fig. 9b). In 2015 and 2016, the Prochlorococcus abundances were found to be higher than usual in the North Pacific Ocean in this (Fig. 2a) and other studies56,57. Irrespective of the underlying drivers for the observed interannual variability, we speculate that an ecosystem tipping point was reached in the hotspot under the prevailing conditions in June 2017, aided by the higher cyanophage abundances yet smaller Prochlorococcus population sizes. In this scenario, picocyanobacterial populations were subjected to high infection levels that resulted in an accumulation of cyanophages, initiating a stronger than usual positive-feedback loop between infection and virus production, and precipitating the unexpected Prochlorococcus decline. Continued observations in the North Pacific Ocean are needed to evaluate the potential link between seasonality and/or large-scale climate forcing as ultimate drivers affecting virus–host interactions.Predicting basin-scale virus dynamicsMeasurements of cyanobacterial and cyanophage abundances rely on discrete sample collection from shipboard oceanographic expeditions, which limits the geographical and seasonal extent of available data. Therefore, we developed a multiple regression model based on high-resolution satellite data of temperature and chlorophyll to predict cyanophage abundances, a key proxy of cyanobacterial infection (Pearson’s r = 0.61, two-sided P = 1.7 × 10−8, degrees of freedom = 68, n = 70). We used the model to estimate the geographical extent of the virus hotspot. The model accurately predicted the location of the hotspot and cyanophage abundances along a fourth transect in April 2019 (Supplementary Table 1), with the majority of observations falling within the 95% confidence intervals of the model predictions (Fig. 5a–c). Application of the model to the larger region predicted that the virus hotspot formed a boundary extending across the North Pacific Ocean, with lower cyanophage abundances on both sides (Fig. 5d,e and Supplementary Fig. 1). This boundary had the hallmarks of the hotspot with a core that was dominated by T7-like cyanophages and the flanking gyre regions dominated by T4-like cyanophages. Thus, this feature may be more appropriately termed a ‘hot-zone’ due to its substantial projected aerial extent. Assuming the infection levels observed in the hotspot in June 2017 were similar throughout the hot-zone, the potential habitat loss for Prochlorococcus would be about 3.2 × 106 km2, approximately half of the cumulative area loss of the Amazonian rainforest to date58.Fig. 5: Prediction of cyanophage abundances.a–c, Model-based predictions of cyanophage abundances corresponding to the empirically measured total (a), T4-like (b) and T7-like clade B (c) cyanophage abundances along a transect in the North Pacific in April 2019. The shaded regions show the 95% confidence interval for the model predictions. d,e, Predicted total cyanophages (d) and the ratio of T4-like/T7-like clade B cyanophages (e) in June 2017 in the North Pacific Ocean. The black lines indicate the cruise track. The grey areas represent regions with no values due to cloud cover or that were beyond the limits of the predictive model. The hotspot peak corresponds to yellow regions in d and red regions in e.Full size imageVirus hotspot biogeochemistryWith the ability to predict biogeographic patterns of cyanophages, we evaluated the potential biogeochemical implications of virus-mediated picocyanobacterial lysis and release of organic material in sustaining the bacterial community6,7,8,9. The aerial extent of the hot-zone (approximately 4 × 106 km2) is only 14% of the size of the subtropical gyre (2.9 × 107 km2), and yet the total virus-mediated organic matter released from picocyanobacteria in the hot-zone in June 2017 was estimated to be on par with that for the entire North Pacific Subtropical Gyre (Methods and Supplementary Discussion). We estimate that viral lysate released from picocyanobacteria in the subtropical gyre could sustain 4.4 ± 0.8% of the calculated bacterial carbon demand there (Extended Data Fig. 10). In contrast, viral lysate released in the transition zone could sustain an average of 21 ± 12% of the bacterial carbon demand, reaching 33% in some regions (Extended Data Fig. 10), assuming that the bacterial assimilation and growth efficiencies were similar between the subtropical gyre and the hotspot. Thus, local generation of cyanobacterial viral lysate in the transition zone is likely to be an important source of carbon for the heterotrophic bacterial community that can rapidly utilize large molecular weight dissolved organic matter59 and may have contributed to the increase in their abundances south of the chlorophyll front in 2017 (Extended Data Fig. 1a,e). More

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01 April 2022

Tropical forests have big climate benefits beyond carbon storage

Study finds that trees cool the planet by one-third of a degree through biophysical mechanisms such as humidifying the air.

Freda Kreier

Freda Kreier

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Tropical forests create cloud cover that reflects sunlight and cools the air.Credit: Thomas Marent/Minden Pictures

Tropical forests have a crucial role in cooling Earth’s surface by extracting carbon dioxide from the air. But only two-thirds of their cooling power comes from their ability to suck in CO2 and store it, according to a study1. The other one-third comes from their ability to create clouds, humidify the air and release cooling chemicals.
How much can forests fight climate change?
This is a larger contribution than expected for these ‘biophysical effects’ says Bronson Griscom, a forest climate scientist at the non-profit environmental organization Conservation International, headquartered in Arlington, Virginia. “For a while now, we’ve assumed that carbon dioxide alone is telling us essentially all we need to know about forest–climate interactions,” he says. But this study confirms that tropical forests have other significant ways of plugging into the climate system, he says.The analysis, published in Frontiers in Forests and Global Change on 24 March1, could enable scientists to improve their climate models, while helping governments to devise better conservation and climate strategies.The findings underscore growing concerns about rampant deforestation across the tropics. Scientists warn that one-third of the world’s tropical forests have been mown down in the past few centuries, and another one-third has been degraded by logging and development. This, when combined with climate change, could transform vast swathes of forest into grasslands2.“This study gives us even more reasons why tropical deforestation is bad for the climate,” says Nancy Harris, forest-research director at the World Resources Institute in Washington DC.More than a carbon spongeForests are major players in the global carbon cycle because they soak up CO2 from the atmosphere as they grow. Tropical forests, in particular, store around one-quarter of all terrestrial carbon on the planet, making them “centrepieces for climate policy” in their home countries, Griscom says.
Tropical forests may be carbon sources, not sinks
“There’s clear evidence that the tropics are producing excellent climate benefits for the entire planet,” says Deborah Lawrence, an environmental scientist at the University of Virginia in Charlottesville and a co-author of the latest study. She and her colleagues analysed the cooling capacity of forests around the globe, in particular considering biophysical effects alongside carbon storage. Tropical forests, they found, can cool Earth by a whole 1 °C — and biophysical effects contribute significantly.Although scientists knew about these effects, they hadn’t understood to what extent the various factors counter global warming.Trees in the tropics provide shade, but they also act as giant humidifiers by pulling water from the ground and emitting it from their leaves, which helps to cool the surrounding area in a way similar to sweating, Griscom says.“If you go into a forest, it immediately is a considerably cooler environment,” he says.This transpiration, in turn, creates the right conditions for clouds, which like snow and ice in the Arctic, can reflect sunlight higher into the atmosphere and further cool the surroundings. Trees also release organic compounds — for example, pine-scented terpenes — that react with other chemicals in the atmosphere to sometimes create a net cooling effect.Locally coolTo quantify these effects, Lawrence and her colleagues compared how the various effects of forests around the world feed into the climate system, breaking down their contributions in bands of ten degrees of latitude. When they considered only the biophysical effects, the researchers found that the world’s forests collectively cool the surface of the planet by around 0.5 °C.
When will the Amazon hit a tipping point?
Tropical forests are responsible for most of that cooling. But this band of trees across Latin America, Central Africa and southeast Asia is under increasing pressure from climate change and deforestation. Both of these human-caused impacts can lead rainforests to dry out, says Christopher Boulton, a geographer at the University of Exeter, UK. Last month, he and his colleagues published a review2 of nearly 30 years’ worth of satellite images of the Amazon, the largest rainforest in the world. By measuring the biomass of the vegetation in the images, the team discovered that three-quarters of the Amazon is losing resilience — the ability to recover from an extreme weather event such as a drought.Threats to tropical rainforests are dangerous not only for the global climate, but also for communities that neighbour the forests, Lawrence says. She and her colleagues found that the cooling caused by biophysical effects was especially significant locally. Having a rainforest nearby can help to protect an area’s agriculture and cities from heatwaves, Lawrence says. “Every tenth of a degree matters in limiting extreme weather. And where you have forests, the extremes are minimized.”Governments across the tropics have struggled to conserve their forests despite more than two decades of global campaigns to halt deforestation, promote sustainable development and protect the climate. Lawrence says that her team’s findings make it clear that protecting forests is a matter of self-interest, and has immediate benefits for local communities.

doi: https://doi.org/10.1038/d41586-022-00934-6

ReferencesLawrence, D., Coe, M., Walker, W., Verchot, L. & Vandecar, K. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2022.756115 (2022).Article 

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Study systemThe cuckoo catfish (Synodontis multipunctatus) belongs to the African catfish family Mochokidae. The genus Synodontis, with 131 species distributed across African freshwaters57, gave rise to a small radiation in Lake Tanganyika, with 10 described endemic species58. The taxonomy of the group is not well established59 and we use the name S. multipunctatus as this species is confirmed as a brood parasite30 and the name was used in previous studies4,30,32,37,42. Cuckoo catfish primarily parasitise mouthbrooding cichlids from the tribe Tropheini30, but species from other lineages can also be parasitised59.Experimental designAll experiments took place between January and August 2020 at the Institute of Vertebrate Biology, Czech Republic. Prior to experimental use, fish were housed in mixed-sex groups in tanks equipped with shelter and internal filtration. Cuckoo catfish were F1 generation of commercially imported wild-caught parents (10 pairs). Host cichlids were descendant of wild fish imported from Kalambo, Zambia. Experimental tanks (420 L; length 150 cm, depth 70 cm, height 40 cm) were equipped with internal filtration, fine gravel (2–4 mm diameter), half a clay pot as a shelter on each side of the tank, and one artificial plant in the centre of each tank. Water temperature was maintained at 27 °C (±1 °C) and the dark – light regime was set to 11 h:13 h. In total, we stocked 18 tanks with 4 males and 12 females of the mouthbrooding cichlid Astatotilapia burtoni and introduced 3 cuckoo catfish pairs of one of three different experience levels. Naïve catfish (n = 36 individuals) had no prior experience with cichlids. Experienced catfish (n = 36) were housed together with reproductive cichlids for 12 months prior to the experiment and were age-matched to naïve catfish (5 years old). Highly experienced catfish (n = 36) were raised, coexisted and reproduced with cichlids for 7 years (and were on average 7–8% larger than both naïve and experienced catfish; mean ± SE, naïve: 116.2 ± 1.9 mm, experienced: 117.1 ± 1.5 mm, highly experienced: 125.6 ± 1.4 mm; Linear Model (LM): experienced vs. highly experienced, estimate ± S.E = 8.44 ± 2.29, t = 3.68, P = 0.0004, experienced vs. naïve, estimate ± S.E = −0.94 ± 2.29, t = −0.41, P = 0.681, n = 108). Additionally, both naïve and experienced cuckoo catfish were bred using in-vitro fertilisation32 to avoid cichlid imprinting (i.e., priming with cichlid cues), while highly experienced catfish were bred under natural conditions within the buccal cavities of their hosts. Each experimental tank contained catfish with the same experience level. Due to space limitations, we split the experiment into two consecutive phases with 3 replicate tanks for each treatment within both phases (in total 9 experimental tanks per phase). Between the two experimental phases, host cichlids were placed together and haphazardly assigned to new experimental tanks. During the second phase, we removed some cichlids from the tanks because of injuries caused by their intraspecific aggression (3 males and 3 females in total), and those hosts were not replaced. Over an experimental phase, cuckoo catfish and cichlids freely interacted for 15–16 weeks. During this period, each tank was checked for mouthbrooding cichlids twice each week (Tuesday and Friday). We caught the mouthbrooding females, gently washed the eggs out of their mouths using a jet of water from a Pasteur pipette, measured their body size to the nearest mm, and released them back to their experimental tank. For each female, we counted the number of cichlid eggs and cuckoo catfish eggs (if present). At the end of each experimental phase, we measured body size of all cuckoo catfish to the nearest mm. There was no significant difference between the number of cichlid spawnings between naïve and experienced catfish treatments (Generalised Linear Models with negative binomial error distribution, estimate ± S.E.: −0.093 ± 0.145, z = −0.644, P = 0.519), nor between naïve and highly experienced catfish (estimate ± S.E.: −0.269 ± 0.148, z = −1.810, P = 0.070).Behavioural recordingOver the experimental period, we successfully recorded 18 videos of spawning events (Lamax x3.1 ATLAS cameras; naïve catfish treatment, n = 9; experienced catfish treatment, n = 6; highly experienced catfish treatment, n = 3). One camera was placed near the spawning site approximately 20 cm away from spawning activity and a second camera was placed outside the experimental tank to obtain an overall view. Nine spawnings were recorded from the start (n = 7 naïve catfish experiments and 2 experienced catfish experiments) and nine spawnings were recorded from the timepoint when we recognised ongoing spawning activity (n = 2 naïve, 4 experienced, and 3 highly experienced catfish experiments). From the video footage taken for each spawning, we scored all overt aggression that host cichlids directed towards cuckoo catfish, counted the number of intruding catfish during each distinct cichlid spawning behaviour (i.e., male and female cichlid interact in a repeated succession of quivering and T-positions), measured the delay of intruding catfish to each distinct spawning behaviour (i.e., the time from the start of spawning behaviour until the first catfish directly approaches the spawning cichlids), and recorded the presence or absence of catfish during each spawning behaviour. Additionally, we recorded whether cichlids used the available shelters for spawning as this might have impeded catfish recognition of the spawning activity. When spawning was recorded from the start, scoring started 100 s before we detected the first egg laid (cichlid or cuckoo catfish). When spawning was already ongoing, the scoring started immediately after the cameras were in place. Mounting of the cameras did not interrupt the normal behaviour of cichlids or catfish. For all video footage, scoring ended 100 s after the last male-female interaction within the spawning site. To estimate the duration of male T-positions during spawnings, we measured the time period from the start of male nuzzling near female genital papilla until the female turned around either to collect eggs or start nuzzling near the male´s genital papilla (n = 115 male T-positions from 12 cichlid spawnings).Statistical analysisWe used R v. 3.5.1 (R Development Core Team, 2018) for all statistical analyses. All statistical tests were two-sided. First, we compared body size among the three cuckoo catfish experience levels using a Linear Model with catfish size (mm) as response variable and ‘treatment’ (naïve, experienced, and highly experienced catfish) as predictor variable. Second, we formally tested whether the number of host spawnings varied between the treatment groups (total numbers: naïve = 191 spawnings, experienced = 174 spawnings, highly experienced = 146 spawnings). To obtain an insight into temporal dynamics of cichlid spawning, we calculated the number of cichlid spawnings for each treatment in each quarter of the duration of the experimental period. We fitted a GLM with a negative binomial error distribution (to account for slightly overdispersed data) with the number of cichlid spawnings as the response variable and our treatment groups as predictors.To test how experience with host spawning (treatment) affected cuckoo catfish ability to place their eggs in the care of the host, we compared (1) the number of parasitised cichlid clutches among the three catfish experience groups (prevalence of parasitism), (2) the mean number of catfish eggs introduced into cichlid clutches among the three treatment levels (mean parasite egg abundance, the mean number of catfish eggs calculated across all cichlid broods, (3) mean parasite clutch size (the number of catfish eggs calculated only across parasitised cichlid broods), and examined (4) temporal dynamics of all three measures of parasite success within each treatment group throughout the duration of the experiment.To test for differences in prevalence of parasitism among different cuckoo catfish experience treatments, we applied a Generalised Linear Mixed-effects Model (GLMM, R package glmmTMB)60 with a binomial error distribution. We fitted the occurrence of ‘catfish parasitism’ (1 = yes, 0 = no) as the binary response variable and ‘treatment effect’ (i.e., ‘catfish experience’), ‘time progress of experiment’ (1–113 days) and ‘host female body size’ (in mm) as predictor variables. We additionally fitted an interaction between treatment (‘catfish experience’) and ‘time progress of experiment’ to the model to test whether parasitism rate changed over time at treatment-specific rates. We included tank identity (‘tank ID’) as a random intercept to account for nonindependence of data obtained from the same tank.Next, we tested whether the mean number of parasite eggs that were accepted by host females during one spawning bout differed between catfish experience treatments. We applied two GLMMs (R package glmmTMB)60 with a negative binomial error distribution (i.e., nbinom1) to account for over-dispersed count data. We applied GLMMs on the mean abundance of catfish eggs (across all host clutches) and on mean clutch size of cuckoo catfish using a subset of clutches that were parasitised. For both GLMMs, we included the ‘number of cuckoo catfish eggs per clutch’ as the response variable and treatment (‘catfish experience’), ‘time progress of experiment’, and their interaction as predictor variables. We additionally fitted ‘host female body size’ as a predictor variable because larger female cichlids are capable of laying more eggs and may appear more attractive hosts to cuckoo catfish. Further, a higher number of host eggs may increase the number of opportunities for cuckoo catfish to deposit their own eggs in the host clutch. ‘Tank ID’ was included as random intercept to account for nonindependence of data.To test whether cuckoo catfish presence affected cichlid spawning activity, we applied a GLMM (R package glmmTMB)60 with Gaussian error distribution (which provided superior model fit compared to Poisson and negative binomial distributions by ‘simulateResiduals’ and ‘testDispersion’ functions in the R package DHARMa)61. We fitted the ‘number of host eggs’ per clutch as the response variable and treatment (‘catfish experience’), ‘host female body size’, ‘time progress of experiment’, and ‘experimental phase’ (1st or 2nd phase) as predictor variables. We also included ‘tank ID’ as random intercept to account for nonindependence of data. The full model further included an interaction between treatment and ‘time progress of experiment’ to accommodate the possibility that host egg numbers may be affected differently across catfish experience treatments over time. As this full model predicted no difference in temporal aspect of host clutch size among treatments (‘catfish experience’: ‘time progress’, experienced: z = 0.92, P = 0.360, highly experienced: z = 1.46, P = 0.143), we subsequently dropped the interaction term from the final model.We used data collected from video footage to investigate whether naïve, experienced and highly experienced cuckoo catfish differed in their response to host spawnings and, additionally, if catfish from the three treatments elicited different host reactions towards them by applying Linear Mixed-effect Models using the R packages lme462 and glmmTMB60. To account for different starting times of recordings, we calculated either the rate of behaviour per minute of observation (i.e., for aggression) or their relative values (i.e., for the number of host courtships that cuckoo catfish missed).First, we tested whether host spawning pairs increased their aggressions towards cuckoo catfish over the experimental period to rule out the presence of host adaptation to cuckoo catfish intrusions, which would interfere with our aim of understanding parasite learning. We fitted a Generalised Linear Mixed-effects Model (GLMM, R package glmmTMB) with a negative binomial error distribution. The number of overt aggressive behaviours that the spawning pair performed towards cuckoo catfish per minute of catfish presence at the spawning site (summed over male and female cichlid) was fitted as the response variable and treatment (‘catfish experience’) as the predictor variable. We further included ‘time progress of experiment’ and ‘experimental phase’ as predictors to account for their possible effect on host aggression. We additionally included ‘tank ID’ as random intercept in the model to account for individual variation in host aggression levels among experimental tanks.To investigate if naïve cuckoo catfish missed more opportunities to parasitise cichlids than experienced and highly experienced catfish, we fitted a GLMM (R package lme4) with a binomial error distribution. We included the proportion of missed spawning behaviours (coded as ‘missed spawnings behaviours’ versus ‘intruded spawning behaviours’, based on count data for each spawning) as the response variable (‘spawnings missed’) and treatment (‘catfish experience’) as a predictor variable. We fitted ‘tank ID’ as a random intercept to the model to account for nonindependence of data within tanks, and we additionally fitted a random intercept based on whether the spawning was covered by a shelter or not (‘sheltered spawn’, yes / no) since spawning in a shelter may have been less apparent to catfish.We tested whether cuckoo catfish experience played a role in the timing of their intrusion to specific spawning behaviours by fitting a GLMM (R package lme4) with a Gamma error distribution to account for a positive skew in the data distribution. We included the mean delay of catfish to the first appearance of cichlid T-position in seconds (‘catfish delay’, see main text and Supplementary Movie 1 for a detailed description of cichlid spawning sequence) as the response variable and ‘catfish experience’ as the predictor variable. We included ‘tank ID’ and ‘sheltered spawn’ as random intercepts.Finally, we fitted a GLMM with a Poisson error distribution to test whether cuckoo catfish learn to synchronise their intrusion behaviour as they gain experience through interactions with their hosts. We included the maximum number of catfish during a specific cichlid spawning behaviour (‘intruder number’, count data) as the response variable and ‘catfish experience’ as the predictor variable. To account for nonindependence of data within experimental tanks and spawnings, we included a random intercept where each spawning was nested within ‘tank ID’ in the model.Ethical complianceResearch adhered to all national and institutional animal care and use guidelines, was administered under permit No. CZ62760203 and was approved by ethical boards of the Institute of Vertebrate Biology and the Czech Academy of Sciences (approval No. 32-2019).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Forest Ecology and Forest Management Group, Wageningen University, Wageningen, the NetherlandsPieter A. Zuidema & Ute Sass-KlaassenSchool of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USAFlurin BabstLaboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USAFlurin Babst, Valerie Trouet, Zakia Hassan Khamisi, Paul R. Sheppard & Ramzi TouchanDepartment of Plant Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, BrazilPeter Groenendijk & José Roberto Vieira AragãoWorld Agroforestry Centre (ICRAF), Addis Ababa, EthiopiaAbrham AbiyuDepartment of Microbiology and Parasitology, Universidad Nacional Autónoma de México, Mexico City, MexicoRodolfo Acuña-SotoLaboratory of Protection and Forest Management, Department of Forest Engineering, Universidade Regional de Blumenau, Santa Catarina, BrazilEduardo Adenesky-FilhoDepartment of Biology, Wilfrid Laurier University, Waterloo, Ontario, CanadaRaquel Alfaro-SánchezDepartment of Forest Sciences, Luiz de Queiroz College of Agriculture, University of Sao Paulo, Piracicaba, BrazilGabriel Assis-Pereira, Claudia Fontana & Mario Tomazello-FilhoTree-Ring Laboratory, Forest Science Department, Federal University of Lavras, Lavras, BrazilGabriel Assis-Pereira & Ana Carolina BarbosaCAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, ChinaXue Bai, Ze-Xin Fan, Shankar Panthi & Zhe-Kun ZhouDepartment of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “L. Vanvitelli”, Caserta, ItalyGiovanna BattipagliaService of Wood Biology, Royal Museum for Central Africa, Tervuren, BelgiumHans Beeckman, Camille Couralet & Benjamin ToirambeBrazilian Agricultural Research Corporation (Embrapa), Embrapa Forestry, Colombo, BrazilPaulo Cesar BotossoU.S. Department of Agriculture, Forest Service, NWCG Member Agency, Washington, DC, USATim BradleyInstitute of Geography, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, GermanyAchim Bräuning, Mahmuda Islam, Mulugeta Mokria & Mizanur RahmanSchool of Geography, University of Leeds, Leeds, UKRoel Brienen & Emanuel GloorLamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USABrendan M. Buckley & Rosanne D’ArrigoInstituto Pirenaico de Ecología (IPE-CSIC), Zaragoza, SpainJ. Julio CamareroCentre for Functional Ecology, Department of Life Sciences, Faculty of Sciences and Technology, University of Coimbra, Coimbra, PortugalAna Carvalho & Cristina NabaisDepartment of Botany, Institute of Biosciences, University of São Paulo, São Paulo, BrazilGregório Ceccantini, Bruno Barçante Ladvocat Cintra & Giuliano Maselli LocosselliInstituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Centro Nacional de Investigación Disciplinaría en Relación Agua-Suelo-Planta-Atmósfera (CENID-RASPA), Gómez Palacio, MéxicoLibrado R. Centeno-Erguera, Julián Cerano-Paredes & Jose Villanueva-DiazInstituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Campo Experimental Centro – Altos de Jalisco, Tepatitlán de Morelos, MéxicoÁlvaro Agustín Chávez-DuránDepartment of Geosciences, University of Arkansas, Fayetteville, AR, USAMalcolm K. Cleaveland & Daniela Granato-SouzaDepartment of Forest Sciences, Universidad Nacional de Colombia – Sede Medellín, Medellín, ColombiaJorge Ignacio del ValleMaster School for Carpentry and Cabinetmaking, Ebern, GermanyOliver DünischDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USABrian J. EnquistSanta Fe Institute, Santa Fe, NM, USABrian J. EnquistDepartment of Biological Sciences, University of Joinville Region ‐ UNIVILLE, Joinville, BrazilKarin Esemann-QuadrosPostgraduate Program in Forestry, Regional University of Blumenau – FURB, Blumenau, BrazilKarin Esemann-QuadrosCollege of Life Science, Climate Science Center and Department of Earth Science, Addis Ababa University, Addis Ababa, EthiopiaZewdu EshetuDepartamento de Dendrocronología e Historia Ambiental, IANIGLA, CCT-CONICET-Mendoza, Mendoza, ArgentinaM. Eugenia Ferrero, Lidio Lopez, Fidel Alejandro Roig & Ricardo VillalbaLaboratorio de Dendrocronología, Universidad Continental, Huancayo, PerúM. Eugenia Ferrero, Janet G. Inga & Edilson Jimmy Requena-RojasDepartment of Crop Sciences, Tropical Plant Production and Agricultural Systems Modelling, Göttingen University, Göttingen, GermanyEsther FichtlerInstitute of Pacific Islands Forestry, USDA Forest Service Pacific Southwest Research Station, Hilo, HI, USAKainana S. Francisco & Mulugeta MokriaWorld Agroforestry Centre (ICRAF), Nairobi, KenyaAster GebrekirstosFlanders Heritage Agency, Brussels, BelgiumKristof HanecaDepartment of Geography and Geological Sciences, University of Idaho, Moscow, ID, USAGrant Logan HarleyGerman Archaeological Institute DAI, Berlin, GermanyIngo HeinrichGeography Department, Humboldt University Berlin, Berlin, GermanyIngo HeinrichGFZ German Research Centre for Geosciences, Potsdam, GermanyIngo Heinrich & Gerd HelleDepartment of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet, BangladeshMahmuda Islam & Mizanur RahmanFaculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czech RepublicYu-mei JiangUS Fish and Wildlife Service, Albuquerque, NM, USAMark KaibDepartment of Ecology and Biogeography, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University, Toruń, PolandMarcin KoprowskiCentre for Climate Change Research, Nicolaus Copernicus University, Toruń, PolandMarcin KoprowskiWater Systems and Global Change Group, Wageningen University and Research, Wageningen, the NetherlandsBart KruijtInstituto Nacional de Innovación Agraria, Programa Nacional de Investigación Forestal, Huancayo, PerúEva LaymeEnvironmental Systems Analysis Group, Wageningen University and Research, Wageningen, the NetherlandsRik LeemansDepartment of Natural Resource Management, South Dakota State University, Brookings, USA, SDA. Joshua LefflerLaboratory of Plant Anatomy and Dendrochronology, Department of Biology, Universidade Federal de Sergipe, Sergipe, BrazilClaudio Sergio Lisi, Mariana Alves Pagotto & Adauto de Souza Ribeiro Department of Geography, Swansea University, Swansea, UKNeil J. Loader & Iain RobertsonDepartamento Forestal, Universidad Autónoma Agraria Antonio Narro, Saltillo, MexicoMaría I. López-HernándezCITAB – Department of Forestry Sciences and Landscape (CIFAP), University of Trás-os-Montes and Alto Douro, Vila Real, PortugalJosé Luís Penetra Cerveira LousadaEscuela de Ciencias Biológicas, Universidad Pedagógica y Tecnológica de Colombia (UPTC), Tunja, ColombiaHooz A. MendivelsoBrazilian Agricultural Research Corporation (Embrapa), Embrapa Amazônia Ocidental, Manaus, BrazilValdinez Ribeiro MontóiaIHE Delft, Delft, the NetherlandsEddy MoorsVU University Amsterdam, Amsterdam, the NetherlandsEddy MoorsDepartment of Biomaterials Science and Technology, School of Natural Resources, The Copperbelt University, Kitwe, ZambiaJustine NgomaLaboratory of Ecology and Dendrology of the Federal Institute of Sergipe, São Cristovão, BrazilFrancisco de Carvalho Nogueira JúniorLaboratory of Plant Ecology, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, BrazilJuliano Morales Oliveira & Gabriela Morais OlmedoBIOAPLIC, Departamento de Botánica, Universidade de Santiago de Compostela, EPSE, Lugo, SpainGonzalo Pérez-De-LisLaboratorio de Dendrocronología, Carrera de Ingeniería Forestal, Universidad Nacional de Loja, Loja, EcuadorDarwin Pucha-CofrepFaculty of Environment and Resource studies, Mahidol University, Nakhon Pathom, ThailandNathsuda PumijumnongFacultad de Ciencias Agrarias, Universidad del Cauca, Popayán, ColombiaJorge Andres RamirezHémera Centro de Observación de la Tierra, Escuela de Ingeniería Forestal, Facultad de Ciencias, Universidad Mayor, Santiago, ChileFidel Alejandro Roig & Alejandro Venegas-GonzálezInstituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Centro de Investigación Regional Pacífico Centro – Campo Experimental, Centro Altos de Jalisco, MéxicoErnesto Alonso Rubio-CamachoNational Institute for Amazon Research, Petrópolis, Manaus, BrazilJochen SchöngartDepartment of Earth Sciences, Freie Universität Berlin, Berlin, GermanyFranziska SlottaDepartment of Earth and Environmental Systems, Indiana State University, Terre Haute, IN, USAJames H. SpeerDepartment of Geography, University of Alabama, Tuscaloosa, AL, USAMatthew D. TherrellDepartment of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USAMax C. A. TorbensonDepartment of Geography, Johannes Gutenberg University, Mainz, GermanyMax C. A. TorbensonDepartment of Plant and Environmental Sciences, School of Natural Resources, The Copperbelt University, Kitwe, ZambiaRoyd VinyaForest and Nature Management, Van Hall Larenstein University of Applied Sciences, Velp, the NetherlandsMart VlamSchool of Teacher Training for Secondary Education Tilburg, Fontys University of Applied Sciences, Tilburg, the NetherlandsTommy WilsP.A.Z., P.G. and V.T. initiated the tropical tree-ring network; P.A.Z., F.B., P.G. and V.T. designed the study; all co-authors except F.B. contributed tree-ring data; F.B. and P.G. analysed the data, with important contributions from P.A.Z.; P.A.Z. and V.T. wrote the manuscript, with important contributions from F.B. and P.G. All co-authors read and approved the manuscript. More

NEWS
31 March 2022

Funding battles stymie ambitious plan to protect global biodiversity

Researchers are disappointed with the progress — but haven’t lost hope.

Natasha Gilbert

Natasha Gilbert

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Animals such as this orangutan in Indonesia are endangered because of illegal deforestation.Credit: Jami Tarris/Future Publishing via Getty

Scientists are frustrated with countries’ progress towards inking a new deal to protect the natural world. Government officials from around the globe met in Geneva, Switzerland, on 14–29 March to find common ground on a draft of the deal, known as the post-2020 global biodiversity framework, but discussions stalled, mostly over financing. Negotiators say they will now have to meet again before a highly anticipated United Nations biodiversity summit later this year, where the deal was to be signed.The framework so far sets out 4 broad goals, including slowing species extinction, and 21 mostly quantitative targets, such as protecting at least 30% of the world’s land and seas. It is part of an international treaty known as the UN Convention on Biological Diversity, and aims to address the global biodiversity crisis, which could see one million plant and animal species go extinct in the next few decades because of factors such as climate change, human activity and disease.
China takes centre stage in global biodiversity push
The COVID-19 pandemic has already slowed discussions of the deal. Over the past two years, countries’ negotiators met only virtually; the Geneva meeting was the first in-person gathering since the pandemic began. When it ended, Basile van Havre, one of the chairs of the framework negotiations working group, said that because negotiators couldn’t agree on goals, additional discussions will need to take place in June in Nairobi. The convention’s pivotal summit — its Conference of the Parties (COP15) — is expected to be held in Kunming, China, in August and September, but no firm date has been set.Anne Larigauderie, executive secretary of the Intergovernmental Platform on Biodiversity and Ecosystem Services in Bonn, Germany, who attended the Geneva gathering, told Nature: “We are leaving the meeting with no quantitative elements. I was hoping for more progress.”Robert Watson, a retired environmental scientist at the University of East Anglia, UK, says the quantitative targets are crucial to conserving biodiversity and monitoring progress towards that goal. He calls on governments to “bite the bullet and negotiate an appropriate deal that both protects and restores biodiversity”.Finance fightMany who were at the meeting say that disagreements over funding for biodiversity conservation were the main hold-up to negotiations. For example, the draft deal proposed that US$10 billion of funding per year should flow from developed nations to low- and middle-income countries to help them to implement the biodiversity framework. But many think this is not enough. A group of conservation organizations has called for at least $60 billion per year to flow to poorer nations.
Biodiversity moves beyond counting species
The consumption habits of wealthy nations are among the key drivers of biodiversity loss. And poorer nations are often home to areas rich in biodiversity, but have fewer means to conserve them.“The most challenging aspect is the amount of financing that wealthy nations are committing to developing nations,” says Brian O’Donnell, director of the Campaign for Nature in Washington DC, a partnership of private charities and conservation organizations advocating a deal to safeguard biodiversity. “Nations need to up their level of financing to get progress in the COP.”Other nations, particularly low-income ones, probably don’t want to agree “unless they have assurances of resources to allow them to implement the new framework”, Larigauderie says.Countries including Argentina and Brazil are largely responsible for stalling the deal, several sources close to the negotiations told Nature. They asked to remain anonymous because the negotiations are confidential.
The world’s species are playing musical chairs: how will it end?
No agreement could be reached even on targets with broad international support, such as protecting at least 30% of the world’s land and seas by 2030. O’Donnell says that just one country blocked agreement on this target, questioning its scientific basis.Van Havre downplayed the lack of progress, saying that the brinksmanship at the meeting was part of a “normal negotiating process”. He told reporters: “We are happy with the progress made.” Further delays ‘unacceptable’A bright spot in the negotiations, van Havre said, was a last-minute “major step forward” in discussions on how to fairly and equitably share the benefits of digital sequence information (DSI). DSI consists of genetic data collected from plants, animals and other organisms.
Why deforestation and extinctions make pandemics more likely
When pressed, however, van Havre admitted that the progress was simply an agreement between countries to continue discussing a way forward.Thomas Brooks, chief scientist at the International Union for Conservation of Nature in Gland, Switzerland, says that DSI discussions have actually been fraught. Communities from biodiverse-rich regions where genetic material is collected have little control over the commercialization of the data that come from it, and no way to recoup financial and other benefits, he explains.Like biodiversity financing, DSI rights could hold up negotiations on the overall framework. Low-income countries want a fair and equitable share of the benefits from genetic material that originates in their lands, but rich nations don’t want unnecessary barriers to sharing the information.“We are a long way from a watershed moment, and there remain genuine disagreements,” Brooks says. However, he is optimistic that progress will eventually be made.
The biodiversity leader who is fighting for nature amid a pandemic
Some conservation organizations take hope from new provisional language in the deal that calls for halting all human-caused species extinctions. The previous draft of the deal proposed only a reduction in the rate and risk of extinctions, says Paul Todd, an environmental lawyer at the Natural Resources Defense Council, a non-profit group based in New York City.Given how much work governments must do to reach agreement on the deal, Watson says the extra Nairobi meeting is a “logical” move. But he warns: “Any further delay would be unacceptable.”“This isn’t even the hard work,” Todd says. “Implementing the deal will be the real work.”

doi: https://doi.org/10.1038/d41586-022-00916-8

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